Baskaran P

@vit.ac.in

Senior Assistant Professor, School of Computer Science and Engineering

Baskaran P


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https://researchid.co/baskarcse06

EDUCATION

PhD - infomation and Communication Engineering
22

Scopus Publications

111

Scholar Citations

6

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • A causal multimodal framework for privacy-preserving early-stage cancer detection and adaptive testing
    S. Sivaprakash, P. Baskaran
    Scientific Reports, 2026
    Early detection of cancer at stage I is critical for improving survival rates, yet existing diagnostic tools often face trade-offs between sensitivity, specificity, and clinical scalability. While liquid biopsies, radiomics, and breathomics independently offer promise, their isolated use struggles with robustness, leading to false positives or missed early lesions. To overcome these challenges, this research proposes CausaLMED, a causal multimodal framework that integrates cfDNA fragmentomics, exhaled breathomics, imaging radiomics, and digital pathology embeddings through a causal graph-based fusion mechanism. Unlike conventional ensemble models, CausaLMED explicitly disentangles causal dependencies across modalities, thereby reducing bias from confounders such as lifestyle factors, imaging vendor variability, and population heterogeneity. The framework incorporates an uncertainty-aware adaptive testing policy, which dynamically selects the next diagnostic modality using a partially observable Markov decision process, ensuring cost-effectiveness while minimizing patient burden. Federated learning with differential privacy safeguards institutional data sharing, enabling large-scale, secure model training. Experimental validation on retrospective multimodal datasets demonstrates that CausaLMED achieves a 96.7% accuracy, 94.2% sensitivity for stage I cancers, and maintains 99.1% specificity, significantly outperforming single-modality baselines by over 8%. Moreover, the adaptive testing policy reduces unnecessary imaging referrals by 23%, highlighting both efficiency and clinical practicality. By unifying causal learning, adaptive diagnostics, and privacy-preserving collaboration, CausaLMED presents a transformative paradigm for clinically viable early-stage cancer detection.
  • PCANN: Principal Convolutional Analysis Neural Network for Block Chain based Diabetic Retinopathy Detection
    Somasundaram Krishnamoorthy, Sivakumar Paulraj, Baskaran Periyasamy, Arun Kumar Ramamoorthy
    Current Eye Research, 2026
    PURPOSE: Diabetic retinopathy is an ophthalmic disease that impairs the retinal blood vessels. Diabetic retinopathy can lead to blindness when it is not examined in earlier phases. Adversely, the accurate diabetic retinopathy recognition phase is prominently complicated and needs experienced human analysis of fundus images. Blockchain technology helps share data by allowing users to select what information to share and control who can access it, which is important for managing electronic health records in healthcare sector. Nevertheless, the privacy of user data is compromised due to the training data, which is revealed to unauthorized users. METHODS: In this work, a superior module for diabetic retinopathy classification based on Blockchain using principal convolutional analysis neural network is designed. Here, the simulation of Blockchain is carried out. Here, the input image is pre-processed using the Gaussian filter. LadderNet is deployed for lesion segmentation, and the segmentation of blood vessel is done using the Sine-Net model. Moreover, feature extraction is performed with the input image, lesion-segmented image, and blood vessel-segmented image. Finally, diabetic retinopathy classification is executed utilizing the proposed principal convolutional analysis neural network, which is classified into normal, mild non-proliferative diabetic retinopathy, moderate non-proliferative diabetic retinopathy, severe non-proliferative diabetic retinopathy, and proliferative. RESULTS: The Blockchain enabled principal convolutional analysis neural network obtains superior values of 90.9%, 91.9%, 92.5%, 89.4%, 88.4%, and 75.5% in terms of metrics like accuracy, true positive rate, true negative rate, positive predictive value, negative predictive value, and Mathews correlation coefficient. CONCLUSION: The integration of principal convolutional analysis neural network with Blockchain enhances data integrity and patient privacy, making it a promising solution for early diagnosis and treatment. Also, this approach ensures accurate and efficient detection of diabetic retinopathy.
  • Hybrid CNN-GNN Framework for Enhanced Optimization and Performance Analysis of Frequency-Selective Surface Antennas
    SatheeshKumar Palanisamy, Sathya Karunanithi, Baskaran Periyasamy, Srithar Samidurai, Ayodeji Olalekan Salau
    International Journal of Communication Systems, 2025
