WORKING AS A ASSISTANT PROFESSOR IN VISHNU INSTITUTE OF TECHNOLOGY BHIMAVARAM
EDUCATION
M.TECH IN COMPUTER SCIENCE AND ENGINEERING
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Computer Engineering, Hardware and Architecture, Software
2
Scopus Publications
3476
Scholar Citations
28
Scholar h-index
63
Scholar i10-index
Scopus Publications
Machine Learning Ensemble Model for Heart Disease Prediction N. Nagasoudhamani, Addala Revathi, Dadinaboina A. K. Rao, Gudapati Dianakamal, Tammineni Rama Tulasi, S. Rajasekhar Reddy Proceedings of the International Conference on Intelligent Computing and Control Systems Iciccs 2025, 2025 One of the most pressing problems in world health today is the lack of accurate prediction models that can help with the early diagnosis and rapid treatment of cardiovascular disease. The primary focus of this study is to use machine learning techniques for the prediction of certain forms of cardiovascular illness, including Other Cardiovascular illness, Stable Angina, Coronary Artery Disease, and Unstable Angina. Age, sex, kind of chest pain, resting blood pressure, serum cholesterol, fasting blood sugar, and other important clinical diagnostic factors are included in the dataset. Current models that achieve 80% prediction accuracy include Logistic Regression and Naive Bayes. Unfortunately, they can't handle complex patterns in data and rely on linear assumptions, which limits their effectiveness. To enhance the precision of predictions, we advocate for the use of cutting-edge ML methods such as Random Forest and Gradient Boosting. Complex feature-nonlinear connection interactions are no match for these algorithms. Accuracy rates of 93% and 95%, respectively, have been achieved by these models by the use of their skills to tighten decision bounds and minimize errors via iterative learning. This research shows that these models have a chance to outperform the current system, providing clinicians with a reliable tool for better cardiac ailment classification, which would enhance healthcare choices and patient outcomes.
Machine Learning Approaches for Anomaly Detection in Network Security: Challenges, Methods and Advances D S B N S Rekha, V.S.S.P. Raju Gottumukkala, Poodi Venkata Vijaya Durga, Kolapalli Jistnasai Upendra, Shalini Eda, Gudapati Dianakamal Proceedings of the 9th International Conference on Communication and Electronics Systems Icces 2024, 2024 Nowadays cyberattacks are become a very serious problem in Networking, online transactions, and everywhere. So the complexity of network infrastructures has given serious difficulties for network security in recent years. To reduce cyberattacks Machine Learning(ML) has provided a reliable solution for network anomaly detection in various settings by including software-defined networks (SDNs), automobile networks, and the Internet of Things (IoT). This paper provides an overview of various machine learning (ML) methods for anomaly detection using supervised, unsupervised, and deep learning models. Long Short-Term Memory (LSTM) networks and Convolution Neural Networks (CNNs) are the best Deep Learning (DL) models for detecting complex and before undiscovered threats. In order to improve detection accuracy and computing efficiency, this work investigates the degree to which these techniques are applied in a number of contexts, including smart metering systems, vehicular ad hoc networks (VANETs), and Internet of Things network security.
