INTERFACING PRIMAL RELIGION OF THE HAMAI (ZELIANGRONG), CHRISTIANITY, HERAKA, AND TINGKAO RAGWANG CHAPRIAK
This chapter explores the intertwinement between four religious traditions, namely (1) Characheng (primal religion of the Hamai) and its offshoots, (2) Heraka, (3) Tingkao Ragwang Chapriak (TRC), and (4) Christianity in contemporary Hamai (Zeliangrong) communities. The influence of the primal Hamai religion on Christianity is unquestionable, and at the same time, these two traditions hold sway over Heraka and TRC in varying degrees. The impacts of the interaction are at the levels of consciousness, belief systems, practices, and values. The chapter brings out the asymmetric encounter between reformed religious traditions (Heraka) of the Hamai and the proselytisation of Christianity in the Hamai communities that had led to the extinction of the primal religion of the former. Remarkably, Heraka and TRC are counter-proselytising movements against Christianity based on the primal belief system and synthesis of Christian and Hindu belief systems. For this purpose, the research employs comparative and dialogical approaches to explore and analyse the interconnection among the above religions. It argues that the current forms of Christianity, Heraka, and TRC in Hamai tribes are unique in themselves, and at the same time, they are also cyclically inspired by one another in the process of their encounters. 2025 selection and editorial matter, Maguni Charan Behera; individual chapters, the contributors.
The Routledge Handbook of Contemporary Readings on Tribe and Religions in India: Emerging Negotiations, pp. 239-255.
Maisuangdibou M., Department of English and Cultural Studies, Christ (Deemed to be University), Bangalore, India
Examining Pre- and Post-ERP Adoption Challenges Confronting SMES: A Study on South Indian Companies
Challenges; Employee training; ERP adoption; SME; Technology
In todays dynamic and fiercely competitive business world, small and medium-sized enterprises (SMEs) can benefit greatly from using enterprise resource planning (ERP) systems. Because these technologies may revolutionize decision-making, data management, and operational efficiency, they are a compelling choice for SMEs who aspire to thrive in their respective industries. This study provides an in-depth analysis of the impact of Enterprise Resource Planning (ERP) systems on small and medium-sized enterprises (SMEs), focusing on the challenges encountered during the pre and post-adoption phases. The research incorporates quantitative data from 36 SMEs that have undergone ERP implementation. The study aims to unravel the challenges of ERP adoption in SMEs through statistical analysis, including paired and independent samples t-tests, Pearson Chi-square tests, and non-parametric tests such as Wilcoxon Signed Rank and Mann-Whitney tests. The findings reveal a significant reduction in operational challenges post-ERP adoption, highlighting the efficiency gains from ERP systems. The findings advocate for a strategic approach to ERP adoption, emphasizing the importance of vendor selection and the nuanced role of employee training in ensuring the successful integration and utilization of ERP systems within SMEs. It is also important to establish a continuous monitoring and evaluation mechanism to resolve issues when and where occurred. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Mathew L.; Nagarathinam A.; Chellasamy A.
Contributions to Management Science, Vol-Part F3767, pp. 167-175.
Springer Science and Business Media Deutschland GmbH
Mathew L., CHRIST University, Bangalore, India; Nagarathinam A., CHRIST University, Bangalore, India; Chellasamy A., CHRIST University, Bangalore, India
Food Quality Indicator-Based Intelligent Food Packaging
Foodborne illnesses caused by microbial growth and consumption of spoiled food items can lead to severe health issues. Monitoring real-time food quality through indicators/sensors has been an important priority for food industries, researchers, consumers, and regulatory bodies in this context. Intelligent packaging (IP), a type of food packaging, uses an indicator component to track and alert consumers on the quality of packaged food from the stage of manufacture to consumption in real time. Intelligent packaging helps reduce food waste and ensure consumer safety. This book chapter will discuss various food quality indicators, including humidity, oxygen, carbon dioxide, pH, and microbial indicators, and their applications in IP. 2025 John Wiley & Sons Ltd. All rights reserved.
Chaithra K.P.; Gayathri B.; Aiswarya P.S.; Vinod T.P.
