Magesh S
@mteschennai.com
Researcher
Dr M. G. R. Educational And Research Institute
As a leader and self-sufficient innovator, Prof. S. Magesh is comfortable in any role from the executive boardroom to the service platform. He is a tech-savvy negotiator known for healthy business development and relationship building skills. His innate ability to build high-performance teams, including his strategically selected freelance staff, has served as a growth catalyst and infused productive energy into the workplace. He is an empanelled Language Trainer with Words Worth & Officer's Training Academy (Military Academy), Chennai. He is a visionary resource person, orator, and acclaimed National Trainer. His expertise spans both corporate and academia, and he has been acclaimed for his scholastic achievements. He won many Awards and Accolades during his academic and entrepreneurial career. He received the "Distinguished Innovator & Edupreneur Award" bestowed on 7th November 2017 and certified as a "Distinguished Fellow" by the National Foundation for Entrepreneurship Development,
EDUCATION
Prof. S. Magesh is a notable academician and committed entrepreneur. Apart from his Engineering background and as a perpetual learner, he holds multifarious credentials in diverse areas from Engineering, Science, Arts, Administration, Technology, Linguistics, and Management. He has commenced his academic career as Lecturer in the year 1999 and after that elevated to the level of Assistant Professor, Associate Professor, Professor, and Head in the Department of Computer Science and Engineering with his distinguished career spanning in engineering institutions over the period of 15 years and ten years Corporate Experience. He has served in various academies ranging from Engineering Colleges and Varsities in different parts of Tamil Nadu till the year 2014. He has published 20 refereed International Indexed journals, including Web of Science, ESCI, SCOPUS, Springer, Compendex (Elsevier Engineering Index) to his credit, 2 Patents, and a Book with ISBN.
RESEARCH INTERESTS
Internet of Things, Deep & Machine Learning, Artificial Intelligence, Wireless Sensor Networks, Marketing
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
S. Magesh and P.S. Raja Kumar
Yayasan Riset dan Pengembangan Intelektual
Congenital heart disease (CHD) is the most prevalent congenital ailment. One in every four newborns born with serious coronary artery disease will require surgery or other early therapy. Early identification of CHD in the fetal heart, on the other hand, is more critical for diagnosis. Extracting information from ultrasound (US) images is a difficult and time-consuming job. Deep learning (Dl) CNNs have been frequently utilized in fetal echocardiography for CAD identification to overcome this difficulty. In this work, a DL based neural network is proposed for classifying the normal and abnormal fetal heart based on US images. A total of 363 pregnant women between the ages of 18 and 34 weeks who had coronary artery disease or fetal good hearts were included. These US images are pre-processed using SCRAB (scalable range based adaptive bilateral filter) for eliminating the noise artifacts. The relevant features are extracted from the US images and classify them into normal and CHD by using the deep Reg net network. The proposed model integrates the Reg net -module with the CNN architecture to diminish the computational complexity and, simultaneously, attains an effectual classification accuracy. The proposed network attains higher accuracy of 98.4% for the normal and 97.2% for CHD. To confirm the efficiency of the proposed Reg net is compared to the various deep learning networks.
S. Magesh and P.S. Rajakumar
Elsevier BV
Kumar K.R., Iyapparaja M., Niveditha V.R., S. Magesh, G. Magesh, and Shanmugasundaram Marappan
Emerald
Purpose This paper has used the well-known machine learning (ML) computational algorithm with Internet of Things (IoT) devices to predict the COVID-19 disease and to analyze the peak rate of the disease in the world. ML is the best tool to analyze and predict the object in reasonable time with great level of accuracy. The Purpose of this paper is to develop a model to predict the coronavirus by considering majorly related symptoms, attributes and also to predict and analyze the peak rate of the disease. Design/methodology/approach COVID-19 or coronavirus disease threatens the human lives in various ways, which leads to deaths in most of the cases. It affects the respiratory organs slowly and this penetration leads to multiple organ failure, which causes death in some cases having poor immunity system. In recent times, it has drawn the international attention because of the pandemic threat that is harder to control the spreading of infection around the world. Findings This proposed model is implemented by support vector machine classifier and Bayesian network algorithm, which yields high accuracy. The K-means algorithm has been applied for clustering the data set models. For data collection, IoT devices and related sensors were used in the identified hotspots. The data sets were collected from the selected hotspots, which are placed on the regions selected by the government agencies. The proposed COVID-19 prediction models improve the accuracy of the prediction and peak accuracy ratio. This model is also tested with best, worst and average cases of data set to achieve the better prediction rate. Originality/value From that hotspots, the IoT devices were fixed and accessed through wireless sensors (802.11) to transfer the data to the authors’ database, which is dedicated in data collection server. The data set and the proposed model yield good results and perform well with expected accuracy rate in the analysis and monitoring of the recovery rate of COVID-19.
