Applications and future directions in multimodal large language model: opportunities and challenges
automation; customer care; healthcare assistant; information management; machine learning; Multimodal large language models
Multimodal large language models (MLLMs) are an application of artificial intelligence that is rapidly growing by integrating numerous use cases. MLLMs have the capability to process data from several sources, including structured and unstructured. It enables large language models (LLMs) to give insights to the user by analyzing data from various formats. The traditional way of analyzing the data was done with a single data format. While using MLLMs, multiple data modalities are handled to manage complicated multimodal tasks, like generating content, multimodal perception, and augmenting human-computer interaction. This chapter discusses in detail the insights from data from multiple modalities and domains of the use cases of MLLMs. We also discuss the advantages of MLLMs and explain the transformative benefits from unimodal systems to multimodal systems in different sectors. We also focus on the ethical usage of MLLMs by addressing the challenges related to privacy, operational limits, bias, computational difficulties, and data scarcity. The scarce assessment metrics and trials in accomplishing robust explainability are also discussed. To train these MLLMs, acquiring and training the data utilizes more computation and power consumption, along with addressing the data security and privacy concerns. The chapter also discusses the sensible usage of AI through different problems and practices. Detailed analysis and strategies for addressing global challenges and promoting novelty in model development, outlining how these MLLMs shape the upcoming technological innovation focusing on ethical application of technology with an advantage on society. 2026 Elsevier Inc. All rights reserved.
Chandra, J.; Malviya, Meenakshi; Sabu, Samson; Rajendran, Rajesh Kanna; Joseph, Alwin
Challenges and Applications of Generative Large Language Models;pp.219-241
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1016/B978-0-443-33592-1.00006-1 https://www.scopus.com/pages/publications/105032983757?origin=resultslist
Chandra J., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Malviya M., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Sabu S., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Rajendran R.K., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Joseph A., Department of Computer Science, Christ University, Karnataka, Bengaluru, India
APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN BUSINESS AND FINANCE 5.0
AI has evolved as a burgeoning technology in many industries, including the financial and banking sectors, which are facing greater challenges in data management, identity theft, and fraud as transactions and other company processes shift online and gain popularity. As systems using deep learning technology are able to detect data patterns and spot suspicious activity and probable fraud, AI can advance many financial and business activities. This book, Applications of Artificial Intelligence in Business and Finance 5.0, provides a valuable overview of how artificial intelligence (AI) applications are transforming global businesses and financial organizations, looking at the newest artificial intelligence-based solutions for e-commerce, corporate management, finance, banking and trading, and more. Chapters look at using artificial intelligence and machine learning techniques to forecast and assess different financial risks such as liquidity risk, volatility risk, and credit risk. The book also describes the use of natural language processing and text mining paired with machine learning models to assist in guiding sophisticated investors and corporate managers in financial decision making. Other topics include cryptocurrency in emerging markets; the role of artificial intelligence in making a positive impact on sustainable development; the use of fintech for micro, small and medium enterprises; the role of AI in financial education; the application of artificial intelligence in cyber security; and more. With a cross-disciplinary theme, this volume will be helpful to those in the corporate world, including professionals in business, finance, the e-commerce, economic sociology, political science, public administration, mass media and communications, information systems, development studies, among others. 2025 by Apple Academic Press, Inc.
Applications of Artificial Intelligence in Business and Finance 5.0, pp. 1-260.
https://doi.org/10.1201/9781003535133 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216633177&doi=10.1201%2f9781003535133&partnerID=40&md5=7a21b2aa324e632caf22efbd54dc0dc4
Goel R., Symbiosis Centre for Management Studies, Symbiosis International Deemed University, Noida, Pune, India; Garg V., Christ University, India; Floris M., Department of Economics and Business, University of Cagliari, Italy
Applications of artificial intelligence in Echo Global Logistics
artificial intelligence; Echo Global Logistics; industrial internet of things; internet of things; supply chain operations; transportation management system (TMS)
Echo Global Logistics is a premier provider of business process outsourcing, using technology to meet its clients logistics and transportation needs. They deliver substantial transportation savings to clients while providing top-tier service, thanks to state-of-the-art web-based technologies, dedicated service teams, and significant purchasing power. The most significant business risk in 2023 will be supply chain interruptions, which can impact cash flow, growth, and shareholder value. Echo Global Logistics has introduced an innovative self-service website called Echo Ship, designed for shippers of less-than-truckload (LTL) shipments. Echo Ship simplifies LTL shipping with excellent visibility, outstanding functionality, and a quick, user-friendly design. Logistics is evolving at Echo Global Logistics, with patented technology incorporating the latest developments in the most flexible and reliable transport management system (TMS) currently available. This TMS is developed using Artificial Intelligence (AI), machine learning, and complex load-matching algorithms. Echos unique software is user-friendly, adaptable, and highly scalable, addressing the evolving needs of carriers and shippers regarding transportation management, enabling customers to move their goods swiftly, securely, and affordably. A transportation management company leverages AI to provide supply chain solutions that optimize transportation and logistics needs. The list of services also encompasses executive dashboard presentations, rate negotiation, transportation procurement, shipment execution and tracking, carrier management, carrier selection, reporting, compliance, and comprehensive shipment reports, Over the next five years, supply chain companies anticipate a twofold increase in the use of machine automation in their operations. Similarly, there is a projected 40% compound annual growth rate (CAGR) over the next seven years, going from $1.67 billion in 2018 to $12.44 billion in 2024. Supply chain executives are often time-constrained, making it challenging to attend numerous meetings for solution implementation. Actionable insights from integrated AI tools can remove bottlenecks and unlock real-time value. This is vital because supply chain businesses require more action rather than excessive analysis. This chapter delves into the AI and supply chain practices at Echo Global Logistics, illustrating how AI-based solutions reduce costs, enhance supply chains, boost productivity, and improve service quality. It aims to determine whether the company can transform its products and services, creating new value propositions for Echo Global Logistics customers with the aid of AI. 2024 by Elsevier Inc. All rights reserved, including those for text and data mining, AI training, and similar technologies.
