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Economic Insights: The Computational Intelligence Perspective on Finance
Using technological advancements and shifting risk landscapes as a driving force, this abstract investigates the revolutionary approaches that have reshaped risk mitigation in contemporary contexts. Introducing a new era of proactive risk management has been made possible by the combination of artificial intelligence (AI), machine learning (ML), and predictive analytics. Organizations are able to recognize patterns and anticipate potential risks with an accuracy that has never been seen before, thanks to these technologies, which analyze vast datasets. By extracting valuable insights from unstructured data sources, natural language processing (NLP) and sentiment analysis broaden the scope of risk assessment with their respective capabilities. Blockchain technology improves both transparency and security, particularly in the realm of financial transactions, thereby lowering the likelihood of fraudulent activity. Cloud computing makes dynamic risk modeling easier to accomplish, which in turn makes it possible to simulate real-time scenarios. The cumulative effect of these innovations not only improves the efficiency of risk reduction, but it also helps organizations develop risk management frameworks that are more agile and resilient. When navigating the complexities of a risk landscape that is constantly shifting, it is essential to strike a balance between technological advancements, ethical considerations, and transparency. 2026 by Apple Academic Press, Inc. -
The Road Ahead: Charting Future Research Directions in Computational Intelligence
The synergy between neuroscience and computational intelligence fosters a dynamic exchange that significantly advances both fields. At its core, this convergence focuses on emulating the brains intricate mechanisms to inspire and enhance machine learning (ML) models. Neural networks (NNs), foundational to computational intelligence, are modeled after biological neurons and drive artificial neural networks (ANNs) that excel in tasks simulating human cognition. Neuromorphic computing furthers this concept by designing hardware and software with braininspired architectures, enabling energy-efficient AI systems. A notable breakthrough is brain-computer interfaces (BCIs), which translate neural signals into actionable commands, offering transformative solutions for individuals with paralysis. Additionally, cognitive computing leverages neuroscience insights to emulate higher-order mental processes, enabling the development of intelligent, context-aware systems. Conversely, ML algorithms, especially in pattern recognition, empower neuroscience by analyzing large-scale brain imaging data to uncover hidden patterns and correlations. This bidirectional interaction accelerates discoveries in neurology and psychology while deepening our understanding of brain function. Together, neuroscience and computational intelligence form a powerful alliance, shaping the future of intelligent technologies and brain science. 2026 by Apple Academic Press, Inc. -
Financial Insights Unleashed: Computational Intelligence in Practice
This study explores how artificial intelligence (AI) is changing the banking sector and identifies important uses and consequences. Fintech is evolving, from AI-powered credit risk evaluations to the real-time effects of news and social media on markets. Customary financial procedures are being transformed by the incorporation of blockchain technology, smart contracts, and collaborative models that combine human and AI. It is important to balance risk and return while implementing computational intelligence (CI), and there are several obstacles associated with this technical advance, such as ethical issues and data privacy solutions. This 168changing financial story emphasizes the importance of finding a careful balance between innovation and responsibility. Financial decision-making will hopefully become data-driven, customer-focused, and morally based in the future thanks to the cooperative synergy between humans and AI. The development of a robust and thoughtful financial landscape depends critically on ongoing monitoring, adaptability, and dedication to ethical AI practices. 2026 by Apple Academic Press, Inc. -
Technology-Enabled Smart Healthcare toward Smart Society 5.0
The emerging technologies such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, 3D printing, and 5G networking (referred as AIB35) in the context of smart healthcare are being greatly explored by smart healthcare providers to move forward in an all-encompassing manner. The field of healthcare requires scientists to implement intelligent solutions into the daily routines of smart aging patients to improve the efficiency and precision of diagnostic and therapeutic procedures. The concept of smart aging refers to a program that makes it possible for elders to have a superior quality of life by embracing high-end technology solutions to diagnosis and disease. Healthcare technologies are a wide range of innovations, including connected devices, appliances, and procedures, as well as drugs, medicine, and vaccine that improve the level and quality of care provided at economic cost structures. The purpose of this study is to first present a listing of the principal technologies that underpin smart healthcare, and then to present it in several significant domains. After that, we elaborate on the issues that now exist with smart healthcare and attempt to provide potential solutions to these issues. In the final part of this chapter, we take a glance into the road ahead and assess the prospects of smart healthcare toward building a smart society 5.0. 2026 by Apple Academic Press, Inc. -
The Vital Role of Artificial Intelligence in the Dynamics of Human Resource Management
