Dr.Karthikeyan Kaliyaperumal

@ambou.edu.et

Associate Professor @ IoT-HH Campus -School of Informatics
Ambo University

Dr.Karthikeyan Kaliyaperumal


               Download Compact PDF  
https://researchid.co/kirithicraj
Kaliyaperumal Ph.D, D.Sc, is working as an Associate Professor in the Department of Information Technology, School of Informatics, IoT-HH Campus, AMBO University, Ethiopia affiliated to the Ministry of Science & Higher Education. He had more than 25+ years of Teaching, Research and Administrative experience in various reputed Colleges, Universities/ Institutions. He has produced more than 28 research students in the field of Computer Science & Engineering and IT. He has published a number of international journal papers and books related to the Computer Science, Engineering and IT with index of Scopus, SCI and SCIE. He has published important research papers in academic and scientific journals as well in different professional conference proceedings and workshops. He has involved the Academic Professional Membership bodies of Elseiver, Springer, IEEE, IET and many more. He received the best award of various discipline of teaching and research activities.

EDUCATION

Ph.D - Doctor of Philosophy
D.Sc - Doctor of Science

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Information Systems, Computer Science, Computer Networks and Communications
44

Scopus Publications

1687

Scholar Citations

16

Scholar h-index

26

Scholar i10-index

Scopus Publications

  • Intelligent location-routing for sustainable reverse supply chain of end-of-life vehicles considering awareness cost and carbon penalty
    Haowei Yan, Yilin Liu, Karthikeyan Kaliyaperumal
    Journal of Engineering Research Kuwait, 2025
    This paper presents a comprehensive investigation into optimizing the reverse supply chain for end-of-life vehicles (ELVs) with a focus on sustainability considerations, including awareness cost and carbon penalty. A novel three-objective mathematical model is developed, leveraging fuzzy logic to address the complex nature of the problem. Two multi-objective algorithms, the Grey Wolf Optimizer (GWO) based on the Pareto boundary and the NSGA-II algorithm, are deployed to solve the model. A case study in China is conducted to validate the proposed model, with results compared against the outcomes of the algorithms. Additionally, a sensitivity analysis approach is employed to assess the impact of awareness cost and carbon penalty parameters. Furthermore, the Taguchi experimental design method is utilized to identify the optimal combination of parameter values. The findings reveal that the GWO algorithm surpasses NSGA-II in terms of solution quality and diversity, although NSGA-II demonstrates superior performance in uniformity and computational time. The sensitivity analysis highlights the positive correlation between increased awareness cost and various performance metrics, such as the number of collected vehicles, economic profit, and social profit, while also indicating a reduction in environmental impacts . Conversely, escalating carbon penalties leads to a decrease in the acceptance and processing of vehicles within the supply chain, resulting in diminished chain and social profits, despite the decrease in carbon penalties.
  • An Efficient Technique for Identifying Distributed Denial of Service Active Assaults Using Deep Neural Networks Based on the Adaptive System Intelligence Paradigm
    Karthikeyan Kaliyaperumal, Raja Sarath Kumar Boddu, Sai Kiran Oruganti, Guidsa Tesema Kebesa, Mohsen Aghaeiboorkheili, Rajendran Bhojan
    International Journal of Basic and Applied Sciences, 2025
    A collection of interconnected devices that exchange data online is known as the Internet of Things. The IoT environment's diverse components make the distributed denial-of-service attack a security risk. One of the most important tasks in creating a smarter environment for ‎end users is detecting DDoS attacks in the Internet of Things. A new version of the optimized Elman recurrent neural network (ERNN) is ‎proposed to detect DDoS active attacks in Internet of Things scenarios. The proposed detection approach optimizes the weight and bias of ‎ERNN (ABCO-ERNN) using a novel adaptive bacterial colony optimization (ABCO) technique. The ABCO algorithm uses an adaptable ‎step size to increase the BCO's capacity for both exploration and exploitation. The four datasets, BoT-IoT, CIC-IDS2017, CIC-DDoS2019, ‎and IoTID20, are used to compare performance, and five distinct performance measures, including accuracy, precision, sensitivity, specificity-‎ty, and f-measure, are considered. When compared to previous literature algorithms, the proposed ABCO-ERNN detection approach ‎produced a high detection rate according to the experimental results‎.
