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Integrating Artificial Intelligence in the Blue Economy: A Case-Based Study of Smart Marine Ecosystems for Climate Change Mitigation and Circular Economy
The Blue Economy represents a critical pathway toward sustainable development, balancing economic growth with the conservation of marine ecosystems. This chapter explores the transformative role of Artificial Intelligence (AI) in advancing sustainable practices within the Blue Economy through a series of global case studies. It examines how leading companies and initiativessuch as Sinay, Saildrone, Sea.AI, Buffalo Automation, IBM, Microsoft, Ocean Cleanup, and Veolia Groupleverage AI-driven solutions for real-time environmental monitoring, predictive climate modeling, autonomous navigation, and circular waste management. Key insights reveal that AI enables data-driven decision-making, reduces carbon emissions, enhances operational efficiency, and supports closed-loop recycling systems, while confronting challenges related to data integration, regulatory frameworks, and economic viability. Policy implications highlight the urgent need for adaptive regulations, open data ecosystems, and public-private partnerships to scale smart marine ecosystems globally. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. -
Integrating Behavioural Science using the Psycho-Intelligence Framework in Connected Systems
The fast-growing convergence of neuroscience, behaviour computing, and adaptive artificial intelligence (AI) offers the possibility to transform human, machine interaction. This work presents Psycho-Intelligence, a new, closed-loop system that merges electroencephalography (EEG) and inertial motion unit (IMU) signals to adaptively recognise and react to users' cognitive and affective states. Levying low-cost wearable sensors (Muse EEG and MPU-6050), the system has real-time signal acquisition, sophisticated preprocessing, spectral and statistical feature extraction, as well as multimodal fusion features. Dimensionality reduction and feature selection techniques, including Principal Component Analysis and XGBoost gain metrics, enhance learning optimally. Multiple machine learning algorithms like Random Forest, SVM and XGBoost are trained to identify engagement states with high accuracy, warranted by extensive testing through cross-validation, ROC AUC, and F1-scores. The pipeline is incorporated into an adaptive feedback system that can regulate chatbot tone, learning material, or interactive graphics based on detected user states. Statistical validation with linear mixed models confirms the robustness of EEG-derived measurements in engagement prediction. The research establishes a new paradigm for emotionally intelligent AI systems and provides a technical foundation for ethical, real-time psycho-behavioural intelligence for communication networks, education systems, and cognitive health monitoring. 2025 IEEE. -
Integrating Big Data Management with Machine Learning in Cloud Environments
In past years, the consideration of cloud environments for big data management and machine learning techniques has increased exponentially. However, the massive amount of data that is made by companies and Internet of Things (IoT) devices has presented the industry with a challenge in storage. In addition, conventional data management methods are unable to manage the complexity, diversity, and volume of this big data. Consequently, cloud environments integrate big data management and machine learning techniques. Combining big data management through cloud environments equipped with machine learning has multiple benefits. First, it is an efficient way to process and analyze large datasets in a distributed and scalable manner. Second, it helps businesses to provide insights and make time to make datadriven decisions. Thirdly, it enables the system to learn from data on an ongoing basis, which helps improve the quality and accuracy of the data. Now, this integration also enhances the overall performance of cloud systems as data management tasks are automated, minimizing manual efforts. Utilizing Big Data Management and Machine Learning Techniques in Cloud Environment to Address the Drawbacks of Conventional Data Management Approaches and Exploit the Value of Big Data These two technologies, when integrated, will allow the enterprise to manage significant amounts of data and derive important insights to arrive at better decisions. Cloud environments provide the automation and scalability required to push this integration further and change considerable data use in businesses. 2025 IEEE. -
Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiology
Introduction: Neuroradiology encounters considerable difficulties owing to imaging data's intricacy and high-dimensional characteristics. Conventional diagnostic techniques often encounter challenges regarding precision and scalability, resulting in delays and possible misinterpretations. This paper presents the Big-Data Analytics-based Diagnostics (BDA-D) framework, a revolutionary method using computational models derived from neural architectures and sophisticated analytics to tackle these difficulties. Methods: The BDA-D architecture utilizes data mining, pattern recognition, and machine learning to glean useful neuroanatomical characteristics from massive datasets. By simulating human thought processes, this method speeds up clinical decision-making and improves diagnostic accuracy. To evaluate the effectiveness of the framework, it is put to the test in a clinical environment. Results and Discussion: Diagnostic precision, processing