Dr. Sathish Kumar M

@hindustanuniv.ac.in

Assistant Professor and Computer Applications
Hindustan Institute of Technology and Science

Dr. Sathish Kumar M


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RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Computer Science Applications, Computer Networks and Communications, Information Systems
33

Scopus Publications

335

Scholar Citations

11

Scholar h-index

12

Scholar i10-index

Scopus Publications

  • Blockchain Based Cloud Storage System
    V. Sri Sairam, M. Sathish Kumar
    Proceedings of 8th International Conference on Trends in Electronics and Informatics Icoei 2025, 2025
    The rapid growth of data generation in various sectors, including healthcare, finance, and enterprise, has created an ever-increasing demand for secure, scalable, and cost-effective cloud storage solutions. Traditional cloud storage systems usually encounter difficulties with data security, privacy, and centralized control, leading to problems such as unauthorized access to data, data breaches, and single points of failure. Hence, we propose an active revocation-based three-factor Maka scheme for granting user access control using Schnorr signatures and provide a formal proof for the security of our scheme under the random oracle security model, which shows that our scheme can achieve certain requirements in the multi-server scenario. Performance evaluation shows that the proposed scheme is suitable for computation resource-limited mobile devices. Full-fledged implementations under meaningful performance assessments confirm the viability of the proposed scheme.
  • OSE: Optimizing User Segmentation in E-Commerce Using APRIORI Algorithm for Personalized Product Recommendations
    Sai Prasanth B, Sathish Kumar M
    Proceedings of International Conference on Visual Analytics and Data Visualization Icvadv 2025, 2025
    E-commerce platforms has led to an overwhelming increase in product choices, making it more challenging for users to find relevant products. To tackle this issue, a robust ECommerce Product Recommendation System is crucial. This system utilizes machine learning techniques, specifically user segmentation and the Apriori algorithm, to personalize recommendations based on user behavior, preferences, and historical data. The research aims to enhance the personalization of the shopping experience, improving user satisfaction, increasing sales, and fostering customer retention. The project focuses on developing a personalized recommendation system that combines Collaborative Filtering (both user-based and itembased) and the Apriori algorithm for association rule mining to recommend products based on frequent itemsets. The system will be trained on an anonymized e-commerce dataset containing user interactions such as purchase history, ratings, and product metadata. Key performance metrics like Precision, Recall, and F1Score will be used to evaluate the recommendation system's accuracy and effectiveness. The system will be designed to provide real-time recommendations tailored to user preferences, enabling e-commerce platforms to increase customer engagement and drive sales. Additionally, it will incorporate strategies to address coldstart problems (new users and products) and will adapt to evolving user preferences over time, ensuring that the recommendations remain dynamic and highly personalized.
  • SmartPark Visionaire: AI-Driven Advanced Parking Slot Detection
    N. Santosh, M. Sathish Kumar
    Proceedings of 8th International Conference on Inventive Computation Technologies Icict 2025, 2025
