Dr. Manoj kumar M
@jyothyit.ac.in
Associate Professor, Department of Computer Science & Engineering
Jyothy Institute of Technology, Bangalore
Dr. Manoj Kumar M, Associate Professor in the Department of Computer Science and Engineering, received his doctoral degree from Jain University, Bangalore in the year 2021. He has studied M. Tech. in Computer Science and Engineering from Nitte Meenakshi Institute of Technology, Bangalore and BE in Computer Science and Engineering from Dr. Ambedkar Institute of Technology, Bangalore, M. Tech. in Computer Science and Engineering from Nitte Meenakshi Institute of Technology, Bangalore affiliated to Visvesvaraya Technological University, Karnataka in 2010 and 2012 respectively. He has 2 years of Industry experience and 10 years of academic experience. His research interests include Cloud Computing, Artificial Intelligence and Machine Learning, Data Science and IOT. He has published 18 research papers in various International journals. He has one patent his name. He is life member in CSI, ISTE. He has reviewed several research articles for reputed journals and international conferences.
RESEARCH INTERESTS
Cloud Computing, Image Processing, Big data Analytics
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
N. Shravan, M. Manoj Kumar, Sriraag Jayanth, R. S. Bindu, B. R. Madhu, and K. S. Sreekeshava
Springer Nature Singapore
Manoj Kumar M. V., S. L. Shiva Darshan, Prashanth B. S, and Vishnu Yarlagadda
IGI Global
The importance of cybersecurity in the contemporary digital age is profound. In this chapter, the authors will traverse through the complex and evolving landscape of cybersecurity, exploring its progression, the driving forces behind it, the key challenges it faces, and its future trajectory. With an in-depth analysis of various threat actors and types of cyber threats, the authors will delve into the tools and technologies developed to combat these threats. The authors also explore and compare different cybersecurity approaches, studying their effectiveness and their implications. Through real-world case studies of major cyber-attacks, the chapter will provide insightful lessons learned and the impact they had on the cybersecurity landscape. We will also discuss the often-overlooked human factor in cybersecurity, focusing on the significance of cybersecurity training and the psychology of social engineering attacks. By providing a comprehensive overview of the field, this chapter aims to equip the reader with a well-rounded understanding of cybersecurity.
Manoj Kumar M. V., S. L. Shiva Darshan, Prashanth B. S., and Vishnu Yarlagadda
IGI Global
In today's interconnected digital world, the threat of malware looms large, posing significant risks to individuals, businesses, and governments. This chapter serves as a comprehensive introduction to the critical field of malware analysis and detection. The chapter begins with a definition of malware, exploring its various forms and the historical perspective of its evolution. The authors delve into the different types of malware, including viruses, worms, Trojans, ransomware, and more, understanding their unique behaviors and propagation methods. Building upon this foundation, they introduce the fundamental concepts of malware analysis methodologies, including static and dynamic analysis, reverse engineering, virtualization, and sandboxing. These techniques enable cybersecurity professionals to gain insights into malware behavior and functionality. To address this challenge, the chapter introduces advanced malware analysis techniques, such as memory forensics, behavioral analysis, kernel-level rootkit detection, and machine learning-based analysis.
Udo Christian Braendle, Nasser Almuraqab, M. V. Manoj Kumar, and Ananth Rao
Springer International Publishing
Aditya Ranjan, Aditya Narayan Singh, Amit Kumar, B S Prashanth, and M V Manoj Kumar
IEEE
A court case governance system is a decentralised judicial system that uses blockchain technology to create a tamperproof, transparent, and secure form of record-keeping for legal processes. A distributed ledger powered by a network of computers is known as blockchain technology. Every transaction is digitally entered into the ledger, encrypted, and verified by the network of users. A court case governance system using blockchain technology can produce smart contracts, which are self-executing contracts with the contents of the agreement between the buyer and seller being directly placed into lines of code. Decentralising the process of automating contracts and arbitrating legal issues is possible with the help of these smart contracts. Users are given the power to draught and execute intelligent contracts, and disputes are resolved through decentralised arbitration. This strategy enables a method to incentivize jurors to evaluate cases fairly and accurately since judgements are upheld by smart contracts.
