1,603 skills found · Page 22 of 54
MariaGrozdeva / Design And Analysis Of Algorithms FMI 2021 2022Repository with examples for the " Design and analysis of algorithms" course given by me @ Faculty of Mathematics and Informatics, Sofia University (2021/22, summer semester)
dcleres / Parkinson Disease MLA comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform
fiteen / Sorting Algorithm📊 Animation and analysis of classical sorting algorithms.（动画详解十大经典排序算法）
code-shoily / Yog ExAn Elixir library for Graph Algorithms and Network Analysis
VNOpenAI / OpenControlOpenControl is a python package that implements basic algorithms for the analysis and design of optimal feedback controllers.
burakozpoyraz / MATLAB 101MATLAB-101 Course 💻: Fundamentals of MATLAB programming, algorithm design, data analysis, and visualization through 12 hours of hands-on lectures
CDW-OffSec / NetScaler Password Hash Type 5An analysis of the user password hashing algorithm used by Citrix NetScaler
AbdouRoumi / HasheramaA Windows string hashing toolkit for security research and malware analysis.Research implementation of malware-focused algorithms from VX Underground collection. For educational and research purposes only.
adamshch / GraFT AnalysisThis repository contains code for morphology-free analysis of functional fluorescence microscopy. The focal algorithm, Graph-Filtered Time-trace (GraFT) Dictionary Learning, is published in Charles et al. 2022 in the IEEE Transactions of Image Processing.
aravinda-1402 / Store Demand Forecasting Using Time Series And Neural NetworksThe primary objective of this project is to develop a cutting-edge forecasting model utilizing advanced machine-learning algorithms and sophisticated time-series analysis techniques. The model aims to deliver precise predictions of future sales across diverse retail outlets.
Ayokanmi-Adejola / Heathcare HubA Digital Healthcare hub designed to provide advanced preventative disease analysis. Key features include advanced algorithmic analysis of health data, personalized risk assessment, and tailored treatment recommendations.
oliverchampion / PCA DenoisingThe PCA denoising matlab algorithm used in the publication "Principal component analysis for fast and model-free denoising of multi b-value diffusion-weighted MR images" by Oliver J Gurney-Champion et al. in physics in medicine and biology in 2019.
MaxineXiong / ImageAI Flask AppsThis project offers two Flask applications that utilize ImageAI's image prediction algorithms and object detection models. These apps enable users to upload images and videos for object recognition, detection and analysis, providing accurate prediction results, confidence scores, raw data of detected objects at frame-level, and object insights.
DhanushN2005 / Market Basket Analysis Uding ECLATJupyter Notebook implementing Market Basket Analysis using the ECLAT algorithm to discover frequent itemsets from transactional data. 📘 README.md # Market Basket Analysis Using ECLAT This repository demonstrates how to perform **Market Basket Analysis** using the **ECLAT**
meganindya / Btech AssignmentsA repository of my BTech assignments for different papers.
QM4RS / Frida Java Crypto Spy📦 frida-java-crypto-spy 🕵️♂️ A Frida script to hook and log Java Cipher operations (init, update, doFinal, and updateAAD) in Android apps. Designed to extract algorithm, mode, key, IV, AAD, and encrypted/decrypted data for analysis and reverse engineering purposes.
mohsin-riad / Epileptic Seizure RecognitionThe aim of this repo is to contribute to the diagnosis of epilepsy by taking advantage of the engineering. So, for diagnosing of epileptic seizures from EEG signals are transformed discrete wavelet and auto regressive models. After these transformations, extract data is applied input for Back-propagation, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM) ,ANN ,Logistic Regression and Principal Component Analysis algorithms.
Code-XYZxyz / Real Time Interferometric Measurement Control For Photopolymer Additive ManufacturingThis is a comprehensive MATLAB-based software platform developed for real-time measurement and feedback control of a custom mask-projection photopolymerization based additive manufacturing system (referred as "ECPL", i.e., Exposure Controlled Projection Lithography) using a lab-built interferometry (referred as "ICM&M", i.e., Interferometric Curing Monitoring and Measurement). A graphical user interface using the graphical user interface development environment (GUIDE) of MATLAB was created to implement the ICM&M method for the ECPL process. The software interfaces with the hardware of the ECPL system’s ultraviolet lamp and DMD, and the ICM&M system’s camera. It was designed to streamline the operation of the ECPL process with the aid of parallel computing that implements online both the ICM&M acquisition and measurement analysis as well as the feedback control method. The application logs the acquired interferogram video data, performs numerical computations for the ICM&M measurement algorithms and control law, saves the real-time data and measurement results for all voxels in the region of interest. Meanwhile, it displays interferogram frames and visualize the photocuring process without a substantial sacrifice in temporal performance of other key functions such as data acquisition and measurement & control analysis. The software could be extended to real-time process measurement and control for other additive manufacturing systems, for example, metal based additive manufacturing aided by in-situ thermal images analysis.
infomindgithub / Machine Learning Engineer Nanodegree Capstone PROJECT ANALYSIS*****PROJECT SPECIFICATION: Machine Learning Capstone Analysis Project***** This capstone project involves machine learning modeling and analysis of clinical, demographic, and brain related derived anatomic measures from human MRI (magnetic resonance imaging) tests (http://www.oasis-brains.org/). The objectives of these measurements are to diagnose the level of Dementia in the individuals and the probability that these individuals may have Alzheimer's Disease (AD). In published studies, Machine Learning has been applied to Alzheimer’s/Dementia identification from MRI scans and related data in the academic papers/theses in References 10 and 11 listed in the References Section below. Recently, a close relative of mine had to undergo a sequence of MRI tests for cognition difficulties.The motivation for choosing this topic for the Capstone project arose from the desire to understand and analyze potential for Dementia and AD from MRI related data. Cognitive testing, clinical assessments and demographic data related to these MRI tests are used in this project. This Capstone project does not use the MRI "imaging" data and does not focus on AD, focusses only on Dementia. *****Conclusions, Justification, and Reflections***** [Student adequately summarizes the end-to-end problem solution and discusses one or two particular aspects of the project they found interesting or difficult.] The formulation of OASIS data (Ref 1 and 2) in terms of a dementia classification problem based on demographic and clinical data only (and without directly using the MRI image data), is a simplification that has major advantages and appeal. This means the trained model can classify whether an individual has dementia or not with about 87% accuracy, without having to wait for radiological interpretation of MRI scans. This can provide an early alert for intervention and initiation of treatment for those with onset of dementia. The assumption that the combined cross-sectional and longitudinal datasets would lead to dementia label classification of acceptable accuracy came out to be true. The method required careful data cleaning and data preparation work, converting it to a binary classification problem, as outlined in this notebook. At the outset it was not clear which algorithm(s) would be more appropriate for the binary and multi-label classification problem. The approach of spot checking the algorithms early for accuracy led to the determination of a smaller set of algorithms with higher accuracy (e.g. Gadient Boosting and Random Forest) for a deeper dive examination, e.g. use of a k-fold cross-validation approach in classifying the CDR label. The neural network benchmark model accuracy of 78% for binary classification was exceeded by the classification accuracy of the main output of this study, the trained Gradient Boosting and Random Forest classification models. This builds confidence in the latter model for further training with new data and further classification use for new patients.
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.
