471
20
16488
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Dublin Core
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Book
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Intersection of AI and business intelligence in data-driven decision-making
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In today's rapidly evolving business landscape, organizations are inundated with vast amounts of data, making it increasingly challenging to extract meaningful insights and make informed decisions. The traditional business intelligence (BI) approach must often address the complexity and speed required for effective decision-making in this data-rich environment. As a result, many businesses need help to leverage their data to drive sustainable growth and remain competitive. Intersection of AI and Business Intelligence in Data-Driven Decision-Making presents a transformative solution to this pressing challenge. By exploring the convergence of artificial intelligence (AI) and BI, our book provides a comprehensive framework for leveraging AI-powered BI to revolutionize data analysis, predictive modeling, and decision-making processes. Readers will gain valuable insights into practical applications, emerging trends, and ethical considerations, inspiring and exciting them about the potential of AI in driving business success. Through in-depth discussions, case studies, and best practices, this book equips professionals, researchers, and students with the knowledge and tools needed to navigate the complexities of AI-powered business intelligence. Whether you're looking to predict trends, analyze consumer behavior, or optimize supply chains, this book offers actionable strategies and techniques for implementing AI-powered BI solutions in your organization. 2024 by IGI Global. All rights reserved.
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Natarajan A.K.; Galety M.G.; Iwendi C.; Das D.; Shankar A.
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Intersection of AI and Business Intelligence in Data-Driven Decision-Making, pp. 1-486.
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IGI Global
Date
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2024-01-01
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<a href="https://doi.org/10.4018/979-8-3693-5288-5" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-5288-5</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215356172&doi=10.4018%2F979-8-3693-5288-5&partnerID=40&md5=878d751b15cd07279579681d7520cc41" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215356172&doi=10.4018%2f979-8-3693-5288-5&partnerID=40&md5=878d751b15cd07279579681d7520cc41</a>
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ISBN: 979-836935289-2; 979-836935288-5
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Online
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English
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Book
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Natarajan A.K., Samarkand International University of Technology, Uzbekistan; Galety M.G., Samarkand International University of Technology, Uzbekistan; Iwendi C., University of Bolton, United Kingdom; Das D., Christ University, India; Shankar A., University of Warwick, United Kingdom
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Editorials
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Title
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Preface
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[No abstract available]
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Natarajan A.K.; Galety M.G.; Iwendi C.; Das D.; Shankar A.
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Intersection of AI and Business Intelligence in Data-Driven Decision-Making, pp. xviii-xix.
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IGI Global
Date
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2024-01-01
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<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215357716&partnerID=40&md5=31f3ed585064f095c8ffa69c3667675b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215357716&partnerID=40&md5=31f3ed585064f095c8ffa69c3667675b</a>
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ISBN: 979-836935289-2; 979-836935288-5
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Online
Language
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English
Type
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Editorial
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Natarajan A.K., Samarkand International University of Technology, Uzbekistan; Galety M.G., Samarkand International University of Technology, Uzbekistan; Iwendi C., University of Bolton, United Kingdom; Das D., Christ University, India; Shankar A., University of Warwick, United Kingdom
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Book Chapter
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Cyber secure man-in-the-middle attack intrusion detection using machine learning algorithms
Description
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The main objective of this chapter is to enhance security system in network communication by using machine learning algorithm. Cyber security network attack issues and possible machine learning solutions are also elaborated. The basic network communication component and working principle are also addressed. Cyber security and data analytics are two major pillars in modern technology. Data attackers try to attack network data in the name of man-in-the-middle attack. Machine learning algorithm is providing numerous solutions for this cyber-attack. Application of machine learning algorithm is also discussed in this chapter. The proposed method is to solve man-in-the-middle attack problem by using reinforcement machine learning algorithm. The reinforcement learning is to create virtual agent that should predict cyber-attack based on previous history. This proposed solution is to avoid future cyber middle man attack in network transmission. 2022 by IGI Global. All rights reserved.
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Natarajan J.
Source
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Research Anthology on Machine Learning Techniques, Methods, and Applications, pp. 976-1001.
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IGI Global
Date
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2022-01-01
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<a href="https://doi.org/10.4018/978-1-6684-6291-1.ch051" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/978-1-6684-6291-1.ch051</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137301265&doi=10.4018%2F978-1-6684-6291-1.ch051&partnerID=40&md5=d3ea01ce2cfb55a827cbcdb353bf44a6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137301265&doi=10.4018%2f978-1-6684-6291-1.ch051&partnerID=40&md5=d3ea01ce2cfb55a827cbcdb353bf44a6</a>
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ISBN: 978-166846292-8; 1668462915; 978-166846291-1
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Online
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English
Type
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Book chapter
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Natarajan J., Christ University, India
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Dublin Core
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Title
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Book Chapter
Book Chapter
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Title
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Cyber secure man-in-the- middle attack intrusion detection using machine learning algorithms
Description
An account of the resource
The main objective of this chapter is to enhance security system in network communication by using machine learning algorithm. Cyber security network attack issues and possible machine learning solutions are also elaborated. The basic network communication component and working principle are also addressed. Cyber security and data analytics are two major pillars in modern technology. Data attackers try to attack network data in the name of man-in-the-middle attack. Machine learning algorithm is providing numerous solutions for this cyber-attack. Application of machine learning algorithm is also discussed in this chapter. The proposed method is to solve man-in-the-middle attack problem by using reinforcement machine learning algorithm. The reinforcement learning is to create virtual agent that should predict cyber-attack based on previous history. This proposed solution is to avoid future cyber middle man attack in network transmission. 2020, IGI Global.
