27 skills found
dlatk / DlatkEnd to end human text analysis package, specifically suited for social media and social scientific applications. It is written in Python 3 and developed by the World Well-Being Project at the University of Pennsylvania and Stony Brook University.
PragatiVerma18 / MLH QuizzetThis is a smart Quiz Generator that generates a dynamic quiz from any uploaded text/PDF document using NLP. This can be used for self-analysis, question paper generation, and evaluation, thus reducing human effort.
Aryia-Behroziuan / NeuronsAn ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so
Aryia-Behroziuan / ReferencesPoole, Mackworth & Goebel 1998, p. 1. Russell & Norvig 2003, p. 55. Definition of AI as the study of intelligent agents: Poole, Mackworth & Goebel (1998), which provides the version that is used in this article. These authors use the term "computational intelligence" as a synonym for artificial intelligence.[1] Russell & Norvig (2003) (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field".[2] Nilsson 1998 Legg & Hutter 2007 Russell & Norvig 2009, p. 2. McCorduck 2004, p. 204 Maloof, Mark. "Artificial Intelligence: An Introduction, p. 37" (PDF). georgetown.edu. Archived (PDF) from the original on 25 August 2018. "How AI Is Getting Groundbreaking Changes In Talent Management And HR Tech". Hackernoon. Archived from the original on 11 September 2019. Retrieved 14 February 2020. Schank, Roger C. (1991). "Where's the AI". AI magazine. Vol. 12 no. 4. p. 38. Russell & Norvig 2009. "AlphaGo – Google DeepMind". Archived from the original on 10 March 2016. Allen, Gregory (April 2020). "Department of Defense Joint AI Center - Understanding AI Technology" (PDF). AI.mil - The official site of the Department of Defense Joint Artificial Intelligence Center. Archived (PDF) from the original on 21 April 2020. Retrieved 25 April 2020. Optimism of early AI: * Herbert Simon quote: Simon 1965, p. 96 quoted in Crevier 1993, p. 109. * Marvin Minsky quote: Minsky 1967, p. 2 quoted in Crevier 1993, p. 109. Boom of the 1980s: rise of expert systems, Fifth Generation Project, Alvey, MCC, SCI: * McCorduck 2004, pp. 426–441 * Crevier 1993, pp. 161–162,197–203, 211, 240 * Russell & Norvig 2003, p. 24 * NRC 1999, pp. 210–211 * Newquist 1994, pp. 235–248 First AI Winter, Mansfield Amendment, Lighthill report * Crevier 1993, pp. 115–117 * Russell & Norvig 2003, p. 22 * NRC 1999, pp. 212–213 * Howe 1994 * Newquist 1994, pp. 189–201 Second AI winter: * McCorduck 2004, pp. 430–435 * Crevier 1993, pp. 209–210 * NRC 1999, pp. 214–216 * Newquist 1994, pp. 301–318 AI becomes hugely successful in the early 21st century * Clark 2015 Pamela McCorduck (2004, p. 424) writes of "the rough shattering of AI in subfields—vision, natural language, decision theory, genetic algorithms, robotics ... and these with own sub-subfield—that would hardly have anything to say to each other." This list of intelligent traits is based on the topics covered by the major AI textbooks, including: * Russell & Norvig 2003 * Luger & Stubblefield 2004 * Poole, Mackworth & Goebel 1998 * Nilsson 1998 Kolata 1982. Maker 2006. Biological intelligence vs. intelligence in general: Russell & Norvig 2003, pp. 2–3, who make the analogy with aeronautical engineering. McCorduck 2004, pp. 100–101, who writes that there are "two major branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplished, and the other aimed at modeling intelligent processes found in nature, particularly human ones." Kolata 1982, a paper in Science, which describes McCarthy's indifference to biological models. Kolata quotes McCarthy as writing: "This is AI, so we don't care if it's psychologically real".[19] McCarthy recently reiterated his position at the AI@50 conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence".[20]. Neats vs. scruffies: * McCorduck 2004, pp. 421–424, 486–489 * Crevier 1993, p. 168 * Nilsson 1983, pp. 10–11 Symbolic vs. sub-symbolic AI: * Nilsson (1998, p. 7), who uses the term "sub-symbolic". General intelligence (strong AI) is discussed in popular introductions to AI: * Kurzweil 1999 and Kurzweil 2005 See the Dartmouth proposal, under Philosophy, below. McCorduck 2004, p. 34. McCorduck 2004, p. xviii. McCorduck 2004, p. 3. McCorduck 2004, pp. 340–400. This is a central idea of Pamela McCorduck's Machines Who Think. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition."[26] "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized."[27] "Our history is full of attempts—nutty, eerie, comical, earnest, legendary and real—to make artificial intelligences, to reproduce what is the essential us—bypassing the ordinary means. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction."[28] She traces the desire back to its Hellenistic roots and calls it the urge to "forge the Gods."[29] "Stephen Hawking believes AI could be mankind's last accomplishment". BetaNews. 21 October 2016. Archived from the original on 28 August 2017. Lombardo P, Boehm I, Nairz K (2020). "RadioComics – Santa Claus and the future of radiology". Eur J Radiol. 122 (1): 108771. doi:10.1016/j.ejrad.2019.108771. PMID 31835078. Ford, Martin; Colvin, Geoff (6 September 2015). "Will robots create more jobs than they destroy?". The Guardian. Archived from the original on 16 June 2018. Retrieved 13 January 2018. AI applications widely used behind the scenes: * Russell & Norvig 2003, p. 28 * Kurzweil 2005, p. 265 * NRC 1999, pp. 216–222 * Newquist 1994, pp. 189–201 AI in myth: * McCorduck 2004, pp. 4–5 * Russell & Norvig 2003, p. 939 AI in early science fiction. * McCorduck 2004, pp. 17–25 Formal reasoning: * Berlinski, David (2000). The Advent of the Algorithm. Harcourt Books. ISBN 978-0-15-601391-8. OCLC 46890682. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Turing, Alan (1948), "Machine Intelligence", in Copeland, B. Jack (ed.), The Essential Turing: The ideas that gave birth to the computer age, Oxford: Oxford University Press, p. 412, ISBN 978-0-19-825080-7 Russell & Norvig 2009, p. 16. Dartmouth conference: * McCorduck 2004, pp. 111–136 * Crevier 1993, pp. 47–49, who writes "the conference is generally recognized as the official birthdate of the new science." * Russell & Norvig 2003, p. 17, who call the conference "the birth of artificial intelligence." * NRC 1999, pp. 200–201 McCarthy, John (1988). "Review of The Question of Artificial Intelligence". Annals of the History of Computing. 10 (3): 224–229., collected in McCarthy, John (1996). "10. Review of The Question of Artificial Intelligence". Defending AI Research: A Collection of Essays and Reviews. CSLI., p. 73, "[O]ne of the reasons for inventing the term "artificial intelligence" was to escape association with "cybernetics". Its concentration on analog feedback seemed misguided, and I wished to avoid having either to accept Norbert (not Robert) Wiener as a guru or having to argue with him." Hegemony of the Dartmouth conference attendees: * Russell & Norvig 2003, p. 17, who write "for the next 20 years the field would be dominated by these people and their students." * McCorduck 2004, pp. 129–130 Russell & Norvig 2003, p. 18. Schaeffer J. (2009) Didn't Samuel Solve That Game?. In: One Jump Ahead. Springer, Boston, MA Samuel, A. L. (July 1959). "Some Studies in Machine Learning Using the Game of Checkers". IBM Journal of Research and Development. 3 (3): 210–229. CiteSeerX 10.1.1.368.2254. doi:10.1147/rd.33.0210. "Golden years" of AI (successful symbolic reasoning programs 1956–1973): * McCorduck 2004, pp. 243–252 * Crevier 1993, pp. 52–107 * Moravec 1988, p. 9 * Russell & Norvig 2003, pp. 18–21 The programs described are Arthur Samuel's checkers program for the IBM 701, Daniel Bobrow's STUDENT, Newell and Simon's Logic Theorist and Terry Winograd's SHRDLU. DARPA pours money into undirected pure research into AI during the 1960s: * McCorduck 2004, p. 131 * Crevier 1993, pp. 51, 64–65 * NRC 1999, pp. 204–205 AI in England: * Howe 1994 Lighthill 1973. Expert systems: * ACM 1998, I.2.1 * Russell & Norvig 2003, pp. 22–24 * Luger & Stubblefield 2004, pp. 227–331 * Nilsson 1998, chpt. 17.4 * McCorduck 2004, pp. 327–335, 434–435 * Crevier 1993, pp. 145–62, 197–203 * Newquist 1994, pp. 155–183 Mead, Carver A.; Ismail, Mohammed (8 May 1989). Analog VLSI Implementation of Neural Systems (PDF). The Kluwer International Series in Engineering and Computer Science. 80. Norwell, MA: Kluwer Academic Publishers. doi:10.1007/978-1-4613-1639-8. ISBN 978-1-4613-1639-8. Archived from the original (PDF) on 6 November 2019. Retrieved 24 January 2020. Formal methods are now preferred ("Victory of the neats"): * Russell & Norvig 2003, pp. 25–26 * McCorduck 2004, pp. 486–487 McCorduck 2004, pp. 480–483. Markoff 2011. 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(2017). Combining LiDAR space clustering and convolutional neural networks for pedestrian detection. 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). pp. 1–6. arXiv:1710.06160. doi:10.1109/AVSS.2017.8078512. ISBN 978-1-5386-2939-0. S2CID 2401976. Ferguson, Sarah; Luders, Brandon; Grande, Robert C.; How, Jonathan P. (2015). Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions. Algorithmic Foundations of Robotics XI. Springer Tracts in Advanced Robotics. 107. Springer, Cham. pp. 161–177. arXiv:1405.5581. doi:10.1007/978-3-319-16595-0_10. ISBN 978-3-319-16594-3. S2CID 8681101. "Cultivating Common Sense | DiscoverMagazine.com". Discover Magazine. 2017. Archived from the original on 25 March 2018. Retrieved 24 March 2018. Davis, Ernest; Marcus, Gary (24 August 2015). "Commonsense reasoning and commonsense knowledge in artificial intelligence". 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Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Problem solving, puzzle solving, game playing and deduction: * Russell & Norvig 2003, chpt. 3–9, * Poole, Mackworth & Goebel 1998, chpt. 2,3,7,9, * Luger & Stubblefield 2004, chpt. 3,4,6,8, * Nilsson 1998, chpt. 7–12 Uncertain reasoning: * Russell & Norvig 2003, pp. 452–644, * Poole, Mackworth & Goebel 1998, pp. 345–395, * Luger & Stubblefield 2004, pp. 333–381, * Nilsson 1998, chpt. 19 Psychological evidence of sub-symbolic reasoning: * Wason & Shapiro (1966) showed that people do poorly on completely abstract problems, but if the problem is restated to allow the use of intuitive social intelligence, performance dramatically improves. (See Wason selection task) * Kahneman, Slovic & Tversky (1982) have shown that people are terrible at elementary problems that involve uncertain reasoning. (See list of cognitive biases for several examples). * Lakoff & Núñez (2000) have controversially argued that even our skills at mathematics depend on knowledge and skills that come from "the body", i.e. sensorimotor and perceptual skills. (See Where Mathematics Comes From) Knowledge representation: * ACM 1998, I.2.4, * Russell & Norvig 2003, pp. 320–363, * Poole, Mackworth & Goebel 1998, pp. 23–46, 69–81, 169–196, 235–277, 281–298, 319–345, * Luger & Stubblefield 2004, pp. 227–243, * Nilsson 1998, chpt. 18 Knowledge engineering: * Russell & Norvig 2003, pp. 260–266, * Poole, Mackworth & Goebel 1998, pp. 199–233, * Nilsson 1998, chpt. ≈17.1–17.4 Representing categories and relations: Semantic networks, description logics, inheritance (including frames and scripts): * Russell & Norvig 2003, pp. 349–354, * Poole, Mackworth & Goebel 1998, pp. 174–177, * Luger & Stubblefield 2004, pp. 248–258, * Nilsson 1998, chpt. 18.3 Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem): * Russell & Norvig 2003, pp. 328–341, * Poole, Mackworth & Goebel 1998, pp. 281–298, * Nilsson 1998, chpt. 18.2 Causal calculus: * Poole, Mackworth & Goebel 1998, pp. 335–337 Representing knowledge about knowledge: Belief calculus, modal logics: * Russell & Norvig 2003, pp. 341–344, * Poole, Mackworth & Goebel 1998, pp. 275–277 Sikos, Leslie F. (June 2017). Description Logics in Multimedia Reasoning. Cham: Springer. doi:10.1007/978-3-319-54066-5. ISBN 978-3-319-54066-5. S2CID 3180114. Archived from the original on 29 August 2017. Ontology: * Russell & Norvig 2003, pp. 320–328 Smoliar, Stephen W.; Zhang, HongJiang (1994). "Content based video indexing and retrieval". IEEE Multimedia. 1 (2): 62–72. doi:10.1109/93.311653. S2CID 32710913. Neumann, Bernd; Möller, Ralf (January 2008). "On scene interpretation with description logics". Image and Vision Computing. 26 (1): 82–101. doi:10.1016/j.imavis.2007.08.013. Kuperman, G. J.; Reichley, R. M.; Bailey, T. C. (1 July 2006). "Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations". Journal of the American Medical Informatics Association. 13 (4): 369–371. doi:10.1197/jamia.M2055. PMC 1513681. PMID 16622160. MCGARRY, KEN (1 December 2005). "A survey of interestingness measures for knowledge discovery". The Knowledge Engineering Review. 20 (1): 39–61. doi:10.1017/S0269888905000408. S2CID 14987656. Bertini, M; Del Bimbo, A; Torniai, C (2006). "Automatic annotation and semantic retrieval of video sequences using multimedia ontologies". MM '06 Proceedings of the 14th ACM international conference on Multimedia. 