Exploring the Impact of Latent and Obscure Factors on Left-Censored Data: Bayesian Approaches and Case Study
Bayesian estimation; Frailty model; Left censoring; Reversed hazard rate; Survival distributions
In the realm of scientific investigation, traditional survival studies have historically focused on mitigating failures over time. However, when both observed and unobserved variables remain enigmatic, adverse consequences can arise. Frailty models offer a promising approach to understanding the effects of these latent factors. In this scholarly work, we hypothesize that frailty has a lasting impact on the reversed hazard rate. Notably, our research highlights the reliability of generalized Lindley frailty models, rooted in the generalized log-logistic type II distribution, as a robust framework for capturing the widespread influence of inherent variability. To estimate the associated parameters, we employ diverse loss functions such as SELF, MQSELF, and PLF within a Bayesian framework, forming the foundation for Markov Chain Monte Carlo methodology. We subsequently utilize Bayesian assessment strategies to assess the effectiveness of our proposed models. To illustrate their superiority, we employ data from renowned Australian twins as a demonstrative case study, establishing the innovative models advantages over those relying on inverse Gaussian and gamma frailty distributions. This study delves into the impact of hidden and obscure factors on left-censored data, utilizing Bayesian methodologies, with a specific emphasis on the application of generalized Lindley frailty models. Our findings contribute to a deeper understanding of survival analysis, particularly when dealing with complex and unobservable covariates. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Gupta P.; Pandey A.; Hanagal D.D.; Tyagi S.
Springer Series in Reliability Engineering, Vol-Part F2569, pp. 293-320.
Springer Science and Business Media Deutschland GmbH
Gupta P., Department of Statistics, Central University of Rajasthan, Rajasthan, India; Pandey A., Department of Statistics, Central University of Rajasthan, Rajasthan, India; Hanagal D.D., Department of Statistics, Savitri Bai Phule Pune University, Pune, India; Tyagi S., Department of Statistics, Central University of Rajasthan, Rajasthan, India, Department of Statistics and Data Science, Christ Deemed to Be University, Bangalore, India
Polyoxometalates and redox-active molecular clusters for supercapacitors
electrochemical energy storage; molecular clusters; Polyoxometalates; rechargeable batteries; supercapacitors
Hybrid electric vehicles and portable electronic devices become inevitable part of our daily life and it is necessary to develop efficient energy storage devices to supply them power. Supercapacitors (SCs) are electrochemical energy storage devices with high power densities. The electrochemical performances of a SC depend mainly on the electrode-active material used in it. An efficient electrode-active material should have qualities such as large surface area, porous structure, uniform pore distribution, good chemical and electrochemical stabilities, and good mechanical strength, to name a few. Mesoporous electrode architecture is highly preferred to obtain maximum electrolyte-ion accessibility that can boost the electrochemical performance of the SC electrode. The various electrode-active materials developed to date are transition metal oxides, electronically conducting polymers, carbon nanomaterials, etc. Polyoxometalates (POMs) are comparatively novel electrode candidates that possess excellent structural stability during the reversible redox reactions. A unique characteristic such as higher oxidation state possessed by POMs makes them an ideal platform to accept and release electrons during the electrochemical charge storage. POMs are considered to be a polyatomic anion, which hold early transition metals like Mo, V, W, etc., and are linked to an oxygen atom in a three-dimensional cluster. The cluster formation of POMs enables higher stability and easy to prepare composites with other materials such as carbon nanomaterial, electronically conducting polymers, etc. The preparation of hybrid electrode architectures by anchoring of POMs helps in producing a large number of electroactive sites for the enhanced electrochemical reactions to occur. This chapter explains the salient features and functionalities of POMs and redox-active molecular clusters that affect the SC performance. 2024 Elsevier Inc. All rights reserved.
Thomas S.A.; Baby A.; Balakrishnan S.P.; Rajendran D.N.; Cherusseri J.
Supercapacitors: Materials, Design, and Commercialization, pp. 221-243.
