SUBASH CHANDRA BOSE S
Verified @gmail.com
ASSISTANT PROFESSOR/COMPUTER SCIENCE
RESEARCH INTERESTS
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.
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Rajesh Natarajan, Santosh Reddy P, Subash Chandra Bose, H.L. Gururaj, Francesco Flammini, and Shanmugapriya Velmurugan
Elsevier BV
S. Subash Chandra Bose, Badria Sulaiman Alfurhood, Gururaj H L, Francesco Flammini, Rajesh Natarajan, and Sheela Shankarappa Jaya
MDPI AG
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.
S. Subash Chandra Bose, Rajesh Natarajan, Gururaj H L, Francesco Flammini, and P. V. Praveen Sundar
MDPI AG
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.
S .Subash Chandra Bose, Vinay D R, Yeligeti Raju, N. Bhavana, Anirbit Sengupta, and Prabhishek Singh
IEEE
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.
S. Subash Chandra Bose, A. Vinoth Kumar, Anitha Premkumar, M. Deepika, and M. Gokilavani
Soft Computing Springer Science and Business Media LLC
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.
R. Manikandan, Rajesh Kumar Maurya, Tariq Rasheed, S. Subash Chandra Bose, José Luis Arias-Gonzáles, Udit Mamodiya, and Ashish Tiwari
Taru Publications
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.
Chithra V, Karthikeyan B, Subash Chandra Bose S, Suman Babu Ch V, Shriranjani. J, and Abinaya R
IEEE
The creation of embedded systems has shown to be a dependable method for keeping track of and improving the industrial environment. The project's aim is to create a structure that can be applied globally at any size to monitor the environmental factors, correct errors, and stop impending accidents in the workplace. Remote monitoring of factors like temperature, humidity, CO2 levels, and many more is now possible because to the development of tiny sensor devices and wireless technology. The main board will be an Arduino, and sensors will gather all of the real-time environmental data. This real-time data will be communicated via IoT (Node MCU) to the web server, where it can be seen on a webpage. This real-time data can also be viewed in the display also. Users can ingress this data from anywhere through the Internet. Motors and drivers are used to rectify and prevent accident that is about to occur in the industry. This proposed system will serve to be an important part in industrial development.
S. Subash Chandra Bose, Natarajan Sivanandam, and P. V. Praveen Sundar
Springer Science and Business Media LLC
S.SubashChandra Bose*, , Dr.T. Christopher, and
Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Tumour detection medical applications utilize classification techniques to categorize malicious and non-malicious tumour features to provide an efficient medical diagnosis of the human individual under investigation. One way to enable efficient classification, Feature extraction methods are used to eliminate the redundant features and obtain the most relevant features. However, the challenges concerning the dimension and quantum of tumour dataset persist. Toward this goal, this paper aims to maximize the malicious tumour classification accuracy using two reliable ensemble classifiers namely Bootstrap Aggregation and k-nearest neighbour. Tumour features extracted by Aggregate Linear Discriminate Analysis (LDA) and the feature distance is calculated with iterative scattering matrix algorithm. The extracted features are further refined by aggregation to select most effective feature values. After this, an ensemble classifier technique is employed to construct malicious and non-malicious tumour classes. The tumour classification based on an ensemble of bagging and k-nearest neighbour. Simulation is carried out on Tumour Repository data set to show that proposed ensemble classifiers have considerably better tumour detection accuracy than existing conventional techniques. Numerical performance evaluations show that 8% improvement by proposed method in tumour classification accuracy for malicious tumour detection in human individuals.