    The parking conflicts are now held most important among the urban problems leading to its own traffic congestion and wastage of fuel. To relieve the situation, SmartPark is hereby proposed, an intelligent car parking system implementing the YOLOv8 state-of-the-art object detection model capable of identifying parking slots in realtime and accurately. It is trained over a very large dataset of annotated parking lot images by applying different preprocessing and augmentation techniques to further improve the detection accuracy. Performance parameters of the model are determined via measuring mean Average Precision (mAP), confusion matrix analysis, and latency comparison, with the very commendable performance of accuracy and efficiency over the previous YOLO versions. The results strongly signify that SmartPark shows very high precision in detecting free and occupied parking slots, thus greatly reducing time in parking searches and contributing to traffic decongestion. The blend of deep learning and real-time detection supports SmartPark's proposition for smart city infrastructure paving way for automated parking governance and pleasant user experience. In summary, this paper underscores the real-world applicability of YOLOv8 to parking systems that subsequently optimize urban mobility and utilization of resources.
  • A Hybrid B-Tree and Enhanced Merkle Hash Tree Approach for Secure Cloud Data Management
    Thiruneelakandan. S, Sherin Eliyas, Sathish Kumar M
    Proceedings of International Conference on Visual Analytics and Data Visualization Icvadv 2025, 2025
    Data security, integrity verification, and efficient retrieval issues are much more pronounced with large datasets in cloud storage systems. The angle of this paper is the proposal of hybrid architecture of AES, EMHT, B-tree indexing, and POR techniques to complete the optimization of cloud operations. The way the system was evaluated against the traditional approaches was based on several key metrics such as encryption time, integrity verification time, update time, and memory usage for datasets up to the 15GB limit. The experimental results show that the proposed system has 6.50% less verification time and 38.44% better update time when compared to existing methods without compromising the effectiveness of encryption. These indicate that the proposed approach is cost-effective, secure, and scalable in a large-scale cloud storage environment. The contribution of this paper is a practical solution for future enhanced data management and security in modern cloud systems.
  • Enhancing Weather Prediction Accuracy with CNN-based Machine Learning
    Brionel Justin Raj J, Sherin Eliyas, Sathish Kumar
    Proceedings of 8th International Conference on Inventive Computation Technologies Icict 2025, 2025
    The accurate as well as efficient prediction of the weathers is considered a big scientific and technological challenge due to the highly dynamic and complex nature of the atmosphere under which such severe developments occur. This project thus focuses on the utilization of datamining techniques, mostly clustering and classification, to improve the accuracy of prediction-enabled weather forecasting systems. Historical and real-time weather data, including satellite images, radar scans, etc., are utilized to extract patterns and relationships, which lend towards predictive modeling. A Multilayer Perceptron (MLP) is used for mapping temperature, humidity, and rainfall as different weather variables' classes. The pre-processing of raw data into useful and relevant features plays a very important role in improving model performance. In this context, effective data mining and machine learning methods put forward by this study will contribute toward more reliable weather prediction against the universe of concern regarding accuracy and efficiency on meteorological systems.
  • Detecting and Predicting Learner's Dropout Using KNN Algorithm
    Guru Priya V, Sherin Eliyas, Sathish Kumar M
    2024 Opju International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4 0 Otcon 2024, 2024
