88 20 16481 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Abraham, Manu Mariyan; Dev, Shampa Title A name given to the resource AI-driven surveillance in India: Reconciling privacy, national security and legal oversight Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Journal of Data Protection and Privacy;Volume;8;Issue;2;pp.183-207 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.69554/BZBZ5748" target="_blank" rel="noreferrer noopener">https://doi.org/10.69554/BZBZ5748</a> <a href="https://www.scopus.com/pages/publications/105024409516?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105024409516?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Abraham M.M., School of Law, CHRIST (Deemed to be University), Bangalore, India; Dev S., School of Law, CHRIST (Deemed to be University), Bangalore, India Description An account of the resource Artificial intelligence (AI) is having a significant impact on how the surveillance apparatus in India operates. Along with the numerous possibilities, the indoctrination of AI in surveillance mechanisms poses serious privacy concerns. The conflict between state surveillance and the fundamental right of privacy is apparent even at the conceptual level. On the one hand, the rise of advanced surveillance mechanisms has been an abetting factor in this conflict, while on the other hand, many theorists have been at work to find a harmonisation between them. Throughout Indian history, surveillance apparatus has helped thwart threats to national security and maintain the nations integrity. The apparent disadvantage of surveillance can be its intrusion into citizens right to privacy, which poses several legal challenges. This paper explores how incorporating AI in surveillance mechanisms enhances Indias surveillance apparatus and influences the conflict between national security and privacy rights. The paper examines how revolutionary AI technologies such as predictive policing, facial recognition (FRT) and AI-enhanced monitoring systems aggravate the apparent conflict between national security interests and the fundamental right to privacy, as adjudged in the Puttaswamy judgment. The paper critically analyses the existing legal architecture, which consists of the Telecommunications Act and the IT Act, and highlights its shortcomings. Further, the paper traverses how legal frameworks of other jurisdictions such as the European Union (EU) AI Act, the Canadian AI and Data Act (AIDA) and the US regulatory guidelines could guide India in determining a well-rounded regulatory approach. Additionally, the paper proposes adopting a context-based or risk-based approach to AI regulation and the practical challenges therewith in an attempt to harmonise the state security imperative with citizens privacy rights without obstructing technological advancement. The comparative analysis of different regulatory guidelines and legislations and the potential regulations would provide practical insights for the legislature, law enforcement and other stakeholders. The paper ultimately argues that there is an exigence for a comprehensive regulatory framework to conciliate national security and privacy rights in the AI-powered digital landscape. This article is also included in The Business and Management Collection which can be accessed at https://hstalks.com/business/. Henry Stewart Publications. Subject The topic of the resource artificial intelligence; national security; right to privacy; state surveillance Publisher An entity responsible for making the resource available Henry Stewart Publications Relation A related resource ISSN: 23981679; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Akshithaa Parvathavardhini, K.P.; Chandra, J. Title A name given to the resource AI-Driven Time Series Models for Rainfall Prediction: A Machine Learning Approach Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE 14th International Conference on Communication Systems and Network Technologies, CSNT 2025;pp.42-46 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/CSNT64827.2025.10968081" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CSNT64827.2025.10968081</a> <a href="https://www.scopus.com/pages/publications/105004555733?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105004555733?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Akshithaa Parvathavardhini K.P., Department of Computer Science Christ (Deemed to be University), Bangalore, India; Chandra J., Department of Computer Science Christ (Deemed to be University), Bangalore, India Description An account of the resource Rainfall variability plays a critical role in agriculture, water resources, and disaster management in India. This study focuses on the usage of a SARIMAX model to analyze and predict rainfall patterns while accounting for both non-seasonal and seasonal components. The model adequately captures seasonal variations, illustrating the relevance of incorporating seasonal factors into forecasting. However, the non-seasonal components did not considerably improve the models performance. Diagnostic tests demonstrate that the model handles autocorrelation satisfactorily; however, the residuals exhibit irregularities, as evidenced by skewness and large tails. While the model has fair prediction accuracy, the findings show areas for development, particularly in fine-tuning non-seasonal dynamics and eliminating residual abnormalities. This research emphasizes the importance of seasonality in rainfall forecasting and lays the framework for future model improvements. The study provides valuable information on rainfall patterns, supporting better planning and management. 2025 IEEE. Subject The topic of the resource ARIMA; Climate patterns; Rainfall variability; SARIMAX; Seasonal decomposition; Time series analysis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153193-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Akshithaa Parvathavardhini, K.P.; Chandra, J. Title A name given to the resource AI-Driven Time Series Models for Rainfall Prediction: A Machine Learning Approach Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE 14th International Conference on Communication Systems and Network Technologies, CSNT 2025;pp.42-46 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/CSNT64827.2025.10968081" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CSNT64827.2025.10968081</a> <a href="https://www.scopus.com/pages/publications/105004555733?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105004555733?