Cognitive behavioral neuroscience in organizational settings
In a world characterized by constant changes, organizations grapple with the complex task of understanding and enhancing human behavior within their ranks. The burgeoning interest in cognitive behavioral neuroscience (CBN) for unraveling the intricacies of organizational dynamics has paved the way for a groundbreaking shift. However, the application of CBN in Human Resource Development and Management (HRDM) remains in its infancy, creating a void between scientific inquiry and practical implementation. As organizations yearn for evidence-based strategies to enhance talent identification, team selection, training, and overall performance, a critical need emerges for a comprehensive guide that bridges this gap. Cognitive Behavioral Neuroscience in Organizational Settings is a groundbreaking book that illuminates the unexplored territory of CBN in HRDM. Positioned as a catalyst for change, this comprehensive guide serves as the linchpin connecting theoretical foundations with real-world applications. Seamlessly navigating through the basics of neuroscience, the anatomy and functions of the brain, and the role of neuroscience in organizational behavior, establishes the groundwork for a new academic discipline. By delving into higher cognitive processes, artificial intelligence integration, neuroscience methods, and CBN-based interventions, the book offers a roadmap to revolutionize how organizations understand, manage, and enhance their human capital. This book is not just a scholarly exploration; it is a call to action. For academic scholars seeking to contribute to the evolution of CBN in HRDM, this is a unique opportunity to be at the forefront of a transformative movement. Pioneering the integration of innovative neuroscience techniques, artificial intelligence, and practical interventions, this guide empowers scholars to actively shape the future of organizational development. 2024 by IGI Global. All rights reserved.
Cognitive Behavioral Neuroscience in Organizational Settings, pp. 1-331.
Reddy K.J., Christ University, India; Alfonso T., Christ University, India
Cognitive Behavioral Therapy for Tinnitus: An Applied Bioanalytical Perspective
Biomarkers; Cognitive Behavioral Therapy; Neuroimaging; Psychometrics; Tinnitus
Tinnitus affects 1015% of adults worldwide, with 23% experiencing severe and chronic symptoms that impair quality of life. Conventional treatments often provide limited long-term relief, while Cognitive Behavioral Therapy (CBT) has emerged as an evidence-based intervention. The present study evaluated the effectiveness of CBT for tinnitus through an integrated psychometric and bioanalytical approach. A total of 100 participants were randomized into a CBT group (n = 50) and a waitlist control group (n = 50). The CBT program consisted of 12 weekly sessions delivered over 12 weeks. Psychometric assessments included the Tinnitus Handicap Inventory (THI), Hospital Anxiety and Depression Scale (HADS), Pittsburgh Sleep Quality Index (PSQI), and WHOQOL-BREF. Bioanalytical measures comprised functional MRI, EEG, salivary cortisol, heart rate variability (HRV), and inflammatory cytokines (IL-6, TNF-?). The CBT group achieved a 45% reduction in THI scores (58.4 9.2 to 32.1 8.5), while the control group showed only a 5% change. Anxiety and depression scores decreased by 42% and 40%, respectively, and sleep quality improved by 38%. Morning cortisol increased by 48%, HRV improved by 38%, and inflammatory cytokines decreased significantly (IL-6: ?26%; TNF-?: ?19%). Neuroimaging and EEG findings confirmed reduced auditory cortex hyperactivity and abnormal connectivity. These results demonstrate that CBT not only alleviates tinnitus distress but also induces measurable neurophysiological and systemic changes, reinforcing its role as a cornerstone of evidence-based tinnitus management. 2025, Green Publication. All rights reserved.
Anuja., V.; Rangaswamy, Rashmi; Shukla, Priyanka; Das, Papri; Prabavathy, M.; Lodha, Niraj
Journal of Applied Bioanalysis;Volume;11;Issue;4;pp.1077-1084
https://doi.org/10.53555/jab.v11i4.479 https://www.scopus.com/pages/publications/105015397911?origin=resultslist
Anuja. V., S. A. Collage of Arts & Science Chennai, Tamil Nadu, India; Rangaswamy R., Christ University, India; Shukla P., School of Arts, Humanities & Social Sciences, CSJMU, Kanpur, India; Das P., Institute of Nursing, Brainware University, West Bengal, India; Prabavathy M., Centre for Differently Abled Persons Bharathidasan University, Tamil Nadu, India; Lodha N., Lodha Hospital and Research Centre, Rajasthan, Pali, India
Cognitive Cloud Computing: Building Intelligent Systems for Tomorrow
This book serves as a comprehensive guide, covering the fundamentals of cloud computing, advanced concepts, and practical applications. Discusses cyber-physical systems, cloud integration for environmental management, and cloud security intelligence systems for sustainable operations. Presents case studies based on computational intelligence-based optimization for sustainable operations and cloud integration. Emphasizes how cloud computing revolutionizes traditional processes, enabling direct input of assembly details into a computation model, streamlining development cycles, and reducing costs. Bridges the gap between theory and practice by offering guidelines on problem encoding and implementation strategies, empowering readers to apply their acquired knowledge to solve complex industrial problems. Covers wireless security in the cloud era, mechatronics and cloud integration, and cloud-enabled manufacturing for eco-friendly operations. The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer engineering, and information technology. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors.
