Optimizing COPD management: Machine learning solutions for early detection and data privacy D. Shiny Irene, D. Vinod, P. Nancy, K. Anitha Environmental Monitoring Technologies for Improving Global Human Health, 2025 Worldwide, Chronic obstructive pulmonary disease (COPD) is one of the reasons for the high number of deaths. Patients who have COPD face many struggles and lose their quality of life. Machine learning has the ability to enhance the life of the patient through the proper treatments by doctors in the early stages. Machine Learning technology has gained more attention in the medical sector, its main objective is to enhance the accuracy and speed of the physicians. Machine learning-based models help to find the disease at its early stage. This work imparts an overall analysis of machine learning utilization in disease diagnosis and data privacy-preserving. In this work, COPD diagnosis by the machine learning method is overall analyzed. This imparts utilization of two machine learning methods in identifying COPD and its severity level namely Support Vector Machine (SVM) and Random Forest (RF). Both techniques have high-level impacts in medical impacts by lower training time. This work examines some pre-processing steps in obtaining quality input.
Application of machine learning methods to forecast potential dangers in chemical and gas industries P. Nancy, K. Anitha, D. Shiny Irene, D. Vinod Environmental Monitoring Technologies for Improving Global Human Health, 2025 Employers have a responsibility to prioritize the safety of their workers and should regularly inquire about their well-being to assess their levels of stress and comfort. Through the analysis and comprehension of their workers' requirements, organizations can generate superior quality products. Utilizing predictive analytics to mitigate the frequency and severity of catastrophic events would be advantageous, since Occupational Health and Safety anticipates substantial direct expenses associated with such incidents. If technology can accurately predict the timing and location of accidents, inspection data will be very valuable in directing immediate injury prevention measures and reducing the probability of future incidents. This article presents machine learning techniques for assessing and evaluating risks in the chemical and gas industries using K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Naive Bayes (NB) methods. The support vector machine (SVM) approach demonstrates high performance in criteria such as accuracy, sensitivity, and specificity
Integrating content-based image retrieval to assess the medical effects of environmental pollution on human health K. Anitha, D. Shiny Irene, D. Vinod, P. Nancy, S. Radhika Environmental Monitoring Technologies for Improving Global Human Health, 2025 The medical impact of environmental pollution is profound, linked to respiratory and cardiovascular diseases and cancer. Traditional studies rely on correlating environmental and health data, but Content-Based Image Retrieval (CBIR) offers a new level of precision by analyzing medical and environmental images such as lung scans and pollution maps. CBIR, especially when combined with neural networks and machine learning, enhances automated pattern recognition, revealing links between pollution exposure and health outcomes. For instance, satellite images of polluted areas can be matched with medical scans from local populations, providing evidence of pollution's health impact. In clinical settings, CBIR supports diagnosis by comparing patient scans with pollution-related image markers, enabling early detection and personalized care. Integrating CBIR into this research improves risk assessment, prevention, and public health strategies, advancing our understanding of pollution's role in disease and fostering healthier communities globally.
Enhanced CX Diagnosis with EHRTRWB Optimized CNN-LSTM Vision Transformer Architecture Shiny Irene D, Vinod D, Sai Venkat Atluri, Gottumukkala Naga Venkata Manikanta Sai 2025 6th International Conference on Data Intelligence and Cognitive Informatics Icdici 2025, 2025 Accurate and prompt identification of thoracic diseases using Chest X-ray (CX) images continues to pose a significant challenge in medical imaging. This study presents an innovative deep learning approach that integrates an Ensemble Hybrid Recurrent Transformer-based Random Wolf Bird (EHRTRWB) optimization algorithm with a hybrid model architecture composed of Convolutional-Neural Networks (CNN), Long Short-Term Memory (LSTMS) networks, and Vision Transformers (VT). The proposed model exploits CNN for spatial feature extraction, LSTMs for modeling temporal dependencies, and VT for capturing global contextual information through attention mechanisms. The EHRTRWB optimization algorithm is applied to refine model parameters and enhance feature selection, leading to improved classification performance and reduced overfitting. Evaluations on standard CX datasets show that the proposed framework surpasses current leading methods in terms of accuracy, precision, recall, and F1-score. These results underscore the effectiveness of merging conventional deep learning models with transformer architectures and evolutionary optimization to enhance automated disease detection from radiographic images.