S. Magesh, P.S. Rajakumar, T.V. Ananthan, and J. Indumathi
IOS Press
Deep learning has become a recent explosion in our everyday lives. Being one of the leading machine learning tools among various tools, deep learning contributes a lot to image analysis and the vision of the computer. This tool is considered enormous for image analysis, especially in detecting fetal cardiac abnormalities in a parallel computing environment. Screening congenital cardiac disease (CCD) is challenging in achieving accuracy in terms of diagnoses concerning the manual process. Hence in this proposed work, optimized ultrasound image (USI) based Artificial Neural Network (ANN), a deep learning tool, has proven in exploiting the dissimilitude prognoses of cardiomyopathies and in predicting perinatal mortality of congenital cardiac disease (CCD). Fetal Cardiac parameters are evaluated using the myocardial performance index (MPI), a biomarker of global cardiac function providing statistics on various periods during diastolic and systolic phases. This paper also discusses some potential trends of deep learning application in ultrasound image analysis in detecting and predicting the abnormalities in fetal cardiac function.
V. R. Niveditha, D. Usha, P. S. Rajakumar, B. Dwarakanath, and Magesh S.
IGI Global
Security over internet communication has now become difficult as technology is increasingly more effective and faster, particularly in resource limited devices such as wireless sensors, embedded devices, internet of things (IoT), radio frequency identification (RFID) tags, etc. However, IoT is expected to connect billions of computers as a hopeful technology for the future. Hence, security, privacy, and authentication services must protect the communication in IoT. There are several recent considerations, such as restricted computing capacity, register width, RAM size, specific operating environment, ROM size, etc. that have compelled IoT to utilize conventional measures of security. These technologies require greater data speeds, high throughput, expanded power, lower bandwidth, and high efficiency. In addition, IoT has transformed the world in light of these new ideas by offering smooth communication between heterogeneous networks (HetNets).
K. Manikandan, Niveditha V. R., Sudha K., Magesh S., and Radha Rammohan S.
IGI Global
RFM (recency, frequency, monetary) examination is a method to perceive high-response customers in promoting progressions and to improve general response rates, which is remarkable and is comprehensively associated today. Less extensively understood is the estimation of applying RFM scoring to a customer database and evaluating customer advantage. A customer who has passed by an e-keeping cash site recently (R) and frequently (F) and influenced a huge amount of monetary to esteem (M) through portion and standing solicitations is presumably going to visit and make portions yet again. After appraisal of the customer's lead using specific RFM criteria, the RFM score is related to the bank excitement, with a high RFM score being more important to the bank by and by and later on.
R. Kumar, Fadi Al-Turjman, L. Anand, Abhishek Kumar, S. Magesh, K. Vengatesan, R. Sitharthan, and M. Rajesh
Springer Science and Business Media LLC
Attributable to the modernization of Artificial Intelligence (AI) procedures in healthcare services, various developments including Support Vector Machine (SVM), and profound learning. For example, Convolutional Neural systems (CNN) have prevalently engaged in a significant job of various classificational investigation in lung malignant growth, and different infections. In this paper, Parallel based SVM (P-SVM) and IoT has been utilized to examine the ideal order of lung infections caused by genomic sequence. The proposed method develops a new methodology to locate the ideal characterization of lung sicknesses and determine its growth in its early stages, to control the growth and prevent lung sickness. Further, in the investigation, the P-SVM calculation has been created for arranging high-dimensional distinctive lung ailment datasets. The data used in the assessment has been fetched from real-time data through cloud and IoT. The acquired outcome demonstrates that the developed P-SVM calculation has 83% higher accuracy and 88% precision in characterization with ideal informational collections when contrasted with other learning methods.
