Advanced Machine Intelligence Research Lab

ALT UAP, BUBT, and AMIR Lab collaborated on Paper No. 13, published in the prestigious Elsevier Journal, Data in Brief (Q2). Titled "Data in Brief," the article introduces HQA-Data, a unique historical question-answer generation dataset from user dialogues. Authors include Sabbir Hosen, Jannatul Ferdous Eva, Ayman Hasib, Aloke Kumar Saha, M.F. Mridha, Anwar Hussen Wadud. The dataset, derived from chat logs, is invaluable for training and testing NLP algorithms, enhancing conversational AI in business, and serving as a learning resource for students. With applicability in diverse languages and domains, the dataset is a versatile tool for NLP and AI enthusiasts. Congratulations to the authors and AMIR Lab Teams! 🌐👏 #Research #NLP #AI #Chatbots #Dataset #Innovation #Tech #AcademicCollaboration

ALT AMIR Lab's recent paper in IEEE Access titled "PlantDet: A Robust Multi-Model Ensemble Method Based on Deep Learning For Plant Disease Detection" introduces the PlantDet model, integrating InceptionResNetV2, EfficientNet, and Xception models with various optimization techniques. The model surpasses the state-of-the-art in the Rice Leaf dataset and outperforms existing models, including robust ensemble models, for the Betel Leaf dataset. The paper emphasizes transparency through Grad-CAM++ and Score-CAM methods with the Xception model. The AMIR Lab Team achieves notable success, acknowledged in the fields of machine learning and deep learning. #amirlab #IEEE #research #deeplearning #machinelearning