Smart Food Packaging Systems: Smart Food Packaging Systems, pp. 337-359.
https://doi.org/10.1002/9781394189595.ch13 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211306716&doi=10.1002%2f9781394189595.ch13&partnerID=40&md5=fd7512bb864c10ff5a4a8cfa99ac313a
Chaithra K.P., Department of Chemistry, Christ University, Karnataka, Bangalore, India; Gayathri B., Department of Chemistry, Christ University, Karnataka, Bangalore, India; Aiswarya P.S., Department of Chemistry, Christ University, Karnataka, Bangalore, India; Vinod T.P., Department of Chemistry, Christ University, Karnataka, Bangalore, India
Importance of Genetic Model in Huntingtons Disease
Huntingtons disease (HD) is the first defect-mapped autosomal-dominant, progressive neurodegenerative disorder with a distinct phenotype found in 1983, which contributed to the concept of human genome project. Thus, the search for genetic defects pioneered various mapping and gene study technological prototypes that culminated in identification of distinct disorders. Huntingtons disease has many symptoms, including chorea and dystonia, incoordination, cognitive decline and dementia, and behavioral difficulties. This disease can manifest any time over the age of 30 years, with the first sign and symptom being behavioral changes, which might include lack of emotions, periods of aggression, excitement, and anger. HD duration varies, ranging from 10 to 25 years or more depending on the individual. It is caused by a single gene defect on chromosome number 4, wherein a person requires only a single copy of the defected gene to show the symptoms; that is, a person with parent with the HD gene has a 50% chance of suffering. The disease becomes prominent in a human being as a result of mutations in a gene called Huntington that is located on the p arm (short arm) of chromosome 4 (4p 16.3). This chapter discusses the genetic model of Huntingtons disease and its importance. An increase in the normal number of repeat CAG (cytosine, adenine, and guanine) segments, (i.e. > 35 CAG) is seen in the Huntington (HTT) mutation that causes the disease. The severity of the disease depends on the sized expansion (i.e. increasing CAG repeats will accelerate the age of onset of the disease). Continuing studies of genetic modifiers-genes whose natural polymorphic variation contribute to the alteration and development of the D gene-offers to open new gateways for early diagnosis by unlocking the biochemical changes that occur years before diagnosis, thereby providing validated target protein and pathways for rational therapeutic interventions. This is also added as a section in this chapter. 2025 selection and editorial matter, Sachchida Nand Rai, Sandeep Singh, Santosh Kumar Singh.
Neurodegenerative Diseases: Translational Models, Mechanisms, and Therapeutics, pp. 109-119.
Bajaj M., School of Innovation in Biodesign, Translational Health Science and Technology Institute, Faridabad, India; Palan D., School of Innovation in Biodesign, Translational Health Science and Technology Institute, Faridabad, India; Gandhi M., School of Innovation in Biodesign, Translational Health Science and Technology Institute, Faridabad, India, Department of Chemistry, CHRIST (Deemed to be University), Bengaluru, India
Experimental Evaluation of Deep Learning-Assisted Brain Tumor Identification with Advanced Classification Methodology
Neelaveni P.; Warrier G.S.; Tamilarasi K.; Gopila M.; Thandaiah Prabu R.
Deep Learning in Biomedical Signal and Medical Imaging, pp. 189-206.
Neelaveni P., Rajalakshmi Engineering College, Tamil Nadu, Chennai, India; Warrier G.S., Christ (Deemed to be University), Karnataka, Bangalore, India; Tamilarasi K., Jeppiaar Institute of Technology, Tamil Nadu, Chennai, India; Gopila M., Sona college of Technology, Tamil Nadu, Salem, India; Thandaiah Prabu R., Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, Chennai, India
Mapping Fire, Earthquake and Bio-hazard in Delhi: A Micro-level Study
Biohazard and waste management; Earthquake; Fire hazard; Vulnerable
Delhi, being Indias capital territory, is a massive metropolitan area that is extremely vulnerable to various types of disasters because of the widely spread built-up area that houses the population from all over the country. Delhi lies in Seismic Zone IV14, which makes the area sensitive to disasters. Another major problem that Delhi is currently facing is of proper garbage disposal, since the density of the population is high, tons of waste is generated. A fair share of the waste generated also includes biomedical waste. Delhi generates more biomedical waste than it can process. The area chosen for the present study is Chirag Delhi and Sheikh Sarai, located in south Delhi. This area is urbanized, and a home to a large number of people. The area is populated, poorly managed and highly vulnerable to disasters. The study area also has two colleges situated near the residential area because of which the area is subjected to a lot of traffic jam. The purpose of choosing this area for this study is its vulnerability to disasters like fire, earthquake and biohazard. The study area has pockets with high rise buildings or ill-designed high-risk areas without specific consideration for earthquake resistance. Moreover, the area lacks proper waste management. It has been identified that the area is a highly vulnerable place when it comes to hazards like fire, earthquake and biohazards. The people living there are in a constant threat for their lives. One of the major problems is that the community lacks dedication and determination, which has been tested through a schedule and observation method, to change their circumstances and bring about a change in the area that would benefit them and their families. The Editor(s)(ifapplicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Making India Disaster Resilient: Challenges and Future Perspectives, pp. 211-227.
