467 skills found · Page 5 of 16
nathanrooy / Spatial AnalysisA collection of algorithms I use for the analysis of geospatial data. Written in C, wrapped in Python.
yifanzhu1592 / Data Structures And Algorithm Analysis In CAll codes in the book "Data Structures and Algorithm Analysis in C (2nd Edition) (Mark Allen Weiss)"
farzadasgari / ProadvProADV is a Python package designed for efficient processing and analysis of acoustic Doppler velocimeter (ADV) data. It offers advanced cleaning algorithms for robust despiking and noise removal, comprehensive statistical functions for calculating essential measures, and further analysis capabilities.
evelinesurbakti / Automated Keywords Extraction Of Data Analyst Job Descriptions From Indeed Using NLPScraped job description and leveraged the concepts of Natural Language Processing (NLP) and GloVe Algorithm to extract the keywords through data and performed analysis. Presenting the vital keywords from data analyst job summary from the Indeed website..
leeeric9527 / Data Mining R利用R语言编写的数据挖掘大作业。着重分析朴素贝叶斯判别分析算法、 AdaBoost 算法以及随机森林算法在口红销量预测中的效果， 并在随机森林算法中进行模型优化。Using R language data mining big homework. The effects of Naive Bayesian Discriminant Analysis (Naive Bayesian Discriminant Analysis), AdaBoost and Random Forest Algorithms on lipstick sales forecasting were analyzed, and the model was optimized in Random Forest Algorithms.
Shaobinggao / Multi Illuminant Based Color ConstancyCombining bottom-up and top-down visual mechanisms for color constancy under varying illumination. This repository contains the datasets and codes published for color constancy under varying illmunations. -----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------ Copyright (c) 2019 All rights reserved. This doucuments are a rough version for summarizing the results and codes in publication [1], which is available only for research purpose. We preserve the rights to further correct and update the data. This dataset contains three datasets for color constancy under varying illuminations, which are used in publication [1]. real-world dataset with multi-illuminant: the real-world dataset contains 37 images captured under vairous non-uniform light sources. synthetic dataset with multi-illuminant: the dataset with the synthetic multiple illuminants contains 100 images. MCC-BU+TD: This dataset contains results of multiple MCC algorithms on several real-world images taken from the web, which could be easily used and compared in any research publications. More information please refer to readme.txt in each folder. If you use this dataset for the evaluation of your approach and producing the results, please cite our work as follows: [1] S. Gao, Y. Ren, M. Zhang and Y. Li, "Combining bottom-up and top-down visual mechanisms for color constancy under varying illumination," in IEEE Transactions on Image Processing. doi: 10.1109/TIP.2019.2908783 [2] X.-S. Zhang, S.-B. Gao, R.-X. Li, X.-Y. Du, C.-Y. Li, and Y.-J. Li, “A retinal mechanism inspired color constancy model,” IEEE Transactions on Image Processing, vol. 25, no. 3, pp. 1219–1232, 2016. [3] K.-F. Yang, S.-B. Gao, Y.-J. Li, and Y. Li, “Efficient illuminant estimation for color constancy using grey pixels,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 2254–2263. [4] Gao, S. B., Yang, K. F., Li, C. Y., & Li, Y. J. (2015). Color constancy using double-opponency. IEEE transactions on pattern analysis and machine intelligence, 37(10), 1973-1985. Any questions and comments are welcome to gaoshaobing@scu.edu.cn
pmla / Polyhedral Template MatchingPolyhedral Template Matching algorithm for analysis of molecular dynamics simulation data
reddyprasade / Machine Learning Interview PreparationPrepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!