Thomas S.A., Department of Physics, Government College for Women (Affiliated to University of Kerala), Kerala, Thiruvananthapuram, India; Baby A., Department of Chemistry, CHRIST (Deemed to be) University, Karnataka, Bengaluru, India; Balakrishnan S.P., Department of Chemistry, CHRIST (Deemed to be) University, Karnataka, Bengaluru, India; Rajendran D.N., Department of Physics, Government College for Women (Affiliated to University of Kerala), Kerala, Thiruvananthapuram, India; Cherusseri J., Research Centre for Nano-Materials and Energy Technology (RCNMET), School of Engineering and Technology, Sunway University, No. 5 Jalan University, Selangor, Bandar Sunway, Subang Jaya, Malaysia
Bioactive Compounds and Biological Activities of Ensete Species
Antioxidants; Ensete; Pharmacological activities; Phytochemicals; Traditional medicine
Ensete, commonly known as the false banana, is a plant of the subtropical and tropical regions of Asia and Africa. Ensete has received global attention in the past decade. The various parts of the plant, such as the fruits, fruit peel, corm, pseudostem, seed, leaves, flowers, sap, and roots, have been used in traditional medicine to treat various ailments. Starch and other minor/trace components found in Ensete plants have been used as tablet binders, disintegrants, pharmaceutical gelling agents, and sustained release agents in pharmaceuticals and nutraceuticals. Ensete has been used as a staple and co-staple food by Ethiopians and has many ethnomedicinal uses. The present chapter validates the historic use of various parts of Ensete in treating ailments by providing detailed information on the phytochemicals present in the plant and discussing various biological properties such as antioxidant, antimicrobial, antidiabetic, immunomodulatory, hypolipidemic, cytotoxic, antiurolithiatic, antiestrogenic, nephroprotective, and hepatoprotective properties. Springer Nature Switzerland AG 2024.
Banadka A.; Toppo V.; Sudheer W.N.; Ramakrishnan R.; Lakshmaiah V.V.; Al-Khayri J.M.; Nagella P.
Reference Series in Phytochemistry, Vol-Part F2504, pp. 77-103.
Springer Science and Business Media B.V.
Banadka A., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Toppo V., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Sudheer W.N., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Ramakrishnan R., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Lakshmaiah V.V., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Al-Khayri J.M., Department of Agricultural Biotechnology, College of Agriculture and Food Sciences, King Faisal University, Al-Ahsa, Saudi Arabia; Nagella P., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India
Leveraging Deep Learning in Hate Speech Analysis on Social Platform
The scope and usage of the Internet have surpassed the expected growth and have proven beyond the basic purpose of being used for networking and telecommunications. It serves as the backbone of the web, and one of the predominant domains that uses the Internet is social media. The concept was conceived in the early 1990s and went on to grow as a powerful medium of people networking along with the Internet. Social networking sites (SNS) acquired a predominant element of the Internet owing to their use and services they offer through the Internet. A few of the most used social networking sites include Twitter and Facebook, which are used synonymous to expressions of text. These SNS allow the users to post photos, videos, and other multimedia content along with text and voice messages that are shared among other users. As with any technology or application, these also have the risk of users posting offensive material and textual content. Hate is being spread through messages, which are in the form of text and also through other materials posted. There is no control to check for the message for the hate content as and when it is posted, and by the time it is deleted by admins, it could have already reached millions of users. This chapter proposes a technique for detecting hate texts in reviews from registered users in the Twitter dataset. The proposed work makes use of improved principle component analysis (IPCA) and modified convolution neural network (MCNN) for detecting hate texts. The advantage of natural language processing is used for building an automated system for the analysis of syntax and semantics of the words. The proposed methodology consists of phases like pre-processing, feature extraction, and process to classify the text. The white spaces in the text are removed through normalization in the pre-processing phase, and also remove special characters such as question marks, punctuations, and exclamatory symbols to remove stop words. The features that are pre-processed are then subjected to feature extraction using IPCA. A set of correlated features are made used for identifying more important features in the data set under consideration. Next, the classification is done for identifying the hate text or for any language abuse. MCNN is applied for the classification of the text into HATE and NON-HATE from the text with better accuracy. The experiments prove that the proposed method has a high level of accuracy even for a large dataset. The results show that the proposed method has better performance in terms of precision, recall, and F-measure when compared with other state-of-the-art methods. 2024 Taylor & Francis Group, LLC.
Manikandan R.; Hariharasitaraman S.; Ramkumar S.; Gobinath R.
Deep Learning for Smart Healthcare: Trends, Challenges and Applications, pp. 38-53.
Manikandan R., CHRIST (Deemed to be University), India, School of Business and Management, India; Hariharasitaraman S., Division of Cyber Security and Digital Forensics, Faculty of Computing Science and Engineering, VIT Bhopal University, India; Ramkumar S., Christ University, Bangalore, India; Gobinath R., Christ University, Bangalore, India
Challenges and Issues in Health Care and Clinical Studies Using Deep Learning
Deep learning is a subset of machine learning, which has more than three layers of neural networks. Neural networks resemble the functioning of human behavior in nature. These neural networks are capable of producing results with single layers, but multiple layers help in producing accurate results with increased precision rate. Deep learning supports a number of artificial intelligence (AI)-based applications and services, which helps in increased automated devices, data analysis, and many more physical tasks in various fields. Deep learning technology has become part of human day-to-day life. It is involved in every aspect of daily routine like voice-based searches, operating a device, baking transactions, and many more. Deep learning allows the healthcare industry to examine data quickly without compromising accuracy. Deep learning uses mathematical models designed to work almost like the human brain. Multiple layers of networking and technology enable unmatched computing capability and the ability to traverse and analyze through vast sets of data that would have previously been lost, forgotten, or missed. 2024 Taylor & Francis Group, LLC.