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Akshithaa Parvathavardhini K.P., Department of Computer Science Christ (Deemed to be University), Bangalore, India; Chandra J., Department of Computer Science Christ (Deemed to be University), Bangalore, India Description An account of the resource Rainfall variability plays a critical role in agriculture, water resources, and disaster management in India. This study focuses on the usage of a SARIMAX model to analyze and predict rainfall patterns while accounting for both non-seasonal and seasonal components. The model adequately captures seasonal variations, illustrating the relevance of incorporating seasonal factors into forecasting. However, the non-seasonal components did not considerably improve the models performance. Diagnostic tests demonstrate that the model handles autocorrelation satisfactorily; however, the residuals exhibit irregularities, as evidenced by skewness and large tails. While the model has fair prediction accuracy, the findings show areas for development, particularly in fine-tuning non-seasonal dynamics and eliminating residual abnormalities. This research emphasizes the importance of seasonality in rainfall forecasting and lays the framework for future model improvements. The study provides valuable information on rainfall patterns, supporting better planning and management. 2025 IEEE. Subject The topic of the resource ARIMA; Climate patterns; Rainfall variability; SARIMAX; Seasonal decomposition; Time series analysis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153193-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sharafudheen, Nashwa; Rohini, V.; Malviya, Meenakshi Title A name given to the resource AI-Driven Tutorial Code Learning System: Personalized Programming Education Through Adaptive Instruction and Gamification Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of 2025 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IConSCEPT66142.2025.11437327" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IConSCEPT66142.2025.11437327</a> <a href="https://www.scopus.com/pages/publications/105036597327?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036597327?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sharafudheen N., CHRIST (Deemed to be University), Department of Computer Applications, Bengaluru, India; Rohini V., CHRIST (Deemed to be University), Department of Computer Applications, Bengaluru, India; Malviya M., CHRIST (Deemed to be University), Department of Computer Applications, Bengaluru, India Description An account of the resource Existing programming education faces critical challenges, such as lack of personalization, restricted feedback tools, and scalability limitations that hinder efficient learning outcomes. This paper presents an AI-powered tutorial code learning system to transform programming education through personalized and adaptive instruction. The system integrates advanced components, including learner modeling, intelligent content recommendation, error analysis, adaptive evaluation, gamification, learning analytics, integration frameworks, quality assurance, security, and scalability layers. To evaluate the system, the study employs a mixed-methods research approach, incorporating embedded case studies and a randomized controlled trial (RCT). Rigorous data collection methods, system measure validation, undergraduate program participant selection, quality assurance protocols, statistical analysis, and ethical considerations are utilized in this work. The architecture demonstrates potential for scalable and globally accessible programming education, addressing traditional challenges through personalized learning protocols. Unlike traditional platforms offering static content and limited feedback, this AI-powered system acts as a personalized tutor, providing active problem-solving and continuous learner engagement. The adaptive system delivers optimal learning paths based on individual student needs and has the potential to transform programming education delivery and outcomes. 2025 IEEE. Subject The topic of the resource adaptive learning; artificial intelligence in education; gamification; intelligent tutoring systems; personalized learning; programming education Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833157120-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Joan Sandeep Larson, M.; Sivakumar, R.; Murugesan, Ranjani Title A name given to the resource AI-Enabled Brand-to-Generic Medication Recommendation System for Pharmacy: Addressing Consumer Brand-Name Bias Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived 2026 International Conference on Communication, Computing and Emerging Technologies, IC3ET 2026;pp.358-363 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IC3ET64989.2026.11467238" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC3ET64989.2026.11467238</a> <a href="https://www.scopus.com/pages/publications/105037427915?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037427915?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Joan Sandeep Larson M., Statistics and Data Science, CHRIST (Deemed to Be University), Bangalore, India; Sivakumar R., Statistics and Data Science, CHRIST (Deemed to Be University), Bangalore, India; Murugesan R., Statistics and Data Science, CHRIST (Deemed to Be University), Bangalore, India Description An account of the resource In countries like India, many people choose branded medicines over equally effective generic options, which drives up healthcare costs. This paper introduces a smart recommendation system that can tell whether a medication query is about a brand-name or a generic drug. It then offers more affordable alternatives based on the active ingredients. By using natural language processing and retrieval-augmented generation (RAG) with a detailed medicine database, the system accurately classifies and recommends options. Its conversational interface mimics a real pharmacy interaction, helping users make informed choices while saving money. Tests show the system responds quickly, usually within 7 seconds, and provides accurate answers over 80 % of the time for straightforward queries. Ultimately, this tool addresses the information gaps about generic drugs and branded ones and is easy to use for both consumers and pharmacists. 