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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. -
Integrating LLMs into Smart Home Architecture: Design, Implementation, and Experimental Insights
Smart home systems are a key application of the Internet of Things (IoT) paradigm. These devices are either directly or indirectly connected to a network in order to perceive actions based on user willingness or sensing. As Information and Communication Technology (ICT) advances, Large Language Models (LLMs) are growing increasingly potent. Agents with LLM capabilities could be very supportive for smart home systems because of their natural language comprehension. In this paper, we introduce an LLM-powered smart home agent with real-time connectivity to smart home devices. This proposal has been experimented with and evaluated with typical smart home use cases. This experimental architecture illustrates a realtime scenario of a standard smart home, where sensors, lights, and switches are interconnected in a multi-tiered environment. The sensor, gateway, and switch modules are connected to the smart home edge server via a Web of Things (WoT) interface. The experiment has been conducted with various types of smart home use case prompts, including status requests, control requests, automation requests, and reasoning requests. The outcome of the experiment indicates that the addition of LLM to smart homes excels in natural conversational patterns compared to keywordbased agents. The prompt response time, which is unsuitable for time-sensitive tasks like anomaly detection, is a drawback, and edge LLMs could be a solution. 2025 IEEE. -
Integrating machine learning techniques for Air Quality Index forecasting and insights from pollutant-meteorological dynamics in sustainable urban environments
Air pollution poses a significant environmental and health challenge in Delhi, India. This research focuses on predicting the Air Quality Index (AQI) for Delhi utilizing machine learning techniques. The research methodology encompasses comprehensive steps such as data collection, preprocessing, analysis, and modeling. Data comprising various pollutants and meteorological parameters were gathered from the Central Pollution Control Board (CPCB) spanning from January 1, 2016, to December 30, 2022. Missing values were imputed using the IterativeImputer method with RandomForestRegressor as the estimator. Data normalization and variance reduction were achieved through Box-Cox transformation. Spearman Rank Correlation analysis was employed to explore relationships between features and AQI. Initial evaluation of nine machine learning algorithms identified Random Forest and XGBoost as the top performers based on accuracy. These algorithms were further optimized using 5-fold cross-validation with RandomizedSearchCV. The results demonstrated the efficacy of both algorithms in AQI prediction. Notably, PM2.5 and CO concentrations emerged are most influential features, highlighting the potential for AQI improvement in Delhi through the reduction of these pollutants. This research distinguishes itself through a meticulous examination of the complex interconnections between pollutants and AQI, providing invaluable insights to inform targeted interventions and enduring policies geared towards improving air quality in Delhi. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Integrating Machine Learning with Financial Risk Modeling for Portfolio Management
Financial markets may be unpredictable and volatile; the ability to perform proper risk forecasting and effectiveness in performing an efficient portfolio is of primary importance when making wise investment choices. The nonlinear trends, and time dependence applied in financial data are usually not captured in conventional predictive models. The research is suggesting a new hybrid architecture LSTXplain that combines with and is afforded capabilities of SHAP, and exogenized with LSTM networks as well as Experimental learning. The aim of this paper, which is entitled Integrating Machine Learning with Financial Risk Modeling to Portfolio Management is to combine sequential learning with interpretability in an attempt to deepen financial risk prediction and portfolio optimization. The model is intended to forecast various measurements of financial risk, such as volatility and Value-at-Risk, and is also likely to establish the causes of each of these estimates. LSTXplain uses historical stock prices, technical features and optionally, sentiment scores designed using financial news to train a robust deep learner. Model outputs are then fed through SHAP that allocates a value of importance of a feature and discover that this allows analysts to know and trust what the model does. In order to compare the framework, Yahoo Finance data was applied, and the findings were compared to the traditional models ARIMA, SVM, Random Forest, and MLP. It has a prediction accuracy of over 98 percent which does not just complement the risk forecasting but enables a portfolio management to act. The analysis is a bridge between the performance of DL and explainable AI in the financial risk prediction. Statistical significance were applied to prove that such improvements are significant, and it is established that results are significant at p<0.05. 2025 IEEE. -
Integrating mindfulness and addiction awareness in higher education: Strengthening resilience and promoting well-being
This chapter explored integrating mindfulness and addiction awareness within higher education. The journey uncovers these practices' profound potential in enhancing student resilience and well-being. The transformative impact of a mindful approach is underscored by examining their symbiotic relationship, individual benefits, and intersection with microlearning. From understanding addiction's prevalence among students to fostering a compassionate learning environment, the discussion navigates ethical considerations, cultural sensitivity, and challenges. A resounding call to action resonates, urging higher education institutions to embed these practices strategically, cultivating an environment prioritizing holistic student growth and development. The promise lies in a brighter future-a generation of self-aware, resilient individuals empowered to navigate challenges with poise, empathy, and well-being. 2024, IGI Global. -