George, Jossy; Upreti, Kamal; Poonia, Ramesh Chandra; Gautam, Ankit; Nadeem, Danish
Cognitive Cloud Computing: Building Intelligent Systems for Tomorrow;pp.1-337
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1201/9781003569251 https://www.scopus.com/pages/publications/105024619829?origin=resultslist
George J., CHRIST (Deemed to be University), BGR Campus, Karnataka, Bengaluru, India; Upreti K., Department of Computer Science, Delhi NCR, Ghaziabad, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Gautam A., BUSINESSNEXT, India; Nadeem D., R&D, German Research Center for Artificial Intelligence, United Kingdom
Cognitive computing: merging modalities for human like artificial intelligence
Artificial Intelligence; Cognitive process; human-centered computing; humancomputer interaction; natural language processing
Artificial intelligence (AI) has transformed with cognitive computing, which integrates different modalities such as vision, language, and reasoning to mimic human thought processes. Cognitive computing systems can recognize patterns, understand natural language, and make complex decisions by integrating these modalities. As a result of the interaction between machines, neural networks, and natural language processing, large amounts of data are analyzed, learning continuously from their interactions. As a result of this multimodal integration, the system is able to comprehend and interpret human communication with greater accuracy and context knowledge. Cognitive computing has made significant advances by processing and synthesizing information from a variety of sources, enabling a more holistic understanding of complicated situations. For instance, these systems can better understand context when visual data is combined with textual information. Using cognitive computing to analyze medical images, patients' histories, and clinical notes, healthcare professionals can find it especially helpful in diagnosing patients and planning treatment. As a result of cognitive computing, we can create systems that not only perform specific tasks but also mimic human cognitive functions, so that we can make better decisions. Human-like AI has the potential to revolutionize various industries with intelligent, context-aware solutions that increase productivity and decision-making. The advancement of cognitive computing has made it possible for humans and machines to communicate more intuitively and efficiently, bridging the gap between them. 2026 Elsevier Inc. All rights reserved.
Davidson, Benny Godwin J.; Muttungal, Peter V.; Tur, Anna
Multimodal Learning Using Heterogeneous Data;pp.217-221
https://doi.org/10.1016/B978-0-443-27528-9.00006-1 https://www.scopus.com/pages/publications/105032961118?origin=resultslist
Davidson B.G.J., School of Business, University of the Fraser Valley, Abbotsford, BC, Canada; Muttungal P.V., Department of Psychology, Christ University, Karnataka, Bangalore, India; Tur A., Acsenda School of Management, Vancouver, BC, Canada
Cognitive Engagement Scale (CES) in an Online Environment: Construction and Validation
Cognitive Engagement Scale; MOOC; Online Environment; Scale Construction; Validation
Researchers have demonstrated linkages between active engagement of students with learning material and greater learning gains. Cognitive engagement is a significant component of educational experience. Understanding the challenges associated with cognitive engagement and measuring cognitive engagement in a MOOC environment is challenging. It is the need of the hour with online learning being equivalent to classroom learning in todays dynamic academic environment. The present study aims to construct cognitive engagement scale (CES) to measure the cognitive engagement of learners who sign up for the massive open online courses (MOOC). The aim of this study is dual-fold: firstly, to conceptualize the cognitive dimension of learner engagement within MOOCs, and secondly, to construct a theoretically informed scale for assessing cognitive engagement in online environments. Study presents a detailed process of the scale development, which included item generation, item evaluation, pilot testing, testing psychometric properties of the scale, and scale refinement. The researchers crafted the initial questionnaire drawing from both existing literature and personal insights. Subject matter experts then validated the items within the questionnaire and ensured its reliability through a pilot study, where it was administered to a sample of 100 participants The final version of the scale captures the four dimensions of cognitive engagement: Passive receiving, active manipulating, constructive generating, and interactive dialoguing. The present study contributes to the growing literature on cognitive engagement and adds to the existing literature of MOOC engagement scale with focus on cognitive engagement exclusively. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Lecture Notes in Networks and Systems, Vol-1222 LNNS, pp. 73-86.