  • An analysis of alternative machine learning and deep learning algorithms for categorization and detection of various active network assaults
    Dr. Karthikeyan Kaliyaperumal, Prof. Raja Sarath Kumar Boddu, Prof. Sai Kiran Oruganti
    International Journal of Basic and Applied Sciences, 2025
    Attacks on networks have grown increasingly widespread because of the exponential growth in internet traffic and the rapid progress of ‎network technology. A network attack occurs when a person gains illegal entry into a network. This includes any attempt to destroy the network, which might have disastrous consequences. Organizations depend significantly on tried-and-true network infrastructure security fea-‎tures like firewalls, encryption, and antivirus software. However, these strategies provide some defence against increasingly sophisti-‎cated attacks and viruses. Machine learning (ML) and deep learning (DL) are two important key concepts of artificial intelligence that gained ‎popularity around the turn of the century. The focus on statistical methodologies and data in these techniques may considerably improve ‎computing power by training computers to think like people. So, to address the inadequacies of non-intelligent solutions, computer ‎scientists started to use intelligent approaches in network security. This article provides a thorough examination of numerous deep learning ‎and machine learning methods for attack detection and classification.
  • A Real-Time Deep Neural Network-Based System’s Ability to Identify and Classify Active Assaults on Networks
    Karthikeyan Kaliyaperumal, Raja Sarath Kumar Boddu, Sai Kiran Oruganti
    Journal of Advances in Information Technology, 2025
    In the context of computer science and technology, a group of linked devices, or nodes, that exchange data, resources, or services with one another is referred to as a network.An active network attack refers to a malicious activity in which an attacker deliberately attempts to disrupt, manipulate, or gain unauthorized access to a computer network or its resources.In today's digital context, network security is of vital importance as cyber threats continue to evolve in terms of sophistication and frequency.Active network attacks pose significant challenges to traditional detection methods, necessitating the exploration of advanced techniques such as deep learning.This research proposes a novel approach for the identification and classification of active network attacks based on deep learning methodologies.To achieve this, an experimental research analysis design was used.A comprehensive review of deep learning approaches appropriate for network attack detection was undertaken.The proposed methodology involves the development of a model based on deep learning that was learned using a dataset comprising diverse network traffic data which is Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD).This study utilizes a comprehensive preprocessing pipeline, including data cleaning, feature selection for categorical variables and standardization of numerical features to prepare the dataset for modeling.To extract the pertinent information, preprocessing approaches are used.Metrics like as accuracy, precision, recall, F1-Score, and confusion matrix are used to evaluate performance as a result from deep learning models Deep Neural Network (DNN), Convolution Neural Networks (CNN), Long Short Term Memory(LSTM), Bi-Long Short Term Memory (Bi-LSTM) and Gated Recurrent Units (GRU) experiments done, Bi-LSTM model scored the best result of 99.15% and 99.12% accuracy for binary and multi classification, respectively.
  • Adaptive heuristic edge assisted fog computing design for healthcare data optimization
    Syed Sabir Mohamed S, Gopi R, Thiruppathy Kesavan V, Karthikeyan Kaliyaperumal
    Journal of Cloud Computing, 2024
