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Cognitive Fuzzy-based Behavioral Learning System for Augmenting the Automated Multi-issue Negotiation in the E-commerce Applications
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. -
Cognitive IoT-Integrated Real-Time Transaction Monitoring System for Financial Security
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. -
Cognitive Load Optimization in Digital (ESL) Learning: A Hybrid BERT and FNN Approach for Adaptive Content Personalization
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. -
Cognitive marketing and purchase decision with reference to pop up and banner advertisements
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. -
Cognitive outcomes prediction in children using machine learning and big data analytics
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. -
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 technology for the Indian higher education: A Language teaching and Learning application
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. -
Cognitive technology in human capital management: a decision analysis model in the banking sector during COVID-19 scenario
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. -
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. -
CoInMPro: Confidential Inference and Model Protection Using Secure Multi-Party Computation
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. -
Cold spray deposition of hydroxyapatite powder onto magnesium substrates for biomaterial applications
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. -
Collaborative intrusion detection system in cognitive smart city network (CSC-Net)
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. -
Collaborative Model for Sustainable Energy Utilization in Cloud Infrastructure
As the infrastructures of cloud computing provides paramount services to worldwide users, persistent applications are congregated using large scale data centres at the customer sides. For such wide platforms, virtualization technique has been incorporated for multiplexing the essential sources available. Due to the extensive application variations in the workloads, it is significant to handle the resource allocation methodologies of the virtual machines (VM) for assuring the Quality of Service (QoS) of cloud. On concentrating this, the paper proposed a Decentralized Energy-Aware Collaborative Model (DEACM) for effectively managing the data centres in cloud infrastructures. Initially, the optimal model for system management and power management are declared. Then, functions of workload vectors and data collection about workloads has been carried out for optimal selection of virtual machines to migrate for balancing loads efficiently. This can be further applied for Target-based VM Migration Algorithm for determining the migrating target for VM. Moreover, the algorithm involved in energy utilization with managed QoS. The developed DEACM is evaluated using CloudSim platform and the results are discussed. The results exemplify that the DEACM can balance the workload across variety of machines optimally and provide reduced energy consumption to the complete system efficiently. 2025 IEEE. -
Collaborative Processes in the Development of the International Competences for Undergraduate Psychology (ICUP) Model
Across all nations, undergraduate psychology programmes aim to promote the acquisition of foundational psychology competences. Yet, until recently, a universally recognised model outlining essential competences did not exist. The International Collaboration on Undergraduate Psychology Outcomes (ICUPO) addressed this gap by developing the International Competences for Undergraduate Psychology (ICUP) Model. The aim of this article is to provide guidance about how other groups might successfully approach similar efforts to delineate discipline-specific key competences. We describe the processes that led to the development of the ICUP Model, framed by group development theory (Preparing, Forming, Storming, Norming, and Performing Stages), with additional consideration of individual ICUPO Committee member psychological needs for competence, relatedness, and autonomy. Each group development Stage section (a) describes project activities relevant to the characteristics of that Stage, and (b) lists key strategies employed and lessons learned, as well as commentary on psychological needs. To further enhance the value of this endeavour, the Discussion includes (a) commentary on the strengths and limitations of these theories for understanding and enhancing the effectiveness of such project processes, and (b) actionable insights for educational leaders undertaking similar projects. 2025 The Author(s). International Journal of Psychology published by John Wiley & Sons Ltd on behalf of International Union of Psychological Science. -
Collaborative security approaches for IoT ecosystems
The surge in Internet of Things (IoT) devices has transformed numerous industries by enabling unparalleled connectivity and data sharing. Yet, this rapid growth has also exposed critical security vulnerabilities. This chapter delves into collaborative security strategies aimed at improving the safety and reliability of IoT ecosystems. A major vulnerability is weak authentication and authorization, often stemming from poor password practices or insufficient authentication mechanisms. Such flaws can result in unauthorized access, data breaches, and serious cyber threats, including Distributed Denial of Service (DDoS) attacks and Man-in-the-Middle (MITM) attacks. DDoS attacks can overwhelm essential IoT systems, like those in smart cities or healthcare, while MITM attacks can jeopardize data integrity during communication between devices and cloud services. Given that IoT devices frequently handle sensitive information, including personal and health data, ensuring their security is vital to avoid detrimental outcomes for users. Physical security risks also present a challenge, as the physical compromise of IoT devices can disrupt systems or pose risks to individuals. To address these threats, this chapter recommends several cybersecurity measures, such as secure design and development practices, robust authentication methods, and advanced encryption techniques. Secure device design should include mechanisms for safe firmware updates and employ Trusted Platform Modules for secure key storage. Effective authentication can be enhanced with multifactor methods, role-based access controls, and digital certificates. Data protection should involve encrypting data both in transit and at rest, as well as employing techniques like data anonymization and differential privacy. 2026 Elsevier Inc. All rights reserved.. -
Collaborative service robots and employee career sustainability: a mixed-methods study of trust and innovativeness as moderators