An investigation on detection of botnets in online social networks P. Nancy, A. Devipriya, K. Anitha, D. Vinod, R. Anto Arockia Rosaline Online Social Networks in Business Frameworks, 2024 An online social network (OSN) is a web-based platform that enables users to create a profile including personal information, therefore establishing their identity on the network. Subsequently, subsequent users may engage in mutual interaction, share content, and see the relevant posts, comments, and activities of each other. The widespread accessibility and user-friendly characteristics of online social networks have significantly fueled their rapid expansion. Socialbots are one of the newest and most advanced forms of cybercrime that have taken up residence on social media platforms. Since the inception of OSNs, malicious individuals have used them to engage in a range of illicit activities, including spamming, identity theft, dissemination of false information, and distribution of fraudulent content. Therefore, several strategies have been implemented to tackle these issues. Socialbots pose a significant risk to online social networks (OSNs) because to their heightened sophistication and cunning compared to traditional spammers and bots. Identifying these bots is a challenging and enjoyable task due to their constant evasive behavior. In this chapter, we have presented a comprehensive examination of the most advanced methods and techniques used to analyze, characterize, and detect different versions of socialbots in various online social networks (OSNs). The purpose of this examination is to better understand the current difficulties and to suggest efficient strategies for characterizing, detecting, and mitigating the impact of socialbots.
Design and development of techniques for fake profile detection in online social networks R. Anto Arockia Rosaline, D. Vinod, P. Nancy, K. Anitha, A. Devipriya Online Social Networks in Business Frameworks, 2024 Today, Facebook and Twitter are among the top 10 trafficked websites. Most social networking sites provide mobile applications or services. Unwary users using SNs on websites may push spammers to behave destructively. Extra security measures protect users’ personal information on social media. People are quite eager to provide personal information on most sites. Adjust this data privacy setting to private or public. Only trustworthy network users may read sensitive data. SNSs’ unreliable identity identification is a major problem. So, the user may see someone else's genuine identity. Accepting friend requests and sharing personal information usually makes persons more popular. Even if they don't know the individual, most social media users click on links. This behavior may indicate spamming. Spam detection systems must prevent account access by spammers. This article presents a system using machine learning to identify spam profiles in online social networks. The particle swarm optimization approach is used to choose features from the social network data set. The classification model is trained using a dataset, using both the Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms.
Investigation of machine learning approaches on security analysis of cryptographic algorithms Suresh Anand M., Anitha K., Devipriya A., Manikandan N., Vinod D. Machine Learning and Cryptographic Solutions for Data Protection and Network Security, 2024 The distributed denial of service (DDoS) assault was a kind of intrusion in the cloud computing environment that severely affects the end user by injecting illegitimate packets. To obtain performance, a hybrid improved wolf optimizer with asymmetric key Goldwasser cryptography (IWO-AKGC) algorithm was proposed based on combining the exploitation ability of security and exploration capability of machine learning. In addition to the selection of parameters, a proposed hybrid IWO-AKGC technique is used for weighting and bias coefficients in neural network models. This has led to an immediate improvement in communication security for the delivery of different types of data services via clouds, thanks to the proposed IWO-AKGC method. The recommended hybrid optimizer successfully addresses the drawbacks of conventional methods, such as local stagnation problems, delayed convergence problems, and local and global optimal trapping problems. Thus, secured data communication is obtained for cloud service provisioning. The proposed model proved to be a better model for DDoS intrusion detection.
Unlocking biologica insights: Harnessing machine learning for analysis in complex biological data P. Nancy, G. Padmapriya, M. Suresh Anand, D. Vinod, R. Anto Arockia Rosaline Machine Learning and Cryptographic Solutions for Data Protection and Network Security, 2024 Recently, researchers in the field of bioinformatics have demonstrated a significant amount of interest in the use of computational methods for the purpose of forecasting clinical outcomes. The processing of biological data has been significantly improved over the past several years thanks to the development of technologies such as machine learning, deep learning, evolutionary algorithms, and other related technologies. By utilizing these technical breakthroughs, it is possible to handle biological data sets that contain more complex interrelationships. The prediction of cancer using microarray data may be accomplished by the utilization of a variety of machine learning techniques, such as clustering and classification algorithms, for instance. It is now possible for computers to learn from samples taken from the actual world, rather than being explicitly programmed to do so. As a result of the fact that the acquisition and interpretation of pictures is essential to the accurate evaluation and diagnosis of illnesses.
