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Prediction of health insurance premium using bidirectional long short-term memory network with local interpretable model-agnostic explanations
This research proposes an application of deep learning techniques towards the prediction of insurance premiums using ConvLSTM, BI-LSTM, and CNN-LSTM models. Nowadays, Insurance is becoming more sophisticated, there is a need for better models that predict premiums so that risk factors that can be properly valued. The aim of this study is to improve the accuracy and reliability of insurance premium prediction using deep learning methods. The main challenge is the shallow traditional models, whose capturing of temporal dependencies is ineffective and results are not explainable resulting in very few stakeholders having any trust to the predictions. To solve this, this study compared three models: ConvLSTM model, BI-LSTM and CNN LSTM. Of these, the BI-LSTM model was the most effective because it was able to learn bidirectional sequential patterns. These patterns were enhanced using L2 regularization, dropout and dense layers to improve generalization. The dataset used comes from a Kaggle repository, which contained actual insurance data incorporating age, BMI, region and smoking as attributes. Results showed that BI-LSTM had performed the best as compare to other models in terms of accuracy and loss minimization. Important findings highlighted features such as age, smoking, and BMI as pivotal to estimating premiums. Also, to make the model explainable, we incorporated Explainable AI using LIME which delivers interpretable explanations by showing and visualizing the most important features for single predictions. 2026 selection and editorial matter, K. V. Sambasivarao, and Anasuya Sesha Roopa Devi Bhima; individual chapters, the contributors. All rights reserved. -
Influence of Pandemic-Induced Risk Awareness on Life Insurance Preferences
The COVID-19 Pandemic has created significant challenges and adjustments in several areas, including life and health insurance policies. By reviewing investors' views on life insurance as a possible investment route and studying the development of health and life insurance policies after COVID-19, this study aims to investigate the complicated elements of these changes. By means of a thorough examination of current patterns, beliefs, and obstacles within the life insurance domain, this study aims to explain the intricate relationship among outside factors, industry modifications, and personal perspectives. The study starts with a thorough analysis of the literature, which offers a theoretical framework for comprehending the ideas of life insurance, and how the COVID-19 pandemic has affected the insurance market. The latest developments in the life insurance industry since the pandemic's start are then examined using empirical research techniques, such as surveys and data analysis. This section tries to clarify the major changes in policies and practices through an examination of industry reports, changes in regulations, and market dynamics. The research looks at perceptions and trends as well as the difficulties investors have when choosing life insurance policies. It looks for typical obstacles, worries, and myths that prevent people from using life insurance products through a mix of qualitative and quantitative analysis. By comprehending these difficulties, the study hopes to shed light on possible approaches for getting over obstacles and boosting investor confidence in life insurance as a sound financial choice. Overall, by providing a thorough examination of the development of health and life insurance policies following COVID-19 and the perspectives on life insurance as an investment source, this study adds to the body of knowledge subsequently in existence. It offers insightful information to policymakers, industry players, and individual investors alike by addressing the goals of examining current trends, looking into investor views, and comprehending the difficulties experienced by investors. 2026 selection and editorial matter, Dr. Harold Andrew Patrick and Dr. Ravichandran Krishnamoorthy; individual chapters, the contributors. -
Comparative Analysis of Banking StocksBSE BANKEX vs. NEPSE
