Improved detection of disease identification in computational biology and Smart farming through advanced machine learning techniques and integrative data analysis to enhance accuracy, early diagnosis, and personalized treatment strategies.
Identify and report innovative and novel research outcomes on applications of IoT, AI, machine learning, deep learning, remote sensing, and autonomous systems in smart farming and precision livestock.
Developing low-resource language models to enhance natural language processing capabilities and improve accessibility for underrepresented languages.
Efficient learning for edge intelligence to maintain privacy and handle data heterogeneity, leveraging advanced algorithms and techniques EG: Federated learning, KNOWLEDGE DISTILLATION, encryption.
Model optimization through hyperparameter tuning, pruning, and model ensemble techniques to enhance performance, reduce computational costs, and improve generalization across various tasks and datasets.