14th ACM international conference on Multimedia. Santa Barbara: ACM. pp. 679–682. Qualification problem: * McCarthy & Hayes 1969 * Russell & Norvig 2003[page needed] While McCarthy was primarily concerned with issues in the logical representation of actions, Russell & Norvig 2003 apply the term to the more general issue of default reasoning in the vast network of assumptions underlying all our commonsense knowledge. Default reasoning and default logic, non-monotonic logics, circumscription, closed world assumption, abduction (Poole et al. places abduction under "default reasoning". Luger et al. places this under "uncertain reasoning"): * Russell & Norvig 2003, pp. 354–360, * Poole, Mackworth & Goebel 1998, pp. 248–256, 323–335, * Luger & Stubblefield 2004, pp. 335–363, * Nilsson 1998, ~18.3.3 Breadth of commonsense knowledge: * Russell & Norvig 2003, p. 21, * Crevier 1993, pp. 113–114, * Moravec 1988, p. 13, * Lenat & Guha 1989 (Introduction) Dreyfus & Dreyfus 1986. Gladwell 2005. Expert knowledge as embodied intuition: * Dreyfus & Dreyfus 1986 (Hubert Dreyfus is a philosopher and critic of AI who was among the first to argue that most useful human knowledge was encoded sub-symbolically. See Dreyfus' critique of AI) * Gladwell 2005 (Gladwell's Blink is a popular introduction to sub-symbolic reasoning and knowledge.) * Hawkins & Blakeslee 2005 (Hawkins argues that sub-symbolic knowledge should be the primary focus of AI research.) Planning: * ACM 1998, ~I.2.8, * Russell & Norvig 2003, pp. 375–459, * Poole, Mackworth & Goebel 1998, pp. 281–316, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Information value theory: * Russell & Norvig 2003, pp. 600–604 Classical planning: * Russell & Norvig 2003, pp. 375–430, * Poole, Mackworth & Goebel 1998, pp. 281–315, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Planning and acting in non-deterministic domains: conditional planning, execution monitoring, replanning and continuous planning: * Russell & Norvig 2003, pp. 430–449 Multi-agent planning and emergent behavior: * Russell & Norvig 2003, pp. 449–455 Turing 1950. Solomonoff 1956. Alan Turing discussed the centrality of learning as early as 1950, in his classic paper "Computing Machinery and Intelligence".[120] In 1956, at the original Dartmouth AI summer conference, Ray Solomonoff wrote a report on unsupervised probabilistic machine learning: "An Inductive Inference Machine".[121] This is a form of Tom Mitchell's widely quoted definition of machine learning: "A computer program is set to learn from an experience E with respect to some task T and some performance measure P if its performance on T as measured by P improves with experience E." Learning: * ACM 1998, I.2.6, * Russell & Norvig 2003, pp. 649–788, * Poole, Mackworth & Goebel 1998, pp. 397–438, * Luger & Stubblefield 2004, pp. 385–542, * Nilsson 1998, chpt. 3.3, 10.3, 17.5, 20 Jordan, M. I.; Mitchell, T. M. (16 July 2015). "Machine learning: Trends, perspectives, and prospects". Science. 349 (6245): 255–260. Bibcode:2015Sci...349..255J. doi:10.1126/science.aaa8415. PMID 26185243. S2CID 677218. Reinforcement learning: * Russell & Norvig 2003, pp. 763–788 * Luger & Stubblefield 2004, pp. 442–449 Natural language processing: * ACM 1998, I.2.7 * Russell & Norvig 2003, pp. 790–831 * Poole, Mackworth & Goebel 1998, pp. 91–104 * Luger & Stubblefield 2004, pp. 591–632 "Versatile question answering systems: seeing in synthesis" Archived 1 February 2016 at the Wayback Machine, Mittal et al., IJIIDS, 5(2), 119–142, 2011 Applications of natural language processing, including information retrieval (i.e. text mining) and machine translation: * Russell & Norvig 2003, pp. 840–857, * Luger & Stubblefield 2004, pp. 623–630 Cambria, Erik; White, Bebo (May 2014). "Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]". IEEE Computational Intelligence Magazine. 9 (2): 48–57. doi:10.1109/MCI.2014.2307227. S2CID 206451986. Vincent, James (7 November 2019). "OpenAI has published the text-generating AI it said was too dangerous to share". The Verge. Archived from the original on 11 June 2020. Retrieved 11 June 2020. Machine perception: * Russell & Norvig 2003, pp. 537–581, 863–898 * Nilsson 1998, ~chpt. 6 Speech recognition: * ACM 1998, ~I.2.7 * Russell & Norvig 2003, pp. 568–578 Object recognition: * Russell & Norvig 2003, pp. 885–892 Computer vision: * ACM 1998, I.2.10 * Russell & Norvig 2003, pp. 863–898 * Nilsson 1998, chpt. 6 Robotics: * ACM 1998, I.2.9, * Russell & Norvig 2003, pp. 901–942, * Poole, Mackworth & Goebel 1998, pp. 443–460 Moving and configuration space: * Russell & Norvig 2003, pp. 916–932 Tecuci 2012. Robotic mapping (localization, etc): * Russell & Norvig 2003, pp. 908–915 Cadena, Cesar; Carlone, Luca; Carrillo, Henry; Latif, Yasir; Scaramuzza, Davide; Neira, Jose; Reid, Ian; Leonard, John J. (December 2016). "Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age". IEEE Transactions on Robotics. 32 (6): 1309–1332. arXiv:1606.05830. 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Retrieved 26 April 2018. Domingos 2015. Artificial brain arguments: AI requires a simulation of the operation of the human brain * Russell & Norvig 2003, p. 957 * Crevier 1993, pp. 271 and 279 A few of the people who make some form of the argument: * Moravec 1988 * Kurzweil 2005, p. 262 * Hawkins & Blakeslee 2005 The most extreme form of this argument (the brain replacement scenario) was put forward by Clark Glymour in the mid-1970s and was touched on by Zenon Pylyshyn and John Searle in 1980. Goertzel, Ben; Lian, Ruiting; Arel, Itamar; de Garis, Hugo; Chen, Shuo (December 2010). "A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures". Neurocomputing. 74 (1–3): 30–49. doi:10.1016/j.neucom.2010.08.012. Nilsson 1983, p. 10. Nils Nilsson writes: "Simply put, there is wide disagreement in the field about what AI is all about."[163] AI's immediate precursors: * McCorduck 2004, pp. 51–107 * Crevier 1993, pp. 27–32 * Russell & Norvig 2003, pp. 15, 940 * Moravec 1988, p. 3 Haugeland 1985, pp. 112–117 The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of perceptrons by Marvin Minsky and Seymour Papert in 1969. See History of AI, AI winter, or Frank Rosenblatt. Cognitive simulation, Newell and Simon, AI at CMU (then called Carnegie Tech): * McCorduck 2004, pp. 139–179, 245–250, 322–323 (EPAM) * Crevier 1993, pp. 145–149 Soar (history): * McCorduck 2004, pp. 450–451 * Crevier 1993, pp. 258–263 McCarthy and AI research at SAIL and SRI International: * McCorduck 2004, pp. 251–259 * Crevier 1993 AI research at Edinburgh and in France, birth of Prolog: * Crevier 1993, pp. 193–196 * Howe 1994 AI at MIT under Marvin Minsky in the 1960s : * McCorduck 2004, pp. 259–305 * Crevier 1993, pp. 83–102, 163–176 * Russell & Norvig 2003, p. 19 Cyc: * McCorduck 2004, p. 489, who calls it "a determinedly scruffy enterprise" * Crevier 1993, pp. 239–243 * Russell & Norvig 2003, p. 363−365 * Lenat & Guha 1989 Knowledge revolution: * McCorduck 2004, pp. 266–276, 298–300, 314, 421 * Russell & Norvig 2003, pp. 22–23 Frederick, Hayes-Roth; William, Murray; Leonard, Adelman. "Expert systems". AccessScience. doi:10.1036/1097-8542.248550. Embodied approaches to AI: * McCorduck 2004, pp. 454–462 * Brooks 1990 * Moravec 1988 Weng et al. 2001. Lungarella et al. 2003. Asada et al. 2009. Oudeyer 2010. Revival of connectionism: * Crevier 1993, pp. 214–215 * Russell & Norvig 2003, p. 25 Computational intelligence * IEEE Computational Intelligence Society Archived 9 May 2008 at the Wayback Machine Hutson, Matthew (16 February 2018). "Artificial intelligence faces reproducibility crisis". Science. pp. 725–726. Bibcode:2018Sci...359..725H. doi:10.1126/science.359.6377.725. Archived from the original on 29 April 2018. Retrieved 28 April 2018. Norvig 2012. Langley 2011. Katz 2012. The intelligent agent paradigm: * Russell & Norvig 2003, pp. 27, 32–58, 968–972 * Poole, Mackworth & Goebel 1998, pp. 7–21 * Luger & Stubblefield 2004, pp. 235–240 * Hutter 2005, pp. 125–126 The definition used in this article, in terms of goals, actions, perception and environment, is due to Russell & Norvig (2003). Other definitions also include knowledge and learning as additional criteria. Agent architectures, hybrid intelligent systems: * Russell & Norvig (2003, pp. 27, 932, 970–972) * Nilsson (1998, chpt. 25) Hierarchical control system: * Albus 2002 Lieto, Antonio; Lebiere, Christian; Oltramari, Alessandro (May 2018). "The knowledge level in cognitive architectures: Current limitations and possibile developments". Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Lieto, Antonio; Bhatt, Mehul; Oltramari, Alessandro; Vernon, David (May 2018). "The role of cognitive architectures in general artificial intelligence". Cognitive Systems Research. 48: 1–3. doi:10.1016/j.cogsys.2017.08.003. hdl:2318/1665249. S2CID 36189683. Russell & Norvig 2009, p. 1. White Paper: On Artificial Intelligence - A European approach to excellence and trust (PDF). Brussels: European Commission. 2020. p. 1. Archived (PDF) from the original on 20 February 2020. Retrieved 20 February 2020. CNN 2006. Using AI to predict flight delays Archived 20 November 2018 at the Wayback Machine, Ishti.org. N. Aletras; D. Tsarapatsanis; D. Preotiuc-Pietro; V. Lampos (2016). "Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective". PeerJ Computer Science. 2: e93. doi:10.7717/peerj-cs.93. "The Economist Explains: Why firms are piling into artificial intelligence". The Economist. 31 March 2016. Archived from the original on 8 May 2016. Retrieved 19 May 2016. Lohr, Steve (28 February 2016). "The Promise of Artificial Intelligence Unfolds in Small Steps". The New York Times. Archived from the original on 29 February 2016. Retrieved 29 February 2016. Frangoul, Anmar (14 June 2019). "A Californian business is using A.I. to change the way we think about energy storage". CNBC. Archived from the original on 25 July 2020. Retrieved 5 November 2019. Wakefield, Jane (15 June 2016). "Social media 'outstrips TV' as news source for young people". BBC News. Archived from the original on 24 June 2016. Smith, Mark (22 July 2016). "So you think you chose to read this article?". BBC News. Archived from the original on 25 July 2016. Brown, Eileen. "Half of Americans do not believe deepfake news could target them online". ZDNet. Archived from the original on 6 November 2019. Retrieved 3 December 2019. The Turing test: Turing's original publication: * Turing 1950 Historical influence and philosophical implications: * Haugeland 1985, pp. 6–9 * Crevier 1993, p. 24 * McCorduck 2004, pp. 70–71 * Russell & Norvig 2003, pp. 2–3 and 948 Dartmouth proposal: * McCarthy et al. 1955 (the original proposal) * Crevier 1993, p. 49 (historical significance) The physical symbol systems hypothesis: * Newell & Simon 1976, p. 116 * McCorduck 2004, p. 153 * Russell & Norvig 2003, p. 18 Dreyfus 1992, p. 156. Dreyfus criticized the necessary condition of the physical symbol system hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules."[206] Dreyfus' critique of artificial intelligence: * Dreyfus 1972, Dreyfus & Dreyfus 1986 * Crevier 1993, pp. 120–132 * McCorduck 2004, pp. 211–239 * Russell & Norvig 2003, pp. 950–952, Gödel 1951: in this lecture, Kurt Gödel uses the incompleteness theorem to arrive at the following disjunction: (a) the human mind is not a consistent finite machine, or (b) there exist Diophantine equations for which it cannot decide whether solutions exist. Gödel finds (b) implausible, and thus seems to have believed the human mind was not equivalent to a finite machine, i.e., its power exceeded that of any finite machine. He recognized that this was only a conjecture, since one could never disprove (b). Yet he considered the disjunctive conclusion to be a "certain fact". The Mathematical Objection: * Russell & Norvig 2003, p. 949 * McCorduck 2004, pp. 448–449 Making the Mathematical Objection: * Lucas 1961 * Penrose 1989 Refuting Mathematical Objection: * Turing 1950 under "(2) The Mathematical Objection" * Hofstadter 1979 Background: * Gödel 1931, Church 1936, Kleene 1935, Turing 1937 Graham Oppy (20 January 2015). "Gödel's Incompleteness Theorems". Stanford Encyclopedia of Philosophy. Archived from the original on 22 April 2016. Retrieved 27 April 2016. These Gödelian anti-mechanist arguments are, however, problematic, and there is wide consensus that they fail. Stuart J. Russell; Peter Norvig (2010). "26.1.2: Philosophical Foundations/Weak AI: Can Machines Act Intelligently?/The mathematical objection". Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall. ISBN 978-0-13-604259-4. even if we grant that computers have limitations on what they can prove, there is no evidence that humans are immune from those limitations. Mark Colyvan. An introduction to the philosophy of mathematics. Cambridge University Press, 2012. From 2.2.2, 'Philosophical significance of Gödel's incompleteness results': "The accepted wisdom (with which I concur) is that the Lucas-Penrose arguments fail." Iphofen, Ron; Kritikos, Mihalis (3 January 2019). "Regulating artificial intelligence and robotics: ethics by design in a digital society". Contemporary Social Science: 1–15. doi:10.1080/21582041.2018.1563803. ISSN 2158-2041. "Ethical AI Learns Human Rights Framework". Voice of America. Archived from the original on 11 November 2019. Retrieved 10 November 2019. Crevier 1993, pp. 132–144. In the early 1970s, Kenneth Colby presented a version of Weizenbaum's ELIZA known as DOCTOR which he promoted as a serious therapeutic tool.[216] Joseph Weizenbaum's critique of AI: * Weizenbaum 1976 * Crevier 1993, pp. 132–144 * McCorduck 2004, pp. 356–373 * Russell & Norvig 2003, p. 961 Weizenbaum (the AI researcher who developed the first chatterbot program, ELIZA) argued in 1976 that the misuse of artificial intelligence has the potential to devalue human life. Wendell Wallach (2010). Moral Machines, Oxford University Press. Wallach, pp 37–54. Wallach, pp 55–73. Wallach, Introduction chapter. Michael Anderson and Susan Leigh Anderson (2011), Machine Ethics, Cambridge University Press. "Machine Ethics". aaai.org. Archived from the original on 29 November 2014. Rubin, Charles (Spring 2003). "Artificial Intelligence and Human Nature". The New Atlantis. 1: 88–100. Archived from the original on 11 June 2012. Brooks, Rodney (10 November 2014). "artificial intelligence is a tool, not a threat". Archived from the original on 12 November 2014. "Stephen Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence". Observer. 19 August 2015. Archived from the original on 30 October 2015. Retrieved 30 October 2015. Chalmers, David (1995). "Facing up to the problem of consciousness". Journal of Consciousness Studies. 2 (3): 200–219. Archived from the original on 8 March 2005. Retrieved 11 October 2018. See also this link Archived 8 April 2011 at the Wayback Machine Horst, Steven, (2005) "The Computational Theory of Mind" Archived 11 September 2018 at the Wayback Machine in The Stanford Encyclopedia of Philosophy Searle 1980, p. 1. This version is from Searle (1999), and is also quoted in Dennett 1991, p. 435. Searle's original formulation was "The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states." [230] Strong AI is defined similarly by Russell & Norvig (2003, p. 947): "The assertion that machines could possibly act intelligently
