Dr. Saiyed Faiayaz Waris, B.E, M.E Ph.D . currently working as Assistant Professor in the Department of Artificial Intelligence and Data Science ,Koneru Lakshmaiah Education Foundation (Deemed to be University), India. He is having 17 years of teaching experience and published more than 24 research articles in various peer reviewed Journals. . His research interests include Artificial Intelligence, Machine Learning, Deep Learning, Big Data and Analytics and Internet of Things. He has presented several papers in conference proceedings.
EDUCATION
Ph.D. (Computer Science and Engineering)
Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology,
Chennai, 2024
M.E. (Computer Science and Engineering )
Sathyabama Institute of Science and Technology( Deemed to be University),2009
B.E. (Computer Science and Engineering )
Anna University , Chennai ,2005
Diploma in Electrical and Electronic Engineering ,Directorate of Technical Education (Chennai),2002
Precise Anomaly Recognition Using Advanced Federated Learning in Resource-Restricted WSN With Unreliable Connections L. Bhagyalakshmi, K. Krishnamoorthy, Saiyed Faiayaz Waris, M. Karthiga International Journal of Communication Systems, 2026 This work presents an advanced federated learning (FL) framework that integrates a CNN–GRU + FedTrust model for precise anomaly recognition in resource‐constrained wireless sensor networks (WSNs) with unreliable connections. Unlike traditional centralized and heterogeneous FL approaches, the proposed system achieves higher accuracy, efficiency, and robustness by combining lightweight CNN–GRU feature extraction with trust‐weighted aggregation through the FedTrust mechanism. The model leverages top‐k gradient sparsification to reduce communication overhead and energy‐aware client selection (EAC) to optimize energy use, ensuring sustainable network performance. Evaluations on real‐world datasets—WUSTL Wireless Sensor Data and Intel Lab Data—demonstrate the model's superiority, achieving 98.4% accuracy, 0.98 F1 score, and faster convergence than Autoencoder‐FL, GNN‐FL, and hierarchical FL methods. It also exhibits enhanced robustness under client dropout (96.1%) and noise (92.7%) conditions, significantly outperforming existing FL techniques. By efficiently capturing both spatial and temporal patterns while maintaining privacy and energy balance, the CNN–GRU + FedTrust framework delivers reliable and scalable anomaly detection across diverse IoT and smart‐industry environments. This hybrid design establishes a new benchmark for energy‐efficient, trustworthy, and high‐precision FL‐based anomaly detection in next‐generation WSNs.
AI-DRIVEN PREDICTIVE ANALYTICS FOR PERFORMANCE EVALUATION AND STORAGE OPTIMISATION OF OXIDATION-PRONE MATERIALS USING HYPERSPECTRAL IMAGING AND QUANTUM SENSORS Oxidation Communications, 2025
Analysis of ECG Signals to Prediction of Ischemic Heart Disease Using Hybrid Neuro-fractal Analysis International Journal of Intelligent Systems and Applications in Engineering, 2024
Wild Horse Optimizer and Support Vector Machine (SVM) Classifier Predicts the Heart Disease Converging Nature-Motivated Optimization and Machine Learning Journal of Angiotherapy, 2024 Background: As one of the leading causes of morbidity and mortality worldwide, the diagnosis of acute cardiovascular disease (acute CVD) in emergency settings remains a significant challenge. Machine learning (ML) is artificial intelligence that allows computer programs to learn from and analyze large data sets without human intervention. As a diagnostic tool, ML presents numerous advantages, such as convenience, speed, and affordability. Methods: This research utilized a new model for predicting heart disease for feature selection(Wild Horse Optimizer, WHO) and another classification model (Support Vector Machine, SVM). In the WHO algorithm, like envisioning social behaviors of wild horses, we observe that wild horses display diverse behaviors like leading, grazing, moving, mating and chase. One odd behavior about wild horses is that young foals will break away from the herd to avoid the breed in its related herd before reaching maturity. It defines the decencies of the horses. Discussion: Multiple ML techniques were employed to train classifiers using the Cleveland heart disease dataset after conducting feature selection using WHO algorithm. The performance of these models was evaluated based on different parameters like Sensitivity, Accuracy, Specificity, and AUC. The SVM classifier model trained using the WHO approach proved better than the existing methods. In the research, we proposed a Wild Horse Optimization algorithm to optimize the features for Heart Disease prediction. After feature optimization, the optimized dataset is then given to Support Vector machine classification, which performs the classification of heart disease. After evaluating different experimental results, authors conclude that combining this feature selection algorithm and SVM as a classifier for forecasting heart disease is very effective. With the help of this approach, we can improve the early detection of heart disease and effectively manage severe heart disease. The experimental result graph shows an increased accuracy rate of the spectra feature subject.