Sanoria V., School of Education, Christ University, Bangalore, India; Chandrakanta, Department of Geography, Shaheed Bhagat Singh College, University of Delhi, New Delhi, India
Antifouling Nanoparticle Coatings for Post-Harvest Food Preservation
antifouling; food monitoring; food preservation; food waste; nanocoatings; Nanoparticles; sensing; zinc oxide nanoparticles
The reports of the World Food Preservation Center exhibit that the entire number of inhabitants in the globe will reach 9.6 billion by the year 2050. With the increasing population, there is a growing pressure on agricultural produce which is further amplified by the losses between harvest and retail (~17%) and other wastages (~17%). Globally, this is a major concern, and thus, there is no surprise to look into current research developments in food preservation. This chapter provides comprehensive reports on the recent trends in various nanoparticle coatings for the aforementioned application. Silicon dioxide (SiO2), titanium dioxide (TiO2), and zinc oxide (ZnO) nano regime coatings on silicon substrate and polymer substrates have been discussed for antifouling applications. This chapter is categorized as follows: Section I delivers the introduction about the significance of food preservation in reference to the reported statistics. In section II, the discussion starts with the materials and methods for post-harvest food saving. Major recent advances in terms of materials or methods to increase the shelf life of cuisine are portrayed in the same section II. Section III entails the appropriate computational methods to envision the interaction of food residuals with coated nanolayers through sensing. The final section (IV) delivers a guideline on feasible research implications to address the shortcomings in food preservation from a broader perspective. 2024 Scrivener Publishing LLC.
Novel Anti-Corrosion and Anti-Fouling Coatings and Thin Films, pp. 1-11.
Thriveni G., Department of Electronics and Communication Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Murthy H., Department of Electronics and Communication Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Anusha C.H., Department of Mechanical Engineering, Vignan Institute of Technology and Science - Deshmukhi, Telangana, Hyderabad, India
Portable and Automated Healthcare Platform Integrated with IoT Technology
Artificial intelligence; Data analysis; IoT; Machine learning; Real time systems; Wearable sensors
The diverse applications of Internet of Things (IoT), Artificial Intelligence (AI) in Bioelectronics - face recognition, pulse wave monitoring and insulin level measurements have been discussed in this chapter. This chapter also emphasizes on the IoT and its applications in biological sensors. The chapter focuses on IoT systems, such as neural networks and other channels that are integrated into a secure healthcare monitoring system in order to make the system operate as a smart model in healthcare sector that determines the priority based on health parameters gathered from the sensor nodes. In this work, the different approaches of IoT with distinct methodologies are also deliberated. 2024 Scrivener Publishing LLC.
Internet of Things in Bioelectronics: Emerging Technologies and Applications, pp. 251-261.
https://doi.org/10.1002/9781394241903.ch12 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208348956&doi=10.1002%2f9781394241903.ch12&partnerID=40&md5=7b6ff698a7d99b163149dbeea79be25b
Preetham Noel P., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bengaluru, India; Kishorekumar R., Department of Electronics and Communication Engineering, School of Engineering and Technology, Christ University, Bengaluru, India
Bibliometric Analysis of AI Research in Sustainable Smart Cities
Smart cities have the potential to improve city-wide governance, environmental sustainability, sustainable transportation, and economic growth. Urban areas may find these advantages useful in their pursuit of SDG-11 objectives. A key component of smart city architecture is the addition of artificial intelligence (AI) and other smart technology into urban areas. The Artificial Neural Network (ANN) is a major machine learning approach. A number of review studies have already been published, reflecting the substantial interest in artificial neural networks (ANN) for smart city applications. In the past, researchers have shown an interest in studying structural monitoring applications, transportation systems, cybersecurity, and the Internet of Things (IoT). But knowledge about how ANN can help Smart Cities achieve SDG-11 is limited. This paper provides a systematic bibliometric analysis of present research trends on artificial neural networks for smart cities, with an emphasis on SDG-11. The research employed a keyword-based search to obtain 131 papers for content analysis and 743 papers for descriptive analysis. Both the amount of interest in the topic and the tendency for related topics to cluster have increased exponentially, according to the findings. Urbanization, Transportation, and Eco-friendly were identified as the main topics of this study. Specifically, this evaluation focuses on particular SDG-11 issues and provides insights on research trends and thematic importance. 2025 Saravanan Krishnan, A. Jose Anand and Raghvendra Kumar.