Springer Science and Business Media Deutschland GmbH
Tripathi V.V., Christ University, Bangalore, India; Prakasha G.S., Christ University, Bangalore, India; Lapina M., North-Caucasus Federal University, Stavropol, Russian Federation
Tripathi, Vijaya Vani; Prakasha, G.S.; Lapina, Maria
Lecture Notes in Networks and Systems;Volume;1222 LNNS;pp.73-86
https://doi.org/10.1007/978-3-031-78776-8_8 https://www.scopus.com/pages/publications/85215502199?origin=resultslist
Cognitive Fuzzy-based Behavioral Learning System for Augmenting the Automated Multi-issue Negotiation in the E-commerce Applications
Cognitive learning; E-commerce application; Fuzzy-based behavioral learning; Multi-agent system; Negotiation system
Evolution of agent-based technology presents behavioral learning and sustainable negotiation challenges in e-commerce applications. In particular, the challenge of designing the negotiation strategy to incorporate sustainability in e-commerce business that can leverage the agent to reach its objectives by increasing the negotiation coordination and cooperation with the opponent agents. Therefore, the proposed research introduces the negotiation strategy sustainable solution using a cognitive fuzzy-based behavioral learning system which can change the preferences of negotiating agents according to human psychological characteristics. It will mimic the attitudes of human risk, patience and regret during the course of bilateral negotiation and also change the preference structures according to the fuzzy logic rules. As a result, the proposed negotiation strategy makes significant improvements on various parameters such as utility value, success rate, total negotiation time, and communication overhead while changing the negotiation rounds from 50 to 500. Since this system leverages the negotiation strategy of the agent by taking appropriate decisions to reach better agreement based on the interest, belief and psychological characteristics of negotiating opponents. Moreover, the usage of negotiation in the cloud-based platform can leverage the e-commerce applications to handle as many requests as possible due to its dynamic elasticity. 2022 Taiwan Academic Network Management Committee. All rights reserved.
Rajavel R.; Komarasamy D.; Meenakshisundaram I.; Gubiniova K.; Iwendi C.
Journal of Internet Technology, Vol-23, No. 6, pp. 1335-1342.
Taiwan Academic Network Management Committee
Rajavel R., Department of Computer Science and Engineering, CHRIST University, India; Komarasamy D., Department of Computer Science and Engineering, Kongu Engineering College, India; Meenakshisundaram I., SITE School, Vellore Institute of Technology, India; Gubiniova K., Faculty of Management, Comenius University, Slovakia; Iwendi C., School of Creative Technologies, University of Bolton, United Kingdom
Cognitive IoT-Integrated Real-Time Transaction Monitoring System for Financial Security
Anomaly Detection; Cognitive IoT; Feature Selection; Federated Learning; Financial Security; LSTM-Autoencoder; Real-Time Monitoring
As financial transactions become more complex in terms of the digitally connected environment, smart and real-time monitoring systems are required to fight off fraud and guarantee safety. The paper proposes a Cognitive IoT based Real-Time Transaction Monitoring System which is a proposed system to the financial sector. The proposed model resorts to the Z-Score normalization as a means of pre-processing the data and Recursive Feature Elimination with Random Forest as a method of selecting the most relevant features affecting the behavior of transactions. As the main type of classifier, a hybrid LSTM-Autoencoder model is used that makes it possible to reliably detect anomalies both using sequential and reconstruction-based learning. Developed on top of TensorFlow and its federated learning extensions, the system maintains the privacy of its user data by supporting decentralized training of edge devices. Experimental tests indicate higher results over the detection accuracy, which reduces false positives, and real-time reactivity. It is a scalable, safe, flexible system that can be used to monitor financial transactions efficiently, using the combined potential of the cognitive computer, the IoT environment, and powerful machine learning to respond to the dynamic nature of financial security issues. 2025 IEEE.
