Optimizing Translation Accuracy and Contextual Sensitivity in Language: A Comparative Evaluation of AI-Based and Human Translation Approaches

Paulina Williams-Onyeji

doi:https://doi.org/10.54216/IJAIET.050203

American Journal of Business and Operations Research | 2026

Data-Driven Customer Retention for SMEs: Predicting Repeat Purchase and Customer Value

The strategic importance of customer retention in small and medium-sized enterprises (SMEs) is due to the fact that the resources are limited, and the indiscriminate customer acquisition and customer retention campaigns are economically inefficient. However, the descriptive reporting used by many SMEs does not have the advantages of transactiondriven analytics that allows differentiating between high-value and low-yield customer relationships. This paper creates a repli-cable customer-analytics pipeline in SME-type retail environments, using publicly available transactional data. In con-trast to the macro-level forecasting research, the paper integrates customer value segmentation with the futureoriented repeat-purchase prediction and translates the results into retention actions explicitly. The customer-level features were based on invoices, quantities, prices, product variety, and return behavior and were derived using the public Online Retail dataset. Observation windows on a monthly were transformed into a repeat-purchase 90-day problem. Three predictive models—logistic regression, random forest, and gradient boosting—were compared after customer segmentation based on recency, frequency, and monetary behavior. The findings indicate that random forest model had the highest discrimination (ROC-AUC = 0.750; PR-AUC = 0.821), followed by logistic regression, which was only slightly less than it and more interpretable. Segment analysis also showed a very concentrated revenue base with Champions having 27.5 percent of the customers but 67.2 percent of recent revenue and 81.0 rate of repeat purchasing. The paper provides a submission-ready, transparently reproducible, and managerially understandable design that is particularly applicable in SMEs that want low-cost retention analytics, customer ranking, and allocation of marketing resources.

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Ilknur Ozturk mail

link https://doi.org/10.54216/AJBOR.140201

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2026

Data-Driven Capital Allocation in Manufacturing Firms: An Investment Analytics Study Using Public Panel Data

This paper evolves a business data analytics approach to capital allocation by exploring how the use of public panel data can aid in estimating, classifying, and profiling strategic firms. The paper examines the claim that lagged market value, capital stock, and growth signals can explain the current investment behavior and hint when the investment activity is unusually high using the public-domain Grunfeld investment data, which has annual observations of major U.S. manufacturing firms. The empirical design is deliberately non-standard as compared to typical forecasting research and it consists of three analytical layers; fixed-effects panel estimation, supervised classification of high-investment periods, and firm-level strategic segmentation. The findings indicate that the growth of lagged investment, lagged capital stock and firm value is highly correlated with the present level of investment, and that machine-learning classifiers offer helpful discrimination of high in-vestment periods. Strategic segmentation exercise also indicates the clear profiles of firms that can be used to prioritize resources and track capital. The value of the paper is two-fold. First, it illustrates how an old, conventional, public data may be re-used as a new business data analytics example to support decision-making. Second, it interprets quantitative results into a managerial advice on capital planning, growth monitoring, and portfolio-style firm evaluation. Accordingly, the paper provides a reproducible submission-ready study that has a different structure than the traditional business intelligence forecasting papers and is more in line with the requirements of strategic financial analysis and data-driven capital allocation.

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Syed Muhammad Mudassir Abbas Naqvi mail -

Ahmed Usman mail

link https://doi.org/10.54216/AJBOR.140202

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2026

A Business Intelligence Framework for Short-Term Consumer Demand Forecasting Using Public Macroeconomic Indicators

