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Robust feature selection using rough set-based ant-lion optimizer for data classification
The selection of an algorithm to tackle a certain problem is a vital undertaking that necessitates both time and knowledge. Non-functional needs, such as the size, quality, and nature of the data, must frequently be taken into account. To develop a generalized machine learning model for any domain, the most relevant features must be chosen because noisy and irrelevant characteristics degrade data mining performance. However, the selection of the dominating features is still dependent on the search technique. When there are a high number of input features, stochastic optimization can be applied to the search space. In this research, the authors investigate the ant lion optimization (ALO), a natureinspired algorithm that mimics the hunting process of ant lions and is further stimulated to identify the smallest reducts. They also investigate rough set-based ant lion optimizer for feature selection. The actual results reveal that the ant lion-based rough set reduct selects a better feature subset and classifies them more accurately. 2022 Information Resources Management Association. All rights reserved. -
Robust Regression Approaches for the FamaFrench 5-Factor Model: A Real Data Study
The FamaFrench five-factor model (FF-5) is one of the advancements of capital asset pricing models (CAPM). Along with other FF-Models, it aims to understand companies and portfolios over a period, Analyzing better return capacity over the five factors such as SMBBusiness Size, HML-Spread between high and low book to market ratio, RMW- Robustness in operating profitability and CMA-investment style to be conservative or aggressive. FF-5-factor regression model widely uses Ordinary Least Squares estimator to estimate the parameter. However, due to the volatility of the markets over the years and not-normal periods, OLS estimators face setbacks due to the assumption violations that are a pre-requisite. This article presents an effort made to improve the performance of the FF-5-factor model using the Robust Dawoud-Kibria estimator. The performance of the FF-5-factor model is compared with other robust estimators such as M, MM, and MMS with MSE criteria. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Robust Rice Leaf Disease Detection using Advanced Preprocessing and Deep CNNs for Class Imbalance Resolution
This study addresses the growing challenges posed by plant diseases, particularly in the rice industry, which is vital for many communities. The research propose a robust framework that integrates Deep Convolutional Neural Networks (Deep CNN) with advanced preprocessing techniques to identify rice leaf diseases, including Brown Spot, Leaf Blast, Hispa, and healthy leaves. Our approach employs normalization to enhance convergence during training and data augmentation to improve model generalizability. Additionally, implement the Synthetic Minority Over-sampling Technique (SMOTE) to create synthetic samples for under-represented classes, addressing class imbalance within the dataset. Experimental results demonstrate the model's impressive accuracy, achieving 98.2% for Brown Spot, 97.5% for Leaf Blast, 94.3% for Hispa, and 96.8% for healthy leaves. Furthermore, our method outperforms established CNN architectures such as AlexNet, VGG16, and ResNet50, showcasing the effectiveness of sophisticated preprocessing in enhancing plant disease detection systems and supporting food security initiatives. 2025 IEEE. -
Robust Statistical Depth Methods for Medical Data: A Focus on Location Estimation and Classification
In robust statistics, data depth functions are extremely powerful and can provide measures of central tendency beyond the ordinary means and medians. These functions provide a sense of depth to points in multivariate space, providing by default a center-outward ranking of observations, which is resistant to outliers and which can be applied to complex and high-dimensional data. Various data depth processes are considered to determine the most optimal location measure with real and simulated data. The performance of Mahalanobis Depth (MD), Half-space Depth (HSD), Zonoid Depth (ZD), Projection Depth (PD), and Spatial Depth (SPD) are compared on some health datasets including the Pima Indians Diabetes Dataset and the Wisconsin Breast Cancer (WBCD) Dataset. The results of these procedures are studied based on calculated depth values and error rates in the discriminant analysis. The findings suggest that the highest depth values are always exhibited by Spatial Depth (SPD), with better robustness and stability without losing accuracy, thus making it the best option. Nevertheless, Mahalanobis Depth (MD) also performs well, which is why it is highly applicable to the robust statistical modelling. Moreover, a new Generalized Mahalanobis Depth (GMD) has been proposed, based on robust location and scatter estimators to eliminate the weaknesses of classical MD. The GMD is more robust to contamination and is valid with singular or ill-conditioned covariance structures, and to high-dimensional data of relevance to real-world data, achieving lower misclassification rates. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Rock abrading in South India /
Encyclopedia of Global Archaeology, pp.1-10 -
Role of Additive Manufacturing and Thermal Spray Processed Materials in Electric Vehicle (EV) and Hybrid Electric Vehicle (HEV) Applications
