1,600 skills found · Page 10 of 54
AlessandroCorradini / Stanford University Algorithms Design And AnalysisAlgorithms - Design and Analysis offered by Stanford University
NourozR / Reconstruction And Compression Of Color ImagesReconstruction and Compression of Color Images Using Principal Component Analysis (PCA) Algorithm
sky-bro / HIT Algo哈工大研究生课程: 高级算法设计与分析 (Advanced algorithm design and analysis), 实验 作业 课件
jmscslgroup / StrymA real time CAN vehicle data logging, analysis and visualization tool to work with USB-CAN Interface for developing in-vehicle AI algorithms for self-driving and automation
Marketscrape / Marketscrape WebMarketscrape is a user-friendly web scraper for Facebook Marketplace that utilizes AI to help users assess the value of each listing. By providing real-time analysis and advanced machine learning algorithms, Marketscrape empowers users to make informed purchasing decisions and find the best deals on Facebook Marketplace.
archienorman11 / Thesis Bitcoin ClusteringThe Bitcoin currency is a publicly available, transparent, large scale network in which every single transaction can be analysed. Multiple tools are used to extract binary information, pre-process data and train machine learning models from the decentralised blockchain. As Bitcoin popularity increases both with consumers and businesses alike, this paper looks at the threat to privacy faced by users through commercial adoption by deriving user attributes, transaction properties and inherent idioms of the network. We define the Bitcoin network protocol, describe heuristics for clustering, mine the web for publicly available user information and finally train supervised learning models. We show that two machine learning algorithms perform successfully in clustering the Bitcoin transactions based on only graphical metrics measured from the transaction network. The Logistic Regression algorithm achieves an F1 score of 0.731 and the Support Vector Machines achieves an F1 score of 0.727. This work demonstrates the value of machine learning and network analysis for business intelligence; on the other hand it also reveals the potential threats to user privacy.
rohanmistry231 / Parkinsons Disease ClassificationA Python-based machine learning project for classifying Parkinson's disease using patient data and algorithms like XGBoost and Random Forest. Includes data preprocessing, feature analysis, and model evaluation with Scikit-learn and Pandas for accurate predictions.
cho-zhang / COMP 550 AlgorithmAnalysisJava code for Algorithms and Data Structures Class
sharmaroshan / Wine Quality PredictionsPredicting the Quality of Red Wine using Machine Learning Algorithms for Regression Analysis, Data Visualizations and Data Analysis.
citiususc / StacStatistical Tests for Algorithms Comparison (STAC) is a new platform for statistical analysis to verify the results obtained from computational intelligence algorithms.
sharmaroshan / Big Mart Sales PredictionUsing Machine Learning Algorithms for Regression Analysis to predict the sales pattern and Using Data Analysis and Data Visualizations to Support it.
mGalarnyk / DSE230 Data Analysis Using Hadoop And Spark UCSDMap-reduce, streaming analysis, and external memory algorithms and their implementation using the Hadoop and its eco-system: HBase, Hive, Pig and Spark. The class will include assignment of analyzing large existing databases.
mahaloz / CfgutilsUtility library for analysis of Control Flow Graphs, home to the Basque CFGED algorithm.
ajayshewale / Sentiment Analysis Of Text Data Tweets This project addresses the problem of sentiment analysis on Twitter. The goal of this project was to predict sentiment for the given Twitter post using Python. Sentiment analysis can predict many different emotions attached to the text, but in this report, only 3 major were considered: positive, negative and neutral. The training dataset was small (just over 5900 examples) and the data within it was highly skewed, which greatly impacted on the difficulty of building a good classifier. After creating a lot of custom features, utilizing bag-of-words representations and applying the Extreme Gradient Boosting algorithm, the classification accuracy at the level of 58% was achieved. Analysing the public sentiment as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like the stock exchange.
ppizarro / Coursera Stanford Algorithms1Coursera: Algorithms: Design and Analysis, Part 1 (Stanford)
vesselinux / YaarxYet Another Toolkit for Analysis of ARX Cryptographic Algorithms
Hazrat-Ali9 / Algorithm Analysis Design Lab🚋 Algorithm 🚃 Analysis 🚞 Design Lab 🚁 an amazing 🛩 hands-on ✈ repository 🚢 built learners ⛱ want to deeply 🚟 understand 🛸 how algorithms 🚈 work how 🍑 they’re designed 🍊 and how to 🍔 analyze their 🍏 performance 🫑 This lab-centric 🏘 repo is perfect 🚅 university 🧸 students 🏀 coding ⛸ enthusiasts and ⚽ interview 🎮 prep ⛸ warriors🥎
sharmaroshan / Students Performance AnalyticsStudents Performance Evaluation using Feature Engineering, Feature Extraction, Manipulation of Data, Data Analysis, Data Visualization and at lat applying Classification Algorithms from Machine Learning to Separate Students with different grades
Superzchen / IFeatureOmega GUIiFeatureOmega is a comprehensive platform for generating, analyzing and visualizing more than 170 representations for biological sequences, 3D structures and ligands. To the best of our knowledge, iFeatureOmega supplies the largest number of feature extraction and analysis approaches for most molecule types compared to other pipelines. Three versions (i.e. iFeatureOmega-Web, iFeatureOmega-GUI and iFeatureOmega-CLI) of iFeatureOmega have been made available to cater to both experienced bioinformaticians and biologists with limited programming expertise. iFeatureOmega also expands its functionality by integrating 15 feature analysis algorithms (including ten cluster algorithms, three dimensionality reduction algorithms and two feature normalization algorithms) and providing nine types of interactive plots for statistical features visualization (including histogram, kernel density plot, heatmap, boxplot, line chart, scatter plot, circular plot, protein three dimensional structure plot and ligand structure plot). iFeatureOmega is an open-source platform for academic purposes. The web server can be accessed through http://ifeature2.erc.monash.edu and the GUI and CLI versions can be download at: https://github.com/Superzchen/iFeatureOmega-GUI and https://github.com/Superzchen/iFeatureOmega-CLI, respectively.
zhouzhouwen / An Improved PINNs With The Adaptive Weight Sampling And DE AlgorithmAn improved and generic PINNs for fluid dynamic analysis is proposed. This approach incorporates three key improvements: residual-based adaptive sampling, which automatically samples points in areas with larger residuals; adaptive loss weights, which balance the loss terms effectively; and the utilization of the DE optimization algorithm
