International Journal of Business & Computational Science
The International Journal of Business & Computational Sciences (IJB&CS) is a multidisciplinary, peer-reviewed, open-access journal that publishes high-quality research in the fields of business and computational sciences. Our journal is committed to ensuring the transparency, accessibility, and integrity of scholarly research.
Assessing the Market Potential for Classic Car Modifications in Kuwait: A Feasibility Study and Strategic Business Plan Development
Background: The global automotive industry is increasingly shifting toward sustainability, technological modernization, and personalized vehicle ownership. At the same time, the classic car market continues to expand due to nostalgia, heritage value, and collector interest. However, classic vehicle owners in many regions, including Kuwait, face challenges such as poor reliability, higher emissions, and limited access to specialized restoration or modification services. Emerging trends in the automotive sector highlight the growing popularity of “restomod” approaches, which integrate modern technologies, such as Electronic Fuel Injection (EFI), catalytic converters, and performance upgrades, into classic vehicles while preserving their original aesthetics. Despite Kuwait’s strong automotive culture and high disposable income, limited research has explored the feasibility of eco-friendly classic car modification services within the country. Objective: This study aims to assess the market potential for eco-friendly classic car modification services in Kuwait and develop a strategic business plan for a specialized automotive venture called Classic Paradise. Methods: A qualitative research design guided by a pragmatist philosophy was employed to evaluate the feasibility of the proposed business concept. Primary data were collected through surveys, stakeholder interviews, and ethnographic observations within Kuwait’s automotive community. The survey involved 39 respondents and assessed interest in classic vehicles and modification services. Interviews with industry stakeholders, including mechanics, garage owners, showroom managers, and enthusiasts, provided insights into market demand and operational challenges. Secondary data from academic literature, industry reports, and government publications supplemented the analysis. Analytical tools including SWOT, PESTLE, and thematic analysis were used to interpret the data. Results: The findings indicate strong interest in classic vehicles, with 76.9% of respondents expressing interest in owning one for personal enjoyment. However, respondents highlighted challenges related to maintenance, reliability, and sourcing specialized parts. Interviews also revealed a significant market gap for professionally modified classic vehicles integrating modern and eco-friendly technologies. Conclusion: Kuwait presents a promising niche market for eco-friendly classic car modification services. By combining heritage vehicle aesthetics with modern engineering solutions, Classic Paradise can address market gaps while aligning with evolving automotive trends.
Copyright (c) 2025 Abdullah Jameel Al-Munaie (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Design And Implementation of an AI-Powered Chatbot for Procurement Management Systems Using Laravel, GPT, And Pinecone – A Case Study
Background: The integration of AI-powered chatbots into enterprise systems, particularly in sensitive domains such as procurement, presents both opportunities and challenges. While conversational AI can enhance accessibility and efficiency, direct interaction with databases through natural language queries introduces significant security risks, including SQL injection and potential data exposure. There is a growing need for architectures that balance usability, performance, and ethical compliance. Methods: This study employed a Design Science Research (DSR) methodology involving iterative design, development, and real-world evaluation. The system was developed using Laravel, MySQL, OpenAI GPT models, and the Pinecone vector database. An initial architecture based on direct SQL query generation was replaced with a Retrieval-Augmented Generation (RAG) approach, where only trace identifiers were embedded in the vector database. This design enabled controlled data retrieval while preventing direct exposure of backend systems. Findings: The redesigned RAG-based system demonstrated substantial improvements in both performance and security. Results showed a 62% reduction in backend query load, an average response time of 1.2 seconds, and a trace retrieval accuracy of 93%. User satisfaction was also high, indicating effective real-world applicability. The findings confirm that separating language models from direct database access enhances operational efficiency while mitigating security risks. Conclusion: The study demonstrates that secure architectural design-specifically the adoption of RAG—can enable the responsible deployment of AI chatbots in enterprise environments. By embedding principles such as data minimization, transparency, and regulatory compliance into system design, organizations can achieve both efficiency and trustworthiness. Future research should focus on scalability, customization, and reducing reliance on external AI services.
Copyright (c) 2025 Muhammad Zubair (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Energy Efficiency Strategies for Industrial Manufacturing: Technological and Managerial Perspectives
Background: Industrial manufacturing is a major contributor to global energy consumption and greenhouse gas emissions, creating an urgent need for energy-efficient strategies to support sustainable development. Both technological advancements and managerial practices are increasingly recognized as critical drivers of improved energy performance in industrial systems. Methods: This study employs a conceptual and literature-based analytical approach, reviewing existing research on energy efficiency in industrial manufacturing. It examines two key dimensions: (1) technological strategies, including advanced manufacturing technologies, energy-efficient machinery, process optimization, and digital solutions such as the Industrial Internet of Things (IIoT) and artificial intelligence (AI); and (2) managerial strategies, including energy management systems, organizational behavior, policy frameworks, and continuous improvement practices. Results: The analysis indicates that technological innovations significantly enhance energy efficiency when effectively implemented. However, their impact is maximized when supported by strong managerial commitment and structured governance. Evidence from prior studies shows that industries adopting integrated energy strategies can reduce energy consumption by 10–30%, while simultaneously improving productivity and competitiveness. Conclusion: Achieving long-term energy efficiency in industrial manufacturing requires a holistic approach that aligns technological innovation with strategic management practices. The integration of advanced technologies with effective managerial frameworks is essential for meeting sustainability goals, complying with regulatory requirements, and ensuring industrial competitiveness.
