Dr. Abhilash .KS

@educorp.co.in

Managing Director
Educorp Centre for Research & Advanced Studies Pvt. Ltd.

Dr. Abhilash .KS


               Download Compact PDF  
https://researchid.co/dr.abhilashks

RESEARCH, TEACHING, or OTHER INTERESTS

Engineering, Business, Management and Accounting, Multidisciplinary, Energy
33

Scopus Publications

164

Scholar Citations

7

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • HIPAA-Compliant Hybrid Cloud for EHR Mortality and Readmission Risk Prediction
    Arjun Warrier, Abhilash K S
    Proceedings of the 7th International Conference on Innovative Data Communication Technologies and Application Icidca 2025, 2025
    The rapid digitization of healthcare has transformed how patient data is collected, stored, and utilized, bringing both opportunities and challenges for modern medical systems. With hospitals relying on Electronic Health Records (EHRs), predictive models are vital for clinical outcomes and timely intervention. Yet, deploying EHRs in shared environments raises serious challenges of privacy, regulatory compliance, and secure management of sensitive patient information. This study introduces a HIPAA (Health Insurance Portability and Accountability Act)-compliant hybrid cloud framework that integrates homomorphic encryption with logistic regression for privacy-preserving EHR analysis. The architecture employs the Cheon–Kim–Kim–Song (CKKS) scheme, enabling computations to be performed directly on encrypted data without decryption. Sensitive patient records remain encrypted within the private cloud, while encrypted computations are securely carried out in the public cloud. Logistic regression functions as the predictive model in the public cloud, delivering outcomes on mortality and readmission risk. Once decrypted in the private cloud, predictions are mapped back to patients, and clinicians are alerted through secure hospital systems. Experimental evaluation demonstrates the robustness of the framework, achieving an accuracy of 98.7% for mortality prediction and 98.3% for readmission risk, with precision, recall, and F1-scores consistently above 96%. The findings confirm that the system balances predictive accuracy with robust data protection, offering a practical framework for secure AI in healthcare that supports proactive decisions while preserving patient confidentiality.
  • Hybrid Edge-Cloud AI Gateway with 1D-CNN for Real-Time Anomaly Detection and Temporal Fusion Transformer for Healthcare Data Streams
    Arjun Warrier, Abhilash K S
    Proceedings of the 7th International Conference on Innovative Data Communication Technologies and Application Icidca 2025, 2025
    The continuous generation of physiological data from wearable devices, bedside monitors, and electronic health records presents both an opportunity and a challenge for modern healthcare systems. Timely identification of anomalies and accurate risk prediction are critical for patient safety, yet conventional cloud-only processing introduces latency, energy overhead, and privacy concerns. This study proposes a hybrid Edge-Cloud AI Gateway designed to overcome these limitations by combining lightweight edge intelligence with scalable cloud analytics. At the edge layer, a 1D Convolutional Neural Network (1D-CNN) enables real-time anomaly detection of significant signs such as electrocardiogram (ECG), blood oxygen saturation (SpO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>), heart rate (HR), and blood pressure (BP), ensuring immediate alerts for critical deviations with minimal latency. Non-critical data streams are routed to the cloud, where a Temporal Fusion Transformer (TFT) performs multi-horizon risk prediction, capturing long-term dependencies and providing uncertainty-aware forecasts. Experiments conducted on the MIMIC-III critical care dataset demonstrate that the framework achieves 99.25% accuracy in anomaly detection, reduces latency by over 98% compared to cloud-only setups, and lowers energy consumption by nearly 80%. Furthermore, the TFT maintains robust forecasting performance, with R2 values above 0.95 even at 60-minute prediction horizons. These results highlight the dual benefits of the architecture: rapid local responsiveness and reliable predictive analytics. By bridging low-latency anomaly detection with advanced risk forecasting, the proposed Edge-Cloud AI Gateway paves the way for scalable, secure, and proactive healthcare monitoring, supporting timely interventions and reducing the burden on clinical resources.