The Healthcare IoTs as a Paradigm Shift in Healthcare Management, Patient Treatment, and Healthcare Data Processing Amit Amit, Prabha Rani Sikdar, C. Raja, Saiyed Faiayaz Waris, Manoj Kumar .T, et al. Journal of Intelligent Systems and Internet of Things, 2024 When it comes to hospital administration, patient care, and medical data analysis, the Healthcare Internet of Things (HIoT) is nothing short of a paradigm revolution. We dive into this new paradigm to examine its far-reaching effects and revolutionary possibilities in the healthcare system. The context is established by introducing HIoT as a game-changing development in healthcare. Using the IoT to network several devices, this model paves the way for real-time patient monitoring, streamlined inventory management, and integrated telemedicine. The healthcare industry as we know it will be transformed by HIoT as it strives to improve resource allocation, simplify operations, and provide proactive patient care. Our investigation includes a thorough appraisal of how HIoT will affect many facets of medical treatment. We use many research approaches and quality indicators for this evaluation. We may evaluate the viability and scalability of HIoT solutions by testing them in experimental settings that mimic real-world healthcare settings. To provide a precise depiction of the healthcare system, dataset environments use well maintained medical data sources. The performance and efficacy of HIoT technologies may be evaluated using measurable criteria such as sensitivity (0.94), specificity (0.89), F1-Score (0.91), ROC-AUC (0.95), and cost savings ($150,000). To determine the relative importance of each part of the HIoT ecosystem, researchers undertake ablation studies. Our findings provide a clear picture of the disruptive potential of HIoT. Better patient outcomes may be ensured via early interventions thanks to the improved accuracy (0.92), efficiency (9.2), and satisfaction (9.2) provided by the suggested HIoT technique for patient monitoring. When healthcare and telemedicine are combined, the success rate of remote consultations increases to 95%, response times decrease to 15 minutes, and more people have access to medical treatment.
Intelligent Voice Assistant by Using OpenCV Approach CH.M.H. Saibaba, Saiyed Faiayaz Waris, S.Hrushikesava Raju, VSRK Sarma, Vijaya Chandra Jadala, et al. Proceedings of the 2nd International Conference on Electronics and Sustainable Communication Systems Icesc 2021, 2021
Analysis of Cluster Head Selection Methods in WSN A. Arulmurugan, Saiyed Faiayaz Waris, N. Bhagyalakshmi Proceedings of the 6th International Conference on Inventive Computation Technologies Icict 2021, 2021
Fault Tolerance-Based Attack Detection Using Ensemble Classifier Machine Learning with IOT Security Big Data Management in Sensing Applications in AI and Iot, 2021
Smart Eye Testing S. Hrushikesava Raju, Lakshmi Ramani Burra, Saiyed Faiayaz Waris, S. Kavitha, S. Dorababu Advances in Intelligent Systems and Computing, 2021
IoT as a Health Guide Tool Dr.S Hrushikesava Raju, Dr Lakshmi Ramani Burra, Saiyed Faiayaz Waris, S Kavitha Iop Conference Series Materials Science and Engineering, 2020
An efficient patch based local principal component analysis technique in image denoising process with soft computing approach International Journal of Scientific and Technology Research, 2020
Smart Catcher of weighted Objects Radha Mothukuri, Dr. S. Hrushikesava Raju, S. Dorababu, Saiyed Faiayaz Waris Iop Conference Series Materials Science and Engineering, 2020
Smart Instant Charging of Power Banks N Sunanda, S. Hrushikesava Raju, Saiyed Faiayaz Waris, Ashok Koulagaji Iop Conference Series Materials Science and Engineering, 2020
RECENT SCHOLAR PUBLICATIONS
Precise Anomaly Recognition Using Advanced Federated Learning in Resource‐Restricted WSN With Unreliable Connections L Bhagyalakshmi, K Krishnamoorthy, SF Waris, M Karthiga International Journal of Communication Systems 39 (5), e70424 , 2026 2026