    Frequency‐selective surface (FSS) antennas are critical in modern communication systems, where optimizing their design for enhanced performance is essential. However, traditional methods often struggle with the complexity of FSS structures, leading to suboptimal designs. This paper addresses these limitations by proposing a novel CNN‐GNN hybrid network (CGHN) framework for FSS antenna optimization. The proposed methodology integrates convolutional neural networks (CNNs) for efficient feature extraction of spatial patterns within FSS designs and graph neural networks (GNNs) to model the relational dependencies between unit cells. This approach ensures that both local features and global interactions are captured, leading to more accurate and optimized antenna designs. The objective is to enhance the performance of FSS antennas by leveraging the complementary strengths of CNNs and GNNs, with an emphasis on improving design accuracy and efficiency. The novelty lies in the combination of CNN's localized pattern recognition with GNN's relational learning, which together enable a comprehensive understanding of the antenna's behavior. The proposed CGHN framework achieves a 96.78% accuracy rate in predicting optimal FSS designs, with a 23.84% boost in performance due to CNN‐driven feature extraction. Additionally, implementing stochastic gradient descent with gradient clipping increased the F1 score by 15%. Compared with existing techniques, the proposed method demonstrates significant improvements in both accuracy and efficiency, making it a superior choice for FSS antenna design optimization.
  • MediHealth: An AI-Driven Mobile Solution for Enhanced Health Care Decision-Making and Accessibility
    Sukrutha Raj, Shashank Shekhar, Baskaran. P
    3rd International Conference on Intelligent Data Communication Technologies and Internet of Things Idciot 2025, 2025
    Mobile health (mHealth) applications have revolutionized healthcare by enhancing patient engagement, providing tools for self-management, and supporting better health outcomes. This paper explores the development of an innovative mHealth application designed to empower patients in making informed healthcare decisions. The application leverages advanced features such as AI powered chatbots, personalized healthcare recommendations, and a report and prescription summarizer to simplify complex medical information. By allowing users to easily find appropriate healthcare professionals, access detailed prescription data, and receive symptom-based specialist recommendations, the app facilitates effective self-management of chronic conditions. Furthermore, it offers proactive care prompts, personalized advice, and health behavior reminders to promote better overall health outcomes. Despite challenges in data security, privacy, and equitable access, the study emphasizes the importance of a user-centered design approach in ensuring the widespread adoption and success of mHealth applications. Ultimately, the goal is to enhance healthcare delivery by improving patient engagement, facilitating seamless access to healthcare resources, and enabling well-informed decision-making. This application presents a holistic solution for advancing patient care across diverse healthcare settings.
  • Enhancing Plant Disease Detection Using Attention-Augmented Residual Networks and Faster Region-Convolutional Networks
    K. Sathya, Arunkumar Balakrishnan, P. Baskaran, Arun Kumar Ramamoorthy
    IEEE Access, 2025
    Rapid and accurate detection of plant diseases is crucial for agricultural productivity and food security. Traditional methods are labor-intensive and often unreliable. To overcome these limitations, this research introduces an innovative approach that integrates attention mechanisms into residual networks (ResNets) and utilizes Generative Adversarial Networks (GANs) for data augmentation. The method incorporates Attention-Augmented Residual Networks (AARN), which enhance feature extraction and classification by focusing on critical image regions. A Conditional GAN (cGAN) generates synthetic images of diseased and healthy plants, increasing dataset diversity. By combining AARN with Faster Region-Convolutional Neural Network (Faster-RCNN), detection capabilities are further enhanced. Training the AARN model on this augmented dataset improves generalization, achieving an impressive 98.78% accuracy in plant disease classification. The attention-augmented residuals boost the Faster-RCNN’s effectiveness by 23.84%, improving feature relevance and reducing overfitting. Comparative analysis shows that this method outperforms existing techniques in accuracy, precision, recall, and F1-score, offering a robust solution for plant disease detection. This integration of advanced deep learning techniques significantly improves automated plant disease identification, benefiting agricultural management practices.
  • Herbguard: An Ensemble Deep Learning Framework With Efficientnet and Vision Transformers for Fine-Grained Classification of Medicinal and Poisonous Plants