Hemalatha Yadav, J.; Arora, Kapil; Salgotra, Priyanka; Dawod, NajimAubed; Patil, Amit Kumar; Kataria, Abhinav
Proceedings - 2025 International Conference on Recent Innovation in Science Engineering and Technology, ICRISET 2025;
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICRISET64803.2025.11251638 https://www.scopus.com/pages/publications/105031406686?origin=resultslist
Hemalatha Yadav J., Alliance University, Bangalore, India; Arora K., Alliance University, Alliance School of Business, Department of Management, Bangalore, India; Salgotra P., Maharishi Markandeshwar (Deemed to be) University, Maharishi Markandeshwar Institute of Management, (Ambala) Haryana, Mullana, India; Dawod N., Al-Nisour University College, Department of Medical Laboratories, Nisour Seq. Karkh, Baghdad, Iraq; Patil A.K., Mit Art, Design and Technology University, Department of Ece, Pune, India; Kataria A., Christ Deemed to be University, Delhi NCR Campus, India
Cognitive Load Optimization in Digital (ESL) Learning: A Hybrid BERT and FNN Approach for Adaptive Content Personalization
adaptive content personalization; artificial intelligence-based English as a secondary language learning; Cognitive load management
Traditional English as a Secondary Language (ESL) learning platform rely on static content delivery, often failing to adapt to individual learners cognitive capacities, leading to inefficient comprehension and increased cognitive load. A novel hybrid Feedforward Neural Network and Bidirectional Encoder Representation Transformer (FNN-BERT) framework stands as our solution because it performs dynamic content personalization through predictions of real-time cognitive load. The proposed approach incorporates Feedforward Neural Networks (FNN) alongside Bidirectional Encoder Representations from Transformers (BERT) to process behavioral analytics for optimized content complexity adjustment and adaptive and scalable learning delivery. Real-time adaptability, scalability and high computational needs of current models reduce their effectiveness in personalized learning environments. Through the application of Test of English for International Communication (TOEIC), International English Language Testing System (IELTS) and Test of English as a Foreign Language (TOEFL) datasets, our methodology uses Feedforward Neural Network (FNN) to forecast cognitive load based on student engagement behaviors and application errors then Bidirectional Encoders Representations from Transformer (BERT) processes content difficulty adjustments automatically. The proposed model delivers a 95.3% accuracy rate, 96.22% precision level, 96.1% recall capability and 97.2% F1-score which surpasses conventional Artificial Intelligence-based English as a Secondary Language (ESL) learning systems. The system makes use of Python for its implementation to improve understanding as well as student focus and mental processing speed. Personalized content presentation methods lead to lower cognitive strain which simultaneously advances student achievement numbers. The research adds value to smart educational frameworks through its introduction of a scalable framework that allows adaptable learning systems for English as a second language (ESL). The following research steps include simplifying system complexity while adding multimodal learning signals including eye monitoring and speech recognition and further developing the model across various educational subject areas. The research works as a promising foundation which propels AI real-time adaptive education systems for students from various backgrounds. (2025), (Science and Information Organization). All Rights Reserved.
Ramesh, Komminni; Thomas, Christine Ann; Osei-Asiamah, Joel; Pagidipati, Bhuvaneswari; Muniyandy, Elangovan; Reddy, B. V. Suresh; El-Ebiary, Yousef A. Baker
International Journal of Advanced Computer Science and Applications;Volume;16;Issue;4;pp.564-576
https://doi.org/10.14569/IJACSA.2025.0160457 https://www.scopus.com/pages/publications/105004016496?origin=resultslist
Ramesh K., BoS, Anurag Engineering College, Suryapet District, Telangana, Kodad, India; Thomas C.A., Department of English and Cultural Studies, Christ University, Bengaluru, India; Osei-Asiamah J., Department of Science and Technology Education, University of South Africa (Unisa), Gauteng Province, Pretoria, South Africa; Pagidipati B., Dept. of English and Foreign Languages, Sagi Rama Krishnam Raju Engineering College (A), West Godavari Dt, Andhra Pradesh, Bhimavaram, 534204, India; Muniyandy E., Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India, Applied Science Research Center, Applied Science Private University, Amman, Jordan; Reddy B.V.S., Department of CSE, Koneru Lakshmaiah Education Foundation, AP, Vaddeswaram, India; El-Ebiary Y.A.B., Faculty of Informatics and Computing, UniSZA University, Malaysia
Cognitive marketing and purchase decision with reference to pop up and banner advertisements
Banner advertisements; Consumers buying behavior; Pop-Up advertisements; Ugly truth
The aim of this research paper is to employ a mixed research approach and to check how the past data differs from the present and hence it uses an argument mapping to find the reality using focus group. Since genders have different opinion on pop-up and banner advertisements, two focus groups, one group consisting the female gender and the other focus group consisting the male respondents have been taken for the data collection. Small sample has been used for the argument mapping (N=45/Male) and (N=47/Female). A series of steps has been conducted in the argument mapping and relevant maps have been developed for drawing inference. It is found that, male have no patience to deal with the pop-up and banner advertisements but women are keener and patient enough to make the best use of these advertisements. On the other hand a questionnaire was framed from the variables found from the literature review and the same was distributed to both the genders and it was found collectively that though pop-up advertisements and banner advertisements are useful in some way, it is always considered to be a negative aspect. Misleading advertisements, data security scam are a few negative aspects of such advertisements and hence, there are a lot of ugly truth behind pop up and banner advertisements. The mixed research approach (triangulation) between the quantitative and qualitative is a new initiative taken by the researchers in this research and holds originality of the study. 2018 Academic Research Publishing Group.