Deep Learning for Smart Healthcare: Trends, Challenges and Applications, pp. 165-182.
Devi M.R., School of Information Science, Presidency University, Bangalore, India; Sindhu V., Department of Computer Science, Christ University, Yeshwanthpur campus, Bangalore, India; Devi S., Presidency University, Bangalore, India, School of Information Science, Presidency University, Bangalore, India
Inkjet printing of MOx-based heterostructures for gas sensing and safety applicationsRecent trends, challenges, and future scope
Acetone; Acetylene; Doping; Ethanol; Formaldehyde; Heterostructures; Ink formulation; Inkjet printing; Metal oxide; Metal oxide semiconductor; Methanol; MOx; Nanocomposites; Nanostructures; Toluene; VOC
Volatile organic compounds (VOCs) are pollutants that affect air quality and human health. Detection of VOCs is important for environmental safety. Metal oxide semiconductor (MOS) is a promising product for gas sensors due to its advantages of easy fabrication, low cost, and good portability. Their performance is greatly affected by microstructure, defects, catalysts, heterojunctions, and moisture. Metal oxidebased nanomaterials serve as a platform to identify various VOCs with high sensitivity due to their wide bandgap, n-type transport, and excellent electrical properties. Gas detection devices based on doping, altered morphology, and heterostructure have been shown to be effective against VOCs. Inkjet printing (IJP) is a promising process for the room-temperature deposition of functional metal oxides for sensing applications. However, the development of metal oxide ink requires a careful selection of the precursors, solvents, and additives. This section will focus on the production of various metal oxide (MOx)-based sensors such as ZnO, SnO2, MoO3, CuO, Cu2O, Mn3O4, and WO3 for the detection of VOCs such as acetylene, toluene, ethanol, formaldehyde, and acetone. It will summarize recent research and advances in large-scale printing of MOx-based nanocomposites. This work illustrates the need to explore new composite materials, structures, and morphology as well as other methods for better and faster transformation. The role of solvents in ink stabilization and printing and the behavior of ink rheological parameters in the IJP spraying process will also be discussed. Ink formulations for the synthesis of functional nanocomposites will be analyzed and presented for future scope and challenges. 2024 Elsevier Inc. All rights reserved.
Complex and Composite Metal Oxides for Gas, VOC and Humidity Sensors, Volume 2: Technology and New Trends, pp. 133-157.
Murthy H., Department of Electronics and Communication Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, India
AI applications at the scheduling and resource allocation schemes in web medium
Resources including business, informational, personal, and financial resources are required, with support from users, to maintain and implement the resource representations. Resource provisioning seeks to meet user needs by supplying the appropriate resources at the appropriate time at a lower cost. A service provider oversees supplying resources to all applications, and among the methods of resource management that they can employ are time-based, cost-based, on-demand, and bargain-based. These general approaches to resource provisioning and scheduling are based on recent developments in heterogeneity in 6G networks, including cloud computing, fog computing, and autonomic computing, to allocate and schedule resources while keeping an eye on service performance and adjusting as needed to meet the needs of cloud users. The proposed work increases resource allocation through cost reduction and, as a result, increases the availability of the services at the device levels without compromising performance parameters such as availability, efficiency, authentication, and authorization. The wide metropolitan area network (6G Networks) wireless heterogeneity is presented in this chapter's technological problems. Memory, network performance, and other factors were heterogeneous in fog nodes. Here, the Load balancing algorithm's Priority ordering is applied to make use of wireless model properties. This chapter focuses on various load balancing and scheduling strategies along with a few machine learning techniques applied to fog nodes and clustering techniques. 2024 selection and editorial matter, Dr. Abraham George and G. Ramana Murthy; individual chapters, the contributors.
Towards Wireless Heterogeneity in 6G Networks, pp. 107-126.
Shekhar R., Department of Computer Science and Engineering, Alliance University, Bangalore, India; Paul P.M., Department of Computer Science and Engineering, Alliance University, Bangalore, India; Jingle D.J., Department of Computer Science and Engineering, Christ University, Bangalore, India
Genetic Modification of Enzymes for Biomass Hydrolysis
Lignocellulose biomass is an economically viable and most abundant energy source. The synthesis of renewable energy-based fuel from lignocellulosic biomass is a replacement for fossil fuel. Cellulases are the biocatalysts that hydrolyze the ?-1,4-glycosidic bond in cellulose to release carbohydrate moieties that can be converted to ethanol, butanol, and other compounds. However, little enzymatic activity and product yield, and thermal stability are hurdles in the deconstruction of lignocellulose. Current progress in synthetic and omics technologies has resulted in several works in metabolic and genetic engineering that have paved the way for efficient conversion of lignocellulose to fuel in the last decades. Several works have attempted to apply genetic and metabolic engineering in the synthesis of stable and highly active cellulases at lower cost. This chapter reviews various genetic engineering technologies for enhancing cellulase synthesis and catalytic efficiency. 2024 selection and editorial matter, Reeta Rani Singhania, Anil Kumar Patel, Htor A. Ruiz, Ashok Pandey; individual chapters, the contributors.
