Advancements in Retail Price Optimization: Leveraging Machine Learning Models for Profitability and Competitiveness

Journal of Business and Management Studies, Год журнала: 2024, Номер 6(3), С. 103 - 110

Опубликована: Май 23, 2024

Retail price optimization is essential for maximizing profitability and maintaining competitiveness in today's dynamic retail landscape. This study addresses as a regression problem, utilizing machine learning models to predict optimal points products. Leveraging factors such product attributes, competitor pricing dynamics, customer behaviors, analysis provides structured approach understanding the intricate relationships between variables. Among various techniques, Random Forest Regressor emerges potent strategy, offering robustness versatility tackling complex tasks. Results indicate that outperforms Decision Tree Logistic Regression regarding accuracy, precision, recall, overall predictive performance. With achieving an accuracy of 94%, it demonstrates superior capability capturing data patterns making accurate predictions prices. By leveraging advanced analytics retailers can optimize strategies, maximize profits, maintain market. underscores importance continuously analyzing refining strategies gain competitive edge

Язык: Английский

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Enhancing Credit Card Fraud Detection: A Comprehensive Study of Machine Learning Algorithms and Performance Evaluation

SSRN Electronic Journal, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Revolutionizing Organizational Decision-Making for Banking Sector: A Machine Learning Approach with CNNs in Business Intelligence and Management

Journal of Business and Management Studies, Год журнала: 2024, Номер 6(3), С. 111 - 118

Опубликована: Май 23, 2024

This research investigates the transformative impact of deep learning, particularly Convolutional Neural Networks (CNNs) such as VGG16, ResNet50, and InceptionV3, on organizational management business intelligence within banking sector. Employing a comprehensive methodology, study emphasizes crucial role high-quality datasets in harnessing learning for improved decision-making. Results reveal superior performance CNN models over traditional algorithms, with (VGG16) achieving an impressive accuracy rate 90%. These findings underscore potential extracting valuable insights from complex data, presenting paradigm shift optimizing various processes. The article concludes by highlighting importance investing infrastructure expertise successful integration, while also addressing ethical privacy considerations. contributes to evolving discourse applications management, offering banks navigating challenges global market.

Язык: Английский

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Enhancing Credit Card Fraud Detection: A Comprehensive Study of Machine Learning Algorithms and Performance Evaluation

Journal of Business and Management Studies, Год журнала: 2024, Номер 6(3), С. 252 - 259

Опубликована: Июнь 13, 2024

Credit card fraud detection remains a significant challenge for financial institutions and consumers globally, prompting the adoption of advanced data analytics machine learning techniques. In this study, we investigate methodology performance evaluation various algorithms credit detection, emphasizing preprocessing techniques model effectiveness. Through thorough dataset analysis experimentation using cross-validation approaches, assess logistic regression, decision trees, random forest classifiers, Naïve Bayes K-nearest neighbors (KNN), artificial neural networks (ANN-DL). Key metrics such as accuracy, sensitivity, specificity, F1-score are compared to identify most effective models detecting fraudulent transactions. Additionally, explore impact different folds in on performance, providing insights into classifiers' robustness stability. Our findings contribute ongoing efforts develop efficient systems, offering valuable researchers striving combat effectively.

Язык: Английский

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Analyzing the Nexus between AI Innovation and Ecological Footprint in Nordic Region: Impact of Banking Development and Stock Market Capitalization using Panel ARDL method

Journal of Environmental Science and Economics, Год журнала: 2024, Номер 3(3), С. 41 - 68

Опубликована: Сен. 1, 2024

This study investigates the impact of Artificial Intelligence (AI) innovation on ecological footprint in Nordic region from 1990 to 2020, alongside effects banking development, stock market capitalization, economic growth, and urbanization. Utilizing STIRPAT model, incorporates cross-sectional dependence slope homogeneity tests, revealing issues heterogeneity dependence. The analysis employs both first second-generation panel unit root confirming that variables are free problems. Panel cointegration tests demonstrate cointegrated long run. To explore short- long-term relationships, utilizes Autoregressive Distributed Lag (ARDL) model. ARDL results indicate urbanization positively correlate with short Conversely, AI development negatively footprint. validate estimations, robustness checks performed using Fully Modified OLS, Dynamic Fixed Effects all which support initial findings. Furthermore, D-H causality test identify causal relationships. show a unidirectional relationship between innovation, urbanization, In contrast, bidirectional exists growth footprint, as well

Язык: Английский

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Leveraging AI for a Greener Future: Exploring the Economic and Financial Impacts on Sustainable Environment in the United States

Journal of Environmental Science and Economics, Год журнала: 2024, Номер 3(3), С. 1 - 30

Опубликована: Авг. 25, 2024

In response to increasing environmental challenges, the United States has deliberately adopted technical advancements promote sustainable development. This includes efforts decrease pollution, improve energy efficiency, and encourage use of environmentally friendly technology in different industries. study investigates role Artificial Intelligence (AI) promoting sustainability from 1990 2019. It also examines impacts financial development, ICT use, economic growth on Load Capacity Factor (LCF). Various unit root tests revealed no issues mixed integration orders among variables. The Autoregressive Distributive Lag (ARDL) model explored cointegration, indicating long-run relationships ARDL findings confirm Curve hypothesis for States, with AI positively correlating LCF both short long run. Conversely, development population significantly reduce LCF. Robustness checks using FMOLS, DOLS, CCR estimation approaches align results. Granger causality reveal unidirectional growth, AI, bidirectional between Diagnostic results are free heterogeneity, serial correlation, specification errors. underscores importance enhancing while highlighting adverse

Язык: Английский

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