Integrating Renewable Energy in Airports: A Roadmap Towards Carbon-Neutral Aviation Hubs
This chapter explains how one of the means of achieving carbon-neutral airports is by the airports integrating renewable energy. It examines how solar, wind, geothermal and hydropower technology can be used to curb carbon emission, reduce energy costs, and make the aviation industry environmentally sustainable. It lists the best practice, the impediments to the implementation, and the policy recommendations to the successful implementation based on the world case studies such as San Diego, Amsterdam Schiphol, and Denver airports. The discussion notes financial, technological and regulatory challenges, and predicts future trends of smart grids, energy storage, and electric ground equipment that can turn airports to sustainable energy centers that will support low-carbon aviation. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Integrating rod-shaped nickel molybdate@polypyrrole matrix for sustainable adsorptive removal of organic dye: Kinetics, isotherm, and thermodynamics study
Water pollution presents a significant global challenge that impacts the environment. The release of industrial effluents significantly contributes to this. Adsorption studies offer a sustainable and cost-effective solution to efficiently remove organic pollutants from water. The current study comprises a polypyrrole/nickel molybdate composite for the effective adsorption of organic dyes, such as methylene blue, from aqueous solutions. The catalyst has been comprehensively characterized using various techniques, including XRD, FE-SEM, FT-IR, HR-TEM, XPS, BET, TGA, zeta potential, and DLS analysis. Adsorption studies demonstrate up to 97% removal efficiency in 60 min. This study also evaluates the impact of various parameters, such as temperature, pH, dye concentration, and quantity of the catalyst, on the adsorption efficiency. The R2 value of 0.99 that is obtained in the kinetics study suggests the suitability of the adsorption process toward pseudo-second-order kinetics. The adsorption isotherm study reveals that the adsorption follows Freundlich's adsorption isotherm. The maximum adsorption capacity of the study is found to be 17.76 mg/g. Investigations into thermodynamic study give a ?H value of ?19.21 J/mol K, indicating the exothermic behavior, and ?G of ?6.95 KJ/mol, suggesting the spontaneity of the composite during the adsorption process. These results demonstrate the potential of the developed material as an effective adsorbent for removing organic dyes from water sources. 2023 Wiley Periodicals LLC. -
Integrating Simple Temporal Attention for Improved Video Summarization
Simple Temporal Attention (STA) in video summarization can improve deep learning model performance while tackling complexity and multi-view dependency problems. Many of the current models are too complex and dependent on multi-view setups to be scalable in single-camera settings. The suggested STA mechanism reduces model complexity without sacrificing accuracy, making it easier to recognize important moments in videos. To further increase the efficacy of summarization, a spatio-temporal mechanism is also introduced to capture crucial dynamics between video frames. The approach is evaluated on two benchmark datasets, UCF50 and TVSum, demonstrating significant improvements in model performance. This study provides a scalable solution for video summarization by highlighting the useful advantages of integrating STA for producing succinct and informative video summaries through a comparison of different deep learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Integrating SMOTE and Heterogeneous Ensemble Methods for Online FraudDetection
In the continuous evolving digital era, the escalation of online fraud demands a robust and efficient mechanism for its detection and prevention. In the recent years there has been a significant increase in the online bank transactions. The research delves into the integration of different machine learning algorithms and to enhance the models adaptability, Synthetic Minority Oversampling Technique (SMOTE) has been utilized. The approach addresses the challenges of data imbalance and also strengthens the overall detection performance. Through an extensive literature review the study highlights the limitations in the existing issues in online financial fraud. The proposed model employs a heterogeneous ensemble model consisting of K-Nearest Neighbors (KNN), Random Forest, and XGBoost. KNN functions as an anomaly detector, identifying irregularities in transactional data. Simultaneously, Random Forest assesses feature significance and detects intricate patterns, contributing to a comprehensive understanding of fraudulent activity. XGBoost, known for its computational efficiency, ensures real-time responsiveness by adapting to emerging fraud tactics. The system also introduces a soft voting mechanism that seamlessly integrates individual algorithm predictions, resulting in a robust and highly accurate ensemble fraud detection system. Validation on an authentic bank fraud dataset underscores the framework's prowess, showcasing superior fraud detection capabilities and a significant reduction in false positives. The purpose of adopting this approach is to enhance the financial security and safeguard the consumers assets. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Integrating spiritual disposition intervention into behavioral medicine: A case report on systemic lupus erythematosus from India
Background: Systemic Lupus Erythematosus (SLE) is a chronic inflammatory systemic autoimmune disease. The disease manifests as the bodys immune cells start attacking healthy connective tissue, which affects the skin, kidneys, blood vessels, brain, and other vital organs. As with any other chronic illness, the disease has psychological implications. Purpose: Literature suggests patients with SLE experience anxiety, depression, anger, and stress along with physiological symptoms. There is a strong association between the occurrence of stress and the onset of the disease. These psychological symptoms can be ameliorated through spiritual activities such as meditation, mindfulness, journaling, and reading. Mehtod: This case report is based on the importance of spirituality in the healthcare system. The study focuses on the concept of a whole-person-centered approach to the medical care industry. Spirituality has been proven to have a positive effect on health and illness. Hence, a 10-week intervention with 30 sessions focusing on spiritual dispositions was provided to the patient for this study, along with regular pharmacological treatment. The present case report is of a 56-year-old woman from New Delhi, India, who was diagnosed with SLE 2years ago. Results: The results reveal the positive effect of the intervention, as it led to a significant decrease in stress levels and depressive symptoms; it also resulted in improved quality of life, an enhanced coping style, and bolstered health hardiness. There was an increase in the score of a spiritual personality. Conlcusion: Spiritual Disposition as an intervention was sucessfull in reducing psychological implications of the disease thus leading to overall positve growth in the patient. The Author(s) 2024. -