Krushali S.; Jojo N.; Anand Shankar Raja M.
Journal of Social Sciences Research, Vol-4, No. 12, pp. 718-735.
https://doi.org/10.32861/jssr.412.718.735 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060457242&doi=10.32861%2fjssr.412.718.735&partnerID=40&md5=5773f8ed260d6da4eecce8d357bf6b99
Krushali S., Department of Commerce, CHRIST (Deemed to be university), Bengaluru, India; Jojo N., Department of Commerce, CHRIST (Deemed to be university), Bengaluru, India; Anand Shankar Raja M., Department of Commerce, CHRIST (Deemed to be university), Bengaluru, India
Cognitive outcomes prediction in children using machine learning and big data analytics
big data analytics; Cognitive outcomes; Machine learning
This study explores the potential of machine learning (ML) and big data analytics in predicting cognitive outcomes in children, aiming to enhance early identification and intervention strategies. Leveraging a diverse dataset comprising neurocognitive assessments, genetic markers, socio-economic factors, and environmental variables, our research employs advanced ML algorithms to develop predictive models. The interdisciplinary approach integrates neuroscience, psychology, and data science to discern patterns and correlations within the expansive dataset. The study emphasizes the importance of early cognitive assessment for optimal child development and academic success. By harnessing the power of big data, our models seek to uncover nuanced relationships that traditional methodologies may overlook. Preliminary results indicate promising accuracy in predicting cognitive outcomes, offering a valuable tool for educators, healthcare professionals, and policymakers. Additionally, the model's interpretability allows for a deeper understanding of the factors influencing cognitive development. Ethical considerations, privacy safeguards, and data governance are integral components of this research, ensuring responsible use of sensitive information. The implications of this study extend beyond academia, with the potential to inform educational policies, personalized learning strategies, and targeted interventions for at-risk populations. As technological advancements continue, the integration of ML and big data analytics in predicting cognitive outcomes heralds a new era in pediatric research, promoting proactive approaches to support children's cognitive well-being. 2024 IEEE.
Behera N.R.; Thiyagarajan V.S.; Manoharan G.; Veena C.; Bikash Acharjee P.; Murthy A.
4th International Conference on Innovative Practices in Technology and Management 2024, ICIPTM 2024
Behera N.R., Swiss School of Business and Management Geneva, Petit-Lancy, 1213, Switzerland; Thiyagarajan V.S., Karpaga Vinayaga College of Engineering and Technology, Department of Cse, India; Manoharan G., Sr University, School of Business, Telangana, Warangal, India; Veena C., Institute of Aeronautical Engineering, Dundigal, Hyderabad, India; Bikash Acharjee P., Christ University Pune, Computer Science, India; Murthy A., Nitte ( Deemed to Be University), Nmam Institute of Technology, Department of Mca, Udupi, India
Cognitive Style and Academic Achievement among School Students
The aim of the present study was to explore cognitive style and academic achievement among school students using a quantitative approach. The study involved a total of 423 students from grade VIth and VIIth. Students from both private and government schools participated in the study. The study used group embedded figures test by Witkin et al. (1971) and classroom achievement test by Singh & Gupta (2007) to determine the participants field independent and dependent cognitive styles and academic achievement. T test was used to compare the academic achievement of field independent ?? field dependent cognitive style students. Whereas, two-way Anova test was done to analysis the interaction effect of grades, gender, and type of school along with cognitive style on academic achievement of students. The findings of the present research showed that there was a significant difference between field independent and dependent students' on academic achievement. It also revealed that students with field independent cognitive style performed significantly better than field dependent students. However grades had a significant main effect on academic achievement of students. There was no interaction effect found between grades and cognitive style on academic achievement of students. In addition, it was also found that there was no interaction effect of type of school and cognitive style on academic achievement of students. The findings of the study benefits teachers by indicating significant classroom implications which will help them to develop effective learning materials and strategies which are suitable for their student in order to utilize their cognitive style strength effectively. It helps students in making effective decisions regarding ones enrolment in higher education courses and career choices. Key Words: Field Independent-Dependent Cognitive Style, Academic Achievement, Grades, Gender and Type of School.