Gunda Sai Yeshwanth, B. Annappa, Shubham Dodia, and M. V. Manoj Kumar
Springer Nature Singapore
Abhinav Antani, B. Annappa, Shubham Dodia, and M. V. Manoj Kumar
Springer Nature Singapore
Prashanth P. Wagle and M. V. Manoj Kumar
Springer Nature Singapore
Neeshad Kumar Sakure, M. V. Manoj Kumar, B. S. Prashanth, H. R. Sneha, and Likewin Thomas
Springer Nature Singapore
Ananth Rao, M. V. Manoj Kumar, Nanda Kumar B. V. S. Sashtry, Immanuel Azaad Moonesar, Arkalgud Ramaprasad, Alicia Núñez, B. Annappa, Karan Bhanot, and Wathiq Mansoor
Frontiers Media SA
COPYRIGHT © 2022 Rao, Manoj Kumar, Sashtry, Moonesar, Ramaprasad, Núñez, Annappa, Bhanot and Mansoor. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: AI and Healthcare Financial Management (HFM) towards sustainable development
K Aditya Shastry, V Vijayakumar, Manoj Kumar M V, Manjunatha B A, and Chandrashekhar B N
MDPI AG
“Alzheimer’s disease” (AD) is a neurodegenerative disorder in which the memory shrinks and neurons die. “Dementia” is described as a gradual decline in mental, psychological, and interpersonal qualities that hinders a person’s ability to function autonomously. AD is the most common degenerative brain disease. Among the first signs of AD are missing recent incidents or conversations. “Deep learning” (DL) is a type of “machine learning” (ML) that allows computers to learn by doing, much like people do. DL techniques can attain cutting-edge precision, beating individuals in certain cases. A large quantity of tagged information with multi-layered “neural network” architectures is used to perform analysis. Because significant advancements in computed tomography have resulted in sizable heterogeneous brain signals, the use of DL for the timely identification as well as automatic classification of AD has piqued attention lately. With these considerations in mind, this paper provides an in-depth examination of the various DL approaches and their implementations for the identification and diagnosis of AD. Diverse research challenges are also explored, as well as current methods in the field.
Ananth Rao, Manoj Kumar M. V., Immanuel Azaad Moonesar, Shadi Atalla, B. S. Prashanth, Gaurav Joshi, Tarun K. Soni, Thi Le, Anuj Verma, and Hazem Marashdeh
Frontiers Media SA
The paper models investor sentiments (IS) to attract investments for Health Sector and Growth in emerging markets, viz., India, Mainland China, and the UAE, by asking questions such as: What specific healthcare sector opportunities are available in the three markets? Are the USA-IS key IS predictors in the three economies? How important are macroeconomic and sociocultural factors in predicting IS in these markets? How important are economic crises and pandemic events in predicting IS in these markets? Is there contemporaneous relation in predicting IS across the three countries in terms of USA-IS, and, if yes, is the magnitude of the impact of USA-IS uniform across the three countries' IS? The artificial neural network (ANN) model is applied to weekly time-series data from January 2003 to December 2020 to capture behavioral elements in the investors' decision-making in these emerging economies. The empirical findings confirmed the superiority of the ANN framework over the traditional logistic model in capturing the cognitive behavior of investors. Health predictor—current health expenditure as a percentage of GDP, USA IS predictor—spread, and Macro-factor GDP—annual growth % are the common predictors across the 3 economies that positively impacted the emerging markets' IS behavior. USA (S&P 500) return is the only common predictor across the three economies that negatively impacted the emerging markets' IS behavior. However, the magnitude of both positive and negative impacts varies across the countries, signifying unique, diverse socioeconomic, cultural, and market features in each of the 3 economies. The results have four key implications: Firstly, US market sentiments are an essential factor influencing stock markets in these countries. Secondly, there is a need for developing a robust sentiment proxy on similar lines to the USA in the three countries. Thirdly, investment opportunities in the healthcare sector in these economies have been identified for potential investments by the investors. Fourthly, this study is the first study to investigate investors' sentiments in these three fast-emerging economies to attract investments in the Health Sector and Growth in the backdrop of UN's 2030 SDG 3 and SDG 8 targets to be achieved by these economies.