speed, and dependability are all enhanced by experimental validation. By detecting even the most minute neuroanatomical changes, BDA-D allows for more accurate diagnosis than traditional approaches. Based on the results, neuroradiologists may improve their practices by using cutting-edge computational methods to close the gap between data-driven analytics and their practical use in the clinic. BDA-D discovers important patterns from high-dimensional neuroimaging data through biologically inspired neural networks, reaching a remarkable diagnosis accuracy of 97.18%. Its 95.42% increase in processing speed allows rapid study of important disorders such as strokes and neurodegenerative diseases. BDA-D reduces inter-observer variability with a dependable value of 94.96%, increasing clinical confidence in AI-assisted diagnosis. Conclusion: A revolutionary change in neurodiagnostics, the BDA-D framework improves efficiency and reliability. This method has the potential to completely transform neuroradiology by combining big-data analytics with sophisticated computer models. It will allow for more rapid and precise diagnosis. 2025 The Authors -
INTEGRATING CLIMATE CHANGE, SOCIAL RESPONSIBILITY AND ELECTRONIC FINANCIAL INCLUSION: A PATHWAY TO SUSTAINABLE DEVELOPMENT
Purpose: This study explores the intersection of climate change social responsibility and electronic financial inclusion (EFI) as critical components of sustainable development. The research aims to identify the synergies between these domains and their potential to drive inclusive growth and resilience. Design/Methodology/Approach: The study integrates literature review and case studies to analyse the role of EFI in enhancing access to financial services, particularly for marginalised communities. It also examines corporate social responsibility (CSR) initiatives aimed at mitigating climate change and promoting environmental sustainability. The research study highlights successful integration models and best practices that demonstrate the impact of multistakeholder collaboration. Findings: The findings reveal that EFI significantly contributes to poverty reduction and economic empowerment by expanding financial access in underserved regions. Moreover, corporate initiatives in climate change mitigation, when aligned with social responsibility, enhance business resilience and foster sustainable practices. The study emphasises the importance of supportive policy frameworks and technological innovations in scaling these efforts. Research Limitations/Implications: The studys focus on case studies may limit the generalisability of the findings. Future research could explore broader geographic regions and diverse economic contexts. Originality/Value: This paper contributes to the understanding of how integrating climate action, social responsibility and EFI can create resilient, equitable and sustainable systems. It offers valuable insights for policymakers, businesses and practitioners aiming to advance sustainable development through innovative and inclusive strategies. 2025 Ernesto D. R. S. Gonzalez, Rajeev Sijariya, Amit Kumar Singh and Vikas Garg Published under exclusive licence by Emerald Publishing Limited. -
Integrating cyber-physical systems with intelligent transportation: Challenges and opportunities
Cyber-physical systems (CPS) are revolutionizing the transportation sector, wherein physical processes are combined with computational systems to create efficient, reliable, and safe transportation solutions. This chapter discusses the ways in which CPS impact contemporary transportation development. The theoretical and practical aspects of CPS have been considered as they follow with the intelligent traffic management systems and driverless cars within this scope of work. The first half of the chapter is then applied to architectural design in CPS, discussing how elements of the physical worldinteraction with cars and roads, for exampleare coupled with cyber systems, such as cloud computing, IoT, and communication networks. Important technical breakthroughs in these areas highlight the key aspects that make real-time decision-making and optimization of systems possible: 5G, edge computing, and artificial intelligence. The chapter also reviews simulation-based techniques in analyzing vehicle behavior and traffic flow, which encompasses insights into how CPS might improve traffic safety and efficiency. Simulations can study very complex transportation scenarios like collision avoidance and control of traffic without the need for real data. The chapter discusses cybersecurity risks, legal issues, and the need for standardized infrastructure to support intelligent transportation systems. It also focuses on the challenges presented by laws and policies in the field of CPS. The interaction of drivers, passengers, and traffic operators with these devices further helps grasp the human factor as well as the experience of a CPS user. The final section of the chapter discusses future directions of CPS research and development, specifically regarding how blockchain technology and quantum computing might advance transportation networks. This chapter will, therefore, give the reader a holistic understanding of how CPS may change the face of transportation in the future by bringing its non-data-driven components to the fore. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
Integrating deep learning in an IoT model to build smart applications for sustainable cities