  • Latency-Aware Edge-Cloud Architecture for 5G IoT Integration
    Varinder Kumar Sharma, K S Abhilash
    Proceedings of the 6th International Conference on Electronics and Sustainable Communication Systems Icesc 2025, 2025
    The rapid growth of 5G-enabled Internet of Things (IoT) applications-such as industrial robotics, connected vehicles, and augmented/virtual reality (AR/VR)faces a fundamental issue: existing cloud-only and edge-only infrastructures cannot consistently deliver the required ultralow latency and high reliability. To address this issue, this study proposes a multi-layered architecture for integrating 5Genabled IoT devices into edge-cloud continuums, underpinned by a latency-aware orchestration framework. The architecture incorporates formal end-to-end latency modelling and a Latency-Aware Graph Placement (LAGP) strategy, enabling dynamic workload distribution between Mobile Edge Computing (MEC) nodes and central cloud resources. Experimental evaluations validate the effectiveness of the proposed approach across diverse workloads. The system consistently achieved superior Service Level Agreement (SLA) compliance, maintaining $95-97 \%$ reliability under dynamic network conditions and outperforming edge-only baselines by $12-16 \%$. Key improvements were observed in industrial robot control, AR/VR streaming, and connected vehicle scenarios, where the architecture ensured bounded latency ($\lt 10 \mathrm{~ms}$ median), minimized jitter, and accelerated hazard alert delivery. Furthermore, resource utilization analysis revealed stable MEC load balancing around $\mathbf{6 5 - 7 0 \%}$, effectively preventing overload while leveraging cloud elasticity. These outcomes confirm that the proposed edge-cloud continuum not only addresses the shortcomings of existing infrastructures but also establishes a robust, scalable, and resilient foundation for next-generation mission-critical 5G IoT services.
  • Multi-Agent Systems for Collaborative and Proactive Fraud Prevention in Distributed AI-Driven Financial Platforms
    Sreenivasarao Amirineni, Abhilash K S
    Proceedings of the 9th International Conference on Electronics Communication and Aerospace Technology Iceca 2025, 2025
    The increasing sophistication of fraudulent activities in digital finance demands advanced detection and prevention mechanisms capable of adapting to evolving threats while ensuring data privacy. Traditional centralized fraud detection systems are hindered by from limited data diversity, delayed response times, and privacy concerns due to direct data sharing between institutions. These constraints hinder their ability to detect novel, cross-platform fraud patterns in real time. To address these limitations, this study proposes a Multi-Agent System (MAS) integrated with Federated Learning (FL) and Deep Q-Network (DQN)-based Reinforcement Learning (RL) for collaborative and proactive fraud prevention in distributed AI-driven financial platforms. The MAS architecture deploys specialized agents such as anomaly Detection, behavioral analysis, risk scoring, and intervention, operating in parallel to analyze diverse transaction and behavioral features. FL ensures privacy-preserving model training across institutions using local datasets, while the RL-driven Intervention Agent dynamically adjusts fraud response strategies based on operational outcomes, balancing fraud prevention, false alarms, and latency. The framework was evaluated using the IEEE-CIS Fraud