    Predicting student dropout is crucial for early intervention and support, addressing a significant loss of potential human capital in the education system. This paper presents a machine-learning approach to predict student dropout using a dataset of student records. Our model leverages various features including demographic information, academic performance, attendance records, and fee payment details to estimate the probability of dropout. A survey conducted among college students in Tamil Nadu sheds light on reasons for discontinuing courses, emphasizing the multifaceted nature of the dropout problem. The proposed predictive model will be evaluated using a large dataset, comparing its effectiveness and accuracy with existing models. This research aims to facilitate proactive interventions by educational institutions, leveraging technology and data analytics to improve retention rates amidst the challenges posed by the pandemic and beyond. Student dropout prediction is paramount for early intervention in the education system, mitigating the loss of potential human capital. This study employs a machine-learning approach to forecast student dropout utilizing a comprehensive dataset comprising demographic information, academic performance, attendance records, and fee payment details. A survey conducted among college students across Tamil Nadu highlights the multifaceted nature of dropout, encompassing socioeconomic and psychological factors. The proposed predictive model will undergo rigorous evaluation against existing benchmarks to assess its efficacy and accuracy. By enabling proactive interventions, this research endeavors to enhance retention rates through the integration of technology and data analytics, particularly in the wake of the pandemic, shaping the future of education.
  • Classifying Emotions and Engagement in Online Learning Based on Single Facial Recognition using CNN and Mobilenet-v2
    Ravikumar Yadav B, Sherin Eliyas, Sathish Kumar M
    2024 Opju International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4 0 Otcon 2024, 2024
    Maintaining student engagement and attention is crucial for effective online learning. This study proposes a novel approach for real-time student engagement assessment during online sessions, leveraging the power of facial expression recognition. Combining the Haar Cascade Classifier for efficient face and eye detection with the lightweight MobileNet-v2 convolutional neural network architecture, the system accurately classifies seven fundamental emotions: happiness, sadness, surprise, fear, anger, disgust, and neutral. These categorized emotions provide a comprehensive understanding of each student’s attentiveness. This nonintrusive approach, implemented within a Flask-based web framework, offers educators valuable insights into classroom dynamics, enabling early identification of disengaged students and facilitating personalized pedagogical interventions. Furthermore, the system generates dashboards and reports on student behavior patterns, promoting data-driven decisio-nmaking and continuous improvement of instructional strategies. This study showcases the potential of machine learning for transforming online education and enhancing student engagement by providing a reliable and efficient method for real-time attentiveness monitoring.
  • An Innovative Design of SH System using T and L Techniques
    S Ramesh, M Sathish Kumar, Purushottam Das, Sherin Eliyas
    2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2024, 2024
    The project begins with gathering a lot of data from different hotels and accommodation facilities in different places. The recommendation system is built on this large dataset, which allows it to provide customised recommendations according to user needs. An essential component of this dataset’s enrichment is user input. Through the process of integrating and evaluating user preferences and views, the system leverages an extensive data set that is crucial for making appropriate hotel recommendations. Because of this user-centric approach, the suggested solutions are guaranteed to closely match the preferences of travellers for their lodging. The problem of selecting the ideal hotel for a certain trip time is one of the main issues our project attempts to solve. The technology does a great job of offering insightful data that helps consumers find the perfect hotel for their trip dates. The technique for the project takes a multifaceted approach. There is much focus on data preparation in addition to data collection. This stage of preparation guarantees that the data gathered is streamlined and optimised for further examination. Utilising the NLTK package and the Python programming language, the system makes use of sophisticated methods like tokenization and lemmatization This is a crucial stage since it enables the system to interpret and handle text-based data effectively. The user-facing interface that displays suggestions is one of the project’s primary deliverables. A carefully selected list of the top five hotels that most closely fit their needs is given to users. This carefully chosen assortment streamlines the decision-making process and provides consumers with practical options to suit their demands for lodging.