    Patient care, research, and decision-making are all aided by real-time medical data analysis in today’s rapidly developing healthcare system. The significance of this research comes in the fact that it has the ability to completely change the healthcare system by relocating computing resources closer to the data source, hence facilitating more rapid and accurate analysis of medical data. Latency, privacy concerns, and inability to scale are common in traditional cloud-centric techniques. With their ability to process data close to where it is created, edge and fog computing have the potential to revolutionize medical analysis. The healthcare industry has unique opportunities and problems for the application of edge and fog computing. There must be an emphasis on data security and privacy, workload flexibility, interoperability, resource optimization, and data integration without any interruptions. In this research, it is suggested the Adaptive Heuristic Edge assisted Fog Computing design (AHE-FCD) to solve these issues using a novel architecture meant to improve medical analysis. Together, edge devices and fog nodes may perform distributed data processing and analytics with the help of AHE-FCD. Heuristic algorithms are often employed for optimization issues that establishing an optimum solution using standard approaches is difficult and impossible. Heuristic algorithms utilize search algorithms to explore the search space and identify a result. Improved patient care, medical research, and healthcare process efficiency are all possible to AHE-FCD real-time, low-latency analysis at the edge and fog layers. Improved medical analysis with minimal latency, high reliability, and data privacy are all likely to emerge from the study’s findings. As a result, rather from being centralized, operations in a sophisticated distributed system occur at several end points. That helps the situation quicker to detect possible dangers prior to propagate across the network. The AHE-FCD is a promising breakthrough that moves us closer to the realization of advanced medical analysis systems, where prompt and well-informed decision-making is essential to providing excellent healthcare.
  • Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture
    O. Sri Nagesh, Raja Rao Budaraju, Shriram S. Kulkarni, M. Vinay, Samuel-Soma M. Ajibade, Meenu Chopra, Malik Jawarneh, Karthikeyan Kaliyaperumal
    Discover Sustainability, 2024
    Due to the limited availability of natural resources, it is essential that agricultural productivity keep pace with population growth. Despite unfavorable weather circumstances, this project's major objective is to boost production. As a consequence of technological advancements in agriculture, precision farming as a way for enhancing crop yields is gaining appeal and becoming more prevalent. When it comes to predicting future data, machine learning employs a number of methods, including the creation of models and the acquisition of prediction rules based on past data. In this manuscript, we examine various techniques to machine learning, as well as an automated agricultural yield projection model based on selecting the most relevant features. For the purpose of selecting features, the Grey Level Co-occurrence Matrix method is utilised. For classification, we make use of the AdaBoost Decision Tree, Artificial Neural Network (ANN), and K-Nearest Neighbour (KNN) algorithms. The data set that was used in this study is simply a compilation of information about a variety of topics, including yield, pesticide use, rainfall, and average temperature. This data collection consists of 33 characteristics or qualities in total. The crops soya beans, maze, potato, rice, paddy, wheat, and sorghum are included in this data collection. This data collection was made possible through the collaboration of the Food and Agriculture Organisation (FAO) and the World Data Bank, both of which make their data available to the public. The AdaBoost decision tree has achieved the highest level of accuracy possible when used to anticipate agricultural yield. Both the accuracy rate and the recall rate are quite high at 99 percent.
  • Integration of BWT scrambling and data compression in an innovative system enhances protection and versatile management of sensor feeds (SEC)
    M. Baritha Begum, Karthikeyan Kaliyaperumal
    Heliyon, 2024