RECENT SCHOLAR PUBLICATIONS
Exploring thermal management approaches in cloud computing environments A Amahrouch, S El Kafhali, Y Saadi Next Energy 11, 100605 , 2026 2026
Efficient unsupervised segmentation method for continuous Arabic and English speech HA Mait, N Aboutabit, M Amnay Annals of Telecommunications, 1-12 , 2026 2026
for Interpretable Energy Load Prediction in Artificial Intelligence Systems O Ghandour, S El Kafhali, M Hanini Artificial Intelligence and Cognitive Sciences for Emerging Technologies … , 2026 2026
Attention-enhanced BiLSTM-ANN framework with CNN-based feature selection for advanced threat detection M Tayebi, S El Kafhali International Journal of Machine Learning and Cybernetics 17 (2), 52 , 2026 2026 Citations: 2
A Survey of Adaptive Scheduling Techniques, Goals, and Challenges in Kubernetes S El Kafhali Archives of Computational Methods in Engineering, 1-24 , 2026 2026
DSGTA: A Dynamic and Stochastic Game-Theoretic Allocation Model for Scalable and Efficient Resource Management in Multi-Tenant Cloud Environments S El Kafhali, O Ghandour Future Internet 17 (12), 583 , 2025 2025 Citations: 2
Scalable overload prediction in cloud computing using a hybrid queuing-theoretic and machine learning framework O Ghandour, S El Kafhali, I El Mir Computing 107 (12), 231 , 2025 2025 Citations: 1
Enhancing IoT security with advanced GAN architectures for cyberattacks detection M Tayebi, S El Kafhali Cluster Computing 28 (15), 1-18 , 2025 2025 Citations: 2
A novel approach based on XGBoost classifier and Bayesian optimization for credit card fraud detection M Tayebi, S El Kafhali Cyber Security and Applications 3, 100093 , 2025 2025 Citations: 18
Images and Captions as Windows into Personality: Exploring the Impact of Demographic Factors S El Bahy, N Aboutabit, I Hafidi International Conference of Machine Intelligence and Computer Science … , 2025 2025
Character-Level Modeling of Subwords Extracted from Historical Arabic Manuscripts Using BLSTM and BGRU M Dahbali, N Aboutabit, N Lamghari International Conference of Machine Intelligence and Computer Science … , 2025 2025
Enhancing LightLog with BERT-Based Contextual Embeddings A Zizouan, I Hafidi, N Aboutabit International Conference of Machine Intelligence and Computer Science … , 2025 2025
Comparison of C3D, Autoencoder, and Hybrid C3D-Autoencoder Approach for Arabic Sign Language Recognition I Bouhanou, N Aboutabit International Conference of Machine Intelligence and Computer Science … , 2025 2025
Artificial Intelligence and Green Computing: Proceedings of the 2nd International Conference on Artificial Intelligence and Green Computing ICAIGC 2025 N Idrissi, A Hair, M Lazaar, Y Saadi, H Chakib, M Erritali, S El Kafhali Springer Nature , 2025 2025
Optimizing workflow scheduling for efficient resource utilization in scalable cloud computing data centers H Mikram, S El Kafhali SIMULATION 101 (11), 1133-1151 , 2025 2025 Citations: 3
AI-Driven Adaptive VM Placement Using Performance-to-Power Ratio for Sustainable Data Center Management A Amahrouch, Y Saadi, S El Kafhali Artificial Intelligence and Applications , 2025 2025 Citations: 1
Performance analysis of recurrent neural networks for intrusion detection systems in Industrial-Internet of Things M Tayebi, S El Kafhali Franklin Open 12, 100310 , 2025 2025 Citations: 16
Game-Theoretic Feature Attribution for Interpretable Energy Load Prediction in Artificial Intelligence Systems O Ghandour, S El Kafhali, M Hanini International Conference on Artificial Intelligence and Cognitive Science … , 2025 2025
Performance Evaluation of Hybrid Metaheuristic Algorithms for Workflow Scheduling in Cloud Environments M Bouqaffa, S El Kafhali International Conference on Artificial Intelligence and Cognitive Science … , 2025 2025
A Hybrid Ensemble Learning Framework for Early Dropout Prediction in Learning Management Systems ZS Hafdi, S El Kafhali International Conference on Artificial Intelligence and Cognitive Science … , 2025 2025 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Cyber security in iot-based cloud computing: A comprehensive survey W Ahmad, A Rasool, AR Javed, T Baker, Z Jalil Electronics 11 (1), 16 , 2021 2021 Citations: 462
