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Artificial Neural Network with Firefly Algorithm-Based Collaborative Spectrum Sensing in Cognitive Radio Networks
Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network. Copyright 2023 KSII. -
Artificial Intelligence: Economic Impact, Labor Productivity, and Policy Implications
Artificial intelligence (AI) is a transformative technology that changes automation and cognitive functions traditionally performed by humans. This research examines the various economic effects of AI, emphasizing its ability to enhance productivity and disrupt labor markets. AI, while it may have automated tasks, has also created new job opportunities and transformed existing roles. The rise of AI has led to significant economic disturbances, especially in terms of unemployment. Today, businesses are more inclined toward AI rather than a human workforce because it is more cost-effective and time-effective. This tendency is evident not only in the financial sector but also in education and e-commerce where the use of artificial intelligence has significantly improved service quality and productivity. However, this transition also presents challenges like joblessness and an educated workforce that rightly deserves strong policy frameworks that put ethical guidelines, global cooperation, and optimistic breakthroughs first while tackling social inequalities. In spite of advancements, further experiential research is required to grasp the consequences of these policy approaches completely. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Artificial intelligence: Blockchain integration for modern business
In the rapidly evolving landscape of modern business, the integration of artificial intelligence (AI) and blockchain technologies has emerged as a potent strategy to address various challenges and unlock new opportunities. This chapter presents a comprehensive overview of the integration of AI and blockchain, highlighting its significance and potential implications for businesses across diverse sectors. The synergy between AI and blockchain offers novel solutions for enhancing transparency, security, and efficiency in business operations. AI algorithms enable the automation of complex tasks, data analysis, and decisionmaking processes, while blockchain provides a decentralized, immutable ledger for secure and transparent data management. By combining these technologies, businesses can streamline processes, reduce costs, mitigate risks, and create new business models. Few key applications of AI-Blockchain integration in modern business include supply chain management, financial services, healthcare, identity verification, and intellectual property protection. 2024, IGI Global. All rights reserved. -
Artificial intelligence: A new model for online proctoring in education
As a result of technological advancements, society is becoming increasingly computerized. Massive open online courses and other forms of remote instruction continue to grow in popularity and reach. COVID-19's global impact has boosted the demand for similar courses by a factor of ten. The ability to successfully assign distant online examinations is a crucial limiting factor in this next stage of education's adaptability. Human proctoring is now the most frequent method of evaluation, which involves either forcing test takers to visit an examination centre or watching them visually and audibly throughout tests via a webcam. However, such approaches are time-consuming and expensive. In this paper, we provide a multimedia solution for semi-automated proctoring that does not require any extra gear other than the student's computer's webcam and microphone. The system continuously monitors and analyses the user based on gaze detection, lip movement, the number of individuals in the room, and mobile phone detection, and captures audio in real time through the microphone and transforms it to text for assessment using speech recognition. Access the words gathered by speech recognition and match them for keywords with the questions being asked for higher accuracy using Natural Language Processing. If any inconsistencies are discovered, they are reported to the proctor, who can investigate and take appropriate action. Extensive experimental findings illustrate the correctness, resilience, and efficiency of our online exam proctoring system, as well as how it allows a single proctor to simultaneously monitor several test takers. 2023 Author(s). -
Artificial Intelligence: A Catalyst for Change in the Indian Automobile Industry
AI is becoming a major game-changer economically and technologically across various sectors in the world. The Indian automotive industry is one such area of development. This paper discusses AIs impact on Indian automotive sector right from supply chain management, boost in production, smart AI systems through predictive maintenance, customization capabilities and development of autonomous vehicles. The Indian automotive industry is one such industry that greatly adds to the countrys GDP and employment, but at the same time presents challenges in terms of infrastructure, logistics and changing consumer needs that AI can address. With the advent of campaigns like Make in India and Digital India, India seeks to position itself as one of the leading figures in international production, and for this, the adoption of AI measures seems of strategic