Creator
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Natarajan J.
Source
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AI and Big Data's Potential for Disruptive Innovation, pp. 291-316.
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IGI Global
Date
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2019-01-01
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<a href="https://doi.org/10.4018/978-1-5225-9687-5.ch011" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/978-1-5225-9687-5.ch011</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077315652&doi=10.4018%2F978-1-5225-9687-5.ch011&partnerID=40&md5=77f1f7c7e1c5ab9f65322dc2e19ddbd8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077315652&doi=10.4018%2f978-1-5225-9687-5.ch011&partnerID=40&md5=77f1f7c7e1c5ab9f65322dc2e19ddbd8</a>
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ISBN: 978-152259689-9; 1522596879; 978-179981055-1
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Online
Language
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English
Type
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Book chapter
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Natarajan J., Christ University, Department of Computer Science and Engineering, Bangalore, India
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Conference Papers
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Transparent Data Encryption: Comparative Analysis and Performance Evaluation of Oracle Databases
Subject
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Cons of Encryption; Cyber-Attack; DBA; TDE
Description
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This Transparent Data Encryption (TDE) can provide enormous benefits to the Relational Databases in the aspects of Data Security, Cryptographic Encryption, and Compliances. For every transaction, the stored data must be decrypted before applying the updates as well as should be encrypted before permanently storing back at the storage level. By adding this extra functionality to the database, the general thinking denotes that the Database (DB) going to hit some performance overhead at the CPU and storage level. However, The Oracle Corporation has adversely claimed that their latest Oracle DB version 19c TDE feature can provide significant improvement in the optimization of CPU and no overhead at the storage level for data processing. Impressively, it is true. the results of this paper prove too. Most interestingly the results also revealed about highly impacted components in the servers which are not yet disclosed in any of the previous research work. This paper completely concentrates on CPU, IO, and RAM performance analysis and identifying the bottlenecks along with possible solutions. 2020 IEEE.
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Natarajan K.; Shaik V.
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Proceedings - 2020 5th International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2020, pp. 137-142.
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Institute of Electrical and Electronics Engineers Inc.
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2020-01-01
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<a href="https://doi.org/10.1109/ICRCICN50933.2020.9296168" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICRCICN50933.2020.9296168</a>
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ISBN: 978-172818818-8
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Online
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English
Type
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Conference paper
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Natarajan K., Christ Deemed to Be University, Department of Computer Science and Engineering, Karnataka, India; Shaik V., Christ Deemed to Be University, Department of Computer Science and Engineering, Karnataka, India
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Title
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Book Chapter
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Title
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Ensemble Model of Machine Learning for Integrating Risk in Software Effort Estimation
Subject
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Actual effort; Effort driver; Estimated effort; Predictive model; Risk score
Description
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The development of software involves expending a significant quantum of time, effort, cost, and other resources, and effort estimation is an important aspect. Though there are many software estimation models, risks are not adequately considered in the estimation process leading to wide gap between the estimated and actual efforts. Higher the level of accuracy of estimated effort, better would be the compliance of the software project in terms of completion within the budget and schedule. This study has been undertaken to integrate risk in effort estimation process so as to minimize the gap between the estimated and the actual efforts. This is achieved through consideration of risk score as an effort driver in the computation of effort estimates and formulating a machine learning model. It has been identified that risk score reveals feature importance and the predictive model with integration of risk score in the effort estimates indicated an enhanced fit. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Natarajan R.; Balachandran K.
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Lecture Notes on Data Engineering and Communications Technologies, Vol-114, pp. 635-644.
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Springer Science and Business Media Deutschland GmbH
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2022-01-01
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<a href="https://doi.org/10.1007/978-981-16-9416-5_46" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-9416-5_46</a>
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ISSN: 23674512
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Online
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English
Type
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Book chapter
Coverage
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Natarajan R., School of Business and Management, CHRIST (Deemed To Be University), Bangalore, India; Balachandran K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed To Be University), Bangalore, India
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Articles
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Growth of some urinary crystals and studies on inhibitors and promoters. II. X-ray studies and inhibitory or promotery role of some substances
Description
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Best conditions were established for the gel growth of three urinary crystals viz., calcium oxalate monohydrate, calcium hydrogen phosphate dihydrate and ammonium magnesium phosphate hexahydrate. The crystals grown were characterized using single crystal X-ray diffraction techniques and density measurements. Crystal growth experiments were carried out by incorporating the extracts or juices of some natural products in the gel media. By carefully observing the changes in the growth of crystals (compared to control experiments carried out at the same conditions), results about the inhibitory or promotery role of the substance incorporated were obtained. It was found that the extracts or juices of many of the naturally occurring substances have interesting inhibitory or promotery effects. These results may have useful applications in the treatment of recurrent stone patients.