2026 IEEE. Subject The topic of the resource brand-generic classification; healthcare cost reduction; medicine recommendation; natural language processing; pharmaceutical alternatives; retrieval-augmented generation (RAG) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158105-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Upreti, Kamal; George, Jossy P.; Malik, Khushboo; Radhakrishnan, G. V.; Ga-B?aszczykowska, Agnieszka Title A name given to the resource AI-Enabled Early Detection of Chemo-Induced Cardiotoxicity Patterns Using ECG Time Series Data Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived American Journal of Clinical Oncology: Cancer Clinical Trials; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1097/COC.0000000000001280" target="_blank" rel="noreferrer noopener">https://doi.org/10.1097/COC.0000000000001280</a> <a href="https://www.scopus.com/pages/publications/105029467813?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029467813?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Upreti K., Department of Computer Science, Christ University, Delhi NCR, Uttar Pradesh, Ghaziabad, India; George J.P., Department of Computer Science, Christ University, Delhi NCR, Uttar Pradesh, Ghaziabad, India; Malik K., Department of Computer Science, Christ University, Delhi NCR, Uttar Pradesh, Ghaziabad, India; Radhakrishnan G.V., Department of Computer Science, KIIT School of Management (KSOM), Bhubaneswar, India; Ga-B?aszczykowska A., School of Law, War Studies University, Warsaw, Poland Description An account of the resource Objectives: Chemotherapy-induced cardiotoxicity is still a major clinical problem, usually appearing subclinically before structural or symptomatic cardiac dysfunction appears. Standard surveillance methods use imaging and biomarkers, which are time-intensive and money-intensive and can only identify damage at more advanced levels. Electrocardiography (ECG) provides a low-cost, non-invasive method that can detect early electrophysiological changes but is not fully utilized in cardio-oncology. The present work was designed to build an explainable machine learning model for predicting chemo-like cardiotoxicity patterns at an early stage from single-lead ECG signals. Methods: A public ECG data set (n=4997 segments) underwent preprocessing and was converted to 18 temporal, morphologic, and spectral features. Two ensemble learning algorithmsRandom Forest and XGBoostwere trained and validated with stratified splits. Model performance was assessed with ROCAUC, PRAUC, and F1-score with 1000 bootstrap resampling. Feature interpretability was evaluated through permutation importance and SHAP analysis. Results: Both models scored near-perfect classification (ROCAUC and PRAUC>0.99, F1-score ? 0.986). Spectral entropy, band3 (high-energy frequency), QT surrogate, and peak count were the top features ranking alongside early cardiotoxicity indicators like repolarization instability and autonomic imbalance. Conclusions: The feature-driven, interpretable ML architecture suggested here shows that single-lead ECG has the potential to be an affordable and clinically relevant tool for the early detection of chemotherapy-induced cardiotoxicity. The method provides a feasible route toward implementation in precision cardio-oncology, particularly in resource-poor or ambulatory environments. 2025 Subject The topic of the resource cardio-oncology; cardiotoxicity; chemotherapy; ECG; precision medicine Publisher An entity responsible for making the resource available Lippincott Williams and Wilkins Relation A related resource ISSN: 2773732; CODEN: AJCOD Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource AI-enabled risk identification and traffic prediction in vehicular Ad hoc Networks Description An account of the resource The proposed research presents a two-fold approach for advancing Vehicular Ad-Hoc Networks (VANETs). Firstly, it introduces a Residual Convolutional Neural Network (RCNN) architecture to extract real-time traffic data features, enabling accurate traffic flow prediction and hazard identification. The RCNN model, trained and tested on real- world data, outperforms existing models in both accuracy and efficiency, promising improved road safety and traffic management within VANETs. Secondly, the study introduces a Genetic Algorithm-enhanced Convolutional Neural Network (GACNN) routing algorithm, challenging traditional VANET routing methods with metaheuristic techniques. Experiments in various VANET network scenarios confirm GACNN's superior performance over existing routing protocols, marking a significant step toward more efficient and adaptive VANET traffic management. 2024 Author(s). Creator An entity primarily responsible for making the resource Kanna R.R.; Priya T.M.; Sivakumar V.; Nataraj C.; Musa A.I.A.; Devi M.R. Source A related resource from which the described resource is derived AIP Conference Proceedings, Vol-3161, No. 1 Publisher An entity responsible for making the resource available American Institute of Physics Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1063/5.0229499" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0229499</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203969723&doi=10.1063%2F5.0229499&partnerID=40&md5=75e8a9507ea50b888aeebf62ce66357f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203969723&doi=10.1063%2f5.0229499&partnerID=40&md5=75e8a9507ea50b888aeebf62ce66357f</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 0094243X Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kanna R.R., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India; Priya T.M., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India; Sivakumar V., Faculty of Computing Engineering and Technology, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia; Nataraj C., Faculty of Computing Engineering and Technology, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia; Musa A.I.A., Department of Computer Science, College of Computer, Qassim University, Riyadh, Saudi Arabia; Devi M.R., School of Information Science, Presidency University, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Tiwari, Vibha; Gupta, Rohit; Telang, Akshada; Tiwari, Akshra; Geddam, Rebakah; Awais, Muhammad; Khan, Muhammad Ahmed; Ghayvat, Hemant Title A name given to the resource AI-enhanced approaches for personalized cardiac treatment: insights from ECG data Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived npj Systems Biology and Applications;Volume;12;Issue;1;Article No.;72; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1038/s41540-026-00702-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1038/s41540-026-00702-6</a> <a href="https://www.scopus.com/pages/publications/105039872408?