abebual / Predicting ICU Patient Clinical Deterioration ReportFor this project, I used publicly available Electronic Health Records (EHRs) datasets. The MIT Media Lab for Computational Physiology has developed MIMIC-IIIv1.4 dataset based on 46,520 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center of Boston between 2001 and 2012. MIMIC-IIIv1.4 dataset is freely available to researchers across the world. A formal request should be made directly to www.mimic.physionet.org, to gain access to the data. There is a required course on human research ‘Data or Specimens Only Research’ prior to data access request. I have secured one here -www.citiprogram.org/verify/?kb6607b78-5821-4de5-8cad-daf929f7fbbf-33486907. We built flexible and better performing model using the same 17 variables used in the SAPS II severity prediction model. The question ‘Can we improve the prediction performance of widely used severity scores using a more flexible model?’ is the central question of our project. I used the exact 17 variables used to develop the SAPS II severity prediction algorithm. These are 13 physiological variables, three underlying (chronic) disease variables and one admission variable. The physiological variables includes demographic (age), vital (Glasgow Comma Scale, systolic blood pressure, Oxygenation, Renal, White blood cells count, serum bicarbonate level, blood sodium level, blood potassium level, and blood bilirubin level). The three underlying disease variables includes Acquired Immunodeficiency Syndrome (AIDS), metastatic cancer, and hematologic malignancy. Finally, whether admission was scheduled surgical or unscheduled surgical was included in the model. The dataset has 26 relational tables including patient’s hospital admission, callout information when patient was ready for discharge, caregiver information, electronic charted events including vital signs and any additional information relevant to patient care, patient demographic data, list of services the patient was admitted or transferred under, ICU stay types, diagnoses types, laboratory measurments, microbiology tests and sensitivity, prescription data and billing information. Although I have full access to the MIMIC-IIIv1.4 datasets, I can not share any part of the data publicly. If you are interested to learn more about the data, there is a MIMIC III Demo dataset based on 100 patients https://mimic.physionet.org/gettingstarted/demo/. If you are interested to requesting access to the data - https://mimic.physionet.org/gettingstarted/access/. Linked repositories: Exploratory-Data-Analysis-Clinical-Deterioration, Data-Wrangling-MIMICIII-Database, Clinical-Deterioration-Prediction-Model--Inferential-Statistics, Clinical-Deterioration-Prediction-Model--Ensemble-Algorithms-, Clinical-Deterioration-Prediction-Model--Logistic-Regression, Clinical-Deterioration-Prediction-Model---KNN © 2020 GitHub, Inc.
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.
mirzayasirabdullahbaig07 / Top MachineLearning DeepLearning ProjectsA curated collection of 10+ end-to-end AI/ML/DL projects showcasing real-world problem-solving, data analysis, and model deployment using Streamlit. Each project demonstrates the use of cutting-edge algorithms in healthcare, finance, NLP, and computer vision. Perfect for learning, inspiration.
sonarsushant / Car Evaluation Dataset ClassificationExploratory data analysis of Car Evalutation Dataset. Prediction of classes using various classification algorithms.
mmalekzadeh / Replacement AutoencoderReplacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis (IoTDI'18)
nelson123-lab / Gender Based Cleaning AlgorithmWelcome to advanced Image Data Cleansing Algorithm, a powerful tool designed to enhance data quality by accurately determining the gender of individuals in images through facial analysis. With its robust face detection capabilities, the algorithm efficiently identifies and verifies gender information and facilitating data cleansing processes.
BlockchainLabs / AeonAbout: AEON was launched on 6.6.2014 at 6:00 PM UTC, with no premine or instamine. AEON is for people who want to pay and live freely, who want to be part of the cryptocurrency revolution and want to try something new. It is based on the CryptoNote protocol and uses the CryptoNight-Lite[1] algorithm, and features: - True anonymity & data protection - Untraceable payments uses ring signature - Unlinkable transactions with random data by the sender - Blockchain analysis resistant - CPU/GPU mining, ASIC-resistant Roadmap April 26, 2015 - new roadmap announced Mobile-friendly PoW and block time (released) GUI wallet (in progress) 32-bit and ARM support (released, but requires low memory footprint below) Low memory footprint (in progress) Signature trimming Blockchain pruning (test release available) Multisig and payment channels (instant payments) Development Team: Lead developer: smooth Release engineering, Q/A, support: Arux Other roles: open (PM smooth) Original developer (as Monero fork): anonymous Bounties: None currently open. You can send donations for the AEON bounty fund and development. Code: AEON address: WmsSWgtT1JPg5e3cK41hKXSHVpKW7e47bjgiKmWZkYrhSS5LhRemNyqayaSBtAQ6517eo5PtH9wxHVmM78JDZSUu2W8PqRiNs View Key: 71bf19a7348ede17fa487167710dac401ef1556851bfd36b76040facf051630b Specifications: PoW algorithm: CryptoNight-Lite[1] Max supply: ~18.4 million[2] Block reward: Smoothly varying using the formula (M−A) / (218) / (1012) where M = 264 −1 and A = supply mined to date.