Detection Dataset, which was partitioned to simulate multiple financial platforms. Experimental results demonstrate superior performance compared to baseline methods, achieving 98.34% accuracy, 97.92% precision, 98.47% recall, 98.19% F1-Score, and 99.12% AUC with an average decision latency of 42.5 ms, enabling real-time deployment. These findings confirm the proposed MASFL-RL approach’s ability to deliver high detection accuracy while maintaining low latency and preserving data privacy. By enabling collaborative intelligence without compromising sensitive information, the framework offers a scalable and adaptive fraud prevention solution suitable for large-scale, distributed financial ecosystems.
  • Hierarchical Cloud-IoT Architecture for AI-Powered Intelligent Disaster Response
    Varinder Kumar Sharma, Abhilash K S
    Proceedings of the 7th International Conference on Innovative Data Communication Technologies and Application Icidca 2025, 2025
    Natural and human-induced disasters such as floods, earthquakes, wildfires, and cyclones present significant threats to communities by disrupting lives, infrastructure, and ecosystems. The unpredictability of these events makes disaster management a critical discipline, requiring systems that can anticipate hazards, provide timely alerts, and support rapid decision making. Conventional disaster management approaches, including manual monitoring, centralized data collection, and statistical prediction models, have shown limited scalability, slower response, and reduced adaptability when challenged with complex and dynamic environments. To address these challenges, this study introduces a hierarchical Cloud-Internet of Things (IoT) framework supported by fifth generation (5G) connectivity, designed to integrate real-time sensing, edge analytics, and cloud-based forecasting into a unified disaster response system. At the edge, an Autoencoder enables low-latency anomaly detection, while the cloud employs a Long Short-Term Memory (LSTM) model to forecast disaster evolution and support proactive planning. The proposed approach was evaluated using IoT sensor-based dataset, demonstrating anomaly detection accuracy consistently exceeding 98.5%, forecasting accuracy with a determination coefficient (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) of up to 0.997, and end-to-end latency below 2 seconds. Robustness testing under varying network conditions further demonstrated the stability of the framework, with timely response rates of 99.4% under normal 5G conditions, 98.1% under degraded performance, and 96.8% even under severe congestion. These findings establish the proposed framework as a reliable and scalable solution for real-time disaster management, combining rapid local responsiveness with accurate predictive intelligence to support effective emergency interventions.
  • AI-Driven Fraud Detection in IoT-Enabled Payment Ecosystems: Challenges, Hybrid Edge-Cloud Framework, and Emerging Trends
    Sreenivasarao Amirineni, K S Abhilash
    Proceedings of the 6th International Conference on Electronics and Sustainable Communication Systems Icesc 2025, 2025
    Financial fraud in IoT-enabled payment systems has emerged as a significant challenge due to the rapid adoption of digital transactions, real-time processing requirements, and the increasing sophistication of fraud schemes. Fraud detection involves identifying unauthorized, anomalous, or suspicious transactions to prevent monetary losses and safeguard user trust. Traditional detection approaches, such as rule-based systems and classical machine learning (ML) models like Logistic Regression (LR), Random Forest (RF), and XGBoost (XGB), provide a baseline capability for flagging simple or known patterns. However, these methods often struggle with temporal transaction dynamics, networked collusion, and previously unseen fraudulent behaviors, leading to high false positives or missed attacks. To address these limitations, a hybrid AI-driven framework is