    Amrit Baskota, Shubham Ghimire, Abiskar Ghimire, P. Baskaran
    IEEE Access, 2025
    Classifying whether a plant is herbal or poisonous is a significant challenge for trekkers, hikers, and nature enthusiasts, particularly in remote areas where several unfamiliar plant species are encountered. Consumption or even close contact to some poisonous plants species may lead to serious health risks, highlighting the need of an intelligent and real-time classification system. In this study, we an approach based on deep-learning based to classify plants into several species under herbal and poisonous categories. The system employs Convolutional Neural Network (CNN) based EfficientNetV2-S, which is optimized for local feature extraction, and transformer-based ViT-Tiny, which is capable of capturing global dependencies across images. Both models are trained using a two-stage fine-tuning strategy with label smoothing, MixUp, and CutMix augmentations. Preprocessing steps include CLAHE-based contrast enhancement, HSV masking and GrabCut segmentation, that are applied to training images focus on relevant plant regions. The models are evaluated on 48 different plant species, consisting of 40 herbal and 8 poisonous species ultimately achieving species-level accuracies of 95.86% (EfficientNet) and 96.69% (ViT) on validation dataset. A soft-voting ensemble of the two models further improves species-level accuracy to 97.10% upon validation and 98.43% upon testing, while category-level accuracy remains consistently above 99.7% for all the models. These results demonstrate that combining convolutional and transformer-based approaches leads to a robust, highly accurate classification system that is capable of distinguishing varieties of medicinal and poisonous plants, offering a practical tool for safe-trekking, biodiversity monitoring, and herbal medicine research.
  • Enhancing Agricultural Decision Making with Machine Learning
    Baskaran P, Deepesh, Dhruv, Devender
    Proceedings 2025 5th International Conference on Expert Clouds and Applications Icoeca 2025, 2025
    Agriculture is the backbone of the Indian economy, but the farmers are unable to pick the right crop because of multi-dimensional environmental conditions such as soil type, climate, and market demand. This research solves this issue by developing an intelligent crop suggestion system using machine learning to provide precise, data-driven crop suggestions. The system takes into account variables such as soil fertility, weather, and market demand to maximize productivity and minimize losses. The system used various machine learning algorithms such as KNN, Decision Trees, Random Forest, and Naive Bayes, and Random Forest gave the best accuracy of 99.89% for a test sample of 9,240 samples out of a total of 46,200 records. The model delivered high precision, recall, and F1-scores for 22 types of crops to make consistent recommendations with fluctuating climatic conditions. Ensemble learning was also used to enhance accuracy by taking multiple algorithmic outputs and using them to make more precise decisions. A web application was developed with six key features: AI-based crop prediction, data analytics, community support, multilingual support, best agriculture practices, and crop analytics. Multilingual support allows accessibility by farmers with different linguistic backgrounds, and the community feature allows knowledge sharing and collaboration. The outcome of this research is a viable and scalable solution empowering Indian farmers with technology-driven knowledge to maximize crop selection, enhance sustainability, and enhance rural economic growth. By bridging the gap between advanced machine learning techniques and real-world agriculture needs, the system offers a data-driven solution to enhance farm productivity and enhance informed decision-making in modern agriculture.
  • Virtualized Deployment of Predictive Analytics in the Automotive Sector: Auto-scaling for containerized applications
    Navadhesh.M, Nivitha K, Muthunagai.S. U, Sivaprakash S, Baskaran P
    Proceedings of the 4th International Conference on Innovative Mechanisms for Industry Applications Icimia 2025, 2025