Cognitive synergy: Enhancing late career engagement with ergonomic solutions
The chapter explores the intricate relationship between cognitive ergonomics and late career employees, emphasizing the challenges and opportunities of an aging workforce. It combines research findings and case studies to understand how cognitive aging affects job performance and satisfaction. A central theme is the importance of technology training and support for older workers. As technology advances, organizations must ensure their older employees have the skills to navigate these changes. This includes training in new software and tools, and ongoing support. Flexible work arrangements are also crucial, reducing stress and fatigue from long commutes and rigid schedules. Health screenings and age-friendly workplaces are key. Regular health screenings and access to healthcare can address physical and cognitive challenges. Designing workspaces and processes for older workers fosters inclusivity and diversity. In conclusion, the chapter offers recommendations for organizations to leverage the late career workforce. 2024 by IGI Global. All rights reserved.
Cognitive Behavioral Neuroscience in Organizational Settings, pp. 184-194.
Moharkonda P.S., Christ University, India; Smitha A.G., Christ University, India; Vijaya R., Christ University, India
Cognitive technology for the Indian higher education: A Language teaching and Learning application
Cognitive technology; Education technology; Learning management system; Machine Learning; Natural Language Processing
Past decade witnessed a technological boom in the world. Regardless of the age every person in the world owns a mobile device which can be connected to internet. The technologies and applications for these mobile devices are one of the inevitable part people's day to day lives. The past decade also evidenced the development of Artificial Intelligence, Machine Learning (ML), Natural Language Processing (NLP), Image Processing (IP), Speech Recognition (SR) and Big DataAnalytics (BDA), etc. which lead to the development of Intelligent applications for the fields like business, health care, weather, media, etc. The field which uses the technology in a slow pace is education system. This paper is majorly focused on the Indian higher education system and the technologies used in their teaching and learning. One of the major drawbacks of Indian higher education system is the traditional teacher centric teaching and learning process. The usage of technology in their education system limited to chock and board to power point presentation. Some of the elite Universities in India uses Massive Online Open Courses (MOOC) but majority of the education institution still follows the old method of teaching and learning. This paper profiles cognitive technology based applications which can be used for the betterment of current system. The proposed model in this paper is for the language course learning. The application is centered on ML and NLP. Copyright 2019 American Scientific Publishers All rights reserved.
Journal of Computational and Theoretical Nanoscience, Vol-16, No. 45813, pp. 2368-2371.
https://doi.org/10.1166/jctn.2019.7901 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068585970&doi=10.1166%2fjctn.2019.7901&partnerID=40&md5=daa03a43d1bf117c2caf3d0b6b99d6d2
George F.J.P., Department of Management, Christ Institute of Management, Lavasa, Maharashtra, 412112, India; Vinay M., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, Karnataka, 560029, India
Cognitive technology in human capital management: a decision analysis model in the banking sector during COVID-19 scenario
AI; artificial intelligence; banking; cognitive technologies; COVID-19; HCM; human capital management; machine learning; ML
Cognitive technologies are products of the artificial intelligence (AI) domain which execute tasks that only humans used to perform. The impact of cognitive technologies on the management of human capital (HC) has a massive effect in the banking sector. This paper studies the transformation of cognitive technology to human capital management (HCM) in the banking sector during the COVID-19 pandemic. The study draws data from 201 bank employees working in private, public, and foreign banks using a multi-stage sampling method in India. A number of hypotheses were framed and tested using multivariate and regression analyses. The results from the study indicate a significant change in the performances of bank employees statistically during the transformation of cognitive technologies. Cognitive technologies such as payment, product customisation, self-services, workload alleviation, automated back-office function, and a personalised experience significantly contribute to the HCM. 2024 Inderscience Enterprises Ltd.
Arumugam S.K.; Abey J.; Seranmadevi R.; Srivastava S.; Dwivedi A.
