We envisage our lab as a beacon of innovation and knowledge, where curiosity drives the creation of technology that is sustainable, ethical, and beneficial for humanity.
Deep Learning
Healthcare AI
Datasets
EducationCurrent initiatives from across the lab, spanning healthcare, intelligent systems and applied machine learning.
In a dynamic landscape where industry and academia often drift apart, we introduce Acastry - an innovative solution designed to harmonize and enhance the relationship between these realms.
In today's world, leukemia cancer, a type of blood cancer, is a lethal condition that affects a person's body.
Anupoma Angasree Toma, Nabajyoti Sharma
In the post-pandemic era, the use and demand for blended learning platforms have grown significantly, leading to a diverse mix of online…
MD Abdul Munim
In an uphill setting where poultry farming is a paramount yet perilous business, we present an IoT-Based Smart Farming Automation System…
Mahbubur Rahman, Aftar Ahmad Sami
In our upcoming work, we plan to develop a robust deep learning model for Retinal OCT images.
Tanzil Ebad Chowdhury, Biggo Bushon Routh
Recent peer-reviewed work from the lab in AI, machine learning and intelligent systems.
All publicationsIEEE Access, vol. 12, pp. 181009-181024
Iftekhar Ahmed, Biggo Bushon Routh, Md. Saidur Rahman Kohinoor, Shadman Sakib, Md Mahfuzur Rahman and Farag Azzedin
IEEE Access, vol. 12, pp. 120454-120473
Khadiza Akther, Md. Saidur Rahman Kohinoor, Bushra Siddika Priya, Md. Jamaner Rahaman, Md. Mahfuzur Rahman and Md. Shafiullah
2023 26th International Conference on Computer and Information Technology (ICCIT), pp. 1-6
Nabajyoti Sharma, Anupoma Angasree Toma and Md. Saidur Rahman Kohinoor
Recordings of our researchers presenting their work at conferences and workshops.
Watch on YouTubeAssessing the Robustness of Machine Learning Algorithms for Cardiovascular Disease Detection Across Diverse Clinical Datasets.
Deep Learning-Based Local Fish Classification: A Comparative Study of VGG16 Models and Multiple Classifiers.
Assessing the Robustness of Machine Learning Algorithms for Cardiovascular Disease Detection Across Diverse Clinical Datasets.
Announcements, milestones, awards and events from across our research and academic life.
We welcome students, researchers and collaborators who want to build technology that is sustainable, ethical and useful to people.