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039872408?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Tiwari V., Centre for Artificial Intelligence, Madhav Institute of Technology and Science (Deemed University), Madhya Pradesh, Gwalior, India, Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University, Vj Sweden; Gupta R., Centre for Artificial Intelligence, Madhav Institute of Technology and Science (Deemed University), Madhya Pradesh, Gwalior, India; Telang A., Centre for Artificial Intelligence, Madhav Institute of Technology and Science (Deemed University), Madhya Pradesh, Gwalior, India; Tiwari A., School of Sciences, Christ University, Uttar Pradesh, Ghaziabad, India; Geddam R., Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University, Vj Sweden, Unitedworld Institute of Technology, Karnavati University, Gujarat, Gandhinagar, India; Awais M., Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States; Khan M.A., Department of Electrical Engineering, Stanford University, Stanford, CA, United States; Ghayvat H., Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University, Vj Sweden, IMT, Department of Humanities and Technology, Roskilde University, Universitetsvej 1, Roskilde, Denmark Description An account of the resource The analysis of drug-induced alterations in the electrocardiogram (ECG) is essential in measuring cardiac safety, but manual analysis is not always accurate enough to identify subtle but important effects. This paper examines how machine learning (ML) models can be used to categorize various pharmacological treatments according to their distinct ECG patterns to establish a platform of individualized therapeutic evaluation. Using the public ECG Effects of Dofetilide, Moxifloxacin, Dofetilide+Mexiletine, Dofetilide+Lidocaine and Moxifloxacin+Diltiazem (ECGDMMLD) database, key electrophysiological features were extractedincluding heart rate variability (HRV) and standard cardiac intervals (RR, PR, QT, QRS) to train and compare three different classifiers: XGBoost, Random Forest, and a Support Vector Machine (SVM). The analysis showed that tree-based ensemble techniques were very useful in this task. The XGBoost model had a better classification accuracy of 98.1%, which was closely followed by the random forest at 97.3%. Conversely, the SVM had much lower accuracy, implying that it was not as well adapted to the complexity of the high-dimensional ECG data. These results establish that ML models, particularly XGBoost, can accurately decode complex drug-induced cardiac signatures from ECG data. This work is a powerful demonstration of the proof-of-concept of automated and data-driven analytics integration into clinical processes to enhance drug safety and promote personalized medicine. The Author(s) 2026. Publisher An entity responsible for making the resource available Nature Research Relation A related resource ISSN: 20567189; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource AI-Enhanced IoT Data Analytics for Risk Management in Banking Operations Subject The topic of the resource AI; Banking Operations; Data Analytics; Financial Risk; IoT; Real-time Analysis; Risk Management Description An account of the resource Using IoT data analytics in conjunction with artificial intelligence (AI) has the potential to improve banking operations' risk management. Sophisticated analytical methods are necessary for the detection and management of possible risks due to the increasing complexity and amount of data generated by the banking industry. This research proposes a novel method for analysing real-time data from IoT devices by employing artificial intelligence algorithms. The risks associated with financial transactions and operations can be better and more accurately assessed using this method. Through the integration of AI's pattern recognition, anomaly detection, and predictive modelling capabilities with the massive amounts of data generated by Internet of Things devices, this project aims to substantially enhance the efficacy and efficiency of risk management approaches in the banking sector. Research like this could lead to innovative solutions that make financial institutions more resistant to rising risks by enhancing decision-making, reducing operational weaknesses, and so on. 2024 IEEE. Creator An entity primarily responsible for making the resource Thirumagal P.G.; Vaddepalli S.; Das T.; Das S.; Madem S.; Immaculate P.S. Source A related resource from which the described resource is derived 5th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024 - Proceedings, pp. 177-181. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICRTCST61793.2024.10578533" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICRTCST61793.2024.10578533</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199358472&doi=10.1109%2FICRTCST61793.2024.10578533&partnerID=40&md5=cb3c7936415ed5b0a248ef13d338b17b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199358472&doi=10.1109%2fICRTCST61793.2024.10578533&partnerID=40&md5=cb3c7936415ed5b0a248ef13d338b17b</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835035137-8 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Thirumagal P.G., VISTAS, Department of MBA, Tamil Nadu, Chennai, India; Vaddepalli S., University of Technology and Applied Sciences, Department of Economics and Business Administration, India; Das T., Christ University, Department of School of Business Management, UP, Ghaziabad, India; Das S., Christ University, Department of School of Commerce, Finance & Accountancy, Delhi NCR Campus, UP, Ghaziabad, India; Madem S., Vignan's Institute of Information Technology, Department of Management Studies, India; Immaculate P.S., Sri SaiRam Institute of Technology, Department of MBA, West Tambaram, TamilNadu, Chennai, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Upreti, Kamal; Divakaran, Prakash; Patil, S.S.; Dadhich, Priyanka; Gautam, Anjali; Vats, Prashant Title A name given to the resource AI-Optimized Erection of Landslide-Resistant Retaining Structures Through Heterogeneous Composite Nanomaterials: A Computerized Algorithmic Breakthrough Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Communications in Computer and Information Science;Volume;2072 CCIS;pp.50-62 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-031-84062-3_5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-84062-3_5</a> <a href="https://www.scopus.com/pages/publications/105002379033?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002379033?