My research concentration is Data Mining with Artificial Intelligence (Bio-Medical Health Care Applications)At Present my research concentration is on Artificial Intelligence, Machine Learning,and Deep Learning & Data Science.
12
Scopus Publications
126
Scholar Citations
6
Scholar h-index
5
Scholar i10-index
Scopus Publications
Image Classification with Hypertensive Angle Disease Detection with Geometric Local Derivative Pre-Processing Anand Rajasekaran Communications on Applied Nonlinear Analysis, 2025 It is recognized as conjunctivitis stands as that of the second-greatest source of disability. Early detection and management of ophthalmology are essential for preventing disease owing to the benign character of loss of vision mostly in early stages of the illness and the permanent condition of vision in later stages. Straight retinal inspection, commercial digital images, laser scanners spectroscopy, scanning infrared additional factor and confocal tomography (OCT) images can all is used it to identify hypertension. These findings suggest using a Vertical Harmonic oscillator Dynamic Hinge Supporting Machine Classifier (Embedded systems) to predict glaucoma. There really are three stages to the Repository system. All those are ophthalmology identification, extraction and classification, and processing. The retina source images first were analyzed using a Geometric Local Derivative Pre - processor architecture to extract the major characteristics required for early diagnosis. The precompiled images then are submitted to Harmonic oscillator Discontinuous Single - input single Probability distribution Extraction Of features to identify significant characteristics with the sensitivity for disease prediction. Lastly, Inter Hinge Gradient Boosting Retinal Identification employs the derived features to detect glaucoma early and effectively. The Retina Mri image datasets was utilized in Simulation experiments to examine the effectiveness of the suggested method, embedded systems. Computational complexity, sensitivities, and correctness performance criteria must be looked at with respect to different Optical image quantities.
Iterative Reflect Perceptual Sammon and Machine Learning-Based Bagging Classification for Efficient Tumor Detection S. Subash Chandra Bose, Rajesh Natarajan, Gururaj H L, Francesco Flammini, P. V. Praveen Sundar Sustainability Switzerland, 2023 A tumor is an abnormal development of cells in the human body. A tumor develops when cells divide without any control. Tumors change their size from a small to large lump. Tumors appear anywhere in the body. The early stage of diagnosis is an essential one in disease treatment. Many researchers carried out their research on different tumor detection methods. However, the tumor detection accuracy level was not improved and tumor detection time consumption not minimized. In order to address these problems, an Iterative Reflect Perceptual Sammon Bagging Classification (IRPS-BAC) Method is introduced. The aim is to accurately detect brain tumors as early as possible and make the method suitable for real-time applications. The IRPS-BAC Method comprises two processes, namely, feature selection and classification using the iterative reflect perceptual sammon feature selection process and bagging classification process. In the IRPS-BAC Method, an input of medical data are gathered from the Epileptic Seizure Recognition Data Set and Cervical Cancer Risk Classification database. After that, iterative reflect perceptual sammon feature selection process is carried out to select the relevant features. Iterative reflect perceptual divergence computes the variation between two features. After that, sammon mapping projects the similar and dissimilar features into feature space. By this manner, the relevant features get selected using the IRPS-BAC Method. With the help of selected relevant features, bagging classification process is carried out. In bagging classification process, internal node processes the selected features and leaf node to make the tumor decision as normal or cancerous one based on information gain. This, in turn, helps to reduce the time complexity and error rate. The performance of the proposed IRPS-BAC Method is determined by two benchmark datasets through comparing the parameter such as tumor detection time, tumor detection accuracy and error rate with the existing approaches. In the Epileptic Seizure Recognition Data Set, the proposed IRPS-BAC Method improves tumor detection accuracy by 16%, with minimum time period and the error rate of 41 ms and 58% for tumor detection as compared to existing methods. By using Cervical Cancer Risk Classification, the proposed IRPS-BAC Method exhibited higher classification performance measures, including accuracy (14%), time (46 ms), and error rate (61%), than the current conventional approaches.
