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

Larisa

doi:https://doi.org/10.54216/AJBOR.120201

American Journal of Business and Operations Research | 2024

PSO-Driven Parametric Estimation and Simulation for Banking Applications Using Soft Computing Approaches

For the purposes of maintaining a healthy liquid balancing and maximizing cash flow, accurate cash forecasting is very necessary for banking operations. In order to overcome the shortcomings of conventional forecasting techniques, such as linear regression, which do not take into account dynamic elements like pay impacts and vacations, this research, offers a Cash Management Model (PSO-CMM) that is based on Particle Swarm Optimization. Taking into account a number of characteristics, such as working days, holiday impacts, and pay patterns, PSO-CMM improves its coefficients for cash prediction. This allows for both short-term and long-term predictions. By swarm intelligence, the model is able to improve the accuracy of its predictions, hence providing greater resilience to continuously modifying surroundings. In addition to the development of linear and hybrid models that combine PSO with artificial neural networks (ANNs), the incorporation of adaptive computing approaches to improve weights is one of the most important advances. Furthermore, in order to prevent local optimums and to promote universal convergence, erratic patterns were incorporated in the most sophisticated systems. The results of this evaluation revealed a significant rise in the accuracy of cash projections. This study presents a comprehensive methodology for predicting cash requirements, which makes it possible for micro financial organizations to get useful insights and improves their operating effectiveness in situations that are always changing. When compared with Normal Data, the suggested PSO-CMM method's overall accuracy is around 91%.

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Yogesh Khandokar mail

link https://doi.org/10.54216/AJBOR.110204

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2024

Optimized Cash Forecasting Models for Banking Applications Using Soft Computing Techniques

The Artificial Neural Network-based Cash Forecasting Model (ANN-CFM) is introduced in this part as one way of mitigating the vices that are characterised with linear approach to financial management. This paradigm is quite helpful when the analysis is focused on non-linear and, generally, troublesome data. ANN-CFM, therefore, simultaneously takes both the linear and non-linear information for improving on the cash forecasting. Due to this fact, it is able to realise and leverage over advantage from the computational competence that neural systems provide. The hidden, output and input layers use randomised initial biased and weights. These include biases together with weighting that is altered regarding a standard basis with the use of a learning strategy to try to find the greatest cash needs. This design is actually composed of three various layering. This is exactly what the ANN-CFM is capable of dealing with and it accepts inputs, both for LT and ST forecasting. Among these inputs, you have factors of the working days, wages’ impact, and the impacts of holidays. The ANN-CFM is a system that revolutionises the way a human would perform his/her decisions and is a highly parallelized and efficient analytical tool for large data. As a result, this results in enhancement of precision to that which is predicted. The kind of architecture used in the system is feed forward neural network, which uses back propagation to help in reducing the numbers of errors that prevail at the time of prediction. In this part, extensive application of ANN, including its ability to learn in environments that may be constantly evolving is also highlighted. Thus, this innovative approach allows for sure receipt of accurate solutions for the management of these funds by companies operating in the financial industry. Comparing it with Normal Data, it is clear that the ANN-CFM technique proposed here provides an overall accuracy of approximately 95%.

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Manjeet rege mail

link https://doi.org/10.54216/AJBOR.110205

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2025

Optimizing Business Process through Fault-Tolerant Scheduling in Cloud Environments: A Comparative Study

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.

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Anil Audumbar Pise mail

link https://doi.org/10.54216/AJBOR.120101

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new

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

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