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AI-Based Medical Assistance forProactive Healthcare Predictions andServices
Healthcare systems must adapt to the requirements of the digital era. The proposed healthcare Artificial Intelligence (AI) assistance provides a safe and user-friendly platform for physicians, patients, and administrators to meet their specific needs. The systems architecture prioritizes user authentication and role-based access control to ensure that only authorized users have access to certain features. The technology allows patients to input their symptoms, which is the platforms cornerstone offering. The technology uses a Machine Learning (ML) model and a large medical database to properly forecast probable illnesses based on the symptoms presented. This predictive feature helps individuals make educated decisions about their health and seek medical assistance proactively. The systems creative approach extends to online consultations. Patients may seek consultations, schedule appointments, and conduct secure video chats from the comfort of their homes. This online consultation service offers a convenient and flexible option for medical treatment, especially for people with restricted mobility or wanting immediate assistance. This paper evaluates disease prediction using parameters like accuracy and confusion matrix performance. The neural network model performs better for the above parameters in comparison to the random forest and K-nearest neighbor ML models. The proposed system uses ML technology to deliver fast, accurate, and secure medical services, breaking down traditional healthcare barriers. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
AI-based online interview bot with an interactive dashboard
In recent years, video interviews have become increasingly popular in the recruitment process due to their convenience and efficiency. However, evaluating a candidates communication skills and perceived personality traits from a video interview can be challenging. The agent utilizes natural language processing and computer vision techniques to analyze the candidates verbal and nonverbal behavior during the interview. Specifically, the agent focuses on linguistic features such as fluency, grammar, and vocabulary, as well as nonverbal cues such as facial expressions and body language. Based on these features, the agent predicts the candidates communication skills and perceived personality traits. To validate the effectiveness of the agent, a Talk was conducted with a group of participants who completed video interviews with and without the agent. The results show that the agents predictions of communication skills and perceived personality traits are highly correlated with the ratings given by human evaluators. Additionally, the agent is able to provide valuable insights into the candidates performance that may not be immediately apparent to human evaluators. Overall, the intelligent video interview agent proposed here has the potential to improve the recruitment process by providing more accurate and objective evaluations of candidates communication skills and perceived personality traits. 2025 selection and editorial matter, A. Vadivel, K. Meena, P. Sumathy, Henry Selvaraj, P. Shanmugavadivu and Shaila S. G.; individual chapters, the contributors. -
AI-based online proctoring: a review of the state-of-the-art techniques and open challenges
So far, this pandemic has severely affected the education sector. As education undergoes a brilliant transformation with advancing technology, the digital acquisition of knowledge has yet to find widespread use - virtual exams. Faraway Proctoring offers several advantages of using manual and primarily based technology. Although this allows students to take an exam in any field with specific technical requirements, it eliminates the need for physical research centers. It is cost-effective and easy to plan, which can be challenging to manage, especially during aggressive trials. Finally, the paper discusses the performance characteristics of different styles of web-based inspection systems, along with their limitations and challenges. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
AI-based Power Screening Solution for SARS-CoV2 Infection: A Sociodemographic Survey and COVID-19 Cough Detector
Globally, the confirmed coronavirus (SARS-CoV2) cases are being increasing day by day. Coronavirus (COVID-19) causes an acute infection in the respiratory tract that started spreading in late 2019. Huge datasets of SARS-CoV2 patients can be incorporated and analyzed by machine learning strategies for understanding the pattern of pathological spread and helps to analyze the accuracy and speed of novel therapeutic methodologies, also detect the susceptible people depends on their physiological and genetic aspects. To identify the possible cases faster and rapidly, we propose the Artificial Intelligence (AI) power screening solution for SARS- CoV2 infection that can be deployable through the mobile application. It collects the details of the travel history, symptoms, common signs, gender, age and diagnosis of the cough sound. To examine the sharpness of pathomorphological variations in respiratory tracts induced by SARS-CoV2, that compared to other respiratory illnesses to address this issue. To overcome the shortage of SARS-CoV2 datasets, we apply the transfer learning technique. Multipronged mediator for risk-averse Artificial Intelligence Architecture is induced for minimizing the false diagnosis of risk-stemming from the problem of complex dimensionality. This proposed application provides early detection and prior screening for SARS-CoV2 cases. Huge data points can be processed through AI framework that can examine the users and classify them into "Probably COVID", "Probably not COVID"and "Result indeterminate". 2021 The Authors. Published by Elsevier B.V. -