Human resources play a pivotal role in ensuring the growth of the organizations. Competency mapping, identifying skill gaps, and right talent identification are vital for enhancing the effective functioning of the organizations. As the organizations size increases, these functions become a challenge. In addition to managing the day-to-day operations, organizations are moving toward implementing effective processes for human resource development. Automating and adopting data-driven processes aids in transforming HR into an effective arm of the organization. The advent of artificial intelligence (AI) tools is becoming part of several organizations. These tools relieve HR personnel from spending time on routine tasks and drive them toward strategic decision-making. Organizations are finding AI is crucial in driving innovation in their business models. Thus, transforming processes and creating disruption are warranted to attain a competitive edge. The introduction of AI-driven systems in business organizations leads to significant changes in the demographic gambit of the workforce. It transforms the characteristics and significance of jobs and the relationship between employers and employees. The interaction between people and technology, and the customer experience in a rapidly changing market will be influenced by AI. The practices adopted by organizations in Middle East countries are also discussed. 2026 by Apple Academic Press, Inc. -
Navigating Prosperity: A Bibliometric Analysis of Financial Literacy and Sustainable Development Goals
The field of financial literacy and SDGs research has advanced significantly as countries place a greater emphasis on financial education and skill development. From 2017 to 2024, 1,017 SCOPUS papers were retrieved for this study. The study gives an overview of studies, trends, and future directions. The efficacy of financial literacy (FL), its impact on economic expansion, and its capacity to promote sustainable practices have all been extensively studied. Prospective pursuits involve advancing financial competencies and assessing their influence on income distribution, evaluating international cooperation initiatives, promoting environmentally friendly technologies, and designating green tax proceeds for environmental initiatives. The constantly growing corpus of research offers crucial direction for decision-makers dedicated to boosting FL and accomplishing Sustainable Development Goals (SDGs). 2026 by Apple Academic Press, Inc. -
Bibliometric Analysis on Multiobjective Optimization and Metaheuristic Algorithm
For difficult optimization issues, metaheuristic algorithms are effective methods for obtaining workable solutions quickly. In the past few years, continuous efforts have been put forward by researchers to develop new effective and robust metaheuristic algorithms for solving engineering optimization problems. The research aims to find the advancements made in multi-objective optimization and metaheuristic algorithms. Metadata of 4149 articles were extracted from Scopus from the year 2000 onward and bibliometric analysis was done with the help of the VOSviewer software. It was found that Mirjalili. S. has the highest number of citations (4011). IEEE Access has published the maximum number of documents (128), the University of Tehrans School of Industrial Engineering has contributed the most in this field of research (43 documents), and China has the most contribution among all the countries with 977 documents. In recent years, the terms optimization algorithms, exploration and exploitation, learning systems, decision-making, uncertainty analysis, sustainable development, supply chains, neural networks, forecasting, machine learning, and cloud computing are being mostly used by researchers. 2026 by Apple Academic Press, Inc. -
Unconventional Adjudication: Promise of Blockchain-Based Dispute Resolution
Blockchain technology is called disruptive technology. In the 1990s, the internet had the potential to revolutionize the entire industrial sector in the same way blockchain technology will change the face of society in the 2000s. Blockchain is the technology that also provides a platform for dispute resolution via smart contracts. The technology aims to create an anonymous and decentralized platform for transactions that do not require any state monitoring or intermediary. Blockchain is a decentralized network that generates an immutable record of transactions. A smart contract is a self-executing software application that executes a function automatically. When the prerequisites are met, the contract automatically takes effect. Blockchain-based dispute resolution (BDR) platforms provide services to resolve disputes resulting from blockchain and smart contract transactions, as well as traditional disputes unrelated to blockchain transactions. Blockchain-based DAO (decentralized autonomous organizations) are established, which are used to settle healthcare-related disputes between doctors, patients, insurance claims, etc. The blockchain platform offers unconventional processes for adjudication that are also distinct, unstructured, and unregulated. The platforms do not adhere to the accepted norms of alternative dispute resolution (ADR) and online dispute resolution (ODR) systems, raising concerns about the platforms authenticity and legitimacy, as well as the need for regulation. The purpose of this chapter is to examine the application of blockchain technology in dispute resolution and to comprehend regulatory issues. 2025 selection and editorial matter, Dr. Javaid Iqbal, Dr. Alwi M. Bamhdi, Dr. Bilal Ahmad Pandow, and Dr. Faheem Syeed Masoodi; individual chapters, the contributors. -
AI in Mechatronics Engineering