Equities may also be termed as shareholder's equity that make the holder an owner of corporate equity and empower him to vote in the annual general meeting of the company. Equity, both in its common and preferred form, is used by investors to understand risk and reward patterns in order to identify and minimize losses, and equally, to maximize gains. Relative to the risk-return trade off, this paper seeks to examine the Indian and Nepali banking equities performance. Currently the banking industry holds a large part of the GDP of the two trading partners; in Nepal it accounts for 18% and in India it accounts for 7.7%. Nepal is a very import oriented economy and the most part of this import money is made through remittances while India has diverse industries that constitute its economy. The empirical analysis is based on the five selected commercial banks; three banks from BSE Bankex of India and two banks from NEPSE Banking Sub-Index of Nepal based on market capitalization. Employing the historical data of five years (from April 2017 to March 2022), it can use Mean, Standard Deviation, Correlation, Regression, and ANOVA to make analysis. Analysis reveals that Indian banking equities exhibit better returns than the Nepali equities over the comparable period. Annualized returns help identify benchmark banks including ICICI Bank and NIC Asia Bank. However, there is more risk in Indian equities because they offer the capacity of higher returns. Thus, the Nepali banking equities have lower risk but produce only mediocre returns with several banks even negative returns. Indian banks provide much better investment opportunities and higher returns even though they are more risky. It helps investors determine profitable equities based on thorough risk-return assessments for equities. 2026 selection and editorial matter, Dr. Harold Andrew Patrick and Dr. Ravichandran Krishnamoorthy; individual chapters, the contributors. -
Celebrity Endorsements in Fashion Purchases
This study investigates the impact of celebrity endorsements on consumer purchase intentions in the fashion apparel sector, focusing on three key variables: celebrity likeability, which is often aligned to cultural norms, and the celebrity familiarity. Information was obtained from 100 participants across India, and chi-square analysis was applied to the hypotheses. The analysis shows that all of these factors are significantly related to attitudes toward purchasing at a less than 0.05 level of significance. Four factors were determined to have significant impact with celebrity likeability coming out strongly to support the notion that consumers buy endorsed products to emulate the celebrity. Cultural fit adds consistency to trust and identity, and familiarity enhances recall, and confidence on the brands. In view of these observations, marketers ought to look at strategic celebrity selection more intensely. The endorser choice is highly recommended to be selected in accordance with the values and preferences of the target market to have the most influence on the buying decision. This paper reveals the need to adopt targeted and culturally appropriate appeals in influencing purchase behaviour in the Indian fashion domain. 2026 selection and editorial matter, Dr. Harold Andrew Patrick and Dr. Ravichandran Krishnamoorthy; individual chapters, the contributors. -
Regulating the Speed of Innovation: A Legal and Ethical Framework for 6G Deployment in Smart Societies
The expected use of 6G technologies contains unmatched breakthroughs in hyperconnectivity, real-time holographic communication, and AI-supported immersivity. There is, however, in this technological leap, a complex legal-ethical-regulatory issue scene that must be addressed now, worldwide. This chapter provides a cross-disciplinary argument on a missing legal regime that can best govern 6G-enabled ecosystems and especially referring to the governance of the real-time artificial intelligence, XR/VR applications, the privacy consequences surrounding data, and nanobots and human rights concerns in a 6G future. This chapter can be taken as comparative legal, following which the emerging 6G regulatory principles are explored in the example of the European Union (Digital Services Act, AI Act), United States (AI Bill of Rights, FCC policies), and India (Digital Personal Data Protection Act, 2023). It also closes in foreign legal materials such as the Budapest Conference on Cybercrime and General Comment No. 25 (2021) on the right to privacy in the online world of the UN Human Rights Committee. The e-Governance model of Estonia, with significant use of AI and XR to enhance the functionality of the communication network is analyzed as a case study in this view to show the potential, as well as the challenges such hyper-automation can bring about. This chapter is then contrasted with the situation in China, which currently has a 6G surveillance infrastructure and explains why algorithms, mass surveillance, and illegal profiling are risky without some regulation. Besides, this chapter explores transnational data transfer, cyber sovereignty, and cross-border information law enforcement, which require global uniformity by all nations. It is insisted that the precautionary principle and technology impact assessments (TIAs) must be conducted as prerequisites to widespread 6G implementation in smart cities, smart tourism, and healthcare fields. The chapter ends by proposing an International 6G Governance Charter expressing the need to secure legally binding protection of AI-integrated XR systems, the obligation to be transparent, and enforcement-based rights of users within the ultra-fast communication space. 2026 selection and editorial matter, Upinder Kaur, Aparna Kumari, Hemant Kumar Saini, Surbhi B. Khan, and Mariya Ouaissa; individual chapters, the contributors. -