Computational Intelligence Techniques for Sustainable Supply Chain Management, pp. 405-428.
Chaurasia A., School of Commerce, Finance and Accountancy Christ (Deemed to be University), Uttar Pradesh, Ghaziabad, India; Parashar B., VIT-Bhopal University, Madhya Pradesh, Bhopal, India; Kautish S., LBEF Campus (APU Malaysia) Kathmandu, Nepal, Model Institute of Engineering and Technology, Jammu and Kashmir, Jammu, India
Applications of artificial intelligence techniques in modern banking sectors
AI-powered decision-making instruments are cutting-edge technology that has the potential to displace conventional banking procedures. This chapter emphasizes the critical role artificial intelligence (AI) has played in guiding the banking industry toward expansion. AI techniques including robotics, deep learning, facial recognition, natural language processing, and more are used to achieve this goal. This chapter provides an overview of the use of AI approaches in several banking functional domains, such as loan approval, customer lifecycle management, customer services, alarm systems, and so on. It also highlights the benefits and difficulties that AI-driven financial apps provide. In summary, artificial intelligence (AI) has enormous promise in banking, but it also confronts several obstacles that, if correctly recognized and overcome, might broaden its use. This chapter is an invaluable tool for researchers, lawmakers, and bank officials who want to learn more about the unrealized potential of artificial intelligence in banking. 2024, IGI Global. All rights reserved.
Devapitchai J.J.; Dheenadhayalan K.; Rajeshkumar L.; Meena M.R.; Soundarya M.; Sowmiya M.S.; Udhaya K.; Vethamanikam G.H.A.; Manickam T.
Blockchain Applications for Smart Contract Technologies, pp. 279-298.
Devapitchai J.J., St. Joseph's College of Engineering, Chennai, India; Dheenadhayalan K., Mepco Schlenk Engineering College, India; Rajeshkumar L., St. Joseph's College of Engineering, Chennai, India; Meena M.R., Dr. Umayal Ramanathan College for Women, India; Soundarya M., Sathyabama Institute of Science and Technology, India; Sowmiya M.S., Alagappa University, India; Udhaya K., Alagappa University, India; Vethamanikam G.H.A., Ayya Nadar Janaki Ammal College, India; Manickam T., Christ University, India
Applications of artificial intelligence to neurological disorders: Current technologies and open problems
Alzheimers disease; Artificial intelligence; Autism spectrum disorder; Dementia; Epilepsy; Machine learning; Mild cognitive impairment; Multiple sclerosis; Neurological disorder; Parkinsons disease; Schizophrenia; Stroke
Neurological disorders are caused by structural, biochemical, and electrical abnormalities involving the central and peripheral nervous system. These disorders may be congenital, developmental, or acute onset in nature. Some of the conditions respond to surgical interventions while most require pharmacological intervention and management, and are also likely to be progressive in nature. Owing to a high global burden of the most common neurological disorders, such as dementia, stroke, epilepsy, Parkinsons disease, multiple sclerosis, migraine, and tension-type headache, there exist multiple challenges in early diagnosis, management, and prevention domains, which are further amplified in regions with inadequate medical services. In such situations, technology ought to play an inevitable role. In this chapter, we review artificial intelligence (AI) and machine learning (ML) technologies for mitigating the challenges posed by neurological disorders. To that end, we follow three steps. First, we present the taxonomy of neurological disorders, derived from well-established findings in the medical literature. Second, we identify challenges posed by each of the common disorders in the taxonomy that can be defined as computational problems. Finally, we review AI/ML algorithms that have either stood the test of time or shown the promise to solve each of these problems. We also discuss open problems that are yet to have an effective solution for the challenges posed by neurological disorders. This chapter covers a wide range of disorders and AI/ML techniques with the goal to expose researchers and practitioners in neurological disorders and AI/ML to each others field, leading to fruitful collaborations and effective solutions. 2022 Elsevier Inc. All rights reserved.