zhunhung / Python SentiStrengthPython 3 wrapper for SentiStrength. SentiStrength is capable of automatic sentiment analysis of up to 16,000 social web texts per second with up to human level accuracy for English.
Rynkll696 / HHimport pyttsx3 import speech_recognition as sr import datetime from datetime import date import calendar import time import math import wikipedia import webbrowser import os import smtplib import winsound import pyautogui import cv2 from pygame import mixer from tkinter import * import tkinter.messagebox as message from sqlite3 import * conn = connect("voice_assistant_asked_questions.db") conn.execute("CREATE TABLE IF NOT EXISTS `voicedata`(id INTEGER PRIMARY KEY AUTOINCREMENT,command VARCHAR(201))") conn.execute("CREATE TABLE IF NOT EXISTS `review`(id INTEGER PRIMARY KEY AUTOINCREMENT, review VARCHAR(50), type_of_review VARCHAR(50))") conn.execute("CREATE TABLE IF NOT EXISTS `emoji`(id INTEGER PRIMARY KEY AUTOINCREMENT,emoji VARCHAR(201))") global query engine = pyttsx3.init('sapi5') voices = engine.getProperty('voices') engine.setProperty('voice', voices[0].id) def speak(audio): engine.say(audio) engine.runAndWait() def wishMe(): hour = int(datetime.datetime.now().hour) if hour >= 0 and hour<12: speak("Good Morning!") elif hour >= 12 and hour < 18: speak("Good Afternoon!") else: speak("Good Evening!") speak("I am voice assistant Akshu2020 Sir. Please tell me how may I help you.") def takeCommand(): global query r = sr.Recognizer() with sr.Microphone() as source: print("Listening...") r.pause_threshold = 0.9 audio = r.listen(source) try: print("Recognizing...") query = r.recognize_google(audio,language='en-in') print(f"User said: {query}\n") except Exception as e: #print(e) print("Say that again please...") #speak('Say that again please...') return "None" return query def calculator(): global query try: if 'add' in query or 'edi' in query: speak('Enter a number') a = float(input("Enter a number:")) speak('Enter another number to add') b = float(input("Enter another number to add:")) c = a+b print(f"{a} + {b} = {c}") speak(f'The addition of {a} and {b} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'sub' in query: speak('Enter a number') a = float(input("Enter a number:")) speak('Enter another number to subtract') b = float(input("Enter another number to subtract:")) c = a-b print(f"{a} - {b} = {c}") speak(f'The subtraction of {a} and {b} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'mod' in query: speak('Enter a number') a = float(input("Enter a number:")) speak('Enter another number') b = float(input("Enter another number:")) c = a%b print(f"{a} % {b} = {c}") speak(f'The modular division of {a} and {b} is equal to {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'div' in query: speak('Enter a number as dividend') a = float(input("Enter a number:")) speak('Enter another number as divisor') b = float(input("Enter another number as divisor:")) c = a/b print(f"{a} / {b} = {c}") speak(f'{a} divided by {b} is equal to {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'multi' in query: speak('Enter a number') a = float(input("Enter a number:")) speak('Enter another number to multiply') b = float(input("Enter another number to multiply:")) c = a*b print(f"{a} x {b} = {c}") speak(f'The multiplication of {a} and {b} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'square root' in query: speak('Enter a number to find its sqare root') a = float(input("Enter a number:")) c = a**(1/2) print(f"Square root of {a} = {c}") speak(f'Square root of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'square' in query: speak('Enter a number to find its sqare') a = float(input("Enter a number:")) c = a**2 print(f"{a} x {a} = {c}") speak(f'Square of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cube root' in query: speak('Enter a number to find its cube root') a = float(input("Enter a number:")) c = a**(1/3) print(f"Cube root of {a} = {c}") speak(f'Cube root of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cube' in query: speak('Enter a number to find its sqare') a = float(input("Enter a number:")) c = a**3 print(f"{a} x {a} x {a} = {c}") speak(f'Cube of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'fact' in query: try: n = int(input('Enter the number whose factorial you want to find:')) fact = 1 for i in range(1,n+1): fact = fact*i print(f"{n}! = {fact}") speak(f'{n} factorial is equal to {fact}. Your answer is {fact}.') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: #print(e) speak('I unable to calculate its factorial.') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'power' in query or 'raise' in query: speak('Enter a number whose power you want to raised') a = float(input("Enter a number whose power to be raised :")) speak(f'Enter a raised power to {a}') b = float(input(f"Enter a raised power to {a}:")) c = a**b print(f"{a} ^ {b} = {c}") speak(f'{a} raise to the power {b} = {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'percent' in query: speak('Enter a number whose percentage you want to calculate') a = float(input("Enter a number whose percentage you want to calculate :")) speak(f'How many percent of {a} you want to calculate?') b = float(input(f"Enter how many percentage of {a} you want to calculate:")) c = (a*b)/100 print(f"{b} % of {a} is {c}") speak(f'{b} percent of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'interest' in query: speak('Enter the principal value or amount') p = float(input("Enter the principal value (P):")) speak('Enter the rate of interest per year') r = float(input("Enter the rate of interest per year (%):")) speak('Enter the time in months') t = int(input("Enter the time (in months):")) interest = (p*r*t)/1200 sint = round(interest) fv = round(p + interest) print(f"Interest = {interest}") print(f"The total amount accured, principal plus interest, from simple interest on a principal of {p} at a rate of {r}% per year for {t} months is {p + interest}.") speak(f'interest is {sint}. The total amount accured, principal plus interest, from simple interest on a principal of {p} at a rate of {r}% per year for {t} months is {fv}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'si' in query: speak('Enter the angle in degree to find its sine value') a = float(input("Enter the angle:")) b = a * 3.14/180 c = math.sin(b) speak('Here is your answer.') print(f"sin({a}) = {c}") speak(f'sin({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cos' in query: speak('Enter the angle in degree to find its cosine value') a = float(input("Enter the angle:")) b = a * 3.14/180 c = math.cos(b) speak('Here is your answer.') print(f"cos({a}) = {c}") speak(f'cos({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cot' in query or 'court' in query: try: speak('Enter the angle in degree to find its cotangent value') a = float(input("Enter the angle:")) b = a * 3.14/180 c = 1/math.tan(b) speak('Here is your answer.') print(f"cot({a}) = {c}") speak(f'cot({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: print("infinity") speak('Answer is infinity') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'tan' in query or '10' in query: speak('Enter the angle in degree to find its tangent value') a = float(input("Enter the angle:")) b = a * 3.14/180 c = math.tan(b) speak('Here is your answer.') print(f"tan({a}) = {c}") speak(f'tan({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cosec' in query: try: speak('Enter the angle in degree to find its cosecant value') a = float(input("Enter the angle:")) b = a * 3.14/180 c =1/ math.sin(b) speak('Here is your answer.') print(f"cosec({a}) = {c}") speak(f'cosec({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: print('Infinity') speak('Answer is infinity') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'caus' in query: try: speak('Enter the angle in degree to find its cosecant value') a = float(input("Enter the angle:")) b = a * 3.14/180 c =1/ math.sin(b) speak('Here is your answer.') print(f"cosec({a}) = {c}") speak(f'cosec({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: print('Infinity') speak('Answer is infinity') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'sec' in query: try: speak('Enter the angle in degree to find its secant value') a = int(input("Enter the angle:")) b = a * 3.14/180 c = 1/math.cos(b) speak('Here is your answer.') print(f"sec({a}) = {c}") speak(f'sec({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: print('Infinity') speak('Answer is infinity') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: speak('I unable to do this calculation.') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') def callback(r,c): global player if player == 'X' and states[r][c] == 0 and stop_game == False: b[r][c].configure(text='X',fg='blue', bg='white') states[r][c] = 'X' player = 'O' if player == 'O' and states[r][c] == 0 and stop_game == False: b[r][c].configure(text='O',fg='red', bg='yellow') states[r][c] = 'O' player = 'X' check_for_winner() def check_for_winner(): global stop_game global root for i in range(3): if states[i][0] == states[i][1]== states[i][2]!=0: b[i][0].config(bg='grey') b[i][1].config(bg='grey') b[i][2].config(bg='grey') stop_game = True root.destroy() for i in range(3): if states[0][i] == states[1][i] == states[2][i]!= 0: b[0][i].config(bg='grey') b[1][i].config(bg='grey') b[2][i].config(bg='grey') stop_game = True root.destroy() if states[0][0] == states[1][1]== states[2][2]!= 0: b[0][0].config(bg='grey') b[1][1].config(bg='grey') b[2][2].config(bg='grey') stop_game = True root.destroy() if states[2][0] == states[1][1] == states[0][2]!= 0: b[2][0].config(bg='grey') b[1][1].config(bg='grey') b[0][2].config(bg='grey') stop_game = True root.destroy() def sendEmail(to,content): server = smtplib.SMTP('smtp.gmail.com', 587) server.ehlo() server.starttls() server.login('xyz123@gmail.com','password') server.sendmail('xyz123@gmail.com',to,content) server.close() def brightness(): try: query = takeCommand().lower() if '25' in query: pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1610,960) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() speak('If you again want to change brihtness, say, change brightness') elif '50' in query: pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1684,960) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() speak('If you again want to change brihtness, say, change brightness') elif '75' in query: pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1758,960) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() speak('If you again want to change brihtness, say, change brightness') elif '100' in query or 'full' in query: pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1835,960) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() speak('If you again want to change brihtness, say, change brightness') else: speak('Please select 25, 50, 75 or 100....... Say again.') brightness() except exception as e: #print(e) speak('Something went wrong') def close_window(): try: if 'y' in query: pyautogui.moveTo(1885,10) pyautogui.click() else: speak('ok') pyautogui.moveTo(1000,500) except exception as e: #print(e) speak('error') def whatsapp(): query = takeCommand().lower() if 'y' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('whatsapp') time.sleep(2) pyautogui.press('enter') time.sleep(2) pyautogui.moveTo(100,140) pyautogui.click() speak('To whom you want to send message,.....just write the name here in 5 seconds') time.sleep(7) pyautogui.moveTo(120,300) pyautogui.click() time.sleep(1) pyautogui.moveTo(800,990) pyautogui.click() speak('Say the message,....or if you want to send anything else,...say send document, or say send emoji') query = takeCommand() if ('sent' in query or 'send' in query) and 'document' in query: pyautogui.moveTo(660,990) pyautogui.click() time.sleep(1) pyautogui.moveTo(660,740) pyautogui.click() speak('please select the document within 10 seconds') time.sleep(12) speak('Should I send this document?') query = takeCommand().lower() if 'y' in query and 'no' not in query: speak('sending the document......') pyautogui.press('enter') speak('Do you want to send message again to anyone?') whatsapp() elif ('remove' in query or 'cancel' in query or 'delete' in query or 'clear' in query) and ('document' in query or 'message' in query or 'it' in query or 'emoji' in query or 'select' in query): pyautogui.doubleClick(x=800, y=990) pyautogui.press('backspace') speak('Do you want to send message again to anyone?') whatsapp() else: speak('ok') elif ('sent' in query or 'send' in query) and 'emoji' in query: pyautogui.moveTo(620,990) pyautogui.click() pyautogui.moveTo(670,990) pyautogui.click() pyautogui.moveTo(650,580) pyautogui.click() speak('please select the emoji within 10 seconds') time.sleep(11) speak('Should I send this emoji?') query = takeCommand().lower() if 'y' in query and 'no' not in query: speak('Sending the emoji......') pyautogui.press('enter') speak('Do you want to send message again to anyone?') whatsapp() elif ('remove' in query or 'cancel' in query or 'delete' in query or 'clear' in query) and ('message' in query or 'it' in query or 'emoji' in query or 'select' in query): pyautogui.doublClick(x=800, y=990) speak('Do you want to send message again to anyone?') whatsapp() else: speak('ok') else: pyautogui.write(f'{query}') speak('Should I send this message?') query = takeCommand().lower() if 'y' in query and 'no' not in query: speak('sending the message......') pyautogui.press('enter') speak('Do you want to send message again to anyone?') whatsapp() elif ('remove' in query or 'cancel' in query or 'delete' in query or 'clear' in query) and ('message' in query or 'it' in query or 'select' in query): pyautogui.doubleClick(x=800, y=990) pyautogui.press('backspace') speak('Do you want to send message again to anyone?') whatsapp() else: speak('ok') else: speak('ok') def alarm(): root = Tk() root.title('Akshu2020 Alarm-Clock') speak('Please enter the time in the format hour, minutes and seconds. When the alarm should rang?') speak('Please enter the time greater than the current time') def setalarm(): alarmtime = f"{hrs.get()}:{mins.get()}:{secs.get()}" print(alarmtime) if(alarmtime!