    The proposed Sensor Data Encryption with Compression (SEC) system is presented as an innovative solution for sensor data processing, aiming to achieve optimal efficiency while improving security and adaptability by integrating robust cryptography with advanced compression techniques. The system's objective is to create a reliable framework for managing sensitive sensor data by effectively tackling challenges in data processing through a balanced combination of strong cryptography and sophisticated compression methods. The SEC system utilizes advanced encryption and compression techniques, including the scrambling of Burrows-Wheeler Transform (BWT), Move-to-Front (MTF) encoding, Run-Length Encoding (RLE), and Huffman coding, to attain exceptional compression efficiency while maintaining data integrity. The practical implementation of the SEC system shows an impressive 85 % enhancement in data compression efficiency, as evidenced by reduced Bits per Character (BPC) across various sensor data inputs, along with improved resistance to cryptanalysis due to an increased unicity distance. Consequently, the SEC system demonstrates itself to be a robust and efficient solution, achieving superior data compression and enhanced cryptographic resilience, thereby addressing critical challenges in sensor data processing.
  • Machine Learning in Education, Finance and Management: Applications and Future Trends
    Harikumar Pallathadka, Guna Sekhar Sajja, Malik Mustafa, Karthikeyan Kaliyaperumal, Indrajit Patra, Samrat Ray, Abhishek Raghuvanshi
    Aip Conference Proceedings, 2023
  • Forecasting road traffic accident using deep artificial neural network approach in case of Oromia Special Zone
    Kannaiya Raja, Karthikeyan Kaliyaperumal, L. Velmurugan, Subash Thanappan
    Soft Computing, 2023
  • A novel optimization approach for rural development based on sustainable agriculture planning, considering the energy and water consumption nexus
    Jing Luo, Ya-Ping Chang, Karthikeyan Kaliyaperumal
    Energy Exploration and Exploitation, 2023
    In today's world, there are many changes and transformations in different sectors of the economy, and today's world economy has become more dependent on each other. The economy of many developing countries is highly dependent on agriculture. In addition, in developed countries, the agricultural sector is of special importance, so they are currently turning to the production of more organic and healthy products. One of the reasons that caused the agricultural sector to receive more attention in the economy is the existential importance of this sector in providing the raw materials of a part of the industry and the food industry needed in that country. Accordingly, in this research, the optimal cultivation pattern is determined using a linear multi-objective mathematical model, including economic, social, and environmental objectives. Moreover, the uncertainty in the input parameters of the proposed model is considered, and a robust optimization approach is applied to deal with this uncertainty. After collecting the research data for the crop year 2021-2022, the proposed robust counterpart formulation is optimized using GAMS software. The numerical results show that increasing the level of conservatism in the robust model cause to decrease in the profit of agriculture as well as increasing the total consumed water. Moreover, the results of the current research can help the decision-makers of the agricultural sector in achieving the goal of obtaining the highest profit.
  • Retracted: Graph-based content matching for web of things through heuristic boost algorithm
    Abdul Hameed Kalifullah, Kondamudi Bhavana Raj, Junath Naseer Ahamed, Rakesh Yemineni, Karthikeyan Kaliyaperumal, Sheshang Degadwala
    Iet Communications, 2023
  • A New Mathematical Model for Economic Ordering with Preventive Maintenance and Reworking in a Supply Chain
    Guolei Ding, Karthikeyan Kaliyaperumal, Xiaoguang Wang
    Discrete Dynamics in Nature and Society, 2023
  • Securing the Health Care Data Using Hybrid Rabin and Routing Algorithm
    Karthikeyan Kaliyaperumal, N. Kannaiya Raja, L. Velmurugan, Bacha Uma Woyessa
    2023 5th International Conference on Electrical Computer and Communication Technologies Icecct 2023, 2023
  • Improving Energy Efficiency in Piezoelectric Effect Based Synchronous Multicast Protocol (PESM) in Wireless Sensor Networks
