Srinivasan S

@rmd.ac.in

Professor, Department of Computer Science and Engieering
R.M.D Engineering College

Srinivasan S


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https://researchid.co/shree1980

RESEARCH INTERESTS

Distributed Computing, Cloud Computing, and Machine Learning
27

Scopus Publications

218

Scholar Citations

9

Scholar h-index

7

Scholar i10-index

Scopus Publications

  • Enhanced task distribution and adaptive resource management utilizing spike-induced graph neural networks with optimized offloading mechanisms in fog computing for improved efficiency
    S. V. Juno Bella Gracia, S. Srinivasan
    Cluster Computing, 2026
  • Attention-Residual Multi-Modal Fusion Framework for Crisis Categorization in Social Media Feeds
    S. Sheeba Rachel, S. Srinivasan
    Optical Memory and Neural Networks Information Optics, 2026
    Abstract Crisis categorization in social media feeds perform an important part in modern disaster management and response techniques. With the increasing employ of social platforms as a primary source of information during crises, effective categorization algorithms are essential for quickly and accurately assessing the severity and impact of events. This study introduces the Attention Residual Multi Modal (ARMM) Fusion Framework, which addresses difficulties in MM data processing for damage assessment. For image processing, the system uses Visual Refinement with Feature Forge, which includes Bilateral Filtering for noise reduction and edge preservation, Bicubic Interpolation for upscaling, and Residual Network with Drop Block for detailed and robust image feature extraction. The framework cleans and pre-processes text using an LSTM-Residual with Embedding Network, converting it into compact vector representations, and then uses residual LSTM connections to capture temporal dependencies and maintain feature integrity for robust text feature extraction. Image and text information are then combined and processed using a MM channel attention method, which improves sensitivity to informative features. The proposed method produces outstanding performance metrics, including precision of 98.00%, recall of 94.12%, F1 score of 95.86%, and accuracy of 96.13%. This method efficiently identifies damage severity (severe, medium, or minor) in tweets that include both images and text, leading risk management strategies (rescue, volunteering and contribution) depending on the assessed damage.
  • Exploring the potential and limitations of Fog Computing toward efficient & scalable solutions in modern network
    S V Juno Bella Gracia, S. Srinivasan
    Journal of Integrated Science and Technology, 2026
    Fog computing has emerged as a pivotal paradigm to support latency-sensitive, bandwidth-efficient, and context-aware services across diverse application domains such as healthcare, smart mobility, and industrial Internet of Things (IIoT). Unlike prior reviews that offer broad overviews, this study presents a comparative, standards-driven analysis of fog computing frameworks, uniquely categorizing them into four performance pillars: interoperability, security and privacy, latency and Quality of Service (QoS), and energy efficiency with resource management. By synthesizing recent developments from 2020 to 2024, the review uniquely maps specific frameworks to these standards and evaluates their trade-offs, limitations, and application-specific suitability. The paper further distinguishes itself by integrating advanced strategies such as federated learning, blockchain trust models, deep reinforcement learning, and edge AI and analyzing their implications in fog architectures. Summary tables and architectural illustrations enhance understanding, while key gaps—like power inefficiencies and scalability bottlenecks—are critically discussed. Finally, the review offers targeted future directions, emphasizing the role of edge intelligence, adaptive orchestration, and standardization in building resilient fog-enabled systems. This structured review provides a roadmap for researchers and practitioners aiming to develop scalable, secure, and intelligent fog computing solutions.
  • A novel DLDRM: Deep learning–based flood disaster risk management framework by multimodal social media data
    S. Sheeba Rachel, S. Srinivasan
    Risk Analysis, 2025