Additive manufacturing (AM) significantly contributes to the development of electric vehicles (EVs) and hybrid electric vehicles (HEVs), providing lightweight, complex, and customized components. This study explores AMs role in advancing EV and HEV technology, with a special focus on integrating thermal spray coatings (TSCs) to enhance component performance. By employing TSCs in AM-fabricated components, manufacturers can improve surface characteristics, wear resistance, and corrosion protection critical factors for long-lasting EV/HEV systems. The synergy between AM and TSC enhances key parts such as battery enclosures, thermal management systems, and structural frameworks by optimizing their thermal insulation, durability, and energy efficiency. Additionally, AM enables efficient material use and lightweighting, which reduces vehicle weight and enhances energy conservation, addressing industry needs for sustainable solutions. This chapter reviews the current applications and future potential of TSC in AM components, highlighting its role in meeting the rigorous demands of the automotive sector. Findings suggest that combining AM and TSC opens pathways for advanced, sustainable EV and HEV designs, aligning with the global shift toward cleaner energy and resource-efficient manufacturing. 2026 selection and editorial matter, R. Suresh, C. Durga Prasad, Satish Kumar, K.N. Bharath, and Ajith G. Joshi; individual chapters, the contributors. -
Role of AI in Computational Risk Modeling of Financial Stability and Portfolio Risk: A New Perspective
The need to assess climate change-related risks and their impact on the financial stability of banks is imperative. Innovations in technology, especially AI andML algorithms, have improved the efficiency and accuracy of risk analysis models. The obstacle for banks is assessing the climate risk exposure due to their lending portfolio. The climate data are uncertain and unavailable, and the granularity of the data is questionable. To overcome these issues, in this chapter, a hybrid risk predictive model is proposed. It uses a combination of ResNet-50 (to analyze and quantify spatial image data) and CoViaR (risk prediction) models. Using the ResNet-50 model, a climate change risk score is developed from images and feature extraction, which is correlated with the emission volume of the borrower firms. Then, using the proposed model, the impact of climate change-related risk on the lending portfolio is evaluated to understand the financial stability of banks through capital. 2025, Bentham Books imprint. -
Role of AI in Enhancing Customer Experience in Online Shopping
AI-powered tools and applications may provide customers with a positive, effective, and customized purchasing experience. By studying client preferences and behaviours, AI systems can anticipate future customer needs, improving and personalizing the shopping experience. The main aim of this study is to examine the role of artificial intelligence (AI) on enhancing customer experience. The results of this study revealed that there is a positive significant relationship between AI features like perceived convenience, personalization and AI-enabled service quality and Customer experience. A total of 416 responses were analysed using a structured questionnaire. The findings indicate significant role of trust as factor, mediating the effects of independent variables on customer experience. Data was analysed using T-test, ANOVA and regression. 2024 IEEE. -
Role of AI in revolutionizing clinical care
Artificial Intelligence (AI) is significantly reshaping clinical care, driving advancements in diagnostics, treatment, and patient management. With its ability to analyze vast amounts of data rapidly, AI is transforming how healthcare professionals approach patient care, thereby improving outcomes and operational efficiency. One of the primary roles of AI in clinical care is enhancing diagnostic precision. Furthermore, AI-driven chatbots and virtual health assistants are revolutionizing patient engagement and support. These tools provide instant responses to health inquiries, manage appointment scheduling, and offer reminders for medication, which fosters adherence and ensures that patients remain informed about their health journey. Lastly, AI's role in streamlining administrative processes cannot be overstated. Automating routine tasks such as billing and data entry allows healthcare professionals to focus more on patient care rather than administrative burdens. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Role of AI in Strengthening ESG Governance: Perspective From Industry Experts
Socially conscious investors, especially Gen Z, value ethics over money. ESG reports are as important as financial reports for them. ESG ratings from various sources can puzzle these Gen Z investors, as there is no standardization in ESG data. Firstly, the chapter focuses on the need to integrate AI into ESG reporting by highlighting the limitations of mere frameworks such as GRI, SASB, and ISSB. Secondly, it emphasizes the difference between traditional reporting and AI-integrated ESG reporting. It also points out the challenges of AI integration and ways to overcome these challenges. Lastly, the chapter also proposes the need for a unified framework, making it easier for investors to compare and make decisions. 2024, IGI Global. All rights reserved. -
Role of AI in the inventory management of agri-fresh produce at HOPCOMS
Inventory management is vital for maintaining the efficiency of supply chain management. Fruits and vegetables being perishable in nature should involve inventory management to avoid wastage and loss in terms of over stocking and stock out situations. The present study focuses on the role of artificial intelligence (AI)-powered inventory management of fruits and vegetables at HOPCOMS, a cooperative society founded in Bangalore. In the road to satisfy the customers, it is necessary for the society to come up with different strategies to manage the inventories in which a retailer confronts overstock and stock out situation, affecting the profit of the society. Therefore, a study was conducted with the help of structured questionnaire among 122 retailers of HOPCOMS outlets in Bangalore. The results obtained from the study suggest that inventory valuation method positively influences AI-powered demand forecasting and customer order fulfillment, and AI-powered demand forecasting is positively related to customer order fulfillment. 2023, IGI Global. All rights reserved. -
Role of AI tools in evaluating brand performance