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Natarajan S.; Ramachandran E.; Blisin Suja D.
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Crystal Research and Technology, Vol-32, No. 4, pp. 553-559.
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Wiley-VCH Verlag
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1997-01-01
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<a href="https://doi.org/10.1002/crat.2170320409" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/crat.2170320409</a>
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ISSN: 2321300; CODEN: CRTED
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Online
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English
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Article
Coverage
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Natarajan S., Department of Physics, Madurai Kamaraj University, Madurai - 625021, India; Ramachandran E., Department of Physics, Madurai Kamaraj University, Madurai - 625021, India, Department of Physics, Thiruvalluvar College, Papanasam - 627425, India; Blisin Suja D., Department of Physics, Madurai Kamaraj University, Madurai - 625021, India, Department of Physics, Christ College, Bangalore - 560029, India
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Articles
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Exploring tourists metaverse experience using destination spatial presence quality & perceived augmentation: metaverse exploration, physical expedition (MEPE)
Subject
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brand equity; FOMO; Metaverse; perceived augmentation; spatial presence; tourism
Description
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A recent surge of interest surrounds metaverse tourism, with researchers highlighting its potential to revolutionize the tourism industry and attract new travellers. This article delves into the key features of a tourist's metaverse experience that influence their desire to visit a destination in the real world using systems theory. In addition, the current study also explores the moderating role of FOMO (Fear of missing out) in few of the proposed relationships. The study is a cross-sectional descriptive investigation carried out among Indian tourists chosen based on the simple random sampling technique and is analyzed using the Smart PLS software. The findings of the study reveal that several attributes of a tourist's metaverse experience, including entertainment, interaction, trendiness, novelty, and intimacy, significantly enhance both the perceived quality of spatial presence within the destination and the level of perceived augmentation experienced by tourists. Notably, both these factors then exert a significant positive influence on a destination's brand equity, ultimately explaining tourists' intentions to visit the physical location. Interestingly, the moderating role of Fear of Missing Out (FOMO) suggests that the relationship between brand equity and the likelihood of tourists undertaking a physical visit is strengthened as their perceived FOMO increases. 2024 Informa UK Limited, trading as Taylor & Francis Group.
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Natarajan T.; Pragha P.; Dhalmahapatra K.; Veera Raghavan D.R.
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Current Issues in Tourism
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Routledge
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2024-01-01
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<a href="https://doi.org/10.1080/13683500.2024.2372652" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/13683500.2024.2372652</a>
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ISSN: 13683500
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Online
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English
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Article
Coverage
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Natarajan T., Department of Management Studies, National Institute of Technology, Tiruchirappalli, India; Pragha P., Department of Management Studies, National Institute of Technology, Tiruchirappalli, India; Dhalmahapatra K., Indian Institute of Management Shillong, Meghalaya, India; Veera Raghavan D.R., School of Business and Management, CHRIST (Deemed to be) University, Bangalore, India
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Articles
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Title
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Unveiling metaverse sentiments using machine learning approaches
Subject
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Behavior; Information technology; Neural nets; Qualitative research
Description
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Purpose: The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers ones intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience. Design/methodology/approach: The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently. Findings: The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models. Research limitations/implications: Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverses experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverses economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust. Social implications: In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators. Originality/value: The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models. 2024, Emerald Publishing Limited.
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Natarajan T.; Pragha P.; Dhalmahapatra K.; Veera Raghavan D.R.