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Upreti K., Department of Computer Science, Christ University, Delhi NCR, Ghaziabad, India; Divakaran P., Department of Management, Himalayan University, Arunachal Pradesh, Itanagar, India; Patil S.S., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Dr. D.Y. Patil Vidyapeeth, Pune (Deemed to be University), Pimpri-Chinchwad, India; Dadhich P., Department of Computer Science, DPG Degree College, Sector-43, Haryana, Gurugram, India; Gautam A., Department of Computer Science, DPG Degree College, Sector-43, Haryana, Gurugram, India; Vats P., Department of CSE, SCSE, Manipal University Jaipur, Rajasthan, Jaipur, India Description An account of the resource The proposed work relates to the field of environmental protection and ensuring the environmental safety of urban development and the population from erosion and landslide phenomena. It can be used to create territorial plans for the development of recreational areas in areas subject to these natural and man-made impacts. The technical result of the proposed work is to ensure the reduction of natural and man-made impacts on urban and similar settlements through the use of new technological solutions for the creation of structures using heterogeneous composite nanomaterials. A technical result is achieved by equipping the territory with buildings and structures, creating a base and a soil-reinforced array, with the location of blocks in it, made as soil-filled shells, on the basis, a soil-filled shell-stay-base is mounted with a soil-filled shell base, a rigid frame is mounted and the front wall, which is fixed with the second attachment point, fix the soil-filled shell, its upper part is made waterproof and equipped with a water outlet through the second attachment point, to which the rigid frame and the front wall are fixed, they are fixed with the third fastening unit, to which a soil-filled shell-plate with a storm drain of one or more arm tapes with a drainage system filled with a sorbent and placed in a waterproof shell. The front wall is additionally covered with a polymer material with seeds. All structural elements are made of heterogeneous composite nanomaterials. As a polymeric material with seeds, the material PINEMA is used. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. Subject The topic of the resource AI-optimized; Composite Nanomaterials; Computerized and Algorithmic; Erection; Heterogeneous; Landslide-Resistant; Retaining Structures Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18650929; ISBN: 978-303184061-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Madan Kumar, C.; Shekhar Jadhav, Rohini; Sondawale, Shweta P; Angulakshmi, M.; Bikash Acharjee, Purnendu; Arockia Dhanraj, Joshuva Title A name given to the resource AI-Powered Analytics in IT Services and the Opportunities and Challenges for Scalable Growth Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings - 2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies, SISIMPACT 2025;pp.734-739 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/SISIMPACT67725.2025.11439473" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/SISIMPACT67725.2025.11439473</a> <a href="https://www.scopus.com/pages/publications/105037464223?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037464223?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Madan Kumar C., School of CS & AI, SR University, Telangana, Warangal, India; Shekhar Jadhav R., Vishwakarma Institute of Technology, Department of Computer Engineering, Maharashtra, Pune, India; Sondawale S.P., School of Engineering and Sciences, MIT ADT University GLA University, Maharashtra, Pune, 412201, India; Angulakshmi M., School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Tamil Nadu, Vellore, India; Bikash Acharjee P., Computer Science, CHRIST University, Bengaluru, India; Arockia Dhanraj J., Research and Development Cell, Lovely Professional University, Punjab, Phagwara, 144411, India Description An account of the resource The technological advancement has witnessed high levels of efficiency in operations, decision-making and customer engagement because of the integration of AI enabled analytics into IT-based services. The paper examines the influence of AI-based solutions that are transforming IT service models, with focus on the effect on scalability, predictive maintenance, and intelligent automation. The studies to point out the opportunities presented by AI describe improved personalization of service delivery, fewer instances of downtime, and data-based optimization approaches. Nonetheless, it also addresses such vital issues as the privacy or interpretability of data and the infrastructural requirements of scaling AI solutions. It is suggested to replace these limitations with a hybrid framework resolving limitations by integrating the advantages of cloud-native frameworks with edge-intelligent systems. Experimental study conducted among medium sized IT companies revealed that the speed in delivering the services increased by 5 0%, 6 0% lower error rates and over 55% reduction in downtime. The results hint at the possibility of bearing AI-driven analytics-based IT services when the required control and strategic design are employed. 2025 IEEE. Subject The topic of the resource AI-powered analytics; Cloud-native architecture; Edge intelligence; Ethical AI deployment; IT services Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155787-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Vinodha, D.; Mary Anita, E.A.; Lapina, Maria Title A name given to the resource AI-Powered Botnet Detection Systems: A Critical Review of Current Approaches and Challenges Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1456 LNNS;pp.93-104 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-07275-7_10" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-07275-7_10</a> <a href="https://www.scopus.com/pages/publications/105023167339?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023167339?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Vinodha D., Christ University, Bangalore, India; Mary Anita E.A., Christ University, Bangalore, India; Lapina M., North Caucasus Federal University, Stavropol, Russian Federation Description An account of the resource In the era of information technology, Botnets have become the most persistent cyber threat, capable of launching large-scale attacks like Distributed Denial of Service. stealing sensitive information and disturbing online services. Botnets have evolved from simple networks to complicated distributed networks including IoT devices, making them pervasive, harder to track, and destroy. Machine learning and Deep learning based models are emerging to detect bot attacks by analyzing large datasets and detecting patterns and anomalies. The state of the art methodologies for detecting bot infection are reviewed deeply and compared based on adopted methodologies, dataset and feature selection mechanism. The paper further discusses the pros and cons of existing methodologies. Finally, research gaps are presented to help future research on enhancing bot detection. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Botnet; Deep Learning; Internet of Things; Machine learning; network traffic analysis Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303207274-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Fernandes, Melsa Magdalene; Mary Anita, E.A.; Lapina, Maria; Lapin, Vitalii Title A name given to the resource AI-Powered Disaster Management System Using Satellite Imagery: A Survey Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1585 LNNS;pp.161-167 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-01831-1_15" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-01831-1_15</a> <a href="https://www.scopus.com/pages/publications/105020726887?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020726887?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Fernandes M.M., Department of CSE, Christ University, Bangalore Kengeri Campus Kanmanike, Kumbalgodu, Mysore Road, Karnataka, Bangalore, 560074, India; Mary Anita E.A., Department of CSE, Christ University, Bangalore Kengeri Campus Kanmanike, Kumbalgodu, Mysore Road, Karnataka, Bangalore, 560074, India; Lapina M., Department of Computational Mathematics and Cybernetics, North Caucasus Federal University, Pushkina Street, 1, Stavropol, 355017, Russian Federation; Lapin V., Department of Computational Mathematics and Cybernetics, North Caucasus Federal University, Pushkina Street, 1, Stavropol, 355017, Russian Federation Description An account of the resource Disaster management is all about time; timely response and an accurate assessment are the basis on which disaster damage may be limited and lives saved. Traditional methods of disaster response rely on human analysis and manual interpretation of satellite images, which are slow and prone to human error. Here, AI can prove to be a technology capable of using ML and DL algorithms to analyze vast quantities of satellite imagery in real time. AI-based systems can work to detect areas affected, assess the severity of the damage, and predict the evolution of disasters for better response and resource allocation. The paper presents recent developments in AI-based disaster management with the assistance of satellite imagery, sketching out major challenges and future research directions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. Subject The topic of the resource AI; Damage Assessment; Disaster Management; Machine Learning; Satellite Imagery Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303201830-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Balaji, K. Title A name given to the resource AI-Powered Ergonomics: Transforming Workplace Health and Safety Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Cases on AI Innovations in Occupational Health and Safety;pp.51-72 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3693-9301-7.ch003" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-9301-7.ch003</a> <a href="https://www.scopus.com/pages/publications/105014459271?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105014459271?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Balaji K., Christ University, Bengaluru, India Description An account of the resource The integration of Artificial Intelligence (AI) into workplace ergonomics is revolutionizing the approach to employee health and safety. AI-powered ergonomic solutions leverage advanced data analytics, machine learning algorithms, and real-time monitoring to optimize workplace design, predict risks, and minimize injury-related costs. This article explores the transformative role of AI in identifying and mitigating ergonomic risks, particularly musculoskeletal disorders (MSDs) and repetitive strain injuries (RSIs), which remain prevalent across various industries. By using AI-driven wearables, motion sensors, and intelligent software, organizations can continuously analyze worker posture, movements, and environmental conditions, enabling proactive interventions. Additionally, AI models enable personalized recommendations for workplace adjustments, thereby enhancing employee comfort, productivity, and overall well-being. This article emphasizes how AI is reshaping workplace ergonomics, offering a datadriven, predictive approach to injury prevention and fostering a safer environment. 2025, IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-836939303-1; 979-836939301-7; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Surenthran David, P.; Dheenadhayalan, K.; Pruno Suson, W.; Devapitchai, Joel Jebadurai; Manickam, Thirupathi Title A name given to the resource AI-Powered Financial Inclusion: Bridging the Gap for the Unbanked Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Utilizing AI and Machine Learning in Financial Analysis;pp.449-479 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3693-8507-4.ch0024" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-8507-4.ch0024</a> <a href="https://www.scopus.com/pages/publications/105007274525?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105007274525?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Surenthran David P., St. Joseph's College of Engineering, India; Dheenadhayalan K., Sri Sai Ram Engineering College, Chennai, India; Pruno Suson W., St. Joseph's College of Engineering, India; Devapitchai J.J., St. Joseph's College of Engineering, India; Manickam T., Christ University, India Description An account of the resource Financial inclusion is crucial for both economic growth and decreasing poverty levels, however, around 1.7 billion adults worldwide still do not have access to banking services. AI- driven financial technologies offer creative solutions to close this divide through offering easy, cost- effective, and effective financial services. This chapter delves into the present situation of financial inclusion, the main AI technologies driving changes, successful examples, obstacles, and future outlook. Through the use of artificial intelligence, we have the ability to develop a financial system that is more inclusive, giving power to individuals and promoting economic growth. 