This repository consolidates projects completed across various academic and research courses, including Computing Lab, Design Lab, Distributed Systems, Deep Learning Term Project, and MTP Final Year Project. Each folder contains subdirectories for individual projects with their respective descriptions, source codes, and deliverables.

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├── Computing Lab Projects/                     # Projects from the Computing Lab course
│   ├── Linear Programming and Integer Programming/
│   ├── Multi-threading/
│   ├── Network Programming/
│   ├── OS Programming/
│   ├── Web Scrapping using Python/
├── Design Lab Projects/                        # Projects from the Design Lab course
│   ├── Chat Server with GPT-2 Chatbot/
│   ├── Crawling_Covid_Statistics_and_News/
│   ├── Deep Learning with PyTorch/
│   ├── Machine Learning using scikit-learn/
├── Distributed Systems/                        # Distributed Systems-related projects
│   ├── Distributed-Database-with-Sharding-and-WAL/
│   ├── Distributed-Systems-Assignment-1-Customizable-Load-Balancer/
│   ├── Scalable-Database-with-Sharding/
├── Deep Learning Project/Deep Learning Term Project: Automatic Image Captioning/
│   ├── Codes/
│   ├── Deep Learning Term Project_3.pdf
│   ├── README.md
├── MTP Final Year Project/                     # Final year MTP research
│   ├── CAGE_TAG_WEB_SERVER.rar
│   ├── README.md

This folder contains projects focused on:

• Linear Programming: Optimization using linear and integer programming techniques.
• Multi-threading: Implementations of thread synchronization, mutex locks, and semaphores.
• Network Programming: Client-server applications and protocols using socket programming.
• OS Programming: Process synchronization and a custom Linux shell implementation.
• Web Scraping using Python: Automated data extraction from dynamic websites.

This folder includes:

• Chat Server with GPT-2 Chatbot: A peer-to-peer chat system with a built-in chatbot for FAQs.
• Crawling Covid Statistics and News: Data scraping using Lex-Yacc and MapReduce for analytics.
• Deep Learning with PyTorch: Projects utilizing PyTorch for training and evaluating neural networks.
• Machine Learning using scikit-learn: Applied scikit-learn for supervised and unsupervised ML tasks.

This folder consists of:

• Distributed Database with Sharding and WAL: Ensures fault tolerance and consistency in distributed databases.
• Customizable Load Balancer: Implements dynamic load distribution across servers.
• Scalable Database with Sharding: Focuses on horizontal scaling and replication techniques.

• Developed encoder-decoder models for Automatic Image Captioning using:
  • CNN-RNN architecture.
  • Vision Transformer (ViT) encoder with a Transformer decoder.
• Metrics: Evaluated using CIDEr, ROUGE-L, and SPICE.
• Deliverables: Source code, notebooks, and detailed project report.

• Objective: Predict Transcription Start Site (TSS) signals associated with CAGE tags.
• Implementation: Used Bi-LSTM with alignment-free embeddings for sequence classification.
• Web Server: CAGE Tag Web Server

• Objective: Neoepitope prediction for personalized cancer vaccines.
• Pipeline: Processes NGS data, tumor peptides, and HLA typing.
• Web Server: DeepPROTECTNeo Web Server

• Each folder contains:
  • README.md: Project-specific descriptions and instructions.
  • Source codes and notebooks.
  • Supporting files such as datasets, scripts, and reports.