Ambeshwar Kumar, R. Manikandan, S. Magesh, Rizwan Patan, S. Ramesh, and Deepak Gupta
Elsevier
S. Magesh, Sujatha Jamuna Anand, Niveditha V.R, Y. Pavan Kumar Reddy, and P.S. Rajakumar
IOS Press
Pervasive computing has made life easy with communication devices. Today devise collaboration has enhanced everywhere in this environment. It has made computing devices invisible and the services. This pervasive framework provides applications with interactions, numerous cooperation and accessibility, and integration. The proposed work enumerates the applications, pervasive security challenges. It provides security predicaments by assigning certificate credentials, access controls, trust management, and some security techniques to overcome the security paradigms in these distributed networks with IoT and the pervasive computing framework. The work also encounters security perplexities in handling the security threats and user interaction issues. Nevertheless, security techniques are listed for various pervasive applications in distinct domains such as healthcare, industries, and transforming sensitive information. The smart applications with smart environments perhaps force towards the new technologies in the pervasive computing outlook. The work also embedded with middleware with the context-based situation in these pervasive applications
Magesh S., Niveditha V. R., Radha RamMohan S, Amandeep Singh K., and Bessy Deborah P.
IGI Global
Image processing concepts are used in the biomedical domain. Brain tumors are among the dreadful diseases. The primary brain tumors start with errors which are the mutations that take place in the part of DNA. This mutation makes the cells breed in a huge manner and makes the healthier cells die. The mass of the unhealthier cells is called tumor, or the unwanted cell growth in the tissues of the brain are called brain tumors. In this chapter, images from the positron emission tomography (PET) scan and MRI scan are fused as a one image, and from that image, neural network concepts are applied to detect the tumor. The main intention of this proposed approach is to segment and identify brain tumors in an automatic manner using image fusion with neural network concepts. Segmentation of brain images is needed to segment properly from other brain tissues. Perfect detection of size and position of the brain tumor plays an essential role in the identification of the tumor.
Senthil Murugan Nagarajan, Muthukumaran V., Vinoth Kumar V., Beschi I. S., and S. Magesh
IGI Global
The workflow between business and manufacturing system level is changing leading to delay in exploring the context of innovative ideas and solutions. Smart manufacturing systems progress rapid growth in integrating the operational capabilities of networking functionality and communication services with cloud-based enterprise architectures through runtime environment. Fine tuning aims to process intelligent management, flexible monitoring, dynamic network services using internet of things (IoT)-based service oriented architecture (SOA) solutions in numerous enterprise systems. SOA is an architectural pattern for building software business systems based on loosely coupled enterprise infrastructure services and components. The IoT-based SOA enterprise systems incorporate data elicitation, integrating agile methodologies, orchestrate underlying black-box services by promoting growth in manufacturer enterprises workflow. This chapter proposes the integration of standard workflow model between business system level and manufacturing production level with an IoT-enabled SOA framework.
S. Magesh, V. R. Niveditha, Ambeshwar Kumar, R. Manikandan, and P. S. Rajakumar
Springer Singapore
Magesh S., Niveditha V.R., Rajakumar P.S., Radha RamMohan S., and Natrayan L.
Emerald
Purpose The current and on-going coronavirus (COVID-19) has disrupted many human lives all over the world and seems very difficult to confront this global crisis as the infection is transmitted by physical contact. As no vaccine or medical treatment made available till date, the only solution is to detect the COVID-19 cases, block the transmission, isolate the infected and protect the susceptible population. In this scenario, the pervasive computing becomes essential, as it is environment-centric and data acquisition via smart devices provides better way for analysing diseases with various parameters. Design/methodology/approach For data collection, Infrared Thermometer, Hikvision’s Thermographic Camera and Acoustic device are deployed. Data-imputation is carried out by principal component analysis. A mathematical model susceptible, infected and recovered (SIR) is implemented for classifying COVID-19 cases. The recurrent neural network (RNN) with long-term short memory is enacted to predict the COVID-19 disease. Findings Machine learning models are very efficient in predicting diseases. In the proposed research work, besides contribution of smart devices, Artificial Intelligence detector is deployed to reduce false alarms. A mathematical model SIR is integrated with machine learning techniques for better classification. Implementation of RNN with Long Short Term Memory (LSTM) model furnishes better prediction holding the previous history. Originality/value The proposed research collected COVID −19 data using three types of sensors for temperature sensing and detecting the respiratory rate. After pre-processing, 300 instances are taken for experimental results considering the demographic features: Sex, Patient Age, Temperature, Finding and Clinical Trials. Classification is performed using SIR mode and finally predicted 188 confirmed cases using RNN with LSTM model.
