User-friendly Gateway to the IPFS Network. My project acts as a bridge from Web 2.0 to Web 3.0, simplifying the complexities of decentralised networks. It offers a service similar to centralised systems, making it more familiar to users. The IPFS gateway allows users to transfer larger files compared to common solutions like messengers or emails and reduces the complexity of sharing over cloud storage. For private sharing, file encryption can restrict unauthorised viewing since IPFS is a public network. Ip-fs.cloud is probably the fastest way to share files between multiple people simultaneously. Users only need to add a file reference to a "Public Wall" for public access. The gateway also supports video streaming directly from the IPFS network, enabling users to upload videos from their phones and play them almost immediately on any HTML5-capable web browser. Apart from simplifying the adoption of distributed networks, this represents a significant technological shift since Web 2.0. Decentralised systems are more efficient, reduce business expenses, and waste less energy due to direct peer-to-peer data transfer, benefiting our ecosystem. IPFS, as a decentralised network with a distributed file system, avoids central choke points. Any node with the content cached can serve it, enabling torrent-like file sharing where peers download files from multiple sources concurrently, speeding up the process.

The aim of this project is to monitor network activities and inform the administrator if any trackable or unexpected events occur. There are three main parts to this project: an application on the computer side, a controller, and a website to display collected data to the administrator. The benefits of such an application are that it can be installed on multiple devices and all collected data can be displayed at a single point (the website). The website can be accessible 24/7 from any part of the globe. All monitored devices are displayed as a list. If a user prefers, email notifications can be sent about events that have occurred. Additionally, users will have some control over remote devices, such as closing applications running on the device and shutting down or restarting computers. An API endpoint will also be provided, so it can be easily integrated into other applications.

A Computer Vision Application for O-Ring Analysis inspects and classifies O-rings based on their condition using image processing techniques. The process begins with thresholding, which converts the image to a binary format to separate the O-ring from the background. To enhance the shape and remove noise, dilation and erosion are applied, ensuring a clearer representation of the object. The system then extracts regions of interest, isolating the O-ring for further analysis. Perimeter detection is used to examine the contour, identifying irregularities, breaks, or distortions. Statistical analysis follows, measuring key properties such as circularity, thickness variation, and surface defects. Based on these extracted features, the application classifies the O-ring into categories such as good, damaged, or ripped, using predefined thresholds. This automated approach improves quality control in manufacturing by providing fast, accurate, and consistent defect detection.

Huffman coding is a scheme that assigns variable-length bit-codes (binary strings) to characters, such that the lengths of the codes depend on the frequencies of the characters in a typical message. As a result, encoded messages take less space (as compared to fixed-length encoding such as ASCII or UNICODE) since the letters that appear more frequently are assigned shorter codes. This is performed by first building a Huffman coding Tree based on a given set of frequencies. From the tree, bit-codes for each character are determined and then used to encode a message. The tree is also used to decode an encoded message as it provides a way to determine which bit sequences translate back to a character.

A Business Website for Nail Polishing Services showcases a professional nail care brand. Designed with a stylish and modern aesthetic, the website features high-quality images, a portfolio of nail art designs, and customer testimonials to enhance credibility.

A COVID-19 Lockdown Simulation App models the spread of the virus under different lockdown scenarios to evaluate their effectiveness in reducing infections. The app simulates a population where individuals interact and transmit the virus based on parameters like infection rate, recovery time, and mobility restrictions. can adjust variables such as lockdown start time, intensity (full or partial), and duration to observe their impact on case numbers over time. The simulation visualises outcomes with graphs and heatmaps, helping policymakers and researchers understand the trade-offs between public health and economic impact.

A Machine Learning System for Chest Infection Analysis and Classification leverages advanced algorithms to detect and categorise chest infections from medical images such as X-rays or CT scans. The system processes input images using techniques like image preprocessing, noise reduction, and feature extraction to enhance diagnostic accuracy. A deep learning model, such as a Convolutional Neural Network (CNN), analyses the images to identify patterns associated with infections like pneumonia, tuberculosis, or COVID-19. The model is trained on labeled medical datasets, learning to differentiate between normal and infected lungs. Statistical measures, including accuracy, sensitivity, and specificity, help evaluate its performance. After analysis, the system classifies the infection and provides a confidence score, assisting healthcare professionals in diagnosis. The model can be integrated into a cloud-based platform for remote accessibility or deployed in hospitals to support radiologists in making faster, data-driven decisions.

Tumor is an advanced mobile application designed to assist in the early detection of cancer through blood analysis, powered by AI and machine learning technologies. By analysing blood samples, the app utilises sophisticated algorithms to identify biomarkers and other indicators that may suggest the presence of cancerous cells. The machine learning models continuously improve their detection capabilities, providing highly accurate insights that can help healthcare professionals make informed decisions for early diagnosis and treatment.

Project leverages advanced artificial intelligence (AI) and machine learning (ML) technologies which may help to combat the spread of misinformation. Using sophisticated algorithms, the system analyses given textual content such as news articles, social media posts, and other content to assess their credibility. By evaluating linguistic patterns and context, it accurately flags potential fake news, helping users distinguish reliable information from misleading or fabricated stories.

An RSS Reader App for Android allows users to subscribe to and read RSS feeds, providing a streamlined way to stay updated with news and articles. The app fetches and parses RSS feeds from user-specified sources, displaying content in a clean and user-friendly interface. Users can categorise feeds, bookmark articles, and search through saved content for easy access. To ensure seamless data synchronisation across devices, the app stores user preferences, subscriptions, and bookmarked articles on Firebase. Firebase Firestore manages real-time data storage, while Firebase Authentication allows secure user login. The app periodically updates feeds in the background and supports offline reading by caching recent articles. With push notifications for new articles, customisable themes, and an intuitive design, the app enhances the RSS reading experience while leveraging Firebase for efficient cloud-based storage and synchronisation.