    This paper focuses on developing a predictive analytics application tailored for the automotive industry. The primary aim is to harness vast quantities of data from vehicle operations and consumer behaviors to enhance strategic decision-making and market competitiveness. Utilizing the ’quikr-cars-scraped’ dataset, the application employs advanced machine learning models to analyze vehicle specifications and market trends, providing actionable insights into car pricing, carbon emissions, launch year, and usage patterns. The proposed architecture is built for scalability and easy deployment, leveraging containerization with Docker to ensure consistent performance across various computing environments. A multi-model machine learning pipeline is integrated, featuring algorithms such as Linear Regression, Decision Trees, Random Forest, and Support Vector Machines, each chosen for its efficacy in handling specific types of predictive tasks. The Flask web application serves as the user interface, allowing real-time data interaction and retrieval of predictions, facilitating an intuitive user experience. The implementation involves preprocessing data to ensure accuracy, developing and testing individual predictive models, and deploying these models in a structured pipeline within the Flask application. The final product demonstrates significant potential to transform automotive data into strategic assets, driving innovations and operational efficiencies in the automotive sector.
  • Smart COVIDNet: designing an IoT-based COVID-19 disease prediction framework using attentive and adaptive-derived ensemble deep learning
    D. Karthikeyan, P. Baskaran, S. K. Somasundaram, K. Sathya, S. Srithar
    Knowledge and Information Systems, 2024
  • RETRACTION:Deep iterative fuzzy pooling in unmanned robotics and autonomous systems for Cyber-Physical systems
    V. Ravindra Krishna Chandar, P. Baskaran, G. Mohanraj, D. Karthikeyan
    Journal of Intelligent and Fuzzy Systems, 2024
    Unmanned robotics and autonomous systems (URAS) are integral components of contemporary Cyber-Physical Systems (CPS), allowing vast applications across many domains. However, due to uncertainties and ambiguous data in real-world environments, ensuring robust and efficient decision-making in URAS is difficult. By capturing and reasoning with linguistic data, fuzzy logic has emerged as a potent tool for addressing such uncertainties. Deep Iterative Fuzzy Pooling (DIFP) is a novel method proposed in this paper for improving decision-making in URAS within CPS. The DIFP integrates the capabilities of deep learning and fuzzy logic to effectively pool and aggregate information from multiple sources, thereby facilitating more precise and trustworthy decision-making. This research presents the architecture and operational principles of DIFP and demonstrates its efficacy in various URAS scenarios through extensive simulations and experiments. The proposed method demonstrated a high-performance level, with an accuracy of 98.86%, precision of 95.30%, recall of 97.32%, F score of 96.26%, and a notably low false positive rate of 4.17%. The results show that DIFP substantially improves decision-making performance relative to conventional methods, making it a promising technique for enhancing the autonomy and dependability of URAS in CPS.
  • An Improved Red Panda Optimizer for Effective Construction Site Design
    Malathy N, Sathya K, Baskaran P, Somasundaram SK
    International Research Journal of Multidisciplinary Scope, 2024
  • Biomedical Named Entity Recognition Using Scikit-Learn And Keras
    P. Baskaran, Somasundaram SK, Thirunavukarasan M, Sathya K
    2023 International Conference on Computer Science and Emerging Technologies Cset 2023, 2023
  • An integrated model for energy conservation in IoT-enabled WSN using adaptive regional clustering and monkey inspired optimization
    P. Baskaran, K. Karuppasamy
    Journal of Intelligent and Fuzzy Systems, 2022
  • COVID-19 Regulation Analysis Using Deep Learning
    P. Baskaran, R. Rengarajan, S. Naveen Kumar, V. Vijay
    Lecture Notes in Networks and Systems, 2022
  • Secured Cloud Data Outsourcing Model Using Two Party Integrity Scheme
    S Ahamed Ali, Y Justindhas, M Lakshmanan, P Hari Kumar, P Baskaran
    2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2022, 2022
  • Retraction: Improved Haar Cascade Feature Extraction and Access Control Framework for Rich Internet Applications
    S Srithar, I Mettildha Mary, P Baskaran, T Maheswaran
    Journal of Physics Conference Series, 2021
  • Hybrid teaching learning approach for improving network lifetime in wireless sensor networks
    P. Baskaran, K. Karuppasamy
    Computers Materials and Continua, 2021
  • DMRM: Enhanced QoS using Dynamic Multipath Routing for MANETS
    Baskaran P, K. Karuppasamy
    Proceedings 2nd International Conference on Smart Electronics and Communication Icosec 2021, 2021
  • InceptionResNetV2 for Plant Leaf Disease Classification
    M Naveenkumar, S Srithar, B Rajesh Kumar, S Alagumuthukrishnan, P Baskaran
    Proceedings of the 5th International Conference on I Smac Iot in Social Mobile Analytics and Cloud I Smac 2021, 2021
  • Smart recognition and apprise system with IoT
    International Journal of Control and Automation, 2020
  • Time slot based data sharing with time slot based password
    P. Baskaran, E G. Anurag, S. Kaviyarasu, A V Eniyavan, M. Karthikeyan
    Proceedings of 2017 International Conference on Innovations in Information Embedded and Communication Systems Iciiecs 2017, 2017
  • Implementation of mobile application for medication using android
    International Journal of Applied Engineering Research, 2015