Chandra J.; Rangaswamy M.; Banerjee B.; Prajapati A.; Akhtar Z.; Sakauye K.; Joseph A.
Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence, pp. 243-272.
Chandra J., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Rangaswamy M., Department of Psychology, CHRIST (Deemed to be University), Bangalore, India; Banerjee B., Institute for Intelligent Systems, Department of Electrical & Computer Engineering, University of Memphis, Memphis, TN, United States, Department of Electrical & Computer Engineering, University of Memphis, Memphis, TN, United States; Prajapati A., Department of Electrical & Computer Engineering, University of Memphis, Memphis, TN, United States; Akhtar Z., Department of Network and Computer Security, State University, New York Polytechnic Institute, Utica, NY, United States; Sakauye K., Department of Psychiatry, University of Tennessee, Health Science Center, Memphis, TN, United States; Joseph A., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India
Applications of bioconvection for tiny particles due to two concentric cylinders when role of Lorentz force is significant
The bioconvection flow of tiny fluid conveying the nanoparticles has been investigated between two concentric cylinders. The contribution of Lorenz force is also focused to inspect the bioconvection thermal transport of tiny particles. The tiny particles are assumed to flow between two concentric cylinders of different radii. The first cylinder remains at rest while flow is induced due to second cylinder which rotates with uniform velocity. Furthermore, the movement of tiny particles follows the principle of thermophoresis and Brownian motion as a part of thermal and mass gradient. Similarly, the gyro-tactic microorganisms swim in the nanofluid as a response to the density gradient and constitute bio-convection. The problem is modeled by using the certain laws. The numerical outcomes are computed by using RKF-45 method. The graphical simulations are performed for flow parameters with specific range like 1?Re?5, 1?Ha?5, 0.5?Nt?2.5, 1?Nb?3, 0.2?Sc?1.8, 0.2?Pe?1.0 and 0.2???1.0. It is observed that the flow velocity decreases with the increase in the Hartmann number that signifies the magnetic field. This outcome indicates that the flow velocity can be controlled externally through the magnetic field. Also, the increase in the Schmidt numbers increases the nanoparticle concentration and the motile density. 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Zhang L.; Puneeth V.; Khan M.I.; El-Zahar E.R.; Manjunath N.; Shah N.A.; Chung J.D.; Khan S.U.; Khan M.I.
All Open Access; Gold Open Access; Green Open Access
ISSN: 19326203; PubMed ID: 35503769; CODEN: POLNC
https://doi.org/10.1371/journal.pone.0265026 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129485959&doi=10.1371%2fjournal.pone.0265026&partnerID=40&md5=71b42626d697aa41bc11c34140b7ee1d
Zhang L., Yellow River Institute of Hydraulic Research, YRCC, Zhengzhou, China, Henan Engineering Research Center of Hydropower Engineering Abrasion Test and Protection, Zhengzhou, China; Puneeth V., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, India; Khan M.I., Department of Mathematics and Statistics, Riphah International University, Islamabad, Pakistan; El-Zahar E.R., Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia, Department of Basic Engineering Science, Faculty of Engineering, Menoufia University, Shebin El-Kom, Egypt; Manjunath N., Department of Sciences and Humanities, CHRIST (Deemed to be University), Bengaluru, India; Shah N.A., Department of Mechanical Engineering, Sejong University, Seoul, South Korea; Chung J.D., Department of Mechanical Engineering, Sejong University, Seoul, South Korea; Khan S.U., Department of Mathematics, COMSATS University Islamabad, Sahiwal, Pakistan; Khan M.I., Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
Applications of brain-computer interfaces in automated financial services
The purpose of this study is to evaluate the possibility that brain-computer interfaces (BCIs) could bring about a revolutionary transformation in the realm of automated financial services. Brain-computer interfaces (BCIs) hold the promise of revolutionizing the way financial transactions are carried out, increasing security measures, and bodying stoner gestures. This is to be accomplished by providing direct communication between the brain and external bias. Among the subjects that are covered in this study are verification methods, real-time decision-making, and client participation in financial services. The study delves into the intricate workings of BCIs. Through the use of neural data, brain-computer interfaces (BCIs) can supply an unknown position of intelligence into the gestures and preferences of stoners. Because of this, financial institutions can offer services that are more effective and more efficiently adapted to the specific needs of each client. This inquiry emphasizes key breakthroughs in BCI technology. 2025, IGI Global Scientific Publishing.
Mekhmonov, Sultonali; Kulshrestha, Nitin; Shetty, Keerthi; Hussein, Abdullah Abed; Jasim, Jasim Ahmede; Udupa, Prakruthi N.