    The impacted community and humanitarian organizations have used social media platforms extensively over the past 10 years to disseminate information during a disaster. Even though numerous researches have been conducted in recent times to categorize useful and non‐informational posts on social media, the majority of these studies are unimodal, that is, they separately employed documented or pictorial information to improve deep learning (DL) approaches. In this research, a multimodal DL approach will be created by integrating the complementary data offered by the text and visual Twitter posts made by members of the affected community discussing the same occurrence. For the classification of multimodal disaster data, we suggested a novel DLDRM: DL‐based disaster risk management structure. We contrast DLDRM with the most widely used bilinear multimodal models for visual question answering, including VGG 16, VGG 19, ResNet 50, DenseNet 121, and RegNet Y320. Accuracy, Precision, Recall, and F1‐score were achieved utilizing DLDRM of 99%, 92.5%, 84.08%, and 98.5%. By emphasizing more pertinent aspects of text and image tweets, the proposed DL‐based multimodal technique surpasses the present state‐of‐the‐art fusion technique on the benchmark multimodal disaster dataset.
  • Boosted sooty tern and piranhav foraging meta-heuristic optimized cluster head selection-based routing algorithm for extending network lifetime in WSNs
    R. S. Amshavalli, D. Devi, S. Srinivasan, R ShaliniRajan, S Anitha Jebamani
    Peer to Peer Networking and Applications, 2025
  • A study on AI Ml based data segregation & feature extraction methods of multimodal data during flood disaster
    S. Sheeba Rachel, S. Srinivasan
    Aip Conference Proceedings, 2025
  • Recognition of Identifying Malicious Cyber Attack Intention in Cyber Physical System
    P Vasanthan, S. Srinivasan
    2025 International Conference on Computing and Communication Technologies Iccct 2025, 2025
    As the threat over the cyber-attack is predominantly increasing in every aspects such as based on network traffic, spoofing, phishing attacks the intrusion detection system is used. The network layer is maliciously attacked by the intruder. The intruder tries to capture network packets of the connection between client and server. Based upon the encryption standard, the intruder tries to comprise the user system. Intrusion cannot be easily detected since it is a manual process. In this novel approach, deep learning reinforcement technique is implemented. The bidirectional end to end communication along with the Bayesian inverse reinforcement learning is used to detect the malicious cyber intention in cyber physical system. Inverse Bayesian reinforcement learning technique is initiated to identify the behavior of the pattern recognition and to resample the dataset. This deals with the risk factor and determines whether the connection is comprised. In this work, the novel application is introduced with deep reinforcement learning to identify the intruder. The intrusion detection is labeled in dataset. These dataset implements reward functions to detect the intrusion over the cps.
  • A Innovative Network Security Regulations Dependent on Improved Support Vector Machine from the Outlook of Modern Cities
    Vasanthan P, S. Srinivasan
    2025 International Conference on Computing and Communication Technologies Iccct 2025, 2025
    The concerns about the security created by the PC have gotten more advanced and complicated. Interruption detection is a pragmatic subject in the area of PC security whose essential target is to identify uncommon assault or attacks and to guarantee the safety of inside frameworks. This paper likewise suggests a semi-class interruption recognition strategy that joins various classifiers to mastermind exemptions and regular activities in a PC framework. In the consideration preference tree learning-iterative dichotomy 3, the maltreatment recognition method is developed and is gathered by using the cumulative knowledge based on the peculiarity detection system performed by one class- uphold vector machine. As of late, individuals have paid more thoughtfulness regarding ID/interruption avoidance framework, which is firmly identified with the insurance and use of framework the executives. A couple of AI principles including neural framework, genetic programming, and progressed uphold vector machines, Bayesian framework, multivariate adaptable backslide splines, feathery deduction systems and other analogical frameworks has been scrutinized for the layout of interruption identification framework. In this article, we suggest a combination strategy dependent on DTL-ID3 and OC-SVM assess the presentation of the extended procedure by utilizing a particular dataset and a hybrid technique to upgrade the precision of IDS/IPS when stood out from a solitary help vector machine.
  • Autism Spectrum Disorder Detection using Navie Bayes Tree Technique
    K. Raju, S. Preethi, Srinivasan S, K. Manikandan, N. Ramshankar, A. Pandiaraj