Decision Fault Tree Learning and Differential Lyapunov Optimal Control for Path Tracking S. Subash Chandra Bose, Badria Sulaiman Alfurhood, Gururaj H L, Francesco Flammini, Rajesh Natarajan, et al. Entropy, 2023 This paper considers the main challenges for all components engaged in the driving task suggested by the automation of road vehicles or autonomous cars. Numerous autonomous vehicle developers often invest an important amount of time and effort in fine-tuning and measuring the route tracking to obtain reliable tracking performance over a wide range of autonomous vehicle speed and road curvature diversities. However, a number of automated vehicles were not considered for fault-tolerant trajectory tracking methods. Motivated by this, the current research study of the Differential Lyapunov Stochastic and Decision Defect Tree Learning (DLS-DFTL) method is proposed to handle fault detection and course tracking for autonomous vehicle problems. Initially, Differential Lyapunov Stochastic Optimal Control (SOC) with customizable Z-matrices is to precisely design the path tracking for a particular target vehicle while successfully managing the noise and fault issues that arise from the localization and path planning. With the autonomous vehicle’s low ceilings, a recommendation trajectory generation model is created to support such a safety justification. Then, to detect an unexpected deviation caused by a fault, a fault detection technique known as Decision Fault Tree Learning (DFTL) is built. The DLS-DFTL method can be used to find and locate problems in expansive, intricate communication networks. We conducted various tests and showed the applicability of DFTL. By offering some analysis of the experimental outcomes, the suggested method produces significant accuracy. In addition to a thorough study that compares the results to state-of-the-art techniques, simulation was also used to quantify the rate and time of defect detection. The experimental result shows that the proposed DLS-DFTL enhances the fault detection rate (38%), reduces the loss rate (14%), and has a faster fault detection time (24%) than the state of art methods.
A Deep Learning-Based Fog Computing and cloud computing for Orchestration S .Subash Chandra Bose, Vinay D R, Yeligeti Raju, N. Bhavana, Anirbit Sengupta, et al. Proceedings 2022 2nd International Conference on Innovative Sustainable Computational Technologies Cisct 2022, 2023 Fog computing is defined as a decentralized infrastructure that locations storage and processing aspects at the side of the cloud, the place records sources such as software customers and sensors exist. The Fog Computing is the time period coined via Cisco that refers to extending cloud computing to an area of the enterprise’s network. Thus, it is additionally recognized as Edge Computing or Fogging. It allows the operation of computing, storage, and networking offerings between give up units and computing facts centers. Fog computing is defined as a decentralized infrastructure that locations storage and processing aspects at the side of the cloud, the place records sources such as software customers and sensors exist. The fog computing Intelligence as Artificial Intelligence (AI) is furnished by way of Fog Nodes in cooperation with Clouds. In Fog Nodes several sorts of AI studying can be realized - such as e.g., Machine Learning (ML), Deep Learning (DL). Thanks to the Genius of Fog Nodes, for example, we communicate of Intelligent IoT.
Biserial targeted feature projection based radial kernel regressive deep belief neural learning for covid-19 prediction S. Subash Chandra Bose, A. Vinoth Kumar, Anitha Premkumar, M. Deepika, M. Gokilavani Soft Computing, 2023 Coronavirus disease 2019 (COVID-19) is a highly infectious viral disease caused by the novel SARS-CoV-2 virus. Different prediction techniques have been developed to predict the coronavirus disease’s existence in patients. However, the accurate prediction was not improved and time consumption was not minimized. In order to address these existing problems, a novel technique called Biserial Targeted Feature Projection-based Radial Kernel Regressive Deep Belief Neural Learning (BTFP-RKRDBNL) is introduced to perform accurate disease prediction with lesser time consumption. The BTFP-RKRDBNL techniques perform disease prediction with the help of different layers such as two visible layers namely input and layer and two hidden layers. Initially, the features and data are collected from the dataset and transmitted to the input layer. The Point Biserial Correlative Target feature projection is used to select relevant features and other irrelevant features are removed with minimizing the disease prediction time. Then the relevant features are sent to the hidden layer 2. Next, Radial Kernel Regression is applied to analyze the training features and testing disease features to identify the disease with higher accuracy and a lesser false positive rate. Experimental analysis is planned to measure the prediction accuracy, sensitivity, and specificity, and prediction time for different numbers of patients. The result illustrates that the method increases the prediction accuracy, sensitivity, and specificity by 10, 6, and 21% and reduces the prediction time by 10% as compared to state-of-the-art works.
