62 skills found · Page 1 of 3
Nicholasli1995 / VisualizingNDFOfficial PyTorch implementation of "Visualizing the Decision-making Process in Deep Neural Decision Forest", CVPR 2019 Workshops on Explainable AI
Okes2024 / Machine Learning To Predict Suicide RiskThis project applies machine learning to predict suicide risk using synthetic psychological, demographic, and behavioral data. It demonstrates risk classification with Logistic Regression and Random Forest, evaluates performance metrics, and visualizes insights, highlighting the potential of AI in mental health research and awareness.
Forest-Protocols / Forest AINetwork of AI Protocols
crillab / PyxaiPyXAI (Python eXplainable AI) is a Python library (version 3.6 or later) allowing to bring formal explanations suited to (regression or classification) tree-based ML models (Decision Trees, Random Forests, Boosted Trees, ...).
Otutu11 / Carbon Sequestration Potential Mapping With AIThis project demonstrates an AI-driven workflow for synthetic carbon sequestration potential mapping. It generates geospatial eco-variables, builds a Random Forest model, evaluates performance, interprets feature importances, and produces prediction maps—designed for teaching, research, and demonstration, not real-world policy or operational use.
Shabopp / FraudDetectionUsingGANAI-powered fraud detection and prevention system using GANs and Random Forest for secure digital transactions.
Beckybams / Illegal Logging Detection With DronesIllegal Logging Detection with Drones uses AI-powered computer vision and drone imagery to identify deforestation and illegal logging activities. By analyzing aerial images with machine learning models, the system enables efficient forest monitoring, supports environmental protection efforts.
Dogiye12 / AI Based Forest Fire Detection SystemReal-time forest fire detection using satellite data (MODIS, VIIRS) and CNN-based classification.
AmirhosseinHonardoust / AI Personal Study TrackerAn AI-driven productivity tracking app built with Python, Streamlit, SQLite, and Machine Learning. It logs and analyzes study sessions, predicts productivity using Random Forest models, and visualizes key insights to help learners improve focus, habits, and overall academic efficiency.
SaravanavelE / Real Time Urban Air Quality Intelligence Alert SystemReal-Time Urban Air Quality Intelligence & Alert System is a fully streaming, AI-driven air quality monitoring system that ingests sensor data from urban pollution stations across Indian cities, processes it in real-time using the Pathway framework, applies a trained Random Forest classifier to assess health risk levels (LOW / MEDIUM / HIGH).
Ranjith00005 / BiasLens🔍 AI-powered news bias detection using Random Forest on 3.6M BEAD dataset
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.
d2cml-ai / 14.388 PyThis material has been created based on the tutorials of the course 14.388 Inference on Causal and Structural Parameters Using ML and AI in the Department of Economics at MIT taught by Professor Victor Chernozukhov. All the scripts were in R and we decided to translate them into Python, so students can manage both programing languages. Jannis Kueck and V. Chernozukhov have also published the original R Codes in Kaggle. In adition, we included tutorials on Heterogenous Treatment Effects Using Causal Trees and Causal Forest from Susan Athey’s Machine Learning and Causal Inference course. We aim to add more empirical examples were the ML and CI tools can be applied using both programming languages.
The-Swarm-Corporation / ForexTreeSwarmA sophisticated forex market analysis system using a swarm of specialized AI agents organized in a forest structure to provide comprehensive market insights and trading recommendations.