Concepts and Applications of Brain-Computer Interfaces;pp.197-210
https://doi.org/10.4018/979-8-3693-7427-6.ch011 https://www.scopus.com/pages/publications/105007492503?origin=resultslist
Mekhmonov S., Tashkent State University of Economics, Uzbekistan; Kulshrestha N., Christ University, India; Shetty K., NMAM Institute of Technology, Nitte, India; Hussein A.A., Al-Manara College for Medical Sciences, Iraq; Jasim J.A., Al-Nisour University College, Iraq; Udupa P.N., Dr. H.N. National College of Engineering, India
Applications of Classification and Recommendation Techniques to Analyze Soil Data and Water Using IOT
As we are moving to a computerized and scientific world, data becomes an intrinsic part of our life. Agriculture sector is still unorganized with regard to automation and data analytics. This task is accomplished through sensors, data mining and analysis. In this paper, we propose real-time sensors to detect the soil features and predict the suitable crop cultivation using trained dataset. This would help the farmers to predict the type of cultivation to be done depending on the soil features. Today, the farmer can understand what type of cultivation will be prepared in the soil. Also, people of the upcoming generation will be using that sensor, different plant can be make. The cost of cultivation can be improved. Water level of the soil can be easily predicted. Which type of plant will be produced in the different soil can be predicted. So, this new type of cultivation followed by the next generation also. This paper has presented an improved by the pH sensor, water level sensor. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Thaiyalnayaki S.; Sambandam R.K.; Reddy T.H.S.; Reddy A.H.V.; Sai K.N.; Srinivas V.
Smart Innovation, Systems and Technologies, Vol-334, pp. 215-223.
Springer Science and Business Media Deutschland GmbH
Thaiyalnayaki S., Department of Computer Science and Engineering, School of Computing, Bharath Institute of Higher Education and Research (Deemed to be University), Selaiyur, Chennai, India; Sambandam R.K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bangalore, India; Reddy T.H.S., Department of Computer Science and Engineering, School of Computing, Bharath Institute of Higher Education and Research (Deemed to be University), Selaiyur, Chennai, India; Reddy A.H.V., Department of Computer Science and Engineering, School of Computing, Bharath Institute of Higher Education and Research (Deemed to be University), Selaiyur, Chennai, India; Sai K.N., Department of Computer Science and Engineering, School of Computing, Bharath Institute of Higher Education and Research (Deemed to be University), Selaiyur, Chennai, India; Srinivas V., Department of Computer Science and Engineering, School of Computing, Bharath Institute of Higher Education and Research (Deemed to be University), Selaiyur, Chennai, India
Applications of Digital Technologies and Artificial Intelligence in Cryptocurrency - A Multi-Dimensional Perspective
blockchain; Cryptocurrency; deep learning; FinTech technologies; hybrid analytics approach; machine learning; security attacks; sensitivity analysis
The paradigm shift requires spreading the light of decentralized ledger technology, extraordinarily implementing cryptocurrencies, and being visible as a game-changer. Blockchain technology, along with cryptocurrencies like Bitcoin, Ethereum, and Litecoin, is a tool for global economic transformation that is rapidly gaining traction in the finance industry. However, these technologies have had low popularity in the consumer market. Many platforms have been misunderstood and ignored when there is an obvious hole in among them. The basic idea behind cryptocurrency is that it is a network-based, totally virtual exchange medium that utilizes cryptographic algorithms such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5) to secure the data. Transactions within the blockchain era are secure, transparent, traceable, and irreversible. Cryptocurrencies have gained a reputation in practically all sectors, including the monetary sector, due to these properties. The uncertainty and dynamism of their expenses, however, hazard investments substantially despite cryptocurrencies growing popularity amongst approval bodies. Studying cryptocurrency charge prediction is fast becoming a trending subject matter in the global research community. Several device mastering and deep mastering algorithms, like Gated Recurrence Units (GRUs), Neural nets (NNs), and nearly short-term memory, were employed by the scientists to analyze and forecast cryptocurrency prices. As a part of this chapter, we discuss numerous aspects of cryptographic protection and their related issues. Specifically, the research addresses the state-of-the-art by examining the underlying consensus mechanism, cryptocurrency, attack style, and applications of cryptocurrencies from a unique perspective. Secondly, we investigate the usability of blockchain generation by examining the behavioral factors that influence customers decision to use blockchain-based technology. To identify the best crypto mining strategy, the research employs an Analytic Hierarchy Process (AHP) and Fuzzy-TOPSIS hybrid analytics framework. Furthermore, it identifies the top-quality mining methods by evaluating providers overall performance during cryptocurrency mining. 2023 Scrivener Publishing LLC.
Fintech and Cryptocurrency, pp. 221-249.