proposed, combining baseline classifiers with advanced models, including Long Short-Term Memory (LSTM) networks for capturing temporal spending patterns, Graph Neural Networks (GNN) for modelling interaccount relationships, and Autoencoders (AE) for unsupervised anomaly detection under label-scarce conditions. The framework leverages ensemble techniques to fuse model outputs, producing a robust risk score while maintaining low detection latency suitable for IoT environments. Evaluation on simulated mobile payment data demonstrates that the model achieves a precision of $96.8 \%$, recall of $94.5 \%$, F1-Score of $95.6 \%$, ROC-AUC of 0.98, and inference latency of 38 ms, outperforming individual baseline and advanced models. The results highlight the framework’s effectiveness in capturing complex and novel fraud patterns. Future trends include the integration of federated learning for privacy-preserving crossplatform training, adaptive thresholding for dynamic risk assessment, and edge-cloud collaboration for scalable real-time deployment. The proposed model provides a practical and extensible solution for enhancing security in IoT payment systems.
  • From Mechanisms To Mitigation: Addressing the Threat of Antimicrobial Resistance Across Ecosystems
    Sruthi SanilKumar, Abhilash K S
    Proceedings of International Conference on Modern Sustainable Systems Cmss 2025, 2025
    Antimicrobial resistance (AMR) represents a growing global health crisis, with a key challenge being the fragmented understanding of its cross-sectoral transmission across human, animal, and environmental ecosystems. AMR results in the evolution of multidrug-resistant pathogens, resulting in prolonged illness, higher death rates, and increased healthcare costs. This review addresses the growing threat of AMR, focusing on its mechanisms, transmission, and reduction strategies across diverse ecosystems. AMR arises from both manmade and natural activities, notably through the overuse of antibiotics in human healthcare, agriculture, and aquaculture, leading to multidrug-resistant pathogens that severely compromise public health. The analysis highlights the roles of specific sectors, such as livestock farming and wastewater management, in promoting the spread of resistance genes, thereby posing risks to both environmental and human health. The paper emphasizes the importance of understanding AMR mechanisms to develop current intervention strategies, including the "One Health" approach, which promotes cross-sectoral collaboration to address AMR comprehensively. This review synthesizes recent findings and highlights the importance of advanced technologies, antimicrobial stewardship, and coordinated policies as essential strategies to mitigate AMR and support global health and environmental sustainability.
  • Listen and Segment: A GNN-Based Network with Attention Mechanism
    Vurimi Bhanu Pranay, S. Karthik, S. K. Abhilash
    Lecture Notes in Electrical Engineering, 2024
  • AF-CPACNet: AnchorFree Crowd Parsing Attention-Based Characteristic Segmentation Network
    S. Raghavendra, S. K. Abhilash, Venu Madhav Nookala, Yashwanth Nanjappa
    IEEE Access, 2024
    Multi-human parsing involves the task of segmenting and identifying different human parts within images that contain multiple people. It is a crucial task in computer vision, particularly for applications such as human pose estimation, scene understanding, and virtual reality. This paper explores the various features and techniques used in multi-human parsing, including the use of deep learning models like convolutional neural networks (CNNs) and attention mechanisms to accurately detect and segment human body parts in crowded or complex environments. Anchor boxes often fail to capture the diverse variations in human body shapes and poses accurately, leading to suboptimal performance in human parsing tasks. To address these limitations, we introduce AF-CPACNet, a novel model that eliminates the need for anchor boxes by adopting a