Secure Quantum Key Distribution Protocol for Hybrid Classical-Quantum Networks SF Waris, L Bhagyalakshmi, MM Babu, MS Rani, H Poddar, SK Suman 2025 IEEE 5th International Conference on ICT in Business Industry … , 2026 2026 Citations: 13
State estimation for DFIG-based wind turbines under voltage dips using multiresolution sinusoidal neural network-Tasmanian Devil optimization in Internet of Things enabled systems SR Menaka, SK Bhoi, SF Waris, E Mohan Energy 326, 136344 , 2025 2025 Citations: 12
Real-Time Energy-Efficient framework for Multi-Source Harvesting and Adaptive Communication IIoT Networks KP Sinha, HA Riyadh, YM Roopa, HSA Saiyed Faiayaz Waris , Hamatta, ... Sustainable Computing: Informatics and Systems, 101150 , 2025 2025 Citations: 35
AI-DRIVEN PREDICTIVE ANALYTICS FOR PERFORMANCE EVALUATION AND STORAGE OPTIMISATION OF OXIDATION-PRONE MATERIALS USING HYPERSPECTRAL IMAGING AND QUANTUM SENSORS. NS PATANKAR, A LAKSHMI, SF WARIS, T NERLEKAR, M PRABHAKAR, ... Oxidation Communications 48 (1) , 2025 2025
Future-proofing entertainment: Navigating market changes in television and internet video services through predictive modeling Y Nanjappa, MGV Kumar, K Vanisree, DVD Rao, SF Waris, N Chinthamu 2024 Citations: 4
The Healthcare IoTs as a Paradigm Shift in Healthcare Management, Patient Treatment, and Healthcare Data Processing. AK Chandanan, PR Sikdar, C Raja, SF Waris, K Bhopate Journal of Intelligent Systems & Internet of Things 13 (2) , 2024 2024
Wild Horse Optimizer and Support Vector Machine (SVM) Classifier Predicts the Heart Disease Converging Nature-Motivated Optimization and Machine Learning V Saiyed Faiayaz ,Mandala, SND Surabhi, VR Balaji, DR Patil 2024 Citations: 2
Removing Gaussian Image Noise Using Advanced Non-Local Means Filtering Techniques A Suresh, SF Waris, B Mohankumar, RA Arun, G Saravanan 2023 International Conference on New Frontiers in Communication, Automation … , 2024 2024 Citations: 3
Exploring subcellular location anomalies: a novel quantum bioimaging paradigm K Kumar, SH Abbas, M Gupta, SF Waris, D Bordoloi, MK Kirubakaran Optical and Quantum Electronics 56 (4), 657 , 2024 2024 Citations: 3
Analysis of ECG Signals to Prediction of Ischemic Heart Disease Using Hybrid Neuro-fractal Analysis SF Waris International Journal of Intelligent Systems and Applications in Engineering … , 2024 2024 Citations: 2
Sensor Based Street Light Changing Robo DPJ Saiyed Faiayaz Waris, Mr.Narender Chinthamu , Dr. Bechoo Lal, Dr Prasun ... 2023
Athlete Fitness Monitoring with the Application of Wearable IoT Devices N Chinthamu, V Moyal, R Sharma, TA Mohanaprakash, SF Waris, ... 2023 4th International Conference on Smart Electronics and Communication … , 2023 2023 Citations: 4
Sparse-Attention Based Deep Convolutional Neural Network for Pneumonia Detection SF Waris, VNR Karthikeya, MVL Supraja 2023 3rd International Conference on Intelligent Technologies (CONIT), 1-8 , 2023 2023 Citations: 1
An Investigation on Disease Diagnosis and Prediction by Using Modified KMean clustering and Combined CNN and ELM Classification Techniques SK saiyed faiayaz waris International Journal of Communication Networks and Information Security 14 … , 2022 2022 Citations: 2
Fault Tolerance-Based Attack Detection Using Ensemble Classifier Machine Learning with IOT Security A Arulmurugan, R Kaviarasan, SF Waris Big data management in Sensing, 115-148 , 2022 2022
Coronary Heart Artery Problem Detection and Evaluation employing Deep Neural Network. SK saiyed faiayaz waris NeuroQuantology 20 (8), 281-290 , 2022 2022
Prediction of heart conditions by consensus K -nearest neighbor algorithm and convolution neural network SF Waris, S Koteeswaran International Journal of Modeling, Simulation, and Scientific Computing 13 … , 2022 2022 Citations: 2
Role of Big Data Learning in Text Analytics of Internet of Things saiyed faiayaz waris Guangdianzi Jiguang/Journal of Optoelectronics Laser 41 (3), 94-98 , 2022 2022
A hybrid framework for efficient detection of fake currency notes M Santhi, S Hrushikesava Raju, S Adinarayna, V Lokanadham Naidu, ... Innovations in Electronics and Communication Engineering: Proceedings of the … , 2022 2022 Citations: 6