Madhavan A.; Arun K.B.; Nair B.G.; Singhania R.R.; Raveendran S.; Awasthi M.K.; Pandey A.; Binod P.
Biomass Hydrolyzing Enzymes: Basics, Advancements, and Applications, pp. 101-111.
Madhavan A., School of Biotechnology, Amrita Vishwa Vidyapeetham, Kerala, Kollam, India; Arun K.B., Department of Life Sciences, Christ (Deemed to be University), Karnataka, Bengaluru, India; Nair B.G., School of Biotechnology, Amrita Vishwa Vidyapeetham, Kerala, Kollam, India; Singhania R.R., Institute of Aquatic Science and Technology, National Kaohsiung University of Science and Technology, Kaohsiung City, Taiwan, Centre for Energy and Environmental Sustainability, Uttar Pradesh, Lucknow, India; Raveendran S., Department of Food Technology, T K M Institute of Technology, Kerala, Kollam, India; Awasthi M.K., College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province, China; Pandey A., Centre for Energy and Environmental Sustainability, Uttar Pradesh, Lucknow, India, Centre for Innovation and Translational Research, CSIR-Indian Institute of Toxicology Research, Uttar Pradesh, Lucknow, India, Kyung Hee University, Seoul, South Korea, School of Engineering, University of Petroleum and Energy Studies, Dehradun, India; Binod P., Microbial Processes and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology, Kerala, Thiruvananthapuram, India
Data Analytics and ML for Optimized Performance in Industry 4.0
Industry 4.0, the fourth industrial revolution, has revolutionized manufacturing and production systems by integrating Data Analytics (DA) and Machine Learning (ML) techniques. Predictive maintenance, which predicts equipment malfunctions and schedules maintenance in advance, is a crucial application of DA and ML within Industry 4.0. It reduces downtime, improves productivity, and lowers costs. Demand forecasting, which uses historical data and ML algorithms to predict future product demand, and anomaly detection, which identifies abnormal patterns or events within large datasets, are also critical applications of DA and ML in Industry 4.0. They enhance operational efficiency and reduce costs. However, the adoption of DA and ML presents several challenges for organizations, including infrastructure, personnel, ethical, and privacy concerns. To realize the benefits of DA and ML, companies must invest in appropriate hardware and software and develop the necessary expertise. They must also handle data responsibly and transparently to ensure privacy and ethical standards. Despite these challenges, the integration of DA and ML in Industry 4.0 is critical for optimized performance, improved productivity, and cost savings. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors.
Daya Sagar K.V.; Ramachandran K.K.; Acharjee P.B.; Singh P.; Satyala H.; Singh B.
Robotics and Automation in Industry 4.0: Smart Industries and Intelligent Technologies, Vol-45689, pp. 275-287.
https://doi.org/10.1201/9781003317456-15 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191824754&doi=10.1201%2f9781003317456-15&partnerID=40&md5=ee2e7c5fd88fee6b4f0ebadbac00c861
Daya Sagar K.V., Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India; Ramachandran K.K., Department of ManagementCommerce/International Business, Dr. G R D College of Science, Coimbatore, India; Acharjee P.B., Christ University, Pune, India; Singh P., Department of CSE Guru Ghasidas Vishwavidyalaya, Bilaspur, India; Satyala H., Department of Operation Management, Indian Institute of Management, Ranchi, India; Singh B., Department of Food Science and Technology, IK Gujral Punjab Technical University, Punjab, India
Carbon-Based and TMDs-Based Materials as Catalyst Support for Fuel Cells
Global energy consumption and environmental pollution caused by the extensive use of fossil fuels have increased the need to look forward to more renewable energy sources. Fuel cell, one of the promising energy conversion devices, has the potential to outsmart the existing devices but has several setbacks to be employed on a larger scale. One of the hindrances is the sluggish oxygen reduction reaction kinetics at the cathode and hence requires electrocatalysts to improve its overall performance. This chapter provides a brief overview of graphene and transition metal dichalcogenides (TMDs)- based composites that have the potential to be used as a catalyst support. 2024 World Scientific Publishing Company.
Carbon and Tmds Nanomaterials for Energy Applications, pp. 107-131.