The way organizations monitor and control brand performance has been significantly changed by the digital environment. Even though they are useful, traditional measurements frequently miss real-time changes in the market. AI-powered solutions are becoming essential resources in order to tackle this, providing a more precise and up-to-date view of brand health. This chapter explores how artificial intelligence (AI) is transforming brand performance evaluation. It does so by examining important metrics, analytics techniques, and particular AI solutions that are revolutionizing the market. Artificial intelligence (AI) enables organizations to make data-driven decisions with previously unheard-of speed and precision, from assessing brand awareness and consumer loyalty to forecasting future trends. In today's quickly changing digital landscape, marketers, analysts, and company executives can obtain a competitive advantage by comprehending how AI tools can improve brand performance evaluation. 2025, IGI Global Scientific Publishing. All rights reserved. -
Role of Antibiotics in Animal Feed: Prospects and Future Challenges
Antibiotics were discovered more than 50 years ago and are now commonly employed in cattle and poultry production to prevent animal illnesses and increase livestock productivity. They were quickly embraced as an integral part of livestock feeding programs after their benefits were recognized. To meet the increasing demand for animal protein for human consumption, antibiotics in animal feed are also highly sought after because they enhance the quality and quantity of meat as well as growth efficiency. Antibiotic overuse, however, has led to the emergence of resistant strains and presented health risks to people. The most significant negative impact of antibiotic residues entering the food chain has been the spread of antibiotic-resistant organisms to humans due to the transportable nature of resistance. Because of these drawbacks, there are many alternate strategies suggested for combating resistance. These include vaccines, bacteriocins, antimicrobial peptides, and phage therapy, to name a few. Even though they are not yet as effective as antibiotics, they can be utilized in preventive and management strategies. Overall, the combination of suggested alternative interventions with limited antibiotic use appears to be promising in combating antimicrobial resistance. In general, antibiotic usage in animal foods should be regulated because of these adverse repercussions, along with a need to concentrate on improving animal nutrition and the production of high-quality animal products. 2026 selection and editorial matter, Arti Gupta and Ram Prasad; individual chapters, the contributors. -
Role of Artificial Intelligence and Robotics in Shaping the Students: A Higher Educational Perspective
An unprecedented shift in technology has begun in the modern era. Robotics and artificial intelligence (AI) advancements have created fresh positions while de-skilling or retraining many existing ones. Technical developments at higher education institutions (HEIs) protect students against potential changes in their field of study brought on by A) and prepare them for success in the workplace. This research aims to investigate how, over the past 150 years; globalization has fundamentally changed human civilization. Conventional education confronts enormous challenges as energy, the internet of things, and the cyber-physical systems they oversee diminish. One may argue that energy, the internet of things, and the cyber-physical systems that are under its jurisdiction are the foundations of all future education. The demise of these systems presents a significant threat to traditional schooling. Students' screen time is increased by this action, which has an impact on their mental health. Five-fold cross-validation with 210 students from Delhi NCR and abroad is beneficial for the classification techniques SVM, Naive Bayes, and Random Forest. The study examined the factors that contributed to an increased rate of mental health issues among undergraduate students in Delhi, India, following the introduction of the COVID-19 virus. The results have demonstrated that while technology's practical applications will likely have a positive influence on education in the future, there may be negative effects as well. This is an opportunity for educators and learners to support excellence and remove obstacles that prevent many kids and schools from achieving it. Therefore, in the future, every nation will need to create an education system that is more technologically sophisticated. 2024 IEEE. -
Role of Artificial Intelligence in Combating Climate Change: The Green Algorithm
In a globalized tech-driven world, Artificial intelligence (AI) is a powerful weapon, providing pathways to reduce climate change. AI has a wide range of applications against climate change, including predictive analytics, environmental monitoring, and energy management. AI improves predictive analytics, providing information about disaster relief and climatic trends. Cutting-edge models anticipate extreme weather, rising sea-levels, and climate-related phenomena, enabling governments and communities to plan better for the society. AI-driven simulations help with urban planning and creation of climate resilient infrastructure. Application of AI in climate action confronts obstacles despite its potential. Important issues include algorithmic bias, privacy, and effects of computing on environment. To ensure AIs constructive contribution without unexpected consequences and addressing abovementionedconcerns requires strong governance frameworks, ethicalconsiderations, and ongoing research. This chapter examines how AI transforms climate action and enhances environmental preservation. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. -
Role of artificial intelligence in customer engagement and experience