Deep Learning-Based Approach for Automated Cataract Detection
Advancements in deep learning approaches is of profound significance in the early detection of cataracts. Automated cataract detection using deep learning approaches is proposed in this chapter. Initially, two pretrained custom convolutional neural network (CNN) architectures, VGG-19 and MobileNetV2, were implemented to detect cataracts. ODIR-5K dataset is used for training, testing, and validating these models, and it has almost 6,400 fundus images. This preprocessed dataset provides the metadata of the available images and is labeled with diagnostic keywords. Since the dataset is highly imbalanced, class weighting techniques are utilized to avoid the impact of the imbalanced dataset. The performance of the models is evaluated, and results show that the ensemble approach outperforms other pretrained models, demonstrating the efficacy of hybrid CNN architecture in enhancing the accuracy of the diagnosis process. 2026 selection and editorial matter, T. Ananth Kumar, R. Rajmohan, M. Niranjanamurthy and G. Sambasivam. -
From Preprocessing to Prediction: An Analytical Study on Diabetes Data
Early detection of diabetes is crucial for improving a patients long-term health. In this chapter, we study diabetes and diabetes-related factors. We also delve into various imputation techniques used to address missing data. Missing data is generally a very critical issue in healthcare analytics, as a limited history of clinical records often leads to biased analysis and suboptimal model representation. This chapter gives a detailed literature review of data imputation methods. In this chapter, we have done two case studies. In the first case study, mean, median, and mode imputation techniques are applied to artificially created missing values to examine their effect on the structure and distribution of the data. The second case study captures a prediction model for a diabetes diagnosis using the same dataset. Here, a random forest prediction model is created to predict the possible presence of diabetes. An accuracy of 97.07% is achieved on the test data, which shows that diabetes can be predicted by considering other dependable variables. 2026 selection and editorial matter, Syed Nisar Hussain Bukhari; individual chapters, the contributors. -
Tech-Enabled Transformations in Gender-Inclusive Healthcare: A Critical Interpretive Synthesis of Artificial Intelligence in India
While artificial intelligence (AI) has the potential to revolutionize healthcare, it also runs the risk of exacerbating structural injustices for Indias gender-diverse communities. This critical interpretive synthesis explores the effects of AI-enabled health devices on LGBTQIA+ inclusion, drawing on intersectional feminism, queer theory, and constructivist learning. Reviewing 30 interdisciplinary studies (20102024), three themes emerge: (1) algorithmic bias: AI systems replicate gendered and caste-based exclusions through non-representative datasets and heteronormative design; (2) structural barriers: infrastructural patriarchy, limited gender-sensitive governance, and gaps in Indias AI policy; and (3) digital inequities: low digital literacy, moral conservatism, and caste hierarchies restrict access to affirming care. Research shows AI often erases or misgenders trans and nonbinary identities, causing epistemic harm. Nonetheless, inclusive innovation is possible through participatory, queer-led AI design. The study warns that without centering intersectional justice, AI in healthcare risks amplifying marginalization and epistemic violence. It recommends co-creation with LGBTQIA+ stakeholders, gender-sensitive audits, and care-centered policy reforms. Rejecting techno-solutionism, it advances a Global South, justice-focused approach that prioritizes equity, contextual awareness, and lived realities in AI healthcare design. 2026 selection and editorial matter, Syed Nisar Hussain Bukhari; individual chapters, the contributors. -
An Adversarial-Resilient Multi-Agent AI Framework for Autonomous Robotic Warfare Defense