Adaptive cloud orchestration resource selection using rough set theory R. Manikandan, Rajesh Kumar Maurya, Tariq Rasheed, S. Subash Chandra Bose, José Luis Arias-Gonzáles, et al. Journal of Interdisciplinary Mathematics, 2023 The recent research is developing in a vast speed to develop the cloud orchestration system. In cloud system the remotely managed servers are storing, finding, removing, replacing and retrieving the various services in an adaptive optimized manner. The lot of services are provided by the vast number of providers in the market with the help of approximation theory by the rough set system (RST). RST finds in helping in getting the efficient cloud resources as a service to the users. The proposed OCRS (Optimized Cost Resource System) approach is being simulated and compared with the existing cloud simulator. The simulator gives the approximate results in many parameters of cloud services. In all aspects our algorithm is performing better.
Hybrid support vector machine based Markov clustering for tumor detection from bio-molecular data Arpn Journal of Engineering and Applied Sciences, 2018
RECENT SCHOLAR PUBLICATIONS
Weighted Hermitian Wavelet Multilayer Extreme Learning Machine for Building Detection Using Unmanned Aerial Vehicle Images SCBS Franklin Alex Joseph A+ Manikandan A , Umi Salma Basha , Shanmugapriya ... International Research Journal of Multidisciplinary Technovation 8 (2), 75-91 , 2026 2026
Ai-Driven Cybersecurity Defense for Military Networks SSC Bose, V Shanmugapriya, N Mahadev, M Pyingkodi, R Natarajan, ... 2025 IEEE International Conference for Women in Innovation, Technology … , 2025 2025
Image Classification with Hypertensive Angle Disease Detection with Geometric Local Derivative Pre-Processing SCBS Anand Rajasekaran, Rajesh Natarajan, Shankar R, Sowmya V L, R vikkram Communications on Applied Nonlinear Analysis 32 (2), 593-609 , 2025 2025
Retraction Note: Biserial targeted feature projection based radial kernel regressive deep belief neural learning for covid-19 prediction S Subash Chandra Bose, A Vinoth Kumar, A Premkumar, M Deepika, ... Soft Computing 28 (Suppl 1), 23-23 , 2024 2024
Fault detection and state estimation in robotic automatic control using machine learning R Natarajan, S Reddy, SC Bose, HL Gururaj, F Flammini, S Velmurugan Array 19, 100298 , 2023 2023 Citations: 27
A Review of Significant Challenges with Quantum Communication and Computing VSA S.Subash Chandra Bose International Journal of Data Informatics and Intelligent computing (IJDIIC … , 2023 2023 Citations: 12
Adaptive cloud orchestration resource selection using rough set theory UMAT R. Manikandan*, Rajesh Kumar Maurya, Tariq Rasheed, S. Subash Chandra ... Journal of Interdisciplinary Mathematics 26 (3), 311–320 , 2023 2023 Citations: 47
Iterative Reflect Perceptual Sammon and Machine Learning-Based Bagging Classification for Efficient Tumor Detection SSC Bose, R Natarajan, G HL, F Flammini, PV Praveen Sundar Sustainability 15 (5), 4602 , 2023 2023 Citations: 5
Decision fault tree learning and differential lyapunov optimal control for path tracking SSC Bose, BS Alfurhood, F Flammini, R Natarajan, SS Jaya Entropy 25 (3), 443 , 2023 2023 Citations: 12
RETRACTED ARTICLE: Biserial targeted feature projection based radial kernel regressive deep belief neural learning for covid-19 prediction: S. Subash Chandra Bose, A. Vinoth … S Subash Chandra Bose, A Vinoth Kumar, A Premkumar, M Deepika, ... Soft Computing 27 (3), 1651-1662 , 2023 2023 Citations: 3