dia2018 / What Is The Difference Between AI And Machine LearningArtificial Intelligence and Machine Learning have empowered our lives to a large extent. The number of advancements made in this space has revolutionized our society and continue making society a better place to live in. In terms of perception, both Artificial Intelligence and Machine Learning are often used in the same context which leads to confusion. AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI which makes decisions while learning patterns from the input data. In this blog, we would dissect each term and understand how Artificial Intelligence and Machine Learning are related to each other. What is Artificial Intelligence? The term Artificial Intelligence was recognized first in the year 1956 by John Mccarthy in an AI conference. In layman terms, Artificial Intelligence is about creating intelligent machines which could perform human-like actions. AI is not a modern-day phenomenon. In fact, it has been around since the advent of computers. The only thing that has changed is how we perceive AI and define its applications in the present world. The exponential growth of AI in the last decade or so has affected every sphere of our lives. Starting from a simple google search which gives the best results of a query to the creation of Siri or Alexa, one of the significant breakthroughs of the 21st century is Artificial Intelligence. The Four types of Artificial Intelligence are:- Reactive AI – This type of AI lacks historical data to perform actions, and completely reacts to a certain action taken at the moment. It works on the principle of Deep Reinforcement learning where a prize is awarded for any successful action and penalized vice versa. Google’s AlphaGo defeated experts in Go using this approach. Limited Memory – In the case of the limited memory, the past data is kept on adding to the memory. For example, in the case of selecting the best restaurant, the past locations would be taken into account and would be suggested accordingly. Theory of Mind – Such type of AI is yet to be built as it involves dealing with human emotions, and psychology. Face and gesture detection comes close but nothing advanced enough to understand human emotions. Self-Aware – This is the future advancement of AI which could configure self-representations. The machines could be conscious, and super-intelligent. Two of the most common usage of AI is in the field of Computer Vision, and Natural Language Processing. Computer Vision is the study of identifying objects such as Face Recognition, Real-time object detection, and so on. Detection of such movements could go a long way in analyzing the sentiments conveyed by a human being. Natural Language Processing, on the other hand, deals with textual data to extract insights or sentiments from it. From ChatBot Development to Speech Recognition like Amazon’s Alexa or Apple’s Siri all uses Natural Language to extract relevant meaning from the data. It is one of the widely popular fields of AI which has found its usefulness in every organization. One other application of AI which has gained popularity in recent times is the self-driving cars. It uses reinforcement learning technique to learn its best moves and identify the restrictions or blockage in front of the road. Many automobile companies are gradually adopting the concept of self-driving cars. What is Machine Learning? Machine Learning is a state-of-the-art subset of Artificial Intelligence which let machines learn from past data, and make accurate predictions. Machine Learning has been around for decades, and the first ML application that got popular was the Email Spam Filter Classification. The system is trained with a set of emails labeled as ‘spam’ and ‘not spam’ known as the training instance. Then a new set of unknown emails is fed to the trained system which then categorizes it as ‘spam’ or ‘not spam.’ All these predictions are made by a certain group of Regression, and Classification algorithms like – Linear Regression, Logistic Regression, Decision Tree, Random Forest, XGBoost, and so on. The usability of these algorithms varies based on the problem statement and the data set in operation. Along with these basic algorithms, a sub-field of Machine Learning which has gained immense popularity in recent times is Deep Learning. However, Deep Learning requires enormous computational power and works best with a massive amount of data. It uses neural networks whose architecture is similar to the human brain. Machine Learning could be subdivided into three categories – Supervised Learning – In supervised learning problems, both the input feature and the corresponding target variable is present in the dataset. Unsupervised Learning – The dataset is not labeled in an unsupervised learning problem i.e., only the input features are present, but not the target variable. The algorithms need to find out the separate clusters in the dataset based on certain patterns. Reinforcement Learning – In this type of problems, the learner is rewarded with a prize for every correct move, and penalized for every incorrect move. The application of Machine Learning is diversified in various domains like Banking, Healthcare, Retail, etc. One of the use cases in the banking industry is predicting the probability of credit loan default by a borrower given its past transactions, credit history, debt ratio, annual income, and so on. In Healthcare, Machine Learning is often been used to