Copyright (c) 2024 Abdullah Jameel Al-Munaie (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Privacy-Preserving Full-Outfit Virtual Try-On: A Conceptual Architecture For GDPR-Compliant 3D Avatar Systems
Background: Full-outfit virtual try-on (VTO) systems aim to digitally render both upper and lower garments on real user images while preserving body shape, pose consistency, garment structure, and visual realism. While substantial progress has been made in single-garment VTO using GAN-based, warping-based, and diffusion-based methods, extending these approaches to full-outfit synthesis introduces additional technical and ethical challenges. These include multi-garment alignment, occlusion handling, structural continuity, and increasing concerns related to user data privacy under frameworks such as the General Data Protection Regulation (GDPR) and UK GDPR. Objective: This study aims to systematically analyze existing virtual try-on systems with a focus on architectural design, technical limitations, evaluation practices, and privacy considerations, and to propose a conceptual privacy-preserving framework for full-outfit VTO systems. Methods: A targeted literature review was conducted on representative VTO systems. The analysis examined key architectural paradigms, including warping-based networks, flow-based deformation models, diffusion-based generation, and hybrid 2D–3D pipelines. Technical performance, input requirements, evaluation metrics, and approaches to user data handling were critically assessed. Results: The review identified persistent technical challenges, including multi-garment misalignment, occlusion inconsistencies, texture distortion, and identity drift. Evaluation practices were found to rely heavily on metrics that inadequately capture full-outfit realism and structural coherence. From a privacy perspective, existing systems demonstrate limited and inconsistent integration of privacy-by-design principles, with notable concerns related to data retention, biometric representation, and re-identification risks. Conclusion: Full-outfit VTO systems remain constrained by both technical and privacy-related limitations. This study proposes a conceptual privacy-preserving architecture that separates image acquisition from garment simulation through avatar-based processing, enabling data minimisation and reduced reliance on raw user images. The findings highlight the need for future research to integrate robust technical solutions with standardized privacy-aware design principles to support scalable and ethical real-world deployment.
Copyright (c) 2026 Muhammad Zubair (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Circular Economy Strategies in the Global Paper Manufacturing Industry: A Systematic Review
Background: The global paper manufacturing industry is undergoing a significant transformation due to increasing environmental concerns, regulatory pressures, and the demand for sustainable resource management. As a resource-intensive sector, paper production contributes substantially to deforestation, water consumption, and greenhouse gas emissions. The circular economy (CE) has emerged as a viable framework to mitigate these challenges by enhancing resource efficiency, minimizing waste, and promoting material reuse. Objective: This study aims to systematically review existing literature on circular economy strategies in the global paper manufacturing industry, with a focus on key approaches such as recycling, closed-loop production systems, eco-design, and industrial symbiosis. Methods: A comprehensive review and synthesis of peer-reviewed literature were conducted to evaluate current circular economy practices, technological advancements, and implementation challenges within the paper manufacturing sector. Results: The findings indicate that recycling and fiber recovery remain central to circular practices, supported by advancements in waste paper utilization and pulping technologies. Additional strategies, including the use of bio-based materials, energy recovery systems, and water reuse technologies, have improved sustainability outcomes. However, persistent challenges include technological constraints, contamination in recycled fibers, economic limitations, and inconsistent regulatory frameworks across regions. Developed economies demonstrate greater progress in adopting circular models, whereas developing regions face structural and infrastructural barriers. The integration of digitalization and Industry 4.0 technologies is increasingly enhancing resource optimization and process efficiency. Conclusion: The study highlights the growing adoption of circular economy principles in the paper manufacturing industry while identifying key gaps, particularly in lifecycle assessment integration and the development of scalable circular business models. It underscores the importance of collaborative efforts among policymakers, industry stakeholders, and researchers to accelerate the transition toward a fully circular paper production system.
Copyright (c) 2026 Abdullah Jameel Al-Munaie (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Digital Divide and Economic Vulnerability: Evidencefrom Developing Economies
Background: Despite the rapid pace of global digital transformation, significant disparities in digital access persist, particularly in developing economies. Limited access to quality internet, digital infrastructure, and digital skills in low- and middle-income countries restricts participation in economic activities and reinforces structural inequalities. Existing literature often examines digital access in isolation, with limited focus on its multidimensional relationship with economic vulnerability. Objective: This study aims to examine the relationship between digital access and economic vulnerability in emerging economies using a multidimensional empirical approach. Methods: The study utilizes secondary data obtained from international development databases and employs regression analysis to assess the impact of digital access indicators on key measures of economic vulnerability, including poverty rates, unemployment rates, and income instability across countries. Results: The findings reveal a statistically significant negative association between digital access and economic vulnerability. Higher levels of digital inclusion are associated with reduced poverty, lower unemployment, and improved income stability. Conclusion: The study highlights the critical role of digital infrastructure and connectivity in promoting inclusive economic performance. It contributes to the literature on digital inequality by providing a multidimensional empirical perspective and underscores the need for policy interventions aimed at improving digital infrastructure, affordability, and digital literacy to achieve sustainable economic growth.
Copyright (c) 2026 Md. Jobaer Rahman Rashed, Mahmoud Ahmed, Annatul Islam Hredoy, Md Saiful Islam Bhuiyan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Bias Detection And Fairness Optimization In Nlp-Based Language Assessment Systems
Background: Natural Language Processing (NLP) has significantly transformed educational assessment through the development of automated systems such as Automated Essay Scoring (AES). These systems enable scalable, cost-effective, and efficient evaluation of student writing, particularly in large-scale and standardized testing environments. However, concerns have emerged regarding bias and fairness, especially in evaluating essays from students with diverse linguistic, cultural, and socio-economic backgrounds. Such biases may arise from training data, model design, or evaluation processes, potentially disadvantaging non-native speakers and underrepresented groups. Methods: This study proposes a comprehensive experimental framework to detect bias and optimize fairness in NLP-based language assessment systems. Bias is evaluated at both demographic and linguistic levels using established fairness metrics and explainable artificial intelligence (XAI) techniques. Additionally, multiple fairness optimization strategies, including data balancing, adversarial debiasing, and bias-aware learning approaches, are examined to improve equity without compromising model performance. Results: The findings reveal the presence of measurable bias in several widely used NLP-based scoring models. The application of fairness-oriented interventions demonstrates a significant reduction in scoring disparities across different groups, while maintaining acceptable levels of predictive accuracy. Conclusion: Bias in automated essay scoring systems is a critical challenge that must be addressed to ensure equitable educational assessment. The integration of fairness evaluation and optimization strategies, supported by XAI methods, can substantially improve the transparency and fairness of NLP-based systems. This study contributes to the advancement of responsible AI practices in educational technology and supports the development of more inclusive and trustworthy assessment tools.