Nair A.S., Department of Physics and Electronics CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India; Joseph S., Department of Physics and Electronics CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India; Jafri R.I., Department of Physics and Electronics CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India
Securing Automated Systems with BT: Opportunities and Challenges
The use of automated systems is becoming increasingly prevalent in various industries; however, they pose significant security risks. In order to enhance the security of these systems, Blockchain Technology (BT) provides a promising solution. This chapter discusses the opportunities and challenges associated with using BT to secure automated systems. The role of BT in securing automated systems is discussed, emphasizing its ability to improve security and transparency. Additionally, BT-based systems with enhanced security are examined, such as decentralized data management, immutable and transparent ledgers, reduced cyber-attacks, and secure data sharing. Despite these opportunities, challenges such as high computational power requirements, integration challenges, BT scalability, and regulatory challenges must be addressed. Utilizing BT can create a more secure and transparent system that can help to prevent fraud, hacking, and other forms of cyber-attacks, ultimately enhancing the reliability and safety of automated systems. In conclusion, this paper highlights the potential of using BT for securing automated systems and the need for continued research and development to overcome the challenges associated with its implementation. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors.
Cavaliere L.P.L.; Rawat S.; Sidana N.; Acharjee P.B.; Kharb L.; Podile V.
Robotics and Automation in Industry 4.0: Smart Industries and Intelligent Technologies, Vol-45689, pp. 337-348.
Cavaliere L.P.L., Department of Economics University of Foggia, Foggia, Italy; Rawat S., MM Institute of Computer Technology and Business Management (MCA), Maharishi Markandeshwar (Christ Deemed to be University), Haryana, India; Sidana N., Amity School of Economics, Amity University, Noida, India; Acharjee P.B., Christ University, Pune, India; Kharb L., Department of IT, Jagan Institute of Management Studies, Delhi, India; Podile V., KL Business School, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India
Role of Leadership and Management of Higher Education Institutions (HEI) in Digitalization
24 learning; cultural transformation; digital transformational process; digitalization; higher education institutions; leadership; new education policy; technology-enhanced learning
Throughout this chapter, several updated concepts, terms, and theoretical constructs are proposed about leadership and management of Higher Education Institutions (HEIs) with respect to the current trends and demands. The digital learning (DL) ecosystem and the transformational stages are discussed to elaborate the process of digital transformation at the HEIs. The advantages and benefits of digital education are integrated in the chapter with a view to better understand the challenges and opportunities brought forth by these imperatives. The chiseling role of leadership in the entire process is presented in the context of the digital ecosystem in order to meet the expectations of all the stakeholders. The New Education Policy (NEP) presents itself as a shaping force in accordance with prevailing standards and/or voluntary commitment by the respective HEIs in India. Further to the elaboration of the drivers of digitalization in the HEIs, the key takeaway is introduced as a holistic approach to leadership and management in such an ecosystem. 2024 Apple Academic Press, Inc.
Digitalization of Higher Education: Opportunities and Threats, pp. 95-116.
Sao A., RICS School of Built Environment, Amity University, Uttar Pradesh, NOIDA, India; Kumar A., School of Business & Management, CHRIST (Deemed to be University), NCR Campus, Ghaziabad, India; Ashesh G., School of Business, AURO University, Gujarat, Surat, India
Bioactive Compounds and Biological Activities of Arrowroot (Maranta arundinacea L.)
Bioactive compounds; Biological activities; Maranta arundinacae; Rhizomes; Starch
Arrowroot is one of the most widely studied herbal species belonging to the family Marantaceae, which originated from South America and is mainly found in tropical areas. Species belonging to the Maranta genus attaining worldwide attention due to the bioactive compounds are present in their rhizomes. The nutritional values of the Maranta arundinacea plant parts were explored in traditional medicine and culinary practices. Maranta arundinacea flour is a good source of fiber, starch, and carbohydrate and is extensively utilized as a major ingredient in food products. It is also used as an alternative to wheat as the flour is gluten-free. Dietary fibers present in the Maranta arundinacea are beneficially used in the treatment of digestive disorders such as celiac disease and immune disorders. Its known to stimulate the production of IgM by immune cells. Maranta arundinacea is commonly used for weight management as it is protein-rich and has fewer calories. The rhizome contains substantial amounts of sodium, magnesium, phosphorus, potassium, calcium, iron, and zinc. The processed starch from the Maranta arundinacea rhizomes is broadly used in nutritional food products as well as in pharmacological applications. The bioactive compounds present in the Maranta arundinacea rhizome make it the subject of novel pharmaceutical studies. The current chapter tries to emphasize the general morphology, nutritional benefits and processing, bioactive compounds, and biological activities of the Maranta arundinacea. Springer Nature Switzerland AG 2024.