RECENT SCHOLAR PUBLICATIONS

  • A causal multimodal framework for privacy-preserving early-stage cancer detection and adaptive testing
    S Sivaprakash, P Baskaran
    Scientific Reports , 2026
    2026
  • PCANN: Principal Convolutional Analysis Neural Network for Block Chain based Diabetic Retinopathy Detection
    S Krishnamoorthy, S Paulraj, B Periyasamy, AK Ramamoorthy
    Current Eye Research 51 (3), 285-296 , 2026
    2026
  • Hybrid GAN Model with LSTM-Combined ResNet Discriminator for COVID-19 Classification in CT Images
    S Krishnamoorthy, S Paulraj, S Sriram, B Periyasamy, S Eswaran, ...
    Decision Sciences in Bioinformatics, 58-78 , 2026
    2026
  • Herbguard: An Ensemble Deep Learning Framework With Efficientnet and Vision Transformers for Fine-Grained Classification of Medicinal and Poisonous Plants
    A Baskota, S Ghimire, A Ghimire, P Baskaran
    IEEE Access , 2025
    2025
    Citations: 2
  • Virtualized Deployment of Predictive Analytics in the Automotive Sector: Auto-scaling for containerized applications
    K Nivitha, S Sivaprakash, P Baskaran
    2025 4th International Conference on Innovative Mechanisms for Industry … , 2025
    2025
  • MediHealth: An AI-Driven Mobile Solution for Enhanced Health Care Decision-Making and Accessibility
    S Raj, S Shekhar, B P
    2025 3rd International Conference on Intelligent Data Communication … , 2025
    2025
    Citations: 2
  • Enhancing plant disease detection using Attention-Augmented residual networks and faster Region–Convolutional networks
    K Sathya, A Balakrishnan, P Baskaran, AK Ramamoorthy
    IEEE Access 13, 48625-48642 , 2025
    2025
    Citations: 13
  • Enhancing Agricultural Decision Making with Machine Learning
    P Baskaran
    2025 5th International Conference on Expert Clouds and Applications (ICOECA … , 2025
    2025
  • Hybrid CNN‐GNN Framework for Enhanced Optimization and Performance Analysis of Frequency‐Selective Surface Antennas
    SK Palanisamy, S Karunanithi, B Periyasamy, S Samidurai, AO Salau
    International Journal of Communication Systems 38 (3), e6105 , 2025
    2025
    Citations: 14
  • Smart COVIDNet: designing an IoT-based COVID-19 disease prediction framework using attentive and adaptive-derived ensemble deep learning
    D Karthikeyan, P Baskaran, SK Somasundaram, K Sathya, S Srithar
    Knowledge and Information Systems 66 (4), 2269-2305 , 2024
    2024
    Citations: 3
  • Deep iterative fuzzy pooling in unmanned robotics and autonomous systems for Cyber-Physical systems
    V Ravindra Krishna Chandar, P Baskaran, G Mohanraj, D Karthikeyan
    Journal of Intelligent & Fuzzy Systems 46 (no. 2), 4621-4639 , 2024
    2024
    Citations: 5
  • An Improved Red Panda Optimizer for Effective Construction Site Design
    SSK Malathy N*, Sathya K, Baskaran P
    International Research Journal of Multidisciplinary Scope 5 (1), 568-581 , 2024
    2024
  • Biomedical Named Entity Recognition Using Scikit-Learn And Keras
    P Baskaran, SK Somasundaram, M Thirunavukarasan, K Sathya
    2023 International Conference on Computer Science and Emerging Technologies … , 2023
    2023
    Citations: 1
  • An integrated model for energy conservation in IoT-enabled WSN using adaptive regional clustering and monkey inspired optimization
    P Baskaran, K Karuppasamy
    Journal of Intelligent & Fuzzy Systems 43 (4), 4961-4974 , 2022
    2022
    Citations: 2
  • Secured cloud data outsourcing model using two party integrity scheme
    SA Ali, Y Justindhas, M Lakshmanan, PH Kumar, P Baskaran
    2022 2nd International Conference on Advance Computing and Innovative … , 2022
    2022
    Citations: 20
  • COVID-19 Regulation Analysis Using Deep Learning
    P Baskaran, R Rengarajan, S Naveen Kumar, V Vijay
    Advances in Computational Intelligence and Communication Technology … , 2022
    2022
  • InceptionResNetV2 for plant leaf disease classification
    M Naveenkumar, S Srithar, BR Kumar, S Alagumuthukrishnan, ...
    2021 fifth international conference on I-SMAC (IoT in social, Mobile … , 2021
    2021
    Citations: 22
  • DMRM: Enhanced QoS using Dynamic Multipath Routing for MANETS
    P Baskaran, K Karuppasamy
    2021 2nd International Conference on Smart Electronics and Communication … , 2021
    2021
    Citations: 3
  • Inshore and Offshore Ship Detection and Categorization System using Mask R-CNN
    BP Lakshmanan M, Nithish Kumar V N, Rahul R, Rohith S
    Design Engineering 2021 (08), 529-541 , 2021
    2021
    Citations: 1
  • Hybrid Teaching Learning Approach for Improving Network Lifetime in Wireless Sensor Networks
    KK P. Baskaran1
    Computer,Materials & Continua 70 (1), 1975-1992 , 2021
    2021
    Citations: 6