Jaisingh W., School of Information Science, Presidency University, Karnataka, Bangalore, India; Preethi N., Department of Data Science, Christ University, Karnataka, Bangalore, India; Kavitha R.K., Department of Computer Applications, Kumaraguru College of Technology, Tamil Nadu, Coimbatore, India
Applications of Machine Learning and Deep Learning Models in Brain Imaging Analysis
Brain imaging is an umbrella term including many non-invasive techniques that objectively monitor brain function. Such monitoring leads to understanding how the brain works by presenting selected stimuli. More importantly, brain function monitoring allows physicians to diagnose and predict brain disorders. In the last decade, several machine learning and deep learning models have been developed by researchers to process and analyse brain imaging data for the diagnosis, detection, and prediction of brain disorders, such as stroke, schizophrenia, autism, psychosis, and Alzheimers. This chapter reviews the various applications and properties of machine learning and deep learning models for brain image analysis. The chapter also highlights the deep learning models that have either understood the test of time or shown the promise to solve challenging problems involving brain imaging data. The review also discusses various open issues yet to have practical solutions or methodologies with the help of machine learning and deep learning. The research covers a wide range of imaging modalities, disorders and models to expose researchers and practitioners in neurological disorders and machine learning and deep learning to each others field, hopefully leading to fruitful collaborations and practical solutions for processing brain images. 2024 selection and editorial matter, Anitha S. Pillai and Bindu Menon; individual chapters, the contributors.
Joseph A.; Chandra J.; Banerjee B.; Rangaswamy M.; Reddy J.K.
Machine Learning and Deep Learning in Neuroimaging Data Analysis, pp. 43-56.
Joseph A., Christ University, Pune, India; Chandra J., Christ University, Bangalore, India; Banerjee B., Institute for Intelligent Systems, Department of Electrical & Computer Engineering, University of Memphis, Memphis, TN, United States; Rangaswamy M., Christ University, Bangalore, India; Reddy J.K., Christ University, Bangalore, India
Applications of neuroscience in education practices: A research review in cognitive neuroscience
Applications of neuroscience; Brain-based learning; Circadian rhythms; Circadian rhythms and learning; Classroom applications of neuroscience; Education; Modulating teaching practices; Neuroscience; Neuroscientific research; Research in education; Scientific progress for teaching and learning
The human brain is the most complex and mysterious organ in the body responsible for learning. Applications of neuroscience and genetics need to be comprehended to modulate teaching and learning practices in education. Considering the scope for application of advanced sciences in education practices, this book chapter simplifies and reviews ten critical research findings relevant for students and teachers for classroom applications and for modulating learning patterns for different age groups. The concept is also relevant for parents and the academic fraternity at large. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved.
Neuro-Systemic Applications in Learning, pp. 117-135.
Satralkar M., School of Education, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Cherian J., School of Education, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Thomas K.A., School of Education, Centre for Education Beyond Curriculum (CEDBEC), CHRIST (Deemed to be University), Bengaluru, Karnataka, India
Applications, Enabling Technologies, Vision of Edge Artificial Intelligence for 6G
6G technologies; Edge AI; Federated learning; Intelligent healthcare; Internet of medical things (IoMT); Precision medicine
The advancement in applications of artificial intelligence technologies that propels the ongoing development of wireless networks. Envisioned as a transformative force, 6G is expected to shift wireless evolution from connecting devices to enabling intelligent connections. However, cutting-edge AI systems relying on deep learning and extensive data analytics demand significant computational and communication resources, leading to notable issues such as privacy breaches, network congestion, energy consumption and latency during both inference and training phases. Edge AI emerges as a disruptive technology for 6G by incorporating inference capabilities and model training at the networks edge. This approach seamlessly integrates intelligence, computation, communication and sensing enhancing the security, privacy, effectiveness and efficiency of the 6G technologies. The study includes the applications, enabling technologies dependable and scalable edge AI technology through an integrated approach that encompasses decentralized models of machine learning and strategies of wireless communication. We will delve into new wireless network design principles, optimization methods for resource allocation driven by service requirements, and an all-encompassing architecture of end-to-end system supporting edge AI. Additionally, we will touch upon application scenarios, hardware and software platforms, and standardization for facilitating the commercialization and industrial adoption of edge AI systems. 2025 Apple Academic Press, Inc.
Takore, Tamirat Tagesse; Krishna, Konda Hari; Kumar, K. Suresh; Kumar, R. Ganesh; Jain, Amit; Keerthika, T.