Ravisankar A.; Ibraheem I.K.; Raha S.; Sakthi T.; Ashok J.; Tonk A.
Sustainable Development Goals: Technologies and Opportunities, pp. 189-208.
Ravisankar A., Department of Management Studies, Erode Sengunthar Engineering College, Autonomous, Tamil Nadu, Erode, 638057, India; Ibraheem I.K., Department of Electronics and Communication Engineering, Uruk University, Baghdad, 10001, Iraq; Raha S., Department of Geography, Bhairab Ganguly College, Belgharia, 700056, India; Sakthi T., Department of Mechanical Engineering, National Engineering College, Kovilpatti, 628503, India; Ashok J., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Tonk A., Department of Multidisciplinary Engineering, The NorthCap University, Gurugram, 122017, India
Comprehensive Data Analysis of Anticorrosion, Antifouling Agents, and the Efficiency of Corrosion Inhibitors in CO2 Pipelines
antibacterial; Anticorrosion; antifouling; CO2 pipelines; coatings; CPANOA; PANI; S-FP
This study explores the various methods that are being proposed for their anticorrosion and antifouling capabilities and also reviews the unique properties that make them suitable for such applications. Special attention has also been given to the problem of corrosion in CO2 pipelines, considering the corrosion inhibitors currently being used and performing statistical analysis about if and how various factors such as temperature, flow velocity, pH, and CO2 pressure affect the rate of corrosion of the CO2 pipelines. Tests including ANOVA, correlation, and graph analyses were conducted to explore their relationships, and suitable conclusions were drawn for the data collected. 2024 Scrivener Publishing LLC.
Simhadri V.; Kumar K.P.; Vijaya P.; Biju V.G.
Novel Anti-Corrosion and Anti-Fouling Coatings and Thin Films, pp. 475-495.
https://doi.org/10.1002/9781394234318.ch17 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208008574&doi=10.1002%2f9781394234318.ch17&partnerID=40&md5=cfffd6a8b13f08b74ecc9436920c18af
Simhadri V., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bangalore, India; Kumar K.P., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bangalore, India; Vijaya P., Department of Mathematics and Computer Science, Modern College of Business and Science, PC, Muscat, Oman; Biju V.G., Faculty of Computer Science and Business Informatics, Technical University of Applied Sciences, Wurzburg-Schweinfurt, Germany
Surface functionalized fluorescent carbon nanoparticles and their applications
Carbon dots; Fluorescence mechanisms; Photocatalysis; Photoluminescence; Phototherapy; Quantum confinement effect; Surface functionalization
Fluorescent carbon nanoparticles or carbon dots (CDs) are zero-dimensional nanomaterials embodying physicochemical characteristics appropriate for novel and improved applications in various disciplines. Tunable photoluminescence, photostability, small size, low cost, biocompatibility, etc., are some of the promising features of CDs. The CDs are usually composed of a graphitic core surrounded by shell layers containing various functional groups. Surface functionalization of CDs is known to customize, and regulate the properties of CDs, thereby proliferating their applications. A variety of physical and chemical methods have been used for the preparation of CDs with tailored surfaces. The choice of the synthetic strategy generally depends on the type of surface modification required and the fluorescence behavior expected. This chapter summarizes and discusses the existing strategies for preparing surface functionalized CDs and the resultant fluorescence phenomena. The surface functionalization of CDs can decisively influence their suitability in several applications. In some applications, surface functionalization improves the existing utility, while novel utilities are emerging in others. The influence of surface functionalities of CDs on biomedical and catalytic applications has been discussed in detail in this chapter. CDs have emerged as a promising material for enhancing the performance, sustainability, and safety of various energy storage devices like batteries, supercapacitors etc. Continued research and development in this area could lead to the realization of more efficient and environmentally friendly energy storage solutions. The chapter concludes by discussing the challenges in synthesizing surface functionalized CDs and their acceptability in biomedical and industrial applications. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies.
Fluorescent Carbon Nanoparticles: Fundamentals and Applications, pp. 185-224.
