136 skills found · Page 3 of 5
brunnurs / Binary Classification BertA binary classifier using BERT and the SST-2 dataset
DataNeel / Nx2 Cross ValidationRun Nx2 Cross Validation for multiple binary classifiers in parallel with optional downsampling
omfaku / Cnn Malware ClassificationClassifying malware families by converting their binaries to images and then applying Convolutional Neural Network solutions.
akbloodadarsh / Twitter Sentimental AnalysisI have used Multinomial Naive Bayes, Random Trees Embedding, Random Forest Regressor, Random Forest Classifier, Multinomial Logistic Regression, Linear Support Vector Classifier, Linear Regression, Extra Tree Regressor, Extra Tree Classifier, Decision Tree Classifier, Binary Logistic Regression and calculated accuracy score, confusion matrix and ROC(Receiver Operating Characteristic) and AUC(Area Under Curve) and finally shown how they are classifying the tweet in positive and negative.
jasonmayes / Retraining TensorFlow Classifier Using VideoScript to convert all MP4 videos in a zip archive to JPG frames at a desired FPS with unique names. It will then retrain the top layers of a binary image classifier using TensorFlow using these extracted images.
marcgarnica13 / Ml Interpretability European FootballUnderstanding gender differences in professional European football through Machine Learning interpretability and match actions data. This repository contains the full data pipeline implemented for the study *Understanding gender differences in professional European football through Machine Learning interpretability and match actions data*. We evaluated European male, and female football players' main differential features in-match actions data under the assumption of finding significant differences and established patterns between genders. A methodology for unbiased feature extraction and objective analysis is presented based on data integration and machine learning explainability algorithms. Female (1511) and male (2700) data points were collected from event data categorized by game period and player position. Each data point included the main tactical variables supported by research and industry to evaluate and classify football styles and performance. We set up a supervised classification pipeline to predict the gender of each player by looking at their actions in the game. The comparison methodology did not include any qualitative enrichment or subjective analysis to prevent biased data enhancement or gender-related processing. The pipeline had three representative binary classification models; A logic-based Decision Trees, a probabilistic Logistic Regression and a multilevel perceptron Neural Network. Each model tried to draw the differences between male and female data points, and we extracted the results using machine learning explainability methods to understand the underlying mechanics of the models implemented. A good model predicting accuracy was consistent across the different models deployed. ## Installation Install the required python packages ``` pip install -r requirements.txt ``` To handle heterogeneity and performance efficiently, we use PySpark from [Apache Spark](https://spark.apache.org/). PySpark enables an end-user API for Spark jobs. You might want to check how to set up a local or remote Spark cluster in [their documentation](https://spark.apache.org/docs/latest/api/python/index.html). ## Repository structure This repository is organized as follows: - Preprocessed data from the two different data streams is collecting in [the data folder](data/). For the Opta files, it contains the event-based metrics computed from each match of the 2017 Women's Championship and a single file calculating the event-based metrics from the 2016 Men's Championship published [here](https://figshare.com/collections/Soccer_match_event_dataset/4415000/5). Even though we cannot publish the original data source, the two python scripts implemented to homogenize and integrate both data streams into event-based metrics are included in [the data gathering folder](data_gathering/) folder contains the graphical images and media used for the report. - The [data cleaning folder](data_cleaning/) contains descriptor scripts for both data streams and [the final integration](data_cleaning/merger.py) - [Classification](classification/) contains all the Jupyter notebooks for each model present in the experiment as well as some persistent models for testing.
ajitsingh98 / DNA Classification Machine Learning ProjectClassify DNA sequence into Binary class using different Classification algorithms.
ankitdhall / Google Street View House Numbers Digit LocalizationLocalizing the the digits in images from the Google SVHN (Street View House Numbers) dataset
trayanmomkov / ZecSlashed Zero-Eight Classifier (ZEC) is a simple binary classifier, written in Python and available for Android.
naver-ai / BurnOfficial Pytorch Implementation of Unsupervised Representation Learning for Binary Networks by Joint Classifier Training (CVPR 2022)
KBNLresearch / DacEntity linker for the newspaper collection of the National Library of the Netherlands. Links named entity mentions to DBpedia descriptions using either a binary SVM classifier or a neural net.
greg-stitt-uf / Bnn Fcc ContestDesign contest for fully connected binary neural network classifier
sayalaruano / AMPredSTStreamlit web application to deploy a machine learning binary classifier to predict the activity of antimicrobial peptides
marius-benthin / MaaSource Code for Master Thesis: "Attribution of Malware Binaries to APT Actors using an Ensemble Classifier"
abhiverse01 / CIFAR 10 Neural VisionContains my project code for the - Rewrite of the classic CIFAR-10 classifier. ResNet-style CNN · Dual Binary + Multi-Class · Grad-CAM Explainability · Streamlit UI
ahmedyounis1st / IDS Feature Selection Based On GWO AlgorithmOne critical issue within network security refers to intrusion detection. The nature of intrusion attempts appears to be nonlinear, wherein the network traffic performance is unpredictable, and the problematic space features are numerous. These make intrusion detection systems (IDSs) a challenge within the research arena. Hence, selecting the essential aspects for intrusion detection is crucial in information security and with that, this study identified the related features in building a computationally efficient and effective intrusion system. Accordingly, a modified feature selection (FS) algorithm called modified binary grey wolf optimisation (MBGWO) is proposed in this study. The proposed algorithm is based on binary grey wolf optimisation to boost the performance of IDS. The new FS algorithm selected an optimal number of features. In order to evaluate the proposed algorithm, the benchmark of NSL-KDD network intrusion, which was modified from 99-data set KDD cup to assess issues linked with IDS, had been applied in this study. Additionally, the support vector machine was employed to classify the data set effectively. The proposed FS and classification algorithms enhanced the performance of the IDS in detecting attacks. The simulation outcomes portrayed that the proposed algorithm enhanced the accuracy of intrusion detection and reduction in the number of features.
g8a9 / L3wrapperA lightweight Python 3 wrapper around the binaries of the L3 associative classifier.
byjg / Php Text ClassifierA PHP text classifier supporting binary spam filtering (Robinson-Fisher Bayesian) and multi-class Naive Bayes classification, with optional LLM-assisted active learning fallback.
niklastoe / Classifier Metric UncertaintyBayesian method to determine the metric uncertainty for (binary) classifiers
M-Lin-DM / Convolutional LSTMs For Motion ForcastingKeras-tensorflow code for training a frame-by-frame binary classifier with video input + code for computing targets.