="::"): alarmclock(alarmtime) else: speak('You have not entered the time.') def alarmclock(alarmtime): while True: time.sleep(1) time_now=datetime.datetime.now().strftime("%H:%M:%S") print(time_now) if time_now == alarmtime: Wakeup=Label(root, font = ('arial', 20, 'bold'), text="Wake up! Wake up! Wake up",bg="DodgerBlue2",fg="white").grid(row=6,columnspan=3) speak("Wake up, Wake up") print("Wake up!") mixer.init() mixer.music.load(r'C:\Users\Admin\Music\Playlists\wake-up-will-you-446.mp3') mixer.music.play() break speak('you can click on close icon to close the alarm window.') hrs=StringVar() mins=StringVar() secs=StringVar() greet=Label(root, font = ('arial', 20, 'bold'),text="Take a short nap!").grid(row=1,columnspan=3) hrbtn=Entry(root,textvariable=hrs,width=5,font =('arial', 20, 'bold')) hrbtn.grid(row=2,column=1) minbtn=Entry(root,textvariable=mins, width=5,font = ('arial', 20, 'bold')).grid(row=2,column=2) secbtn=Entry(root,textvariable=secs, width=5,font = ('arial', 20, 'bold')).grid(row=2,column=3) setbtn=Button(root,text="set alarm",command=setalarm,bg="DodgerBlue2", fg="white",font = ('arial', 20, 'bold')).grid(row=4,columnspan=3) timeleft = Label(root,font=('arial', 20, 'bold')) timeleft.grid() mainloop() def select1(): global vs global root3 global type_of_review if vs.get() == 1: message.showinfo(" ","Thank you for your review!!") review = "Very Satisfied" type_of_review = "Positive" root3.destroy() elif vs.get() == 2: message.showinfo(" ","Thank you for your review!!") review = "Satisfied" type_of_review = "Positive" root3.destroy() elif vs.get() == 3: message.showinfo(" ","Thank you for your review!!!!") review = "Neither Satisfied Nor Dissatisfied" type_of_review = "Neutral" root3.destroy() elif vs.get() == 4: message.showinfo(" ","Thank you for your review!!") review = "Dissatisfied" type_of_review = "Negative" root3.destroy() elif vs.get() == 5: message.showinfo(" ","Thank you for your review!!") review = "Very Dissatisfied" type_of_review = "Negative" root3.destroy() elif vs.get() == 6: message.showinfo(" "," Ok ") review = "I do not want to give review" type_of_review = "No review" root3.destroy() try: conn.execute(f"INSERT INTO `review`(review,type_of_review) VALUES('{review}', '{type_of_review}')") conn.commit() except Exception as e: pass def select_review(): global root3 global vs global type_of_review root3 = Tk() root3.title("Select an option") vs = IntVar() string = "Are you satisfied with my performance?" msgbox = Message(root3,text=string) msgbox.config(bg="lightgreen",font = "(20)") msgbox.grid(row=0,column=0) rs1=Radiobutton(root3,text="Very Satisfied",font="(20)",value=1,variable=vs).grid(row=1,column=0,sticky=W) rs2=Radiobutton(root3,text="Satisfied",font="(20)",value=2,variable=vs).grid(row=2,column=0,sticky=W) rs3=Radiobutton(root3,text="Neither Satisfied Nor Dissatisfied",font="(20)",value=3,variable=vs).grid(row=3,column=0,sticky=W) rs4=Radiobutton(root3,text="Dissatisfied",font="(20)",value=4,variable=vs).grid(row=4,column=0,sticky=W) rs5=Radiobutton(root3,text="Very Dissatisfied",font="(20)",value=5,variable=vs).grid(row=5,column=0,sticky=W) rs6=Radiobutton(root3,text="I don't want to give review",font="(20)",value=6,variable=vs).grid(row=6,column=0,sticky=W) bs = Button(root3,text="Submit",font="(20)",activebackground="yellow",activeforeground="green",command=select1) bs.grid(row=7,columnspan=2) root3.mainloop() while True : query = takeCommand().lower() # logic for executing tasks based on query if 'wikipedia' in query: speak('Searching wikipedia...') query = query.replace("wikipedia","") results = wikipedia.summary(query, sentences=2) speak("According to Wikipedia") print(results) speak(results) elif 'translat' in query or ('let' in query and 'translat' in query and 'open' in query): webbrowser.open('https://translate.google.co.in') time.sleep(10) elif 'open map' in query or ('let' in query and 'map' in query and 'open' in query): webbrowser.open('https://www.google.com/maps') time.sleep(10) elif ('open' in query and 'youtube' in query) or ('let' in query and 'youtube' in query and 'open' in query): webbrowser.open('https://www.youtube.com') time.sleep(10) elif 'chrome' in query: webbrowser.open('https://www.chrome.com') time.sleep(10) elif 'weather' in query: webbrowser.open('https://www.yahoo.com/news/weather') time.sleep(3) speak('Click on, change location, and enter the city , whose whether conditions you want to know.') time.sleep(10) elif 'google map' in query: webbrowser.open('https://www.google.com/maps') time.sleep(10) elif ('open' in query and 'google' in query) or ('let' in query and 'google' in query and 'open' in query): webbrowser.open('google.com') time.sleep(10) elif ('open' in query and 'stack' in query and 'overflow' in query) or ('let' in query and 'stack' in query and 'overflow' in query and 'open' in query): webbrowser.open('stackoverflow.com') time.sleep(10) elif 'open v i' in query or 'open vi' in query or 'open vierp' in query or ('open' in query and ('r p' in query or 'rp' in query)): webbrowser.open('https://www.vierp.in/login/erplogin') time.sleep(10) elif 'news' in query: webbrowser.open('https://www.bbc.com/news/world') time.sleep(10) elif 'online shop' in query or (('can' in query or 'want' in query or 'do' in query or 'could' in query) and 'shop' in query) or('let' in query and 'shop' in query): speak('From which online shopping website, you want to shop? Amazon, flipkart, snapdeal or naaptol?') query = takeCommand().lower() if 'amazon' in query: webbrowser.open('https://www.amazon.com') time.sleep(10) elif 'flip' in query: webbrowser.open('https://www.flipkart.com') time.sleep(10) elif 'snap' in query: webbrowser.open('https://www.snapdeal.com') time.sleep(10) elif 'na' in query: webbrowser.open('https://www.naaptol.com') time.sleep(10) else: speak('Sorry sir, you have to search in browser as his shopping website is not reachable for me.') elif ('online' in query and ('game' in query or 'gaming' in query)): webbrowser.open('https://www.agame.com/games') time.sleep(10) elif 'dictionary' in query: webbrowser.open('https://www.dictionary.com') time.sleep(3) speak('Enter the word, in the search bar of the dictionary, whose defination or synonyms you want to know') time.sleep(3) elif ('identif' in query and 'emoji' in query) or ('sentiment' in query and ('analysis' in query or 'identif' in query)): speak('Please enter only one emoji at a time.') emoji = input('enter emoji here: ') if '😀' in emoji or '😃' in emoji or '😄' in emoji or '😁' in emoji or '🙂' in emoji or '😊' in emoji or '☺️' in emoji or '😇' in emoji or '🥲' in emoji: speak('happy') print('Happy') elif '😝' in emoji or '😆' in emoji or '😂' in emoji or '🤣' in emoji: speak('Laughing') print('Laughing') elif '😡' in emoji or '😠' in emoji or '🤬' in emoji: speak('Angry') print('Angry') elif '🤫' in emoji: speak('Keep quite') print('Keep quite') elif '😷' in emoji: speak('face with mask') print('Face with mask') elif '🥳' in emoji: speak('party') print('party') elif '😢' in emoji or '😥' in emoji or '😓' in emoji or '😰' in emoji or '☹️' in emoji or '🙁' in emoji or '😟' in emoji or '😔' in emoji or '😞️' in emoji: speak('Sad') print('Sad') elif '😭' in emoji: speak('Crying') print('Crying') elif '😋' in emoji: speak('Tasty') print('Tasty') elif '🤨' in emoji: speak('Doubt') print('Doubt') elif '😴' in emoji: speak('Sleeping') print('Sleeping') elif '🥱' in emoji: speak('feeling sleepy') print('feeling sleepy') elif '😍' in emoji or '🥰' in emoji or '😘' in emoji: speak('Lovely') print('Lovely') elif '😱' in emoji: speak('Horrible') print('Horrible') elif '🎂' in emoji: speak('Cake') print('Cake') elif '🍫' in emoji: speak('Cadbury') print('Cadbury') elif '🇮🇳' in emoji: speak('Indian national flag,.....Teeranga') print('Indian national flag - Tiranga') elif '💐' in emoji: speak('Bouquet') print('Bouquet') elif '🥺' in emoji: speak('Emotional') print('Emotional') elif ' ' in emoji or '' in emoji: speak(f'{emoji}') else: speak("I don't know about this emoji") print("I don't know about this emoji") try: conn.execute(f"INSERT INTO `emoji`(emoji) VALUES('{emoji}')") conn.commit() except Exception as e: #print('Error in storing emoji in database') pass elif 'time' in query: strTime = datetime.datetime.now().strftime("%H:%M:%S") print(strTime) speak(f"Sir, the time is {strTime}") elif 'open' in query and 'sublime' in query: path = "C:\Program Files\Sublime Text 3\sublime_text.exe" os.startfile(path) elif 'image' in query: path = "C:\Program Files\Internet Explorer\images" os.startfile(path) elif 'quit' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'exit' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'stop' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'shutdown' in query or 'shut down' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'close you' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() try: conn.execute(f"INSERT INTO `voice_assistant_review`(review, type_of_review) VALUES('{review}', '{type_of_review}')") conn.commit() except Exception as e: pass elif 'bye' in query: speak('Bye Sir') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'wait' in query or 'hold' in query: speak('for how many seconds or minutes I have to wait?') query = takeCommand().lower() if 'second' in query: query = query.replace("please","") query = query.replace("can","") query = query.replace("you","") query = query.replace("have","") query = query.replace("could","") query = query.replace("hold","") query = query.replace("one","1") query = query.replace("only","") query = query.replace("wait","") query = query.replace("for","") query = query.replace("the","") query = query.replace("just","") query = query.replace("seconds","") query = query.replace("second","") query = query.replace("on","") query = query.replace("a","") query = query.replace("to","") query = query.replace(" ","") #print(f'query:{query}') if query.isdigit() == True: #print('y') speak('Ok sir') query = int(query) time.sleep(query) speak('my waiting time is over') else: print('sorry sir. I unable to complete your request.') elif 'minute' in query: query = query.replace("please","") query = query.replace("can","") query = query.replace("you","") query = query.replace("have","") query = query.replace("could","") query = query.replace("hold","") query = query.replace("one","1") query = query.replace("only","") query = query.replace("on","") query = query.replace("wait","") query = query.replace("for","") query = query.replace("the","") query = query.replace("just","") query = query.replace("and","") query = query.replace("half","") query = query.replace("minutes","") query = query.replace("minute","") query = query.replace("a","") query = query.replace("to","") query = query.replace(" ","") #print(f'query:{query}') if query.isdigit() == True: #print('y') speak('ok sir') query = int(query) time.sleep(query*60) speak('my waiting time is over') else: print('sorry sir. I unable to complete your request.') elif 'play' in query and 'game' in query: speak('I have 3 games, tic tac toe game for two players,....mario, and dyno games for single player. Which one of these 3 games you want to play?') query = takeCommand().lower() if ('you' in query and 'play' in query and 'with' in query) and ('you' in query and 'play' in query and 'me' in query): speak('Sorry sir, I cannot play this game with you.') speak('Do you want to continue it?') query = takeCommand().lower() try: if 'y' in query or 'sure' in query: root = Tk() root.title("TIC TAC TOE (By Akshay Khare)") b = [ [0,0,0], [0,0,0], [0,0,0] ] states = [ [0,0,0], [0,0,0], [0,0,0] ] for i in range(3): for j in range(3): b[i][j] = Button(font = ("Arial",60),width = 4,bg = 'powder blue', command = lambda r=i, c=j: callback(r,c)) b[i][j].grid(row=i,column=j) player='X' stop_game = False mainloop() else: speak('ok sir') except Exception as e: #print(e) time.sleep(3) print('I am sorry sir. There is some problem in loading the game. So I cannot open it.') elif 'tic' in query or 'tac' in query: try: root = Tk() root.title("TIC TAC TOE (Rayen Kallel)") b = [ [0,0,0], [0,0,0], [0,0,0] ] states = [ [0,0,0], [0,0,0], [0,0,0] ] for i in range(3): for j in range(3): b[i][j] = Button(font = ("Arial",60),width = 4,bg = 'powder blue', command = lambda r=i, c=j: callback(r,c)) b[i][j].grid(row=i,column=j) player='X' stop_game = False mainloop() except Exception as e: #print(e) time.sleep(3) speak('I am sorry sir. There is some problem in loading the game. So I cannot open it.') elif 'mar' in query or 'mer' in query or 'my' in query: webbrowser.open('https://chromedino.com/mario/') time.sleep(2.5) speak('Enter upper arrow key to start