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AI-Powered Threat Detection and Response for Future 6G Networks
As 6G networks come into existence, there will be ultra-low latency, high bandwidth, seamless integration of billions of devices, and a revolution of connectivity. But with these great strides, new challenges about the securing of these systems against very sophisticated cyber attacks have come up. This book discusses how AI can benefit the 6G system to detect and respond threats better. Through the utilization of AI algorithms, machine learning, and deep learning, 6G networks will autonomously identify and mitigate security risks in real time while adapting to dynamic and everevolving attack vectors. AI systems can monitor network traffic in real-time, analyze anomalies, and predict possible vulnerabilities before they are exploited, hence reducing the detection-to-mitigation cycle. As 6G networks become more complex and pervasive, AI will become an indispensable component in maintaining security and enabling a trustworthy digital ecosystem, thereby becoming a core component of the future network defense. 2026 by IGI Global Scientific Publishing. All rights reserved. -
AI-Powered Solutions for Legal Compliance in Industrial Workspaces a Psychological and Labour Law Perspective
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. -
AI-Powered Smart Waste Management Systems Using Cloud Analytics
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. -
AI-Powered Military Logistics and Strategy: A Paradigm Shift in Modern Warfare
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. -
AI-powered marketing strategies in the tourism and hospitality sector
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. -
AI-Powered IoT Framework for Enhancing Building Safety through Stability Detection
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. -
AI-Powered Financial Inclusion: Bridging the Gap for the Unbanked
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. -
AI-Powered Ergonomics: Transforming Workplace Health and Safety
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. -
AI-Powered Disaster Management System Using Satellite Imagery: A Survey
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. -
AI-Powered Botnet Detection Systems: A Critical Review of Current Approaches and Challenges
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. -
AI-Powered Analytics in IT Services and the Opportunities and Challenges for Scalable Growth
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. -
AI-Optimized Erection of Landslide-Resistant Retaining Structures Through Heterogeneous Composite Nanomaterials: A Computerized Algorithmic Breakthrough
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. -
AI-Enhanced IoT Data Analytics for Risk Management in Banking Operations
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. -
AI-enhanced approaches for personalized cardiac treatment: insights from ECG data
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. -
AI-enabled risk identification and traffic prediction in vehicular Ad hoc Networks
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). -
AI-Enabled Early Detection of Chemo-Induced Cardiotoxicity Patterns Using ECG Time Series Data
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 -
AI-Enabled Brand-to-Generic Medication Recommendation System for Pharmacy: Addressing Consumer Brand-Name Bias
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. -
AI-Driven Tutorial Code Learning System: Personalized Programming Education Through Adaptive Instruction and Gamification
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. -
AI-Driven Time Series Models for Rainfall Prediction: A Machine Learning Approach
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. -
AI-Driven Time Series Models for Rainfall Prediction: A Machine Learning Approach
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.