MOST CITED SCHOLAR PUBLICATIONS

  • InceptionResNetV2 for plant leaf disease classification
    M Naveenkumar, S Srithar, BR Kumar, S Alagumuthukrishnan, ...
    2021 fifth international conference on I-SMAC (IoT in social, Mobile … , 2021
    2021
    Citations: 22
  • Secured cloud data outsourcing model using two party integrity scheme
    SA Ali, Y Justindhas, M Lakshmanan, PH Kumar, P Baskaran
    2022 2nd International Conference on Advance Computing and Innovative … , 2022
    2022
    Citations: 20
  • Hybrid CNN‐GNN Framework for Enhanced Optimization and Performance Analysis of Frequency‐Selective Surface Antennas
    SK Palanisamy, S Karunanithi, B Periyasamy, S Samidurai, AO Salau
    International Journal of Communication Systems 38 (3), e6105 , 2025
    2025
    Citations: 14
  • Enhancing plant disease detection using Attention-Augmented residual networks and faster Region–Convolutional networks
    K Sathya, A Balakrishnan, P Baskaran, AK Ramamoorthy
    IEEE Access 13, 48625-48642 , 2025
    2025
    Citations: 13
  • Hybrid Teaching Learning Approach for Improving Network Lifetime in Wireless Sensor Networks
    KK P. Baskaran1
    Computer,Materials & Continua 70 (1), 1975-1992 , 2021
    2021
    Citations: 6
  • loT based water Quality Monitoring System using Machine Learning
    P Baskaran, D Selvapandian, DJ Immanuel, RM Bhavadharini
    International Journal of recent Technology and Engineering (IJRTE) 8 (4) , 2019
    2019
    Citations: 6
  • Time slot based data sharing with time slot based password
    P Baskaran, EG Anurag, S Kaviyarasu, AV Eniyavan, M Karthikeyan
    2017 International Conference on Innovations in Information, Embedded and … , 2017
    2017
    Citations: 6
  • Deep iterative fuzzy pooling in unmanned robotics and autonomous systems for Cyber-Physical systems
    V Ravindra Krishna Chandar, P Baskaran, G Mohanraj, D Karthikeyan
    Journal of Intelligent & Fuzzy Systems 46 (no. 2), 4621-4639 , 2024
    2024
    Citations: 5
  • Improved Haar cascade feature extraction and access control framework for rich internet applications
    S Srithar, I Mettildha Mary, P Baskaran, T Maheswaran
    Journal of Physics: Conference Series 1916 (1), 012019 , 2021
    2021
    Citations: 4
  • Smart COVIDNet: designing an IoT-based COVID-19 disease prediction framework using attentive and adaptive-derived ensemble deep learning