Next-generation AI-enabled defense is essential to deter enemy drones, swarms, and autonomous vehicles in contested and deceptive environments, with modern battlefields becoming increasingly dependent on autonomous robot platforms. To discover and dissect and eliminate hostile autonomous threats in real-time, this study presents an integrated Adversarial-Resilient Swarm Defense AI Framework (AR-SDAI) with a Spatio-Temporal Transformer, a Multi-Agent Reinforcement Learning Countermeasure Module, and a Hybrid Graph Attention Network. To identify hidden or counterfeit threats and improve defense against enemy attacks, the system begins with applying a Transformer-based situational awareness model to merge multi-sensor battlefield data to fuse. Autonomous defense drones are then controlled by a multi-agent reinforcement learning framework to perform actions of dynamic electronic jamming and optimal interception maneuvers in a swarm environment. Finally, the system can find unusual patterns and create human-understandable counter-strategies for human-in-the-loop control with the help of a graph-based explainability layer that models the interactions of adversary swarms as dynamic graphs. Compared to traditional rule-based and CNN-RNN baselines using experiments on a simulated Red-Blue drone warfare test benchmark, the suggested AR-SDAI is better by 23% in threat detection accuracy, 31% in swarm interception success rate, and 19% in response latency. With its provision of robust, explainable, and flexible AI capability for next-generation robotic warfare settings, the paper in general enhances the state of autonomous defense operations. 2026 Saurav Mallik, Sandeep Kumar Mathivanan, Basu Dev Shivahare. -
Cyber resilience through adaptive federated learning
This chapter explores the soon-to-be-reached intersection of cyber resilience and adaptive federated learning (FL), presenting a thorough examination of how FL, and especially through its adaptability mechanisms, can significantly enhance an organizations ability to predict, withstand, recover from, and learn from cyber-attacks. It explores the fundamental concepts of cyber resilience and FL, outlines several adaptive methodologies utilized in FL (e.g., adaptive client selection, aggregation, and optimization), and examines their direct contribution to the formation of resilient and privacy-aware cybersecurity systems. Real-world applications in critical infrastructures, accompanied by an honest review of current limitations and research avenues towards the future, present the revolutionary potential embedded within this synergy-based approach in an increasingly sophisticated and interlinked digital era. 2026 selection and editorial matter, Swati Sah, Rejwan Bin Sulaieman, and Aditya Dayal Tyagi; individual chapters, the contributors. -
Cybersecurity vulnerabilities in federated learning
Federated Learning (FL) has been conceived as a dispersed machine learning paradigm facilitating collaborative learning at edge devices without exposing raw data. The model is amenable to privacy preservation and data protection regulation, for example, General Data Protection Regulation compliance. Yet, more widespread deployment of FL reveals a new and extreme spectrum of cybersecurity risks. These consist of data poisoning attacks that can potentially severely contaminate model integrity, model inversion attacks that can potentially recover sensitive data from exchanged gradients, adversarial manipulations where malicious agents take advantage of model weaknesses, and incidental privacy leakage. The impact and real world implication of these attacks differs, for example, a successful poisoning attack in medicine can result in misdiagnosis, model inversion in the finance sector could leak client confidential data, and adversarial attacks in Internet of Things (IoT) would control autonomous devices with safety consequences. This chapter critically reviews these threats taking into consideration attack feasibility, harm extent, and detectability, inspired by recent case studies illustrating their applicability in real world FL deployments. We also analyze the effectiveness of current state of the art countermeasures like robust aggregation methods, differential privacy, and cryptographic methods like secure multiparty computation and homomorphic encryption. By synthesizing current research on attack paradigms and counterattack architectures, the chapter offers practical knowledge towards constructing secure, robust, and trustworthy FL systems, particularly in high-risk applications like medicine, finance, and critical infrastructure. 2026 selection and editorial matter, Swati Sah, Rejwan Bin Sulaieman, and Aditya Dayal Tyagi; individual chapters, the contributors. -
