RAHIM BAIG
A Graduate with a master's degree in computer science from Rowan University. Have a solid foundation in Python3, HTML5, AWS, Azure. I am eager to kickstart my career as a software engineer. I bring enthusiasm, fresh perspectives, and a strong willingness to learn. I am excited about the opportunity to contribute to Any projects and grow professionally in a collaborative environment.
SKILL SET
Intrusion Detection System
Image Classification Using Kmeans
Image classification, a pivotal task in computer vision, traditionally relies on complex neural network architectures demanding extensive labeled data for training. In contrast, the K-means clustering method presents a simpler yet effective alternative for image classification by segmenting images into distinct clusters based on pixel similarities. This project explores the utilization of K-means clustering to categorize images into meaningful groups without the need for labeled training data. The process involves extracting image features to represent content, applying K-means clustering to partition images into segments, and assigning semantic labels to clusters through methods such as centroid analysis or majority voting. Evaluation metrics such as accuracy and precision gauge the performance of the classification system. By leveraging pixel similarities and clustering algorithms, this approach demonstrates promise in various applications, including image retrieval and unsupervised image classification, offering an efficient solution for organizing and categorizing images.
Social Media Platform
This project aims to develop an intrusion detection system (IDS) leveraging KMeans clustering and Random Forest algorithms for enhanced cybersecurity. By harnessing machine learning techniques, the IDS will analyze network traffic data to identify anomalous patterns indicative of potential intrusions or attacks. KMeans clustering will help detect deviations from normal behavior by grouping similar network traffic data points, while Random Forest will provide classification capabilities to distinguish between benign and malicious activities. Through this approach, the project seeks to improve the accuracy and efficiency of intrusion detection, contributing to more robust cybersecurity defenses against evolving threats.
Project Portfolio
The project aims to develop a dynamic social media platform that fosters meaningful connections and interactions among users. This social media website will provide a user-friendly interface for individuals to create profiles, share content, connect with friends, and engage with communities of interest. Key features include customizable user profiles, a news feed displaying updates from friends and followed accounts, messaging functionality for private communication, and the ability to join or create groups based on shared interests. The platform will prioritize user privacy and data security, implementing robust encryption and user-controlled privacy settings. Additionally, advanced algorithms will curate personalized content recommendations based on user preferences and interactions. The website will support various media formats, including text, images, videos, and links, to encourage diverse forms of expression and engagement. A responsive design will ensure seamless access across devices, including desktops, tablets, and smartphones. Community moderation tools and reporting mechanisms will maintain a safe and inclusive environment, promoting respectful discourse and mitigating harmful behavior. Overall, the social media website project aims to provide an immersive and enriching user experience while fostering genuine connections and facilitating meaningful interactions in a safe and vibrant online community.