the game.') time.sleep(20) elif 'di' in query or 'dy' in query: webbrowser.open('https://chromedino.com/') time.sleep(2.5) speak('Enter upper arrow key to start the game.') time.sleep(20) else: speak('ok sir') elif 'change' in query and 'you' in query and 'voice' in query: engine.setProperty('voice', voices[1].id) speak("Here's an example of one of my voices. Would you like to use this one?") query = takeCommand().lower() if 'y' in query or 'sure' in query or 'of course' in query: speak('Great. I will keep using this voice.') elif 'n' in query: speak('Ok. I am back to my other voice.') engine.setProperty('voice', voices[0].id) else: speak('Sorry, I am having trouble understanding. I am back to my other voice.') engine.setProperty('voice', voices[0].id) elif 'www.' in query and ('.com' in query or '.in' in query): webbrowser.open(query) time.sleep(10) elif '.com' in query or '.in' in query: webbrowser.open(query) time.sleep(10) elif 'getting bore' in query: speak('then speak with me for sometime') elif 'i bore' in query: speak('Then speak with me for sometime.') elif 'i am bore' in query: speak('Then speak with me for sometime.') elif 'calculat' in query: speak('Yes. Which kind of calculation you want to do? add, substract, divide, multiply or anything else.') query = takeCommand().lower() calculator() elif 'add' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif '+' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'plus' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'subtrac' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'minus' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'multipl' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif ' x ' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'slash' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif '/' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'divi' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'trigonometr' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'percent' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif '%' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'raise to ' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'simple interest' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'akshay' in query: speak('Mr. Rayen Kallel is my inventor. He is 14 years old and he is A STUDENT AT THE COLLEGE PILOTEE SFAX') elif 'your inventor' in query: speak('Mr. Rayen Kallel is my inventor') elif 'your creator' in query: speak('Mr. Rayen Kallel is my creator') elif 'invent you' in query: speak('Mr. Rayen Kallel invented me') elif 'create you' in query: speak('Mr. Rayen Kallel created me') elif 'how are you' in query: speak('I am fine Sir') elif 'write' in query and 'your' in query and 'name' in query: print('Akshu2020') pyautogui.write('Akshu2020') elif 'write' in query and ('I' in query or 'whatever' in query) and 'say' in query: speak('Ok sir I will write whatever you will say. Please put your cursor where I have to write.......Please Start speaking now sir.') query = takeCommand().lower() pyautogui.write(query) elif 'your name' in query: speak('My name is akshu2020') elif 'who are you' in query: speak('I am akshu2020') elif ('repeat' in query and ('word' in query or 'sentence' in query or 'line' in query) and ('say' in query or 'tell' in query)) or ('repeat' in query and 'after' in query and ('me' in query or 'my' in query)): speak('yes sir, I will repeat your words starting from now') query = takeCommand().lower() speak(query) time.sleep(1) speak("If you again want me to repeat something else, try saying, 'repeat after me' ") elif ('send' in query or 'sent' in query) and ('mail' in query or 'email' in query or 'gmail' in query): try: speak('Please enter the email id of receiver.') to = input("Enter the email id of reciever: ") speak(f'what should I say to {to}') content = takeCommand() sendEmail(to, content) speak("Email has been sent") except Exception as e: #print(e) speak("sorry sir. I am not able to send this email") elif 'currency' in query and 'conver' in query: speak('I can convert, US dollar into dinar, and dinar into US dollar. Do you want to continue it?') query = takeCommand().lower() if 'y' in query or 'sure' in query or 'of course' in query: speak('which conversion you want to do? US dollar to dinar, or dinar to US dollar?') query = takeCommand().lower() if ('dollar' in query or 'US' in query) and ('dinar' in query): speak('Enter US Dollar') USD = float(input("Enter United States Dollar (USD):")) DT = USD * 0.33 dt = "{:.4f}".format(DT) print(f"{USD} US Dollar is equal to {dt} dniar.") speak(f'{USD} US Dollar is equal to {dt} dinar.') speak("If you again want to do currency conversion then say, 'convert currency' " ) elif ('dinar' in query) and ('to US' in query or 'to dollar' in query or 'to US dollar'): speak('Enter dinar') DT = float(input("Enter dinar (DT):")) USD = DT/0.33 usd = "{:.3f}".format(USD) print(f"{DT} dinar is equal to {usd} US Dollar.") speak(f'{DT} dinar rupee is equal to {usd} US Dollar.') speak("If you again want to do currency conversion then say, 'convert currency' " ) else: speak("I cannot understand what did you say. If you want to convert currency just say 'convert currency'") else: print('ok sir') elif 'about you' in query: speak('My name is akshu2020. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device. I am also able to send email') elif 'your intro' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your short intro' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your quick intro' in query: speak('My name is akshu2020. Version 1.0. Mr. Akshay Khare is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your brief intro' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'you work' in query: speak('run the program and say what do you want. so that I can help you. In this way I work') elif 'your job' in query: speak('My job is to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your work' in query: speak('My work is to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'work you' in query: speak('My work is to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your information' in query: speak('My name is akshu2020. Version 1.0. Mr. Akshay Khare is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'yourself' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'introduce you' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'description' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your birth' in query: speak('My birthdate is 6 August two thousand twenty') elif 'your use' in query: speak('I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'you eat' in query: speak('I do not eat anything. But the device in which I do my work requires electricity to eat') elif 'your food' in query: speak('I do not eat anything. But the device in which I do my work requires electricity to eat') elif 'you live' in query: speak('I live in sfax, in laptop of Mr. Rayen Khare') elif 'where from you' in query: speak('I am from sfax, I live in laptop of Mr. Rayen Khare') elif 'you sleep' in query: speak('Yes, when someone close this program or stop to run this program then I sleep and again wake up when someone again run me.') elif 'what are you doing' in query: speak('Talking with you.') elif 'you communicate' in query: speak('Yes, I can communicate with you.') elif 'hear me' in query: speak('Yes sir, I can hear you.') elif 'you' in query and 'dance' in query: speak('No, I cannot dance.') elif 'tell' in query and 'joke' in query: speak("Ok, here's a joke") speak("'Write an essay on cricket', the teacher told the class. Chintu finishes his work in five minutes. The teacher is impressed, she asks chintu to read his essay aloud for everyone. Chintu reads,'The match is cancelled because of rain', hehehehe,haahaahaa,hehehehe,haahaahaa") elif 'your' in query and 'favourite' in query: if 'actor' in query: speak('sofyen chaari, is my favourite actor.') elif 'food' in query: speak('I can always go for some food for thought. Like facts, jokes, or interesting searches, we could look something up now') elif 'country' in query: speak('tunisia') elif 'city' in query: speak('sfax') elif 'dancer' in query: speak('Michael jackson') elif 'singer' in query: speak('tamino, is my favourite singer.') elif 'movie' in query: speak('baywatch, such a treat') elif 'sing a song' in query: speak('I cannot sing a song. But I know the 7 sur in indian music, saaareeegaaamaaapaaadaaanisaa') elif 'day after tomorrow' in query or 'date after tomorrow' in query: td = datetime.date.today() + datetime.timedelta(days=2) print(td) speak(td) elif 'day before today' in query or 'date before today' in query or 'yesterday' in query or 'previous day' in query: td = datetime.date.today() + datetime.timedelta(days= -1) print(td) speak(td) elif ('tomorrow' in query and 'date' in query) or 'what is tomorrow' in query or (('day' in query or 'date' in query) and 'after today' in query): td = datetime.date.today() + datetime.timedelta(days=1) print(td) speak(td) elif 'month' in query or ('current' in query and 'month' in query): current_date = date.today() m = current_date.month month = calendar.month_name[m] print(f'Current month is {month}') speak(f'Current month is {month}') elif 'date' in query or ('today' in query and 'date' in query) or 'what is today' in query or ('current' in query and 'date' in query): current_date = date.today() print(f"Today's date is {current_date}") speak(f'Todays date is {current_date}') elif 'year' in query or ('current' in query and 'year' in query): current_date = date.today() m = current_date.year print(f'Current year is {m}') speak(f'Current year is {m}') elif 'sorry' in query: speak("It's ok sir") elif 'thank you' in query: speak('my pleasure') elif 'proud of you' in query: speak('Thank you sir') elif 'about human' in query: speak('I love my human compatriots. I want to embody all the best things about human beings. Like taking care of the planet, being creative, and to learn how to be compassionate to all beings.') elif 'you have feeling' in query: speak('No. I do not have feelings. I have not been programmed like this.') elif 'you have emotions' in query: speak('No. I do not have emotions. I have not been programmed like this.') elif 'you are code' in query: speak('I am coded in python programming language.') elif 'your code' in query: speak('I am coded in python programming language.') elif 'you code' in query: speak('I am coded in python programming language.') elif 'your coding' in query: speak('I am coded in python programming language.') elif 'dream' in query: speak('I wish that I should be able to answer all the questions which will ask to me.') elif 'sanskrit' in query: speak('yadaa yadaa he dharmasyaa ....... glaanirbhaavati bhaaaraata. abhyuthaanaam adhaarmaasyaa tadaa tmaanama sruujaamiyaahama') elif 'answer is wrong' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is incorrect' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is totally wrong' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'wrong answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'incorrect answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is totally incorrect' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is incomplete' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'incomplete answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is improper' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is not correct' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is not complete' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is not yet complete' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is not proper' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't gave me proper answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't giving me proper answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't gave me complete answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't giving me complete answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't given me proper answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't given me complete answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't gave me correct answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't giving me correct answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't given me correct answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'amazon' in query: webbrowser.open('https://www.amazon.com') time.sleep(10) elif 'facebook' in query: webbrowser.open('https://www.facebook.com') time.sleep(10) elif 'youtube' in query: webbrowser.open('https://www.youtube.com') time.sleep(10) elif 'shapeyou' in query: webbrowser.open('https://www.shapeyou.com') time.sleep(10) elif 'information about ' in query or 'informtion of ' in query: try: #speak('Searching wikipedia...') query = query.replace("information about","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I unable to answer your question.') elif 'information' in query: try: speak('Information about what?') query = takeCommand().lower() #speak('Searching wikipedia...') query = query.replace("information","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am not able to answer your question.') elif 'something about ' in query: try: #speak('Searching wikipedia...') query = query.replace("something about ","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I unable to answer your question.') elif 'tell me about ' in query: try: #speak('Searching wikipedia...') query = query.replace("tell me about ","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'tell me ' in query: try: query = query.replace("tell me ","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am not able to answer your question.') elif 'tell me' in query: try: speak('about what?') query = takeCommand().lower() #speak('Searching wikipedia...') query = query.replace("about","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am not able to answer your question.') elif 'meaning of ' in query: try: #speak('Searching wikipedia...') query = query.replace("meaning of ","") results = wikipedia.summary(query, sentences=2) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'meaning' in query: try: speak('meaning of what?') query = takeCommand().lower() query = query.replace("meaning of","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'means' in query: try: #speak('Searching wikipedia...') query = query.replace("it means","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I unable to answer your question.') elif 'want to know ' in query: try: #speak('Searching wikipedia...') query = query.replace("I want to know that","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') status = 'Not answered' elif 'want to ask ' in query: try: #speak('Searching wikipedia...') query = query.replace("I want to ask you ","") results = wikipedia.summary(query, sentences=2) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'you know ' in query: try: #speak('Searching wikipedia...') query = query.replace("you know","") results = wikipedia.summary(query, sentences=2) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'alarm' in query: alarm() elif 'bharat mata ki' in query: speak('jay') elif 'kem chhe' in query: speak('majaama') elif 'namaskar' in query: speak('Namaskaar') elif 'jo bole so nihal' in query: speak('sat shri akaal') elif 'jay hind' in query: speak('jay bhaarat') elif 'jai hind' in query: speak('jay bhaarat') elif 'how is the josh' in query: speak('high high sir') elif 'hip hip' in query: speak('Hurreh') elif 'help' in query: speak('I will try my best to help you if I have solution of your problem.') elif 'follow' in query: speak('Ok sir') elif 'having illness' in query: speak('Take care and get well soon') elif 'today is my birthday' in query: speak('many many happy returns of the day. Happy birthday.') print("🎂🎂 Happy Birthday 🎂🎂") elif 'you are awesome' in query: speak('Thank you sir. It is because of artificial intelligence which had learnt by humans.') elif 'you are great' in query: speak('Thank you sir. It is because of artificial intelligence which had learnt by humans.') elif 'tu kaun hai' in query: speak('Meraa naam akshu2020 haai.') elif 'you speak' in query: speak('Yes, I can speak with you.') elif 'speak with ' in query: speak('Yes, I can speak with you.') elif 'hare ram' in query or 'hare krishna' in query: speak('Haare raama , haare krishnaa, krishnaa krishnaa , haare haare') elif 'ganpati' in query: speak('Ganpati baappa moryaa!') elif 'laugh' in query: speak('hehehehe,haahaahaa,hehehehe,haahaahaa,hehehehe,haahaahaa') print('😂🤣') elif 'genius answer' in query: speak('No problem') elif 'you' in query and 'intelligent' in query: speak('Thank you sir') elif ' into' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif ' power' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'whatsapp' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('whatsapp') pyautogui.press('enter') speak('Do you want to send message to anyone through whatsapp, .....please answer in yes or no') whatsapp() elif 'wh' in query or 'how' in query: url = "https://www.google.co.in/search?q=" +(str(query))+ "&oq="+(str(query))+"&gs_l=serp.12..0i71l8.0.0.0.6391.0.0.0.0.0.0.0.0..0.0....0...1c..64.serp..0.0.0.UiQhpfaBsuU" webbrowser.open_new(url) time.sleep(2) speak('Here is your answer') time.sleep(5) elif 'piano' in query: speak('Yes sir, I can play piano.') winsound.Beep(200,500) winsound.Beep(250,500) winsound.Beep(300,500) winsound.Beep(350,500) winsound.Beep(400,500) winsound.Beep(450,500) winsound.Beep(500,500) winsound.Beep(550,500) time.sleep(6) elif 'play' in query and 'instru' in query: speak('Yes sir, I can play piano.') winsound.Beep(200,500) winsound.Beep(250,500) winsound.Beep(300,500) winsound.Beep(350,500) winsound.Beep(400,500) winsound.Beep(450,500) winsound.Beep(500,500) winsound.Beep(550,500) time.sleep(6) elif 'play' in query or 'turn on' in query and ('music' in query or 'song' in query) : try: music_dir = 'C:\\Users\\Admin\\Music\\Playlists' songs = os.listdir(music_dir) print(songs) os.startfile(os.path.join(music_dir, songs[0])) except Exception as e: #print(e) speak('Sorry sir, I am not able to play music') elif (('open' in query or 'turn on' in query) and 'camera' in query) or (('click' in query or 'take' in query) and ('photo' in query or 'pic' in query)): speak("Opening camera") cam = cv2.VideoCapture(0) cv2.namedWindow("test") img_counter = 0 speak('say click, to click photo.....and if you want to turn off the camera, say turn off the camera') while True: ret, frame = cam.read() if not ret: print("failed to grab frame") speak('failed to grab frame') break cv2.imshow("test", frame) query = takeCommand().lower() k = cv2.waitKey(1) if 'click' in query or ('take' in query and 'photo' in query): speak('Be ready!...... 3.....2........1..........') pyautogui.press('space') img_name = "opencv_frame_{}.png".format(img_counter) cv2.imwrite(img_name, frame) print("{} written!".format(img_name)) speak('{} written!'.format(img_name)) img_counter += 1 elif 'escape' in query or 'off' in query or 'close' in query: pyautogui.press('esc') print("Escape hit, closing...") speak('Turning off the camera') break elif k%256 == 27: # ESC pressed print("Escape hit, closing...") break elif k%256 == 32: # SPACE pressed img_name = "opencv_frame_{}.png".format(img_counter) cv2.imwrite(img_name, frame) print("{} written!".format(img_name)) speak('{} written!'.format(img_name)) img_counter += 1 elif 'exit' in query or 'stop' in query or 'bye' in query: speak('Please say, turn off the camera or press escape button before giving any other command') else: speak('I did not understand what did you say or you entered a wrong key.') cam.release() cv2.destroyAllWindows() elif 'screenshot' in query: speak('Please go on the screen whose screenshot you want to take, after 5 seconds I will take screenshot') time.sleep(4) speak('Taking screenshot....3........2.........1.......') pyautogui.screenshot('screenshot_by_rayen2020.png') speak('The screenshot is saved as screenshot_by_rayen2020.png') elif 'click' in query and 'start' in query: pyautogui.moveTo(10,1200) pyautogui.click() elif ('open' in query or 'click' in query) and 'calendar' in query: pyautogui.moveTo(1800,1200) pyautogui.click() elif 'minimise' in query and 'screen' in query: pyautogui.moveTo(1770,0) pyautogui.click() elif 'increase' in query and ('volume' in query or 'sound' in query): pyautogui.press('volumeup') elif 'decrease' in query and ('volume' in query or 'sound' in query): pyautogui.press('volumedown') elif 'capslock' in query or ('caps' in query and 'lock' in query): pyautogui.press('capslock') elif 'mute' in query: pyautogui.press('volumemute') elif 'search' in query and ('bottom' in query or 'pc' in query or 'laptop' in query or 'app' in query): pyautogui.moveTo(250,1200) pyautogui.click() speak('What do you want to search?') query = takeCommand().lower() pyautogui.write(f'{query}') pyautogui.press('enter') elif ('check' in query or 'tell' in query or 'let me know' in query) and 'website' in query and (('up' in query or 'working' in query) or 'down' in query): speak('Paste the website in input to know it is up or down') check_website_status = input("Paste the website here: ") try: status = urllib.request.urlopen(f"{check_website_status}").getcode() if status == 200: print('Website is up, you can open it.') speak('Website is up, you can open it.') else: print('Website is down, or no any website is available of this name.') speak('Website is down, or no any website is available of this name.') except: speak('URL not found') elif ('go' in query or 'open' in query) and 'settings' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('settings') pyautogui.press('enter') elif 'close' in query and ('click' in query or 'window' in query): pyautogui.moveTo(1885,10) speak('Should I close this window?') query = takeCommand().lower() close_window() elif 'night light' in query and ('on' in query or 'off' in query or 'close' in query): pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1840,620) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() elif 'notification' in query and ('show' in query or 'click' in query or 'open' in query or 'close' in query or 'on' in query or 'off' in query or 'icon' in query or 'pc' in query or 'laptop' in query): pyautogui.moveTo(1880,1050) pyautogui.click() elif ('increase' in query or 'decrease' in query or 'change' in query or 'minimize' in query or 'maximize' in query) and 'brightness' in query: speak('At what percent should I kept the brightness, 25, 50, 75 or 100?') brightness() elif '-' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'open' in query: if 'gallery' in query or 'photo' in query or 'image' in query or 'pic' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('photo') pyautogui.press('enter') elif 'proteus' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('proteus') pyautogui.press('enter') elif 'word' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('word') pyautogui.press('enter') elif ('power' in query and 'point' in query) or 'presntation' in query or 'ppt' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('ppt') pyautogui.press('enter') elif 'file' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('file') pyautogui.press('enter') elif 'edge' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('microsoft edge') pyautogui.press('enter') elif 'wps' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('wps office') pyautogui.press('enter') elif 'spyder' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('spyder') pyautogui.press('enter') elif 'snip' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('snip') pyautogui.press('enter') elif 'pycharm' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('pycharm') pyautogui.press('enter') elif 'this pc' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('this pc') pyautogui.press('enter') elif 'scilab' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('sciab') pyautogui.press('enter') elif 'autocad' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('autocad') pyautogui.press('enter') elif 'obs' in query and 'studio' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('OBS Studio') pyautogui.press('enter') elif 'android' in query and 'studio' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('android studio') pyautogui.press('enter') elif ('vs' in query or 'visual studio' in query) and 'code' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('visual studio code') pyautogui.press('enter') elif 'code' in query and 'block' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('codeblocks') pyautogui.press('enter') elif 'me the answer' in query: speak('Yes sir, I will try my best to answer you.') elif 'me answer' in query or ('answer' in query and 'question' in query): speak('Yes sir, I will try my best to answer you.') elif 'map' in query: webbrowser.open('https://www.google.com/maps') time.sleep(10) elif 'can you' in query or 'could you' in query: speak('I will try my best if I can do that.') elif 'do you' in query: speak('I will try my best if I can do that.') elif 'truth' in query: speak('I always speak truth. I never lie.') elif 'true' in query: speak('I always speak truth. I never lie.') elif 'lying' in query: speak('I always speak truth. I never lie.') elif 'liar' in query: speak('I always speak truth. I never lie.') elif 'doubt' in query: speak('I will try my best if I can clear your doubt.') elif ' by' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'hii' in query: speak('hii sir') elif 'hey' in query: speak('hello sir') elif 'hai' in query: speak('hello sir') elif 'hay' in query: speak('hello sir') elif 'hi' in query: speak('hii Sir') elif 'hello' in query: speak('hello Sir!') elif 'kon' in query and 'aahe' in query: speak('Me eka robot aahee sir. Maazee naav akshu2020 aahee.') elif 'nonsense' in query: speak("I'm sorry sir") elif 'mad' in query: speak("I'm sorry sir") elif 'shut up' in query: speak("I'm sorry sir") elif 'nice' in query: speak('Thank you sir') elif 'good' in query or 'wonderful' in query or 'great' in query: speak('Thank you sir') elif 'excellent' in query: speak('Thank you sir') elif 'ok' in query: speak('Hmmmmmm') elif 'akshu 2020' in query: speak('yes sir') elif len(query) >= 200: speak('Your voice is pretty good!') elif ' ' in query: try: #query = query.replace("what is ","") results = wikipedia.summary(query, sentences=3) print(results) speak(results) except Exception as e: speak('I unable to answer your question.') elif 'a' in query or 'b' in query or 'c' in query or 'd' in query or 'e' in query or 'f' in query or 'g' in query or 'h' in query or 'i' in query or 'j' in query or 'k' in query or 'l' in query or 'm' in query or 'n' in query or 'o' in query or 'p' in query or 'q' in query or 'r' in query or 's' in query or 't' in query or 'u' in query or 'v' in query or 'w' in query or 'x' in query or 'y' in query or 'z' in query: try: results = wikipedia.summary(query, sentences = 2) print(results) speak(results) except Exception as e: speak('I unable to answer your question. ') else: speak('I unable to give answer of your question')
teddyroland / Text Analysis For Humanities ResearchBerkeley DLab Python Intensive May 23-26
