2692-2967ISSN (Online)
2770-0216ISSN (Print)
The fault tolerance study carried out in this research explores Bidirectional Long Short-Term Memory (LSTM) and Generative Adversarial Networks (GAN) to improve cloud computing dependability and functionality. Being an integral part of the rage for business operations, cloud-computing fundamentals of resource provisioning and fault tolerance have a bearing on the overall cost-dynamics, ROI and OpEx. Reliability covers such issues as hardware failures, configuration problems and other network issues that may have financial implications and even lead to revenue loss, and failure to meet service level agreement (SLA). The work develops a novel GAN-BiLSTM model for the accurate prediction of faults and the enhancement of recovery management, resulting in resource efficiency and cost of capital reduction (CapEx). Evaluation criteria involve deadline guarantee ratio, average task delay, and system scalability, confirming that the proposed model has better financial performance than DPSO and ANFIS. Cutting wastage of resources and increasing energy capacity in a system, the model displays attractive cost reduction and operating effectiveness for cloud service providers. In the simulation, important results of the model are demonstrated in the business continuity, financial risk reduction as well as maintaining accurate and resourceful service in high demand situations. All these developments have placed the fault tolerant systems powered by machine learning as indispensable instruments that can also enhance profitability, resources utilisation and sustainable competitiveness in the cloud computing business.
Read MoreDoi: https://doi.org/10.54216/AJBOR.120101
Vol. 12 Issue. 1 PP. 01-14, (2025)
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.
Read MoreDoi: https://doi.org/10.54216/AJBOR.120102
Vol. 12 Issue. 1 PP. 15-27, (2025)
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.
Read MoreDoi: https://doi.org/10.54216/AJBOR.120103
Vol. 12 Issue. 1 PP. 28-39, (2025)
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.
Read MoreDoi: https://doi.org/10.54216/AJBOR.120104
Vol. 12 Issue. 1 PP. 40-51, (2025)
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
Read MoreDoi: https://doi.org/10.54216/AJBOR.120105
Vol. 12 Issue. 1 PP. 52-59, (2025)
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.
Read MoreDoi: https://doi.org/10.54216/AJBOR.120106
Vol. 12 Issue. 1 PP. 60-66, (2025)
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.
Read MoreDoi: https://doi.org/10.54216/AJBOR.120107
Vol. 12 Issue. 1 PP. 67-81, (2025)