Jaison J.P.; Kurian J.T.; Scaria S.S.; Gangwar J.; Sebastian J.K.
Reference Series in Phytochemistry, Vol-Part F2504, pp. 523-540.
Jaison J.P., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Kurian J.T., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Scaria S.S., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Gangwar J., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Sebastian J.K., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India
Impact of Work from Home During COVID-19 Scenario
Coronavirus; COVID-19; Lockdown; SARS-CoV-2; Work from home (WFH)
In view of the recent situation, COVID-19 has spread across the world, and every country has to enforce a lockdown to prevent the virus from transmitting further. The worldwide COVID-19 outbreak has led to a large number of professionals work from their homes. Almost all the sectors like IT, academics, government, business, etc. are implementing work from home for safety of their employees and sincerely obeying the social distancing norms. Work from home can be beneficial and fruitful in terms of travel expenses, saving time commuting, working on ones own agenda, etc. But it can also be a pain and take a toll on mental well-being as you are living a quarantined life with little to no social life, which can also impact an individuals efficiency. There are so many barriers to work from home (WFH), like unavailability of resources, poor network connectivity, using digital platform and latest software for non-IT professionals, lack of proper infrastructure, etc. Our chapter focuses on every aspects of WFH during the COVID-19 lockdown period so that well-suited policies and practices can be designed to cope with the issues and hence transforming future of organizations by shifting the tradition of work from office to work from home. 2024 Apple Academic Press, Inc. All rights reserved.
Mahato S.; Pathak L.K.; Malik S.; Keo?lu N.
HR 4.0 Practices in the Post-covid-19 Scenario, pp. 47-69.
Mahato S., Indian Institute of Information Technology (IIIT), Jharkhand, Ranchi, India; Pathak L.K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Malik S., Amity Institute of Biotechnology, Amity University Jharkhand, Jharkhand, Ranchi, India; Keo?lu N., Canakkale Onsekiz Mart University, Canakkale, Turkey
Business Intelligence in Action: Way of Successful Implementation of Automated Systems
This chapter presents an overview of the role of automated systems in Business Intelligence (BI). BI has emerged as a critical element for modern organizations in decision-making processes by analyzing large volumes of data. Automated BI systems offer several advantages over traditional manual systems, including increased efficiency, accuracy, and customized insights. Despite these benefits, there are several limitations and challenges associated with the implementation of automated BI systems. This chapter examines the benefits and limitations of automated BI systems and identifies common success factors for successful implementation. The chapter also explores different types of automated systems, including predictive analytics, machine learning, natural language processing, and robotics process automation. These systems can help organizations analyze and interpret large amounts of data more quickly and accurately, enabling them to make informed decisions. However, despite the potential benefits of automated BI systems, there are several challenges associated with their implementation, including technical expertise and integration issues. To address these challenges, careful planning, collaboration, and ongoing monitoring are essential. In conclusion, this chapter highlights the importance of automated BI systems in modern businesses and provides valuable insights into their benefits and limitations. The chapter also emphasizes the need for careful planning, collaboration, and monitoring for the successful implementation of automated BI systems. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors.
Challa V.N.S.K.; Ramachandran K.K.; Rameshkumar P.M.; Cavaliere L.P.L.; Acharjee P.B.; Dornadula V.H.R.
Robotics and Automation in Industry 4.0: Smart Industries and Intelligent Technologies, Vol-45689, pp. 288-302.