These days, many CS experts focus their efforts on IoT. IoT is an emerging & cutting-edge technology that enables many items, including vehicles and home appliances, to connect and cooperate via mechanisms like machine to machine communication, big data, and AI. It has found use in a wide range of settings, from smart homes and cities, to healthcare and agriculture, to factory automation. Smart cities are becoming smarter, cars are getting more features, and health and fitness devices are getting more sophisticated thanks to the internet of things. Many problems that are directly relevant to the IoT's development have yet to be resolved. The exponential development of IoT has given birth to new problems, including concerns about personal data and security. There is need of a comprehensive approach that tackles the scalability, security, efficiency, and privacy concerns raised by the widespread deployment of IoT. 2023, IGI Global. -
Integrating Diverse Approaches in Medical Image Analysis: PAA-CNN and Feature Extraction Fusion for Classiation of Psychological Disorders using Anatomical Scans
A psychological disorder is a condition that impacts a persons behavior. Due to the contemporary way of life, a large number of individuals suffer from disorders like stress, depression, and other similar ones. These might turn into severe issues that would signiantly impact a persons quality of life. We present a sample framework that uses an Anatomical scan captured along with fMRI. Anatomical scans were used to extract characteristics, which were then utilized to classify using a random forest classir. In a follow-up experiment, CNN is applied to features obtained from the piecewise aggregate approximation method for multi-class classiation of psychological disorders. This method performs noticeably better than the conventional feature extraction techniques, and with this approach, obtained an accuracy of up to 79%. Combining several approaches may boost the classiation and prediction accuracy of medical data. 2025 The Authors. Published by Elsevier B.V. -
Integrating dye-sensitized solar cells and supercapacitors: portable powerpacks for future energy applications
Integrating energy storage and harvesting devices have been major challenges and significant needs of the time for upcoming energy applications. Photosupercapacitors are combined solar cell-supercapacitor devices which can provide next-generation portable powerpacks. Owing to advantages like economic and environmental friendliness, dye-sensitized solar cells (DSSCs) offer vast potential for being integrated with energy accumulation devices like supercapacitors. Over the past few years, various types of harvesting cum storage power devices combining DSSCs and supercapacitors have been reported. Over time the devices have improved in both performance and stability providing a broad outlook to possible future advancements including commercialization. We still have many challenges that are yet to be resolved in order to take these powerpacks to the next level of applications in portable and wearable electronics and communication devices. In this context, a detailed analysis and comparison of already reported photo-powered integrated supercapacitors based on DSSCs would give further insights into future advancements. In this review, we have discussed the development of photosupercapacitors, their fabrication strategies, and different materials used as counter electrodes, electrolytes, and dye sensitizers. Graphical Abstract: (Figure presented.) The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Integrating Education, Technology, and Sustainable Living: Advancing the Circular Economy for Future Generations
Sustainable living is crucial in reducing societys reliance on natural resources, a goal intimately connected to the principles of the circular economy. This conceptual framework highlights the interplay between education, technology, and the standard of living as fundamental aspects driving sustainable practices. Education empowers individuals with the knowledge and skills necessary to raise awareness and encourage sustainable behavior, shaping beliefs and intentions towards resource efficiency. However, recent studies suggests, increased access to information can paradoxically lead to greater complexities and dilemmas, creating challenges in enacting sustainable consumption and inadvertently slowing sustainable development efforts. Furthermore, the role of technology in the circular economy is indispensable. Advanced technological solutions facilitate the efficient use of resources, offering innovative approaches to mitigate environmental impact while enhancing the quality of life. Earlier literatures underscores the importance of education, funding, and innovation in green technology to achieve Sustainable Development Goals (SDGs) by 2030, highlighting the current state and research hotspots in this domain. A comprehensive literature review utilizing the Web of Science database reveals global contributions to SDG research, emphasizing the need for a balanced approach that integrates education, technology, and a sustainable standard of living. Together, these elements contribute to the development of a society that fosters a culture of sustainability. By promoting a circular economy, where resources are reused, recycled, and repurposed, we can ensure that the needs of the present are met without compromising the ability of future generations to thrive. This approach not only preserves environmental health but also cultivates resilient communities capable of navigating the complexities of sustainability in a rapidly changing world. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Integrating Emerging Technologies: Enhancing Supply Chain Optimization Through AI, IoT, and Blockchain