Businesses in the ultramodern period of today concentrate on drawing in customers and changing to meet their wants. The digital revolution has altered customer engagement strategies for businesses, which has an effect on their lifespan. Businesses look for creative methods to improve customer experience in order to remain competitive. During the Industrial Revolution 4.0., artificial intelligence (AI) significantly changed customer management, and it continues to do so today, and will surely continue in future as well. This chapter analyzes the conceptual framework of customer engagement, industries strategies and ethical considerations, data safety and security, mitigating the bias of AI, customer interactions and customer journey, and how artificial intelligence rebuilds, enhances, and optimizes customer management in various industries. Featuring an emphasis on both present and future uses, this chapter investigates how AI might improve corporate offerings, services, and consumer interaction. This chapter delves at how perception analytics, chatbots, and personalized experiences may use AI to improve consumer engagement. It analyzes ethical issues including data privacy, transparency, and prejudice in AI-driven customer management, with an emphasis on fairness and trust. It also examines AI's role in anticipating what customers want and enhancing interactions. This study examines the use of AI to customer management, including obstacles and methods to improve company expansion through the use of pertinent data. This chapter presents an empirical case study on AI's challenges, opportunities, and future potential in customer management and business engagement. 2025 Shakti Swarupa Nayak, Himanshi R Giri, Thirupathi Manickam and Sriram Ananthan. Published under exclusive licence by Emerald Publishing Limited. All rights reserved. -
Role of Artificial Intelligence in Influencing Impulsive Buying Behaviour
This research paper investigates the influence of Artificial Intelligence (AI) on impulsive buying behaviour in the digital commerce domain. The study explores how AI algorithms, data analysis, and customized marketing approaches influence impulsive buying decisions, reshaping traditional understandings of this phenomenon. The analysis draws from a confluence of psychological principles, technological advancements, and marketing strategies, aiming to shed light on how AI not only forecasts but also incites impulsive buying behaviours. The study identifies research gaps, such as the integration of AI with emotional triggers, the comparative effectiveness of AI vs. human influence, and cross-cultural and demographic variability. The research methodology involves a descriptive study with a questionnaire-based survey, and data analysis tools such as ANOVA and paired t-tests. This research contributes to the broader discussion on digital-age consumer behaviors, underscoring the revolutionary role of AI in transforming retail experiences and beyond. 2024 IEEE. -
Role of Artificial Intelligence in Neuroimaging for Cognitive Research
Artificial intelligence (AI)-based solutions are used in most of our daily activities. AI has been adapted and it has found various applications. Cognitive research is one area where AI has been applied to understand the hidden patterns in the data. Neuroimaging techniques investigate the neural basis of cognitive processes like perception, attention, memory, language, reasoning, decision-making, and problem-solving. The irregularities in the cognitive process lead to cognitive disabilities and diseases. Neuroimaging techniques, including magnetic resonance imaging (MRI), functional MRI (fMRI), electroencephalography (EEG), and positron emission tomography (PET), along with other data-gathering techniques, are studied to identify cognitive disorders. The imaging techniques generate large amounts of complex data. AI methods, including machine learning (ML), deep learning (DL), natural language processing (NLP), and computer vision, are applied and used to analyse and interpret the data generated by various imagining techniques. Numerous techniques have been designed, developed, and proposed to handle the neuroimaging data for cognitive research with the help of AI techniques. AI techniques include ML algorithms like decision trees, random forest, support vector machine (SVM), principal component analysis (PCA), and DL algorithms, including convolution neural networks (CNNs), long short-term memory (LSTM), and generative adversarial networks (GANs). Recent advancements in the field of neuroimages use AI techniques to preprocess, process, and analyse the data generated by various neuroimaging modalities. This chapter provides an in-depth analysis and summary of various AI techniques for processing neuroimages for cognitive disorders. 2024 selection and editorial matter, Anitha S. Pillai and Bindu Menon; individual chapters, the contributors. -
Role of Augmented Reality (AR) in Promoting Media Literacy and Sustainability Awareness: A Mixed Method Approach
Augmented reality (AR) has emerged as a transformative tool in education, offering immersive experiences that enhance engagement and understanding across various domains. This study explores the potential of AR in promoting media literacy and sustainability awareness, two critical competencies in the modern information landscape. Through a mixed-methods approach, the research investigates how AR interventions can improve individuals ability to critically assess media content while simultaneously raising awareness about environmental sustainability. The study employs pre- and post-test evaluations, focus groups, and user interaction data to measure changes in media literacy and sustainability awareness among participants exposed to AR-based educational content. Findings indicate that AR significantly enhances media literacy by enabling users to better identify fake news, understand media bias, and critically evaluate information sources. The implications of these findings suggest that AR is not only a powerful tool for enhancing media literacy and sustainability awareness but also a catalyst for promoting informed, responsible, and proactive citizenship in the digital age. 2026 selection and editorial matter, Sonal Trivedi, Vishal Jain, Balamurugan Balusamy, Subhendu Pani, and Danish Ather.