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Kybernetes
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Emerald Publishing
Date
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2024-01-01
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<a href="https://doi.org/10.1108/K-11-2023-2268" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/K-11-2023-2268</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188192750&doi=10.1108%2FK-11-2023-2268&partnerID=40&md5=214cdd4eeb7dcd452f9391b2d9870084" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188192750&doi=10.1108%2fK-11-2023-2268&partnerID=40&md5=214cdd4eeb7dcd452f9391b2d9870084</a>
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ISSN: 0368492X
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English
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Article
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Natarajan T., Department of Management Studies, National Institute of Technology Tiruchirappalli, Tiruchirappalli, India; Pragha P., Department of Management Studies, National Institute of Technology Tiruchirappalli, Tiruchirappalli, India; Dhalmahapatra K., Operations and Quantitative Techniques, IIM Shillong, Shillong, India; Veera Raghavan D.R., School of Business and Management, CHRIST (Deemed to be) University, Bangalore, India
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Does integrated store service quality determine omnichannel customer lifetime value? Role of commitment, relationship proneness, and relationship program receptiveness
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Customer lifetime value; Integrated store; Relationship marketing; Relationship program receptiveness; Relationship proneness; Service quality; SOR theory
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Purpose: Building on the relationship marketing and stimulus-organism-response (SOR) theory, the purpose of this paper is to study the impact of the integrated store service quality (ISSQ) on the omnichannel customer lifetime value (CLV). The mediating role of customer commitment (affective, normative and continuance) and relationship program receptiveness with the moderating role of customer relationship proneness were relied upon to better understand the omnichannel customer profitability metric (CLV). Design/methodology/approach: The study is descriptive and relies upon the cross-sectional data collected using the self-administered structured questionnaires from 785 omnichannel shoppers. A purposive sampling technique was performed in the study. Structural equation modeling was performed using the SMART-PLS 4.0 software to analyze the data. Findings: The results indicate that omnichannel customer commitment (affective, normative and continuance) differentially mediates the relationship between ISSQ and relationship program receptiveness, subsequently impacting the omnichannel CLV. The customer relationship proneness significantly and positively moderated the relationships between different dimensions of customer commitment and relationship program receptiveness. Research limitations/implications: The study relied upon the cross-sectional data from the Indian population aged above 18years for testing the proposed model. Further studies could test the model across different populations to generalize the study results. Originality/value: This study addresses the need to investigate the omnichannel retail store customer profitability and their relationship performance with the store. By testing the customer relationship management model in the omnichannel retail store context, this study is the first to show that ISSQ will impact the customer profitability and relationship performance metric (CLV) through omnichannel customer commitment and relationship program receptiveness. The moderating effect of customer relationship proneness on a few proposed hypotheses was also tested to give managerial recommendations. 2024, Emerald Publishing Limited.
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Natarajan T.; Veera Raghavan D.R.
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TQM Journal
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Emerald Publishing
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2024-01-01
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<a href="https://doi.org/10.1108/TQM-09-2023-0276" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/TQM-09-2023-0276</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182198059&doi=10.1108%2FTQM-09-2023-0276&partnerID=40&md5=905365318c64359989c1066eb1e73a01" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182198059&doi=10.1108%2fTQM-09-2023-0276&partnerID=40&md5=905365318c64359989c1066eb1e73a01</a>
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ISSN: 17542731
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English
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Article
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Natarajan T., National Institute of Technology Tiruchirappalli, Tiruchirappalli, India; Veera Raghavan D.R., Department of Management Studies, National Institute of Technology Tiruchirappalli, Tiruchirappalli, India, School of Business and Management, Christ University, Bangalore, India
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Natarajan, Arul Kumar; Galety, Mohammad Gouse; Mishra, Nilamadhab; Prabu, M.; Manimaran, A.; Ramasamy, Gobi
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Enhancing Log File Analysis in Digital Forensics and Incident Response through Machine Learning
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01-01-2025
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Cyber Security, Forensics and National Security;pp.378-396
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<a href="https://doi.org/10.1201/9781003497868-17" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003497868-17</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105021606008?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105021606008?origin=resultslist</a>
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Natarajan A.K., Samarkand International University of Technology, Samarkand, Uzbekistan; Galety M.G., Samarkand International University of Technology, Samarkand, Uzbekistan; Mishra N., VIT Bhopal University, Bhopal, India; Prabu M., Amrita Vishwa Vidyapeetham Erachakulam, Andhra Pradesh, India; Manimaran A., VIT-AP University, Amaravati, India; Ramasamy G., Christ University, Bengaluru, India
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Log file analysis is crucial for identifying and exploring digital security incidents by recording system and network traffic. The growing volume and complexity of log data do not allow traditional analytical methods to be used, which led to the need for the development of more advanced analytical tools. This chapter shows a new method to infer practical information from the log file analysis using machine learning algorithms combined with Python programming. The technique has the following structure: Data preprocessing, Feature extraction, and then using multiple machine learning models such as RandomForestClassifier, Gradient Boosting Classifier, SVM, XGBoostClassifier, and MLPClassifier. Adding Python greatly improves these advanced models' accuracy and efficiency in analyzing log files. The XGBoostClassifier achieved the highest accuracy, which was 0.9198 as precision, and it indicates good applicability to complicated log data compared to another model in our test. This section compares the machine learning models using the UNSWNb15 dataset, which provides a broad range of network traffic data. The chapter contains some visualizations of flagship results and a detailed discussion about the results, discussing the challenges and limitations of the proposed approach. It also suggests future research directions. The results also typify the specifics of how Python and machine learning can be disrupted to develop digital forensics incident response practicability, bringing forth such innovations that cater to solving the cyber world's rapidly transitioning threat landscapes and tooling up valued scientific knowledge in the domain. 2026 selection and editorial matter, Vinay Aseri, Sumit Kumar Choudhary, and Adarsh Kumar; individual chapters, the contributors.