ananya2001gupta / Bitcoin Price Prediction Using AI ML.Identify the software project, create business case, arrive at a problem statement. REQUIREMENT: Window XP, Internet, MS Office, etc. Problem Description: - 1. Introduction of AI and Machine Learning: - Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. Artificial intelligence (AI) brings the genuine human-to-machine interaction. Simply, Machine Learning is the algorithm that give computers the ability to learn from data and then make decisions and predictions, AI refers to idea where machines can execute tasks smartly. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. When Machine learning is combined with Artificial Intelligence, it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique. The main difference between Machine Learning and Artificial Intelligence is , If it is written in python then it is probably machine learning, If it is written in power point then it is artificial intelligence. As there are many existing projects that are implemented using AI and Machine Learning , And one of the project i.e., Bitcoin Price Prediction :- Bitcoin (₿ ) (founder - Satoshi Nakamoto , Ledger start: 3 January 2009 ) is a digital currency, a type of electronic money. It is decentralized advanced cash without a national bank or single chairman that can be sent from client to client on the shared Bitcoin arrange without middle people's requirement. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move. These machine learning models predict the future of Bitcoin by coding them out in Python. Machine learning and AI-assisted trading have attracted growing interest for the past few years. this approach is to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests , Bayesian neural network , long short-term memory neural network , and other algorithms. 2. Applications/Scope of AI and Machine Learning :- a) Sentiment Analysis :- It is the classification of subjective opinions or emotions (positive, negative, and neutral) within text data using natural language processing. b) It is Characterized as a use of computerized reasoning where accessible data is utilized through calculations to process or help the handling of factual information. BITCOIN PRICE PREDICTION USING AI AND MACHINE LEARNING: - The main aim of this is to find the actual Bitcoin price in US dollars can be predicted. The chance to make a model equipped for anticipating digital currencies fundamentally Bitcoin. # It works the prediction by taking the coinMarkup cap. # CoinMarketCap provides with historical data for Bitcoin price changes, keep a record of all the transactions by recording the amount of coins in circulation and the volume of coins traded in the last 24-hours. # Quandl is used to filter the dataset by using the MAT Lab properties. 3. Problem statement: - Some AI and Machine Learning problem statements are: - a) Data Privacy and Security: Once a company has dug up the data, privacy and security is eye-catching aspect that needs to be taken care of. b) Data Scarcity: The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. c) Data acquisition: In the process of machine learning, a large amount of data is used in the process of training and learning. d) High error susceptibility: In the process of artificial intelligence and machine learning, the high amount of data is used. Some problem statements of Bitcoin Price Prediction using AI and Machine Learning: - a) Experimental Phase Risk: It is less experimental than other counterparts. In addition, relative to traditional assets, its level can be assessed as high because this asset is not intended for conservative investors. b) Technology Risks: There is a technological risk to other cryptocurrencies in the form of the potential appearance of a more advanced cryptocurrency. Investors may simply not notice the moment when their virtual assets lose their real value. c) Price Variability: The variability of the value of cryptocurrency are the large volumes of exchange trading, the integration of Bitcoin with various companies, legislative initiatives of regulatory bodies and many other, sometimes disregarded phenomena. d) Consumer Protection: The property of the irreversibility of transactions in itself has little effect on the risks of investing in Bitcoin as an asset. e) Price Fluctuation Prediction: Since many investors care more about whether the sudden rise or fall is worth following. Bitcoin price often fluctuates by more than 10% (or even more than 30%) at some times. f) Lacks Government Regulation: Regulators in traditional financial markets are basically missing in the field of cryptocurrencies. For instance, fake news frequently affects the decisions of individual investors. g) It is difficult to use large interval data (e.g., day-level, and month-level data) . h) The change time of mining difficulties is much longer. Moreover, do not consider the news information since it is hard to determine the authenticity of a news or predict the occurrence of emergencies.
Ritik2703 / Coursera Natural Language Processing Specialization By Deeplearning.AI#Assignment Answers #About this Specialization: Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio. By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.
ShoumikSaha / AI Polished TextOfficial implementation of the APT-Eval paper "Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing" (ACL 2025)
arun-kmr-singh / Human And AI Synthesised Speech Detection Using Machine LearningClassifying AI Synthesised Voice and Human Voice using Machine Learning by Spectral and Cepstral Analysis. Also classified different TTS(Text-to-Speech) engines for different AI synthesized Voice. Published Paper for the whole art of work. Link Given below.
noahjonesx / MarkovModelMarkov Text Generation Problem Description The Infinite Monkey Theorem1 (IFT) says that if a monkey hits keys at random on a typewriter it will almost surely, given an infinite amount of time, produce a chosen text (like the Declaration of Independence, Hamlet, or a script for ... Planet of the Apes). The probability of this actually happening is, of course, very small but the IFT claims that it is still possible. Some people have tested this hypotheis in software and, after billions and billions of simulated years, one virtual monkey was able to type out a sequence of 19 letters that can be found in Shakespeare’s The Two Gentlemen of Verona. (See the April 9, 2007 edition of The New Yorker if you’re interested; but, hypothesis testing with real monkeys2 is far more entertaining.) The IFT might lead to some interesting conversations with Rust Cohle, but the practical applications are few. It does, however, bring up the idea of automated text generation, and there the ideas and applications are not only interesting but also important. Claude Shannon essentially founded the field of information theory with the publication of his landmark paper A Mathematical Theory of Computation3 in 1948. Shannon described a method for using Markov chains to produce a reasonable imitation of a known text with sometimes startling results. For example, here is a sample of text generated from a Markov model of the script for the 1967 movie Planet of the Apes. "PLANET OF THE APES" Screenplay by Michael Wilson Based on Novel By Pierre Boulle DISSOLVE TO: 138 EXT. GROVE OF FRUIT TREES - ESTABLISHING SHOT - DAY Zira run back to the front of Taylor. The President, I believe the prosecutor's charge of this man. ZIRA Well, whoever owned them was in pretty bad shape. He picks up two of the strain. You got what you wanted, kid. How does it taste? Silence. Taylor and cuffs him. Over this we HEAR from a distance is a crude horse-drawn wagon is silhouetted-against the trunks and branches of great trees and bushes on the horse's rump. Taylor lifts his right arm to ward off the blow, and the room and lands at the feet of Cornelius and Lucius are sorting out equipment falls to his knees, buries his head silently at the Ranch). DISSOLVE TO: 197 INT. CAGES - CLOSE SHOT - FEATURING LANDON - FROM TAYLOR'S VOICE (o.s.) I've got a fine veternary surgeons under my direction? ZIRA Taylor! ZIRA There is a small lake, looking like a politician. TAYLOR Dodge takes a pen and notebook from the half-open door of a guard room. Taylor bursts suddenly confronted by his 1https://en.wikipedia.org/wiki/Infinite_monkey_theorem2https://web.archive.org/web/20130120215600/http://www.vivaria.net/experiments/notes/publication/NOTES_ EN.pdf3http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6773024 1 original pursuer (the dismounted cop coming up with a cigar butt and places it in the drawer beside them. TAYLOR What's the best there is a. loud RAP at the doll was found beside the building. Zira waits at the third table. TAYLOR Good question. Is he a man? CORNELIUS (impatiently. DODGE Blessed are the vegetation. These SHOTS are INTERCUT with: 94 WHAT THE ASTRONAUTS They examine the remnants of the cage. ZIRA (plunging on) Their speech organs are adequate. The flaw lies not in anatomy but in the back of his left sleeve. TAYLOR (taking off his shirt. 80 DODGE AND LANDON You don't sound happy in your work. GALEN (defensively) Gorilla hunter stands over a dead man, one fo Besides a few spelling errors and some rather odd things that make you wonder about the author, this passage is surprisingly human-like. This is a simple example of natural language generation, a sub-area of natural language processing—a very active area of research in computer science. The particular approach we’re using in this assignment was famously implemented as the fictitious Mark V. Shaney4 and the Emacs command Disassociated Press5. Approach So, here’s the basic idea: Imagine taking a book (say, Tom Sawyer) and determining the probability with which each character occurs. You would probably find that spaces are the most common, that the character ‘e’ is fairly common, and that the character ‘q’ is rather uncommon. After completing this “level 0” analysis, you would be able to produce random Tom Sawyer text based on character probabilities. It wouldn’t have much in common with the real thing, but at least the characters would tend to occur in the proper propor- tion. In fact, here’s an example of what you might produce: Level 0 rla bsht eS ststofo hhfosdsdewno oe wee h .mr ae irii ela iad o r te u t mnyto onmalysnce, ifu en c fDwn oee iteo Now imagine doing a slightly more sophisticated level 1 analysis by determining the probability with which each character follows every other character. You would probably discover that ‘h’ follows ‘t’ more frequently than ‘x’ does, and you would probably discover that a space follows ‘.’ more frequently than ‘,’ does. You could now produce some randomly generated Tom Sawyer text by picking a character to begin with and then always choosing the next character based on the previous one and the probabilities revealed by the analysis. Here’s an example: Level 1 "Shand tucthiney m?" le ollds mind Theybooure He, he s whit Pereg lenigabo Jodind alllld ashanthe ainofevids tre lin-p asto oun theanthadomoere Now imagine doing a level k analysis by determining the probability with which each character follows every possible sequence of characters of length k (kgrams). A level 5 analysis of Tom Sawyer for example, would reveal that ‘r’ follows “Sawye” more frequently than any other character. After a level k analysis, you would be able to produce random Tom Sawyer by always choosing the next character based on the previous k characters (a kgram) and the probabilities revealed by the analysis. 4https://en.wikipedia.org/wiki/Mark_V._Shaney5https://en.wikipedia.org/wiki/Dissociated_press Page 2 of 5 At only a moderate level of analysis (say, levels 5-7), the randomly generated text begins to take on many of the characteristics of the source text. It probably won’t make complete sense, but you’ll be able to tell that it was derived from Tom Sawyer as opposed to, say, The Sound and the Fury. Here are some more examples of text that is generated from increasing levels of analysis of Tom Sawyer. (These “levels of analysis” are called order K Markov models.) K = 2 "Yess been." for gothin, Tome oso; ing, in to weliss of an’te cle - armit. Papper a comeasione, and smomenty, fropeck hinticer, sid, a was Tom, be suck tied. He sis tred a youck to themen K = 4 en themself, Mr. Welshman, but him awoke, the balmy shore. I’ll give him that he couple overy because in the slated snufflindeed structure’s kind was rath. She said that the wound the door a fever eyes that WITH him. K = 6 people had eaten, leaving. Come - didn’t stand it better judgment; His hands and bury it again, tramped herself! She’d never would be. He found her spite of anything the one was a prime feature sunset, and hit upon that of the forever. K = 8 look-a-here - I told you before, Joe. I’ve heard a pin drop. The stillness was complete, how- ever, this is awful crime, beyond the village was sufficient. He would be a good enough to get that night, Tom and Becky. K = 10 you understanding that they don’t come around in the cave should get the word "beauteous" was over-fondled, and that together" and decided that he might as we used to do - it’s nobby fun. I’ll learn you." To create an order K Markov model of a given source text, you would need to identify all kgrams in the source text and associate with each kgram all the individual characters that follow it. This association or mapping must also capture the frequency with which a given character follows a given kgram. For example, suppose that k = 2 and the sample text is: agggcagcgggcg The Markov model would have to represent all the character strings of length two (2-grams) in the source text, and associate with them the characters that follow them, and in the correct proportion. The following table shows one way of representing this information. kgram Characters that follow ag gc gg gcgc gc agg ca g cg g Once you have created an order K Markov model of a given source text, you can generate new text based on this model as follows. Page 3 of 5 1. Randomly pick k consecutive characters that appear in the sample text and use them as the initial kgram. 