The rapid rise of technologies like AI, IoT, and blockchain is transforming supply chain management by boosting efficiency, transparency, and resilience. This paper examines how these technologies optimize supply chains, focusing on predictive analytics, real- time monitoring, and secure data exchange. AI excels in automation and decision- making, IoT enables real- time data gathering, and blockchain ensures trust and transparency. Case studies from Amazon, Maersk, Walmart, and Pfizer illustrate improvements in efficiency, risk reduction, and cost savings. Additionally, challenges like high costs, data privacy, and lack of standardization are discussed, along with future trends in edge computing, quantum computing, and digital twins. The study highlights AI, IoT, and blockchain as essential to building smarter, adaptable supply chains capable of thriving in todays global economy. 2025, IGI Global Scientific Publishing. -
Integrating Explainability and Fairness in Credit Risk Prediction: A Hybrid Approach Using Tabnet, LightGBM, SHAP, and Counterfactual Explanations on the FICO Dataset
Transparent and fair credit risk assessment is essential for responsible lending in modern financial systems. This paper presents an interpretable and ethically grounded machine learning framework for loan default prediction using the FICO Explainability Challenge dataset. We combine LightGBM, a high-performing gradient boosting model for tabular data, with TabNet, a deep learning architecture that provides intrinsic interpretability through attentive feature selection. To enhance transparency, SHapley Additive exPlanations (SHAP) are employed for global and local feature attribution, while counterfactual explanations generated using the DiCE framework offer actionable recourse. Fairness is evaluated and mitigated using IBM's AI Fairness 360 toolkit. Experimental results demonstrate that the proposed hybrid approach achieves strong predictive performance while ensuring interpretability and fairness, making it suitable for trustworthy and regulation-compliant credit risk modeling. 2026 IEEE. -
Integrating Explainable Machine Learning (XAI) in Stroke Medicine: Opportunities and Challenges for Early Diagnosis and Prevention
Stroke is a leading cause of mortality and disability worldwide, emphasizing the critical need for early diagnosis and prevention. Machine learning (ML) has demonstrated significant potential in improving stroke prediction and management by analysing complex datasets for risk stratification, diagnosis, and treatment planning. However, the adoption of ML in stroke medicine is limited by the opacity of these models, which can hinder clinical trust and decision-making. Explainable Artificial Intelligence (XAI) addresses this challenge by making ML models more interpretable and transparent, enabling healthcare professionals to understand, validate, and trust their outputs. This research work explores the integration of XAI in stroke medicine, highlighting its potential to enhance early diagnosis, personalized prevention strategies, and treatment planning. We discuss the opportunities XAI provides in identifying high-risk patients, uncovering critical predictors, and enabling informed clinical decisions. Furthermore, we examine challenges such as ensuring model reliability, addressing biases in stroke datasets, and navigating ethical considerations related to patient data privacy and algorithmic accountability. 2025 IEEE. -
Integrating Hesitant Fuzzy Sets with Machine Learning for Enhanced Healthcare Predictive Analytics
This study examines how Hesitant Fuzzy Sets (HFS) and Machine Learning (ML) might improve healthcare predictive analytics. HFS, which accommodates uncertainty and hesitation in decision-making, is used to improve healthcare projections. Predictive analytics methods struggle with data ambiguity and imprecision, resulting in poor decision-making. Traditional ML algorithms may not be able to collect hesitant information, resulting in less accurate patient outcomes and treatment recommendations. The Integrating Hesitant Fuzzy Sets with ML (IHFS-ML) framework overcomes these issues by integrating HFS flexibility with advanced ML approaches. This connection allows the representation of ambiguous patient data for better healthcare analytics. Data pre-processing in the IHFS-ML framework improves healthcare analytics prediction. These methods transform uncertain fuzzy data into an ML-friendly format. Disease prediction, patient risk assessment, and therapeutic effectiveness analysis are recommended. The approach aims to improve healthcare decision-making and deliver new insights by merging hesitant and ambiguous information. IHFS-ML uses HFS to characterize imprecise and confusing patient data. These HFS are combined with powerful ML classifiers like Random Forest (RF) and Logistic Regression. The IHFS-ML system outperforms current prediction accuracy and reliability methods, suggesting it might transform healthcare analytics. HFS improves ML model interpretability, improving patient outcomes and healthcare decisions. Compared to other methods, the IHFS-ML model improves prediction analysis reliability by 99.7%, scalability by 97.6%, data pre-processing efficiency by 97.1%, interpretability by 98.9%, and accuracy by 97.8%. 2025, Research Expansion Alliance (REA). All rights reserved. -
Integrating intelligence: The convergence of computer science and engineering in cyber-physical systems
