Raveendrababu Vempati

FedBiCNN: Federated BiLSTM-CNN Framework for Early Detection of Type III Diabetes Mellitus

Type III Diabetes Mellitus (T3DM) is characterized by insulin resistance in the brain and neurodegenerative disorders, and is the main objective of timely and accurate detection to ensure early clinical intervention. But machine learning systems with central control are not without troubles due to patient privacy, data...

XAI-MaizeNet: An Explainable Deep Learning Framework for Transparent and Interpretable Maize Leaf Disease Detection and Classification

Early and precise diagnosis of maize leaf diseases is vital to reduce crop losses and to ensure sustainable farming production. Nevertheless, traditional Deep Learning models are usually “black boxes,” making them unreliable and unpopular in real-world agricultural decision-making processes. To provide transparent and...

FDSA: Sustainable Task Scheduling for IoT Applications in Multi-Cloud Environments

A Multi-View Hybrid Deep Learning Framework for Interpretable Disease Prediction using Heterogeneous Electronic Health Records

Electronic Health Record (EHR) contains varied and diversified clinical data, including demographic data, laboratory measures, diagnosis histories, medication data and physiological observations. However, there remains the issue of effectively using such multimodal information for illness prediction because of the data...

An Intelligent Edge-AI-Enabled Framework for Maize Disease Detection System Using Deep CNN Models

The proposed work suggests an intelligent Edge-AI-based system of detecting any disease in maize employing deep Convolutional Neural Network (CNN) models to provide the opportunity to detect diseases in real-time with minimum latency and minimum reliance on the cloud.