importance as this will facilitate productivity growth, competitiveness and meeting the aims of sustainable development (Aggarwal et al. in J Technol Forecast Soc Change 170, 2021 [1]; Chui et al. in AI adoption and economic growth: The case of India. McKinsey Global Institute, 2022 [6]). Through case studies of Indian companies and new startups using AI technologies, this research focuses on how AI can tackle complex supply chains, cut production costs and satisfy consumer expectations for going green, safety and personalization. At the same time, AI usage in India has its own challenges such as expensive introduction, lack of skilled labour, protection of personal data and strict rules. This paper posits that given the necessary assistance from the state, together with the cooperation of the industry and investment in AI specialists, the Indian auto industry is able to use AI for scaling in a competitive environment and to become part of Indias economy in a larger context. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Artificial Intelligence, Smart Contracts, and the Groundbreaking Potential of Blockchain technology: Unlock the Next Generation of Innovation
The blockchain technology consists of blocks and is a decentralized network of nodes (miners). Each block is made up of three parts: the data, the hash, and the hash from the previous block. After data has been stored, it is extremely difficult to temper the data. Transactions are verified by miners, who are compensated with a commission for their labor. Readers will gain a comprehensive understanding of blockchain technology from this review article, including how it may be used in a variety of industries including supply chains, healthcare, and banking. Most individuals were already familiar with Bitcoin as one of the well-known blockchain applications. In this section, we'll discuss a few of the countless research publications on the cutting-edge applications of this technology. We'll talk about the challenges that come with actually using these applications as well. Blockchain is an industry that is growing thanks to its more recent applications in a number of fields, such as hospital administration, cryptocurrency use, and other places. Only the manner that blockchain works and runs makes it possible for these applications. 2023 IEEE. -
Artificial intelligence-powered travel apps with mixed reality immersion
Conventional and traditional industry standards have seen a drastic shift in recent years with the technological revolution. The introduction of big data, artificial intelligence (AI), and virtual and augmented reality has created a sophisticated and futuristic environment for businesses to strive for. AI-enabled tourism applications refer to the digital tools and software used in the tourism sector that use AI technology to improve and customize a variety of aspects of the travel experience. Though it has applications in tourism that go beyond conventional approaches, AI is still vital to many industries. This chapter examines how AI provides travelers with intelligent and personalized solutions, enabling them to have immersive travel experiences. This investigation includes virtual travel agents, intelligent itinerary planners, AR navigation, and other cutting-edge AI-powered services that cater to individual preferences and needs. The study acknowledges the dynamic landscape of AI-enabled travel apps as well as the evolving expectations of modern travelers. It addresses the opportunities and challenges brought about by the integration of AI in the tourism sector, highlighting the potential for enhanced user experiences and better travel planning. The chapter also discusses how immersive experiences can be created with technology, shedding light on how AI could encourage travelers to develop closer relationships with their travel destinations. It examines how AI impacts decision-making processes, the customization of travel recommendations, and user satisfaction overall within the context of travel experiences. 2025 Sandhya H. and Bindi Varghese. All rights reserved. -
Artificial intelligence-powered talent acquisition and onboarding
Artificial intelligence (AI)-powered instantaneous recruitment analysis and onboarding of the candidates has gained more popularity in recent years, mainly after the COVID-19 phase, largely due to the incredible growth of data-driven content. Basically, the art of summarizing the entire content of the resume of the candidates and leveraging the resumes that are relevant to the job description takes more time-consuming by using the traditional methods. AI is revolutionizing the landscape of talent acquisition and onboarding for applicants/selected candidates, making it much easier than conventional techniques. By harnessing the power of advanced AI-based algorithms and machine learning, organizations and multinational corporations (MNCs) can streamline their recruitment processes, from identifying top talent to integrating new hires seamlessly. AI-based tools automate time-consuming tasks such as screening of the resumes of the voluminous data, scheduling interviews based on each individual job role, and sending out offer letters for the selected applicants. This automation frees up human resource professionals to focus on strategic initiatives and build stronger relationships with candidates. AI algorithms can analyze vast amounts of data to identify candidates who possess the exact skills and qualifications required for a specific role. This precision reduces the risk of mismatches and ensures a higher quality of hires. This article specifically focuses on the tools and techniques based on AI for making the recruitment process easier and their impacts on the actual landscape, along with some recent case studies. The impact of AI on this field of recruitment and its significance on the job market is well analyzed in this article. By embracing AI, organizations can unlock new opportunities for growth and innovation. AI-driven talent acquisition and onboarding not only improve efficiency and accuracy but also enhance the overall candidate experience and foster a more diverse and inclusive workforce. 2026 Elsevier Inc. All rights reserved. -