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CRC Press
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ISBN: 978-104041196-4; 978-104041198-8;
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English
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Book chapter
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online
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Faculty Publications
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Natarajan, Arul Kumar; Upreti, Kamal; Kshirsagar, Pravin R.; Tak, Tan Kuan
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Generative AI for Healthcare Security: Addressing Privacy Challenges through Anomaly Detection in Healthcare Communications
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01-01-2025
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Advances in Artificial Intelligence for Healthcare Applications;pp.60-71
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<a href="https://doi.org/10.1201/9781003503385-5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003503385-5</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105005686847?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105005686847?origin=resultslist</a>
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Natarajan A.K., Samarkand International University of Technology, Samarkand, Uzbekistan; Upreti K., Christ (Deemed to be University), Delhi NCR, India; Kshirsagar P.R., J D College of Engineering & Management, Maharashtra, Nagpur, India; Tak T.K., Singapore Institute of Technology, Punggol, Singapore
Description
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Cybersecurity within the healthcare sector is paramount due to the sensitive nature of patient data and critical healthcare services. This chapter explores the role of Generative AI (GAI), particularly using BERT embeddings and the Isolation Forest algorithm, in enhancing cybersecurity measures. It begins by discussing the significance of cybersecurity in healthcare and the potential threats healthcare organizations face, emphasizing the need for robust security measures to protect patient data and ensure uninterrupted healthcare services. The chapter provides an overview of GAI and its applications in cybersecurity, focusing on its ability to detect anomalies in healthcare communications. A detailed case study demonstrates the practical implementation of GAI techniques for anomaly detection in healthcare emails, highlighting the effectiveness of BERT embeddings and Isolation Forest in identifying potential security breaches. Furthermore, the chapter discusses the broader implications of generative AI in healthcare cybersecurity, addressing privacy concerns and ethical considerations. The findings underscore the importance of integrating advanced AI technologies with robust privacy-preserving measures to safeguard patient data while promoting technological innovation in healthcare cybersecurity. 2025 selection and editorial matter, Anoop V.S., Suhasini Verma, Usharani Hareesh Govindarajan.
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CRC Press
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ISBN: 978-104035486-5; 978-103282187-0;
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English
Type
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Book chapter
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online
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Faculty Publications
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Natarajan, Jayapandian
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Machine Learning Based Parking Space Classification Using R-CNN and Faster R-CNN FPN Architecture
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01-01-2026
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Lecture Notes in Networks and Systems;Volume;1636 LNNS;pp.64-73
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<a href="https://doi.org/10.1007/978-3-032-05802-7_8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-05802-7_8</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020165029?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020165029?origin=resultslist</a>
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Natarajan J., School of Engineering and Technology, Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India
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This research work aims to create an accurate and economical model for classifying parking space using deep learning techniques. Using current advances in deep learning and computer vision, the proposed model solves urban mobility difficulties, particularly parking management. To address parking space occupancy classification, the research work suggests using two proven deep learning architectures, R-CNN (Region-based Convolutional Neural Networks) and Faster R-CNN FPN (Feature Pyramid Network), as well as insights from previous research. The proposed models take advantage of the R-CNN and Faster R-CNN FPN architectures. This solution uses binary classifiers, such as ResNet50, to assess image patches representing individual parking spaces and offer precise occupancy values. Furthermore, this research investigates the Faster R-CNN FPN architecture, which uses a feature pyramid network to record hierarchical information and reason about complex spatial configurations in parking lots. The proposed models stand out for their ability to use high-resolution photos from real-world parking lots, allowing them to learn discriminative features automatically from raw image data. This differs from traditional methods that rely on handcrafted features, allowing the models to manage a wide range of parking lot circumstances, including changes in weather, illumination, and occlusions caused by surrounding vehicles or barriers. This research intends demonstrate the improved performance and scalability of deep learning models for parking space occupancy classification by conducting extensive testing. Here the implementation method focuses on systematic data collection, annotation, preprocessing, and model training to create machine learning models that can reliably categorize parking spot occupancy, allowing for successful parking management solutions in real-world scenarios. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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Artificial Intelligence; Convolutional Neural Networks; Deep learning; Feature Pyramid Network; Parking Space Classification
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Springer Science and Business Media Deutschland GmbH
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ISSN: 23673370; ISBN: 978-303205801-0;
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English
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Conference paper
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online
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Faculty Publications
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Natarajan, Jayapandian; Marry, Ann; Yasmin, Huda; Eldo, Jessica; Eswaran, Sivaraman; Zade, Nilima; Krishna Kumar, P.R.
