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American Journal of Business and Operations Research | 2025

Gorilla Troop Optimizer-Driven Fault-Tolerant Scheduling for Cloud-Based Business Workflows

The study proposes a GTO-FTASS (Gorilla Troop Optimizer-Based Fault Tolerant Aware Scheduling Scheme) for improving the reliability and performance in the cloud computing context. Cloud systems are more likely to fail due to the architecture of these layers and dependence on both the hardware and software, therefore require more sophisticated fault-tolerant solutions. The preliminary to this work is the design of an adaptive GTO-FTASS with a fitness function based on two constraints: Expected Time of Completion (ETC) and Failure probability that were derived from the gorilla value system. The approach provides resource utilization and task planning with the provision of fault recovery hence reducing exposure to time loss and operational vulnerability. MGS outperforms several state-of-the-art models, such as MTCT, MAXMIN, ACO, NSGA-II, and DCLCA in terms of makes pan, failure ratio and failure slowdown. Finally, the applicability of experimental validation with various situations and fluctuating intensities demonstrates the scalability of the model and its stability under pressure, decreased failure rates and increased effectiveness of performed tasks. Through the approaches to latency, resource, and error correction, GTO-FTASS is an investment that stewards have to make to cut costs and achieve high performance on clouds. The framework also provides competitive benefit and robustness for cloud enterprising in fluctuating and crucial strategic applications.

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Takura Wekwete mail
link https://doi.org/10.54216/AJBOR.120102

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new
American Journal of Business and Operations Research | 2025

Comprehensive Analysis of Stock Price Dynamics Using Ensemble Machine Learning Models for Enhanced Prediction Accuracy

Stock price prediction is an important component of the financial analysis because the results influence the increase in economic growth and investment. This work aims to develop an ensemble SL technique that consists of mainly PCA, PSO, and SVM to achieve better prediction. Hence, through PCA, large numbers of stocked data dimensions are compressed without compromising on the crucial feature of data set. The problem of parameter selection for non-linear datasets is handled by using a bio-inspired optimization technique known as PSO in order to optimize the SVM hyperparameters. As the core accurate predictor model, the SVM employs the Radial Basis Function to provide the substantial regression capacity for sophisticated financial data sets. The ensemble framework was used with actual stock price data and the information set into training and testing sets. The acknowledgement of probable manifold values indicated that the proposed approach is more accurate than conventional approaches, with an accuracy rate of 95.5 %, when benchmarked using RMSE or MAE. In particular, the forecasts of stock prices by integrating PCA for feature reduction and PSO for parameter tuning with SVM regression is a notable improvement. The proposed methodology can be easily applied to scale for financial analytics since it manages to solve for the issues of noisy and non-linear high dimensional data.

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Vinamra Nayak mail
link https://doi.org/10.54216/AJBOR.120103

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new
American Journal of Business and Operations Research | 2025

Optimized Hybrid Machine Learning Approaches for Stock Market Forecasting and Time Series Analysis

The research introduces an innovative hybrid model of KPCA, ESVM, and TLBO to analyze stock price variation and time series forecasting. To handle the issue of high dimensionality of the financial data and the nonlinear dependencies amongst the variables, the model employs KPCA for feature extraction, thus identifying, and retaining only the feature space that is most relevant. Subsequently, the features extracted are passed through ESVM for regression – aiding in correct estimations on stock prices. To improve the outcome, prediction accuracy and to fine transient parameters of the model TLBO as a metaheuristic algorithm is used. The application of KPCA-ESVM-TLBO establishes optimal characteristics from the above methodologies, producing efficiency in tackling complications and nonlinearity of the data structures. KPCA looks for hidden structure; ESVM does regression with the kernel; and TLBO twiddles appropriate knobs such as λ and kernel coefficients. By using real-world financial data sets, the experimental evaluations presented show that the reported method outperforms the conventional benchmarks in relations of predictive accuracy. MAE, RMSE, and accuracy confirm its relevance: predictive accuracy of 99.99%. This approach to using artificial neural networks in tandem with a nearest neighbor algorithm presents the prospect of a potent weapon for forecasting and decision making in ever complex and volatile market.

