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

Ziad

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

Galoitica: Journal of Mathematical Structures and Applications | 2026

New Concepts of MetaStructures: Algebra, Topology, Lattices, Queues, Markov Chains, and Intervals

A MetaStructure is a higher-level framework that treats entire collections of structures as single objects, equipped with natural operations that preserve isomorphisms across different domains. The term “Struc- ture” here refers broadly to mathematical systems as well as real-world models. An Iterated MetaStructure generalizes this idea recursively, generating successive layers in which structures of structures form deeper hierarchical meta-levels. In this work, we extend and investigate the properties of Algebra, Topology, Lattices, Queues, Markov Chains, and Intervals through the lens of MetaStructures and Iterated MetaStructures.

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Takaaki Fujita mail -

Ajoy Kanti Das mail

link https://doi.org/10.54216/GJMSA.130103

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

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Journal of International Economics Research | 2026

Empirical Analysis of Financial Stability of Agro-Clusters in Uzbekistan

This study examines the financial stability of agro-clusters with a focus on identifying key determinants that influence long-term asset growth and overall economic sustainability. Using cross-sectional data, the research applies an Ordinary Least Squares (OLS) regression model to analyze the impact of workers, depreciation coefficient, validity coefficient, and current assets on long-term assets. The empirical results reveal that labor capacity, liquidity, and operational efficiency have a positive and statistically significant effect on financial stability, while the depreciation coefficient shows a negative but insignificant relationship. Diagnostic tests confirm the reliability and robustness of the model, including normality of residuals and absence of heteroscedasticity. The findings highlight the importance of efficient resource management, access to financial capital, and effective asset utilization in strengthening agro-cluster performance. From a policy perspective, the study suggests that improving workforce productivity, enhancing financial accessibility, and promoting modern management practices are essential for achieving sustainable growth in the agricultural sector. The results contribute to the existing literature by providing empirical evidence on the financial dynamics of agro-clusters, particularly in the context of developing economies such as Uzbekistan.

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Dildora Yuldasheva mail

link https://doi.org/10.54216/JIER.040101

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2026

Cybercrime and Digital Competence among Students at a Public University in Lima

This article is part of an exhaustive study that aspired to determine the relationship between cybercrime and digital competence in sixth-cycle undergraduate students at a public university in Lima. The hypothesis was a sincere relationship between the two variables. The methodology applied is a quantitative, basic, correlational approach with a non-experimental cross-sectional design. The results reflected a medium positive correlation between cybercrime and digital competence, with a Kendall's Tau-b coefficient of 0.585 and a significance level of 0.000 (p < 0.05). In conclusion, it was evident that greater digital competence is associated with greater exposure to cybercrime risks, suggesting the need to implement educational strategies aimed at strengthening digital security in the university environment.

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Belén Vila Osores mail

link https://doi.org/10.54216/JCIM.180102

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2026

An Explainable Hybrid SVM Framework for Spam and Malicious Email Detection in Enterprise Information Systems

Email has been a key communication and information-management tool in contemporary organizations, yet it is also one of the most misused avenues to spam, fraud, credential theft, and malicious code delivery. Lightweight and reproducible detection models are especially useful to universities, public institutions, and small-to-medium enterprises which might not have access to costly proprietary filtering infrastructures because of the operational relevance of email security. In this paper I suggest an Explainable Hybrid SVM Framework (EHSF) to detect spam and malicious-risk email in a business information system. The framework integrates TF–IDF representation of text with lightweight risk-based email indicators, such as structural and lexical cues that can be obtained at low computation cost. An external Enron- Spam data were used so that it may be reproducible and will be checked later by the reviewers and readers. The experimentation process was coded in Python and assessed in terms of accuracy, precision, recall, F1-score, ROC-AUC, and confusion-matrix. These findings demonstrate that the suggested Linear SVM-based framework has the highest overall performance with accuracy of 0.9853, precision of 0.9818, recall of 0.9893, F1-score of 0.9855, and ROC-AUC of 0.9981 on the held-out test set. The confusion matrix shows that there were only 34 false negatives and 58 false positives which show that there was a good discrimination between ham and spam classes. Besides the predictive performance, the framework provides an interpretable layer based on the analysis of influential lexical indicators related to risky and legitimate enterprise emails. The research adds a replicable and operationally viable methodology that complies with the needs of cybersecurity and information-management, and is lightweight enough to be implemented in the real-life setting within an organization.

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Mahmoud A. Zaher mail -

Nabil M. Eldakhly mail

link https://doi.org/10.54216/JCIM.180103

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

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Journal of Cognitive Human-Computer Interaction | 2025

Explainable Eye-Tracking-Based Cognitive Workload Classification for Interactive Visual Tasks: A Reproducible Human-Computer Interaction Study Using the Public COLET Dataset

Attention allocation, efficiency of interactions and the formation of errors during human-computer interaction (HCI) are directly influenced by cognitive workload. Eye tracking provides a feasible, non-invasive source of evidence to estimate workload since the behavior of gaze is strongly correlated with visual search, task processing and decision effort. The paper explores explainable cognitive workload classification based on explainable cognitive workload on the public COLET dataset; eye-tracking recordings of 47 subjects completing interactive search tasks of the visual-search with workload labels based on NASA-TLX. The five supervised learning models are tested on binary and four-class problems, and the most successful setup is analyzed via SHAP-based feature attribution. In both tasks, boosting-based ensembles are best at predictive behavior, with XGBoost scoring highest on the overall and binary low-v-high discrimination scores in the best range of performance reported in the original COLET benchmark. The feature analysis attribute shows that the most significant variables are gaze entropy, fixation time, pupil changes, and saccadic movements. The results are consistent with the application of explainable gaze-based models to adaptive interfaces that can adapt to a rising mental load by making the content simpler to present, varying the pacing, or attentive to important information.

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Mahmoud A. Zaher mail -

Nabil M. Eldakhly mail

link https://doi.org/10.54216/JCHCI.100202

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

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Journal of International Economics Research | 2026

Logistics Performance and Global Trade Integration: An Empirical Analysis of the Logistics Performance Index (LPI) Across 153 Countries

How much does logistics efficiency actually matter for a country’s trade performance in today’s volatile global economy? This study explores this question by analyzing a comprehensive dataset of 153 countries for the year 2023. Using a robust OLS regression, the research examines the direct relationship between the Logistics Performance Index (lpi) and national trade-to-GDP ratios, while also accounting for economic development (gdp_pc) and macroeconomic stability (inflation). The empirical results offer clear evidence that logistics is a primary driver of trade success. The model reveals that a better logistics environment has a statistically significant positive impact on trade integration (coefficient = 0.2798, p < 0.05). This suggests that reducing "trade friction" through smarter customs and better infrastructure is essential for global competitiveness. Furthermore, the analysis shows that while higher income levels support trade, price instability remains a major obstacle, with inflation showing a strong negative effect (-0.4174, p < 0.001). These findings lead to a straightforward conclusion: to thrive in the modern market, nations must look beyond physical borders and invest heavily in the speed, reliability, and digital integration of their supply chains. This research provides a practical roadmap for policymakers aiming to enhance their country’s international trade footprint.

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Pardaev Khurshidbek mail -

Muhammad Eid Balbaa mail

link https://doi.org/10.54216/JIER.040105

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

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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