Results for "algorithm-analysis"

Claude Code Claude Desktop GitHub Copilot Cursor Windsurf Cline Zed JetBrains

📄SKILL.md 🤖CLAUDE.md ⚡Claude Commands 📐.cursorrules 📐Cursor Rules 🕹️AGENTS.md 🧬codex.md 🏄.windsurfrules 🔧.clinerules 🧑‍✈️Copilot Instructions

All Development Operations Data Product Marketing Customer Design Sales

1,603 skills found · Page 24 of 54

JRKagumba / 2D Video Pose Estimation Yolov7

Computer-vision based monocular human pose-estimation and biomechanical gait analysis on elite runners. Using Yolov7 pose-estimation algorithm.

universal

gait-analysispose-estimationyolov7

Updated 2mo ago

pakaplace / ICO Whitepaper Analysis

LSI algorithm-based analysis of ICO whitepapers, written in python. Clone and run by calling "python whitepaperAnalysis.py". Returns the top 5 most similar white papers to the one you specified. Additional whitepapers can be scraped using "pdfScraper.py". I was learning python at the time :/

universal

Updated 2y ago

PARCO-LAB / Mocap Refinement

This repository includes implementation codes or links to the authors’ original codes of filtering methods for denoising and completing data generated by software platforms for human motion analysis, allowing readers to easily reproduce all the algorithms in different experimental settings.

universal

Updated 14d ago

kamruleee51 / MRI Pre Processing

Almost in every image processing or analysis work, image pre-preprocessing is crucial step. In medical image analysis, pre-processing is a very important step because the further success or performance of the algorithm mostly dependent on pre-processed image. In this lab, we are working with 3D Brain MRI data. In case of working with brain MRI removing the noise and bias field (which is due to inhomogeneity of the magnetic field) is very important part of preprocessing of brain MRI. To do so, we widely used algorithm Anisotropic diffusion, isotropic diffusion which can diffuse in any direction, and Multiplicative intrinsic component optimization (MICO) have been used for noise removal and bias field correction respectfully. Both quantitative and qualitative performance of the algorithms also have been analyzed.

zed

Updated 6mo ago

nurskurmanbekov / IBM Data Science Professional Certification

This repository provides an online certification program in Data Science and Machine Learning offered by IBM and Coursera. The program covers topics such as data science methodology, data visualization, data analysis, statistical analysis, predictive modeling, and machine learning algorithms. The courses are hands-on and conducted on the IBM Cloud,

universal

big-datacoursera-datacoursera-specialization+12

Updated 1y ago

MohiniPriya / Anomaly Detection Using UnSupervised Machine Learning Algorithms In HVAC System

Applied unsupervised machine learning algorithms (K-Means Clustering and Isolation Forest) on time series data collected from an Air Handling Unit of a building to detect anomalous behavior of the system. Applied exploratory data analysis using Python to identify non-optimal working conditions of the AHU. Designed an automated anomaly detection system and a corrective strategy to control the AHU effectively.

universal

Updated 11mo ago

Jonathandeventer / Master Thesis DCDT

In recent years, community detection has received increased attention thanks to its wide range of applications in many fields. While at first most techniques were focused on discovering communities in static networks, lately the research community’s focus has shifted toward methods that can detect meaningful substructures in evolving networks because of their high relevance in real-life problems. This thesis explores the current availability of empirical comparative studies of dynamic methods and also provides its own qualitative and quantitative comparison with the aim of gaining more insight in the performance of available algorithms that are expected to perform well in the context of social community detection. The qualitative comparison includes 13 algorithms, namely D-GT, Extended BGL, TILES, AFOCS, HOCTracker, OLCPM, DOCET, LabelRankT, FacetNet, DYNMOGA, DEMON and iLCD. The empirical analysis compares TILES, HOCTracker, OLCPM, DEMON and iLCD on synthetic RDyn graphs and the real graph, DBLP. In addition to the results of the empirical and qualitative results of the analysis, the thesis’s value lies in its wide coverage of the dynamic community detection problem.

universal

dhruvbhatia563 / Technocolabs Internship Majpr Project Bitcoin API Price Prediction Based On Twitter Sentiments

Project shows that real-time Twitter data can be used to predict market movement of Bitcoin Price. The goal of this project is to prove whether Twitter data relating to cryptocurrencies can be utilized to develop advantageous crypto coin trading strategies. By way of supervised machine learning techniques, have outlined several machine learning pipelines with the objective of identifying cryptocurrency market movement. The prominent alternative currency ex- amined in this paper is Bitcoin (BTC). Our approach to cleaning data and applying supervised learning algorithms such as logistic regression, Decision Tree Classifier, and LDA leads to a final prediction accuracy exceeding 70%. In order to achieve this result, rigorous error analysis is employed in order to ensure that accurate inputs are utilized at each step of the model.