John V.L., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Chaithra K.P., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Vinod T.P., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bangalore, India
The Shame of Ageing During Fourth Industrial Revolution: A Thematic Analysis of Indian Adults
The Fourth Industrial Revolution (4IR), a term popularised by Klaus Schwab in 2016, connected the physical-biological and the digital world. This is an era of artificial intelligence and computational technologies suited to satiate the needs of the human race. The emphasis is also on a digital identity we have developed alongside our physical and psychological entities. Millennials and Gen Z have a cognizant grip on their digital identity and are known to use the fruits of 4IR in their everyday livelihood. However, with the advent of Industry 4.0, the generation of Baby Boomers and Gen X have had to undergo much re-learning and accommodate the newer ways of integrating digitalization in their lives. The process has brought about occupational threats and shaming related to failure to upgradation and flexibility. This article explores the influences of these social experiences on the identity and self-concept of the quinquagenarians and the sexagenarians. The article follows a qualitative method where using a thematic approach, the emerging themes from the in-depth interviews will be analyzed in detail to form a theoretical framework for shaming among the Indian Baby Boomers and Gen X. The variables in focus are adjustment, coping styles, resilience, the purpose of life, and Self-Image. The study explores the themes of Indian adults, which emerge from interviewing 46 participants, who have been associated with full-time employment and are between 77 and 59 years of age, representing the Baby Boomers, and those between 43 and 58 years of age, representing Gen X. The analysis adopts a psychoanalytic approach, where the data is interpreted using an Eriksonian lens. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Shame and Ageing in a Transforming World, pp. 201-224.
Banerjee T., Christ (Deemed to be University), Bangalore, India; Pal A., company developing next-generation Space Missions, West Bengal, Kolkata, India
Nature of music engagement and its relation to resilient coping, optimism and fear of COVID-19
The COVID-19 pandemic has resulted in unprecedented lockdowns, a work from home culture, social distancing and other measures which badly affected the world populace.Individuals over the globe reported experiencing several psychosocial and psychosomatic problems.Nevertheless, this pandemic allowed us to be with ourselves, to understand the importance of healthy lifestyles and to devote time to our passions and hobbies when we were socially isolated.Against this background, the present study was undertaken to explore the nature of peoples everyday musical engagement and to examine how the experience and functions of music were related to resilient coping, life orientation and fear from COVID-19.In an online survey, a total of 197 participants responded to a questionnaire designed to assess the nature of musical engagement (level of musical training, functional niche of music, listening habits and involvement in musical activities), functions of music (FMS), resilient coping (BRCS), life orientation (LOT-R), and fear of COVID-19 (FCV-19S).Results indicate that for most of the respondents, music listening was a preferred activity during the pandemic which resulted in positive effects on their mood, heart rate and respiratory rates.More than 80 per cent of respondents reported music as a source of pleasure and enjoyment and claimed that it helped to calm them, release their stress, and help them relax.Significant positive correlations were found between the functions of music (memory-based and mood-based), optimism and resilient coping and mood-based functions of music and optimism were found to predict resilient coping among individuals.These results suggest that meaningful and active music engagement may lead to optimism which may result in effective resilient coping during the crisis.Moreover, reflecting upon our everyday musical engagements can promote music as a coping skill. 2025 selection and editorial matter, Asma Parveen and Rajesh Verma; individual chapters, the contributors.
Upadhyay D.K.; Singh T.; Mittal S.; Tushir B.
Mental, Emotional and Behavioural Needs of the General Population Following COVID-19 in India: Findings from Qualitative and Quantitative Studies, pp. 1-14.
Upadhyay D.K.; Singh T., Banaras Hindu University, India, National Academy of Psychology (NAOP), International Association of Applied Psychology (IAAP), India; Mittal S., School of Liberal Arts, Bennett University, India; Tushir B., Christ University, Delhi-NCR, India
Fluorescent carbon nanoparticles for catalytic and photocatalytic applications
Catalytic and photocatalytic applications; Electrocatalysis; Fluorescent carbon nanoparticles; Organic synthesis
In the present times, catalysis is ubiquitous in chemical processes. Catalysts range from macromolecules consisting of enzymes to nanoparticles, including metals/metal oxides and composite materials. Due to their harmlessness, biocompatibility, high stability, versatility, and ease of functionalization, carbon nanomaterials (CNMs) which are fluorescent in nature, are used extensively for catalytic applications. Several studies regarding the catalytic applications of CNMs have been reported. These applications range from homogeneous to heterogeneous catalysis, where CNMs are used as supports for metal/metal oxide nanoparticles. Extensive studies on nanocomposites, doping strategies, and their utility in catalysis have been carried out. Carbon-based electrocatalysts find applications in both storage and conservation of energy. The exceptional properties of these materials make them an apt choice for various environment-friendly organic transformations. Photocatalysis is another area in which CNMs have excelled. Photoluminescence, photostability, and electron transfer properties of CNMs make them potent candidates for several photoinduced reactions. Various CNMs, namely graphene, carbon dots, nanotubes, graphitic carbon nitride, fullerenes, and graphdiyne, find applications in medicine, catalysis, sensing, bioimaging, supercapacitors, and many more. This chapter focuses on the catalytic and photocatalytic applications of CNMs. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies.