  • The Development of Enhancement of Communication in Drones using Various Iot System
    Adhitya Raman, M Sathish Kumar, Sushant Chamoli, Sherin Eliyas
    2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2024, 2024
    Attacks on UAV communication, or unmanned aerial vehicles used in conjunction with radio signals for operations, are the points of the paper. In that way, UAV vehicles can be intercepted because of the use of that type of signal. Using a certain understanding of the specific communication protocol that is used by a UAV, attackers can design software tools to manipulate the intercepted signals. This may involve the forging of commands, modification of the telemetry data, or even the injection of malicious code into the UAV system. An unencrypted path of communication can lead the UAV vehicles to interception, making it possible for a person who conducts this illegal activity to perform further activities like hijacking the payload it’s carrying or forcing it to crash, resulting in loss for the organization. But, this may give a big advantage to an organization because interception can be done in the different ways. This may be done by the other organization in the name of the other things happening so that the organization can detect that some intercepting is done, which it can detect using detection algorithms. And, of course, the more the implementation of the hybrid algorithm is done, the more secure it is from attacks, so it can be done secure by traditional algorithms-RSA-with modern algorithms, for instance, elliptic curve cryptography algorithms. By doing this, communication is made more secure with larger companies like Amazon and Walmart. Being these huge companies, it is better for them to use hybrid-based cryptographic algorithms in order to reduce the risk of interception that may put the organizations at jeopardy.
  • Enhancement of LL and SDM in Case of SMO via FC
    S Lekhaa, M Sathish Kumar, Amit Kumar Mishra, Sherin Eliyas
    2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2024, 2024
    Time is critical in making quick decisions in this era of universal, real-time data creation. Traditional cloud-centric architectures have high latencies and network congestion, which do not allow one to provide quick responses. The paper presents an innovative application of the fog computing paradigm in real-time data processing, with a special focus on scenarios where timely reactions are necessary. In the proposed approach, data processing happens at the fog layer close to the source, thereby reducing latency and network usage. This will show, once again, that fog computing is a bridge for real-time data handling, hence providing fast decision-making, optimization of resources, and creating environmentally friendly and more adaptive services.
  • The Development of SWMS with LT and FT Environment in Fog Based IOT Environment
    K Rathi, M Sathish Kumar, Deepak Singh Rana, Sherin Eliyas
    2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2024, 2024
  • Individualized Mastery Quest: Crafting Customized Question Papers and Dynamic Hint Generation for Personalized Learning Journeys Using Cutting-Edge Rank-Based Algorithm
    Nithin. P, Sherin Eliyas, Sathish Kumar M, Balamurugan Balusamy
    2024 International Conference on Electrical Electronics and Computing Technologies Iceect 2024, 2024
  • The Controlling of Viability of LM Controller in ULR System
    Mukesh, Kamreed Udham Singh, Kadim A. Jabbar, M Sathish Kumar, Laith Hussein, Tareq Hafdhi Abdtawfeeq, Abdul Hassan Majli Jaafar, Ali Saad Alwan