International Journal of Applied Decision Sciences, Vol-17, No. 5, pp. 657-680.
https://doi.org/10.1504/IJADS.2024.140852 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203262596&doi=10.1504%2fIJADS.2024.140852&partnerID=40&md5=8f557dd57542081710d6d2bd2a4bd331
Arumugam S.K., School of Commerce, Presidency University, Bengaluru, India; Abey J., College of Business Administration, Kingdom University, Bahrain; Seranmadevi R., Professional Studies Department, CHRIST (Deemed to be University), Bangalore, India; Srivastava S., Department of Professional Studies, CHRIST (Deemed to be University), Bangalore, India; Dwivedi A., Operations Management and Decision Sciences, Jindal Global Business School, O.P. Jindal Global, Institution of Eminence Deemed to be University, Sonipat, India
COGNITIVE VS. BEHAVIORAL THERAPY IN ADHD: Executive Function Outcomes
This chapter explores the comparative effects of Cognitive Remediation Therapy (CRT) and Behavioral Therapy (BT) on executive functions (EFs)in children with Attention-Deficit/Hyperactivity Disorder (ADHD). Executive dysfunction, a key challenge in ADHD, impairs regulatory control, sustained attention, and task management. Using a quasi-experimental pre-test-post-test design, the study involved eight children aged 712 years diagnosed with ADHD. Participants were purposively sampled and randomly assigned to CRT or BT interventions, delivered thrice weekly over three months. EF was assessed pre-and post-intervention using the Behavior Rating Inventory of Executive Function (BRIEF-2). Results indicated that both therapies significantly improved executive functioning, but in different domains. CRT was more effective in enhancing working memory and cognitive flexibility, while BT demonstrated greater improvements in inhibitory control and behavioral regulation. These findings emphasize the complementary roles of CRT and BT in targeting distinct executive deficits in ADHD. 2026 selection and editorial matter, K. Jayasankara Reddy; individual chapters, the contributors. All rights reserved.
The Routledge International Handbook of Neurocognitive Rehabilitation: Practices, Innovations, and Future Directions;pp.185-195
https://doi.org/10.4324/9781003646662-14 https://www.scopus.com/pages/publications/105028460627?origin=resultslist
Bhuyan B., Christ (Deemed to be University), Bengaluru, India; Singh V., Christ (Deemed to be University), Bengaluru, India
CoInMPro: Confidential Inference and Model Protection Using Secure Multi-Party Computation
Confidential inference and model protection; Machine learning (ML); Privacy; Privacy-preserving machine learning (PPML); Secure multi-party computation (SMPC)
In the twenty-first century, machine learning has revolutionized insight generation by using historical data across domains like health care, finance, and pharma. The effectiveness of machine learning solutions depends largely on the collaboration between data owners, model owners, and ML clients, without privacy concerns. The existing privacy-preserving solutions lack efficient and confidential ML inference. This paper addresses this inefficiency by presenting the Confidential Inference and Model Protection, also known as the CoInMPro, to solve the privacy issue faced by model owners and ML clients. The CoInMPro technique is suggested with an aim to boost the privacy of model parameters and client input during ML inference, without affecting the accuracy and by paying a marginal performance cost. Secure multi-party computation (SMPC) techniques were used to calculate inference results confidentially after sharing client input and model parameters privately from different model owners. The technique was implemented in Python language using the open-source SyMPC library to support the SMPC function. The Boston Housing Dataset was used, and the experiments were run on Azure data science VM using Ubuntu OS. The result suggests CoInMPros effectiveness in addressing privacy concerns of model owners and inference clients, with no sizable impact on accuracy and trade-off. A linear impact on performance was noted with an increase of secure nodes in the SMPC cluster. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes in Networks and Systems, Vol-462, pp. 1-14.
Tiwari K., CHRIST University, Bengaluru, India; Bisht K., CHRIST University, Bengaluru, India; George J.P., CHRIST University, Bengaluru, India
Cold spray deposition of hydroxyapatite powder onto magnesium substrates for biomaterial applications
Characterisation; Cold spray; Fractional factorial design; Hydroxyapatite; Magnesium; Surface response methodology
A simple, modified, cold spray process was developed in which hydroxyapatite powder was coated onto pure magnesium substrates preheated to 350 or 550C and ground to either 240 or 2000 grit surface roughness, with stand-off distances of 20 or 40 mm. The procedure was repeated five and 10 times. The hydroxyapatite coatings did not show any phase changes. Atomic force microscopy revealed a uniform coating topography, and scanning electron microscopy revealed good bonding between the coated layers and the substrates. As the p values were < 0.05, all factors except the number of sprays were considered to be significant. The response optimiser indicated that a 22.7 mm stand-off distance, a 649.2 grit surface roughness and a 496C substrate heating temperature produced good hydroxyapatite coatings of 46.3 ?m thickness, 436.5 MPa nanohardness and 43.9 GPa elastic modulus. The modified cold spray technique with substrate heating showed promising results in terms of product coating thickness and mechanical properties. 2015 Institute of Materials, Minerals and Mining.