S. Magesh, J. Indumathi, and S. Radha RamMohan
EverScience Publications
Edge has become a growing trend in recent years. Bringing computing and analytics remarkably close to the data where it originated is the leading cause of edge computing. As the data is growing day by day, there arises the bottleneck in computation and network layers. Due to the enormous growth of Internet of Things (IoT) devices with its recent applications, the need for real-time computation has readily driven edge computing. Today data processing is an excellent paradigm for real-time data. In the integration of various IoT devices to solve the computing perplexities, created the emergence of the Edge computing. This paper clarifies concepts and contributions of edge computing associated with IoT devices. The proposed work produces a thumbnail survey on edge computing and its performance management towards IoT devices. The characteristics and architecture of Edge computing over IoT devices are furnished. The state-of-the-art on edge computing applications in the real-time scenario is discussed in this article. The proposed work explores the key benefits of Edge computing towards IoT devices, along with the comparative principles of edge computing over the Cloud, are represented. The existing challenges of edge computing are also discussed in this work. Index Terms – Edge Computing, IoT devices, Data Processing, Performance Computing.
Jagadeesan Dhanapal, Badrinath Narayanamurthy, Vijayakumar Shanmugam, Asha Gangadharan, and Magesh S.
Emerald
Purpose The purpose of this paper is to provide a model for prediction of respiratory symptoms in the progression of COVID-19, social distancing, frequent hand washes, wearing of face mask in public are some of the potential measures of preventing the disease from further spreading. In spite of the effects and efforts taken by governments, the pandemic is still uncontrolled in major cities of the world. The proposed technique in this paper introduces a non-intrusive and major screening of vital symptoms and changes in the respiratory organs. Design/methodology/approach The novel coronavirus or Covid-19 has become a serious threat to social and economic growth of many nations worldwide. The pace of progression was significantly higher in the past two months. Identified by severe respiratory illness, fever and coughs, the disease has been threatening the lives of human society. Early detection and prognosis is absolutely necessary to isolate the potential spreaders of the disease and to control the rate of progression. Findings Recent studies have highlighted the changes observed in breathing characteristics of infected patients. Respiratory pattern of Covid-19 patients can be differentiated from the respiratory pattern of normal cold/flu affected patients. Tachypnoea is one among the vital signs identified to be distinguishing feature of Covid-19. The proposed respiratory data capture will commence with facial recognition, use of infrared sensors and machine-learning approaches to classify the respiratory patterns, which finally narrows down as a symptom of Covid-19. Originality/value Proposed system produced outcome of 94% accuracy, precision, recall and a F1-measure as an average in the conducted experiments. This method also proves to be a fruitful solution for large-scale monitoring and categorisation of people based on the symptoms.
Robbi Rahim, S. Murugan, S. Priya, S. Magesh, and R. Manikandan
EverScience Publications
Wireless Sensor Network (WSN) design to be efficient expects better energy optimization methods as nodes in WSN are operated only through batteries. In WSN, energy is a challenging one in the network during transmission of data. To overcome the energy issue in WSN, Taylor based Grey Wolf Optimization algorithm proposed, which is the integration of the Taylor series with Grey Wolf Optimization approach finding optimal hops to accomplish multi-hop routing. This paper shows the multiple objective-based approaches developed to achieve secure energyaware multi-hop routing. Moreover, secure routing is to conserve energy efficiently during routing. The proposed method achieves 23.8% of energy, 75% of Packet Delivery Ratio, 35.8% of delay, 53.2% of network lifetime, and 84.8% of scalability. Index Terms – Taylor Series, Grey Wolf Optimization, Multihop Routing, Energy Efficiency, Security.
S. Magesh and S. Vijayalakshmi
American Scientific Publishers
The paper aspires at discovering the most indispensable factors persuading customer reactions and purchasing commodities after observing online advertisements of social media and recognizing the distinctiveness of clusters of Purchaser having the optimistic reaction, over and above of buying customer clusters after analyzing online advertisement in social media. The selection of attribute and clustering techniques are incorporated in the analysis of data to find significant factors and target customer clusters correspondingly through data mining approach. It has been identifies that there is a strapping correlation between the advertisement being clicked on social media and the fulfillment with commodities, and amidst purchasing commodities online and saving information for supplementary deliberations. The findings also points out the characteristics of product and price Conscious clusters for Purchasers' reaction and procuring after seeing online social media advertisement.