    2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2024, 2024
  • Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease
    Chitradevi Dhakhinamoorthy, Sathish Kumar Mani, Sandeep Kumar Mathivanan, Senthilkumar Mohan, Prabhu Jayagopal, Saurav Mallik, Hong Qin
    Mathematics, 2023
  • Smart Industrial Scanner for Implementation of Relevant Data Parsing from Prescriptions Using SSWF Algorithm
    Jephin V. Jose, Sherin Eliyas, Sathish Kumar, Angeline Benitta
    Lecture Notes in Networks and Systems, 2023
  • An Intellectual Diffused Configuration for High-Level Edge Network Elasticity
    A Karthigeyan, M Sathish Kumar, Hawraa Ali Sabah, Laith Fouad, Adil Abbas Alwan, Laith Fouad
    2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2023, 2023
  • Improving Quality of Services of Fog Computing Through Efficient Work Flow Scheduling
    G. Ravi Prasanth, M. Sathish Kumar, Rabei Raad Ali, Aqeel Ali, Hawraa Ali Sabah, Mustafa Al-Tahee
    2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2023, 2023
  • Student Placement Prediction Using Supervised Machine Learning
    M. Siva Surya, M.Sathish Kumar, D. Gandhimathi
    2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2022, 2022
  • A Prediction of Flight Fare Using K-Nearest Neighbors
    S. Naveen Prasath, Sathish Kumar M, Sherin Eliyas
    2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2022, 2022
  • Efficient resource allocation in fog computing using QTCS model
    M. Iyapparaja, Naif Khalaf Alshammari, M. Sathish Kumar, S. Siva Rama Krishnan, Chiranji Lal Chowdhary
    Computers Materials and Continua, 2022
  • Rank Fraud and Malware Detection in Google Play Using Fairplay
    G. Yugeshwaran, Sathish Kumar M, D.Angeline Benitta, Sherin Eliyas, Sanju Rajan R
    2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2022, 2022
  • Kidney Stone Prediction Using Neural Network Classifier
    Pavithra, Sanjurajan, Chitradevi, Sherin Eliyas, Angeline Benitta, Sathish Kumar
    2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2022, 2022
  • Fog and edge computing simulators systems: research challenges and an overview
    M. Sathish Kumar, M. Iyapparaja
    International Journal of System of Systems Engineering, 2021
  • Fog Computing: State-of-Art, Open Issues, Challenges and Future Directions
    M. Sathish Kumar, M. Iyapparaja
    Learning and Analytics in Intelligent Systems, 2021
  • FogQSYM: An Industry 4.0 Analytical Model for Fog Applications
    M. Iyapparaja, M. Sathish Kumar, S. Siva Rama Krishnan, Chiranji Lal Chowdhary, Byungun Yoon, Saurabh Singh, Gi Hwan Cho
    Computers Materials and Continua, 2021
  • Improving quality-of-service in fog computing through efficient resource allocation
    Sathish Kumar Mani, Iyapparaja Meenakshisundaram
    Computational Intelligence, 2020
  • A queuing theory model for e-health cloud applications
    M. Sathish Kumar, M. Iyappa Raja
    International Journal of Internet Technology and Secured Transactions, 2020
  • Improving Energy Consumption by Using DVFS
    M. Iyapparaja, L. Abirami, M. Sathish Kumar
    Learning and Analytics in Intelligent Systems, 2020
  • Analyzing financial data and mutual funds recommendation by using big data analytics
    Nithya Sampath, Jayakumar Sadhasivam, R Raj Kumar, M Sathish Kumar, Balajee Jeyakumar, P. V PraveenSundar
    Journal of Computational and Theoretical Nanoscience, 2019
  • An analysis on Barrier Coverage in Wireless Sensor networks
    Shakila Basheer, Rincy Merlin Mathew, D Ranjith, M Sathish Kumar, P. V Praveen Sundar, J. M Balajee
    Journal of Computational and Theoretical Nanoscience, 2019
  • A review on utilizing queuing models for improving performance in cloud
    Journal of Advanced Research in Dynamical and Control Systems, 2018
  • Review of gaming and its evolution over networks
    International Journal of Civil Engineering and Technology, 2017
  • A review on performance evaluation techniques in cloud
    M. Sathish Kumar, B. Balamurugan
    Proceedings 2017 2nd International Conference on Recent Trends and Challenges in Computational Models Icrtccm 2017, 2017