Title
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An Application of Improved Support Vector Machine Classifier for the Study of Breast Cancer Detection
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01-01-2025
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Applied Artificial Intelligence and Machine Learning Techniques for Engineering Applications;pp.166-179
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<a href="https://doi.org/10.1201/9781003473442-12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003473442-12</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022042419?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022042419?origin=resultslist</a>
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Natarajan J., Department of Computer Science and Engineering, Christ University Kengeri Campus, Bangalore, India; Marry A., Department of Computer Science and Engineering, Christ University Kengeri Campus, Bangalore, India; Yasmin H., Department of Computer Science and Engineering, Christ University Kengeri Campus, Bangalore, India; Eldo J., Department of Computer Science and Engineering, Christ University Kengeri Campus, Bangalore, India; Eswaran S., Department of Electrical and Computer Engineering, Curtin University Malaysia, Sarawak, Miri, Malaysia; Zade N., Computer Science and Engineering, Symbiosis Institute of Technology Pune, Symbiosis International (Deemed University), Pune, India; Krishna Kumar P.R., Department of Computer Science and Engineering, S.E.A College of Engineering & Technology, Karnataka, Bengaluru, India
Description
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Breast cancer is known to be a major global health challenge, necessitating effective early detection strategies to improve patient outcomes and reduce mortality rates. This research focuses on the application of machine learning algorithms for the detection of breast cancer. The dataset considered includes a wide array of features extracted from breast tissue samples, enabling the evaluation of five different machine learning algorithms. These algorithms were chosen for their proven efficacy in medical diagnostics and their potential to complement traditional diagnostic methods. Among the algorithms evaluated, the support vector machine (SVM) emerged as particularly noteworthy, achieving an impressive accuracy rate of 98.27%. SVM demonstrated robust capabilities in accurately categorising breast cancer cases, effectively distinguishing between benign and malignant tumours with high precision. This underscores SVMs potential as a valuable tool for enhancing breast cancer detection accuracy, thereby aiding clinicians in making informed decisions. Furthermore, this research highlights the importance of leveraging large-scale datasets like WBCD to train machine learning models effectively. Such datasets provide a comprehensive set of features that enable algorithms to discern complex patterns and correlations, which may not be apparent through conventional methods alone. This data-driven approach not only enhances diagnostic accuracy but also lays the groundwork for personalised medicine approaches tailored to individual patient profiles. To summarise, the following study emphasises the transformative role of machine learning in oncology, specifically in early breast cancer detection. Continued research and validation of these algorithms across diverse datasets will be crucial in further improving their effectiveness and applicability in real-world healthcare settings, ultimately benefiting patients globally. 2026 selection and editorial matter, Ravichander Janapati, Usha Desai, Steven Fernandes, Rakesh Sengupta, Shubham Tayal; individual chapters, the contributors.
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CRC Press
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ISBN: 978-104035969-3; 978-103275324-9;
Language
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English
Type
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Book chapter
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online
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Faculty Publications
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Natarajan, Jayapandian; Moozhippurath, Bineesh
Title
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Deep learning ensembles for lung cancer detection in thoracic CT scans leveraging generative adversarial network technology
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01-01-2026
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IAES International Journal of Artificial Intelligence;Volume;15;Issue;2;pp.1605-1612
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<a href="https://doi.org/10.11591/ijai.v15.i2.pp1605-1612" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijai.v15.i2.pp1605-1612</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036311287?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036311287?origin=resultslist</a>
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Natarajan J., Department of Computer Science and Engineering, CHRIST University, Bengaluru, India; Moozhippurath B., Department of Computer Science and Engineering, CHRIST University, Bengaluru, India, Department of Artificial Intelligence and Data Science, Jyothi Engineering College, Thrissur, India
Description
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Effective treatment of lung cancer depends on early and accurate detection, which continues to be a major cause of cancer-related fatalities globally. Conventional diagnostic techniques are useful, but their efficacy in handling large amounts of thoracic computed tomography (CT) scan data is limited by their time-consuming nature and susceptibility to human error. The research here suggests a new deep learning model that integrates generative adversarial networks (GANs) for data improvement with a sophisticated ensemble approach to classification. GANs are employed to generate realistic synthetic CT images, addressing the challenges of limited datasets. The backbone of the proposed approach is a consensus-guided adaptive blending (CGAB) ensemble model that learns to dynamically combine the predictions of three top-performing convolutional neural networks (CNNs): ResNet-152, DenseNet-169, and EfficientNet-B7. The CGAB model improves prediction accuracy through model contribution weighting based on confidence scores and inter-model consensus, while a conflict-resolving auxiliary decision model is used. The approach was tested using the lung image database consortium and the image database resource initiative (LIDC-IDRI) dataset with a detection rate of 97.35, surpassing single-model and traditional ensemble methods. The current work provides a solid and scalable approach to lung cancer detection with better generalization, increased diagnostic consistency, and applicability for clinical use. This is an open access article under the CC BY-SA license.