    D Karthikeyan, P Baskaran, SK Somasundaram, K Sathya, S Srithar
    Knowledge and Information Systems 66 (4), 2269-2305 , 2024
    2024
    Citations: 3
  • DMRM: Enhanced QoS using Dynamic Multipath Routing for MANETS
    P Baskaran, K Karuppasamy
    2021 2nd International Conference on Smart Electronics and Communication … , 2021
    2021
    Citations: 3
  • Herbguard: An Ensemble Deep Learning Framework With Efficientnet and Vision Transformers for Fine-Grained Classification of Medicinal and Poisonous Plants
    A Baskota, S Ghimire, A Ghimire, P Baskaran
    IEEE Access , 2025
    2025
    Citations: 2
  • MediHealth: An AI-Driven Mobile Solution for Enhanced Health Care Decision-Making and Accessibility
    S Raj, S Shekhar, B P
    2025 3rd International Conference on Intelligent Data Communication … , 2025
    2025
    Citations: 2
  • An integrated model for energy conservation in IoT-enabled WSN using adaptive regional clustering and monkey inspired optimization
    P Baskaran, K Karuppasamy
    Journal of Intelligent & Fuzzy Systems 43 (4), 4961-4974 , 2022
    2022
    Citations: 2
  • Biomedical Named Entity Recognition Using Scikit-Learn And Keras
    P Baskaran, SK Somasundaram, M Thirunavukarasan, K Sathya
    2023 International Conference on Computer Science and Emerging Technologies … , 2023
    2023
    Citations: 1
  • Inshore and Offshore Ship Detection and Categorization System using Mask R-CNN
    BP Lakshmanan M, Nithish Kumar V N, Rahul R, Rohith S
    Design Engineering 2021 (08), 529-541 , 2021
    2021
    Citations: 1
  • Design of E-Water Application to Maintain the Flow of Water from Common Faucets by Enabling GSM
    P Baskaran, K Baskaran, V Rajaram
    Artificial Intelligence Techniques for Advanced Computing Applications … , 2020
    2020
    Citations: 1
  • A causal multimodal framework for privacy-preserving early-stage cancer detection and adaptive testing
    S Sivaprakash, P Baskaran
    Scientific Reports , 2026
    2026
  • PCANN: Principal Convolutional Analysis Neural Network for Block Chain based Diabetic Retinopathy Detection
    S Krishnamoorthy, S Paulraj, B Periyasamy, AK Ramamoorthy
    Current Eye Research 51 (3), 285-296 , 2026
    2026
  • Hybrid GAN Model with LSTM-Combined ResNet Discriminator for COVID-19 Classification in CT Images
    S Krishnamoorthy, S Paulraj, S Sriram, B Periyasamy, S Eswaran, ...
    Decision Sciences in Bioinformatics, 58-78 , 2026
    2026