Copyright (c) 2025 Kazi Siam Al Mobin, Afroza Riju (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Explainable Artificial Intelligence in Automated Essay Scoring: A Systematic Review of Transparency In AI-Based Educational Assessment
Background: Artificial intelligence (AI) is increasingly being used in educational assessment, particularly through automated essay scoring (AES) systems that apply machine learning and natural language processing to evaluate textual features such as grammar, coherence, vocabulary, and semantic relevance. While these systems offer efficiency, scalability, and reduced grading time, they raise significant concerns regarding transparency, interpretability, and fairness due to their “black-box” nature. This lack of explainability can undermine trust and accountability in educational decision-making. Methods: This study conducts a systematic review of the literature on explainable artificial intelligence (XAI) in AES. It examines existing AI models used for essay scoring, identifies commonly applied explainability techniques such as SHAP, LIME, and attention-based visualization, and analyzes their role in improving transparency and interpretability across interdisciplinary domains, including educational technology, natural language processing, and learning analytics. Results: The review finds that although AES systems are effective in replicating human-like scoring, most models lack transparency in their decision-making processes. The integration of XAI techniques has shown promise in providing insights into how models assign scores based on textual features. However, research in explainable AES remains fragmented, with limited standardization and varying implementation approaches across studies. Conclusion: Explainable AI has significant potential to enhance transparency, trust, and fairness in automated essay scoring systems. However, there is a need for more cohesive and standardized research efforts to fully realize its benefits. Future work should focus on developing unified frameworks and evaluating the broader educational impact of explainable AES systems.
Copyright (c) 2024 Kazi Siam Al Mobin (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Explainable Artificial Intelligence (XAI) for Clinical Decision Support Systems
Background: Artificial Intelligence (AI) has become increasingly integrated into healthcare, particularly in Clinical Decision Support Systems (CDSS) that assist clinicians in diagnosis, prognosis, and treatment planning. Despite their predictive power, many AI models—especially deep learning algorithms—operate as “black boxes,” limiting clinicians’ trust and acceptance. Explainable Artificial Intelligence (XAI) has emerged as a promising approach to enhance transparency and interpretability in AI-driven healthcare systems. Objective: This study aims to examine the role of Explainable Artificial Intelligence techniques in improving the transparency, interpretability, and clinical adoption of AI-based Clinical Decision Support Systems. Methods: A systematic analysis of existing XAI approaches used in clinical decision support will be conducted. The study will evaluate commonly used interpretability techniques such as SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), and attention-based mechanisms in machine learning models applied to healthcare datasets. Performance metrics, interpretability measures, and clinician usability will be assessed to determine the effectiveness of XAI methods. Results: The analysis is expected to demonstrate that integrating XAI techniques into clinical decision support systems significantly improves model interpretability and clinician trust without substantially compromising predictive performance. Explainable models are anticipated to facilitate better understanding of feature importance and clinical reasoning behind AI predictions. Conclusion: Explainable Artificial Intelligence has the potential to bridge the gap between complex machine learning models and clinical decision-making by providing transparent and interpretable insights. The adoption of XAI-enabled CDSS may enhance trust, accountability, and integration of AI technologies into clinical practice.
Copyright (c) 2026 Fariha Ambreen Chaudhry (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Enhancing Financial Fraud Detection Using Machine Learning Algorithms: A Comparative Analysis of Random Forest, Gradient Boosting, and Deep Neural Networks
Background: Financial fraud has become increasingly sophisticated with the growth of digital banking and online transactions. Traditional rule-based fraud detection systems struggle to identify complex and evolving fraud patterns, highlighting the need for advanced machine learning techniques. Objective: This study aims to evaluate the effectiveness of different machine learning algorithms in detecting fraudulent financial transactions and to identify the most accurate and efficient model for fraud detection. Methods: A comparative experimental study will be conducted using publicly available financial transaction datasets. Machine learning models including Random Forest, Gradient Boosting, and Deep Neural Networks will be trained and evaluated. Model performance will be assessed using accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Results: The findings are expected to show that ensemble learning methods and deep learning models outperform traditional machine learning techniques in detecting complex fraud patterns. Gradient Boosting and Deep Neural Networks are anticipated to demonstrate superior performance in terms of predictive accuracy and fraud detection rates. Conclusion: Machine learning-based fraud detection systems can significantly improve the identification of fraudulent transactions in financial systems. Implementing these models can enhance financial security and reduce economic losses caused by fraud. Keywords: Artificial Intelligence, Fraud Detection, Machine Learning, Financial Technology, Random Forest, Deep Learning
Copyright (c) 2026 Arif Irshad (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Impact of Generative Artificial Intelligence Tools on Knowledge Worker Productivity: A Study of AI-Assisted Writing, Coding, and Research Tasks
Background: Generative Artificial Intelligence (AI) tools such as large language models have rapidly transformed knowledge-intensive work by assisting in tasks including writing, coding, and information synthesis. Despite their increasing adoption, empirical understanding of how these tools influence productivity, efficiency, and task quality among knowledge workers remains limited. Objective: This study aims to investigate the impact of generative AI tools on the productivity and performance of knowledge workers engaged in writing, programming, and research-related tasks. Methods: A mixed-method research design will be employed, combining quantitative productivity measurements with qualitative insights from surveys and semi-structured interviews. Participants will complete selected tasks both with and without AI assistance. Task completion time, output quality, and user satisfaction will be analyzed using statistical methods to determine productivity differences. Results: The study is expected to demonstrate that generative AI tools significantly reduce task completion time while improving the quality and coherence of outputs in writing and research tasks. Coding productivity is anticipated to increase through automated code suggestions and debugging assistance. Conclusion: Generative AI technologies have the potential to substantially augment human capabilities in knowledge work. However, their integration requires careful consideration of issues related to skill dependency, ethical use, and accuracy of generated content.