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Deep learning; Ensemble learning; Generative adversarial networks; Lung cancer detection; Medical imaging; Thoracic computed tomography
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Institute of Advanced Engineering and Science
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ISSN: 20894872;
Language
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English
Type
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Article
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All Open Access; Gold Open Access
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online
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Faculty Publications
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Natarajan, Jayapandian; Vimal, Parekh Hetvi; Sen, Dikshita; Lawrence, Cyril; Eswaran, Sivaraman; Chintale, Pradeep; Patil, Shalmali Arun
Title
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Machine Learning-Based Maternal and Child Mortality Rate Prediction Using Random Forest Algorithm
Date
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01-01-2025
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Revolutionizing Healthcare Services: Unleashing Innovation through Generative AI;pp.251-266
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<a href="https://doi.org/10.1201/9781003531272_14" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003531272_14</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105005058544?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105005058544?origin=resultslist</a>
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Natarajan J., Department of Computer Science and Engineering, Christ University, Bangalore, India; Vimal P.H., Department of Computer Science and Engineering, Christ University, Bangalore, India; Sen D., Department of Computer Science and Engineering, Christ University, Bangalore, India; Lawrence C., Department of Computer Science and Engineering, Christ University, Bangalore, India; Eswaran S., Department of Electrical and Computer Engineering, Curtin University, Sarawak, Malaysia; Chintale P., SEI Investment Company, Downingtown, PA, United States; Patil S.A., Amazon AWS, Seattle, WA, United States
Description
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This research uses a variety of data sources such as maternal age, health records of the mother and/or child, socioeconomic status, medical history, or prenatal care, and details of health indicators to determine the factors most decisive in increasing mortality risks. This entails data acquisition, data cleaning, data transformation and selection, and model building with an example of algorithms such as logistic regression and random forest. The trained models are checked for accuracy and their resilience level is checked using methods like SHapley Additive exPlanations and Local Interpretable Model agnostic Explanations for interpretation. The model is presented in an easy interface that doctors and health practitioners could use to make early and relevant decisions. It keeps updating the performance of established models and is a crucial way of maintaining data security for compliance with the set regulations. The rationale for this project is to offer practical recommendations for healthcare technicians so that more lives of mothers and children could be saved and maternal/child mortality decreased. Random Forest, in particular, has demonstrated superiority due to its ensemble approach, which mixes many decision trees to improve forecast accuracy and robustness. This technique can handle huge datasets with increased dimensionality and effectively lowers the overfitting risk. Additionally, Random Forest improves generalization by averaging the outputs of numerous trees, making it more tolerant to data noise and fluctuation. What makes it superior to single decision tree models is that it can handle both numerical and categorical data and handle missing values without a substantial loss of accuracy. 2025 selection and editorial matter, Babita Singla, Kumar Shalender, Nripendra Singh, and Sandhir Sharma; individual chapters, the contributors.
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CRC Press
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ISBN: 978-104035711-8; 978-103287158-5;
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English
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Book chapter
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Restricted Access; Hardcopy may be available in the library
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online
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https://archives.christuniversity.in/files/original/9f74a47c7a17ef2eea69e788a54d5261.png
ae8597b1fb62da5294ca14f6fc0ece85
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Patents
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Patent
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Patent
Patent
Patents
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Social environment based on sentiments using globalized user review analysis /
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Computer Science
Description
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Patent Number: 202141007727, Applicant: Dr.G Muneeswari.
A simple yet efficient model, called Globalized User Sentiment Analysis (GURA) by using the property that sentiment classification has two opposite class labels (i.e., positive and negative), we first propose a data expansion technique by creating sentiment toggled reviews. The original and switched reviews are constructed in a one-to-one correspondence. Thereafter, we enhance the dual training (DT) algorithm and a dual forecasting (DF) algorithm separately, to make use of the original and switched samples in pairs for training a statistical classifier and make predictions.
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Natarajan, K.
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<a href="https://drive.google.com/file/d/1YnW_VB9sfn84VQ4zorYwKLSQukXqZXQW/view" target="_blank" title="Social environment based on sentiments using globalized user review analysis /" rel="noreferrer noopener">https://drive.google.com/file/d/1YnW_VB9sfn84VQ4zorYwKLSQukXqZXQW/view</a>
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Intellectual Property India
Date
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2021-03-24
Language
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English
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Patent
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Faculty Publications
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Natarajan, Thamaraiselvan; Pragha, P.; Dhalmahapatra, Krantiraditya; Raghavan, Deepak Ramanan Veera
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Exploring tourists metaverse experience using destination spatial presence quality & perceived augmentation: metaverse exploration, physical expedition (MEPE)
Date
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01-01-2025
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Current Issues in Tourism;Volume;28;Issue;14;pp.2343-2365
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<a href="https://doi.org/10.1080/13683500.2024.2372652" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/13683500.2024.2372652</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85197818538?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85197818538?origin=resultslist</a>
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Natarajan T., Department of Management Studies, National Institute of Technology, Tiruchirappalli, India; Pragha P., Department of Management Studies, National Institute of Technology, Tiruchirappalli, India; Dhalmahapatra K., Indian Institute of Management Shillong, Meghalaya, India; Raghavan D.R.V., School of Business and Management, CHRIST (Deemed to be) University, Bangalore, India
Description
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A recent surge of interest surrounds metaverse tourism, with researchers highlighting its potential to revolutionize the tourism industry and attract new travellers. This article delves into the key features of a tourist's metaverse experience that influence their desire to visit a destination in the real world using systems theory. In addition, the current study also explores the moderating role of FOMO (Fear of missing out) in few of the proposed relationships. The study is a cross-sectional descriptive investigation carried out among Indian tourists chosen based on the simple random sampling technique and is analyzed using the Smart PLS software. The findings of the study reveal that several attributes of a tourist's metaverse experience, including entertainment, interaction, trendiness, novelty, and intimacy, significantly enhance both the perceived quality of spatial presence within the destination and the level of perceived augmentation experienced by tourists. Notably, both these factors then exert a significant positive influence on a destination's brand equity, ultimately explaining tourists' intentions to visit the physical location. Interestingly, the moderating role of Fear of Missing Out (FOMO) suggests that the relationship between brand equity and the likelihood of tourists undertaking a physical visit is strengthened as their perceived FOMO increases. 2024 Informa UK Limited, trading as Taylor & Francis Group.