Anand S.; Sunaja Devi K.R.; Sreevidhya K.B.; Pinheiro D.
Fluorescent Carbon Nanoparticles: Fundamentals and Applications, pp. 543-597.
Anand S., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Sunaja Devi K.R., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Sreevidhya K.B., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Pinheiro D., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, India
IoT-Enabled Analysis of COVID Data: Unveiling Insights from Temperature, Pulse Rate, and Oxygen Measurements
COVID-19; Health monitoring systems; IoT-enabled analysis; Measurements; Sensors; Temperature-pulse rate-oxygen
The COVID-19 pandemic has forced unparalleled transformation on healthcare systems around the world, demanding new and improved approaches for effective monitoring and diagnosis. In this context, we present a study titled IoT-Enabled Analysis of COVID Data: Unveiling Insights from Temperature, Pulse Rate, and Oxygen Measurements. The global impact of COVID-19, with millions of confirmed cases and fatalities, underscores the urgency of finding efficient monitoring solutions. To address this crisis, IoT-Enabled Health Monitoring Systems have emerged as a promising tool for remote patient monitoring and infection risk reduction. These systems leverage sensors to collect real-time data on the temperature, pulse rate, and oxygen saturation levels of the subject. The integration of a mobile application enables immediate access to this critical health information. In this study, we explore the use of IoT systems, which have demonstrated accuracy comparable to other devices on the market. By leveraging these technologies, we aim to provide healthcare professionals with valuable insights into patients health status, aiding in early detection, monitoring, and timely intervention. Our research contributes to the efforts to battle the COVID-19 pandemic by highlighting the potential of IoT-enabled monitoring systems in enhancing healthcare delivery, reducing infection risks, and ultimately saving lives. 2024 Scrivener Publishing LLC.
Internet of Things in Bioelectronics: Emerging Technologies and Applications, pp. 297-310.
John J., Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bangalore, India; Kumar K.P., Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bangalore, India; Murthy H., Electronics and Communication Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bangalore, India
Impact of Multi-domain Features for EEG Based Epileptic Seizures Classification
Adaptive Boosting; Data Standardization; EEG; Epileptic Seizure; Frequency domain; Gaussian Nae Bayes; Machine Learning; Multi-domain Features; Time domain
Accurate detection and classification of epileptic seizures play a pivotal role in clinical diagnosis and treatment. This study introduces an innovative approach that leverages multi-domain features extracted from Electroencephalogram (EEG) data in conjunction with Supervised learning classification techniques. Initially, EEG data undergoes preprocessing through data standardization, followed by the extraction of essential features per instance, encompassing combination of Time domain, Frequency domain, and Time-Frequency domain features. These extracted feature combinations are subsequently fed into the machine learning-based boosting classifier Adaptive Boosting (ADABOOST) for an accurate and precise classification of epileptic signals. Validation of the proposed method is conducted using EEG data from the BEED (Bangalore EEG Epilepsy Dataset) and BONN (University of BONN, Germany) database to detect epileptic seizures. The experimental results show remarkably high levels of classification accuracy for various conditions: 99% accuracy for BEED data, 98% accuracy for BONN data for classifying seizures from healthy states, and 91% accuracy for classifying seizure onset from seizure events. Furthermore, the study applies the Gaussian Nae Bayes (GNB) classifier to differentiate various types of epileptic seizures, employing evaluation metrics such as the confusion matrix, ROC curve, and diverse performance measures. This method demonstrates significant potential in supporting experienced neurophysiologists decision in the clinical classification of epileptic seizure types. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Najmusseher; Banu P.K.N.; Azar A.T.; Kamal N.A.
Lecture Notes on Data Engineering and Communications Technologies, Vol-220, pp. 317-329.