multi-head and multi-task architecture. AF-CPACNet consists of two key components: a detection head and an edge-guided parsing module, enabling pixel-level analysis and improving the precision of human body part segmentation. Additionally, a refinement head is incorporated to further enhance semantic parsing quality. The model captures finer details of human body parts by considering color, size, and pattern attributes in a single forward pass while operating in real-time. A specialized loss function is employed to optimize semantic parsing results and improve training efficiency. We evaluate the performance of AF-CPACNet on multiple human parsing datasets, including CCIHP and CIHP, and demonstrate that it significantly outperforms existing state-of-the-art methods. Specifically, AF-CPACNet achieves an 11% improvement on the CIHP dataset and an mIoU of 67.3 on the CCIHP dataset, across both global and instance-level metrics. The open-source code is available at https://github.com/abhigoku10/AF-CPACNet.git.
  • EGA-Net: Edge Guided Attention Network With Label Refinement for Parsing of Animal Body Parts
    S. Raghavendra, S. K. Abhilash, Venu Madhav Nookala, S. Girisha, N. D. Adesh
    IEEE Access, 2024
    In computer vision, semantic segmentation precisely delineates objects at the pixel level. This fundamental idea is constantly evolving by adding new modules and adjustments to suit the unique characteristics of different object classes. Pixel-level semantic segmentation is an intricate and computationally intensive task, especially within the context of part-based approaches. The study proposes a transformer-based attention network that is edge-guided and developed for the precise partitioning of different parts of quadruped animals. The process of labeling masks at the pixel level is a challenging task for various object categories, owing to its inherent complexity, which often results in inaccurate annotations. An additional mechanism is used to enhance pixel-level accuracy between classes, which iteratively refines labels. The model is evaluated using the PascalPart and PartImageNet datasets, using various scales of transformer architectures. Performance is evaluated using metrics such as mean Intersection-over-Union (mIoU), Pixel Accuracy (PA), and mean Accuracy (mA). Ablation studies are conducted to evaluate the model’s performance based on network parameters, while the effectiveness of each component is assessed using Class Activation Maps (CAM). The results show a notable 8% improvement in mIoU scores over existing state-of-the-art architectures, indicating the effectiveness of the proposed model in achieving fine-grained part segmentation, particularly in the context of quadruped animals. The open source code is available at https://github.com/abhigoku10/EGA-Net.
  • A Comprehensive Experimentation on Security-Conserving Storage Verifications and Data Dynamics Over Cloud Storage Environment
    K. Sravan Abhilash, M Vasudevan, M. Jayaprakash, C. Anitha, Kavitha Karthikeyan, Soujanya Maisa
    Proceedings 1st International Conference on Electronics Communication and Signal Processing Icecsp 2024, 2024
  • Online Label Refinement for Weakly Semi-supervised Semantic Vehicle Parsing Using CNN and Transformer
    S. K. Abhilash, Venu Madhav Nookala, S. Karthik, Bhargav Kumar Nammi
    Lecture Notes in Networks and Systems, 2023
  • BADANet: Boundary Aware Dilated Attention Network for Face Parsing
    S. Raghavendra, S. K. Abhilash, Venu Madhav Nookala, N. N. Srinidhi, N. D. Adesh
    IEEE Access, 2023
  • Elevating Amodal Segmentation Using ASH-Net Architecture for Accurate Object Boundary Estimation
    S. Raghavendra, Divya Rao, S. K. Abhilash, Venu Madhav Nookala, Praveen Gurunath Bharathi
    IEEE Access, 2023
  • MHCP-RCNN: Multi-Human Color Parsing Segmentation using Multi-Task Network
    Abhilash SK, Venu Madhav Nookala, Adithya Babu, Karthik S, Mithun VR
    Proceedings of the International Joint Conference on Neural Networks, 2023