Purpose This study aims to explore how the integration of collaborative service robots (cobots) in the workplace influences employees long-term career sustainability. Specifically, it investigates the moderating roles of perceived innovativeness, trust and employee type in this relationship. The research integrates insights from decent work theory, career sustainability theory, perceived innovativeness theory and initial trust theory to develop and validate a comprehensive conceptual framework. Design/methodology/approach A sequential mixed-method design was adopted. In Phase I, qualitative data were gathered through in-depth interviews with ten senior women managers from software development firms in India. Using grounded theory analysis via NVivo, key themes and constructs were identified. In Phase II, a structured questionnaire was developed based on these findings and distributed among employees working in manufacturing, logistics and hospitality sectors industries actively using service robots. A total of 755 usable responses were analysed using structural equation modelling with SmartPLS 4. Findings The analysis confirmed that humanrobot collaboration effectiveness, perceived working conditions and perceived autonomy significantly impact employees career sustainability, as reflected through their health, happiness and productivity. Furthermore, both trust in service robots and perceived organisational innovativeness emerged as significant mediators in these relationships. In addition, the strength of these relationships varied across managerial and non-managerial employees, highlighting the contingent role of employee type in shaping career sustainability outcomes in robot-enabled workplaces. Practical implications The findings provide actionable insights for HR professionals, technology managers and policymakers by underscoring the need for role-sensitive implementation strategies when introducing collaborative robots. Tailoring trust-building and innovation initiatives according to employee roles can help ensure that automation supports, rather than undermines, sustainable career development. Originality/value This study advances the human-centred automation literature by introducing employee type as a critical boundary condition in the relationship between collaborative robotics and career sustainability. Through a multidisciplinary and mixed-methods approach, it offers nuanced theoretical and practical contributions to understanding differential employee experiences in technologically augmented work environments. 2026 Emerald Publishing Limited -
Collaborative Ventures Between Public and Private Sectors in Technology and Sustainability
Governance models are needed in this age when climate change, resource depletion, and environmental degradation are now accelerating the pace of life. This chapter is a systematic review method using thematic-content analysis, in reality, critically analyzes public-private collaborative ventures as public-private partnership (PPP) matters, positioning them as the device to connect government policy frameworks with private sector technological expertise and investment capacity. With renewable energy and waste management, among other low-carbon technology applications, coupled with much public ground governance, one finds PPPs at the interface be-tween technology policy and a green governance agenda. The chapter delves into the dynamics of government factors, barriers, and replicable strategies. It applies them here using the case-study methodology of the Rewa Ultra Mega Solar Project and the Indore Smart City waste management initiative. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Collision avoidance using gazebo simulator
Autonomous cars will make its complete presence on roads in the future. A major feature of autonomous cars currently under research is collision avoidance on roads. Better collision avoidance systems could result in a decrease in number of accidents. Smart collision avoidance systems could handle the increasing amount of vehicles on roads. Collision avoidance system provides alert to the autonomous vehicles if an unavoidable collision is detected. When the collision is definite to happen, collision avoidance system takes action by its own without any driver input (by braking or steering or both). Collision avoidance system does the obstacle avoidance by gathering information about the environment with the help of sensors embedded in the system. The effectiveness of collision avoidance system depends upon the speed at which the system reacts from the gathered inputs. This paper uses the Gazebo simulation to design and implement collision avoidance. This paper also present a simple and effective obstacle avoidance algorithm for a simulated robot. Turtlebots Obstacle Avoider algorithm is attached to the robot in the simulator with the support of ROS(Robotic operating system) to implement collision avoidance. BEIESP. -
Colonial legacies and regional separatism: comparative analysis of statehood demands in Coorg and North Bengal
This article presents a comparative study of statehood demands in two distinct regions of IndiaCoorg (Kodagu) in Karnataka and Darjeeling region of the Eastern Himalayas in West Bengalhighlighting how historical, cultural and political legacies have shaped regional separatist movements in these areas. By analysing the influence of colonial administration, regional identity and political marginalisation, this study uncovers the underlying drivers of these statehood demands and situates them within Indias broader socio-political landscape. The research employs a comparative framework supplemented with primary data to explore the complexities of regional movements, focusing on the interplay between cultural identity, economic disparities and the perceived neglect by central and state governments. The article also delves into how these demands challenge Indias federal structure, raising critical questions about regional autonomy, governance and the management of diverse identities within a democratic framework. Through a nuanced analysis of historical records, political discourse and socio-economic data, this study provides insights into the enduring nature of these movements and their implications for Indias federal polity. By addressing the factors that sustain such demands, the paper contributes to the scholarly discourse on identity politics, regional autonomy, and the governance challenges associated with statehood movements in Indias pluralistic society. 2025 The Round Table Ltd. -
Colonial Migration and Cultural Transformation in India and Burma: Exploring the Role of Transnational Mobility in Shaping Chettinad Heritage
Migration has played an important role in the transformation of tangible and intangible cultural heritage across regions, including in the Global South, especially among postcolonial nations, due to their longstanding people-to-people contacts leading to socio-economic and historical-cultural transferences over time. In the case of India, global migratory forces have irreversibly transformed its tangible and intangible sociocultural landscape into a form of syncretism reflected in our civilizational ethos of Vasudhaiva Kutumbakam. In this context, this paper explores the role of international in-/outmigration in historical and contemporary times toward the evolution of India's regional cultural identities using a case of Chettiars' migration from the hinterlands of Chettinad in present-day Tamil Nadu, which is in the south of the Indian subcontinent, to the far-flung nation state of Burma/Myanmar in the northeast, including their subsequent return, primarily during the 19th and the 20th centuries. Using the 3i Framework (Interests, Ideas and Institutions), the study explains the role of cross-border migration in shaping the tangible and intangible heritage of Chettinad, as reflected in its architecture, cuisine and social customs. 2026 Association for the Study of Ethnicity and Nationalism and John Wiley & Sons Ltd.