Artificial Intelligence-Powered Stock Market Forecasting with Metaheuristic Feature Selection Techniques
This study proposed a hybrid stock market forecasting model which consists of Artificial Intelligence (AI) and metaheuristic feature selection algorithms to improve the accuracy in prediction and efficiency of the prototypical. It uses PSO (Particle Swarm Optimization) algorithm to pick the most relevant feature out of a pool of technical indicators and sentiment data and temporally learns the pattern using the LSTM (Long Short-Term Memory) network. The model yields better learning by diminishing noise and dimensionality and prevents over fitting. The efficiency of the anticipated system is seen through comparative analysis with such baseline models as SVM (Support Vector Machine), RF (Random Forest), and standard LSTM. This prototypical obtained MAE of 11.2RMSE of 18.18, and the mean absolute percentage error (MAPE) of 5.36 percent, with R2 of 0.91 and directional accuracy of 86.4 percent. The above results confirm the effectiveness of the suggested method, providing a solid and generalizable solution in terms of intelligent stock market prediction and investment decision support. 2025 IEEE. -
Artificial Intelligence-Monitored Procedure for Personal Ethical Standard Development Framework in the E-Learning Environment
The changes in the lifestyle of human beings due to the pandemic COVID-19 have affected all walks of human life. As a pillar of human development, the arena of education has a vital role to play in this changing world. The humongous and disruptive technologies that had made inroads into the educational scene as E-learning paved the way for ethical concerns in an unimaginable manner. Artificial intelligence is prudently incorporated for developing an ethical lifestyle for students all over the world. The Personal Ethical Standard Framework would work as a vaccine for the pandemic of the cancerous growth of the unethical habits of learners. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Artificial intelligence-internet of things integration for smart marketing: Challenges and opportunities
The convergence of AI and the internet of things (IoT) has revolutionized various industries, including marketing. This integration offers immense potential for enhancing marketing strategies through real-time data analysis, personalized customer experiences, and predictive analytics. However, it also presents several challenges that need to be addressed for successful implementation. This abstract explores the challenges and opportunities associated with integrating AI and IoT in smart marketing initiatives. It discusses the potential benefits such as improved targeting, increased efficiency, and enhanced customer engagement. Additionally, it examines the challenges such as data privacy concerns, interoperability issues, and the need for skilled personnel. Furthermore, the abstract delves into case studies and examples illustrating successful AI-IoT integration in marketing campaigns. It also highlights emerging trends and future directions in this domain, emphasizing the importance of addressing challenges to unlock the full potential of smart marketing. 2024, IGI Global. All rights reserved. -
Artificial intelligence-enabled project-based learning to augment subject comprehension among commerce graduates
In project-based learning, students participate in and create knowledge, problem solve and interact with their peers. This method is an active learning program that provides a deep grasp of subject matter as well as teaches critical thinking skills. AI technology in project-based learning creates an environment that is dynamic this allows students to succeed academically. A convenience sample of 100 students will be selected for this experiment-based research. The results using the advanced statistical tools and techniques using the SPSS ver 28 and Independent t test Paired t test were used. The results of the study indicate students in the experiment group had better subject comprehension from project based learning when AI based interventions are given. In contrary, the control group which was not administered with any AI interventions showed moderate increase in subject comprehension. The study's implications emphasise that AI-based projects, real-time feedback, personalized learning resources, and virtual simulations help deepen their understanding of commerce subjects. 2025, IGI Global Scientific Publishing. All rights reserved. -
Artificial Intelligence-Enabled Digital Twin for Smart Manufacturing