Regulatory challenges and compliance in federated learning (FL) for financial applications
The financial sector is increasingly turning toward artificial intelligence (AI) for applications such as fraud detection, credit scoring, and risk management. But that makes it contrary to the regulatory environment. New data protection laws such as the General Data Protection Regulation (GDPR) in Europe and the Digital Personal Data Protection Act (DPDPA) in India impose stringent conditions on data residency, minimization, and sovereignty. This chapter argues that traditional centralized AI systems which require sensitive data to be collected for processing at one site simply do not sit well with these legal requirements, thereby creating massive compliance risks for financial institutions. By way of an extensive architectural study and practical application, this chapter demonstrates that the very basic functions of a traditional AI system tend to contravene prohibitions on cross-border transfers of data. Instead, we propose Federated Learning (FL) as a compliance-by-design solution that solves this sticking point. In other words, by inverting the discredited approachand bringing the algorithm to the data rather than the other way aroundFL ensures that practitioners in different institutions and jurisdictions collaborate on model training without sharing raw data. Only aggregated and anonymized updates on the model are sentinherently complying with certain data residency and data minimization principles. Besides advocating for FL as a core compliant innovation pathway, this chapter also touches on a number of regulatory uncertainties and other potential issues arising from this technology, such as liability, model security, and a need for industry-wide standards. To this end, the chapter clearly states that the adoption of privacy-preserving technologies such as FL has become integral. 2026 selection and editorial matter, Swati Sah, Rejwan Bin Sulaieman, and Aditya Dayal Tyagi; individual chapters, the contributors. -
Building Trustworthy 6G Networks with Generative Adversarial Learning
The imminent dawn of sixth-generation (6G) networks promises a future of unparalleled connectivity and communication speeds. However, this technological leap necessitates robust security measures to counter increasingly sophisticated cyberthreats targeting the intricate 6G infrastructure. This chapter investigates the potential of Generative Adversarial Learning (GALs) as a transformative tool for building trustworthy 6G networks. With the advent of 6G networks on the horizon, ensuring trustworthiness in communication systems becomes paramount. This chapter proposes a novel approach leveraging GAL to fortify the security and reliability of 6G networks. In traditional network security paradigms, adversaries exploit vulnerabilities, necessitating constant reactive measures. However, the proactive nature of GANs enables the creation of realistic synthetic data to train robust Intrusion Detection Systems (IDS). By simulating diverse attack scenarios, a GAN-based IDS can identify and adapt to emerging threats, mitigating potential risks in real time. Moreover, GANs facilitate the generation of synthetic network traffic, enabling thorough testing of network defenses without risking actual data. Taking a proactive stance enables network operators to predict and preempt potential vulnerabilities before they are exploited. Our solution involves harnessing the power of Generative Adversarial Networks (GANs) to address 6G network security comprehensively. GANs create authentic network traffic, allowing IDS to be trained effectively in identifying and mitigating actual cyberthreats. Moreover, GANs can learn to discern typical network patterns, thus alerting to potential anomalies that may signify ongoing or imminent attacks. This proactive strategy empowers security teams to maintain an edge in navigating the constantly evolving cyberthreat landscape. 2026 selection and editorial matter, E. Chandra Blessie, Pethuru Raj, and B. Sundaravadivazhagan; individual chapters, the contributors. -
Ethical and Privacy Considerations in GAN-Based Security
