1,603 skills found · Page 23 of 54
mohit9949 / Autonomous Forest Surveillance Safety System Using OpenCVThe current forest surveillance systems methods consume a lot of resources and are less efficient, not reliable and require a constant human presence whose tasks can be easily automated using new technology. To solve these problems we propose an autonomous surveillance system which uses object detection to identify specified animals. It is capable of monitoring forest fires, intruders, wildlife etc, all at once and alerts the concerned officials immediately and precisely. It has a hybrid object detection system using HAAR and Backpropagation neural network algorithms which can be used to train and detect animals and predict from the data obtained respectively. This helps in detecting various unwanted visitors, dangerous animals, or restricted tools into the forest. The system can not only store the video feed but can also determine population , track a specific animal or human and sends the pictures to your email directly along with real-time video monitoring via the internet which allows the users to monitor from anywhere in the world and sends instant alerts to your phone via an SMS even in remote areas in case of emergencies, and it stores all the data in a repository. We can control the system using a windows app which allows us to select which animals to be detected by the camera modules and their alert levels along with other settings and also provides a detailed analysis on various things like forest fires, animal population, trespassed areas etc, to users in simple charts. It is a smart, automatic, modular system which is cheap and easily expandable.
fsrt16 / Introduction To Genomic Data Sciences Breast Cancer Detection# Breast-cancer-risk-prediction > Necessity, who is the mother of invention. – Plato* ## Welcome to my GitHub repository on Using Predictive Analytics model to diagnose breast cancer. --- ### Objective: The repository is a learning exercise to: * Apply the fundamental concepts of machine learning from an available dataset * Evaluate and interpret my results and justify my interpretation based on observed data set * Create notebooks that serve as computational records and document my thought process. The analysis is divided into four sections, saved in juypter notebooks in this repository 1. Identifying the problem and Data Sources 2. Exploratory Data Analysis 3. Pre-Processing the Data 4. Build model to predict whether breast cell tissue is malignant or Benign ### [Notebook 1](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB1_IdentifyProblem%2BDataClean.ipynb): Identifying the problem and Getting data. **Notebook goal:Identify the types of information contained in our data set** In this notebook I used Python modules to import external data sets for the purpose of getting to know/familiarize myself with the data to get a good grasp of the data and think about how to handle the data in different ways. ### [Notebook 2](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB2_ExploratoryDataAnalysis.ipynb) Exploratory Data Analysis **Notebook goal: Explore the variables to assess how they relate to the response variable** In this notebook, I am getting familiar with the data using data exploration and visualization techniques using python libraries (Pandas, matplotlib, seaborn. Familiarity with the data is important which will provide useful knowledge for data pre-processing) ### [Notebook 3](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB3_DataPreprocesing.ipynb) Pre-Processing the data **Notebook goal:Find the most predictive features of the data and filter it so it will enhance the predictive power of the analytics model.** In this notebook I use feature selection to reduce high-dimension data, feature extraction and transformation for dimensionality reduction. This is essential in preparing the data before predictive models are developed. ### [Notebook 4](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB4_PredictiveModelUsingSVM.ipynb) Predictive model using Support Vector Machine (svm) **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I construct a predictive model using SVM machine learning algorithm to predict the diagnosis of a breast tumor. The diagnosis of a breast tumor is a binary variable (benign or malignant). I also evaluate the model using confusion matrix the receiver operating curves (ROC), which are essential in assessing and interpreting the fitted model. ### [Notebook 5](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB_5%20OptimizingSVMClassifier.ipynb): Optimizing the Support Vector Classifier **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I aim to tune parameters of the SVM Classification model using scikit-learn.
anandchauhan21 / Design And Analysis Of Algorithm🎓 Design and Analysis of Algorithm Lab (303105219) – Semester 5 💡 Python-based implementations of core algorithms using Google Colab 📘 Topics: Greedy, Divide & Conquer, Dynamic Programming, Graphs, Strings 🧪 Includes 12 labs as per official BTech syllabus 🚀 Easy to run, modify, and learn interactively!
RGLab / FlowStatsflowStats: algorithms for flow cytometry data analysis using BioConductor tools
coongroup / CompassCoon OMSSA (Open Mass Spectrometry Search Algorithm) Proteomic Analysis Software Suite.
AbdullahArean / Design And Analysis Of Algorithm Data StructureData structure and algorithms are two of the most important aspects of computer science. Data structures allow us to organize and store data, while algorithms allow us to process that data in a meaningful way.
andland / SparseLogisticPCAImplements the algorithm form "Sparse logistic principal components analysis for binary data"
willfavre4 / Algo TradingAlgorithmic Trading and Quantitative Analysis w/ Python, TradeStation, MATLAB, Mathematica, and more.
cfsmile / Alg 4gAlgorithm Design and Analysis Notes for Grad
MachineLearningJournalClub / LearningNLPSome Tutorials & in depth analysis of NLP's algorithms with an ethical flavour
jintrone / TEvATopic Evolution Analysis - an algorithm for analyzing knowledge flow in text based corpora
Hassan-Farid / Design And Analysis Of AlgorithmsNo description available
mskstanmay / DAA LabCompilation of questions from DAA course - Design and Analysis of Algorithms
sahilichake / Indian Crime Data Analysis ForecastingConducted data analysis, statistical analysis, and data visualization on an Indian crime dataset. Applied various machine learning algorithms to gain insights from the data. Utilized Time-Series models for prediction and forecasting based on the crime data analysis.
SaaadRaaa / Truss OptimizationAnalysis and Optimization of Truss Structures using Genetic Algorithms
marthadais / TrajectoriesCompressionAnalysisThe proposed methodology assess how compression algorithms influence the clustering analysis with respect to anomaly detection of vessel trajectories.
aideep1400 / Cattely Cattle Face Images DatasetA sample of front profile images of 50 cattle, with 50 images per cattle, facilitating research in cattle facial recognition, breed classification, and machine learning algorithms for cattle facial feature analysis
NymeriaWang / DeepLearning SCA ASCADRecent works have demonstrated that deep learning algorithms were very efficient to conduct security evaluations of embedded systems and had many advantages compared to the other methods. A comprehensive study of deep learning algorithms when applied in the context of side-channel analysis and we discuss the links with the classical template attacks. We address the question of the choice of the hyper-parameters for the class of multi-layer perceptron networks and convolutional neural networks. Several benchmarks and rationales are given in the context of the analysis of a masked implementation of the AES algorithm.
suneelpatel / Machine Learning With PythonMachine learning is changing the world and if you want to be a part of the ML revolution, this is a great place to start! This repository serves as an excellent introduction to implementing machine learning algorithms in depth such as linear and logistic regression, decision tree, random forest, SVM, Naive Bayes, KNN, K-Mean Cluster, PCA, Time Series Analysis and so on.
Karanchaudhary350 / DiagnoSysDiagnoSys is a comprehensive web application that provides advanced detection and analysis for various health conditions. This project leverages state-of-the-art machine learning algorithms to detect and diagnose COVID-19, Alzheimer's disease, breast cancer, and pneumonia using X-ray and MRI datasets.