An essential book on the applications of AI and digital twin technology in the smart manufacturing sector. In the rapidly evolving landscape of modern manufacturing, the integration of cutting-edge technologies has become imperative for businesses to remain competitive and adaptive. Among these technologies, Artificial Intelligence (AI) stands out as a transformative force, revolutionizing traditional manufacturing processes and making the way for the era of smart manufacturing. At the heart of this technological revolution lies the concept of the Digital Twin-an innovative approach that bridges the physical and digital realms of manufacturing. By creating a virtual representation of physical assets, processes, and systems, organizations can gain unprecedented insights, optimize operations, and enhance decision-making capabilities. This timely book explores the convergence of AI and Digital Twin technologies to empower smart manufacturing initiatives. Through a comprehensive examination of principles, methodologies, and practical applications, it explains the transformative potential of AI-enabled Digital Twins across various facets of the manufacturing lifecycle. From design and prototyping to production and maintenance, AI-enabled Digital Twins offer multifaceted advantages that redefine traditional paradigms. By leveraging AI algorithms for data analysis, predictive modeling, and autonomous optimization, manufacturers can achieve unparalleled levels of efficiency, quality, and agility. This book explains how AI enhances the capabilities of Digital Twins by creating a powerful tool that can optimize production processes, improve product quality, and streamline operations. Note that the Digital Twin in this context is a virtual representation of a physical manufacturing system, including machines, processes, and products. It continuously collects real-time data from sensors and other sources, allowing it to mirror the physical systems behavior and performance. What sets this Digital Twin apart is the incorporation of AI algorithms and machine learning techniques that enable it to analyze and predict outcomes, recommend improvements, and autonomously make adjustments to enhance manufacturing efficiency. This book outlines essential elements, like real-time monitoring of machines, predictive analytics of machines and data, optimization of the resources, quality control of the product, resource management, decision support (timely or quickly accurate decisions). Moreover, this book elucidates the symbiotic relationship between AI and Digital Twins, highlighting how AI augments the capabilities of Digital Twins by infusing them with intelligence, adaptability, and autonomy. Hence, this book promises to enhance competitiveness, reduce operational costs, and facilitate innovation in the manufacturing industry. By harnessing AIs capabilities in conjunction with Digital Twins, manufacturers can achieve a more agile and responsive production environment, ultimately driving the evolution of smart factories and Industry 4.0/5.0. Audience: This book has a wide audience in computer science, artificial intelligence, and manufacturing engineering, as well as engineers in a variety of industrial manufacturing industries. It will also appeal to economists and policymakers working on the circular economy, clean tech investors, industrial decision-makers, and environmental professionals. 2024 Scrivener Publishing LLC. -
Artificial Intelligence-Enabled Digital Twin for Smart Manufacturing
An essential book on the applications of AI and digital twin technology in the smart manufacturing sector. In the rapidly evolving landscape of modern manufacturing, the integration of cutting-edge technologies has become imperative for businesses to remain competitive and adaptive. Among these technologies, Artificial Intelligence (AI) stands out as a transformative force, revolutionizing traditional manufacturing processes and making the way for the era of smart manufacturing. At the heart of this technological revolution lies the concept of the Digital Twin-an innovative approach that bridges the physical and digital realms of manufacturing. By creating a virtual representation of physical assets, processes, and systems, organizations can gain unprecedented insights, optimize operations, and enhance decision-making capabilities. This timely book explores the convergence of AI and Digital Twin technologies to empower smart manufacturing initiatives. Through a comprehensive examination of principles, methodologies, and practical applications, it explains the transformative potential of AI-enabled Digital Twins across various facets of the manufacturing lifecycle. From design and prototyping to production and maintenance, AI-enabled Digital Twins offer multifaceted advantages that redefine traditional paradigms. By leveraging AI algorithms for data analysis, predictive modeling, and autonomous optimization, manufacturers can achieve unparalleled levels of efficiency, quality, and agility. This book explains how AI enhances the capabilities of Digital Twins by creating a powerful tool that can optimize production processes, improve product quality, and streamline operations. Note that the Digital Twin in this context is a virtual representation of a physical manufacturing system, including machines, processes, and products. It continuously collects real-time data from sensors and other sources, allowing it to mirror the physical