A deep learning-based fog computing and cloud computing for orchestration SSC Bose, DR Vinay, Y Raju, N Bhavana, A Sengupta, P Singh 2022 2nd International Conference on Innovative Sustainable Computational … , 2022 2022 Citations: 7
RETRACTED ARTICLE: Design of ensemble classifier using Statistical Gradient and Dynamic Weight LogitBoost for malicious tumor detection S Subash Chandra Bose, N Sivanandam, PV Praveen Sundar Journal of Ambient Intelligence and Humanized Computing 12 (6), 6713-6723 , 2021 2021 Citations: 11
INTELLIGENT DRUG ABUSE ASCERTAIN SYSTEM PN S. Subash Chandra Bose, A. Thilaka , P.V. Praveen Sundar , Dr.G ... IN Patent App. 202,041,026,079 , 2020 2020
A SURVEY ON DATAANALYSIS FOR IoT APPLICATIONS USING DATA MINING TECHNIQUES AND ALGORITHMS SSCBDS Sheeja Journal of Computer Science 13, 262-265 , 2019 2019
Aggregate Linear Discriminate Analyzed Feature Extraction and Ensemble of Bootstrap with Knn Classifier for Malicious Tumour Detection SSCBT Christopher International Journal of Recent Technology and Engineering (IJRTE) 8 (3 … , 2019 2019 Citations: 2
Performance Analysis of Hybrid and Ensemble Techniques for Efficient Malicious Tumor Detection DTC S. Subash Chandra Bose International Journal for Research in Engineering Application & Management 5 (3) , 2019 2019
DEEP LEARNING FEATURE EXTRACTION WITH ENSEMBLE SPECTRAL CLUSTER AND GAUSSIAN MIXTURE FOR MALICIOUS TUMOR DETECTION SSC Bose, T Christopher ICTACT JOURNAL ON SOFT COMPUTING 8 (4), 1750-1757 , 2018 2018
Pragmatic Comparison of Single Clustering With Ensemble Techniques for Efficient Malicious Tumor Detection DTC S. Subash Chandra Bose IJSRCSAMS(International Journal of Scientific Research in Computer Science … , 2018 2018
HYBRID SUPPORT VECTOR MACHINE BASED MARKOV CLUSTERING FOR TUMOR DETECTION FROM BIO-MOLECULAR DATA S SubashChandraBose, T Christopher ARPN Journal of Engineering and Applied Sciences 13 (9), 3271-3279 , 2018 2018
A Survey on Data Analysis for IoT Applications using Data Mining Techniques and Algorithms SSCBT Christopher Journal of Computer Science 10 (1), 18-27 , 2015 2015
MOST CITED SCHOLAR PUBLICATIONS
Adaptive cloud orchestration resource selection using rough set theory UMAT R. Manikandan*, Rajesh Kumar Maurya, Tariq Rasheed, S. Subash Chandra ... Journal of Interdisciplinary Mathematics 26 (3), 311–320 , 2023 2023 Citations: 47
Fault detection and state estimation in robotic automatic control using machine learning R Natarajan, S Reddy, SC Bose, HL Gururaj, F Flammini, S Velmurugan Array 19, 100298 , 2023 2023 Citations: 27
A Review of Significant Challenges with Quantum Communication and Computing VSA S.Subash Chandra Bose International Journal of Data Informatics and Intelligent computing (IJDIIC … , 2023 2023 Citations: 12
Decision fault tree learning and differential lyapunov optimal control for path tracking SSC Bose, BS Alfurhood, F Flammini, R Natarajan, SS Jaya Entropy 25 (3), 443 , 2023 2023 Citations: 12
RETRACTED ARTICLE: Design of ensemble classifier using Statistical Gradient and Dynamic Weight LogitBoost for malicious tumor detection S Subash Chandra Bose, N Sivanandam, PV Praveen Sundar Journal of Ambient Intelligence and Humanized Computing 12 (6), 6713-6723 , 2021 2021 Citations: 11