Subject
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brand equity; FOMO; Metaverse; perceived augmentation; spatial presence; tourism
Publisher
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Routledge
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ISSN: 13683500;
Language
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English
Type
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Article
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Restricted Access; Hardcopy may be available in the library
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online
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Faculty Publications
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Faculty Publications -Articles
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Natarajan, Thamaraiselvan; Pragha, P.; Dhalmahapatra, Krantiraditya; Veera Raghavan, Deepak Ramanan
Title
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Unveiling metaverse sentiments using machine learning approaches
Date
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01-01-2025
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Kybernetes;Volume;54;Issue;8;pp.4114-4137
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<a href="https://doi.org/10.1108/K-11-2023-2268" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/K-11-2023-2268</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85188192750?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85188192750?origin=resultslist</a>
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Natarajan T., Department of Management Studies, National Institute of Technology Tiruchirappalli, Tiruchirappalli, India; Pragha P., Department of Management Studies, National Institute of Technology Tiruchirappalli, Tiruchirappalli, India; Dhalmahapatra K., Operations and Quantitative Techniques, IIM Shillong, Shillong, India; Veera Raghavan D.R., School of Business and Management, CHRIST (Deemed to be) University, Bangalore, India
Description
An account of the resource
Purpose: The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers ones intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience. Design/methodology/approach: The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently. Findings: The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models. Research limitations/implications: Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverses experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverses economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust. Social implications: In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators. Originality/value: The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models. 2024, Emerald Publishing Limited.
Subject
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Behavior; Information technology; Neural nets; Qualitative research
Publisher
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Emerald Publishing
Relation
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ISSN: 0368492X;
Language
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English
Type
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Article
Rights
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Restricted Access; Hardcopy may be available in the library
Format
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online
-
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Faculty Publications
Article
Faculty Publications -Articles
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
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Natarajan, Thamaraiselvan; Veera Raghavan, Deepak Ramanan
Title
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Does integrated store service quality determine omnichannel customer lifetime value? Role of commitment, relationship proneness, and relationship program receptiveness
Date
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01-01-2025
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TQM Journal;Volume;37;Issue;3;pp.800-830
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<a href="https://doi.org/10.1108/TQM-09-2023-0276" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/TQM-09-2023-0276</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85182198059?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85182198059?origin=resultslist</a>
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Natarajan T., National Institute of Technology Tiruchirappalli, Tiruchirappalli, India; Veera Raghavan D.R., Department of Management Studies, National Institute of Technology Tiruchirappalli, Tiruchirappalli, India, School of Business and Management, Christ University, Bangalore, India
Description
An account of the resource
Purpose: Building on the relationship marketing and stimulus-organism-response (SOR) theory, the purpose of this paper is to study the impact of the integrated store service quality (ISSQ) on the omnichannel customer lifetime value (CLV). The mediating role of customer commitment (affective, normative and continuance) and relationship program receptiveness with the moderating role of customer relationship proneness were relied upon to better understand the omnichannel customer profitability metric (CLV). Design/methodology/approach: The study is descriptive and relies upon the cross-sectional data collected using the self-administered structured questionnaires from 785 omnichannel shoppers. A purposive sampling technique was performed in the study. Structural equation modeling was performed using the SMART-PLS 4.0 software to analyze the data. Findings: The results indicate that omnichannel customer commitment (affective, normative and continuance) differentially mediates the relationship between ISSQ and relationship program receptiveness, subsequently impacting the omnichannel CLV. The customer relationship proneness significantly and positively moderated the relationships between different dimensions of customer commitment and relationship program receptiveness. Research limitations/implications: The study relied upon the cross-sectional data from the Indian population aged above 18years for testing the proposed model. Further studies could test the model across different populations to generalize the study results. Originality/value: This study addresses the need to investigate the omnichannel retail store customer profitability and their relationship performance with the store. By testing the customer relationship management model in the omnichannel retail store context, this study is the first to show that ISSQ will impact the customer profitability and relationship performance metric (CLV) through omnichannel customer commitment and relationship program receptiveness. The moderating effect of customer relationship proneness on a few proposed hypotheses was also tested to give managerial recommendations. 2024, Emerald Publishing Limited.
Subject
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Customer lifetime value; Integrated store; Relationship marketing; Relationship program receptiveness; Relationship proneness; Service quality; SOR theory
Publisher
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Emerald Publishing
Relation
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ISSN: 17542731;
Language
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English
Type
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Article
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Restricted Access; Hardcopy may be available in the library
Format
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online