AI-Based Real-Time Class Engagement Emotions Monitoring System
This chapter introduces an AI-based system for real-time monitoring of student engagement by analyzing emotional responses and attention levels during classroom sessions. Using a camera placed in front of students, the system applies computer vision algorithms to detect focus and distraction through eye movements and facial expressions. The core of the system is the Emo-Engage Analysis framework, which classifies engagement into three levels based on eye retention, duration of attentiveness, and indicators of positive emotional response. Drawing on recent research in emotional and attentional regulation, this method offers a fine-grained analysis of student engagement. Aggregated engagement data allow instructors to assess both individual and group dynamics throughout a lesson. These insights support the evaluation of teaching strategies and provide meaningful feedback on instructional impact, helping educators adapt their methods to foster more effective and emotionally supportive learning environments. 2026, IGI Global Scientific Publishing. All rights reserved. -
AI-Based Risk Profiling of Online Human Trafficking Victims: A Multimodal Framework for Proactive Detection
Human trafficking is still a major worldwide crime that is made easier by digital channels, including social media, job advertisements, and websites that offer escort services. This study offers a thorough examination of artificial intelligence (AI) approaches to human trafficking detection and prevention, with a focus on identifying the most susceptible groups. From authorship attribution and geolocation extraction to social network analysis and multimodal detection, we analyze around 20 scholarly papers that include text, photos, audio, and network data. Lack of victim-focused profiling, data scarcity, bias in AI, and ethical deployment issues are some of the main research needs. In response, we provide a conceptual AI system that uses publicly available signals to evaluate individual vulnerability by combining network analysis, natural language processing, and weakly supervised learning. This work emphasizes the importance of ethically grounded AI systems to assist NGOs, law enforcement, and policymakers proactively identify at-risk individuals and prevent exploitation before it occurs. 2025 IEEE. -
AI-Based Security and Privacy Solutions for Edge Computing Using Federated Learning
Edge computing, which reduces latency and bandwidth usage by performing computations on data closer to where they are generated, is generating considerable interest due to the rapid growth of the Internet of Things (IoT) and real-time applications. However, this new architecture brings more security issues, such as cyber-attacks, unauthorized access, and data leakage. This architectural change improves performance, but it also increases the risk for data leakage, unauthorized access and cyberattacks. These distributed/architecturally decentralized scenarios are not something traditional cloud security models are suitable for. Federated learning (FL), a privacy-preserving setting for distributed learning, comes as a new solution which enables edge devices to collaboratively update their models without disclosing locally learned models. When integrated with AI, FL offers intelligent, flexible, and privacy-preserving security to edge ecosystems. This chapter investigates the interplay between edge computing, FL, and AI, providing a detailed analysis of possible future developments, risk mitigation strategies, and existing threats. This chapter studies the pioneering role of AI-supported federated systems in defending the future generation of edge networks through recent research and applied studies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
AI-based wavelet and stacked deep learning architecture for detecting coronavirus (COVID-19) from chest X-ray images
A novel coronavirus (COVID-19), belonging to a family of severe acute respiratory syndrome coronavirus 2 (SARs-CoV-2), was identified in Wuhan city, Hubei, China, in November 2019. The disease had already infected more than 681.529665 million people as of March 13, 2023. Hence, early detection and diagnosis of COVID-19 are essential. For this purpose, radiologists use medical images such as X-ray and computed tomography (CT) images for the diagnosis of COVID-19. It is very difficult for researchers to help radiologists to do automatic diagnoses by using traditional image processing methods. Therefore, a novel artificial intelligence (AI)-based deep learning model to detect COVID-19 from chest X-ray images is proposed. The proposed work uses a wavelet and stacked deep learning architecture (ResNet50, VGG19, Xception, and DarkNet19) named WavStaCovNet-19 to detect COVID-19 from chest X-ray images automatically. The proposed work has been tested on two publicly available datasets and achieved an accuracy of 94.24% and 96.10% on 4 classes and 3 classes, respectively. From the experimental results, we believe that the proposed work can surely be useful in the healthcare domain to detect COVID-19 with less time and cost, and with higher accuracy. 2023 Elsevier Ltd -