DDoS attack detection using machine learning techniques in cloud computing environments M Zekri, S El Kafhali, N Aboutabit, Y Saadi 2017 3rd international conference of cloud computing technologies and … , 2017 2017 Citations: 312
Security Threats, Defense Mechanisms, Challenges, and Future Directions in Cloud Computing: S. El Kafhali et al. S El Kafhali, I El Mir, M Hanini Archives of Computational Methods in Engineering 29 (1), 223-246 , 2022 2022 Citations: 209
DIDDOS: An approach for detection and identification of Distributed Denial of Service (DDoS) cyberattacks using Gated Recurrent Units (GRU) S Ur Rehman, M Khaliq, SI Imtiaz, A Rasool, M Shafiq, AR Javed, Z Jalil, ... Future Generation Computer Systems 118, 453-466 , 2021 2021 Citations: 179
Efficient and dynamic scaling of fog nodes for IoT devices S El Kafhali, K Salah The Journal of Supercomputing 73 (12), 5261-5284 , 2017 2017 Citations: 144
Cybersecurity management in cloud computing: semantic literature review and conceptual framework proposal N Tissir, S El Kafhali, N Aboutabit Journal of Reliable Intelligent Environments 7 (2), 69-84 , 2021 2021 Citations: 139
Security in next generation mobile payment systems: A comprehensive survey W Ahmed, A Rasool, AR Javed, N Kumar, TR Gadekallu, Z Jalil, ... IEEE Access 9, 115932-115950 , 2021 2021 Citations: 127
HEPGA: A new effective hybrid algorithm for scientific workflow scheduling in cloud computing environment H Mikram, S El Kafhali, Y Saadi Simulation modelling practice and theory 130, 102864 , 2024 2024 Citations: 111
Performance modelling and analysis of Internet of Things enabled healthcare monitoring systems S El Kafhali, K Salah IET Networks 8 (1), 48-58 , 2019 2019 Citations: 96
Energy-efficient strategy for virtual machine consolidation in cloud environment Y Saadi, S El Kafhali Soft Computing 24 (19), 14845-14859 , 2020 2020 Citations: 91
Stochastic modelling and analysis of cloud computing data center S El Kafhali, K Salah 2017 20th Conference on Innovations in Clouds, Internet and Networks (ICIN … , 2017 2017 Citations: 66
Modeling and analysis of performance and energy consumption in cloud data centers S El Kafhali, K Salah Arabian Journal for Science and Engineering 43 (12), 7789-7802 , 2018 2018 Citations: 60
Performance analysis of metaheuristics based hyperparameters optimization for fraud transactions detection M Tayebi, S El Kafhali Evolutionary intelligence 17 (2), 921-939 , 2024 2024 Citations: 58
Dynamic scalability model for containerized cloud services S El Kafhali, I El Mir, K Salah, M Hanini Arabian Journal for Science and Engineering 45 (12), 10693-10708 , 2020 2020 Citations: 48
Performance evaluation of IoT-fog-cloud deployment for healthcare services S El Kafhali, K Salah, SB Alla 2018 4th international conference on cloud computing technologies and … , 2018 2018 Citations: 48
Computing Resources Scalability Performance Analysis in Cloud Computing Data Center: O. Ghandour et al. O Ghandour, S El Kafhali, M Hanini Journal of Grid Computing 21 (4), 61 , 2023 2023 Citations: 47
Performance analysis of multi-core VMs hosting cloud SaaS applications S El Kafhali, K Salah Computer Standards & Interfaces 55, 126-135 , 2018 2018 Citations: 47
Architecture to manage internet of things data using blockchain and fog computing S El Kafhali, C Chahir, M Hanini, K Salah Proceedings of the 4th international conference on big data and internet of … , 2019 2019 Citations: 46
Lip shape and hand position fusion for automatic vowel recognition in cued speech for french P Heracleous, N Aboutabit, D Beautemps IEEE Signal Processing Letters 16 (5), 339-342 , 2009 2009 Citations: 42
Generative modeling for imbalanced credit card fraud transaction detection M Tayebi, S El Kafhali Journal of Cybersecurity and Privacy 5 (1), 9 , 2025 2025 Citations: 41
Publications
1.Machine Learning Approaches for Anomaly Detection in Network Security:Challenges,Methods and Advances
2.Recognition of Human Behaviour utilizing multiscale convolutional neural networks
3.Robust Local Filtering to secure Federated learning against adversarial Poisoning
4.Efficient cryptography techniques to ensure cloud security and privacy
5.Machine Learning Ensemble Model for Heart Disease Prediction