MOST CITED SCHOLAR PUBLICATIONS
Tourism enhancer app: user-friendliness of a map with relevant features R Dr. S. Hrushikesava, RB Lakshmi, K Ashok, SF Waris IOP Conference Series: Materials Science and Engineering 981 (2), 022067 , 2020 2020 Citations: 54
IoT as a health guide tool SH Raju, LR Burra, SF Waris, S Kavitha IOP Conf. Ser. Mater. Sci. Eng 981 (4) , 2020 2020 Citations: 50
Heart disease early prediction using a novel machine learning method called improved K-Nearest neighbor classifier in python SF Waris, S Koteeswaran Materials Today: Proceedings , 2021 2021 Citations: 42
Real-Time Energy-Efficient framework for Multi-Source Harvesting and Adaptive Communication IIoT Networks KP Sinha, HA Riyadh, YM Roopa, HSA Saiyed Faiayaz Waris , Hamatta, ... Sustainable Computing: Informatics and Systems, 101150 , 2025 2025 Citations: 35
Smart gas monitoring system for home and industries M Kavitha, DSH Raju, SF Waris, DA Koulagaji IOP Conference Series: Materials Science and Engineering 981 (2), 022003 , 2020 2020 Citations: 29
Smart dark pattern detection: Making aware of misleading patterns through the intended app SH Raju, SF Waris, S Adinarayna, VC Jadala, GS Rao Sentimental Analysis and Deep Learning: Proceedings of ICSADL 2021, 933-947 , 2021 2021 Citations: 23
Eyesight Test through Remote Virtual Doctor Using IoT SH Raju, LR Burra, SF Waris, VL Lalitha, S Dorababu, S Kavitha Smart Computing and Self-Adaptive Systems, 83-95 , 2021 2021 Citations: 21
Smart instant charging of power banks N Sunanda, S Hrushikesava Raju, S Faiayaz Waris, A Koulagaji IOP Conference Series: Materials Science and Engineering 981 (2), 022066 , 2020 2020 Citations: 21
Output-oriented multi-pane mail booster, smart computing and self-adaptive systems SH Raju, VL Lalitha, P Tumuluru, N Sunanda, S Kavitha, SF Waris CRC Press 10, 9781003156123-4 , 2021 2021 Citations: 19
Intelligent voice assistant by using OpenCV approach CHMH Saibaba, SF Waris, SH Raju, V Sarma, VC Jadala, C Prasad 2021 second international conference on electronics and sustainable … , 2021 2021 Citations: 17
Smart Eye Testing SH Raju, LR Burra, SF Waris, S Kavitha, S Dorababu International Conference on Intelligent and Smart Computing in Data … , 2021 2021 Citations: 16
Smart catcher of weighted objects R Mothukuri, DSH Raju, S Dorababu, SF Waris IOP Conference Series: Materials Science and Engineering 981 (2), 022002 , 2020 2020 Citations: 16
IoT as a health guide tool, IOP Conf DSH Raju, DLR Burra, SF Waris, S Kavitha Ser. Mater. Sci. Eng 981 (4) , 2020 2020 Citations: 14
Secure Quantum Key Distribution Protocol for Hybrid Classical-Quantum Networks SF Waris, L Bhagyalakshmi, MM Babu, MS Rani, H Poddar, SK Suman 2025 IEEE 5th International Conference on ICT in Business Industry … , 2026 2026 Citations: 13
Smart Eye Testing, Advances in Intelligent Systems and Computing, 2021, ISCDA 2020, 1312 AISC S Hrushikesava Raju, LR Burra, SF Waris, S Kavitha, S Dorababu 2021 Citations: 13
State estimation for DFIG-based wind turbines under voltage dips using multiresolution sinusoidal neural network-Tasmanian Devil optimization in Internet of Things enabled systems SR Menaka, SK Bhoi, SF Waris, E Mohan Energy 326, 136344 , 2025 2025 Citations: 12
Analysis of cluster head selection methods in WSN A Arulmurugan, SF Waris, N Bhagyalakshmi 2021 6th International Conference on Inventive Computation Technologies … , 2021 2021 Citations: 9
Smart Instant Charging of Power Banks SMART INSTANT CHARGING OF POWER BANKS, IOP Conference Series Materials Science and Engineering N Sunanda, SH Raju, SF Waris, A Koulagaji SRITW, Warangal, Telangana, India 981, 210 , 2020 2020 Citations: 7
A hybrid framework for efficient detection of fake currency notes M Santhi, S Hrushikesava Raju, S Adinarayna, V Lokanadham Naidu, ... Innovations in Electronics and Communication Engineering: Proceedings of the … , 2022 2022 Citations: 6
International Currency Translator using IoT for shopping H Saiyed Faiayaz Waris, M Santhi, S Dorababu IOP Conference Series: Materials Science and Engineering 981 (4), 042014 , 2020 2020 Citations: 5