    Tadele Degefa Geleto, C. Suresh Gnana Dhas, Karthikeyan Kaliyaperumal, S. Noordeen
    Smart Innovation Systems and Technologies, 2023
  • Challenges and Prospects of Implementing Information and Communication Technology for Small-Scale Farmers
    Karthikeyan Kaliyaperumal, V. Mahalakshmi, P.M. Sithar Selvam, T. Priya, B. Lakshmi Narayana, T. Pradeep
    Predictive Analytics in Smart Agriculture, 2023
  • Applications of artificial intelligence in business management, e-commerce and finance
    Harikumar Pallathadka, Edwin Hernan Ramirez-Asis, Telmo Pablo Loli-Poma, Karthikeyan Kaliyaperumal, Randy Joy Magno Ventayen, Mohd Naved
    Materials Today Proceedings, 2023
  • Classification and Detection of Skin Disease Based on Machine Learning and Image Processing Evolutionary Models
    Bordoloi, Dibyahash, Singh, Vijay, Kaliyaperumal, Karthikeyan, Ritonga, Mahyudin, Jawarneh, Malik, et al.
    Computer Assisted Methods in Engineering and Science, 2023
  • Detection of brain tumour using machine learning based framework by classifying MRI images
    P. Nancy, G. Murugesan, Abu Sarwar Zamani, Karthikeyan Kaliyaperumal, Malik Jawarneh, Surendra Kumar Shukla, Samrat Ray, Abhishek Raghuvanshi
    International Journal of Nanotechnology, 2023
  • An Efficient Key Generation Scheme for Secure Sharing of Patients Health Records using Attribute Based Encryption
    Karthikeyan Kaliyaperumal, F. Sammy
    2022 International Conference on Communication Computing and Internet of Things Ic3iot 2022 Proceedings, 2022
  • Predictive Analysis of Clinical Outcomes Using an Enhanced Random Survival Forest for Heart Failure Patients
    E. Laxmi Lydia, Karthikeyan Kaliyaperumal, Jose Moses Gummadi
    Lecture Notes on Data Engineering and Communications Technologies, 2022
  • Balance Harvest from the Forest as a Renewable Resource Using Game Theory
    Ngakan Ketut Acwin Dwijendra, R. Sivaraman, Karthikeyan Kaliyaperumal, Rosario Mireya Romero-Parra
    Discrete Dynamics in Nature and Society, 2022
  • " Pseudomonas fluorescens " as an Antagonist to Control Okra Root Rotting Fungi Disease in Plants
    Harsha Sharma, Mohd Anul Haq, Ashok Kumar Koshariya, Anil Kumar, Sandeep Rout, Karthikeyan Kaliyaperumal
    Journal of Food Quality, 2022
  • Fault Detection and Torque Control in BLDC Motor using AI
    Karthikeyan Kaliyaperumal, T. Sathesh Kumar, Ravi Kant, Mustafa Mahdi Abdulridha, Ramya.D, Anita Gehlot
    Proceedings of the 2022 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2022, 2022
  • Optimized Feature Selection and Image Processing Based Machine Learning Technique for Lung Cancer Detection
    Dr. P. Nancy, S Ravi Kishan, Kantilal Pitambar Rane, Dr. Karthikeyan Kaliyaperumal, Dr. Meenakshi, I Kadek Suartama
    International Journal of Electrical and Electronics Research, 2022
  • User Classification and Stock Market-Based Recommendation Engine Based on Machine Learning and Twitter Analysis
    Prasad N. Achyutha, Sushovan Chaudhury, Subhas Chandra Bose, Rajnish Kler, Jyoti Surve, Karthikeyan Kaliyaperumal
    Mathematical Problems in Engineering, 2022
  • An Optimized Systematic Approach to Identify Bugs in Cloud-Based Software
    Shanmugasundaram Marappan, Archana Kollu, Ismail Keshta, Shehab Mohamed Beram, Sahil Bhende, Karthikeyan Kaliyaperumal
    Scientific Programming, 2022
  • Reliability Model for Joint Production and Preventive Maintenance System based on IoT
    R. Regin, Mamoona Anam, M. Kalyan Chakravarthi, Karthikeyan Kaliyaperumal, Khongdet Phasinam, K. Ashok
    Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy Icais 2022, 2022
  • Identifying Smart Strategies for Effective Agriculture Solution Using Data Mining Techniques
    Anthony Jesus Bustamante Suarez, Barjinder Singh, Firas Husham Almukhtar, Rajnish Kler, Sonali Vyas, Karthikeyan Kaliyaperumal
    Journal of Food Quality, 2022
  • Deep Learning and Machine Learning Based Efficient Framework for Image Based Plant Disease Classification and Detection
    P. Nancy, Harikumar Pallathadka, Mohd Naved, Karthikeyan Kaliyaperumal, K. Arumugam, Vipul Garchar
    2022 International Conference on Advanced Computing Technologies and Applications Icacta 2022, 2022
  • Addressing the Real World Problem of Managing Wireless Communication Systems Using Explainable AI-Based Models through Correlation Analysis