Copyright (c) 2026 Asif Irshad (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
PROMPT LINEAGE AND GOVERNANCE IN LLM-ENABLED DATA ENGINEERING: A REFERENCE ARCHITECTURE
modern data engineering ecosystems to automate data transformation, quality assurance, metadata generation, and analytical reasoning. While these models enhance productivity and adaptability, they introduce significant governance challenges due to their probabilistic behavior and heavy reliance on prompts as executable control artifacts. Unlike traditional data pipelines, where logic is encoded in version-controlled code, LLM-enabled systems often embed prompts in orchestration layers without formal lifecycle management, lineage tracking, or policy enforcement. This absence of prompt governance undermines reproducibility, auditability, and regulatory compliance in enterprise data platforms. This paper proposes a reference architecture for prompt lineage and governance in LLM-enabled data engineering environments. Drawing on principles from DataOps, MLOps, metadata management, and responsible AI, the architecture treats prompts as first-class governed assets. It enables versioning, lineage tracking, metadata capture, and policy enforcement across prompt creation, deployment, and execution. The proposed architecture integrates with modern lakehouse platforms, orchestration engines, and observability tools to provide end-to-end transparency across data, prompts, models, and outputs. This study contributes a structured and practical framework to support scalable, compliant, and trustworthy adoption of LLMs in data engineering workflows.
Copyright (c) 2024 Shambhu Adhikari (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Large Language Models in Modern Data Engineering: A Systematic Review of Architectures, Use Cases, and Limitations
The rapid advancement of large language models (LLMs) since 2022 has significantly reshaped modern data engineering practices. Originally developed for natural language processing tasks, LLMs are increasingly integrated into data engineering workflows, including data ingestion, schema inference, metadata generation, transformation logic synthesis, data quality monitoring, and natural-language interaction with analytical systems. This systematic review examines the role of LLMs in contemporary data engineering, focusing on architectural integration patterns, practical use cases across the data lifecycle, and inherent limitations affecting reliability and governance. Following PRISMA-informed guidelines, peer-reviewed articles, preprints, and industrial reports published between 2022 and 2025 were analyzed. The review identifies Retrieval-Augmented Generation (RAG), hybrid vector-database architectures, and agent-based orchestration frameworks as dominant deployment strategies. Evidence suggests that LLM-assisted pipelines improve developer productivity, reduce manual coding overhead, and enhance accessibility of data platforms for non-technical stakeholders. However, persistent challenges remain, including hallucination, data privacy risks, limited explainability, operational costs, and scalability constraints. The findings emphasize the need for robust architectural safeguards, evaluation benchmarks, and governance frameworks to ensure safe and effective production adoption. This review contributes a structured taxonomy of LLM-centric data engineering architectures and outlines future research directions to support trustworthy, scalable, and auditable data platforms.
Copyright (c) 2025 Shambhu Adhikari (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Application of Machine Learning for Optimizing Oil Well Production and Reservoir Management: A Simulation-Based Approach
Background: The oil and gas industry requires efficient reservoir management and accurate production forecasting to optimize operations and reduce costs. Traditional physics-based models, though reliable, are computationally intensive and require domain expertise. Machine learning (ML) offers a data-driven approach to predict production trends, optimize operational strategies, and enhance decision-making. This study evaluates various ML models, including regression, decision trees, gradient boosting machines (GBM), and deep learning, to determine their effectiveness in oil well production forecasting. Methods: A synthetic dataset simulating reservoir conditions, production histories, and operational parameters was used to train ML models. Linear regression, decision trees, random forests, GBM, and deep learning models were tested. Performance was measured using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Hyperparameter tuning and cross-validation were applied to improve model accuracy, and feature importance analysis was conducted to identify key factors influencing production. Results: GBM achieved the highest accuracy, with an RMSE of 3.5% and an MAE of 2.1%, outperforming other models in production forecasting. Deep learning models captured complex patterns but required high computational resources. Random forests showed strong generalization, making them effective for noisy datasets, while linear regression struggled with non-linearity. Overall, ML models improved forecasting accuracy and enabled real-time optimization of reservoir operations. Conclusion: ML models significantly enhance oil well production forecasting and reservoir management. GBM proved to be the most effective, balancing accuracy and efficiency. Integrating ML into oil well operations can reduce costs and improve decision-making. Future research should focus on real-world datasets and hybrid ML approaches to further refine predictive capabilities.
Copyright (c) 2024 Mohsin Saleem (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Systematic Review of Machine Learning Techniques for Predictive Maintenance In Industrial IOT (IIOT) Environments
Background and Purpose: Predictive Maintenance (PdM) has emerged as a critical component of smart manufacturing, driven by the proliferation of Industrial Internet of Things (IIoT) technologies that enable continuous monitoring of industrial assets. The extensive data generated through interconnected sensors and cyber-physical systems has created new opportunities for real-time equipment diagnostics, early fault detection, and improved operational reliability. Machine Learning (ML) techniques play a central role in transforming these heterogeneous data streams into meaningful insights, reducing unplanned downtime and enhancing productivity. Despite rapid advancements, significant challenges remain regarding model selection, performance evaluation, interpretability, and practical deployment in industrial environments. This study provides a comprehensive synthesis of ML techniques applied to PdM within IIoT ecosystems, examining methodological trends, strengths, limitations, and research gaps. Methods: A systematic review methodology was adopted following PRISMA 2020 guidelines. Peer-reviewed studies published between 2015 and 2025 were retrieved from IEEE Xplore, ACM Digital Library, Scopus, Web of Science, and ScienceDirect. Boolean search strategies were used to identify literature focused on ML-based PdM models applied to IIoT data, cyber-physical systems, sensor networks, and digital twins. Data extracted from eligible studies included ML algorithms, datasets, feature engineering approaches, performance metrics, deployment frameworks, and identified limitations. Comparative and thematic analyses were employed to categorize methods and evaluate their effectiveness across different industrial contexts. Findings: Sixty-two studies met the inclusion criteria. The findings show that Deep Learning (DL) architectures, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and autoencoders, predominate in contemporary PdM research due to their capacity to learn complex temporal and multidimensional sensor patterns. Hybrid models integrating DL with signal processing and classical ML methods demonstrated improved robustness and predictive accuracy. However, the review reveals persistent challenges, including the reliance on controlled or semi-synthetic datasets, limited real-time validation, data imbalance, lack of model interpretability, and constraints in integrating ML solutions with industrial hardware. These limitations hinder the scalability and practical adoption of PdM systems in real-world manufacturing environments. Theoretical Contributions: The review synthesizes key theoretical perspectives underpinning ML-driven PdM. Data-driven modeling theory underscores the importance of high-quality sensor data and feature representations for accurate prediction. Systems theory highlights the interconnected nature of IIoT architectures and the need for interoperability across devices and platforms. Decision-support theory contextualizes the role of predictive analytics in optimizing maintenance planning and operational strategies. Additionally, emerging paradigms such as physics-informed ML and edge intelligence illustrate how theoretical advancements can bridge gaps between algorithmic accuracy and industrial applicability. Conclusion and Implications: ML-enabled PdM offers substantial potential to transform industrial asset management within IIoT environments. To achieve large-scale implementation, future efforts must prioritize data quality improvement, real-time processing capabilities, algorithm explainability, and seamless integration with edge and cloud infrastructures. Research should advance toward federated learning, transfer learning, standardized benchmark datasets, and hybrid physics-data models to enhance model generalizability and industrial adoption. A holistic, technically informed, and context-specific framework is essential for maximizing the impact of ML-driven PdM in smart manufacturing ecosystems.