    Proceedings of 5th International Conference on Pervasive Computing and Social Networking Icpcsn 2025, 2025
    Autism Spectrum Disorder (ASD) impacts the social and communicative abilities of individuals of all ages. Autism screening is a time-consuming and complicated procedure, making it difficult to discover autistic individuals. Recently, machine learning techniques have been applied to streamline the screening process and rapidly discover ASD. However, it is necessary to increase the accuracy of predictions using the limited data available. This research provides a novel approach for detecting ASD using the Nave Bayes (NB) Tree. The proposed method generates a decision tree from a data set using the naive Bayes categorizer at the nodes to assess whether or not a person has autism. The performance of the suggested technique is compared to that of the most commonly used classification algorithms, including Nave Bayes, Support Vector Machine (SVM), and Multilayer Perceptron (MLP). The results of the experiments show that the NB Tree-based technique is better than the current classification methods in terms of classification accuracy, Root Mean Squared Error (RMSE), and precision.
  • Optimized RB-RNN: Development of hybrid deep learning for analyzing student's behaviours in online-learning using brain waves and chatbots
    S. Sageengrana, S. Selvakumar, S. Srinivasan
    Expert Systems with Applications, 2024
  • Local search enhanced optimal Inception-ResNet-v2 for classification of long-term lung diseases in post-COVID-19 patients
    Anusha Sanampudi, S. Srinivasan
    Automatika, 2024
  • Performance analysis of study material recommendation system to reduce dropout in online learning using optimal behavior prediction cluster and online poll bot
    Sageengrana S., Selvakumar S., Srinivasan S.
    Interactive Learning Environments, 2024
  • Network Traffic Cyber Attacks Classification using Supervised Machine Learning Techniques
    15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
  • Efficient Fault Detection by Test Case Prioritization via Test Case Selection
    J. Paul Rajasingh, P. Senthil Kumar, S. Srinivasan
    Journal of Electronic Testing Theory and Applications JETTA, 2023
  • DAuth—Delegated Authorization Framework for Secured Serverless Cloud Computing
    P. Padma, S. Srinivasan
    Wireless Personal Communications, 2023
  • Performance Evaluation of Classifiers for non Communicable Diseases
    S. Srinivasan, S. Selvakumar, Pacha Shobha Rani, D. JayaKumar
    Aip Conference Proceedings, 2023
  • An incremental approach for detecting distributed deadlocks in the generalized model
    Srinivasan Selvaraj
    Computing, 2022
  • AGS: A precise and efficient AI-based hybrid software effort estimation model
    V. Vignaraj Ananth, S. Srinivasan
    International Journal of Business Intelligence and Data Mining, 2021
  • A Blockchain Based Online Voting System: An Indian Scenario
    Srinivasan Selvaraj, P. Shobha Rani, A. Gnanasekar, Vignaraj Anand
    Communications in Computer and Information Science, 2021
  • Comparative study of neural network and tree-based models in solar irradiance prediction
    N. Anbuchezhian, S. Srinivasan, T. Velmurugan, G. Suganya Priyadharshini, R. Krishnamoorthy
    International Review of Mechanical Engineering, 2021
  • Resource Allocation in Cloud Computing Using SFLA and Cuckoo Search Hybridization
    P. Durgadevi, S. Srinivasan
    International Journal of Parallel Programming, 2020
  • A survey on biometric based authentication in cloud computing
    P. Padma, S. Srinivasan
    Proceedings of the International Conference on Inventive Computation Technologies Icict 2016, 2016
  • Energy aware data center using dynamic consolidation techniques: A survey
    E. S. Madhan, S. Srinivasan
    Proceedings of Icccs 2014 IEEE International Conference on Computer Communication and Systems, 2014
  • An improved, centralised algorithm for detection and resolution of distributed deadlock in the generalised model
    S. Srinivasan, R. Rajaram
    International Journal of Parallel Emergent and Distributed Systems, 2012
  • Message-optimal algorithm for detection and resolution of generalized deadlocks in distributed systems
    Informatica Ljubljana, 2011
  • A decentralized deadlock detection and resolution algorithm for generalized model in distributed systems
    Selvaraj Srinivasan, R. Rajaram
    Distributed and Parallel Databases, 2011
  • An optimal, distributed deadlock detection and resolution algorithm for generalized model in distributed systems
    S. Srinivasan, Rajan Vidya, Ramasamy Rajaram
    Communications in Computer and Information Science, 2009