  • DCSP-RCNN: A Network for Person Detection with Color, Size and Pattern Characteristic Parsing
    Abhilash SK, Nischal DS, Shiv Kumar, Venu Nookala, Karthik S
    2023 International Conference on Recent Advances in Information Technology for Sustainable Development Icrais 2023 Proceedings, 2023
  • ClothFormer - A Boundary Aware Self-Attention Network for Human Outfit Parsing
    Vurimi Bhanu Pranay, Nischal DS, Bhargav Kumar Nammi, Shiv Kumar, Abhilash SK
    2023 International Conference on Recent Advances in Information Technology for Sustainable Development Icrais 2023 Proceedings, 2023
  • Efficient Deep Learning Approach to Recognize Person Attributes by Using Hybrid Transformers for Surveillance Scenarios
    S. Raghavendra, Ramyashree, S. K. Abhilash, Venu Madhav Nookala, S. Kaliraj
    IEEE Access, 2023
  • Computer-Aided Detection of Human Lung Nodules on Computer Tomography Images via Novel Optimized Techniques
    Lim J. Seelan, L. Padma Suresh, Abhilash K.S., Vivek P.K.
    Current Medical Imaging, 2022
  • Person Attribute Recognition using Hybrid Transformers for Surveillance Scenarios
    Sk Abhilash, Venu Madhav Nookala
    2022 IEEE International Conference on Distributed Computing VLSI Electrical Circuits and Robotics Discover 2022 Proceedings, 2022
  • A GNN Based Multi-Attribute Pedestrian Recognition Framework for Real World Surveillance Scenarios
    Sk Abhilash, Venu Madhav Nookala, Adithya Babu
    2022 International Conference on Futuristic Technologies Incoft 2022, 2022
  • Cloud-Fog Trustworthy Computing for Information Sharing in Dynamic IoT System
    Bommi Reddy Prasanthi, Dharavath Veeraswamy, Sravan Abhilash, Kesham Ganesh
    Proceedings of 2022 IEEE International Women in Engineering Wie Conference on Electrical and Computer Engineering Wiecon Ece 2022, 2022
  • Automatic Segmentation of Colon Using Multilevel Morphology and Thesholding
    Shijin Kumar P. S, Abhilash K. S, Zina Ravindran, Nisha S Das
    2021 International Conference on Computer Communication and Informatics Iccci 2021, 2021
  • Investigative study on the feasibility of simultaneous movement along multiple axes for helical cut using RTM
    K.S. Abhilash, V.V. Sudheer Babu, A. Nisam Rahman, Ananda Mohan Vemula, P.S. Shijin Kumar
    Materials Today Proceedings, 2021
  • Design and evaluation of solar powered dryer system with integrated thermal energy storage
    , Abhilash K.S, Nisam Rahman A, , Shijin Kumar P.S*, and
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • An Optimized Multiplexing System for Remote Sensing Optical Signals
    Sudhan M.B., Abhilash K.S., Shijin Kumar P.S.
    2019 International Conference on Recent Advances in Energy Efficient Computing and Communication Icraecc 2019, 2019
  • Method to remove the noisy data DROM captured image of iris and identifying the pupil by detecting its centroid
    International Journal of Applied Engineering Research, 2017
  • Algorithm for enhancement of biometric images used in feature extraction and authentication
    International Journal of Applied Engineering Research, 2016
  • FFT architectures for real valued signals based different radices algorithm
    Kodakandla Abhilash, P. Reena Monica
    Indian Journal of Science and Technology, 2015
  • Correlation between phase and optical properties of yttrium-doped hafnium oxide nanocrystalline thin films
    A. Ortega, E.J. Rubio, K. Abhilash, C.V. Ramana
    Optical Materials, 2013
  • Electrical and optical properties of nanocrystalline yttrium-doped hafnium oxide thin films
    M. Noor-A-Alam, K. Abhilash, C.V. Ramana
    Thin Solid Films, 2012
  • Dual Transformation Bimodal Biometrics Based on Feature Level Fusion
    A.C. Ramachandra, L.M. Patnaik, K.R. Venugopal, S.K. Abhilash, K.B. Raja
    Iet Conference Publications, 2012
  • On-line optimization of a crude distillation unit with constraints on product properties
    Kaushik Basak, K. S. Abhilash, Saibal Ganguly, D. N. Saraf
    Industrial and Engineering Chemistry Research, 2002