predict patient’s stay in the hospital, the likelihood of occurrence of a disease, identifying abnormal patterns in the cell, etc. Many software companies have incorporated Machine Learning in their workflow to steadfast the process of testing. Various manual, repetitive tasks are being replaced by machine learning models. Comparison Between AI and Machine Learning Machine Learning is the subset of Artificial Intelligence which has taken the advancement in AI to a whole new level. The thought behind letting the computer learn from themselves and voluminous data that are getting generated from various sources in the present world has led to the emergence of Machine Learning. In Machine Learning, the concept of neural networks plays a significant role in allowing the system to learn from themselves as well as maintaining its speed, and accuracy. The group of neural nets lets a model rectifying its prior decision and make a more accurate prediction next time. Artificial Intelligence is about acquiring knowledge and applying them to ensure success instead of accuracy. It makes the computer intelligent to make smart decisions on its own akin to the decisions made by a human being. The more complex the problem is, the better it is for AI to solve the complexity. On the other hand, Machine Learning is mostly about acquiring knowledge and maintaining better accuracy instead of success. The primary aim is to learn from the data to automate specific tasks. The possibilities around Machine Learning and Neural Networks are endless. A set of sentiments could be understood from raw text. A machine learning application could also listen to music, and even play a piece of appropriate music based on a person’s mood. NLP, a field of AI which has made some ground-breaking innovations in recent years uses Machine Learning to understand the nuances in natural language and learn to respond accordingly. Different sectors like banking, healthcare, manufacturing, etc., are reaping the benefits of Artificial Intelligence, particularly Machine Learning. Several tedious tasks are getting automated through ML which saves both time and money. Machine Learning has been sold these days consistently by marketers even before it has reached its full potential. AI could be seen as something of the old by the marketers who believe Machine Learning is the Holy Grail in the field of analytics. The future is not far when we would see human-like AI. The rapid advancement in technology has taken us closer than ever before to inevitability. The recent progress in the working AI is much down to how Machine Learning operates. Both Artificial Intelligence and Machine Learning has its own business applications and its usage is completely dependent on the requirements of an organization. AI is an age-old concept with Machine Learning picking up the pace in recent times. Companies like TCS, Infosys are yet to unleash the full potential of Machine Learning and trying to incorporate ML in their applications to keep pace with the rapidly growing Analytics space. Conclusion The hype around Artificial Intelligence and Machine Learning are such that various companies and even individuals want to master the skills without even knowing the difference between the two. Often both the terms are misused in the same context. To master Machine Learning, one needs to have a natural intuition about the data, ask the right questions, and find out the correct algorithms to use to build a model. It often doesn’t requiem how computational capacity. On the other hand, AI is about building intelligent systems which require advanced tools and techniques and often used in big companies like Google, Facebook, etc. There is a whole host of resources to master Machine Learning and AI. The Data Science blogs of Dimensionless is a good place to start with. Also, There are Online Data Science Courses which cover the various nitty gritty of Machine Learning.
Nelvinebi / Water Quality Index Prediction Using ML XGBoost RF LSTM This project applies Machine Learning (XGBoost, Random Forest) and Deep Learning (LSTM) to predict the Water Quality Index (WQI) using synthetic environmental data, helping assess water safety, support sustainable management, and demonstrate AI’s role in environmental monitoring and decision-making.
MadhavSingh2236 / Classify Websites Into Amiable Or Malicious Using Raw DataWith WWW being the global platform, various fields inclusive to the same have emerged until hitherto. Due to ever-changing forms of cyber Security, it has become a necessity to classify Malicious websites so as to secure personal content. In this project we have implemented The State-Of-the-Art Decision Tree Machine Learning Models such as Random Forest and Decision Tree to classify URLs as malicious or amiable. Implementation of Classification algorithms for discrete data as well as normal regression model is used in the project. Malevolent URLs have been broadly used to mount different digital assaults including spamming, phishing and malware. Recognition of malignant URLs and distinguishing proof of danger types are basic to upset these assaults. Knowing the sort of a danger empowers assessment of seriousness of the assault and embraces a viable countermeasure. Existing strategies commonly distinguish vindictive URLs of a solitary assault type. In this paper, we propose technique utilizing AI to identify malevolent URLs of all the mainstream assault types. While the World Wide Web has become