RECENT SCHOLAR PUBLICATIONS

  • Intelligent Skin Cancer Detection Using Hybrid Deep Learning and Machine Learning Model
    G Dipesh, S Eliyas, S Kumar
    2026 Third International Conference on Networking and Communications (ICNWC … , 2026
    2026
  • Enhancing Weather Prediction Accuracy with CNN-Based Machine Learning
    S Eliyas, S Kumar
    2025 International Conference on Inventive Computation Technologies (ICICT … , 2025
    2025
    Citations: 1
  • Mechanical Properties of a Composite Formed from Bamboo Granules and Glass Fiber
    S Kumar, S Singh, H Singh, A Goyal
    Asian Review of Mechanical Engineering 13 (2), 37-41 , 2024
    2024
  • High-Accuracy Prostate Cancer Staging Prediction Using K nearest Neighbour (KNN) Classifier: A Machine Learning Approach
    J Chaudhary, A Phulia, AK Pandey, PD Sharma, S Kumar
    EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING 51, S778-S778 , 2024
    2024
  • Correction to: Smart Industrial Scanner for Implementation of Relevant Data Parsing from Prescriptions Using SSWF Algorithm
    JV Jose, S Eliyas, S Kumar, A Benitta
    Mobile Radio Communications and 5G Networks: Proceedings of Third MRCN 2022 … , 2023
    2023
  • Nanotechnology Horizons in Food Process Engineering: Volume 1: Food Preservation, Food Packaging, and Sustainable Agriculture
    MR Goyal, JA Malik, S Kumar, RB Watharkar
    CRC Press , 2023
    2023
    Citations: 1
  • An Intellectual Diffused Configuration for High-Level Edge Network Elasticity
    A Karthigeyan, MS Kumar, HA Sabah, L Fouad, AA Alwan, L Fouad
    2023 3rd International Conference on Advance Computing and Innovative … , 2023
    2023
  • Improving Quality of Services of Fog Computing Through Efficient Work Flow Scheduling
    GR Prasanth, MS Kumar, RR Ali, A Ali, HA Sabah, M Al-Tahee
    2023 3rd International Conference on Advance Computing and Innovative … , 2023
    2023
    Citations: 2
  • Smart Industrial Scanner for Implementation of Relevant Data Parsing from Prescriptions Using SSWF Algorithm
    A Jose, J.V. , Sherin Eliyas, M. , Kumar, S. , Benitta
    Third International Conference on Mobile Radio Communications and 5G … , 2023
    2023
  • Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease
    H Dhakhinamoorthy, C. , Mani, S.K. , Mathivanan, S.K. , ... Mallik, S. , Qin
    Mathematics 11 (5), 1136 , 2023
    2023
    Citations: 33
  • Retraction Notice: Kidney Stone Prediction Using Neural Network Classifier
    S Eliyas, A Benitta, S Kumar
    2022 2nd International Conference on Advance Computing and Innovative … , 2022
    2022
  • Student Placement Prediction Using Supervised Machine Learning
    D Surya, M.S. , Kumar, M.S. , Gandhimathi
    2022 2nd International Conference on Advance Computing and Innovative … , 2022
    2022
    Citations: 18
  • Rank Fraud and Malware Detection in Google Play Using Fairplay
    S Yugeshwaran, G. , Kumar M, S. , Benitta, D.A. , Eliyas, S. , Rajan R
    2022 2nd International Conference on Advance Computing and Innovative … , 2022
    2022
    Citations: 3
  • Kidney Stone Prediction Using Neural Network Classifier
    S Pavithra , Sanjurajan , Chitradevi , ... Benitta, A. , Kumar
    2022 2nd International Conference on Advance Computing and Innovative … , 2022
    2022
    Citations: 23
  • A Prediction of Flight Fare Using K-Nearest Neighbors
    S Prasath, S.N. , Kumar M, S. , Eliyas
    2022 2nd International Conference on Advance Computing and Innovative … , 2022
    2022
    Citations: 9
  • Efficient resource allocation in fog computing using QTCS model
    CL Iyapparaja, M. , Alshammari, N.K. , Kumar, M.S. , Krishnan, S.S.R ...
    Computers, Materials and Continuathis link is disabled 70 (2), 2225-2239 , 2022
    2022
    Citations: 47
  • Intuitionistic Fuzzy Solid Assignment Problems for Multi-Attribute Decision-Making
    DUR Dr.M. Saradha,Dr. V. Vinoth Kumar,S.Satheesh Kumar, Dr. Sathish Kumar
    IN Patent 09/2,022 , 2022
    2022
  • Blockchain-Based Proxy Re-Encryption Scheme for IoT Systems Using Algebraic Structure
    DDAB Mr.Prathap R,Mr.T.Karthikeyan,Mr.Anantha Babu, Mr.V.Manikandan,Dr ...
    IN Patent 06/2,022 , 2022
    2022
  • Bone cancer detection using feature extraction with classification using K-nearest neighbor and decision tree algorithm
    S Kumar, B Sathiyaprasad
    Smart Intell. Comput. Commun. Technol, 347-353 , 2021
    2021
    Citations: 11
  • Fog and edge computing simulators systems: research challenges and an overview
    M Kumar, M.S. , Iyapparaja
    International Journal of System of Systems Engineering 11 (3-4), 202–223 , 2021
    2021
    Citations: 24