Generative Adversarial Networks (GANs) have caused a great transformation in a number of fields, including anomaly detection, healthcare, data encryption, deep learning (DL), machine learning (ML), and entertainment. They are also employed in various security applications. This chapter explores mainly the privacy and ethical concerns associated with the use of GANs. Although GANs have enormous possibilities for security applications such as biometrics, intrusion detection, and data augmentation, implementing them requires careful consideration regarding privacy and ethical issues. GANs ability to produce remarkably lifelike deepfakes poses a risk since it might promote social engineering attacks. Moreover, these discrepancies may be increased by biased security programs that arise from biases in training data. Even with all of the potential benefits, ethical and privacy concerns must be prioritized for the safe and reliable application of GANs in security. This chapter also lays the foundation of GAN-based applications in Cybersecurity. 2026 selection and editorial matter, E. Chandra Blessie, Pethuru Raj, and B. Sundaravadivazhagan; individual chapters, the contributors. -
GAN-Based Metaheuristic Techniques for Data Generation and Imbalance Data Control
In cybersecurity today, the power of features such as threat detection, anomaly spotting, and predictive analytics depends heavily on having abundant, properly dispersed datasets. The actual datasets often fall short, suffering both from a lack of volume and skewed class distribution, for example, a flood of routine network activity records overshadowing the infrequent but vital records of malicious behavior. The performance of data-driven models hinges on access to abundant, well-distributed data. However, real-world datasets frequently exhibit inadequate sample sizes and pronounced class imbalances, limiting the viability of complex models. This chapter proposes a novel strategy for generating synthetic data and effectively managing class imbalance, leveraging the integration of Generative Oppositional Networks (GANs) and sophisticated metaheuristic optimization techniques. Rather than settling for fixed GAN architectures, our approach progressively enhances a dynamic GAN framework by deploying a metaheuristic search to identify optimal network topologies, antidote scaling factors, and training schedules. This iterative calibration enables the model to adaptively respond to the imbalance and ensures a richer, balanced synthetic training environment. This adaptive optimization addresses common GAN training pitfalls, mode collapse, and instability while consistently producing synthetic samples that are both precise and varied. What sets the framework apart is its built-in ability to detect and over-represent minority classes, intelligently augmenting the dataset to correct class imbalance without falling back on naive duplication. By equipping GANs with metaheuristic reasoning, the study seeks to elevate data synthesis beyond current limits, generating more robust and impartial machine learning models in any domain where data collection is limited or systematically skewed. 2026 selection and editorial matter, E. Chandra Blessie, Pethuru Raj, and B. Sundaravadivazhagan; individual chapters, the contributors. -
Enhancing Cybethreat Intelligence Feeds Using Generative Adversarial Networks
Cyberthreat Intelligence (CTI) feeds serve as crucial resources for organizations seeking to fortify their defenses against emerging cyberthreats. However, these feeds often suffer from deficiencies such as incomplete data, false positives, and a lack of contextual information. This chapter proposes an innovative approach to address these challenges by leveraging Generative Adversarial Networks (GANs) to enhance CTI feeds. We introduce ThreatGAN, a novel GAN architecture specifically designed for cyberthreat modeling. Trained on accurate CTI data, ThreatGAN learns to generate synthetic yet realistic threat indicators, including malicious uniform resource locators (URLs), Internet Protocol (IP) addresses, and attack patterns. We demonstrate the efficacy of ThreatGAN in filling gaps in existing feeds, reducing false positives, and providing essential contextual information. The quantitative and qualitative evaluation shows that ThreatGAN significantly improves CTI quality. This technique can strengthen organizations cyber defenses by enabling them to work with higher quality, more complete Threat Intelligence. 2026 selection and editorial matter, E. Chandra Blessie, Pethuru Raj, and B. Sundaravadivazhagan; individual chapters, the contributors. -
Three-level biometric digital voting system
The aim of this research paper is to design and develop a new model of electronic voting machine (EVM) with enhanced features, that are not present in the current EVM model. The paper proposes the new model of EVM that uses biometric identifiers, such as fingerprints, iris recognition, and facial recognition, to make voting more secure and efficient. Previously, voting was done with paper ballots. This method suffered from issues like over voting, lost or misplaced ballots, and it consumed a lot of time in counting paper ballots and was also harmful for the environment due to the use of papers. The new EVM system aims to solve those issues because many security levels are added to this EVM system. It can use biometric identifiers to ensure each voter is authenticated securely and records their votes accurately. This EVM system can prevent fraud, such as voter casting multiple votes or a person illegally voting on behalf of others. The system first verifies the voters, and then the voters can cast their votes. 2026 selection and editorial matter, Dr. Poonam Nandal, Dr. Mamta Dahiya, Dr. Meeta Singh, Dr. Arvind Dagur, Dr. Brijesh Kumar. All rights reserved. -