    S. Surya, Sumeet Gupta, Abolfazl Mehbodniya, Jeidy Panduro-Ramirez, Prabhakara Rao Kapula, Tanweer Alam, Karthikeyan Kaliyaperumal
    Mathematical Problems in Engineering, 2022
  • RETRACTED: Major Challenges and Future Approaches in the Employment of Blockchain and Machine Learning Techniques in the Health and Medicine
    S. K. UmaMaheswaran, G. Lakshmi Vara Prasad, Batyrkhan Omarov, Dalael Saad Abdul-Zahra, Piyush Vashistha, Bhasker Pant, Karthikeyan Kaliyaperumal
    Security and Communication Networks, 2022
  • RETRACTED: Intelligence-based Network Security System to Predict the Possible Threats in Healthcare Data
    K. Vijayakumar, Sangheethaa Sukumaran, D. Murali, R.Venkateswara Reddy, Patteti Krishna, C. Bazil Wilfred, Karthikeyan Kaliyaperumal
    Security and Communication Networks, 2022
  • Metaheuristic Methods for Efficiently Predicting and Classifying Real Life Heart Disease Data Using Machine Learning
    Elia Ramirez-Asis, Magna Guzman-Avalos, Bireshwar Dass Mazumdar, D Lakshmi Padmaja, Manmohan Mishra, Deepali S Hirolikar, Karthikeyan Kaliyaperumal
    Mathematical Problems in Engineering, 2022
  • RETRACTED: Design and Implementation of Advanced Machine Learning Management and Its Impact on Better Healthcare Services: A Multiple Regression Analysis Approach (MRAA)
    M. Kiruthiga Devi, Veena Prasad Vemuri, Mahalakshmi Arumugam, S. K. UmaMaheswaran, Purnendu Bikash Acharjee, Rupali Singh, Karthikeyan Kaliyaperumal
    Computational and Mathematical Methods in Medicine, 2022
  • Multisensor Data and Cross-Validation Technique for Merging Temporal Images for the Agricultural Performance Monitoring System
    Venkata Kanaka Srivani Maddala, K. Jayarajan, M. Braveen, Ranjan Walia, Patteti Krishna, Sivakumar Ponnusamy, Karthikeyan Kaliyaperumal
    Journal of Food Quality, 2022
  • Detection of Pancreatic Cancer in CT Scan Images Using PSO SVM and Image Processing
    Arshiya S. Ansari, Abu Sarwar Zamani, Mohammad Sajid Mohammadi, Meenakshi, Mahyudin Ritonga, Syed Sohail Ahmed, Devabalan Pounraj, Karthikeyan Kaliyaperumal
    Biomed Research International, 2022
  • Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis
    V. Durga Prasad Jasti, Abu Sarwar Zamani, K. Arumugam, Mohd Naved, Harikumar Pallathadka, F. Sammy, Abhishek Raghuvanshi, Karthikeyan Kaliyaperumal
    Security and Communication Networks, 2022
  • Towards Application of Machine Learning in Classification and Prediction of Heart Disease
    Guna Sekhar Sajja, Malik Mustafa, Khongdet Phasinam, Karthikeyan Kaliyaperumal, Randy Joy Magno Ventayen, Thanwamas Kassanuk
    Proceedings of the 2nd International Conference on Electronics and Sustainable Communication Systems Icesc 2021, 2021
  • Retraction Notice: Dynamic Brain Segmentation and Intelligent Deep Neural Networks (2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) DOI: 10.1109/ICACCS51430.2021.9441937)
    Sonali Vyas, Karthikeyan Kaliyaperumal, Bhopendra Singh, Ghassan K. Ali, R. Aarthi, Ahmed Gawish
    2021 7th International Conference on Advanced Computing and Communication Systems Icaccs 2021, 2021
  • Dynamic Brain Segmentation and Intelligent Deep Neural Networks
    Sonali Vyas, Karthikeyan Kaliyaperumal, Bhopendra Singh, Ghassan K. Ali, R. Aarthi, Ahmed Gawish
    2021 7th International Conference on Advanced Computing and Communication Systems Icaccs 2021, 2021
  • Tuning Hyperparameters of Machine Learning Methods for Afan Oromo Hate Speech Text Detection for Social Media
    Naol Bakala Defersha, Kula Kekeba, Karthikeyan Kaliyaperumal
    Proceedings of the 2021 4th International Conference on Computing and Communications Technologies Iccct 2021, 2021
  • Brain Tumor Analysis Using Advanced Textural Feature Extraction Algorithm
    Deepak Mane, Karthikeyan Kaliyaperumal, Saira Khurram, R. Regin, R. Aarthi, Ahmed Gawish
    Proceedings 2nd International Conference on Smart Electronics and Communication Icosec 2021, 2021
  • Rainfall Prediction Using Deep Mining Strategy for Detection
    Karthikeyan Kaliyaperumal, Afikah Rahim, D. K. Sharma, R. Regin, Swati Vashisht, Khongdet Phasinam
    Proceedings 2nd International Conference on Smart Electronics and Communication Icosec 2021, 2021
  • The impact of online social media networking on educational performance in ambo university
    International Journal of Advanced Research in Engineering and Technology, 2020