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Sushmita Mallik mail
link https://doi.org/10.54216/AJBOR.120104

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new
American Journal of Business and Operations Research | 2025

Analysis of the Dynamics of the State External Debt of the Republic of Uzbekistan

This article examines the theoretical aspects of the external debt of the Republic of Uzbekistan. The study analyzed the dynamics of the state external debt of the Republic of Uzbekistan, its structure and creditors and the distribution of external debt by industry. The article also examines the reasons for the increase in public debt and develops strategies for further reducing the state external debt

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Akhmedova Dilafruz Muratovna mail
link https://doi.org/10.54216/AJBOR.120105

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new
American Journal of Business and Operations Research | 2025

Assessment of the Current State of Investment Attractiveness of Uzbekistan

This article provides insights into Uzbekistan’s investment environment for investors and policymakers, focusing on both the investment potential and the overall investment climate of the country. It examines key economic sectors, government policies, and ongoing reforms aimed at improving transparency and attracting foreign investment.

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Eshonkulova Sayyorabonu mail
link https://doi.org/10.54216/AJBOR.120106

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new
American Journal of Business and Operations Research | 2025

Assessing the Impact of Key Marketing Variables on the Diffusion and Commercial Success of Technological Innovations

An all-inclusive profit-maximizing methodology for optimising the cost of selling and warranties term of technical improvements is presented in this research. In order to reduce warranty expenses and maximise product dependability, the model combines preventative maintenance tactics. To predict consumer actions, we use a two-dimensional diffusion of innovations framework that accounts for the impact of pricing and time on uptake rates. The distribution calculated by Weibull is used to simulate breakdown rates, taking into consideration the effect of routine upkeep on lowering the cost of repairs and systems deterioration. While making sure that supply and demand are met, profit management incorporates important cost factors such as manufacturing costs, structural expenses, costs for warranties, and servicing charges. To help manufactures maximise profits, the suggested methodology offers an ordered approach to determining the appropriate guarantee periods and marketplace prices. Validating the theory's practicality and demonstrating large profit benefits via optimum decision-making are computational optimisation methods and instances, such as repaired semiconductors. Variables like as warranties duration as well as service level have a significant influence on economic viability, as shown by sensitivity analysis. Organisations seeking to increase customer happiness, guarantee fiscal viability, and gain edge over competitors in ever-changing marketplaces might find useful insights in the profit maximisation approach, which combines sales methods with technological dependability approaches. The accuracy of Profit Maximisation Model approach is far much higher that of LR, DT, and RF by a margin of around 96.5%. This work suggests that the proposed approach improves the conventional algorithms with respect to prediction accuracy and error minimisation. This is true as evidenced by its exceptional performance on different parameters to demonstrate its reliability and coherence in delivering excellent results.

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Ilknur Ozturk mail
link https://doi.org/10.54216/AJBOR.120107

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new
Journal of International Economics Research | 2026

Affective-Cognitive Prospect Theory: An Integrative Psychological-Economic Model of Decision-Making under Uncertainty

Standard models of decision-making under uncertainty, from expected utility theory to cumulative prospect theory, treat risk preferences as fixed individual traits. Yet a substantial psychological literature shows that risk-relevant preferences shift systematically with the decision-maker’s momentary internal state: affective arousal amplifies loss aversion, while cognitive load and time pressure push judgment toward heuristic, more distorted probability processing. This paper proposes Affective-Cognitive Prospect Theory (ACPT), a state-dependent extension of cumulative prospect theory in which the loss-aversion coefficient is an increasing function of affective arousal and the curvature of the probability-weighting function is a decreasing function of cognitive load, each grounded in an established psychological literature.

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Dmitriy Yagudin mail
link https://doi.org/10.54216/JIER.040106

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new
American Journal of Business and Operations Research | 2025

Optimized Composition of Business Process Web Services via QoS-Based Categorization Using Decision Tree Classifier and Knowledge-Based Decision Support