Najmusseher, Department of Computer Science, CHRIST (Deemed to Be University) Central Campus, Karnataka, Bangalore, 560029, India; Banu P.K.N., Department of Computer Science, CHRIST (Deemed to Be University) Central Campus, Karnataka, Bangalore, 560029, India; Azar A.T., College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13518, Egypt; Kamal N.A., Faculty of Engineering, Cairo University, Giza, Egypt
Critical Analysis of MoS2-Based Systems for Textile Wastewater Treatment
Indiscriminate discharge of toxic organic contaminant-laden wastewater into water bodies is one of the major issues posing a risk to the environment in general and aquatic living systems in particular. Widely used textile dyes are ubiquitous in the effluents emanating from industries. Photocatalysts, due to their efficiency and eco-friendliness, can be effectively used to remove pollutant dyes from the water bodies. Molybdenum disulfide (MoS2), an emerging co-catalyst, has high photocatalytic activity, strong absorptivity, non-toxicity, and low cost; with a graphene-like structure, it offers functional features similar to graphene: high charge carrier transfer, strong wear resistance, and good mechanical strength. However, in aspects such as cost, abundance, versatile morphologies, and tunable band gap with efficient visible light absorption properties, MoS2 scores over graphene. The present chapter discusses the recent advances in nanostructured MoS2 materials for applications in environmental remediation. Special emphasis has been paid to MoS2 and MoS2-based systems for the photocatalytic degradation of various organic contaminants such as malachite green, methyl orange, rhodamine B, and methylene blue that find extensive use in the textile industry. As a result, MoS2 systems play an essential role in nanocomposites, especially in speeding up photo-induced electron transport and lowering electron recombination rates, making them desirable photocatalysts for the degradation of pollutants. The chapter focuses on addressing SDG 3 (Good Health and Wellbeing), SDG 6 (Clean Water and Sanitation), SDG 7 (Clean and Affordable Energy), SDG 9 (Industry, Innovation, and Infrastructure), SDG 12 (Responsible Consumption and Production), SDG 14 (Life Below Water), and SDG 15 (Life on Land). 2025 Moharana Choudhury, Ankur Rajpal, Srijan Goswami, Arghya Chakravorty and Vimala Raghavan.
Madhushree R.; Jose A.; Pinheiro D.; Sunaja Devi K.R.
Analytical Case Studies on Municipal and Biomedical Waste Management: Perspectives on Sustainable Development Goals, pp. 55-82.
Madhushree R., Department of Chemistry, Christ (Deemed to be University), Karnataka, Bangalore, 560029, India; Jose A., Molecular Medicine and Pathology, University of Auckland, Auckland, 1023, New Zealand; Pinheiro D., Department of Chemistry, Christ (Deemed to be University), Karnataka, Bangalore, 560029, India; Sunaja Devi K.R., Department of Chemistry, Christ (Deemed to be University), Karnataka, Bangalore, 560029, India
alternatives; bioeconomics; biological substances; Biomolecules; biotechnology; demand and supply; environment; global market; manufacturer; marine biopolymers; natural resources; pharmaceutical manufacturing
The usage of synthetic polymers such as plastic is a much-debated topic across the globe for a reason; it is not recyclable and harms the environment. However, todays consumers have shifted their preferences to eco-friendly products over harmful products. The biopolymers market globally accounted for about $13.7 billion in 2021, and by 2030, its projected to reach over $35.2 billion, growing at 11.07% [compound annual growth rate (CAGR)]. By 2026, the marine biotechnology sector will be worth $5 billion worldwide. Despite the manufacturing cost of marine biopolymers being higher than that of standard polymers, the market is growing faster because of its benefits across various industries and mainly for stakeholders. The biopolymer industry has evolved due to the depletion of petroleum reservoirs. Key players from countries such as the United States, Brazil, Germany, Netherlands, Italy, United Kingdom, Japan, Germany, and Australia are in the biopolymers market. Different classes of marine biopolymers and their industrial applications prove the precious value of ocean resources to society. 2025 Elsevier Ltd. All rights reserved.
Halaswamy D.; Borehalli Mayegowda S.; Brijesh H.; Bheemappa M.
Marine Biopolymers: Processing, Functionality and Applications, pp. 989-1007.