Copyright (c) 2025 Mahmudul Islam (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Transforming Private Equity Operations Through Ai-Driven Workflow Automation: A Comprehensive Review
Introduction: The private equity (PE) industry is undergoing a rapid transformation driven by technological advancements, particularly artificial intelligence (AI) and workflow automation. Historically dependent on manual data processing, human judgment, and fragmented decision systems, PE firms now face pressure to enhance efficiency, transparency, and scalability. The integration of AI-driven automation tools is revolutionizing deal sourcing, due diligence, portfolio monitoring, risk assessment, and operational management. This review explores how AI-driven workflow automation is reshaping private equity operations and its implications for competitiveness, value creation, and decision-making. Methods: A comprehensive literature review was conducted using databases including Scopus, Web of Science, and ScienceDirect. The search focused on publications from 2015 to 2025 employing combinations of keywords such as “private equity,” “AI automation,” “workflow transformation,” and “digitalization in investment management.” Both peer-reviewed articles and industry reports were included to capture academic and practical insights. The studies were screened for relevance, methodological rigor, and applicability to PE operations. Data were synthesized thematically across five operational domains: deal sourcing, due diligence, portfolio management, compliance, and exit strategies. Results: The review reveals that AI-driven workflow automation significantly enhances operational agility, data accuracy, and strategic foresight in PE firms. Automated systems employing natural language processing (NLP), predictive analytics, and robotic process automation (RPA) streamline repetitive tasks, improve risk modeling, and accelerate decision-making. Firms integrating AI-based deal sourcing tools reported up to 30 - 40% faster transaction screening, while automated due diligence platforms reduced manual workload by 50–60%. Moreover, AI-based portfolio monitoring systems improved real-time visibility and performance tracking, contributing to better fund governance and investor relations. Conclusion: AI-driven workflow automation is transforming the private equity ecosystem from intuition-based management to data-driven decision-making. While implementation challenges such as high costs, data privacy, and talent shortages persist, the long-term benefits include greater operational efficiency, improved return on investment, and sustainable competitive advantage. Future success will depend on how effectively PE firms align AI strategies with governance frameworks and human expertise
Copyright (c) 2025 Wafaa Chmimo (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Edge AI for Energy-Efficient Computing: A Systematic Review of Strategies, Challenges, and Future Directions
Background and Purpose: Edge Artificial Intelligence (AI) has emerged as a crucial solution for minimizing power consumption during real-time data processing in computing devices. Given the increasing demand for energy-efficient AI systems, this study systematically reviews Edge AI methods focused on optimizing energy efficiency. The research highlights existing challenges and explores future research directions to enhance Edge AI capabilities. Methods: This study employs a systematic review methodology, analyzing recent advancements in Edge AI, including new hardware systems, model optimization techniques, and software development tools. The review considers the interplay between model complexity, hardware constraints, security concerns, and the trade-off between performance and energy efficiency. Findings: The analysis reveals that optimizing Edge AI requires a multifaceted approach involving hardware innovations, lightweight models, and adaptive algorithms. Key challenges include balancing computational power with energy constraints, ensuring data security in edge environments, and maintaining real-time processing capabilities. Theoretical Contributions: This research identifies three significant research avenues: (1) integrating neuromorphic computing to enhance efficiency and mimic biological neural processes, (2) leveraging federated learning to improve privacy-preserving model training across distributed edge devices, and (3) developing adaptive AI architectures that dynamically adjust computational resources based on workload demands. Conclusions and Policy Implementations: To advance Edge AI, policymakers and industry leaders must prioritize the development of energy-efficient hardware, encourage research into low-power AI models, and establish regulatory frameworks for secure edge computing. Future work should explore interdisciplinary collaborations to further optimize Edge AI performance.