Advances in AI for Cloud, Edge, and Mobile Computing Applications;pp.213-237
https://www.scopus.com/pages/publications/105005693781?origin=resultslist
Takore T.T., Department of Electrical and Computer Engineering, Wachemo University, India; Krishna K.H., Department of CSE, School of Computing, Mohan Babu University, Sree Sainath Nagar, Rangampeta, Andhra Pradesh, Tirupati, India; Kumar K.S., MBA Department, Panimalar Engineering College, Varadarajapuram, Poonamallee, Tamil Nadu, Chennai, India; Kumar R.G., Department of Computer Science and Engineering, CHRIST (Deemed to be University), School of Engineering and Technology, Kengeri Campus, Kanminike, Karnataka, Bangalore, India; Jain A., Computer Science and Engineering Department, OP Jindal University, Chhattisgarh, Raigarh, India; Keerthika T., Department of IT, Sri Krishna College of Engineering and Technology, Coimbatore, India
Applying a Multi-Agent Simulation Model for Examining Restorative Justice-Based Intervention in the Criminal Justice System: A Legal and Technological Perspective
Artificial Intelligence; Child Sexual Abuse; JADE-NetLoGo Integration; Restorative Justice; Simulation; Victim rights
Previous studies on Restorative Justice (RJ) have focused on the theoretical underpinnings of RJ and its processes. Several systematic literature reviews on RJ point out its potential to assist in victim healing much better compared to the traditional criminal justice system. However, the potential and viability of RJ largely remain in the theoretical landscape. Few empirical studies or simulations have been conducted to explore the viability of this practice in the legal domain. Furthermore, apart from purely studying RJ, literature also points towards its potential use in addressing child sexual abuse cases (CSA) by providing a child and victim-centric approach. However, the practicality of this claim remains scant in present times. Given the gap in the global discourse on the use of RJ as an intervention strategy in the criminal justice system, this paper outlines a computational framework for including RJ into the legal system. The paper does so by applying a Multi-Agent Simulation model (MAS). By utilising JADE for agent orchestration and NetLogo for a visual structure, the framework encodes multiple stakeholders such as accused/ offender, victim, counsellors and judges as autonomous agents with state vectors, utility performance and ACL-communication. The criminal justice system is compared to the restorative justice system using metrics like resolution rate, time, victim healing, rehabilitation and reintegration into the community. Through this paper, a foundation is laid for the potential of RJ in CSA. This paper will enable law and policy makers to consider introducing alternative practices like RJ. 2026 IEEE.
Ali, Khan Afrin Riyasat; Somu, C.S.; Bang, Sanjay
2026 IEEE 3rd International Conference on Emerging Trends in Engineering and Medical Sciences, ICETEMS 2025;
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICETEMS66917.2026.11469421 https://www.scopus.com/pages/publications/105038018558?origin=resultslist
Ali K.A.R., Christ University, Department of Law, Lavasa, India; Somu C.S., Christ University, Department of Law, Lavasa, India; Bang S., Christ University, Department of Law, Lavasa, India
Applying Artificial Bee Colony Algorithm to Improve UWSNs Communication
Artificial Bee Colony algorithm; bio-inspired optimization; communication reliability; data transmission; energy efficiency; network scalability; Underwater Wireless Sensor Networks
The research in this study aims at implementing the ABC algorithm to enhance the communication within UWSNs. The ABC algorithm, motivated by the CPG approach being analogous to that of honey bees searching for food, specifies optimal values for critical parameters of the network such as energy consumption, reliability in data transfer, and scalability. From the analyses conducted in this exposition, it is apparent that the envisaged methodology outperforms other conventional routing parlances in the following ways: minimal energy usage, high data delivery ratios, low packet drops, and longest network lifetime. Therefore, from the above results it can be concluded that, the said ABC algorithm is helping in achieving a better result in terms of improved underwater communication as well as in mitigating with the difficulties of UWSNs. 2024 IEEE.
Habelalmateen M.I.; Kaur A.; Tiwari A.; Shalini M.; Victor M.; Kumar N.S.
2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024
Habelalmateen M.I., The Islamic University, College of Technical Engineering, Department of Computers Techniques Engineering, Najaf, Iraq, The Islamic University of Al Diwaniyah, College of Technical Engineering, Department of Computers Techniques Engineering, Al Diwaniyah, Iraq; Kaur A., Chandigarh Group of Colleges, Chandigarh Engineering College, Department of Computer Science Engineering, Punjab, Mohali, India; Tiwari A., Ies College of Technology, Department of Electronics & Communication Engineering, M.P., Bhopal, India; Shalini M., Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Victor M., Christ (Deemed to be University), School of Business and Management, India; Kumar N.S., (E.Tech.) Srm Institute of Science & Technology, Department of Computer Science & Engineering, Chennai, 26, India
Applying Ensemble Techniques for the Prediction of Alcoholic Liver Cirrhosis
Alcoholic liver disease; Ensemble classifier; Liver cirrhosis; Machine learning; Random forest; Stacked ensemble classifier; XGBoost
More than fifty percent of all liver cognate deaths are caused by alcoholic liver disease (ALD). Excessive drinking over the time leads to alcohol-related steatohepatitis and fatty liver, this in turn can lead to alcoholic liver fibrosis (ALF) and in due course alcohol-related liver cirrhosis (ALC). Detecting ALD at an early stage will reduce the treatment cost to the patient and reduce mortality. In this research, a two-step model is developed for predicting the liver cirrhosis using different ensemble classifiers. Among 41 features recorded during data collection, only 15 features arefound to be effective determinants of the class variable. The proposed stacked ensemble technique for ALD prediction is compared with other ensemble models such as random forest, AdaBoost, and bagging. Through experimentation, it is observed that the proposed model with XGBoost and decision tree as base models and logistic regression as Meta model exhibits prediction accuracy of 93.86%. The prediction accuracy of theproposed stacked ensemble technique is 0.2% better in prediction accuracy and 0.3% reduced error rate in comparison with random forest classifier. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Vinutha M.R.; Chandrika J.; Krishnan B.; Kokatnoor S.A.