Challa V.N.S.K., Department of BBA KL Business School, Koneru Lakshmaiah Education Foundation Green fields, Vaddeswaram, India; Ramachandran K.K., Department of ManagementCommerce/International Business, Dr. G R D College of Science, Coimbatore, India; Rameshkumar P.M., Department of Corporate Secretaryship Dwaraka Doss Goverdhan Doss, Vaishnav College (Autonomous), Chennai, India; Cavaliere L.P.L., Department of Economics University of Foggia, Foggia, Italy; Acharjee P.B., Christ University, Pune, India; Dornadula V.H.R., Sree Venkateswara College of Engineering, Nellore, India
A Smart Internet of Things (IoT) Enabled Agricultural Farming System
Automated Irrigation System; Digital Humidity; Greenhouse System; Industry 4.0; IoT; Smart Farming System; Soil Moisture Monitoring; Temperature Sensing
Industry 4.0 has brought about a profound revolution in recent times. This advancement profoundly impacted technology usage in every aspect and has significantly improved businesses. Agriculture is one of the evergreen economic contributors to Indias GDP. With improvements in adaptability in this sector, the time is ripe for instituting IoT (Internet of Things)-based smart agriculture. Water scarcity and drastic climate change are real issues affecting crop yields, leading to the failure in the timely fulfillment of market demand (Nawandar 2019). The authors have collaborated to address these concerns by creating a system comprising a functional hardware prototype and an android application for regulating irrigation and temperature. The introduction of IoT (Internet of Things) automates crop monitoring and reduces labor costs. By using IoT, (Internet of Things) an earmarked agricultural field is covered with sensors. The sensors are concealed so as not to be affected by the bleakness of the external environment. These sensors work in tandem with drip irrigation following the sensed climatic conditions. The water is pumped directly to the root zone in an optimally sensed manner. The authors developed and tested the system successfully in a greenhouse system. The process initially aims to extract the values of soil parameters by using IoT (Internet of Things) sensors and appropriately control the watering of crops, thus enabling the cultivation of crops even in a hot and dry climate. Crops can be irrigated from a remote location and their temperature can be meticulously regulated to ensure they remain within an optimal range. Water utilization for agricultural crops is optimized with the use of automated irrigation systems that use W.S.N (Wireless - Sensor-Networks) and G.P.R.S (General-Packet-Radio-Service) modules. The algorithm employed in the system to control water usage is based on the needs of the crop and the terrain. The entire system is powered by photovoltaic panels, which are useful in rural and isolated areas without electricity (Raut and Shere 2014). A cellular network is used for duplex communication. Continuous monitoring and irrigation schedule programming are used by web apps to manage irrigation. This is also possible using a browser and web pages. A system with three identical automatic irrigation systems can save water use by up to 90%. 2024 by Nova Science Publishers, Inc. All rights reserved.
Joy J.; Josephine V.L.H.; Angel A.; Abisheakkumar B.; Seles E.S.; John R.
Intelligent Decision Support System for IoT-Enabling Technologies: Opportunities, Challenges and Applications, pp. 59-72.
Joy J., MBA Department, School of Business and Management, Kengeri Campus, Christ University, Bangalore, India; Josephine V.L.H., MBA Department, School of Business and Management, Kengeri Campus, Christ University, Bangalore, India; Angel A., MBA Department, School of Business and Management, Kengeri Campus, Christ University, Bangalore, India; Abisheakkumar B., MBA Department, School of Business and Management, Kengeri Campus, Christ University, Bangalore, India; Seles E.S., MBA Department, School of Business and Management, Kengeri Campus, Christ University, Bangalore, India; John R., College of Computer Science & Information Technology, Department of Computer Science, Jazan University, Jazan, Saudi Arabia
Edge Computing in Aerial Imaging A Research Perspective
Aerial imaging; Edge computing; Internet of drones
Internet of Drones (IoD) is a field that has a vast scope for improvement due to its high adaptability and complex problem statements. Aerial vehicles have been employed in various applications such as rescue operations, agriculture, crop productivity analysis, disaster management, etc. As computing and storage power have increased, satellite imaging and drone imaging have become possible, with vast datasets available for study and experiments. The recent work lies in the edge computing sector, where the captured aerial images are processed at the edge. Our paper focuses on the algorithms and technologies that easily facilitate aerial image processing. The applications and their architectures are focused on which can efficiently function using aerial processing. The various research perspectives in aerial imaging are concentrated on paving the way for further research. 2024 Scrivener Publishing LLC. All rights reserved.
Vetriveeran D.; Sambandam R.K.; Jenefa J.; Leena Sri R.
Advances in Aerial Sensing and Imaging, pp. 355-382.
Vetriveeran D., Department of CSE, Christ University, Karnataka, Bangalore, India; Sambandam R.K., Department of CSE, Christ University, Karnataka, Bangalore, India; Jenefa J., Department of CSE, Christ University, Karnataka, Bangalore, India; Leena Sri R., Department of CSE, Thiagarajar College of Engineering, Tamil Nadu, Madurai, India
Leveraging Financial Data to Optimize Automation: An Industry 4.0 approach
Industry 4.0 is a transformative approach that leverages advanced technologies to enhance business efficiency and productivity. Automation is a crucial aspect of next-generation industry, and leveraging financial data is essential to optimizing the automation process. This chapter discusses the role of financial data in optimizing automation processes using an I-4.0 approach. Financial data is derived from various sources and can be collected through different methods, such as automated data collection, manual entry, or using sensors and Internet of Things (IoT) devices. The integration of these sources can pose challenges for businesses. The chapter outlines techniques for automation optimization, such as machine learning, predictive analytics, and business process reengineering. Optimizing automation using financial data offers various benefits for businesses, including cost savings, improved quality, and increased profitability. However, there are challenges that businesses face in leveraging financial data, including the integration of various data sources and formats and the need for skilled personnel to analyze and interpret the data. The successful implementation of automation and optimization of processes can lead to sustainable growth and enhanced operations, making it crucial for businesses to remain competitive in the I-4.0 era. By leveraging financial data to optimize automation processes, businesses can maximize their potential and drive growth. Overall, this chapter highlights the significance of financial data in automation optimization and provides insights into the benefits and challenges that businesses must consider when leveraging financial data for optimization. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors.