RECENT SCHOLAR PUBLICATIONS

  • Pose-Guided Multi-Scale Vision Transformer for Robust and Accurate Person Re-Identification in Visual Surveillance
    M Shimja, A Ramachandran, K Priya, S Khan, KS Abhilash, S Sreekanth
    2026 6th International Conference on Expert Clouds and Applications (ICOECA … , 2026
    2026
  • Analysis on Plant Disease Classification, Tracking and Forecasting for Farmers by Using a Cloud Based Callaborative Platform and Artificial Intelligence
    DR Reddy, K Chandrakala, P Kumar, KS Abhilash, P Iyyanar
    Adaptive Technologies for Sustainable Growth, 47-53 , 2026
    2026
  • Canopy management in two dragon fruit species through training systems for sustainable fruit production
    G Karunakaran, C Kanupriya, M Arivalagan, RH Laxman, P Kumar, ...
    Scientia Horticulturae 355, 114545 , 2026
    2026
    Citations: 4
  • Autonomous Agentic AI for Clinical Workflow Orchestration: Self-Managing Healthcare Operations
    A Warrier, KS Abhilash
    2025 6th International Conference on IoT Based Control Networks and … , 2025
    2025
    Citations: 3
  • Quantum-Inspired Neural Networks for Accelerated Processing in Edge Devices
    WB Latif, IM Yasin, S Peneti, S Bhukya, K Abhilash
    2025 IEEE 5th International Conference on ICT in Business Industry … , 2025
    2025
    Citations: 6
  • Transforming Energy-Intensive Smart Factories with AI: TCN-based Forecasting and DQN-Driven Operational Optimization for Healthcare Manufacturing
    MR Anand, KS Abhilash
    2025 International Conference on Intelligent Computing, Information and … , 2025
    2025
    Citations: 8
  • Multi-agent systems for collaborative and proactive fraud prevention in distributed AI-driven financial platforms
    S Amirineni, KS Abhilash
    2025 9th International Conference on Electronics, Communication and … , 2025
    2025
    Citations: 9
  • Resilient Cloud Architectures with AI Agents for Enhancing Cybersecurity in Health Information Systems
    A Gundaboina, KS Abhilash
    2025 9th International Conference on Electronics, Communication and … , 2025
    2025
  • Temporal Fusion Transformer Forecasting and MILP Prescriptive Optimization for Hospital Pharmacy Supply Chain Orchestration
    MR Anand, KS Abhilash
    2025 9th International Conference on Electronics, Communication and … , 2025
    2025
    Citations: 2
  • Adaptive Machine Learning Algorithms for Dynamic Right-Turn Signal Control at US Traffic Intersections with Heterogeneous Traffic Flows
    R Kasarla, KS Abhilash
    2025 Third International Conference on Emerging Applications of Material … , 2025
    2025
  • Hierarchical Cloud-IoT Architecture for AI-Powered Intelligent Disaster Response
    VK Sharma, KS Abhilash
    2025 7th International Conference on Innovative Data Communication … , 2025
    2025
    Citations: 3
  • HIPAA-Compliant Hybrid Cloud for EHR Mortality and Readmission Risk Prediction
    A Warrier, KS Abhilash
    2025 7th International Conference on Innovative Data Communication … , 2025
    2025
  • Hybrid Edge-Cloud AI Gateway with 1D-CNN for Real-Time Anomaly Detection and Temporal Fusion Transformer for Healthcare Data Streams
    A Warrier, KS Abhilash
    2025 7th International Conference on Innovative Data Communication … , 2025
    2025
    Citations: 1
  • Sunburn mitigation in dragon fruit ( Hylocereus spp.): unravelling genotype-specific physiological and biochemical responses
    G Karunakaran, C Kanupriya, M Arivalagan, RH Laxman, K Prakash, ...
    Frontiers in Plant Science 16, 1661147 , 2025
    2025
    Citations: 2
  • AI-Driven Fraud Detection in IoT-Enabled Payment Ecosystems: Challenges, Hybrid Edge-Cloud Framework, and Emerging Trends
    S Amirineni, KS Abhilash
    2025 6th International Conference on Electronics and Sustainable … , 2025
    2025
    Citations: 7
  • Latency-Aware Edge-Cloud Architecture for 5G IoT Integration
    VK Sharma, KS Abhilash
    2025 6th International Conference on Electronics and Sustainable … , 2025
    2025
    Citations: 2
  • Leveraging AWS Machine Learning And Datalake Architectures for Large Scale Predictive Modelling in Smart Cities
    R Kasarla, KS Abhilash
    2025 3rd International Conference on Sustainable Computing and Smart Systems … , 2025
    2025
  • Topology-Aware EV Load Allocation using Graph Neural Networks for Distribution Grid Optimization
    A Pedapati, KS Abhilash
    2025 3rd International Conference on Sustainable Computing and Smart Systems … , 2025
    2025
  • Resilient EV Charging Management During Grid Disturbances using Digital Twins and Reinforcement Learning
    A Pedapati, KS Abhilash
    2025 3rd International Conference on Sustainable Computing and Smart Systems … , 2025
    2025
  • From Mechanisms To Mitigation: Addressing the Threat of Antimicrobial Resistance Across Ecosystems
    S SanilKumar, KS Abhilash
    2025 International Conference on Modern Sustainable Systems (CMSS), 92-98 , 2025
    2025