Copyright (c) 2025 Ndansi Seraphin Sigala (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Role of Project Management from Idea to Execution in Entrepreneurial Product Development
Background and Purpose: Entrepreneurial product development is a complex, high-risk process that requires structured management to transform innovative ideas into market-ready products. Startups often operate under conditions of uncertainty, resource constraints, and rapid technological shifts, making project management a critical factor for success. This research examines the role of project management methodologies, including Agile, Lean Startup, and Design Thinking, in supporting entrepreneurs from idea generation to commercialization. By integrating structured frameworks with adaptive, iterative processes, this study aims to identify best practices that enhance product viability, market responsiveness, and operational efficiency in entrepreneurial ventures. Methods: A qualitative research design was employed, incorporating case study analyses and semi-structured interviews with startup founders, product managers, and project leaders. The study investigated project management practices across multiple industries, including technology, healthcare, and consumer products. Thematic analysis was used to identify recurring patterns in project execution, decision-making processes, and the effectiveness of various project management methodologies. Comparative assessments of successful and failed startups provided insights into critical factors influencing product development outcomes. Findings: Project management plays a fundamental role in mitigating risks, optimizing resource allocation, and ensuring structured product development in entrepreneurial ventures. The study finds that Agile methodologies, particularly Scrum and Kanban, enable startups to maintain flexibility through iterative cycles, allowing continuous user feedback integration. Lean Startup principles, centered around the Build-Measure-Learn framework, support rapid prototyping and market validation, reducing wasted resources. Design Thinking complements these approaches by emphasizing user-centric innovation and problem-solving. However, startups face challenges such as resource limitations, time constraints, team coordination issues, and evolving market demands. The research highlights the importance of prioritization frameworks, such as MoSCoW analysis, to focus on essential product features while preventing scope creep. Effective risk management strategies, continuous performance monitoring through Key Performance Indicators (KPIs), and structured communication channels significantly enhance project execution. Case study analyses illustrate that startups that embrace structured project management approaches outperform those that rely solely on ad hoc decision-making. Theoretical Contributions: This study bridges the gap between traditional project management methodologies and the dynamic requirements of entrepreneurial product development. By integrating Institutional Theory, Resource-Based View (RBV), and Lean Thinking, the research demonstrates how structured project management enhances innovation, strategic alignment, and sustainable growth in startups. The findings contribute to the evolving discourse on how project management practices should be tailored to support entrepreneurial agility while maintaining structured execution. Conclusion and Policy Implications: Entrepreneurs benefit from structured project management approaches that combine strategic planning with adaptability. Governments, incubators, and investors should support startup ecosystems by promoting project management education, providing funding for structured development methodologies, and fostering mentorship programs. Future research should explore the integration of AI-driven project management tools to further enhance decision-making, risk assessment, and real-time project tracking in entrepreneurial environments.
Copyright (c) 2025 International Journal of Business & Computational Science https://creativecommons.org/licenses/by-nc-nd/4.0
The Effects of Government Policies On SME Growth in Developing Economies: A Comprehensive Review
Background and Purpose: Small and Medium Enterprises (SMEs) are fundamental to economic development in developing economies, contributing significantly to employment generation, poverty reduction, and GDP growth. Despite their potential, SMEs face numerous constraints, including limited financial access, regulatory burdens, inadequate infrastructure, and challenges in trade integration. This study provides a comprehensive review of the effects of government policies on SME growth, assessing the effectiveness of various policy interventions while identifying the key challenges and sectoral variations that influence their impact. Methods: A systematic literature review methodology was adopted, focusing on scholarly articles, policy reports, and empirical case studies from 2005 to 2025. The study utilized electronic databases such as Scopus, Web of Science, JSTOR, and Google Scholar. Boolean search techniques were applied to retrieve relevant literature, and thematic analysis was used to categorize the findings into distinct policy areas. The review applied comparative analysis to examine policy effectiveness across different regions and economic contexts. Inclusion criteria were set to ensure relevance, prioritizing studies that explicitly examined the intersection of government policies and SME development within developing economies. Findings: Government policies have played a critical role in shaping the SME landscape through financial inclusion initiatives, regulatory reforms, infrastructure development, trade and export promotion, digitalization policies, and sustainability measures. Financial policies, including credit guarantee schemes, microfinance programs, and interest rate subsidies, have improved access to capital. However, challenges such as high collateral requirements, inefficiencies in fund allocation, and the exclusion of informal enterprises persist. Regulatory reforms aimed at simplifying business registration and tax compliance have facilitated SME formalization, yet bureaucratic inefficiencies and corruption continue to undermine their effectiveness. Investments in physical and digital infrastructure have enhanced SME operational capabilities, but regional disparities remain, particularly between urban and rural enterprises. Trade promotion policies have provided SMEs with access to international markets through export processing zones and trade facilitation programs; however, complex certification requirements and non-tariff barriers hinder participation. Digitalization policies have encouraged SME adoption of e-commerce and financial technology solutions, yet cybersecurity risks and digital skill gaps present ongoing challenges. Sustainability policies, including incentives for green technologies and eco-friendly financing, are gaining traction, though high implementation costs and limited financial support restrict their widespread adoption. Theoretical Contributions: The study integrates multiple theoretical perspectives to analyze the effectiveness of government policies in SME development. Institutional theory highlights the role of regulatory stability and governance quality in shaping SME growth. The resource-based view (RBV) emphasizes the importance of financial and technological resource availability in determining SME competitiveness. Entrepreneurship theory illustrates the role of government incentives in fostering a conducive business environment. Market failure theory justifies state intervention in addressing financing gaps and structural barriers, while public choice theory explores the influence of political and interest-group dynamics on SME policymaking. These theoretical frameworks collectively provide a multidimensional understanding of how government policies impact SME growth and sustainability. Conclusion and Policy Implications: The findings suggest that SME growth in developing economies requires a multifaceted policy approach that integrates financial accessibility, regulatory efficiency, infrastructure investment, and trade facilitation. Governments should enhance financial inclusion through alternative credit mechanisms, reduce regulatory bottlenecks to encourage SME formalization, and strengthen institutional capacity to combat corruption. Digital and physical infrastructure investments must be expanded to bridge regional disparities, particularly in underserved rural areas. Trade facilitation policies should address certification and logistical challenges to improve SME integration into global value chains. Sustainability policies should be reinforced with financial incentives and technical support to enable SMEs to transition toward environmentally sustainable practices. A holistic and sector-specific policy framework, aligned with regional economic contexts, is essential for maximizing SME contributions to economic growth and development.