Halaswamy D., School of Business and Management, CHRIST-Deemed to be University, Kengeri Campus, Karnataka, Bengaluru, India; Borehalli Mayegowda S., Department of Psychology, CHRIST-Deemed to be University, Kengeri Campus, Karnataka, Bengaluru, India; Brijesh H., Department of Biotechnology, School of Applied Sciences, REVA University, Karnataka, Bengaluru, India; Bheemappa M., Department of Biotechnology, School of Applied Sciences, REVA University, Karnataka, Bengaluru, India
A Brief Review onDifferent Machine Learning-Based Intrusion Detection Systems
Decision tree algorithm; Intrusion detection; kNN algorithm; Random Forest algorithm; Support vector Machine algorithm
In the contemporary cybersecurity landscape, the proliferation of complex and sophisticated cyber threats necessitates the development of robust Intrusion Detection Systems (IDS) for safeguarding network infrastructures. These threats make it more challenging to maintain the communitys availability, integrity, and confidentiality. To ensure a secure network, community administrators should implement multiple intrusion detection systems (IDS) to monitor and detect unauthorized and malicious activities. An intrusion detection system examines the networks traffic by analyzing data flowing through computers to identify potential security threats or malicious activities. It alerts administrators when suspicious activities are detected. IDS generally performs two types of malicious activity detection: misuse or signature-based detection, which entails collecting and comparing information to a database of known attack signatures, and anomaly detection, which detects any behavior that differs from the standard activity and assumes it to be malicious. The proposed paper offers an overview of how different Machine Learning Algorithms like Random forest, k - Nearest Neighbor, Decision tree, Support Vector Machine, Naive Bayes, and K- means are used for IDS and how these algorithms perform on different well-known datasets, and Their accuracy and performance are evaluated and compared, providing valuable insights for future work. kNN shows an accuracy of 90.925% for Denial of Service Attacks and 98.244% for User To Root attacks. The SVM algorithm shows an accuracy of 93.051% for Probe attacks and 80.385% accuracy for remote-to-local attacks. According to our implementation, these two algorithms work better than the others. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Samanta S.; Sen A.P.; Mukhopadhyay D.; De S.; Bhattacharyya S.; Platos J.
Lecture Notes on Data Engineering and Communications Technologies, Vol-220, pp. 98-108.
Samanta S., Department of Computer Science and Engineering, Cooch Behar Government Engineering College, West Bengal, Cooch Behar, India; Sen A.P., Department of Computer Science and Engineering, Cooch Behar Government Engineering College, West Bengal, Cooch Behar, India; Mukhopadhyay D., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bangalore, India; De S., Department of Computer Science and Engineering, Cooch Behar Government Engineering College, West Bengal, Cooch Behar, India; Bhattacharyya S., VSB Technical University of Ostrava, Ostrava, Czech Republic, Algebra University, Zagreb, Croatia; Platos J., VSB Technical University of Ostrava, Ostrava, Czech Republic
Exploring the synergy of IIoT, AI, and data analytics in Industry 6.0
This chapter delves into the transformative intersection of artificial intelligence (AI), Industrial Internet of Things (IIoT), and data analytics within the context of emerging Industry 6.0. As industries continue to emerge towards greater connectivity and automation, the chapter delivers a comprehensive analysis of the convergence of these cutting-edge technologies in reshaping the industrial landscape. It explores the synergistic relationships among IIoT, AI, and data analytics, examining their collaborative potential to enhance efficiency, productivity, and decision-making processes. The chapter begins by offering an in-depth overview of Industry 6.0, highlighting the technological advancements and paradigm shifts that characterize this era. Subsequently, it dissects the role of IIoT as a pivotal enabler, connecting physical devices and systems to facilitate real-time data exchange. The incorporation of artificial intelligence is explored as a premeditated augmentation, empowering machines to learn, adapt, and optimize operations autonomously. Simultaneously, the chapter investigates the significance of advanced data analytics techniques in extracting actionable insights from big data, fueling informed decision-making and predictive maintenance strategies. Furthermore, the chapter delves into practical applications and case studies showcasing successful implementations of this triad in diverse industrial sectors. 2025 selection and editorial matter, C Kishor Kumar Reddy, Srinath Doss, Lavanya Pamulaparty, Kari Lippert and Ruchi Doshi; individual chapters, the contributors.
Vijay Arputharaj J.; William B.N.J.; Haruna A.A.; Prasad D.D.
Industry 6.0: Technology, Practices, Challenges, and Applications, pp. 1-36.
Vijay Arputharaj J., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; William B.N.J., TE Connectivity, Switzerland; Haruna A.A., Department of Computer Science, University of Hafr Al Batin, Saudi Arabia; Prasad D.D., Department of ECE, Vishnu Institute of Technology, Andhra Pradesh, India