An improved digital image identification system for face recognition using computer vision for green communication Journal of Green Engineering, 2020
Fuzzy quantum computing model for health analytics Journal of Green Engineering, 2020
An efficient incident recovery based information security model using fuzzy rough sets for green business environment Journal of Green Engineering, 2020
Energy efficient resource allocation using hybrid genetic algorithm in cloud Journal of Green Engineering, 2020
Information security assurance model for collaborating business processes Recent Researches in Computer Science Proceedings of the 15th Wseas International Conference on Computers Part of the 15th Wseas Cscc Multiconference, 2011
RECENT SCHOLAR PUBLICATIONS
Enhancing Network Security: A Novel Intrusion Detection System Utilizing Dual-Optimization Techniques for Feature Selection and Classification D Vinod, M Prasad Computer Networks, 112021 , 2026 2026
Deep Learning for Hyperspectral Biomedical Imaging: A Comprehensive Review of Methods, Challenges, and Emerging Trends K Anitha, D Vinod, S Radhika Biomedical Applications in Deep Learning-Enhanced Hyperspectral Imaging, 155-204 , 2026 2026
An Efficient Method for Detection of Routing Attacks and Predicting Data Flow Using Improved Hybrid Altruistic Pack Search Optimization D Vinod, M Prasad 2025 IEEE 9th International Conference on Information and Communication … , 2025 2025
Privacy-Preserving Multiclass Lung Disorder Classification via CNN with Cosine Similarity in Big Data Framework J Sharma, DF Vinod, U Chugh Informatica 49 (10) , 2025 2025
A Privacy Based Deep Learning Algorithm for Big Data Analytics DF Vinod, N Ahlawat An International Journal of Computing and Informatics 49, 455-456 , 2025 2025
Big Data-Driven Multimodal Classification Using Clipped RBMs and Cross-Modality Attention in MMDBN N Ahlawat, DF Vinod 2025
Optimizing COPD Management: Machine Learning Solutions for Early Detection and Data Privacy DS Irene, D Vinod, P Nancy, K Anitha Environmental Monitoring Technologies for Improving Global Human Health, 417-424 , 2025 2025
Application of Machine Learning Methods to Forecast Potential Dangers in Chemical and Gas Industries P Nancy, K Anitha, DS Irene, D Vinod Environmental Monitoring Technologies for Improving Global Human Health, 237-256 , 2025 2025
Integrating Content-Based Image Retrieval to Assess the Medical Effects of Environmental Pollution on Human Health K Anitha, DS Irene, D Vinod, P Nancy, S Radhika Environmental Monitoring Technologies for Improving Global Human Health, 193-218 , 2025 2025
Privacy-Aware Deep Learning Model for Multi-Class Classification in Big Data J Sharma, DF Vinod 2025
nCD and Clipped RBM based multimode DBN for Optimal Classification of Heterogeneous Images in Big Data N Ahlawat, DF Vinod 2025
An Investigation on Detection of Botnets in Online Social Networks P Nancy, A Devipriya, K Anitha, D Vinod, RAA Rosaline Online Social Networks in Business Frameworks, 299-317 , 2024 2024
Design and Development of Techniques for Fake Profile Detection in Online Social Networks RAA Rosaline, D Vinod, P Nancy, K Anitha, A Devipriya Online Social Networks in Business Frameworks, 319-336 , 2024 2024
CodeXchange: Leaping into the Future of AI-Powered Code Editing M Agrawal, J Goyal, M Goyal, P Sukhija, J Sharma, DF Vinod 2024 International Conference on Computational Intelligence and Computing … , 2024 2024 Citations: 2
An heuristic rainfall pattern prediction using dynamic tuning parameters with novel attenuation measurements by comparing random forest over k-nearest neighbour GS Chand, D Vinod AIP Conference Proceedings 2853 (1), 020177 , 2024 2024
Enhanced Multimode DBN for Optimal Classification of Heterogeneous Cancer Images for HealthCare System. N Ahlawat, DF Vinod Frontiers in Health Informatics 13 (2) , 2024 2024
An Empirical Study and Analysis of Artificial Intelligence, Machine Learning, and Big Data for Crypto Healthcare Industries D Ananthavadivel, RAA Rosaline, D Vinod, K Anitha, P Nancy Machine Learning and Cryptographic Solutions for Data Protection and Network … , 2024 2024
Unlocking Biologica Insights: Harnessing Machine Learning for Analysis in Complex Biological Data P Nancy, G Padmapriya, MS Anand, D Vinod, RAA Rosaline Machine Learning and Cryptographic Solutions for Data Protection and Network … , 2024 2024
Green Computing-Based Digital Waste Management and Resource Allocation for Distributed Fog Data Centers N Manikandan, D Vinod, RAA Rosaline, P Nancy, G Premalatha Computational Intelligence for Green Cloud Computing and Digital Waste … , 2024 2024 Citations: 1
A Systematic Review: Future Prospects and Challenges to IoT-Based EHRs in the Healthcare Industry N Manikandan, AD Priya, D Vinod, MS Ananad, G Padmapriya Blockchain and IoT Approaches for Secure Electronic Health Records (EHR), 89-116 , 2024 2024