RECENT SCHOLAR PUBLICATIONS

  • Analytical Evaluation of Machine Learning and Deep Learning Models for Network Intrusion Detection and Vulnerability Analysis
    RB Karthikeyan Kaliyaperumal, Gudisa Tesema Kebesa , Mohsen Aghaeiboorkheili
    International Journal of Recent Trends in Multidisciplinary Research and … , 2026
    2026
  • A Real-Time Deep Neural Network–Based Architecture for Active Network Attack Identification and Classification Models
    K Kaliyaperumal, RSK Boddu, SK Oruganti, GT Kebesa
    Sustainable Global Societies Initiative 1 (1) , 2025
    2025
  • NETWORK ATTACK SYSTEM PREDICTION AND THREAT ANALYSIS USING VARIOUS MACHINE LEARNING AND DEEP LEARNING TECHNIQUES
    K Kaliyaperumal, RS Kumar, SK Oruganti
    SGS-Engineering & Sciences 1 (4) , 2025
    2025
  • Intelligent location-routing for sustainable reverse supply chain of end-of-life vehicles considering awareness cost and carbon penalty
    H Yan, Y Liu, K Kaliyaperumal
    Journal of Engineering Research , 2025
    2025
    Citations: 1
  • Sophisticated Machine Learning Methods for Reliable Network Traffic Data Categorization Models
    K Kaliyaperumal, R Bhojan, M Aghaeiboorkheili
    Interdisciplinary Journal of Papua New Guinea University of Technology 2 (2) , 2025
    2025
  • AN EXPERIMENTAL INVESTIGATION ON THE DETECTION AND CLASSIFICATION OF ACTIVE NETWORK ATTACKS: AN EXPERIMENTAL INVESTIGATION ON THE DETECTION AND CLASSIFICATION OF ACTIVE NETWORK …
    K Kaliyaperumal, RSK Boddu, SK Oruganti
    SGS-Engineering & Sciences 1 (2) , 2025
    2025
  • Design of face recognition based effective automated smart attendance system
    K Kaliyaperumal
    Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) p … , 2025
    2025
    Citations: 1
  • An analytical study of different machine learning and deep learning techniques for classification and detection of various network attacks
    K Kaliyaperumal, RSK Boddu, SK Oruganti
    SGS-Engineering & Sciences 1 (1) , 2025
    2025
  • Adaptive heuristic edge assisted fog computing design for healthcare data optimization
    K Kaliyaperumal
    Journal of Cloud Computing 13 (1), 1-18 , 2024
    2024
    Citations: 25
  • Integration of BWT scrambling and data compression in an innovative system enhances protection and versatile management of sensor feeds (SEC)
    MB Begum, K Kaliyaperumal
    Heliyon 10 (20) , 2024
    2024
    Citations: 16
  • Detection of fake reviewer groups in product reviews using novel bidirectional encoder revolutionary transformer compared with stochastic gradient descent
    K Karthikeyan, BN Devi, CHC Alexander
    AIP Conference Proceedings 3161 (1), 020189 , 2024
    2024
    Citations: 1
  • Using the Greedy Algorithm to Minimize Bandwidth in P2P Networks Over Video Live Streaming
    K Kaliyaperumal, NK Raja
    Next-Gen Technologies in Computational Intelligence, 237-245 , 2024
    2024
  • Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture
    OS Nagesh, RR Budaraju, SS Kulkarni, M Vinay, SSM Ajibade, M Chopra, ...
    Discover Sustainability 5 (1), 78 , 2024
    2024
    Citations: 51
  • Identifying Smart Strategies for Effective Agriculture Solution Using Data Mining Techniques (Retraction of Vol 2022, art no 6600049, 2022)
    AJB Suarez, B Singh, FH Almukhtar, R Kler, S Vyas, K Kaliyaperumal
    JOURNAL OF FOOD QUALITY 2024 , 2024
    2024
  • Design of Mobile Call Drop Reasons Prediction Model using Machine Learning
    DAJR Karthikeyan Kaliyaperumal, N. Kannaiya Raja, T. Ratha Jeyalakshmi
    Journal of Electrical Systems 20 (10) , 2024
    2024
  • Predictive analytics in smart agriculture
    S Krishnan, AJ Anand, N Prasanth, S Goundar, C Ananth
    CRC Press , 2023
    2023
    Citations: 5
  • Challenges and Prospects of Implementing Information and Communication Technology for Small-Scale Farmers
    K Kaliyaperumal, V Mahalakshmi, PMS Selvam, T Priya, BL Narayana, ...
    Predictive Analytics in Smart Agriculture, 156-174 , 2023
    2023
    Citations: 4
  • Machine learning in education, finance and management: Applications and future trends
    H Pallathadka, GS Sajja, M Mustafa, K Kaliyaperumal, I Patra, S Ray, ...
    AIP Conference Proceedings 2587 (1), 090003 , 2023
    2023
    Citations: 2
  • A novel optimization approach for rural development based on sustainable agriculture planning, considering the energy and water consumption nexus
    J Luo, YP Chang, K Kaliyaperumal
    Energy Exploration & Exploitation 41 (5), 1724-1745 , 2023
    2023
    Citations: 2
  • Retracted: Graph‐based content matching for web of things through heuristic boost algorithm
    AH Kalifullah, KB Raj, JN Ahamed, R Yemineni, K Kaliyaperumal, ...
    IET Communications 17 (13), 1626-1636 , 2023
    2023
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • Applications of artificial intelligence in business management, e-commerce and finance