Predictive Analytics and Its Role in Enhancing Consumer Experience in E-Commerce
In today’s digital world, where e-commerce platforms have become an integral part of the consumer landscape, businesses are constantly seeking innovative ways to differentiate themselves in an intensely competitive market. One powerful tool in this quest is predictive analytics. By leveraging data and sophisticated algorithms, predictive analytics enables e-commerce businesses to forecast consumer behavior, personalize shopping experiences, optimize inventory management, and ultimately enhance overall customer satisfaction. This editorial explores the role of predictive analytics in transforming the consumer experience in e-commerce and its implications for the industry. What is Predictive Analytics? Predictive analytics involves using historical data, statistical algorithms, machine learning models, and artificial intelligence (AI) to predict future outcomes based on past patterns. By identifying trends, behaviors, and preferences, businesses can forecast what will happen in the future, allowing them to make more informed decisions. In the context of e-commerce, predictive analytics is used to understand customer behavior, forecast demand, and enhance personalization in a way that elevates the overall consumer experience (1,2). Personalization: Tailoring the Shopping Experience One of the most significant ways predictive analytics enhances the consumer experience in e-commerce is through personalization. Today’s consumers expect personalized experiences that cater to their individual needs and preferences. A one-size-fits-all approach no longer suffices, and e-commerce businesses must use data-driven insights to create more customized experiences. Predictive analytics helps businesses tailor product recommendations to individual customers by analyzing previous purchase history, browsing behavior, and preferences (3,4). For example, Amazon’s recommendation engine is a prime example of predictive analytics at work. The platform analyzes user behavior, such as past purchases and searches, to recommend products that the customer is likely to be interested in (1,5). This personalized approach not only makes shopping more convenient for the customer but also increases the likelihood of conversion and repeat business. Additionally, predictive analytics can optimize marketing campaigns by ensuring that the right message reaches the right audience at the right time. By analyzing customer segments and their behavior, businesses can predict which offers or promotions will resonate most with specific groups (6). For instance, a customer who frequently purchases skincare products may receive targeted promotions for related items such as moisturizers or face masks. This level of targeted marketing improves the overall consumer experience by delivering relevant content and products directly to the consumer (7). Anticipating Customer Needs and Enhancing Customer Service Predictive analytics goes beyond just recommending products; it also plays a crucial role in anticipating customer needs and enhancing customer service. By analyzing data from customer interactions, e-commerce platforms can predict when a customer might need assistance, enabling businesses to offer proactive support (2,8). For example, predictive models can forecast when a customer might encounter an issue with their order—such as delays in shipping or inventory shortages—and preemptively offer solutions. This proactive approach ensures that customers feel valued and heard, reducing frustration and improving overall satisfaction (7). Chatbots powered by AI can also use predictive analytics to identify the type of assistance a customer needs, providing instant and relevant support without the need for human intervention (1,3). Moreover, predictive analytics helps businesses optimize customer service operations by identifying common issues and pain points. For example, if a significant number of customers have issues with a particular product or service, predictive models can flag these trends, enabling businesses to address them promptly (2,8). This data-driven approach leads to a more seamless and efficient customer experience, helping businesses resolve issues before they escalate. Optimizing Inventory Management and Delivery A critical aspect of e-commerce is the timely delivery of products. Predictive analytics plays a pivotal role in optimizing inventory management and ensuring that products are available when customers need them. By analyzing consumer demand patterns, e-commerce businesses can predict which products will be in high demand during certain periods, such as during holidays or promotional events (9). This predictive insight allows businesses to ensure that their inventory levels are aligned with demand, reducing the chances of stockouts or overstocking (7). For example, if an e-commerce platform predicts an increase in demand for a particular product, it can increase stock levels in anticipation, ensuring that customers can make purchases without delays. On the flip side, predictive analytics can also help businesses avoid the costs associated with excess inventory by forecasting slow-moving products and optimizing inventory turnover (9). Additionally, predictive analytics can improve the delivery process by predicting the best delivery routes and timeframes. With the help of AI and machine learning, e-commerce businesses can forecast demand in specific regions and adjust their logistics operations accordingly (10). This ensures faster and more efficient delivery, which is a key driver of consumer satisfaction. In fact, fast and reliable delivery is often cited as one of the top factors influencing consumers’ purchasing decisions (7). Enhancing Pricing Strategies Pricing is another area where predictive analytics can significantly enhance the consumer experience. Dynamic pricing, powered by predictive models, enables e-commerce businesses to adjust prices in real time based on demand, competition, and other external factors 6. For example, an e-commerce platform might lower the price of a product when demand is low or raise the price when demand is high. This creates a sense of urgency and encourages consumers to make a purchase at the right moment (1,5). Furthermore, predictive analytics can help businesses determine the optimal pricing strategy for different customer segments. By analyzing data on customer demographics, purchasing power, and preferences, businesses can create personalized pricing strategies that appeal to different types of consumers (1,6). The Impact on Consumer Trust and Loyalty Ultimately, the use of predictive analytics in e-commerce can significantly enhance consumer trust and loyalty. When customers are offered personalized recommendations, proactive customer service, fast deliveries, and fair prices, they are more likely to trust the brand and return for future purchases (7,6). Predictive analytics helps businesses build long-term relationships with customers by offering them the right products at the right time, creating a seamless and enjoyable shopping experience. In addition, when customers feel that their needs are anticipated and met, they are more likely to recommend the brand to others (7). Positive word-of-mouth and customer referrals are powerful tools in driving business growth, and predictive analytics contributes to this by fostering a deeper connection between the brand and the consumer. Conclusion Predictive analytics has become an indispensable tool in enhancing the consumer experience in e-commerce. By leveraging data and AI, businesses can offer personalized shopping experiences, anticipate customer needs, optimize inventory and delivery processes, and improve pricing strategies. The ability to predict consumer behavior and respond proactively to their needs not only increases customer satisfaction but also drives business growth and loyalty. As e-commerce continues to evolve, predictive analytics will play an increasingly vital role in shaping the future of the industry. Businesses that embrace this technology and leverage its full potential will have a competitive edge in delivering exceptional consumer experiences that keep customers coming back. Predictive analytics is not just about forecasting future trends; it’s about creating a future where businesses and consumers are better connected, and where personalized experiences lead to stronger, more lasting relationships.