Hasniyati M.; Zuhailawati H.; Sivakumar R.; Dhindaw B.K.; Noor S.N.F.M.
Surface Engineering, Vol-31, No. 11, pp. 867-874.
Hasniyati M., Biomaterials Niche Area Group, School of Materials and Mineral Resources Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Penang, 14300, Malaysia; Zuhailawati H., Biomaterials Niche Area Group, School of Materials and Mineral Resources Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Penang, 14300, Malaysia; Sivakumar R., Biomaterials Niche Area Group, School of Materials and Mineral Resources Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Penang, 14300, Malaysia; Dhindaw B.K., Faculty of Engineering, Christ University, Bangalore, 560060, India; Noor S.N.F.M., Paediatric Dentistry Unit, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, Kepala Batas, Penang, 13200, Malaysia
Collaboration between gram panchayat and women self-help groups on rural development in karnataka
Mahatma Gandhi said, Indiaand#8223;s development relays on development in rural India. To see a developed India, we must develop villages in India. India is trying to improve the living standard of rural people since Independence. Government, Non-Government Organizations, Voluntary Groups, and many individuals are making continuous efforts for decades to improve rural condition. There is a positive change and growth, but the achieved results are not satisfactory in relation to need, the available resources, opportunities, and the efforts made. What are the root causes of failures? Are there necessary coordination and collaboration among the development efforts to optimize the fruits and minimize the loss of human and material resources? Gram Panchayat Institutions and Self-Help Groups by women are two of major efforts which became very powerful means to empower and develop rural people. The Constitution (73rd Amendment) Act, 1992 became a land-mark by establishing Panchayat Raj Institutions (PRI) in Indiaand#8223;s effort for rural development and reaching out the democracy to grass-root level by newlineforming Panchayat Raj Institutions with three tier system. The Reservation policy of 72nd newlineAmendment Act was another turning point in empowering effort. NGOs initiated Self-Help newlineGroups in India in early 1990s and later Government also supported and promoted the newlineinitiative. Will the Collaboration between the Gram Panchayat Institutions and Women SelfHelp Groups enhance development of rural area through higher level of Community newlineParticipation is the research question here. newlineThere are many writings, studies and evaluations on Gram Panchayat Institutions and SelfHelp Groups by women to assess the existing condition, and to make the efforts more efficient and effective towards rural empowerment and development. Still, studies on impact of collaboration between Gram Panchayat and Women Self-Help Groups on rural newlinedevelopment are missing.
Collaborative intrusion detection system in cognitive smart city network (CSC-Net)
Artificial Intelligence (AI); Cognitive Smart City Network (CSC-Net); Internet of Things (IoT); Mobile Edge Computing Solution (MEC); Smart environment
Smart environment is about incorporating smart thinking in the environment and implementing the technical intervention that improvise the city's environment. Artificial intelligence (AI) provides solutions in huge technological issues in various aspects of day-to-day life such as autonomous transportation, governance, healthcare, agriculture, maintenance, logistics, and education that are automated, managed, controlled, and accessed remotely with the aid of smart devices. Cognitive computing is denoted as a next-generation AI-dependent method that gives human-computer interactions with personalized services that replicate manual behavior. Simultaneously, massive data is generated from the applications of the smart city like smart transportation, retail industry, healthcare, and governance. It is necessary to obtain a reliable, sustainable, continuous, and secure framework in the cloud centralized infrastructure. In this research article, the authors proposed the architecture of cognitive smart city network (CSC-Net) that defines how data are collected from applications of smart city and scrutinized by cognitive computing. This research article predicts the mobile edge computing solution (MEC) that permits node collaboration between internet of things (IoT) devices for providing secure and reliable communication among smart devices and fog layer, conversely fog layer and cloud layer. This proposed work helps to reduce the excessive traffic flow in smart environment with the support of node to node communication protocols. Collaborative-dependent intrusion detection system (C-IDS) is proposed to solve the data security issues in fog and cloud layers. Copyright 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Daniel D.; Preethi N.; Jakka A.; Eswaran S.
International Journal of Knowledge and Systems Science, Vol-12, No. 1, pp. 60-73.
https://doi.org/10.4018/IJKSS.2021010105 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100421747&doi=10.4018%2fIJKSS.2021010105&partnerID=40&md5=7b4196eb7ac6e98a947d979eb93c5afd
Daniel D., Christ University (Deemed), Kengeri, India; Preethi N., Christ University (Deemed), Kengeri, India; Jakka A., University of Pittsburgh, United States; Eswaran S., PES University, India