Prognosis of relocation disease in animals using aggregation method with optimization techniques
In the most cutting-edge setting, health data is processed by machine learning algorithms, which are used to forecast illnesses. Dementia, especially Alzheimer's disease (AD), is a leading cause of diminished quality of life in the elderly. Early diagnosis by medical professionals increases the likelihood of reducing the aggressiveness of the disease. In this study, we develop a new uncertainty-based clustering model to handle the centroid selection ambiguity and the issue of noisy instances and outliers that lower the efficiency of prediction models. This work employs an uncertainty-based optimization technique to handle the unknown pattern of AD patients, since it is relatively tough to handle unknown patterns with unsupervised learning algorithms. Converting the instances in the AD dataset to the membership value of the dependent variable allows for an accurate determination of whether they belong as AD patterns or non-AD patterns. This proposed study takes a migration-based optimization method to animal migration, where the best instances are chosen as centroids and fresh instances are evaluated for clustering; this minimizes outliers throughout the clustering process by using comparable patterns. To make sure, we check the fitness values of each instance; the ones with the highest values are called centroids. To control the unknowns when dealing with outliers, the fuzzy Euclidean distance is employed. By comparing it to current state-of-the-art clustering methods, the OASIS dataset simulation results show that the proposed uncertainty-based Animal Migration optimization method (UAMO) performs better. 2026 selection and editorial matter, Dr. Poonam Nandal, Dr. Mamta Dahiya, Dr. Meeta Singh, Dr. Arvind Dagur, Dr. Brijesh Kumar. All rights reserved. -
Child safety gadget with home security
The Child Safety Gadget Home Security System introduces the initial five iterations of a technologically advanced system that addresses child safety and home security concerns by integrating innovative subsystems. We started with a primary password and incremented towards advanced functionality like a camera for visual verification, a lock mechanism for entry security, and finally, app integration. Finally, we extend the system with additional capabilities to fully implement security solutions based on features like an OLED display and sensors. The concealed pseudo-passcode alert system, which wrists parents up in emergencies to respond quickly, is central to our system. Our framework prioritizes cost-effectiveness, simplicity of scalability, and use effectiveness to provide an affordable solution that can protect children's safety while increasing the efficiency of intelligent sensor networks in home environments. 2026 selection and editorial matter, Dr. Poonam Nandal, Dr. Mamta Dahiya, Dr. Meeta Singh, Dr. Arvind Dagur, Dr. Brijesh Kumar. All rights reserved. -
Artifcial intelligence for smart city development in BRICS economies: Opportunities and challenges
The urbanization process and migration of labour from rural areas at a fast pace is making the application of artificial intelligence (AI) inevitable for the economies, especially the emerging economies. Today, when we look at the cities, we find them to be sitting on a plethora of data, which are produced through usage of Internet of Things (IoT), sensors, smart meters, telecommunication devices, traffic management systems, installed cameras etc., despite posing ample challenges and creating various opportunities. This chapter strives to understand the status of smart cities in Brazil, Russia, India, China and South Africa (BRICS) as BRICS nations are considered to be doing a lot on this front and they are facing challenges in terms of implementation of the same. This chapter suggests that the inclusivity of the citizens in the AI based smart cities will not only play a requisite part in the evolution of smart cities in an effective manner but also contribute towards the growth of BRICS economies on a whole. 2025 Roli Raghuvanshi, Tanushree Sharma, Ravinder Rena, Rashmi Rai. All rights reserved.