Lecture Notes in Networks and Systems, Vol-462, pp. 433-445.
Vinutha M.R., Department of ISE, Malnad College of Engineering, Hassan, 573201, India; Chandrika J., Department of ISE, Malnad College of Engineering, Hassan, 573201, India; Krishnan B., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Kokatnoor S.A., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India
APPLYING SOLUTION-FOCUSED BRIEF THERAPY IN COGNITIVE REHABILITATION: Insights from Positive Neuropsychology
Neurocognitive rehabilitation refers to the procedure involved in helping patients recover or regain some of the lost functions of the brain after an internal and external injury. Specific psychotherapeutic procedures are also combined with these rehabilitation strategies for the maximum benefit of the patients. Solution-focused brief therapy (SFBT) is a psychotherapy that allows clients to focus their attention on the solution rather than exploring the origin of the problem and focusing on their strengths and resources. It is a brief therapy based on a positive psychology approach. The traditional cognitive rehabilitation techniques focus on deficit remediation, while SFBT offers a strength-based approach that focuses on the clients resources, exceptions to the problems, and goal-oriented behavior. The integration of these approaches will bring a shift in the paradigm of neurorehabilitation by providing a balance between cognitive challenges and preserved strengths. In the realm of cognitive rehabilitation, SFBT can empower individuals with neurological impairments by fostering resilience, adapting coping strategies, and self-efficacy. This chapter explores the innovative application of SFBT principles within cognitive rehabilitation settings, which can be viewed from the lens of positive neurorehabilitation. It will also propose a framework for integrating the CAPE model (Compensatory, Activity, Preventive & Enhancement) with salient principles of SFBT, emphasizing the potential role of positive neuropsychology in cognitive rehabilitation. 2026 selection and editorial matter, K. Jayasankara Reddy; individual chapters, the contributors. All rights reserved.
Rastogi, Kritika; Mukherjee, Sonali; Basu, Sabornee Karmakar
The Routledge International Handbook of Neurocognitive Rehabilitation: Practices, Innovations, and Future Directions;pp.78-86
https://doi.org/10.4324/9781003646662-5 https://www.scopus.com/pages/publications/105028468721?origin=resultslist
Rastogi K., Department of Psychology, CHRIST University, Delhi NCR, India; Mukherjee S., Department of Psychology, CHRIST University, Delhi NCR, India; Basu S.K., Department of Psychology, CHRIST University, Delhi NCR, India
Applying talent acquisition to the test: Assessing productivity in facilities organization /
Pramana Research Journal, Vol.9, Issue 2, pp.197-207, ISSN No: 2249-2976.
Leena N. Fukey , Mudita Sinha and Rhea Pius
Appraisal of prolyl 4-hydroxylase alpha subunit gene polymorphisms in Spondyloepimetaphyseal dysplasia of Handigodu type (SEMDHG)
4-hydroxyproline; Collagen; Handigodu; Prolyl 4-hydroxylase; Spondyloepimetaphyseal dysplasia
Background: The Handigodu variant of Spondyloepimetaphyseal Dysplasia (SEMDHG) is a severe, progressive osteoarthritic disorder characterized by chronic pain and joint degeneration. Clinically, the disorder presents in three distinct phenotypic forms, each exhibiting varying degrees of stature reduction and disease severity. Urine analysis of affected individuals reveals an elevated peptide-bound proline to 4-hydroxyproline ratio relative to controls, suggesting disruptions in collagen metabolism. Given the critical role of prolyl 4-hydroxylase enzymes in stabilizing collagen structure, this study undertook a comprehensive sequence analysis of all three isoforms of prolyl 4-hydroxylase in both affected and unaffected individuals to elucidate potential molecular underpinnings of the disorder. Method: The entire exonic regions and 2000 base pairs upstream of the translation start sites of the P4HA1, P4HA2, and P4HA3 genes were sequenced in a cohort of 300 individuals, comprising 166 affected and 134 unaffected individuals. Results: Sequence analysis of the ? (I), ? (II), and ? (III) subunit genes identified three novel SNPs and a 39-bp deletion variant, in addition to ten previously reported SNPs catalogued in dbSNP. The SNP rs28384495 in P4HA1, the 39-bp deletion variant, and a novel mutation (SNP3) in P4HA3 exhibited significantly different allele frequencies between patients and controls. Genotype association analysis revealed that SNPs in P4HA1 and P4HA3 were associated with Type 2 and Type 3 HD under various genetic models. Notably, all Type 2 HD patients were heterozygous for the 39-bp deletion, whereas all Type 3 HD patients were homozygous for the variant. Haplotype analysis corroborated the findings of the genotype association analysis. Conclusion: This study is the first to account an association between the P4H gene and disease. Further research is needed to evaluate the functional implications of the identified mutations. 2024
Venugopal P.N.; Rao I.A.; Mollick S.A.; Chandrasekar A.