MOST CITED SCHOLAR PUBLICATIONS

  • Electrical and optical properties of nanocrystalline yttrium-doped hafnium oxide thin films
    M Noor-A-Alam, K Abhilash, CV Ramana
    Thin Solid Films 520 (21), 6631-6635 , 2012
    2012
    Citations: 27
  • Correlation between phase and optical properties of yttrium-doped hafnium oxide nanocrystalline thin films
    A Ortega, EJ Rubio, K Abhilash, CV Ramana
    Optical Materials 35 (9), 1728-1734 , 2013
    2013
    Citations: 23
  • Feature level fusion based bimodal biometric using transformation domine techniques
    A Ramachandra, S Abhilash, KB Raja, KR Venugopal, L Patnaik
    IOSR Journal of Computer Engineering (IOSRJCE) 3 (3), 39-46 , 2012
    2012
    Citations: 15
  • Transform domain fingerprint identification based on DTCWT
    JP George
    International Journal of Advanced Computer Science and Applications , 2012
    2012
    Citations: 13
  • Multi-agent systems for collaborative and proactive fraud prevention in distributed AI-driven financial platforms
    S Amirineni, KS Abhilash
    2025 9th International Conference on Electronics, Communication and … , 2025
    2025
    Citations: 9
  • Transforming Energy-Intensive Smart Factories with AI: TCN-based Forecasting and DQN-Driven Operational Optimization for Healthcare Manufacturing
    MR Anand, KS Abhilash
    2025 International Conference on Intelligent Computing, Information and … , 2025
    2025
    Citations: 8
  • Design and implementation of wireless sensor network for environmental monitoring
    MS Andhare, TL Pal, V Jayaram, GS Pillai, V Tripathi, M Krishnaraj, ...
    International Journal of Health Sciences, 431336 , 2022
    2022
    Citations: 8
  • AI-Driven Fraud Detection in IoT-Enabled Payment Ecosystems: Challenges, Hybrid Edge-Cloud Framework, and Emerging Trends
    S Amirineni, KS Abhilash
    2025 6th International Conference on Electronics and Sustainable … , 2025
    2025
    Citations: 7
  • Automatic segmentation of colon using multilevel morphology and thesholding
    Z Ravindran, NS Das
    2021 International Conference on Computer Communication and Informatics … , 2021
    2021
    Citations: 7
  • Quantum-Inspired Neural Networks for Accelerated Processing in Edge Devices
    WB Latif, IM Yasin, S Peneti, S Bhukya, K Abhilash
    2025 IEEE 5th International Conference on ICT in Business Industry … , 2025
    2025
    Citations: 6
  • Canopy management in two dragon fruit species through training systems for sustainable fruit production
    G Karunakaran, C Kanupriya, M Arivalagan, RH Laxman, P Kumar, ...
    Scientia Horticulturae 355, 114545 , 2026
    2026
    Citations: 4
  • Computer-Aided Detection of Human Lung Nodules on Computer Tomography Images via Novel Optimized Techniques
    LJ Seelan, LP Suresh, A KS, V PK
    Current Medical Imaging Reviews 18 (12), 1282-1290 , 2022
    2022
    Citations: 4
  • Investigative study on the feasibility of simultaneous movement along multiple axes for helical cut using RTM
    KS Abhilash, VVS Babu, AN Rahman, AM Vemula, PSS Kumar
    Materials Today: Proceedings 45, 3422-3425 , 2021
    2021
    Citations: 4
  • Autonomous Agentic AI for Clinical Workflow Orchestration: Self-Managing Healthcare Operations
    A Warrier, KS Abhilash
    2025 6th International Conference on IoT Based Control Networks and … , 2025
    2025
    Citations: 3
  • Hierarchical Cloud-IoT Architecture for AI-Powered Intelligent Disaster Response
    VK Sharma, KS Abhilash
    2025 7th International Conference on Innovative Data Communication … , 2025
    2025
    Citations: 3
  • Sripath Roy, K., Abhilash, K, Arvind, BV,(2018). Implementation of asymmetric processing on multi-core processors to implement IOT applications on GNU/Linux framework
    S Poonam Jain, S Pooja
    International Journal of Engineering and Technology (UAE) 7 (2.7), 710-713 , 2017
    2017
    Citations: 3
  • Temporal Fusion Transformer Forecasting and MILP Prescriptive Optimization for Hospital Pharmacy Supply Chain Orchestration
    MR Anand, KS Abhilash
    2025 9th International Conference on Electronics, Communication and … , 2025
    2025
    Citations: 2
  • Sunburn mitigation in dragon fruit ( Hylocereus spp.): unravelling genotype-specific physiological and biochemical responses
    G Karunakaran, C Kanupriya, M Arivalagan, RH Laxman, K Prakash, ...
    Frontiers in Plant Science 16, 1661147 , 2025
    2025
    Citations: 2
  • Latency-Aware Edge-Cloud Architecture for 5G IoT Integration
    VK Sharma, KS Abhilash
    2025 6th International Conference on Electronics and Sustainable … , 2025
    2025
    Citations: 2
  • An innovative approach for efficient detection and classification of malware in 5G-IoT healthcare systems
    L Bharathi, L Bhagyalakshmi, SN Bhavanam, M Sindhuja, KS Abhilash, ...
    2025 Global Conference in Emerging Technology (GINOTECH), 1-5 , 2025
    2025
    Citations: 2