Manoj Kumar M. V., Nanda Kumar Bidare Sastry, Immanuel Azaad Moonesar, and Ananth Rao
Frontiers Media SA
The majority of the world's population is still facing difficulties in getting access to primary healthcare facilities. Universal health coverage (UHC) proposes access to high-quality, affordable primary healthcare for all. The 17 UN sustainable development goals (SDGs) are expected to be executed and achieved by all the 193 countries through national sustainable development strategies and multi-stakeholder partnerships. This article addresses SDG 3.8—access to good quality and affordable healthcare and two subindicators related to societal impact (SDG 3.8.1 and 3.8.2) through two objectives. The first objective is to determine whether health expenditure indicators (HEIs) drive UHC, and the second objective is to analyze the importance of key determinants and their interactions with UHC in three economic blocks: emerging Gulf Cooperation Council (GCC); developing Brazil, Russia, India, China, and South Africa (BRICS) vis-à-vis the developed Australia, UK, and USA (AUKUS). We use the WHO Global Health Indicator database and UHC periodical surveys to evaluate the hypotheses. We apply state-of-the-art machine learning (ML) models and ordinary least square (traditional—OLS regression) methods to see the superiority of artificial intelligence (AI) over traditional ones. The ML Random Forest Tree method is found to be superior to the OLS model in terms of lower root mean square error (RMSE). The ML results indicate that domestic private health expenditure (PVT-D), out-of-pocket expenditure (OOPS) per Capita in US dollars, and voluntary health insurance (VHI) as a percentage of current health expenditure (CHE) are the key factors influencing UHC across the three economic blocks. Our findings have implications for drafting health and finance sector public policies, such as providing affordable social health insurance to the weaker sections of the population, making insurance premiums less expensive and affordable for the masses, and designing healthcare financing policies that are beneficial to the masses. UHC is an important determinant of health for all and requires an in-depth analysis of related factors. Policymakers are often faced with the challenge of prioritizing the economic needs of sectors such as education and food safety, making it difficult for healthcare to receive its due share. In this context, this article attempts to identify the key components that may influence the attainment of UHC and enable policy changes to address them more effectively and efficiently.
Shadi Atalla, Saad Ali Amin, M. V. Manoj Kumar, Nanda Kumar Bidare Sastry, Wathiq Mansoor, and Ananth Rao
Frontiers Media SA
Multi-morbidity is the presence of two or more long-term health conditions, including defined physical or mental health conditions, such as diabetes or schizophrenia. One of the regular and critical health cases is an elderly person with a multi-morbid health condition and special complications who lives alone. These patients are typically not familiar with advanced Information and Communications Technology (ICT), but they are comfortable using smart devices such as wearable watches and mobile phones. The use of ICT improves medical quality, promotes patient security and data security, lowers operational and administrative costs, and gives the people in charge to make informed decisions. Additionally, the use of ICT in healthcare practices greatly reduces human errors, enhances clinical outcomes, ramps up care coordination, boosts practice efficiencies, and helps in collecting data over time. The proposed research concept provides a natural technique to implement preventive health care innovative solutions since several health sensors are embedded in devices that autonomously monitor the patients' health conditions in real-time. This enhances the elder's limited ability to predict and respond to critical health situations. Autonomous monitoring can alert doctors and patients themselves of unexpected health conditions. Real-time monitoring, modeling, and predicting health conditions can trigger swift responses by doctors and health officials in case of emergencies. This study will use data science to stimulate discoveries and breakthroughs in the United Arab Emirates (UAE) and India, which will then be reproduced in other world areas to create major gains in health for people, communities, and populations.
Chaitra C, Chennamma, Vethanayagi R, Manoj Kumar M V, Prashanth B S, Snehah H R, Likewin Thomas, and Shiva Darshan S L
IEEE
In the year 2022, an estimated 2.2 billion people around the globe will have a visual impairment. The problem may be hereditary or due to accidents. Nonetheless, technological advancements in helping visually impaired people have been going on for a long time. The admittance of technical concepts such as robotics, Machine Learning, and Artificial Intelligence for societal needs has proven worthwhile. The blind or visually impaired people learn about their surroundings through other senses, such as touch, hearing, and smell. Our proposed work aims to build an end-to-end solution for visually impaired people to help them grasp the environment by summarizing the images or video streams with the help of Machine Learning paradigms. The proposed work uses a pre-trained Caffe Object Detection model and requires less data for training and detection. We have developed a Client-Server model for our proposed idea wherein the significant computations happen on the server side, which is the Object detection model, and the client App is developed using Android. The app also has a text-to-signal processing feature that helps summarize the objects detected in the form of an audio catalog.