The dynamic and innovative paradigm known as cyber physical systems (CPSs) arises from the merging of digital technology and physical infrastructure. This chapter provides a thorough analysis of CPSs, covering the basic ideas, constituent parts, a range of applications, and their integration with more complex subjects. Fundamentally, CPSs represent the smooth fusion of computational and physical components, enabling real-time control, analysis, and monitoring. The fundamentals of CPSs are explained in this chapter, with a focus on how they facilitate the development of interconnected networks that can coordinate complicated tasks across multiple domains. A close examination of the complex interactions that occur between sensors, actuators, processors, and communication networks in CPS designs demonstrates how these components work together to gather, process, and distribute data. Furthermore, a wide range of industries, including infrastructure, manufacturing, transportation, and healthcare, are impacted by the diverse applications of CPSs. CPSs transform conventional processes, improving efficiency, safety, and production. Examples of these processes include intelligent healthcare devices that monitor patient vitals and smart transportation systems that optimise traffic flow. When CPSs are combined with more complex subjects, they become even more powerful, accelerating innovation and change in a variety of fields. By enabling CPSs to process and analyse data at network edges, edge computing can lower latency and bandwidth consumption. Algorithms for machine learning improve decision-making, allowing CPSs to adjust and gain knowledge from real-world data. By protecting CPSs from cyberattacks, security and resilience measures guarantee the availability and integrity of vital systems. Furthermore, human CPS contact opens up new collaborative paradigms and gives people the ability to communicate with intelligent systems in a natural way. To sum up, this chapter gives readers a thorough grasp of CPSs and how they have revolutionised contemporary life. It adds to the continuing conversation on CPS research, innovation, and implementation by clarifying their basic ideas, elements, applications, and integration with more complex subjects. With ongoing research and cooperation, CPSs have the potential to completely transform our world and bring in a new era of intelligence, creativity, and connectivity. 2025 selection and editorial matter, Kamal Upreti, Nishant Kumar, Mohammad Shabbir Alam, Mohammad Shahnawaz Nasir and Debabrata Samanta; individual chapters, the contributors. -
Integrating k-Means++ with ARCANE: A Scalable Framework for Exact Cluster Unlearning
To address the demand for exact data removal in unsupervised clustering, a novel framework for exact machine unlearning is proposed that integrates the K-Means++ algorithm with ARCANE. This framework combines high-quality cluster initialization with targeted partitioning, allowing a more efficient method for removing data without the need for a naive retraining of the model. The proposed model is compared to a SISA-based approach against synthetic and Iris datasets. The ARCANE K-Means++ model demonstrated superior clustering quality, achieving a Silhouette Score of 0.841 to the baseline's performance of 0.263. ARCANE framework also demonstrated better speedup and predictable unlearning times for typical deletion requests than the SISA model. This is a strong, scalable, and provably-exact method for machine unlearning, providing a new and intuitive framework for developing privacy-preserving AI. 2025 IEEE. -
Integrating Koch Rajbongshi indigenous knowledge into teacher education: Bridging local traditions with global standards
This chapter examines the integration of Koch Rajbongshi indigenous knowledge into teacher education, preserving cultural diversity while aligning with global standards. The Koch community, spanning India, Nepal, Bhutan, and Bangladesh, holds rich cultural assets, including oral traditions and ecological wisdom, which can enhance curricula. Recognizing the Koch Rajbongshi as a Scheduled Tribe and including their language in the Eighth Schedule of the Indian Constitution would aid in preserving their heritage. The chapter proposes strategies for integrating indigenous knowledge into mainstream education, addressing challenges and opportunities. It underscores the collective role of educators, policymakers, and Indigenous communities in fostering inclusive education and highlights global best practices for sustaining indigenous knowledge in formal learning systems. 2025, IGI Global Scientific Publishing. -
Integrating LightGBM and XGBoost for Robust Plant Disease Classification: A Homogenous Stacking Approach
In addressing the critical challenge of early and accurate plant disease diagnosis, this study explores the application of a novel homogeneous multi-layered stacking model utilising Light Gradient Boosting Model (LGBM) and Extreme Gradient Boost (XGB) for the detection of plant diseases. Traditional approaches often rely on basic stacking methods; however, this research seeks to explore the intricacies of altering model architecture, combining the strengths of LGBM and XGB classifiers to build a highly accurate and efficient disease detection system. Comprehensive evaluations were conducted using metrics such as AUCROC curves, Confusion matrix and F1 scores. The ROC curve for the stacked model demonstrated superior performance with a score of 85.12%, compared to 83.09% for the single LightGBM model used for comparative analysis. The future scope of ML in agriculture includes integrating such models with real-time monitoring systems and expanding its applications to diverse crops and environments. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.