systems behavior and performance. What sets this Digital Twin apart is the incorporation of AI algorithms and machine learning techniques that enable it to analyze and predict outcomes, recommend improvements, and autonomously make adjustments to enhance manufacturing efficiency. This book outlines essential elements, like real-time monitoring of machines, predictive analytics of machines and data, optimization of the resources, quality control of the product, resource management, decision support (timely or quickly accurate decisions). Moreover, this book elucidates the symbiotic relationship between AI and Digital Twins, highlighting how AI augments the capabilities of Digital Twins by infusing them with intelligence, adaptability, and autonomy. Hence, this book promises to enhance competitiveness, reduce operational costs, and facilitate innovation in the manufacturing industry. By harnessing AIs capabilities in conjunction with Digital Twins, manufacturers can achieve a more agile and responsive production environment, ultimately driving the evolution of smart factories and Industry 4.0/5.0. Audience: This book has a wide audience in computer science, artificial intelligence, and manufacturing engineering, as well as engineers in a variety of industrial manufacturing industries. It will also appeal to economists and policymakers working on the circular economy, clean tech investors, industrial decision-makers, and environmental professionals. 2024 Scrivener Publishing LLC. -
Artificial Intelligence-Driven Perspectives on Maternal Health: Revealing Important Aspects and Improving Pregnancy Results via Machine Learning
A number of factors, including genetic, environmental and social ones, affect the intricate biological process of pregnancy. The developing foetuss health as well as the mothers must be maintained in the necessary secure equilibrium of these variables. The mothers health, which encompasses her mental as well as physical health, lifestyle decisions, money, social support systems and educational attainment, will determine whether the pregnancy ends well. Medical research has changed as a result of the long-awaited tools for processing for complicated datasets that have been made possible by recent advancements in machine learning models. These models have the ability to identify correlations between characteristics that are difficult for traditional analytical techniques to uncover. Therefore, scientists can improve their understanding of the elements influencing conception and create diagnostic tools by utilizing machine learning technology for timely intervention and customized treatment. Machine learning encompasses various techniques, such as logistic regression, linear regression, random forest, K-Nearest Neighbours and gradient boosting classifier. While Random Forest is an effective way to handle big databases with multiple dimensions and interactions, KNN classifiers are excellent for more organic, data-driven cluster finding of relevant instances and association investigation between various parameters and pregnancy outcomes. Logistic regression only explains the ways in which individual factors affect pregnancy outcomes; it cannot handle binary outcomes as well as linear regression does. We will look for significant determinants of pregnancy outcomes and assess each models performance. Important elements will also be expanded upon. Pregnant patients care, professional practice and improved program decisions may all benefit from this information. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Artificial Intelligence-Driven Lumbar Stenosis Diagnosis: A Deep Learning Pipeline for MRI-Based Segmentation and Classification
Lumbar spinal stenosis is a prevalent musculoskeletal disorder that requires accurate diagnosis through magnetic resonance imaging (MRI). However, manual interpretation of MRI images is time-consuming and subject to inter-observer variability. This study proposes an automated deep learning-based pipeline for lumbar stenosis identification, integrating advanced methodologies for preprocessing, segmentation, feature extraction, and classification. The pipeline consists of Super-Resolution Convolutional Neural Network (SRCNN) for MRI image enhancement, SegNet for segmentation of the spinal canal, intervertebral discs (IVDs), and neural foramen, Convolutional Block Attention Module (CBAM) for feature refinement, and Swin Transformer for final classification. The proposed method was evaluated on a publicly available multicenter lumbar spine MRI dataset, comprising 218 patient studies with 447 MRI series. Model performance was assessed using accuracy, recall, precision, and F1-score, achieving 95.2% accuracy, 89.82% recall, 92.3% precision, and an F1-score of 96.12%. The results demonstrate that SRCNN enhances MRI quality for improved segmentation, CBAM strengthens feature extraction, and Swin Transformer effectively classifies stenosis cases. This study highlights the efficacy of AI-driven methodologies in lumbar spine MRI analysis, offering a potential computer-aided diagnosis (CAD) tool for clinical applications. Future work may focus on optimizing model efficiency and improving generalization across diverse imaging protocols. 2025 IEEE. -
Artificial intelligence-based reverse logistics for improving circular economy performance: adeveloping country perspective
