Santhosh Kumar Medishetti

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...

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

TOTO: An Intelligent Task Scheduling Framework for IoT Applications in Multi-Cloud Environment

Internet of Things (IoT) applications and the increasing demand for cloud-based services have created major challenges in task scheduling for energy efficiency and carbon awareness in multi-cloud environments. This study proposes an Energy and Carbon Emission Aware Task Scheduling framework for IoT applications based o...

Intelligent Task Scheduling Approach for Internet of Health Things with Integrated Cyber-Physical Systems

The Internet of Health Things (IoHT) based Cyber-Physical Systems (CPS) have greatly improved the intelligent provision of healthcare services, but secure, reliable, and latency-sensitive task scheduling is still far from being realized in the face of dynamic network environments, resource heterogeneity, and growing cy...

Multi-Plant Disease Classification using ResNet50-based Deep Ensemble Learning Framework

Agricultural productivity and food security are heavily impacted by plant diseases, and thus there is a high demand for accurate and automated plant disease detection that can be achieved by applying deep learning techniques. This research proposes a Multi-Model Ensemble Method Based on Deep Learning for multi-plant di...

Intelligent Link Failure Prediction and Optimization-Based QoS Routing in MANETs

This paper proposes an Intelligent Link Failure Prediction and QoS-Aware Routing framework for Mobile Ad Hoc Networks (MANETs) using the Harris Hawk Optimization (HHO) algorithm to achieve reliable and efficient data transmission under highly dynamic network conditions. The proposed model integrates proactive link fail...

QUBO: Quantum-Inspired Metaheuristic-based IoT Task Scheduling in Multi-Cloud Environment

Task scheduling in multi-cloud-based IoT is a very relevant issue as the demand on energy-efficient and environmentally conscious computing is growing. The proposed study suggests that a Quantum-Inspired Metaheuristic-based scheduling method based on a Quadratic Unconstrained Binary Optimization (QUBO) model could be u...

Energy and Carbon Emission Aware Task Scheduling in Cloud Computing Using Memetic Optimization Framework

Improvements in memetic-based hybrid scheduling underscore the potential of memetic-based hybrid scheduling to support environmentally sustainable and performance-efficient cloud infrastructures.