Copyright (c) 2024 International Journal of Business & Computational Science https://creativecommons.org/licenses/by-nc-nd/4.0
Latency Optimization in Edge vs. Cloud Computing: A Comparative Study for Real-Time Applications
Background and Purpose: The increasing demand for real-time applications such as autonomous vehicles, industrial automation, and telemedicine has driven the need for low-latency computing solutions. While traditional cloud computing offers extensive computational power, it suffers from latency due to long-distance data transmission. In contrast, edge computing reduces latency by processing data closer to the source, despite facing resource limitations. This study is designed to compare latency optimization strategies in edge versus cloud computing to support the unique demands of real-time applications. Methods: The research employs a comparative analysis framework that focuses on key performance metrics and real-world case studies. It evaluates various optimization techniques including caching, network slicing, AI-driven workload allocation, and data compression. This approach allows for a systematic assessment of the latency performance and resource efficiency of both computing paradigms. Findings: Results indicate that edge computing substantially reduces latency by minimizing the distance data must travel, though it is constrained by limited resources. In contrast, cloud computing, while offering high computational capabilities, introduces latency overhead due to extended data transmission. The findings suggest that hybrid models, which integrate edge and cloud computing, provide the most effective balance by leveraging the strengths of each paradigm to enhance overall system performance. Theoretical Contributions: This study advances the theoretical understanding of distributed computing architectures by clarifying the inherent trade-offs between latency reduction and resource availability. It contributes a novel framework for evaluating hybrid edge-cloud models, thereby enriching the academic discourse on real-time system optimization. Conclusions and Policy Implications: The research concludes that hybrid computing models offer a promising solution for real-time applications by combining low latency with scalable computing power. Policymakers and industry leaders are encouraged to consider integrated edge-cloud infrastructures in future technology deployments, as these models can enhance performance while addressing the limitations of each individual approach.
Sustainable Business Practices: Integrating Environmental, Social, and Governance (ESG) Metrics
Background and Purpose: Sustainability has become a crucial aspect of corporate strategy, driven by increasing stakeholder expectations, regulatory requirements, and consumer demand for responsible business practices. Environmental, Social, and Governance (ESG) metrics serve as a framework for assessing corporate sustainability and ethical impact. This study examines the role of ESG metrics in business management, their influence on corporate performance, and the challenges and opportunities associated with their adoption. Methods: The paper employs a multidisciplinary approach, integrating insights from existing literature, case studies, and empirical data to analyze the implementation of ESG metrics. It explores corporate governance structures, performance indicators, and regulatory frameworks to assess how businesses integrate ESG principles into their operational models. Findings: The study reveals that integrating ESG metrics enhances long-term business sustainability by improving risk management, stakeholder engagement, and financial performance. Companies that align their strategies with ESG principles tend to experience increased investor confidence and competitive advantage. However, challenges such as standardization issues, reporting complexities, and resistance to change hinder widespread adoption. Theoretical Contributions: This research contributes to the growing body of literature on sustainable business practices by offering a comprehensive framework for understanding ESG integration in corporate governance. It highlights the interconnectedness of ESG dimensions and their impact on business resilience and value creation. Conclusions and Policy Implications: The findings underscore the importance of regulatory support and standardized ESG reporting frameworks to enhance transparency and accountability. Policymakers and corporate leaders must collaborate to establish clear guidelines and incentives for ESG adoption, ensuring long-term sustainability and ethical business practices.
AI-Powered Decision-Making: Balancing Automation and Human Oversight in Corporate Governance
Background and Purpose: The integration of Artificial Intelligence (AI) in corporate governance has significantly transformed decision-making processes, enhancing efficiency, transparency, and predictive accuracy. AI-driven analytics, machine learning algorithms, and robotic process automation (RPA) have enabled organizations to optimize risk management, regulatory compliance, and strategic planning. However, concerns regarding ethical accountability, algorithmic biases, and the diminishing role of human oversight necessitate a critical examination of AI’s role in governance. This study aims to explore the benefits and risks associated with AI adoption in corporate governance and highlight the need for a balanced approach that combines automation with human judgment. Methods: This paper employs a multidisciplinary approach, drawing from recent literature, case studies, and empirical analyses to assess the impact of AI on corporate governance. It examines the applications of AI in governance structures, identifies key challenges, and evaluates existing regulatory frameworks governing AI adoption in corporate decision-making. Findings: The study finds that AI-driven governance enhances operational efficiency, reduces compliance risks, and improves data-driven decision-making. However, over-reliance on AI introduces ethical concerns, including biases embedded in algorithms, lack of transparency in decision-making, and the potential marginalization of human judgment in critical corporate affairs. The findings underscore the necessity of hybrid governance models that integrate AI-driven insights with human expertise to mitigate risks and ensure responsible decision-making. Theoretical Contributions: This paper contributes to corporate governance and AI ethics literature by proposing a framework for responsible AI adoption that balances automation with human oversight. It extends existing discussions on algorithmic accountability and governance ethics by emphasizing the role of regulatory mechanisms and ethical AI frameworks in ensuring fair and transparent corporate decision-making. Conclusions and Policy Implications: To harness AI’s potential while mitigating associated risks, organizations must implement governance models that combine AI-driven insights with human expertise. Regulatory bodies should establish comprehensive frameworks to address algorithmic biases, ensure transparency, and uphold ethical standards in AI adoption. A balanced governance approach will foster trust, sustainability, and long-term value creation in corporate decision-making.
Role of AI in Business Framework Revolution in Developed Countries
Background and Purpose: The integration of Artificial Intelligence (AI) into business frameworks has transformed operations, decision-making, and customer engagement, particularly in developed economies. This study explores the profound impact of AI on businesses by examining advancements in automation, data analytics, and strategic decision-making. It also investigates AI-driven business models, associated challenges, ethical considerations, and the evolving regulatory landscape. Methods: This research employs a qualitative approach, utilizing case studies of AI adoption across various industries. It analyzes secondary data from academic literature, industry reports, and regulatory guidelines to assess AI's role in enhancing business efficiency and competitiveness. Findings: The study reveals that AI significantly improves business performance by automating processes, optimizing data-driven strategies, and enabling innovative business models. However, challenges such as data privacy concerns, algorithmic bias, and regulatory complexities persist. The research highlights how businesses navigate these challenges while leveraging AI to gain a competitive advantage. Theoretical Contributions: This paper contributes to the existing literature by providing a comprehensive framework for understanding AI’s role in business transformation. It integrates perspectives from technology adoption theories, competitive strategy, and regulatory compliance, offering a holistic view of AI’s impact on business sustainability and growth. Conclusions and Policy Implications: The study concludes that AI is a critical driver of business innovation and efficiency but requires balanced regulatory oversight to address ethical and operational risks. Policymakers must develop adaptive frameworks that encourage AI-driven growth while ensuring responsible implementation. Businesses should invest in AI governance and ethical AI practices to sustain long-term success in evolving market landscapes.