Business intelligence has emerged to be a high-level managerial competency among organizations that aim to enhance the quality of planning, responsiveness in operations and evidence-based decision making in uncertain market environments. Short-term demand forecasting is one of its most important business applications since fluctuations in demand expectations affect budgeting, inventory planning, staffing, procurement timing and revenue management. The paper formulates and tests a business intelligence system of consumer demand prediction over short-term with the help of the public macroeconomic variables. It aims to show how external economic signals may be converted into an explainable, reproducible, and useful forecasting layer to be used in dashboards and decision support systems. The research forecasts next-period real consumer spending using lagged indicators based on output, disposable income, investment, unemployment, inflation, and short-term interest rates using a publicly available U.S. macroeconomic data, which is periodically updated. Ordinary least squares, ridge regression, random forest and gradient boosting are compared by using a chronological holdout design. The empirical findings indicate that the regression-based models that are interpretable have the best out-of-sample performance, and ordinary least squares model has the lowest error and greatest explanatory power. The results suggest that effective business forecasting support can be offered using transparent analytics without the need to use complex black-box models. The study is valuable because it adds to the body of business intelligence literature a reproducible external-signal prediction pipeline, a comparison of the explainable and non-explainable models in a management context, and a translation of the forecasting results into operational and strategic planning consequences.

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Ilhan Ozturk mail

link https://doi.org/10.54216/AJBOR.140203

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Metaheuristic Optimization Review | 2026

Artificial Intelligence-Assisted Alzheimer’s Disease Research: A Review of Pathology, Early Diagnosis, Biomarkers, Therapeutic Challenges, and Care Implications

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and one of the major causes of cognitive decline, functional impairment, and long-term dependency in older adults. Although AD is often associated with memory loss, its clinical impact extends to language, executive function, attention, behavior, daily living ability, caregiver burden, and healthcare-system demand. This review examines AD as a multifactorial and clinically heterogeneous disorder shaped by interacting pathological, molecular, diagnostic, therapeutic, caregiving, and publichealth dimensions. In addition, the review highlights the growing role of artificial intelligence (AI) in AD research and clinical support. AI-based approaches are increasingly being explored for neuroimaging analysis, biomarker interpretation, cognitive assessment, disease-risk prediction, patient stratification, early detection, and longitudinal monitoring. These methods may support more accurate and timely diagnosis, especially when combined with clinical evaluation, biomarker evidence, and patient history. However, AI should not be considered a replacement for clinical judgment. Its value depends on validation, interpretability, ethical use, data quality, accessibility, and real-world clinical integration. The reviewed literature shows that amyloid beta accumulation, tau pathology, synaptic dysfunction, neuronal loss, neuroinflammation, oxidative stress, vascular contribution, mixed pathology, and brain atrophy all contribute to AD progression and clinical variability. Despite advances in biological understanding, biomarker-based diagnosis, and computational tools, important challenges remain, including subtle early symptoms, overlap with normal aging and other disorders, unequal access to advanced diagnostics, limited clinical deployment of AI models, uncertain translation of biological treatment effects into meaningful functional benefit, and substantial caregiver burden. Overall, this review emphasizes the need for an integrated and patient-centered framework that connects AD pathology, AI-assisted diagnosis, biomarker development, therapeutic innovation, caregiver support, and practical healthcare implementation.

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Ziad Shendy mail

link https://doi.org/10.54216/MOR.060106

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2026

Energy Optimization Problems: A Comprehensive Review of Metaheuristic Algorithms and Recent Advances

Introducing renewable energy into contemporary power systems is crucial to guaranteeing sustainable solutions and improving energy performance. Optimizing energy generation, demand forecasting, and system stability have become difficult with the increasing popularity of renewable energy sources like wind and solar energy systems. This literature review explores recent advances in addressing these challenges by applying artificial intelligence (AI), machine learning (ML), and metaheuristic optimization algorithms. Some of those papers are reviewed because they show advancements in forecasting renewable energy generation, controlling hybrid microgrids, and managing energy in smart grids. Particular attention is given to innovative models such as adaptive dynamic grey wolf-dipper throated optimization (ADGWDTO) for wind speed prediction, the Evolutionary Neural Machine Inference Model (ENMIM) for residential energy consumption, and the Wolf-Inspired Optimized Support Vector Regression (WIOSVR) for building energy forecasts. Further, the review discusses the emergence of hybrid renewable energy systems and evaluates advancements in techno-economic optimization. The works under review explore advancements in prediction performance, system availability, and cost, thus making a real contribution to further developing reliable and effective energy systems. Thus, these findings may be used to change to more sustainable energy systems in urban and off-grid environments. It will also lead to further exploration of new optimization techniques and improved synergistic application of renewable energy into electricity networks worldwide.