AI-Based Yolo V4 Intelligent Traffic Light Control System
With the growing number of city vehicles, traffic management is becoming a persistent challenge. Traffic bottlenecks cause significant disturbances in our everyday lives and raise stress levels, negatively impacting the environment by increasing carbon emissions. Due to the population increase, megacities are experiencing severe challenges and significant delays in their day-to-day activities related to transportation. An intelligent traffic management system is required to assess traffic density regularly and take appropriate action. Even though separate lanes are available for various vehicle types, wait times for commuters at traffic signal points are not reduced. The proposed methodology employs artificial intelligence to collect live images from signals to address this issue in the current system. This approach calculates traffic density, utilizing the image processing technique YOLOv4 for effective traffic congestion management. The YOLOv4 algorithm produces better accuracy in the detection of multiple vehicles. Intelligent monitoring technology uses a signal-switching algorithm at signal intersections to coordinate time distribution and alleviate traffic congestion, resulting in shorter vehicle waiting times. 2022 Boppuru Rudra Prathap et al., published by Sciendo. -
AI-Controlled Wind Turbine Systems: Integrating IoT and Machine Learning for Smart Grids
Advances in renewable energy technologies are pivotal in addressing the challenges posed by the depletion of traditional energy sources and their associated environmental impacts. Among these, wind energy stands out as a promising avenue, with wind turbine farms proliferating globally. However, the unpredictable nature of wind and intricate interplay between turbines necessitate innovative solutions for efficient operation and maintenance. This paper reviews advancements in intelligent control systems, notably those proposed by Smart Wind technologies. These systems leverage a network of sensors and IoT devices to gather real-time data, such as wind speed, temperature, and humidity, to optimize turbine performance. A significant focus is on turbines employing doubly-fed induction generators, which offer benefits like adjustable speed and consistent frequency operation. Their integration into smart grids introduces challenges concerning power system dynamics'security and reliability. This review delves into the dynamics, characteristics, and potential instabilities of such integrations, emphasizing the uncertainties in wind and nonlinear load predictions. A noteworthy finding is the rising prominence of artificial intelligence, particularly machine and deep learning, in predictive diagnostics. These methodologies offer costeffective, accurate, and efficient solutions, holding potential for enhancing power system stability and accuracy in the smart grid context. The Authors, published by EDP Sciences, 2024. -
AI-Driven Consumer Behavior and Decision Making
Artificial Intelligence (AI) has transformed consumer behavior and decision-m aking, impacting purchasing habits, brand relationships, and marketing efforts. This systematic review synthesizes evidence on AI- influenced consumer behavior, analyzing its effects on attitudes, likes, and decision- making. Major areas of study encompass AI- facilitated recommendation systems, personalized engagement marketing, AI in online advertising, and AI- facilitated automation in retail and service sectors. The research presents both the benefits and pitfalls of AI integration, such as better customer experiences, data privacy fears, and information cocoons. AI has revolutionized conventional marketing practices by facilitating hyper-personalization and predictive analyses, engaging customers while incurring ethical issues. Research also highlights the importance of AI in sectors like fashion, entertainment, and business- to- business (B2B) marketing, offering insights into consumer trust and perceptions of privacy. 2026, IGI Global Scientific Publishing. All rights reserved. -
AI-Driven Continuous Learning Analysis and Blockchain Validation: A Review on Innovations in Digital Education
The rapid evolution of digital education has necessitated innovative approaches to enhance learning experiences, provide personalized insights, and ensure the credibility of achievements. This study analyses the challenges in AI-based continuous learning analysis for students and how we can securely share certificates through blockchain technology. The amalgamation of artificial intelligence and blockchains can create a secure, open, and trustworthy environment that schools, students, and employers can make use of. It is important to protect student data along with technological advancements. In response to the dynamic landscape of digital education, novel approaches are essential to enrich learning experiences, offer personalized insights, and maintain the credibility of achievements. This research reviews existing AI-based framework that facilitates ongoing learning assessment for students while ensuring secure certificate sharing through blockchain technology. By amalgamating artificial intelligence and blockchain, a robust ecosystem emerges that fosters transparent, efficient, and secure interactions among educational institutions, learners, and employers. Drawing from these evaluations, the framework suggests customized learning paths, thus amplifying the effectiveness of learning journeys. The objective of this study is to analyze the existing methods and to suggest the latest technologies in education, which will help the education sector to keep track of technological advancements and be in the race. 2025 IEEE. -