zed

Updated 1mo ago

swatijha2496 / FACE RECOGNITION USING OPENCV IN PYTHON

Face is most commonly used biometric to recognize people. Face recognition has received substantial attention from researchers due to human activities found in various applications of security like airport, criminal detection, face tracking, forensic etc. Compared to other biometric traits like palm print, Iris, finger print etc., face biometrics can be non-intrusive. They can be taken even without user’s knowledge and further can be used for security based applications like criminal detection, face tracking, airport security, and forensic surveillance systems. Face recognition involves capturing face image from a video or from a surveillance camera. They are compared with the stored database. Face biometrics involves training known images, classify them with known classes and then they are stored in the database. When a test image is given to the system it is classified and compared with stored database. Face biometrics is a challenging field of research with various limitations imposed for a machine face recognition like variations in head pose, change in illumination, facial expression, aging, occlusion due to accessories etc.,. Various approaches were suggested by researchers in overcoming the limitations stated. 72 Automatic face recognition involves face detection, feature extraction and face recognition. Face recognition algorithms are broadly classified into two classes as image template based and geometric feature based. The template based methods compute correlation between face and one or more model templates to find the face identity. Principal component analysis, linear discriminate analysis, kernel methods etc. are used to construct face templates. The geometric feature based methods are used to analyze explicit local features and their geometric relations (elastic bung graph method). Multi resolution tools such as contour lets, ridge lets were found to be useful for analyzing information content of images and found its application in image processing, pattern recognition, and computer vision. Curvelets transform is used for texture classification and image de-noising. Application of Curvelets transform for feature extraction in image processing is still under research.

priyamittal15 / Implementation Of Different Deep Learning Algorithms For Fracture Detection Image Classification

Using-Deep-Learning-Techniques-perform-Fracture-Detection-Image-Processing Using Different Image Processing techniques Implementing Fracture Detection on X rays Images on 8000 + images of dataset Description About Project: Bones are the stiff organs that protect vital organs such as the brain, heart, lungs, and other internal organs in the human body. There are 206 bones in the human body, all of which has different shapes, sizes, and structures. The femur bones are the largest, and the auditory ossicles are the smallest. Humans suffer from bone fractures on a regular basis. Bone fractures can happen as a result of an accident or any other situation in which the bones are put under a lot of pressure. Oblique, complex, comminute, spiral, greenstick, and transverse bone fractures are among the many forms that can occur. X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and other types of medical imaging techniques are available to detect various types of disorders. So we design the architecture of it using Neural Networks different models, compare the accuracy, and get a result of which model works better for our dataset and which model delivers correct results on a specific related dataset with 10 classes. Basically our main motive is to check that which model works better on our dataset so in future reference we all get an idea that which model gives better type of accuracy for a respective dataset . Proposed Method for Project: we decided to make this project because we have seen a lot of times that report that are generated by computer produce error sometimes so we wanted to find out which model gives good accuracy and produce less error so we start to research over image processing nd those libraries which are used in image processing like Keras , Matplot lib , Image Generator , tensor flow and other libraries and used some of them and implement it on different image processing algorithm like as CNN , VGG-16 Model ,ResNet50 Model , InceptionV3 Model . and then find the best model which gives best accuracy for that we generate classification report using predefined libraries in python such as precision , recall ,r2score , mean square error etc by importing Sklearn. Methodology of Project: Phase 1: Requirement analysis: • Study concepts of Basic Python programming. • Study of Tensor flow, keras and Python API interface . • Study of basic algorithms of Image Processing and neural network And deep learning concepts. • Collect the dataset from different resources and describe it into Different classes(5 Fractured + 5 non fractured). Phase 2: Designing and development: The stages of design and development are further segmented. This step starts with data from the Requirement and Analysis phase, which will lead to the model construction phase, where a model will be created and an algorithm will be devised. After the algorithm design phase is completed, the focus will shift to algorithm analysis and implementation in this project. Phase 3: Coding Phase: Before real coding begins, the task is divided into modules/units and assigned to team members once the system design papers are received. Because code is developed during this phase, it is the developers' primary emphasis. The most time-consuming aspect of the project will be this. This project's implementation begins with the development of a program in the relevant programming language and the production of an error-free executable program. Phase 4: Testing Phase: When it comes to the testing phase, we may test our model based on the classification report it generates, which contains a variety of factors such as accuracy, f1score, precision, and recall, and we can also test our model based on its training and testing accuracy. Phase 5: Deployment Phase: One of our goals is to bring all of the previous steps together and put them into practice. Another goal is to deploy our model into a python-based interface application after comparing the classification reports and determining which model is best for our dataset.

valentineashio / Online Payments Fraud Detection Dataset Case Study

A Data Science/Machine Learning Project. According to Bolster , Global Fraud Index (as at June 2022) is at 10,183 and growing. This is high risk to businesses and customers transacting online. This indicates that traditional rules-based methods of detecting and combating fraud are fast becoming less effective. It becomes imperative for stakeholders to develop innovative means to make transacting online as safe as possible. Artificial intelligence provides viable and efficient solutions via Machine Learning models/algorithms. In this project, I trained a fraud detection model to predict online payment fraud using Blossom Bank PLC as case study. Blosssom Bank ( BB PLC) is a multinational financial services group, that offers retail and investment banking, pension management, assets management and payment services, headquartered in London, UK. Blossom Bank wants to build a machine learning model to predict online payment fraud. Here is the dataset used for this task. With this model, BB PLC will: Keep up with fast evolving technological threats and better prevent the loss of funds (profit) to fraudsters. Accurately detect and identify anomalies in managing online transactions done on its platforms which may go undetected using traditional rules-based methods. 3.Improve quality assurance thus retaining old customers and acquire new ones. This will increase credit/profit base. Improve its policy and decision making. Steps: 1.Loading necessary python libraries. Loading Dataset. Exploratory Data Analysis. Higlighting Relationships and insights. Data Transformation; Using resampling techniques to address Class-imbalace.. Feature Engineering. Model Training. Model Evaluation. Challenges: I encountered a number of challenges during coding which made me run into error reports. these were due to improper documentations, syntax, especially during feature engineering (one-hot encoding: 'fit.transform'). This aspect consumed most of my time I was able to solve these challenges by making extensive research and paying close attention to syntax. I was able to selve the encoding by using 'pd.get_dummies() and making some specifications in the methods.