The Course of Independent Study (CIS) is a 3-credit Open Elective introduced by the University as part of its Outcome-Based Competency-Focused Curriculum (OBCFC), designed as a 10-week mentored research engagement that replaces traditional classroom learning with competency-driven, independent research work. The course enables students to develop core competencies such as problem identification, analytical thinking, research methodology, ethical inquiry, and scholarly communication, and is assessed through structured progress reviews, successful execution of a defined project plan, and the submission of a full-length research paper to a Scopus-indexed journal or conference, followed by a final presentation and viva. The Department of Computer Science and Engineering (CSE) plays a key role in implementing CIS by offering research projects aligned with emerging areas such as Artificial Intelligence, Machine Learning, Data Science, Cybersecurity, Cloud Computing, Blockchain, and Software Engineering, with faculty mentors guiding students through all stages of the research lifecycle using modern tools and real-world datasets available in departmental laboratories and Centres of Excellence. Through a strong emphasis on research documentation, publication, and professional presentation, CIS fosters a robust undergraduate research culture and reinforces the department’s commitment to competency development, research excellence, and industry-relevant education, preparing students for higher studies, innovation, and research-oriented careers.
Few topis for CIS are:
Leveraging Brain Tumor Detection and Classification using Vision Transformer Models and Explainable AI
Trustworthy AI, A Framework for Auditable and Tamper-Resistant Decision System
Explainable AI-Based Student Performance Predictor with Transparent Feedback Insights
Predict and negotiate optimal crop prices in real-time using market, weather, and logistics conditions using multiagent systems.
The Course of Independent Study (CIS) is a 3-credit Open Elective introduced by the University as part of its Outcome-Based Competency-Focused Curriculum (OBCFC), designed as a 10-week mentored research engagement that replaces traditional classroom learning with competency-driven, independent research work. The course enables students to develop core competencies such as problem identification, analytical thinking, research methodology, ethical inquiry, and scholarly communication, and is assessed through structured progress reviews, successful execution of a defined project plan, and the submission of a full-length research paper to a Scopus-indexed journal or conference, followed by a final presentation and viva. The Department of Computer Science and Engineering (CSE) plays a key role in implementing CIS by offering research projects aligned with emerging areas such as Artificial Intelligence, Machine Learning, Data Science, Cybersecurity, Cloud Computing, Blockchain, and Software Engineering, with faculty mentors guiding students through all stages of the research lifecycle using modern tools and real-world datasets available in departmental laboratories and Centres of Excellence. Through a strong emphasis on research documentation, publication, and professional presentation, CIS fosters a robust undergraduate research culture and reinforces the department’s commitment to competency development, research excellence, and industry-relevant education, preparing students for higher studies, innovation, and research-oriented careers.
Few topis for CIS are:
Leveraging Brain Tumor Detection and Classification using Vision Transformer Models and Explainable AI
Trustworthy AI, A Framework for Auditable and Tamper-Resistant Decision System
Explainable AI-Based Student Performance Predictor with Transparent Feedback Insights
Predict and negotiate optimal crop prices in real-time using market, weather, and logistics conditions using multiagent systems.