AI-Driven Credit Assessment in Banks and Non-Banking Finance Companies (NBFCs) in India: A Comprehensive Analysis
This study examines the application of AI frameworks in real- world scenarios, including regulatory compliance, fraud detection, credit scoring, default prediction, and portfolio management (Moscato et al., 2020). By leveraging AI to gain deeper insights into borrower creditworthiness, financial institutions can enhance decision- making, streamline processes, and foster a more stable financial environment. However, alongside its transformative potential, the implementation of AI presents significant challenges. Ensuring data quality, improving model interpretability, adhering to regulatory compliance, and addressing ethical considerations are crucial to achieving fair and unbiased outcomes. This paper highlights both the opportunities and complexities associated with integrating AI into financial systems, emphasizing the need for balanced and responsible adoption. 2025 by IGI Global Scientific Publishing. All rights reserved. -
AI-driven decentralized finance and the future of finance
In the evolving landscape of finance, traditional institutions grapple with challenges ranging from outdated processes to limited accessibility, hindering the industry's ability to meet the diverse needs of a modern, digital-first society. Moreover, as the world embraces Decentralized Finance (DeFi) and Artificial Intelligence (AI) technologies, there becomes a need to bridge the gap between innovation and traditional financial systems. This disconnect not only impedes progress but also limits the potential for financial inclusion and sustainable growth. AI-Driven Decentralized Finance and the Future of Finance addresses the complexities and challenges currently facing the financial industry. By exploring the transformative potential of AI in decentralized finance, this book offers a roadmap for navigating the convergence of technology and finance. From optimizing smart contracts to enhancing security and personalizing financial experiences, the book provides practical insights and real-world examples that empower professionals to leverage AI-driven strategies effectively. With a focus on regulatory challenges, ethical considerations, and emerging trends, this book teaches individuals and organizations how to harness the power of AI in finance. By fostering interdisciplinary collaboration and offering forward-thinking perspectives, the book equips readers with the knowledge and tools needed to navigate the complexities of the digital age. Through this comprehensive exploration, AI-Driven Decentralized Finance and the Future of Finance not only offers solutions to current challenges but also paves the way for a more inclusive, sustainable, and innovative future for finance. 2024 by IGI Global. All rights reserved. -
AI-driven decision-making and optimization in modern agriculture sectors
AI-driven decision-making tools have emerged as a novel technology poised to replace traditional agricultural practices. In this chapter, AI's pivotal role in steering the agricultural sector towards sustainability is highlighted, primarily through the utilization of AI techniques such as robotics, deep learning, the internet of things, image processing, and more. This chapter offers insights into the application of AI techniques in various functional areas of agriculture, including weed management, crop management, and soil management. Additionally, it underlines both the challenges and advantages presented by AIdriven applications in agriculture. In conclusion, the potential of AI in agriculture is vast, but it faces various impediments that, when properly identified and addressed, can expand its scope. This chapter serves as a valuable resource for government authorities, policymakers, and scientists seeking to explore the untapped potential of AI's significance in agriculture. 2024, IGI Global. All rights reserved. -
AI-driven deep learning framework for energy-efficient optimization in IoT-enabled wireless networks
Artificial intelligence (AI) and Internet of Things (IoT)-enabled wireless sensor networks (WSNs) have revolutionized industries by providing automation, real-time monitoring, and analytics that are predictive. WSNs still face significant obstacles such data security, network flexibility, and energy limitations in spite of these developments. In order to optimize energy use in Internet of Things (IoT)-based WSNs, this study introduces a novel Reinforcement Learning-based Energy-Efficient Communication Protocol (RL-EECP) to optimize the lifetime of networks and guarantee effective data transmission. The suggested protocol integrates sleep scheduling, reinforcement learning, and data fusion techniques. Also, an adaptive prioritization approach is introduced that assesses nodes according to the surroundings, significance, and energy consumption. Experiments show that RL- EECP performs better than existing studies in extending node lifetime and preserving excellent network performance. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
AI-driven defense mechanisms for Sybil and DDoS attacks in cloud networks