Purpose: Reverse logistics services are designed to move goods from their point of consumption to an endpoint to capture value or properly dispose of products and materials. Artificial intelligence (AI)-based reverse logistics will help Micro, Small, and medium Enterprises (MSMEs) adequately recycle and reuse the materials in the firms. This research aims to measure the adoption of AI-based reverse logistics to improve circular economy (CE) performance. Design/methodology/approach: In this study, we proposed ten hypotheses using the theory of natural resource-based view and technology, organizational and environmental framework. Data are collected from 363 Indian MSMEs as they are the backbone of the Indian economy, and there is a need for digital transformation in MSMEs. A structural equation modeling approach is applied to analyze and test the hypothesis. Findings: Nine of the ten proposed hypotheses were accepted, and one was rejected. The results revealed that the relative advantage (RA), trust (TR), top management support (TMS), environmental regulations, industry dynamism (ID), compatibility, technology readiness and government support (GS) positively relate to AI-based reverse logistics adoption. AI-based reverse logistics indicated a positive relationship with CE performance. For mediation analysis, the results revealed that RA, TR, TMS and technological readiness are complementary mediation. Still, GS, ID, organizational flexibility, environmental uncertainty and technical capability have no mediation. Practical implications: The study contributed to the CE performance and AI-based reverse logistics literature. The study will help managers understand the importance of AI-based reverse logistics for improving the performance of the CE in MSMEs. This study will help firms reduce their carbon footprint and achieve sustainable development goals. Originality/value: Few studies focused on CE performance, but none measured the adoption of AI-based reverse logistics to enhance MSMEs CE performance. 2024, Emerald Publishing Limited. -
Artificial Intelligence-Based L&E-Refiner forBlind Learners
An Artificial Intelligence (AI)-based scribe known as L &E Refiner for blind learners is a technology that utilizes natural language processing and machine learning techniques to automatically transcribe lectures, books, and other written materials into audio format. This system is designed to provide an accessible learning experience for blind students, allowing them to easily access and interact with educational content. The AI scribe is able to recognize and understand various forms of text, including handwriting, printed text, and digital documents, and convert them into speech output that blind learners easily comprehend. This technology has the potential to significantly improve the accessibility and inclusion of education for blind individuals. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Artificial Intelligence-Based Approaches for Anticipating Financial Market Index Trends
The stock market is an essential component of the world economy and significantly impacts how different countries handle their finances. Predicting stock prices has gained popularity recently since it can offer traders, investors, and policymakers useful information. Making informed financial decisions, lowering risk, and maximizing returns can all be facilitated by accurate stock price projections. Stock price prediction is a current research subject due to improvements in machine learning (ML) techniques, and several methodologies have been put forth in the literature. To increase the accuracy of stock price prediction, one method combines the feature extraction ability of convolutional neural networks (CNNs) with the classification strength of support vector machines (SVMs). CNNs are a subclass of neural networks that have excelled in voice and picture recognition. They can be taught to extract valuable features from the supplied data automatically. Contrarily, SVMs are a well-liked machine learning (ML) technique that has been applied for regression and classification tasks. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Artificial Intelligence- Driven Business Intelligence for ESG Strategy Implementation: Enhancing Corporate Sustainability through Data-Driven Case Studies
In the modern world of growing levels of stakeholder analysis and regulatory pressure, it has become a policy imperative of responsible companies to focus on the ESG (Environmental, Social, and Governance)- based considerations as an essential part of their central strategic effort. The main idea is to evaluate how the use of AI- powered BI tools allows achieving ESG data collection, analysis, visualization, and reporting, contributing to providing more responsible and intelligent decision-making in sustainability- focused organizations. The study follows a qualitative- type, multiple case study design, wherein the analysis of five practical entities representing various industries including energy, finance, manufacturing, and information technology. Information was gathered by conducting in- depth interviews with ESG officers and data analysts and the use of secondary data Results indicate that AI-assisted BI systems enable the ESG data to have a better granularity and a more timely and predictive nature, therefore, causing more responsive risk management and stakeholder engagement. 2026, IGI Global Scientific Publishing. All rights reserved.