Explore our ResearchArea and their impactful contributions.
SPECTRA Lab (Secure Programming, Cryptography & Trust Research Alliance) is the cybersecurity research unit of the SWE Dept. at Green University. Equipped with high-performance PCs (Core i7, 16GB RAM, 1TB SSD, 4GB GPU), the lab focuses on secure software engineering, cryptography, blockchain security, digital forensics, and AI-driven threat detection—empowering future experts in trustworthy computing.
Secure Software Engineering, Cryptographic Protocol Design, Blockchain & Distributed Ledger Security, Network Security & Intrusion Detection, Cyber Threat Intelligence (CTI) & Forensics, IoT & Embedded Security, Privacy-Preserving Technologies, Security in AI & ML Systems, Compliance & Risk Management, Secure Identity & Access Management (IAM),
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NOVA Lab (Next-Gen Open-source & Value-Driven Architecture) is a research cell of the SWE Dept. at Green University focused on modern software architecture, open-source technologies, and DevOps. It empowers students to build scalable, maintainable systems using agile and cloud-native tools. NOVA promotes innovation through real-world collaboration, open-source contributions, and sustainable software engineering practices.
Software Architecture & Design Patterns, Open-Source Software Development, Agile and DevOps Practices, Cloud-Native Application Engineering, Microservices and Containerization (Docker, Kubernetes), Continuous Integration / Continuous Deployment (CI/CD), Software Reusability and Modularity, Software Documentation & Maintenance Automation, Developer Productivity & Tooling Ecosystems, Sustainable & Green Software Engineering,
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AIMS Lab focuses on applying Artificial Intelligence and Machine Learning to software engineering challenges. From intelligent code generation to defect prediction and software analytics, AIMS empowers students to build smarter, adaptive, and efficient software systems. The lab supports projects in NLP, computer vision, ML Ops, and AI-driven software lifecycle automation.
Automated Software Engineering with AI, Defect Prediction and Bug Localization, AI-based Code Generation & Completion, Machine Learning in Software Quality Assurance, Model Optimization & Hyperparameter Tuning, ML for User Behavior Analysis & Recommendation Systems, AI for Software Process Analytics,
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The Human–Computer Interaction (HCI) research cell is dedicated to designing software that not only works—but works beautifully for people. This cell explores how users interact with technology and how systems can be designed to be more usable, accessible, and emotionally engaging. By blending creativity, psychology, and engineering, students and researchers in this lab craft next-generation user experiences that are intuitive, inclusive, and impactful.
User Interface (UI) & User Experience (UX) Design, Usability Testing & Evaluation Methods, Accessible & Inclusive Design (for differently-abled users), Human-Centered AI Interfaces, Gesture, Voice & Multimodal Interaction, Emotional Computing & Affective Interfaces, Augmented & Virtual Reality Interaction Design, Cognitive Load Analysis in Interface Design, User Behavior Analytics & Eye-Tracking Studies, Design Thinking & Prototyping for Software Systems,
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The Big Data Research Cell is dedicated to unlocking the power of large-scale data to drive smarter decisions and intelligent systems. By exploring scalable data processing, real-time analytics, and predictive modeling, this cell empowers students to handle complex, high-volume data challenges. From business intelligence to smart city solutions, the cell bridges the gap between raw data and real-world impact—shaping future-ready engineers and researchers.
Big Data Analytics & Visualization, Distributed Data Processing (Hadoop, Spark), Real-Time Stream Processing (Kafka, Flink), Data Warehousing & Data Lakes, Scalable Machine Learning on Big Data, Data Privacy & Anonymization in Large Datasets, Cloud-based Big Data Infrastructure (AWS, GCP, Azure), Text and Web Mining at Scale, Big Data Applications in Smart Cities & Healthcare, Data Governance & Ethical Data Use,
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