2025 by IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-836938509-8; 979-836938510-4; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource AI-Powered IoT Framework for Enhancing Building Safety through Stability Detection Subject The topic of the resource Accuracy; Building Safety; Computational Time; Ensemble Learning; Fuzzy Logic Systems; Gradient Boosting Machines; Internet of Things; Latency Detection; Sensor Data; Support Vector Machine Description An account of the resource The rapid urbanization and increasing structural complexities of modern buildings have heightened the need for advanced monitoring systems to ensure building safety. The research presents an AI-powered IoT framework that enhances building safety through advanced stability detection mechanisms. The proposed framework employs a novel algorithm, Ensemble Learning with IoT Sensor Data Aggregation (EnIoT-SDA), which integrates ensemble learning techniques with aggregated sensor data to provide accurate and real-time stability assessments of building structures. The effectiveness of EnIoT-SDA was evaluated through a comprehensive simulation analysis, comparing its performance against existing algorithms, including Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Fuzzy Logic Systems (FLS). Simulation metrics, such as accuracy, false positive rate, computational time, and detection latency, were used to assess and compare the algorithms' performance. The results demonstrated that EnIoT-SDA outperformed the existing methods in several key areas, offering improved accuracy and reduced detection latency, thus establishing its potential as a robust solution for building safety monitoring. The study underscores the significant advancements brought by integrating ensemble learning with IoT sensor data and highlights areas for future research and development in this domain. 2024 IEEE. Creator An entity primarily responsible for making the resource Raja S.R.; Devi T.R.; Raj J.R.F.; Sankar V.K.; Krishnan R.S.; Venkatalakshmi R. Source A related resource from which the described resource is derived 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024 - Proceedings, pp. 73-80. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/I-SMAC61858.2024.10714838" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/I-SMAC61858.2024.10714838</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208596171&doi=10.1109%2FI-SMAC61858.2024.10714838&partnerID=40&md5=c26bb54dd5c79f82d2a890c8720ca1b6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208596171&doi=10.1109%2fI-SMAC61858.2024.10714838&partnerID=40&md5=c26bb54dd5c79f82d2a890c8720ca1b6</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835037642-5 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Raja S.R., Hindustan Institute of Technology and Science, Computer Applications, Chennai, India; Devi T.R., Christ University, Department of Computer Science and Engineering, Bangalore, India; Raj J.R.F., Saveetha Institute of Medical and Technical Sciences, Department of Electronics and Communication, Saveetha School of Engineering, Chennai, India; Sankar V.K., Scad College of Engineering and Technology, Department of Civil Engineering, Cheranmahadevi, India; Krishnan R.S., Scad College of Engineering and Technology, Department of Electronics and Communication Engineering, Cheranmahadevi, India; Venkatalakshmi R., Psna College of Engineering and Technology, Department of Information Technology, Dindigul, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource AI-powered marketing strategies in the tourism and hospitality sector Description An account of the resource A highly competitive environment with increased demand for personalized services drives the tourism and hospitality industry to embrace immersive and intelligent technologies. Smart technologies like artificial intelligence (AI) and virtual reality (VR) assist in promotions, marketing brands, customer analysis, and ultimately leading to sustainable businesses. Marketing research is an inevitable element for any businesses that helps in understanding their customers, catering their needs, and turning them into loyal customers. Marketing strategies incorporated with smart technologies are gaining high importance in the tourism and hospitality industries due to three major outcomes such as experience enhancement, revenue improvement and effective operations. Artificial intelligence revamped the hospitality industry with customized services and tailored recommendations based on a wholesome of customer data. Virtual reality technology provides high immersive experience to boost tourism, to enhance customer experience, to influence positive travel decisions. 2024, IGI Global. All rights reserved. Creator An entity primarily responsible for making the resource Daline R.; Thomas J. Source A related resource from which the described resource is derived Utilizing Smart Technology and AI in Hybrid Tourism and Hospitality, pp. 223-242. Publisher An entity responsible for making the resource available IGI Global Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3693-1978-9.ch010" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-1978-9.ch010</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189064779&doi=10.4018%2F979-8-3693-1978-9.ch010&partnerID=40&md5=85361ac8c823cb9d246450ff26800fd8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189064779&doi=10.4018%2f979-8-3693-1978-9.ch010&partnerID=40&md5=85361ac8c823cb9d246450ff26800fd8</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-836931979-6; 979-836931978-9 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Daline R., Christ University, India; Thomas J., Christ University, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Pandey, Ananya Title A name given to the resource AI-Powered Military Logistics and Strategy: A Paradigm Shift in Modern Warfare Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Ethical Challenges of AI and Warfare;pp.45-66 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-3611-4.ch002" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-3611-4.ch002</a> <a href="https://www.scopus.com/pages/publications/105038914733?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038914733?