Mehta K.; Kulshrestha N.; Sisodia D.R.; Dornadula V.H.R.; Priya P.K.; Ranjan R.
Robotics and Automation in Industry 4.0: Smart Industries and Intelligent Technologies, Vol-45689, pp. 198-214.
Mehta K., TAPMI School of Business Manipal University, Jaipur, India; Kulshrestha N., Christ Deemed to be University, Bangalore, India; Sisodia D.R., Department of Management Army Institute of Management and Technology, Greater Noida, India; Dornadula V.H.R., Startups and IIC, Chairman Office, Sree Venkateswara College of Engineering, Nellore, India; Priya P.K., KL Business School, Koneru Lakshmaiah Education Foundation, Guntur, India; Ranjan R., School of Business and Commerce, Manipal University, Jaipur, India
Revolutionizing Healthcare with IoT: Connecting the Dots for Better Patient Outcomes
Healthcare enhances ones physical and emotional well-being via the detection, treatment, and eventual cure of disease, illness, injuries, and other debilitating conditions. The importance of information systems has increased everywhere, particularly in the healthcare sector. Information technology has long benefitted the health business, from electronic health records to cloud-based platforms. Information systems are becoming increasingly important in advancing healthcare and healthcare administration. The pandemic brought virtual space and services to all sectors of the economy, especially healthcare, which was predominantly supported through face-to-face services earlier, but due to the requirement of social distancing, hospitals started offering services in virtual mode. Also, evolution in the information system and the Internet has paved the way for the Healthcare Internet of Things (HIoT). The Healthcare Internet of Things (HIoT) is the interconnection of intelligent objects or devices that enables the development of new healthcare services and applications. HIoT can take many forms, namely medical devices, public health services, innovative technology, medication refills, and remote monitoring. This healthcare data is a new treasure for healthcare stakeholders to improve patients health and experiences while creating revenue opportunities and improving healthcare operations. Thus, HIoT is redefining healthcare by ensuring better care, improved treatment outcomes, and reduced patient costs, as well as better processes and workflows, improved performance, and patient experience for healthcare providers. HIoT devices can also be useful for asset management tasks like controlling inventory at the pharmacy, checking refrigerator temperatures, and controlling humidity and temperature in the environment. Having said the advantages, one cannot deny the challenges it has brought to safety, security, privacy, and scalability aspects. Hence, this chapter will explore the evolution of IoT in healthcare, its elements, applications, and challenges. 2024 selection and editorial matter, Alex Khang.
Rangasamy S.; Rajamohan K.; Basu D.; Menezes K.J.; Chongder A.
AI and IoT Technology and Applications for Smart Healthcare Systems, pp. 204-223.
Rangasamy S., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Rajamohan K., School of Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Basu D., School of Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Menezes K.J., School of Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Chongder A., School of Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, India
Bioactive Compounds and Biological Activities of Taro (Colocasia esculenta (L.). Schott)
Colocasia esculenta; Nutrition; Pharmacological activities; Phytochemistry; Taro
Plants are said to be the finest source of food and phytochemicals. Along with aerial plant components, subterranean tuberous, stems, and roots were often consumed for their phytochemical and nutritional worth. Colocasia esculenta(L.). Schott is an essential plant that is utilized for its nutritional and phytochemical properties. It is commonly called taro, which is very rich in plant secondary metabolites and their respective pharmacological properties. Taro is consumed by people worldwide and serves as a staple food in Asian and African countries, leading to its abundant production. Extensive studies has explored the nutritional composition of taro, which has been identified as a promising source of dietary fiber. Moreover, taro exhibits a wealth of minerals and phytochemicals, including phenols, flavonoids, and various derivatives, which contribute to its diverse pharmacological activities, such as antioxidant, antimicrobial, antidiabetic, anti-inflammatory, and anticancer effects. This chapter provides a comprehensive overview of taro, encompassing its nutritional profile, phytochemistry, and numerous pharmacological properties. Additionally, it addresses the important aspects of biosafety in relation to taro consumption and highlights potential prospects for sustainable production of this remarkable tuber crop. Springer Nature Switzerland AG 2024.
Wudali S.N.; Barwad A.; Banadka A.; Shaikh A.; Al-Khayri J.M.; Nagella P.
Reference Series in Phytochemistry, Vol-Part F2504, pp. 37-59.
Wudali S.N., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Barwad A., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Banadka A., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Shaikh A., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Al-Khayri J.M., Department of Agricultural Biotechnology, College of Agriculture and Food Sciences, King Faisal University, Al-Ahsa, Saudi Arabia; Nagella P., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India