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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. -
Integration of 0.1 GHz to 40 GHz RF and microwave anechoic chamber and the intricacies
The aim of this paper is to highlight and elaborate the construction and establishment of a rectangular anechoic chamber (AC) of dimensions 7 m 4 m 3 m working from 0.1 GHz to 40 GHz. It is an informative checklist giving an insight on the reckoning of chamber dimensions and selection of appropriate absorbers as per the required specifications. It briefs the key features of validation of an anechoic chamber, namely, shielding effectiveness and reflectivity (quiet zone). It describes the intricacies of the integration of systems such as vector network analyzer (VNA), antenna mounting stands, three-axes motorized antenna rotation control circuitry, and customized software. The validation of the established chamber is accomplished for overall shielding effectiveness of ?80 dB and reflectivity of ?40 dB in one cubic meter area at the receiving antenna or the antenna under test (AUT) region far away from transmitter say, at 5.5 m separation. This paper covers the measurement results of three broadband horn antennas which can be used as reference antennas for characterization of other antennas in the chosen frequency range. The entire report will certainly be a guideline for any reader or aspirant who is interested in the development of a similar anechoic chamber and looking for complete intricacies. 2020, Electromagnetics Academy. All rights reserved. -
Integration of blockchain to IoT: Possibilities and pitfalls
[No abstract available] -
Integration of cyber-physical systems with wearable devices:A new paradigm for patient monitoring
The healthcare sector has witnessed significant changes as cyber-physical systems (CPS) bring embedded technologies that are developed from human and physical surroundings to smart objects. Wearable technologies such as wearable fitness trackers, biosensors, and smartwatches stand as good examples of smart healthcare that may improve decision-making and real-time patient monitoring. Through advances in personal health technology, mobile medication, and smart sensing, these technologies aid the medical treatment of the patient. Such advancements enable monitoring and programming health data streams continually, which supports early diagnosis and better care. However, challenges persist in integrating machine learning into health wearables; it still poses a limitation, improving algorithm accuracy and reliability so that the use can be widespread. Advances in skin-based, textile-based, and biofluidic designs have allowed medical wearables to monitor neuro, cardiovascular, and metabolic disorders, which are being further extended to drug delivery systems. The present study identifies gaps and advances in the field using secondary data from articles, journals, and research papers. It highlights future research directions on the clinical applications of wearable technology and its role in routine safety and health monitoring. Findings have indicated that regulated data privacy, equity, and fairness must be pursued to fully realize CPS-enabled wearables in terms of a healthcare revolution. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
Integration of enterprise resource planning system as an effective technology for increasing business productivity
Enterprise Resource Planning (ERP) refers to a potential software, which organisations utilise for managing daily basis activities such as proper accounting, project management, compliance as well as procurement actions within organisational standards for achieving better business performance. This research focuses on understanding ways of ERP usage of businesses for enhancing potential procurement as well as accounting for assuring best performance achievement. Literature from different company reports and other sources has been implemented that brings out an understanding of productivity optimisation of organisations using ERP. It also focuses on illustrating different types of ERP along with assuring better data visibility aspects of the ERP usage for allowing consumers to view real time data while progressing with business relationships and enabling higher procurement standards. The research aims to investigate ways in which different types of ERP are used by organisations for assuring better accounting performance and procurement standards in their marketing environment. Hypothesis is a positive association between ERP utilisation and implementation in organisation and its accounting and procurement standards, achieving high performance in the competitive market. Methodology used in this research involves Exploratory research design with a probability sampling for bringing out best possible outcomes of the research. Sample sizes include secondary sources such as articles, journals and relevant company reports and databases for understanding ways in which ERP helps in attaining suitable accounting and procurement practices of businesses within organisational standards. Results as well as implications indicate an optimal relation of proper risk management through enhancing ERP and usage of most suitable ERP that assures best possible procurement and accounting practices for businesses to get competitive advantage in the market. 2024 Author(s). -
Integration of Franciscan Capuchin philosophical values in higher education: a systematic literature review
This systematic literature review examines the current state of Higher Education (HE) research concerning antecedents related to Franciscan Capuchin philosophical values. Much has been discussed about Franciscan HE in past research. However, the literature lacks studies on Franciscan values integration and implementation practices in their educational institutions. This study employs a systematic literature review exploring research in higher education with particular reference to Franciscan missionaries. The systematic review will assist practitioners and researchers in determining the key factors that led catholic Franciscan missionaries to impart holistic education. It also identifies the research gaps that need to be plugged to keep the body of knowledge in integrating Franciscan philosophical values in HE up-to-date. The research is an original contribution to understanding the research in higher education on catholic philosophical values implementation through a systematic review process considering literature from 1980 to 2024. The value addition is identifying the underpinning theories to be considered in developing a holistic model in HE through the Franciscan lens. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Integration of Hyperspectral Imaging and Deep Learning for Sustainable Mangrove Management and Sustainable Development Goals Assessment
Mangrove forests support biodiversity and provide valuable ecosystem services. Their conservation is important for maintaining these benefits. In addition to this, understanding and preserving these forests is important for the assessment of Sustainable Development Goals (SDGs) such as SDG 1,2,3,6,8,11,12,13,14 and15. This review paper explores how the integration of Hyperspectral Image (HSI) technology and Deep Learning (DL) algorithms is helpful in mangrove conservation and SDGs assessment. HSI in mangrove research allows detailed analysis of tree health, species types and environmental stress factors (includes salinity levels, waterlogging, soil erosion, pollution, habitat fragmentation, disturbances from human activities etc.) with enhanced spectral and spatial resolution. Combining DL algorithms like Convolutional Neural Network (CNN) with HSI data automates mangrove mapping, detects change in mangrove health, estimates carbon sequestration and manages ecological zone. Rich spectral information from HSI empowers DL algorithms to identify patterns and features for accurate and efficient classification tasks in both supervised and unsupervised methods. This review aims to comprehensively summarize the research efforts reported in monitoring mangrove ecosystems through varied remote sensing approaches, algorithms and their support towards SDGs assessment. HSI and DL together offer a powerful approach for researchers, environmentalists and climate activists working towards sustainable development objectives. This paper not only focuses on mangrove conservation but also addresses challenges associated with integrating technologies such as data processing complexities and the need for specialized expertise. This study outlines advancements in HSI technology, DL applications and future directions to drive sustainable management strategies for mangrove ecosystems. The Author(s), under exclusive licence to Society of Wetland Scientists 2025.