Integrating sustainability principles into human resource management: An emerging trend
This chapter "Integrating Sustainability into Human resource management" talks about the fact that Sustainability has emerged as a critical concern for organizations worldwide, demanding a holistic approach that integrates environmental, social, and economic considerations. This abstract discusses the essential role that Human Resource Management plays in achieving sustainable business practices, especially in a competitive environment, such as that of tourism. Human capital, comprising knowledge, skills, abilities, and experience, is the core of an organization's success. Tourism being a people-intensive business, the quality and dedication of the workforce will determine the degree of customer satisfaction, reputation of the brand, and competitiveness. It is not just a normative issue but also a strategic issue where sustainability integration into HRM is essential for the long-term survival of the organizations. 2025, IGI Global Scientific Publishing. All rights reserved. -
Integrating Sustainability with Financial Markets: Risk, Return, and Responsibility
This chapter explores the burgeoning field of green finance and its crucial role in aligning financial markets with sustainable development objectives. It examines how incorporating environmental, social, and governance (ESG) factors influences traditional risk and return profiles, presenting both opportunities and challenges for investors and financial institutions. We delve into innovative financial instruments and strategies designed to mobilize capital towards environmentally sound and socially responsible projects. Furthermore, the chapter critically analyzes the evolving concept of fiduciary responsibility in the context of sustainability, arguing forabroaderinterpretationthatencompasseslong-termvaluecreationandplanetary well-being.Bybridgingthegapbetweenfinancialimperativesandsustainabilitygoals, this chapter underscores the transformative potential of integrating responsibility into the core of financial decision-making for a more resilient and equitable future. Copyright 2026, IGI Global Scientific Publishing. -
Integrating Sustainable Development Goals (SDGs) into Corporate Marketing Strategies: A Technological Approach to Responsible Business
The navigation of businesses increased sustainability with integrated conscious marketplace integration of Sustainable Development Goals in strategies of corporate marketing has emerged as the crucial driver of the business practices. The study explores the company's leveraging technology for aligning the marketing efforts using the objectives of SDG that fosters brand trust for long-term, competitive advantage and stakeholder engagement. The study examines the impact of digital innovation includes artificial intelligence Internet of Things and blockchain technology to promote consumer awareness, ethical sourcing and transparency. Moreover, the present study highlights the case examples of corporations with successful embedded SDG principles in the Framework of marketing to demonstrate the advantages of branding with technological approach to responsible business. Integration of Sustainability in the corporate narratives Using technology driven marketing and the businesses can enhance the environmental and social impact as well as it can strengthen the consumer loyalty based on ethical considerations to shape the purchasing behaviours. A novel approach is developed for integration of SDG in corporate marketing and compared with conventional marketing and the achievement has been recorded. The findings of the study show the importance of strategic marketing enabled with technology for corporate sustainability communication that can emphasise aligned marketing with business necessity. 2025 IEEE. -
Integrating Vertical Greening Systems for Urban Heat Mitigation and Well-being in Bengaluru's High-Rise Buildings: A Literature Review and Pilot Study
Rapid urbanization in Bengaluru has aggravated the Urban Heat Island (UHI) effect and decreased green space in high-rise developments. This phenomenon creates elevated "heat hotspots"that increase cooling energy demand and impact public health, social equity, and economic sustainability. To mitigate these issues, balcony greening and other Vertical Greening Systems (VGS) are considered nature-based solutions. This research paper integrates a comprehensive literature review of VGS performance with a pilot study examining Bengaluru residents' perceptions. The pilot study comprises a cross-sectional survey of 55 participants (95% CI: 13.2%). Existing literature demonstrates VGS effectiveness in reducing surface temperature by 2-4C and ambient temperature by 1-3C, thereby reducing cooling energy requirements by 15-23%. Survey results indicate high acceptance (80.9%, 95% CI: 68.5-89.7%), with 87.5% (95% CI: 76.0-94.1%) recognizing VGS benefits for cooling and psychological stress reduction. However, maintenance burden (54.5%), structural concerns (25.5%), and native flora scarcity (73.2%) were identified as significant barriers. Chi-square analysis revealed statistically significant associations between acceptance levels and perceived benefits (?2 = 18.42, p < 0.001), indicating strong adoption potential when barriers are addressed. This research paper offers critical insights into tropical high-rise vertical greening perceptions, informing climate-resilient urban development policies for Bengaluru and similar megacities. Published under licence by IOP Publishing Ltd.