RECENT SCHOLAR PUBLICATIONS

  • A novel DLDRM: Deep learning–based flood disaster risk management framework by multimodal social media data
    SS Rachel, S Srinivasan
    Risk Analysis 45 (10), 3256-3275 , 2025
    2025
    Citations: 2
  • Boosted sooty tern and piranhav foraging meta-heuristic optimized cluster head selection-based routing algorithm for extending network lifetime in WSNs
    RS Amshavalli, D Devi, S Srinivasan, R ShaliniRajan, SA Jebamani
    Peer-to-Peer Networking and Applications 18 (2), 66 , 2025
    2025
    Citations: 9
  • A study on AI Ml based data segregation & feature extraction methods of multimodal data during flood disaster
    SS Rachel, S Srinivasan
    AIP Conference Proceedings 3162 (1), 020018 , 2025
    2025
  • Performance analysis of study material recommendation system to reduce dropout in online learning using optimal behavior prediction cluster and online poll bot
    S Sageengrana, S Selvakumar, S Srinivasan
    Interactive Learning Environments 32 (9), 5779-5800 , 2024
    2024
  • Identification of Acute Myocardial Infarction from Left Ventricular Wall Rupture Using ResNet 18-Deep Active Learning Algorithms
    R Periyasamy, S Selvaraj
    Traitement du Signal 41 (5), 2495-2505 , 2024
    2024
  • Optimized RB-RNN: Development of hybrid deep learning for analyzing student’s behaviours in online-learning using brain waves and chatbots
    S Sageengrana, S Selvakumar, S Srinivasan
    Expert Systems with Applications 248, 123267 , 2024
    2024
    Citations: 14
  • Network Traffic Cyber Attacks Classification using Supervised Machine Learning Techniques.
    JG Priya, S Srinivasan, C Priyanka, D Harini
    Grenze International Journal of Engineering & Technology (GIJET) 10 , 2024
    2024
  • Efficient fault detection by test case prioritization via test case selection
    JP Rajasingh, PS Kumar, S Srinivasan
    Journal of Electronic Testing 39 (5), 659-677 , 2023
    2023
    Citations: 7
  • Conversion of Waste Face Mask into Carbonized Functional Materials for Environmental Applications
    S Srinivasan, RS Karmukhilnilavan, A Selvam, JWC Wong, K Murugesan
    Solid Waste 2023, 95 , 2023
    2023
  • DAuth—Delegated Authorization Framework for Secured Serverless Cloud Computing
    P Padma, S Srinivasan
    Wireless Personal Communications 129 (3), 1563-1583 , 2023
    2023
    Citations: 11
  • Performance evaluation of classifiers for non communicable diseases
    S Srinivasan, S Selvakumar, PS Rani, D JayaKumar
    AIP Conference Proceedings 2523 (1), 020035 , 2023
    2023
  • An incremental approach for detecting distributed deadlocks in the generalized model
    S Srinivasan
    Computing. Archives for Informatics and Numerical Computation 104 (1), 149-168 , 2022
    2022
    Citations: 2
  • AGS: a precise and efficient AI-based hybrid software effort estimation model
    VV Ananth, S Srinivasan
    International Journal of Business Intelligence and Data Mining 18 (1), 1-16 , 2021
    2021
    Citations: 2
  • Modified K-Nearest Neighbor Algorithm for Noisy Data Set
    SRK S.Srinivasan, P.V Rishi Kiran, N.Krishna Teja
    Annals of the Romanian Society for Cell Biology 25 (4), 16426 – 16433 , 2021
    2021
  • An Eminent Spam Noticing Methodology for IOT Gadgets Using ML Techniques
    KYP D. Jayakumar, S. Srinivasan, G. Meghana, B. Sai Harika
    Revista geintec-gestao inovacao e Technologies 11 (2), 2156-2166 , 2021
    2021
    Citations: 2
  • Application of Machine Learning on Crop Yield Prediction in Agriculture Enforcement
    NS D. Jayakumar, S. Srinivasan ,P. Prithi, Sreelekha Vemula
    Revista geintec-gestao inovacao e Technologies 11 (2), 2142-2154 , 2021
    2021
    Citations: 6
  • Secure Storage of Electronic Health Records on Cloud Using Integrity Verification Auditing
    KDS S. Srinivasan, Kethineni Keerthi, Gummadi Tejaswi
    Revista geintec-gestao inovacao e Technologies 11 (2), 2132– 2141 , 2021
    2021
  • Online Voting Using Blockchain Technology
    IJI Dr.S.Srinivasan, R.Lavanya, C.Lakshmi, S.Malini,” Online Voting Using ...
    International Journal of Innovative Research in Science, Engineering and … , 2020
    2020
  • A Survey on Effort Estimation Techniques in Agile Software Development
    VVV Dr.Srinivasan Selvaraj, Dr.V.Vignaraj Anand, Bhuvaneshwari.M
    International Journal of Interdisciplinary Global Studies 14 (4), 91-95 , 2020
    2020
    Citations: 2
  • Cluster Based Regression Method For Software Effort Estimation
    MB Dr V.Vignaraj Ananth, Dr S.Srinivasan
    Solid State Technology 63 (6), 1696- 1707 , 2020
    2020