https://doi.org/10.1016/j.humgen.2025.201387 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216883798&doi=10.1016%2fj.humgen.2025.201387&partnerID=40&md5=de31c42bb8329ff36f32463273975c87
Venugopal P.N., Anthropological Survey of India, Southern Regional Centre, Karnataka, Mysore, India; Rao I.A., Department of Life Sciences, Christ University, Karnataka, Bengaluru, India; Mollick S.A., Anthropological Survey of India, Southern Regional Centre, Karnataka, Mysore, India; Chandrasekar A., Anthropological Survey of India, Southern Regional Centre, Karnataka, Mysore, India
Venugopal, Pulamaghatta N.; Rao, Isukapatla Arjun; Mollick, Sahid Afrid; Chandrasekar, Adimoolam
Human Gene;Volume;43;Issue;;Article No.;201387;
https://doi.org/10.1016/j.humgen.2025.201387 https://www.scopus.com/pages/publications/85216883798?origin=resultslist
Appraisal of the potential of endophytic bacterium Bacillus amyloliquefaciens from Alternanthera philoxeroides: A triple approach to heavy metal bioremediation, diesel biodegradation, and biosurfactant production
Alternanthera philoxeroides; Bacillus amyloliquefaciens; Bioremediation; Biosurfactant, Diesel biodegradation; Endophyte; Heavy metal
Endophytic microbes have been associated with many positive traits due to their endurance mechanisms. The current study was designed at exploring the potential of the endophytic bacterium Bacillus amyloliquefaciens MEBAphL4 isolated from Alternanthera philoxeroides for biosurfactant production and bioremediation efficiency. This endophyte, isolated from the polluted Madiwala lake in Bangalore, displayed elevated resistance to Cr and Pb till 2000 mg/L. The metal removal efficiency was found to be higher for Cr (25.7 %) at pH 6 and for Pb (92.3 %) at pH 9. Further, the present study also describes biosurfactant production with good emulsification ability (E24-52 %) and stability over a range of pH (8?12), temperature (2040C) and salinity (515 %). Biosurfactant production was enhanced 1.18-fold using the Response Surface Methodology approach and characterised by Fourier Transformation Infra-red Spectroscopy and Ultra-Performance Liquid Chromatography- Mass Spectrometry showing the presence of lipopeptides, fengycin, iturin and surfactin of molecular weights 1463.65, 1043.44 and 1012.56 Da respectively. The potential application of the biosurfactant in degrading various hydrocarbons was evaluated, demonstrating its effectiveness in bioremediation of oil-contaminated sites. Specifically, diesel biodegradation was measured at 56.460.95 %. These findings underscore the potential of B. amyloliquefaciens in environmental applications such as heavy metal biosorption and the bioremediation of contaminated sites, particularly those affected by oil spills and correlates to UN SDG6 of clean water and sanitation. 2024 Elsevier Ltd
Biswas S.; Jayaram S.; Philip I.; Balasubramanian B.; Pappuswamy M.; BarcelD.; Chelliapan S.; Kamyab H.; Sarojini S.; Vasseghian Y.
Journal of Environmental Chemical Engineering, Vol-12, No. 5
https://doi.org/10.1016/j.jece.2024.113454 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197601720&doi=10.1016%2fj.jece.2024.113454&partnerID=40&md5=849c73571cb3c692ecc84ebe98500e93
Biswas S., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India, Equal Contribution.; Jayaram S., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Philip I., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Balasubramanian B., Department of Food Science and Biotechnology, College of Life Science, Sejong University, Seoul, 05006, South Korea, Equal Contribution.; Pappuswamy M., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; BarcelD., Chemistry and Physics Department, University of Almeria, Almer, 04120, Spain; Chelliapan S., Department of Smart Engineering and Advanced Technology, Faculty of Artificial Intelligence, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur, 54100, Malaysia; Kamyab H., Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, 600 077, India, Faculty of Architecture and Urbanism, UTE University, Calle Rumipamba S/N and Bourgeois, Quito, Ecuador; Sarojini S., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Vasseghian Y., Department of Chemical Engineering and Material Science, Yuan Ze University, Taiwan