Determining web services according to Quality of Service (QoS) restrictions is the topic of discussion in this section. Decision tree classifiers are used to accomplish this classification. Because of the ever-changing and expanding nature of online services, it is necessary to accurately categorize them in order to make choosing them more efficient for consumers. It makes use of decision tree techniques, more especially the C5.0 classifier, this is an advancement over older approaches such as the C4.5 classifiers. It incorporates characteristics like as noisy handling, incomplete information administration, and improved decision-making correctness. Web services are classified into four distinct groups: Outstanding, Good, Average, and Poor. These classifications are determined by QoS metrics that include time to response, accessibility, performance, dependability, and success rate. The choice of features is accomplished utilizing an evolutionary algorithm with a wrapper technique with the goal to maximize the effectiveness of this category. This method minimizes the number of repetitive features and improves the method of classification for the purpose of optimization. The resilience and predicted reliability of the algorithm are ensured by additional approaches like as cross-validation and error reduction. These approaches also address difficulties such as overfitting and redundant characteristics. The construction of integrated web services for complicated corporate operations is a particularly valuable use of this technology, which also considerably improves the procedure for making choices for identifying services and consumption. Service 7 stands out with an impressive 98% performance, while Service 6 and Service 3 are also among the top-performing services. Compared to the others, Service 1, Service 2, Service 5, and Service 4 all exhibit comparatively poor results.

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Larisa Ivascu mail
link https://doi.org/10.54216/AJBOR.120201

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new
American Journal of Business and Operations Research | 2025

Leveraging Time Lag-Based Diffusion Models to Predict Innovation Adoption for Optimized Product Development

The suggested models for the spread of technical breakthroughs make use of a phase structure to illustrate the steps involved in becoming familiar with the problem and making a choice. For it to portray genuine adopting conduct, a time-lag factor is included into the dispersion process. Depicts a two-step dissemination process by taking into account the reliance of adopting on the informed group of potential purchasers. Assuming that a prospective customer first becomes intrigued by an upcoming the item's availability and then accepts the novel idea at an ulterior point, a method of analysis for sales functions that incorporates time delay is proposed. The efficient propagation method for invention is shown using the various lag factors. Applying nonlinear regression modelling to worldwide shipping data of Acer PCs and Samsung smartphones experimentally validates the suggested models for mathematics. Several comparison models are used to evaluate the predicting abilities of the suggested models. By integrating a distributed time delay function into the implementation manage, a theoretical intergenerational diffusion model is created. To measure how long it takes for innovation to be eventually accepted, the distributed time lag function that follows the Erlang distributions is used. This framework incorporates switch and substituting, two forms of pragmatist shift behaviour. Using real shipping data of LCD (Liquid Crystal Display) computer monitors from consecutive generations, the predicted effectiveness of the suggested methods is examined and contrasted with well-established research. Here is the total accuracy of the approaches that have been proposed: When contrasted with more conventional models, MGDM 1 achieves a 99.33% accuracy rate, MGDM 2 a 99.81% rate, and MGDM 3 a 99.91% accuracy rate.

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Muddassar Sarfraz mail
link https://doi.org/10.54216/AJBOR.120202

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new
Fusion: Practice and Applications | 2024

Neutrosophic Model for Sentiment Data Analysis

Sentiment analysis has recently become popular in social, political and health related fields, but it has a common limitation of capturing the subjectivity involved in multiple human expressions. In this study, we tackle this concern by presenting a model that is constructed using neutrosophic logic which can incorporate indeterminacy in the evaluation of perceptions. Although some answers may be provided by the traditional methods, they fail to contain the uncertainties and contradictions which are characteristic of natural language, making them difficult to implement in complicated situations. In this methodological gap, the neutrosophic model is presented as a tool capable of overcoming these limitations by explicitly treating uncertainty and balancing definite, indeterminate, and contradictory elements. The integration of machine learning algorithms with neutrosophic techniques helps classify and visualize sentiments embedded in big volume of text data. The findings suggest that this methodology not only enhances the precision in the identification of emotional subtleties but also provides a hybrid platform for integrating imprecise information. His credits are based on the development of a theoretical model which advances the field of sentiment analysis and the development of real-life applications in customer services for example, political analytics and strategic decision making. This methodological advance demonstrates that incorporating neutrosophic logic into sentiment data analysis opens up new possibilities for understanding and modeling the complexities of human perceptions.

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Ned Vıto Quevedo Arnaız mail -
Genaro Vınıcıo Jordan Naranjo mail -
Diego Xavier Chamorro Valencia mail -
Joffre Joffre Paladines Rodríguez mail -
Anna Mixaylovna Aripova mail
link https://doi.org/10.54216/FPA.160214

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new