a stellar application on the Internet, it has likewise gotten a massive danger of digital assaults. Enemies have utilized the Web as a vehicle to convey malignant assaults, for example, phishing, spamming, and malware contamination. For instance, phishing ordinarily includes sending an email apparently from a dependable source to deceive individuals to click a URL (Uniform Resource Locator) contained in the email that joins to a fake page. To address Web-based assaults, an incredible exertion has been coordinated towards identification of noxious URLs. A typical countermeasure is to utilize a boycott of vindictive URLs, which can be built from different sources, particularly human criticisms that are exceptionally precise yet tedious. Boycotting acquires no bogus positives, however is successful just for known noxious URLs. It can't identify obscure malevolent URLs. The very idea of careful match in boycotting these renders it simple to be sidestepped. This shortcoming of blacklisting has been tended to by oddity-based location techniques intended to identify obscure vindictive URLs. In these strategies, a characterization model dependent on discriminative principles or highlights is worked with either information from the earlier or through machine learning. Choice of discriminative standards or highlights assumes a basic function for the presentation of a locator. Online malware assaults become one in everything about chief genuine dangers that need to be tended too frantically. Numerous methodologies that have stood out as promising manners by which of safeguard work, for example, malware grasp utilizing various boycotts. Nonetheless, these standard methodologies ordinarily neglect to watch new assaults due to the adaptability of malignant sites. Consequently, it's hard to deal with state-of-the-art boycotts with data concerning new vindictive sites. Malignant location identification assumes a significant part for a few network protection applications, and unmistakably AI moves toward square measure a promising course. In mix with protection imperatives on information sets of real client traffic, its irksome for scientists and product engineers to measure hostile to malware arrangements against huge scope information sets of practical net traffic. AI strategy [1] region unit utilized so as to characterize the online deals into malignant and benevolent URLs. The appearance of ongoing correspondence innovations has had enormous contact with in the development and advancement of organizations spamming over a few applications just as web based banking, online business, and long range informal communication. In actuality, in the present age it's almost required to have a web presence to run a famous endeavour. Accordingly, the significance of the overall net has ceaselessly been expanding. Unfortunately the mechanical promotions return in expansion to new unobtrusive strategies to assault and trick client. Such assaults grasp noxious sites that sell fake stock, financial extortion by fooling clients into uncovering delicate data that in the long run cause stealing of money or character, or maybe placing in malware inside the clients framework. There square measure a huge kind of procedures to actualize such assaults, similar to explicit hacking attempts, Derive-by abuses, Denial of administration [2], Distributed refusal of administration [1] and bunches of others. Concentrating the changeability of assaults, without a doubt new assault assortments, and furthermore the unnumbered settings inside which such assaults will appears, it's exhausting to style-solid frameworks to find digital security penetrates. The restrictions of customary security the board advancements are getting to an ever increasing extent genuine given this remarkable development of new security dangers, fast changes of new IT advancements, and critical deficiency of security experts. The vast majority of these assaulting strategies are acknowledged through spreading traded off URLs. A primary exploration exertion in pernicious URL recognition has zeroed in on choosing profoundly successful discriminative highlights. Existing techniques were intended to distinguish pernicious URLs of a solitary assault type, for example, spamming, phishing, or malware. In this paper, we propose a strategy utilizing Machine Learning Algorithms on how to distinguish malevolent URLs of all the well known assault types including phishing, spamming and malware contamination, and distinguish the assault types noxious URLs endeavour to dispatch.
aymenhmaidiwastaken / CosmicwatchAI-powered Earth & Space monitoring platform. Analyzes 5,000+ exoplanets, tracks near-Earth asteroids, monitors global wildfires using NASA satellite data with machine learning pipelines (KMeans, Isolation Forest, ARIMA forecasting) and interactive Plotly Dash dashboard.
Nelvinebi / AI Powered Early Warning System For Environmental HazardsAn AI-powered early warning system that detects environmental hazards using synthetic sensor data. Built with Streamlit and a Random Forest model, it predicts risks like extreme temperature, pollution, rainfall, or seismic activity, providing real-time alerts and visual insights to support disaster preparedness and environmental monitoring.
Trojan3877 / LogSight AILogSight-AI is a real-time AIOps platform that ingests Kubernetes logs at > 50 k lines/sec, tokenizes them with a C++ SIMD engine, clusters patterns on-the-fly using HDBSCAN + Isolation Forest