Robotic engineering, with a focus on the combination of artificial intelligence (AI) together with robotics, computers, electronics, and mechanical systems, as well as control system implementations, allows for many inventions. Key applications of AI in mechatronics engineering practice will be advanced production, intelligent robotics, predictive maintenance, and design optimization control. Robotics engineers are able to incorporate AI into their systems such that data can be collected, analyzed, and modeled, then used to enhance the dependability, flexibility, as well as performance of the systems. This chapter researches the engineering integration of AI along with mechatronics and the industries it is disrupting. Moreover, it addresses the basic definition of AI and its main application areas within mechatronics and its prospects toward enabling enhanced control systems, predictive maintenance, design optimization, intelligent robotics, and improved production in any contemporary industry. Such systems may be developed by mechatronics engineers due to the enriched capabilities of AI in data analysis, recognition, and decision-making. This study also addresses the limits and moral issues to the ethics of combining artificial and human power and suggests ideal steps for more study and advancement in the areas outlined. 2026 selection and editorial matter, Pushpalatha Naveenkumar, Vandana Sharma, Gunapriya Devarajan, Azween Abdullah, and Ahmed A. Elngar. -
Punishing poverty: The economic disparity of the poor in the criminal justice system
Equality before law is one of the most significant features of the Indian constitution. Anyone who seeks justice must be provided with legal support without any discrimination. An accused is also assured of penalization based on the tenets of equality irrespective of his ethnicity, religion, economic, social background, etc. Poor parity has led to discriminatory approaches in awarding punishments to offenders belonging to economically marginalised sections of society. The low paying capacity of the poor offenders gives an upper edge to the rich offenders who has better paying capacity of fines or damages and suffer less severe repercussions through the justice system. This paper will conduct a comprehensive study to identify the discrepancies in the penalization process and its implications in the dispensation of justice. It will also explore the factors such as social background, ethnicity, and economic status which play an integral part in influencing the legal and sociological perspectives of the stakeholders of the justice delivery system. It will analyze the judicial trend and legislative framework to ensure equitable justice. It will conclude with suggestions and recommendations for the formulation of robust policies to ensure a just penal administration. 2026 The Author(s). -
Smart Villages and Cities: A Sustainable Imperative for Emerging IndiaA Journey from Painful to Thoughtful
Smart cities and villages refer to making cities and villages more beautiful and fuller of all the requirements that can make the lives of the people living in the cities and villages more comfortable and satisfactory. On 25th June 2015, this initiative was launched by the Central government, and the time limit for completion was 5 years, but due to the pandemic situation, it was further extended by the central government, and this year, all the states are required to submit their respective report about the progress in this regard. In India, 70% of the total population lives in Villages and only 30% are part of urban areas. In India, villages should be a combination of smart and digital villages. If the work is done at the grassroots level, it will 436solve many problems, such as migration, employment, poverty, education, and medical facilities. Sustainability of the environment is one of the basic requirements for humankind globally. In 2015, the United Nations introduced Sustainable Development Goals (SDGs) to address environmental and economic issues and challenges and to provide and promote better and more sustainable pathways for the future generation globally. It is not a matter of concern for a group of countries, but every single species is affected by environmental issues. Owing to the dense population, infrastructure, buildings, and commercialization, cities are more prone and susceptible to the effects of climate change and natural disasters. while increasing the sustainability of urbanization processes is necessary to protect the environment, reduce the risk of disasters, and address climate change, building urban resilience is essential to preventing losses in terms of people, society, and the economy. Goal 11 of the SDGs deals with Sustainable Cities and Communities. Focusing more on urbanization in such a way that comprehensively controls and plans the development of cities and villages using different technologies and sciences to make them smart cities and villages. By ensuring that smart cities and villages will help us in many ways, they can control the migration of the human population from one city to another in search of employment and basic needs. In India, the government should focus on creating smart villages rather than smart cities. Around 69% of the population lives in the villages, and they do not have the basic amenities like electricity, clean drinking water, roads, transportation, and a source of employment. 2026 Jenny Stanford Publishing Pte. Ltd. -
Reengineering the Rural Economy by Leveraging the Use of Financial Services: A Lynchpin
Financial services form the bedrock of the economy, intricately woven into its fabric, exerting a profound influence on its dynamics and growth trajectory. Financial services play a momentous role in the complex web of global economies, delicately interlacing the strands of trade, capital accumulation, and risk mitigation. These services serve as an essential hub, linking people, companies, and governments in a complex web of exchanges and interactions, from the busy streets of big metropolises to the serene settings of rural villages. The chapter emphasizes the importance of financial services in promoting rural businesses by uplifting their stream of earnings by making use of emerging technologies. It also focuses on the challenges and opportunities faced by the rural population in obtaining financial assistance. The chapter also discusses the modern financial service schemes introduced so far for rural development and the suggestions for future implementation. The ways and means to mitigate shortcomings of the development of the rural business due to negligible awareness and restricted accessibility to financial services are also highlighted in the study. 2026 Jenny Stanford Publishing Pte. Ltd. All rights reserved. -
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.