Anand Jaiswal, Anchal Pandey, Srinivas K R, V Avinash, Shiva Darshan S L, Manoj Kumar M V, Prashanth B S, Janardhana D R, Dileep Reddy Bolla, and Vijaya Shetty S
IEEE
Holograms provide us a fascinating view of the three-dimensional world around us. Holograms offer a shifting perspective based on the viewer’s position by enabling the eye to adjust the focal depth and alternately focus on the foreground and background. Researchers have long sought to produce computer-generated holograms, but the process has frequently required a supercomputer to run physics simulations, which is time-consuming and can provide results that are less than photo-realistic. This paper describes a cutting-edge 3D holographic and artificial intelligence technology as a remedy to this drawback. The recommended approach employs the pepper ghost pyramid projection technique to provide 3D holographic output. This method creates the illusion that a 3D holographic output is floating in the air. In order to perform fundamental AI tasks, the system leverages Natural Language Processing to understand user requests and provide answers. In order to enhance the appearance of authentic human-machine interaction, the system additionally employs Motion Machine Learning (Motion ML) to provide a variety of non-verbal indications.
Likewin Thomas, Manoj Kumar M V, Prashanth B S, and Sneha H R
IEEE
Increasing usage of gadgets is what can be seen in present days. Gadgets that tend to reduce human effort and anxiety are used the most. The excessive use of these being in contact has made the world lose its eyes or fall under stress. Thus, the voice assistant application is built, which takes up real voice recognition, processes the requirements according to the client, and responds well. An algorithm named seq2seq is used to accomplish the same. Since it has the feature of completing tasks without eye contact, it diminishes the requirement of continually looking at their screen. The latter task is accomplished using verbally ordering the gadgets to do the respective task. The task accomplished by the voice assistant is having a friendly chat and querying the results. Querying the results include searching through Google and summarizing the term. An additional feature added to it is a blink option as we call to alert the user for every n second as prescribed by the user.
G. Moulshree, M. V. Manoj Kumar, B. S. Prashanth, M. A. Ajay Kumara, S. L. Shivadarshan, H. R. Sneha, and P. Wagle Prashanth
Springer Nature Singapore
Sachin Somanna, N. Vikhyath Shetty, M. Mohammed Afthab, Sagar Neupane, M. V. Manoj Kumar, M. A. Ajay Kumara, Bjarne Berg, and Prashanth P. Wagle
Springer Nature Singapore
B. R. Sreenivasa, C. R. Nirmala, and M. V. Manoj Kumar
Springer Singapore
M. V. Manoj Kumar, B. S. Prashanth, H. R. Sneha, Likewin Thomas, B. Annappa, and Y. V. S. Murthy
Springer Singapore
B. S. Prashanth, M. V. Manoj Kumar, Likewin Thomas, M. A. Ajay Kumar, Dinghao Wu, B. Annappa, Anirudh Hebbar, and Y. V. Srinivasa Murthy
Springer International Publishing
G M Aditya, Aditya Hoode, K Anvesh Rai, Gangadhar Biradar, M.A Ajay Kumara, M V Manoj Kumar, B S Prashanth, H R Sneha, and S L Shivadarshan
IEEE
Normally, the long queues and crowd can be seen at the canteens/hotels whenever there is a lunch break in an organization or within a campus. This paper proposes a solution for eliminating the queue system and introduces the facility to remotely place food orders. Further, this paper proposes a real-time food recommendation system to suggest the dishes to users based on their past orders. The solution has been implemented through a mobile application built using Flutter. The mobile application has been empowered with a machine learning model for recommending the items that the user might like. The proposed method has been tested with 2 vendors catering to 60 customers successfully. The accuracy of the recommendation has been satisfactory.