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Pandey A., Christ University, Bangalore, India Description An account of the resource The function of logistics and strategic planning has expanded beyond traditional supply lines as contemporary warfare becomes more complicated. AI is changing military operations by facilitating autonomous logistics systems, real-time data processing, and quicker decision-making. This change is altering how military strategy is developed and implemented, going beyond efficiency improvements. AI provides a level of adaptability never before achievable, from autonomous transport systems and coordinated battlefield support to predictive maintenance of combat vehicles. An intelligent and robust logistics ecosystem is being fostered by the integration of technologies such as swarm robotics, satellite-based monitoring, and quantum-enhanced optimization. But this change also presents operational, ethical, and policy issues. This chapter addresses the hazards of reliance and misalignment while examining the complex effects of AI on military logistics and strategy. 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. Publisher An entity responsible for making the resource available IGI Global Scientific Publishing Relation A related resource ISBN: 979-833733613-8; 979-833733611-4; 979-833733612-1; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kumar Singh, Samit; Chaurasia, Amrita; Ruzimbaevich, Aybek Kalandarov; Kavitha, G.L. Title A name given to the resource AI-Powered Smart Waste Management Systems Using Cloud Analytics Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCAMS65118.2025.11234652" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCAMS65118.2025.11234652</a> <a href="https://www.scopus.com/pages/publications/105030099221?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030099221?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kumar Singh S., Bhoj Reddy Engineering College for Women, Department of Mechanical Engineering - H & S, Hyderabad, India; Chaurasia A., Christ ( Deemed to be University), School of Commerce, Finance and Accountancy, Delhi NCR, India; Ruzimbaevich A.K., Mamun University, Department of Roman-Germanic Philology, Khiva, Uzbekistan; Kavitha G.L., Kmct Institute of Emerging Technology and Management, Department Cse, Kerala, India Description An account of the resource The increasing volume of trash and urbanisation has made it essential for waste management to be innovative, sustainable and effective. This study offers a smart waste management system that optimises garbage collection, sorting & recycling using AI, Internet of Things and cloud computing. Real-time monitoring, automated garbage categorisation, predictive fill-level forecasting & intelligent route planning are possible with a hybrid edge-cloud architecture. Technology improves operational efficiency & environmental sustainability over conventional trash systems by incorporating machine learning algorithms and real-time sensor data. Multi-modal data fusion, scalability, cloud analytics & adaptive feedback mechanisms enable system improvement. Sensor upgrades and staged implementation tactics assist in handling IoT adoption issues, including challenging trash categorisation accuracy and high startup expenses. This comprehensive strategy supports smart city development and global sustainability. Future improvements include blockchain for waste tracking and 5 G-powered edge AI for automatic garbage processing. 2025 IEEE. Subject The topic of the resource Artificial Intelligence; Cloud Analytics; Hybrid Edge Cloud Architecture; Internet of Things; Route Planning; Waste Management Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159610-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Venugopal, E.; Rikimi; Vinjamuri, Lakshmi Priya; Sachan, Vivek Singh; Samuel, Stephen; Shukhratjon, Ismoilov Title A name given to the resource AI-Powered Solutions for Legal Compliance in Industrial Workspaces a Psychological and Labour Law Perspective Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 International Conference on Sustainability, Innovation and Technology, ICSIT 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSIT65336.2025.11295145" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSIT65336.2025.11295145</a> <a href="https://www.scopus.com/pages/publications/105031628417?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105031628417?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Venugopal E., Saveetha School of Law, Saveetha University, Chennai, India; Rikimi, MIT Institute of Design, MIT ADT University, Department of Graphic Design, Maharashtra, Pune, India; Vinjamuri L.P., Technology and Legal Analyst Law College, Dehradun Uttaranchal University, Uttarakhand, India; Sachan V.S., School of Business Management, CSJM University, Kanpur, India; Samuel S., CHRIST (Deemed to be University), Department of Psychology, Karnataka, Bangalore, India; Shukhratjon I., Tashkent State University of Law, Department of LabourLaw, Tashkent, Uzbekistan Description An account of the resource The complicated issue of the ways to ensure the workplaces in the industries meet the legal standards brings together the psychology of the workplaces, the labour law, and the convolution of the AI design. In this dissertation, the author investigates the use of AI-mediated options to deal with regulatory compliance and psychological and legal issues. The risk associated with the fair labour, discrimination, and safety issues can be addressed with the help of predictive analytics, automated compliance, and AI-compliance monitoring. The use of AI can also be expanded to support worker well-being and mental health through the identification of work stressors, burnout prevention, and creation of a physiologically safe workplace. But in the case of AI, there are ethical or legal considerations around the agency of workers, bias in algorithms, as well as privacy or confidentiality of data. Due to these reasons, it is necessary to adopt the strategy approach, where AI and human observation are used to determine the work decisions trade-offs in an observable and just manner to employers and employees alike. This dissertation also added to the contribution of how AI can benefit the responsible design of industrial workplaces that do not fail to achieve ethical standards, hold to psychological sustainability, and adhere to labour laws, evaluating the psychological effects of labour law actors as well as effects on the workplace. 2025 IEEE. Subject The topic of the resource Industrial workspace; Legal compliance Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153549-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library