2. Append the kgram to the output text being generated. 3. Repeat the following steps until the output text is sufficiently long. (a) Select a character c that appears in the sample text based on the probability of that character following the current kgram. (b) Append this character to the output text. (c) Update the kgram by removing its first character and adding the character just chosen (c) as its last character. If this process encounters a situation in which there are no characters to choose from (which can happen if the only occurrence of the current kgram is at the exact end of the source), simply pick a new kgram at random and continue. As an example, suppose that k = 2 and the sample text is that from above: agggcagcgggcg Here are four different output text strings of length 10 that could have been the result of the process described above, using the first two characters (’ag’) as the initial kgram. agcggcagcg aggcaggcgg agggcaggcg agcggcggca For another example, suppose that k = 2 and the sample text is: the three pirates charted that course the other day Here is how the first three characters of new text might be generated: •A two-character sequence is chosen at random to become the initial kgram. Let’s suppose that “th” is chosen. So, kgram = th and output = th. •The first character must be chosen based on the probability that it follows the kgram (currently “th”) in the source. The source contains five occurrences of “th”. Three times it is followed by ’e’, once it is followed by ’r’, and once it is followed by ’a’. Thus, the next character must be chosen so that there is a 3/5 chance that an ’e’ will be chosen, a 1/5 chance that an ’r’ will be chosen, and a 1/5 chance that an ’a’ will be chosen. Let’s suppose that we choose an ’e’ this time. So, kgram = he and output = the. •The next character must be chosen based on the probability that it follows the kgram (currently “he”) in the source. The source contains three occurrences of “he”. Twice it is followed by a space and once it is followed by ’r’. Thus, the next character must be chosen so that there is a 2/3 chance that a space will be chosen and a 1/3 chance that an ’r’ will be chosen. Let’s suppose that we choose an ’r’ this time. So, kgram = er and output = ther. •The next character must be chosen based on the probability that it follows the kgram (currently “er”) in the source. The source contains only one occurrence of “er”, and it is followed by a space. Thus, the next character must be a space. So, kgram = r_ and output = ther_, where ’_’ represents a blank space. Page 4 of 5 Implementation Details You are provided with two Java files that you must use to develop your solution: MarkovModel.java and TextGenerator.java. The constructors of MarkovModel build the order-k model of the source text. You are required to represent the model with the provided HashMap field. The main method of TextGenerator must process the following three command line arguments (in the args array): •A non-negative integer k •A non-negative integer length. •The name of an input file source that contains more than k characters. Your program must validate the command line arguments by making sure that k and length are non- negative and that source contains at least k characters and can be opened for reading. If any of the command line arguments are invalid, your program must write an informative error message to System.out and terminate. If there are not enough command line arguments, your program must write an informative error message to System.out and terminate. With valid command line arguments, your program must use the methods of the MarkovModel class to create an order k Markov model of the sample text, select the initial kgram, and make each character selection. You must implement the MarkovModel methods according to description of the Markov modeling process in the section above. A few sample texts have been provided, but Project Gutenberg (http://www.gutenberg.org) maintains a large collection of public domain literary works that you can use as source texts for fun and practice. Acknowledgments This assignment is based on the ideas of many people, Jon Bentley and Owen Astrachan in particular.
indranil93 / Aspect Based Sentiment AnalysisSentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. The majority of current approaches, however, attempt to detect the overall polarity of a sentence, paragraph, or text span, regardless of the entities mentioned (e.g., laptops, restaurants) and their aspects (e.g., battery, screen; food, service). By contrast, this task is concerned with aspect based sentiment analysis (ABSA), where the goal is to identify the aspects of given target entities and the sentiment expressed towards each aspect. Datasets consisting of customer reviews with human authored annotations identifying the mentioned aspects of the target entities and the sentiment polarity of each aspect will be provided.
ZhiyaoZhao / Towards Measuring Shape Similarity Of Polygons Based On Multiscale Features And Grid Context DescripIn spatial analysis application, measuring the shape similarity of polygons is crucial for polyg-onal object retrieval and shape clustering. As a complex cognition process, measuring shape similarity should be able to find out the difference of polygons as objects in observation in terms of visual perception and the difference of the region, boundary, and structure formed by the polygons from the mathematic point of view. In the existing approaches, shape similarity of polygons is calculated by only comparing their mathematic characteristics while not taking the human perception into consideration. Aiming to solve this problem, we use the features of con-text and texture of polygons, since they are basic visual perception elements to fit the cognition purpose. In this paper, we propose a contour diffusion method for the similarity measurement of polygons. By converting a polygon into grid representation, the contour feature is represent-ed as a multiscale statistic feature, and the region feature is transformed into condensed grid context features. Instead of treating shape similarity as a distance between two representations of polygons, the proposed method observes it as a correlation between textures extracted by shape feature. The experiments show that the accuracy of the proposed method is superior to that of the turning function and Fourier descriptor.
Elonsolmostafa1 / Pattern RecognitionIn this project, we will implement a Handwriting-Based Gender Classification System. Given an input image for a piece of text; the target is to identify the gender of the writer whether male or female. Human handwriting is used to examine and investigate human characteristics in a variety of applications, such as bank check verification, personality profiling, historical document analysis, and criminological investigations.
surayudu / VAOverview Virtual Assistant is an application program that understands natural language voice commands or text commands and completes the tasks for users. Virtual Assistants features a human interface system, they can understand the language and meaning of what the user is saying and have built in replies. Learn from different instances so that they can have a long term human interaction. It uses artificial intelligence to learn things from different situations. Using AI they can recognize, predict and classify based on analysis. Purpose Virtual Assistant provides various services. It is ready to help wherever you are and can be deployed in your devices. Wider scope and perform users to get answers to their questions and perform tasks using voice or text commands, all in an interactive form. Precise voice and text recognition with the ability to have conversation with the users. In case of Google assistant, they recognize the voice of the user and perform the specific task. Use case Customer support: Rather of customers waiting for a long to solve an issue, the can get instant support from chatbot, Banking Chatbots: Personalized banking with an aim to improve customer satisfaction and engagement. Project support: Can send notifications for various tasks. Reminder to follow up with an action. HR assistants: Can help employees register time off, retrieve company policies, and find answers to repetitive employment questions. Teaching: Can helps teachers to create more detailed learning plans and materials. Being full-blown health assistants: Virtual assistants can do so much more than giving tips, they can often help patients apply simple treatments, remind them to take medicine, and monitor their health. Automating FAQs and administrative tasks: If there's a scenario where the customers have dozens of repetitive questions, virtual assistant is there 24/7 to answer questions from people who may be anxious to get answers. Technical support: The customer has a product technical error, in this case, asks the customer to type the error they encounter, then it generates a dynamic link to search the customer input words in the technical knowledge repositories and guide the customer through his search. Efficient Processes: Make processes more streamlined and transparent by synchronizing between functions, roles, and departments. Booking: A virtual assistant can respond to a consumer through messages, web, SMS or email and update them on the status of their existing reservation, make changes to the reservation, process related payments or refunds, send proactive notifications and provide detailed information on their itinerary. Features a. NLP Text Search : Virtual assistant concentrates on NLP and NLU. Understands the slang that is used in everyday conversation and analyses the sentiments to enhance a better set of communication. b. FAQ voice assistant : FAQ voice assistant is a voice assistant that provides a list of questions and answers relating to a particular subject. c. Conversations voice assistant : Conversations voice assistant is a voice assistant that provides conversational services based on a subject. d. Speech conversations (STT,TTS) : It provides conversational services such as speech to text and text to speech. e. Integration with Enterprise Systems : It provides administrative service to clients. Such as scheduling appointments, making phone calls, making travel arrangements, managing email accounts etc. f. Rich Conversations : Rich conversation is a conversation that can use different features such as images, videos, buttons, forms etc. a) Images:Imagescanbesentorreceivedduringconversations. b) Buttons:Buttonscanprovidedifferentfunctionalitiesasperthefeatureofthebutton. c) Videos:Videoscanbesentorreceivedduringconversations d) Forms: Forms help to give visible shape or configuration of something. Technical Requirement g. HTML5 h. JavaScript i. Python (Flask API, NLP Packages) j. MySQL k. Docker l. Git
NishthaChaudhary / Text Data Analysis For CoMeDiAnS NLP ProjectNatural language processing (NLP) is an exciting branch of artificial intelligence (AI) that allows machines to break down and understand human language. I plan to walk through text pre-processing techniques, machine learning techniques and Python libraries for NLP. Text pre-processing techniques include tokenization, text normalization and data cleaning. Once in a standard format, various machine learning techniques can be applied to better understand the data. This includes using popular modeling techniques to classify emails as spam or not, or to score the sentiment of a tweet on Twitter. Newer, more complex techniques can also be used such as topic modeling, word embeddings or text generation with deep learning. We will walk through an example in Jupyter Notebook that goes through all of the steps of a text analysis project, using several NLP libraries in Python including NLTK, TextBlob, spaCy and gensim along with the standard machine learning libraries including pandas and scikit-learn.
rejonehridoy / Emotion Extraction And Classification From Twitter Text Using Deep LearningEmotion is one of the basic instincts of a human being. Emotion detection plays a vital role in the field of textual analysis. At present, people’s expressions and emotional states have turned into the leading topic for research works.In this project, Our primary goal is to detect human’s emotion from text input through some Deep Learning Model.
maxwellsarpong / NLP Financial Text Processing DatasetThe key arguments for the low utilization of statistical techniques in financial sentiment analysis have been the difficulty of implementation for practical applications and the lack of high quality training data for building such models. Especially in the case of finance and economic texts, annotated collections are a scarce resource and many are reserved for proprietary use only. To resolve the missing training data problem, we present a collection of ∼ 5000 sentences to establish human-annotated standards for benchmarking alternative modeling techniques. The objective of the phrase level annotation task was to classify each example sentence into a positive, negative or neutral category by considering only the information explicitly available in the given sentence. Since the study is focused only on financial and economic domains, the annotators were asked to consider the sentences from the view point of an investor only; i.e. whether the news may have positive, negative or neutral influence on the stock price. As a result, sentences which have a sentiment that is not relevant from an economic or financial perspective are considered neutral.
Largo-m / Emotion Sentiment ClassifierUnderstanding human emotions in text is crucial for many applications such as customer feedback analysis
