Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Jayapandian Natarajan
Christ University
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Research on secure workload execution scheme in heterogeneous cloud environment Fairoz Pasha; Jayapandian Natarajan
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp1047-1054

Abstract

The increasing demand for the hardware, software and infrastructure is playing a big role in the information technology domain towards the need of customer’s specific requirements. Cloud computing is a major backbone for providing such services over the internet. It includes the services such as applications, storage, network, scalability, sharing, virtualization, confidentiality, security, authentication, and integrity. A large number of data intensive workflow applications uses heterogeneous cloud environment for communication and computation operation. An intruder/attacker will utilize these environments for their benefit by flooding malicious links, unwanted information and others. In cloud environment, detecting a malicious device/packet during workflow execution is a critical and challenging task. The various workflow method with security, service level agreement (SLA) and quality of service (QoS) have been modelled in recent time; However, these models are not efficient in detecting malicious users and maintaining high level of QoS or workflow applications. This article focus is on addressing research future direction, issues and challenges of work in meeting secure and efficient workflow execution model for heterogeneous cloud environment.
Deep learning ensembles for lung cancer detection in thoracic CT scans leveraging generative adversarial network technology Bineesh Moozhippurath; Jayapandian Natarajan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1605-1612

Abstract

Effective treatment of lung cancer depends on early and accurate detection, which continues to be a major cause of cancer-related fatalities globally. Conventional diagnostic techniques are useful, but their efficacy in handling large amounts of thoracic computed tomography (CT) scan data is limited by their time-consuming nature and susceptibility to human error. The research here suggests a new deep learning model that integrates generative adversarial networks (GANs) for data improvement with a sophisticated ensemble approach to classification. GANs are employed to generate realistic synthetic CT images, addressing the challenges of limited datasets. The backbone of the proposed approach is a consensus-guided adaptive blending (CGAB) ensemble model that learns to dynamically combine the predictions of three top-performing convolutional neural networks (CNNs): ResNet-152, DenseNet-169, and EfficientNet-B7. The CGAB model improves prediction accuracy through model contribution weighting based on confidence scores and inter-model consensus, while a conflict-resolving auxiliary decision model is used. The approach was tested using the lung image database consortium and the image database resource initiative (LIDC-IDRI) dataset with a detection rate of 97.35%, surpassing single model and traditional ensemble methods. The current work provides a solid and scalable approach to lung cancer detection with better generalization, increased diagnostic consistency, and applicability for clinical use.
Multi objective energy aware integrated cloud scheduling with a consensus-based security Fairoz Pasha; Jayapandian Natarajan
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9885

Abstract

This research presents a multi-objective, energy-aware workflow scheduling framework for heterogeneous cloud–edge environments that addresses both efficiency and data integrity challenges. Conventional encryption-based security mechanisms, although effective in protecting data during task offloading, often introduce significant computational and communication overhead, leading to degraded system performance. To overcome this limitation, this work proposes the consensus security-integrity and quality-aware workflow scheduler (CSIQA-WS), which integrates energy-aware scheduling with a lightweight, consensus-driven security mechanism. The model incorporates automatic service management and an attack prevention module to detect and mitigate malicious behavior during inter-node data transmission while maintaining quality of service (QoS) constraints. A dynamic coordination between edge and cloud resources enables efficient workload distribution and robust resource utilization. Experimental evaluation using scientific workflow benchmarks demonstrates that CSIQA-WS significantly reduces processing time and energy consumption compared to existing approaches. The proposed model achieves up to 92.29% reduction in processing time and consistently improves overall QoS while preserving data integrity in dynamic execution environments. These results indicate that CSIQA-WS provides an effective and scalable solution for secure and energy-efficient workflow scheduling in modern cloud–edge systems.
Co-Authors Bineesh Moozhippurath Fairoz Pasha