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Safina Shokeen mail -

Vishal Srivastava mail

link https://doi.org/10.54216/MOR.060107

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2026

Deep Learning-Based Classification of Brain Tumors from Magnetic Resonance Imaging Scans Using a Convolutional Neural Network Model

Brain tumors are serious neurological conditions that require accurate and timely classification to support medical evaluation and treatment planning. This project presents a deep learning-based system for classifying brain Magnetic Resonance Imaging (MRI) scans into four categories: glioma, meningioma, pituitary tumor, and no tumor. The proposed system uses a Convolutional Neural Network (CNN) trained on a balanced dataset of 7,200 MRI images collected from publicly available sources. The images were preprocessed through RGB conversion, resizing, tensor transformation, and normalization to ensure consistent input for model training and testing. The trained model achieved an overall classification accuracy of 94.31% on a held-out test set of 1,600 MRI images, demonstrating strong performance in multi-class brain tumor classification. A Streamlit-based web application was also developed to allow users to upload MRI images and view the predicted class, confidence score, and probability distribution across the four categories. The system is intended for educational and research purposes only and should not replace professional medical diagnosis, clinical judgment, or radiological evaluation.

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Karim Eldreny mail

link https://doi.org/10.54216/MOR.060108

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Journal of International Economics Research | 2026

The China–Pakistan Economic Corridor and the Dynamics of Regional Connectivity

The China–Pakistan Economic Corridor (CPEC), the flagship project of China’s Belt and Road Initiative (BRI), has emerged as a transformative framework for enhancing regional connectivity and economic integration across South Asia, Central Asia, and the Middle East. This study examines the role of CPEC in strengthening regional connectivity through infrastructure development, energy cooperation, trade facilitation, and strategic partnerships. Drawing on the conceptual relationship between infrastructure and regional integration, the chapter analyzes how investments in transportation networks, energy projects, Gwadar Port, and special economic zones have improved Pakistan’s internal connectivity and created opportunities for broader cross-border linkages. Particular attention is given to the potential of CPEC in fostering connectivity between Pakistan, China, Afghanistan, Central Asia, Iran, and Saudi Arabia. The findings suggest that CPEC has significantly improved Pakistan’s energy and transport infrastructure, reduced logistical constraints, and established the foundations for regional economic cooperation. Furthermore, the corridor provides landlocked Central Asian states and Afghanistan with access to global markets through Gwadar Port while creating new prospects for trade, energy collaboration, and regional integration. The study concludes that although CPEC has laid the groundwork for enhanced regional connectivity, its long-term success depends on political stability, security cooperation, and sustained collaboration among participating countries. Overall, CPEC represents a strategic geo-economic initiative with the potential to reshape regional connectivity and promote shared economic development across a wider Eurasian landscape.

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Maaz Ahmad mail -

Suhrob Gadoev mail -

Arif Khan mail

link https://doi.org/10.54216/JIER.040206

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2026

Business Data Analytics for GCC Travel and Tourism SMEs Under Geopolitical Disruption