A deep learning-based fog computing and cloud computing for orchestration SSC Bose, DR Vinay, Y Raju, N Bhavana, A Sengupta, P Singh 2022 2nd International Conference on Innovative Sustainable Computational … , 2022 2022 Citations: 7
Iterative Reflect Perceptual Sammon and Machine Learning-Based Bagging Classification for Efficient Tumor Detection SSC Bose, R Natarajan, G HL, F Flammini, PV Praveen Sundar Sustainability 15 (5), 4602 , 2023 2023 Citations: 5
RETRACTED ARTICLE: Biserial targeted feature projection based radial kernel regressive deep belief neural learning for covid-19 prediction: S. Subash Chandra Bose, A. Vinoth … S Subash Chandra Bose, A Vinoth Kumar, A Premkumar, M Deepika, ... Soft Computing 27 (3), 1651-1662 , 2023 2023 Citations: 3
Aggregate Linear Discriminate Analyzed Feature Extraction and Ensemble of Bootstrap with Knn Classifier for Malicious Tumour Detection SSCBT Christopher International Journal of Recent Technology and Engineering (IJRTE) 8 (3 … , 2019 2019 Citations: 2
Weighted Hermitian Wavelet Multilayer Extreme Learning Machine for Building Detection Using Unmanned Aerial Vehicle Images SCBS Franklin Alex Joseph A+ Manikandan A , Umi Salma Basha , Shanmugapriya ... International Research Journal of Multidisciplinary Technovation 8 (2), 75-91 , 2026 2026
Ai-Driven Cybersecurity Defense for Military Networks SSC Bose, V Shanmugapriya, N Mahadev, M Pyingkodi, R Natarajan, ... 2025 IEEE International Conference for Women in Innovation, Technology … , 2025 2025
Image Classification with Hypertensive Angle Disease Detection with Geometric Local Derivative Pre-Processing SCBS Anand Rajasekaran, Rajesh Natarajan, Shankar R, Sowmya V L, R vikkram Communications on Applied Nonlinear Analysis 32 (2), 593-609 , 2025 2025
Retraction Note: Biserial targeted feature projection based radial kernel regressive deep belief neural learning for covid-19 prediction S Subash Chandra Bose, A Vinoth Kumar, A Premkumar, M Deepika, ... Soft Computing 28 (Suppl 1), 23-23 , 2024 2024
INTELLIGENT DRUG ABUSE ASCERTAIN SYSTEM PN S. Subash Chandra Bose, A. Thilaka , P.V. Praveen Sundar , Dr.G ... IN Patent App. 202,041,026,079 , 2020 2020
A SURVEY ON DATAANALYSIS FOR IoT APPLICATIONS USING DATA MINING TECHNIQUES AND ALGORITHMS SSCBDS Sheeja Journal of Computer Science 13, 262-265 , 2019 2019
Performance Analysis of Hybrid and Ensemble Techniques for Efficient Malicious Tumor Detection DTC S. Subash Chandra Bose International Journal for Research in Engineering Application & Management 5 (3) , 2019 2019
DEEP LEARNING FEATURE EXTRACTION WITH ENSEMBLE SPECTRAL CLUSTER AND GAUSSIAN MIXTURE FOR MALICIOUS TUMOR DETECTION SSC Bose, T Christopher ICTACT JOURNAL ON SOFT COMPUTING 8 (4), 1750-1757 , 2018 2018
Pragmatic Comparison of Single Clustering With Ensemble Techniques for Efficient Malicious Tumor Detection DTC S. Subash Chandra Bose IJSRCSAMS(International Journal of Scientific Research in Computer Science … , 2018 2018
HYBRID SUPPORT VECTOR MACHINE BASED MARKOV CLUSTERING FOR TUMOR DETECTION FROM BIO-MOLECULAR DATA S SubashChandraBose, T Christopher ARPN Journal of Engineering and Applied Sciences 13 (9), 3271-3279 , 2018 2018
A Survey on Data Analysis for IoT Applications using Data Mining Techniques and Algorithms SSCBT Christopher Journal of Computer Science 10 (1), 18-27 , 2015 2015