MOST CITED SCHOLAR PUBLICATIONS

  • A survey on biometric based authentication in cloud computing
    P Padma, S Srinivasan
    Inventive Computation Technologies (ICICT), International Conference on 1, 1-5 , 2016
    2016
    Citations: 43
  • Resource allocation in Cloud computing using SFLA and Cuckoo search hybridization
    P Durgadevi, S Srinivasan
    International Journal of Parallel Programming 48 (3), 549-565 , 2018
    2018
    Citations: 40
  • A decentralized deadlock detection and resolution algorithm for generalized model in distributed systems
    S Srinivasan, R Rajaram
    Distributed and Parallel Databases 29 (4), 261-276 , 2011
    2011
    Citations: 31
  • Optimized RB-RNN: Development of hybrid deep learning for analyzing student’s behaviours in online-learning using brain waves and chatbots
    S Sageengrana, S Selvakumar, S Srinivasan
    Expert Systems with Applications 248, 123267 , 2024
    2024
    Citations: 14
  • DAuth—Delegated Authorization Framework for Secured Serverless Cloud Computing
    P Padma, S Srinivasan
    Wireless Personal Communications 129 (3), 1563-1583 , 2023
    2023
    Citations: 11
  • An efficient detection and resolution of generalized deadlocks in distributed systems
    S Selvaraj, R Ramasamy
    International Journal of Computer Applications 1 (19), 1-7 , 2010
    2010
    Citations: 11
  • Task scheduling using amalgamation of metaheuristics swarm optimization algorithm and cuckoo search in cloud computing environment
    P Durgadevi, DS Srinivasan
    Journal for Research 1 (9) , 2015
    2015
    Citations: 10
  • Boosted sooty tern and piranhav foraging meta-heuristic optimized cluster head selection-based routing algorithm for extending network lifetime in WSNs
    RS Amshavalli, D Devi, S Srinivasan, R ShaliniRajan, SA Jebamani
    Peer-to-Peer Networking and Applications 18 (2), 66 , 2025
    2025
    Citations: 9
  • Ant Colony Optimization Algorithm forScheduling Cloud Tasks
    JJ Srinivasan Selvaraj
    International Journal of Computer Technology & Applications 7 (3), 491-494 , 2016
    2016
    Citations: 9
  • Efficient fault detection by test case prioritization via test case selection
    JP Rajasingh, PS Kumar, S Srinivasan
    Journal of Electronic Testing 39 (5), 659-677 , 2023
    2023
    Citations: 7
  • An improved, centralised algorithm for detection and resolution of distributed deadlock in the generalised model
    S Srinivasan, R Rajaram
    International Journal of Parallel, Emergent and Distributed Systems 27 (3 … , 2012
    2012
    Citations: 7
  • Application of Machine Learning on Crop Yield Prediction in Agriculture Enforcement
    NS D. Jayakumar, S. Srinivasan ,P. Prithi, Sreelekha Vemula
    Revista geintec-gestao inovacao e Technologies 11 (2), 2142-2154 , 2021
    2021
    Citations: 6
  • Mobile Controlled Automated wheelchair for Disabilities
    DPE Srinivasan Selvaraj, A.Ganasekar, Pacha Shobha Rani
    International Journal of Innovative Technology and Exploring Engineering 9 … , 2019
    2019
    Citations: 6
  • A novel DLDRM: Deep learning–based flood disaster risk management framework by multimodal social media data
    SS Rachel, S Srinivasan
    Risk Analysis 45 (10), 3256-3275 , 2025
    2025
    Citations: 2
  • An incremental approach for detecting distributed deadlocks in the generalized model
    S Srinivasan
    Computing. Archives for Informatics and Numerical Computation 104 (1), 149-168 , 2022
    2022
    Citations: 2
  • AGS: a precise and efficient AI-based hybrid software effort estimation model
    VV Ananth, S Srinivasan
    International Journal of Business Intelligence and Data Mining 18 (1), 1-16 , 2021
    2021
    Citations: 2
  • An Eminent Spam Noticing Methodology for IOT Gadgets Using ML Techniques
    KYP D. Jayakumar, S. Srinivasan, G. Meghana, B. Sai Harika
    Revista geintec-gestao inovacao e Technologies 11 (2), 2156-2166 , 2021
    2021
    Citations: 2
  • A Survey on Effort Estimation Techniques in Agile Software Development
    VVV Dr.Srinivasan Selvaraj, Dr.V.Vignaraj Anand, Bhuvaneshwari.M
    International Journal of Interdisciplinary Global Studies 14 (4), 91-95 , 2020
    2020
    Citations: 2
  • An Optimal, Distributed Deadlock Detection and Resolution Algorithm for Generalized Model in Distributed Systems
    S Srinivasan, R Vidya, R Rajaram
    International Conference on Contemporary Computing, 70-80 , 2009
    2009
    Citations: 2
  • In vitro Callus Regeneration and Biochemical Analysis in the Medicinal Plant Phyllanthus niruri L.
    JJ Jeyakumar, S Srinivasan
    British Biomedical Bulletin , 2013
    2013
    Citations: 1