    H Pallathadka, EH Ramirez-Asis, TP Loli-Poma, K Kaliyaperumal, ...
    Materials Today: Proceedings 80, 2610-2613 , 2023
    2023
    Citations: 544
  • Computational technique based on machine learning and image processing for medical image analysis of breast cancer diagnosis
    VDP Jasti, AS Zamani, K Arumugam, M Naved, H Pallathadka, F Sammy, ...
    Security and communication networks 2022, 1-7 , 2022
    2022
    Citations: 289
  • Biogas production from co-digestion of brewery wastes [BW] and cattle dung [CD]
    S Tewelde, K Eyalarasan, R Radhamani, K Karthikeyan
    Int. J. Latest Trends Agric. Food Sci 2 (2), 90-93 , 2012
    2012
    Citations: 57
  • Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture
    OS Nagesh, RR Budaraju, SS Kulkarni, M Vinay, SSM Ajibade, M Chopra, ...
    Discover Sustainability 5 (1), 78 , 2024
    2024
    Citations: 51
  • Multisensor Data and Cross-Validation Technique for Merging Temporal Images for the Agricultural Performance Monitoring System
    K Kaliyaperumal
    Journal of food quality 2022, 10 , 2022
    2022
    Citations: 44
  • User Classification and Stock Market‐Based Recommendation Engine Based on Machine Learning and Twitter Analysis
    PN Achyutha, S Chaudhury, SC Bose, R Kler, J Surve, K Kaliyaperumal
    Mathematical Problems in Engineering 2022 (1), 4644855 , 2022
    2022
    Citations: 43
  • Classification and detection of skin disease based on machine learning and image processing evolutionary models
    D Bordoloi, V Singh, K Kaliyaperumal, M Ritonga, M Jawarneh, ...
    Computer Assisted Methods in Engineering and Science 30 (2), 247-256 , 2023
    2023
    Citations: 42
  • Rainfall prediction using deep mining strategy for detection
    K Kaliyaperumal, A Rahim, DK Sharma, R Regin, S Vashisht, K Phasinam
    2021 2nd International Conference on Smart Electronics and Communication … , 2021
    2021
    Citations: 38
  • Towards application of machine learning in classification and prediction of heart disease
    GS Sajja, M Mustafa, K Phasinam, K Kaliyaperumal, RJM Ventayen, ...
    2021 Second International Conference on Electronics and Sustainable … , 2021
    2021
    Citations: 36
  • Deep learning and machine learning based efficient framework for image based plant disease classification and detection
    P Nancy, H Pallathadka, M Naved, K Kaliyaperumal, K Arumugam, ...
    2022 International Conference on Advanced Computing Technologies and … , 2022
    2022
    Citations: 34
  • Forecasting road traffic accident using deep artificial neural network approach in case of Oromia Special Zone
    K Kaliyaperumal
    Soft Computing , 2023
    2023
    Citations: 28
  • Comparative analysis of data centric routing protocols for wireless sensor networks
    K Karthikeyan, M Kavitha
    International journal of scientific and research publications 3 (1), 1-6 , 2013
    2013
    Citations: 27
  • Adaptive heuristic edge assisted fog computing design for healthcare data optimization
    K Kaliyaperumal
    Journal of Cloud Computing 13 (1), 1-18 , 2024
    2024
    Citations: 25
  • “ Pseudomonas fluorescens ” as an Antagonist to Control Okra Root Rotting Fungi Disease in Plants
    H Sharma, MA Haq, AK Koshariya, A Kumar, S Rout, K Kaliyaperumal
    Journal of Food Quality 2022 (1), 5608543 , 2022
    2022
    Citations: 25
  • Design and Implementation of Advanced Machine Learning Management and Its Impact on Better Healthcare Services: A Multiple Regression Analysis Approach (MRAA)
    K Kaliyaperumal
    Computational and Mathematical Methods in Medicine 2022, 7 , 2022
    2022
    Citations: 21
  • Integration of BWT scrambling and data compression in an innovative system enhances protection and versatile management of sensor feeds (SEC)
    MB Begum, K Kaliyaperumal
    Heliyon 10 (20) , 2024
    2024
    Citations: 16
  • Towards investigation of various security and privacy issues in internet of things
    M Mustafa, D Virmani, K Kaliyaperumal, K Phasinam, T Santosh
    Design Engineering, 1747-1758 , 2021
    2021
    Citations: 16
  • Metaheuristic methods for efficiently predicting and classifying real life heart disease data using machine learning
    E Ramirez-Asis, M Guzman-Avalos, BD Mazumdar, DL Padmaja, ...
    Mathematical Problems in Engineering 2022 (1), 4824323 , 2022
    2022
    Citations: 13
  • Page rank based design and implementation of search engine optimization
    K Karthikeyan, M Sangeetha
    International Journal of Computer Applications 40 (4), 13-18 , 2012
    2012
    Citations: 13
  • Sentiment analysis for Afaan Oromoo using combined convolutional neural network and bidirectional long short-term memory
    M Oljira, K Sori, K Kaliyaperumal, BN Sima
    Int. J. Adv. Res. Eng. Technol 11 (11), 101-112 , 2020
    2020
    Citations: 12