The paper creates the business data analytics vision of how small and medium-sized enterprises (SMEs) of GCC travel and tourism ecosystems can mitigate the commercial disruption as the perceived cost, uncertainty, or inconvenience of air travel increases due to geopolitical friction in the region. Since there is a lack of public GCC micro-level booking and itinerary data, the research paper relies on a similar public dataset: the travel mode-choice dataset published under the name of statsmodels and initially based on the intercity mode-choice literature. The benchmark is operationalized as an analogue of disruption-sensitive travel demand reallocation and poses a managerial question, not a simply transport question: in the event of a shock that increases generalized cost and waiting frictions on the most exposed mode what are the most likely demand reallocations and how should SMEs respond? Empirical design transforms the data in the long-format alternative-choice form into an analytical platform that is business-facing and integrates multinomial logit, random forest, gradient boosting, and scenario stress testing into a single analytical framework. The findings indicate that the random forest model provides the best out of sample predictive performance (accuracy 0.981; macro-F1 0.973), whereas the multinomial logit model is useful in translating scenarios that can be understood. Average predicted air share decreases by 28.0 to 16.1 percent with simulated air-travel disruption, and train-like substitutes acquire most of the share. The results suggest that GCC travel, hospitality, and mobility SMEs cannot afford to trust one open channel when a period of geopolitical escalation occurs, but rather they should develop substitution-ready packages, flexible repricing guidelines, and portfolios of partnering that encompass low-friction options. The article adds a unique business analytics template of demand reallocation sensitive to crisis through the use of repeatable public information and underlines practical resilience solutions as opposed to self-forecasting wars.

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Shummaila Afzal mail -

Sidra Sohail mail -

Sana Ullah mail

link https://doi.org/10.54216/AJBOR.140204

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2026

International Perspectives on Transforming Teaching and Learning in Higher Education

Background: The rapid development of artificial intelligence (AI) is changing the sphere of higher education, creating a pressing need for graduates with competencies related to AI. Conventional degree-based credentialing models are difficult to adapt to the rate, interdisciplinary, and job alignment demanded in AI-driven economies. Because of this, the other forms of AI credentials, including micro-credentials, certificates, and stackable learning pathways, have become of worldwide significance. This review critically evaluates international views of AI credentialing and reviews the effects of these credentials on changing the teaching and learning practices in higher education. Methods: Narrative review methodology was used to summarize peer-reviewed articles, policy reports, and international frameworks published in 2015-2025. The important sources were Scopus, Web of Science, ERIC, UNESCO, OECD, and publications of the World Economic Forum. Thematic analysis was employed to determine the global trends, pedagogical changes, and policy implications. The review shows an increasing convergence through competency oriented, flexible, and industry-oriented AI credentials in the regions. Such qualifications facilitate curriculum modularization, experiential and personalized learning, and graduate employability. Nevertheless, there are issues of quality assurance, standardization, fair use of AI, and fair access. Conclusion: AI credentials are both a great offer to curriculum innovation, personnel growth, and lifelong learning, and a strong governance and collaboration across sectors. AI credentials are a disruptive process of rethinking teaching and learning in higher education. Their implementation should take place in strategic, ethically based, and globally coordinated action so as to achieve their full potential.

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Ashmun Nisha mail

link https://doi.org/10.54216/IJAIET.050202

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2026

Optimizing Translation Accuracy and Contextual Sensitivity in Language: A Comparative Evaluation of AI-Based and Human Translation Approaches

This study examines the effectiveness of artificial intelligence (AI)–based language translation in comparison with human translation, with particular attention to accuracy and contextual sensitivity in educational settings. As higher education institutions increasingly integrate AI tools into language instruction and multilingual course delivery, evaluating their pedagogical reliability and cultural appropriateness has become essential. Using a mixed-methods design, the study analyzes translation outputs produced by leading AI systems and professional human translators across academic texts, lectures, and instructional materials in multiple language pairs. Quantitative measures assess linguistic accuracy, while qualitative analysis focuses on cultural nuance, disciplinary appropriateness, and contextual meaning. Semi-structured interviews with university lecturers and students further explore perceptions of the benefits and limitations of AI-assisted translation in academic contexts. Findings indicate that although AI tools offer substantial advantages in speed and accessibility, they remain less effective in maintaining disciplinary precision and cultural depth. The study recommends a complementary model in which AI technologies support, rather than replace, human expertise in multilingual academic communication.

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Paulina Williams-Onyeji mail

link https://doi.org/10.54216/IJAIET.050203

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

Details open_in_new