With massive DDoS attacks and Sybil attacks targeting national digital frameworks, financial institutions, and vital infrastructures, India is seeing an unparalleled increase in cyber threats. These attacks reveal significant vulnerabilities in national cybersecurity by jeopardizing system availability and integrity. Sybil attacks use numerous falsified identities to get unauthorized control over trust-based systems, while DDoS attacks flood networks with illegal traffic, making services inaccessible. This study investigates advanced machine learning (ML)-based identification and prevention strategies, including support vector machine (SVM), random forest (RF), decision tree (DT), and logistic regression (LR). To identify attack patterns, the methodology entails gathering actual network traffic data, preprocessing it to extract key information, and then using these models. To identify the best strategy, a comparison study is carried out depending on various parameters such as accuracy, precision, recall, and computing efficiency. The research suggests that random forest outperforms other ML algorithms in detecting Sybil attacks and DDoS attacks, achieving the maximum stability and accuracy. Nevertheless, the classification method is improved by merging decision trees and logistic regression, which further increases detection accuracy. In order to actively fight changing cyber threats, our findings highlight how important it is to include machine learning-driven security frameworks into India's cybersecurity infrastructure. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
AI-Driven Early Diagnosis of Acute Liver Failure: A Machine Learning Perspective
The liver performs a valuable role in operating proper metabolism. This organ in the human body is responsible for maintaining and preserving overall health and well-being. However, when it fails to function optimally, it can cause severe and significant health complications. Liver diseases are multifactorial conditions that can be challenging to diagnose and treat. Early detection of any disease is beneficial for effective treatment and diagnosis of patients' conditions. Machine Learning algorithms create a great platform for analyzing medical data that helps improve disease detection procedures. This paper aims to get a better understanding of ML algorithms for detecting diseases associated with the liver. The paper tries to explore various machine learning techniques for predicting accurate liver diseases. It uses various parameters as symptoms and calculates ALF (Acute Liver Failure) based on the parameters and ALF predicts in-case the person is suffering from a Liver disease or not. Accuracy was calculated with various ML techniques i.e. Logistic Regression Classification, KNN Classification, Decision Tree, Random Forest and Support Vector Machine. Among all these, Logistic Regression was found to be most effective in identifying and predicting the outcome of the dataset compared to other algorithms. SVM has a higher cross-validation score but Accuracy, precision and recall are very low thus, cannot select this model. 2025 IEEE. -
AI-driven emotion recognition systems for sustainable mental health care: an engineering perspective
Emotion recognition systems are transforming human-computer interaction (HCI) applications by enabling AI-driven, adaptive, and responsive mental health interventions. This study explores AI-based emotion recognition technologies using facial expressions, voice analysis, text-based sentiment processing, and physiological signals to develop scalable, real-time mental health support systems. Utilizing datasets such as FER2013, JAFFE, and CK+, our research examines deep learning models, including EfficientNet-XGBoost, which achieved over 90% accuracy across key evaluation metrics. Unlike traditional mental health interventions, AI-driven systems provide cost-effective, accessible, and sustainable solutions through telemedicine, wearable biosensors, and virtual counselors. The study also highlights critical challenges such as algorithmic bias, ethical AI compliance, and the energy consumption of deep learning models. By integrating machine learning, cloud-based deployment, and edge computing, this research contributes to the development of sustainable, ethical, and user-centric AI solutions for mental health care. Future directions include AI model optimization for energy-efficient deployments and the creation of diverse, inclusive datasets to improve performance across global populations. 2025, Intelektual Pustaka Media Utama. All rights reserved. -
AI-Driven Energy Efficiency in Buildings and Urban Environments
In buildings, AI-driven energy management systems (EMS) are revolutionizing the way energy is consumed. Smart building technologies equipped with AI can monitor environmental conditions, occupancy patterns, and equipment performance to fine-tune their operations, leveraging intensive machine learning to learn the preferences of building occupants and adapt accordingly to ensure comfort while minimizing energy use. Predictive maintenance, powered by AI, also contributes to energy efficiency by identifying equipment that operates inefficiently or is at risk of failure, enabling timely interventions that prevent energy waste. The industrial sector, a significant consumer of global energy, is another arena where AI is making remarkable strides in enhancing energy efficiency. 2026 by IGI Global Scientific Publishing. All rights reserved.