MOST CITED SCHOLAR PUBLICATIONS
Characterization and in vitro evaluation of electrospun chitosan/polycaprolactone blend fibrous mat for skin tissue engineering T Prasad, EA Shabeena, D Vinod, TV Kumary, PR Anil Kumar Journal of Materials Science: Materials in Medicine 26 (1), 28 , 2015 2015.0 Citations: 118
An ex vivo evaluation of the efficacy of andrographolide in modulating differential expression of transcription factors and target genes in periodontal cells and its potential … R Ambili, P Janam, PSS Babu, M Prasad, D Vinod, PRA Kumar, ... Journal of ethnopharmacology 196, 160-167 , 2017 2017.0 Citations: 32
Differential expression of transcription factors NF-κB and STAT3 in periodontal ligament fibroblasts and gingiva of healthy and diseased individuals R Ambili, P Janam, PSS Babu, M Prasad, D Vinod, PRA Kumar, ... Archives of oral biology 82, 19-26 , 2017 2017.0 Citations: 25
An improved security assurance model for collaborating small material business processes D Vinod, N Bharathiraja, M Anand, A Antonidoss Materials Today: Proceedings 46, 4077-4081 , 2021 2021.0 Citations: 20
A filter based feature set selection approach for big data classification of patient records DF Vinod, V Vasudevan 2016 International Conference on Electrical, Electronics, and Optimization … , 2016 2016.0 Citations: 17
Severity prediction over Parkinson’s disease prediction by using the deep Brooke inception net classifier R Sarankumar, D Vinod, K Anitha, G Manohar, KS Vijayanand, B Pant, ... Computational Intelligence and Neuroscience 2022 (1), 7223197 , 2022 2022.0 Citations: 16
RA (2015). Characterization and in vitro evaluation of electrospun chitosan/polycaprolactone blend fibrous mat for skin tissue engineering T Prasad, EA Shabeena, D Vinod, TV Kumary, P Kumar J. Mater. Sci 26, 28 , 0 Citations: 13
A novel hybrid automatic intrusion detection system using machine learning technique for anomalous detection based on traffic prediction D Vinod, M Prasad 2023 International Conference on Networking and Communications (ICNWC), 1-7 , 2023 2023.0 Citations: 6
Evaluation and design of robotic hand picking operations using intelligent motor unit R Gnanavel, D Vinod, MK Nalini, K Dhinakaran, D Elantamilan 2022 international conference on advances in computing, communication and … , 2022 2022.0 Citations: 6
A hybrid algorithm to perform dynamic node energy and link stability through invoking data from 5G wireless sensor based network K Dhinakaran, D Elantamilan, R Gnanavel, D Vinod, MK Nalini 2022 International Conference on Advances in Computing, Communication and … , 2022 2022.0 Citations: 5
Clipped RBM and DBN Based Mechanism for Optimal Classification of Brain Cancer N Ahlawat, DF Vinod ICT with Intelligent Applications: Proceedings of ICTIS 2022, Volume 1, 295-304 , 2022 2022.0 Citations: 4
A hybrid algorithm for secure image based encryption and steganographic technique in combination with DET and AES algorithms D Vinod, MK Nalini, K Dhinakaran, D Elantamilan, R Gnanavel 2022 International Conference on Advances in Computing, Communication and … , 2022 2022.0 Citations: 4
A Predictable Information Security Based Context Aware Trust Model for Organization Management: A Statistical Analysis CS Vinod Duraivelu Journal of Software 11 (6), 577-588 , 2016 2016.0 Citations: 4
A formal forecasting approach to predict flood disaster and recovery strategy using machine learning D Vinod, KS Vijayanand, AS Kumar AIP Conference Proceedings 2519 (1), 050057 , 2022 2022.0 Citations: 3
An Enhanced Diabetic Retinopathy Prediction Model Using Novel Optical Coherence Tomography by Comparing Random Forest Over K-Means Algorithm J Ch Electrochemical Society Transactions 107 (1), 13119-13129 , 2022 2022.0 Citations: 3
Combination of local feature extraction for image retrieval S Sankara Narayanan, D Vinod, S Athisayamani, A Robert Singh Proceedings of Third International Conference on Sustainable Computing … , 2022 2022.0 Citations: 3
A secure electronic voting system using blockchain technology K Dhinakaran, PM Britto Hrudaya Raj, D Vinod Proceedings of the Second International Conference on Information Management … , 2021 2021.0 Citations: 3
LNTP-MDBN: Big Data Integrated Learning Framework for Heterogeneous Image Set Classification DF Vinod, V Vasudevan Current Medical Imaging Reviews 15 (2), 227-236 , 2019 2019.0 Citations: 3
Design, synthesis, characterization and screening of thiophene derivatives for anti-inflammatory activity AJ Suresh, K Anitha, D Vinod Journal of Chemical, Biological and Physical Sciences (JCBPS) 1 (2), 304 , 2011 2011.0 Citations: 3
CodeXchange: Leaping into the Future of AI-Powered Code Editing M Agrawal, J Goyal, M Goyal, P Sukhija, J Sharma, DF Vinod 2024 International Conference on Computational Intelligence and Computing … , 2024 2024.0 Citations: 2
