This repository contains the code and documentation for a computational framework that leverages machine learning techniques to enable accurate classification of bacterial species, even closely related strains.

The framework integrates genomic analysis methods, such as motif screening and single nucleotide polymorphism (SNP) extraction, to derive informative features from bacterial genomes. These genomic insights are then fed into machine learning models, which are trained to reliably differentiate between bacterial species based on their distinctive patterns and characteristics.

The Dash Cam Video Dataset is a comprehensive collection of real-world road footage captured across various Indian roads, focusing on lane conditions and traffic dynamics. Indian roads are often characterized by inconsistent lane markings, unstructured traffic flow, and frequent obstructions, making lane detection and traffic identification a challenging task for autonomous vehicle systems.

The BNS (Bharatiya Nyay Sanhita) dataset is a comprehensive collection of legal texts which was web-scraped.. It consists of chapters and their respective sections, capturing detailed legal content relevant to the recently introduced BNS framework in India. This dataset was gathered using a Python-based web scraping script leveraging Selenium WebDriver, ensuring accuracy and completeness. Available in CSV formats, the dataset facilitates ease of access for legal research, natural language processing (NLP) tasks, and AI-based legal assistance applications.

This paper presents an enhanced methodology for network anomaly detection in Industrial IoT (IIoT) systems using advanced data aggregation and Mutual Information (MI)-based feature selection. The focus is on transforming raw network traffic into meaningful, aggregated forms that capture crucial temporal and statistical patterns. A refined set of 150 features including unique IP counts, TCP acknowledgment patterns, and ICMP sequence ratios was identified using MI to enhance detection accuracy.