MOST CITED SCHOLAR PUBLICATIONS

  • Improving quality-of-service in fog computing through efficient resource allocation
    SKMI Meenakshisundaram
    Computational Intelligence 36 (4), 1527-1547 , 2020
    2020
    Citations: 56
  • Efficient resource allocation in fog computing using QTCS model
    CL Iyapparaja, M. , Alshammari, N.K. , Kumar, M.S. , Krishnan, S.S.R ...
    Computers, Materials and Continuathis link is disabled 70 (2), 2225-2239 , 2022
    2022
    Citations: 47
  • Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease
    H Dhakhinamoorthy, C. , Mani, S.K. , Mathivanan, S.K. , ... Mallik, S. , Qin
    Mathematics 11 (5), 1136 , 2023
    2023
    Citations: 33
  • A queuing theory model for e-health cloud applications
    MSKMI Raja
    International Journal of Internet Technology and Secured Transactions 10 (5 … , 2020
    2020
    Citations: 30
  • Fog and edge computing simulators systems: research challenges and an overview
    M Kumar, M.S. , Iyapparaja
    International Journal of System of Systems Engineering 11 (3-4), 202–223 , 2021
    2021
    Citations: 24
  • Kidney Stone Prediction Using Neural Network Classifier
    S Pavithra , Sanjurajan , Chitradevi , ... Benitta, A. , Kumar
    2022 2nd International Conference on Advance Computing and Innovative … , 2022
    2022
    Citations: 23
  • FogQSYM: An Industry 4.0 Analytical Model for Fog Applications
    SSGHC M. Iyapparaja,M. Sathish Kumar, S. Siva Rama Krishnan, Chiranji Lal ...
    Computers,Materials & Continua 69 (3), 3164-3178 , 2021
    2021
    Citations: 19
  • Student Placement Prediction Using Supervised Machine Learning
    D Surya, M.S. , Kumar, M.S. , Gandhimathi
    2022 2nd International Conference on Advance Computing and Innovative … , 2022
    2022
    Citations: 18
  • A Review on Utilizing Queuing Models for Improving Performance in Cloud
    MS Kumar, MI Raja
    Journal of Advanced Research in Dynamical and Control Systems 10 (14), 1730-1741 , 2018
    2018
    Citations: 13
  • Bamboo preservation compendium. INBAR-CIBART
    W Liese, S Kumar
    ABS-Technical Report , 2003
    2003
    Citations: 13
  • Bone cancer detection using feature extraction with classification using K-nearest neighbor and decision tree algorithm
    S Kumar, B Sathiyaprasad
    Smart Intell. Comput. Commun. Technol, 347-353 , 2021
    2021
    Citations: 11
  • An Analysis on Barrier Coverage in Wireless Sensor Networks
    JMB Shakila Basheer, Rincy Merlin Mathew, D. Ranjith, M. Sathish Kumar, P. V ...
    Journal of Computational and Theoretical Nanoscience 16, 2599–2603 , 2019
    2019
    Citations: 10
  • A Prediction of Flight Fare Using K-Nearest Neighbors
    S Prasath, S.N. , Kumar M, S. , Eliyas
    2022 2nd International Conference on Advance Computing and Innovative … , 2022
    2022
    Citations: 9
  • A review on performance evaluation techniques in cloud
    MS Kumar, B Balamurugan
    2017 Second International Conference on Recent Trends and Challenges in … , 2017
    2017
    Citations: 5
  • REVIEW OF GAMING AND ITS EVOLUTION OVER NETWORKS
    AR Jayakumar Sadhasivam, Mohan Kubendiran, Prajeesh Tomy, Balajee Jeyakumar ...
    International Journal of Civil Engineering and Technology 8 (11), 0976-6316 , 2017
    2017
    Citations: 5
  • Bamboo preservation compendium. International Network for Bamboo and Rattan, Beijing
    W Liese, S Kumar
    China, Technical report 22 , 2003
    2003
    Citations: 5
  • Rank Fraud and Malware Detection in Google Play Using Fairplay
    S Yugeshwaran, G. , Kumar M, S. , Benitta, D.A. , Eliyas, S. , Rajan R
    2022 2nd International Conference on Advance Computing and Innovative … , 2022
    2022
    Citations: 3
  • Improving Quality of Services of Fog Computing Through Efficient Work Flow Scheduling
    GR Prasanth, MS Kumar, RR Ali, A Ali, HA Sabah, M Al-Tahee
    2023 3rd International Conference on Advance Computing and Innovative … , 2023
    2023
    Citations: 2
  • Fog computing: state-of-art, open issues, challenges and future directions
    M Sathish Kumar, M Iyapparaja
    International Conference on Innovative Computing and Cutting-edge … , 2020
    2020
    Citations: 2
  • Analyzing Financial Data and Mutual Funds Recommendation by Using Big Data Analytics
    PVPS Nithya Sampath1, Jayakumar Sadhasivam, R. Raj Kumar, M. Sathish Kumar ...
    Journal of Computational and Theoretical Nanoscience 16, 2414–2418 , 2019
    2019
    Citations: 2