[3] Block time: 240 seconds[3] Difficulty: Retargets at every block RPC-bind-port: 11180 P2P-bind-port: 11181 Downloads: Current release 0.9.6.0 (source code, 64 bit Windows binaries) bootstrap for linux-x64 (by community member Phantas 2016-03-10) bootstrap for Windows-x64 (by community member Phantas 2016-03-11) bootstrap for OS X (by community member sammy007 2015-08-08) GUI for Windows 0.2.3 (by community member h0g0f0g0, src.zip, sha1) Instructions to compile on Windows (provided by community member cryptrol): see bottom of this post Recommended: Use caution with community-provided downloads, check reputation and scan for malware Recommended: Use the --donate option when starting the daemon to donate a portion of your computer power to support the project and the network Links & Resources: Trading: - Bittrex - AEON/BTC - Cryptopia - AEON/BTC (also has DOGE and LTC pairs) - OTC thread - AEON/XMR - Speculation thread (moderated by americanpegasus) Pools: - http://52.8.47.33:8080 - Arux's personal pool (2% fee) - http://98.238.231.31:9000 - The Cryptophilanthropist (2% fee) Block Explorers: - Chainradar - Minergate Community: - Reddit - Steem - Twitter - IRC channel #aeon @ Freenode (Webchat Link) Dead Links / Outdated: cryptocointalk white paper Mining: 1. Compile from source code. 2. Launch aeond and wait until it is synchronized. 3. Launch simplewallet --generate-new-wallet=wallet_name.bin --pass=12345 4. Start mining from the wallet using start_mining command Windows Compilation: (provided by community member cryptrol) Compile steps for Windows x64 using MSVC First of all let's get all the tools we need : - Download and install Microsoft Visual Studio Community 2013 (It's a free version of visual studio with some license limitations). You can uncheck the web development tools and SQL tools since you won't use them for building AEON. This will take time to download and install and you will have to reboot upon completion. - Download and install cMake for windows from : http://www.cmake.org/download/ (Win32 install) - Download Boost 1.57 from http://www.boost.org/users/download/ , use the zip or 7zip archive and extract. You can use c:\boost_1_57_0 since this is what I am using for this steps. - Download and install Github for Windows from https://windows.github.com/ (This also includes a Git shell that we will use later). Now the nasty part compile & build time ! - Build Boost : Open a command line and type : Code: > cd c:\boost_1_57_0 > bootstrap.bat > b2 --toolset=msvc variant=release link=static threading=multi runtime-link=static address-model=64 - Open the Git Shell (or Git bash) depending what you downloaded previously and do. Code: > git clone https://github.com/aeonix/aeon.git > cd aeon > mkdir build > cd build > cmake -G "Visual Studio 12 Win64" -DBOOST_ROOT=c:\boost_1_57_0 -DBOOST_LIBRARYDIR=c:\boost_1_57_0\stage\lib .. > MSBuild Project.sln /p:Configuration=release /m You should now find the exe files under build/src/release . Aeon isn't a cryptocurrency. It's a lifestyle. It's about polished perfection, attained by breaking the rules with calculated mastery of the art. It's about respecting history and pushing innovation forward at the same time. It's about more than just math: it's a vision of a world where luxury is the same as entry-level, and the limits are the heavens themselves. If you're just buying Aeon to get rich, don't even bother. Aeon needs more than just the next wave of crypto speculators: we're looking for the truly elite. But if you think you have what it takes to redefine global finance and discover new magnitudes of wealth in the process... Well, Aeon is ready for you. Are you ready for Aeon?
openGemini / OpenGemini CastorData analysis algorithm library
lucasimi / Tda Mapper PythonA simple and efficient Python implementation of Mapper algorithm for Topological Data Analysis
yagyapandeya / Supervised Music Video Emotion ClassificationThe extended and verified music video emotion analysis dataset for data driven algorithm.
Manishms18 / Audit Risk Prediction For A Financial FirmMACHINE LEARNING : Audit risk analysis using multiple firms' historical data using Regression algorithms like KNN, Kernelized SVM, Decision Tree, Random Forest etc.
santiagocanepa / AutomationTrading Strategy Backtesting SuiteTradingView suite with 15+ indicators and Python scripts generating up to 130,000 combinations, optimized to 3,000 with robust analysis. Automation with Puppeteer for backtesting and JSON to CSV data conversion for insights. Designed to maximize accuracy and performance in algorithmic trading.
