Deep Learning in Stock Market Forecasting: Comparative Analysis of Neural Network Architectures Across NSE and NYSE

Journal of Computer Science and Technology Studies, Journal Year: 2024, Volume and Issue: 6(1), P. 68 - 75

Published: Jan. 13, 2024

This research explores the application of four deep learning architectures—Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Convolutional (CNN)—in predicting stock prices using historical data. Focusing on day-wise closing from National Stock Exchange (NSE) India New York (NYSE), study trains neural network NSE data tests it both NYSE stocks. Surprisingly, CNN model outperforms others, successfully despite being trained Comparative analysis against ARIMA underscores superior performance networks, emphasizing their potential in forecasting market trends. sheds light shared underlying dynamics between distinct markets demonstrates efficacy architectures price prediction.

Language: Английский

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Transforming Breast Cancer Identification: An In-Depth Examination of Advanced Machine Learning Models Applied to Histopathological Images

Journal of Computer Science and Technology Studies, Journal Year: 2024, Volume and Issue: 6(1), P. 155 - 161

Published: Jan. 28, 2024

Breast cancer stands as one of the most prevalent and perilous forms affecting both women men. The detection treatment breast benefit significantly from histopathological images, which carry crucial phenotypic information. To enhance accuracy in detection, Deep Neural Networks (DNNs) are commonly utilized. Our research delves into analysis pre-trained deep transfer learning models, including ResNet50, ResNet101, VGG16, VGG19, for identifying using a dataset comprising 2453 histopathology images. categorizes images two groups: those featuring invasive ductal carcinoma (IDC) without IDC. Through our we observed that ResNet50 outperformed other achieving impressive metrics such rates 92.2%, Area under Curve (AUC) 91.0%, recall 95.7%, minimal loss 3.5%.

Language: Английский

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Unleashing Deep Learning: Transforming E-commerce Profit Prediction with CNNs

Journal of Business and Management Studies, Journal Year: 2024, Volume and Issue: 6(2), P. 126 - 131

Published: April 11, 2024

This research examines the potential of Convolutional Neural Networks (CNNs), including VGG16, ResNet50, and InceptionV3, in predicting ecommerce profits. Emphasizing importance high-quality datasets, study showcases superior performance CNN models over traditional algorithms, particularly noting a notable accuracy rate 92.55% with (VGG16). These results highlight deep learning's capability to extract actionable insights from complex data, offering significant opportunities for revenue optimization operational efficiency improvement. The conclusion underscores need investment infrastructure expertise successful integration, alongside ethical privacy considerations. contributes valuable discourse on learning ecommerce, guidance businesses navigating competitive global market landscape.

Language: Английский

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

Journal of Business and Management Studies, Journal Year: 2024, Volume and Issue: 6(1), P. 230 - 237

Published: Feb. 13, 2024

This research delves into the transformative impact of deep learning, specifically Convolutional Neural Networks (CNNs) such as VGG16, ResNet50, and InceptionV3, on organizational management business intelligence. The study follows a comprehensive methodology, emphasizing importance high-quality datasets in leveraging learning for enhanced decision-making. Results demonstrate superior performance CNN models over traditional algorithms, with (VGG16) achieving an accuracy rate 89.45%. findings underscore potential extracting meaningful insights from complex data, offering paradigm shift optimizing various processes. article concludes by significance investing infrastructure expertise successful integration, ensuring ethical considerations, addressing data privacy concerns. contributes to growing discourse application management, providing valuable resource businesses navigating dynamic landscape global market.

Language: Английский

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Comparative Analysis of Machine Learning Models for Accurate Retail Sales Demand Forecasting

Journal of Computer Science and Technology Studies, Journal Year: 2024, Volume and Issue: 6(1), P. 204 - 210

Published: Feb. 27, 2024

This article compares sales forecasting models, LSTM and LGBM, using retail data from an American multinational company. The study employs a meticulous methodology, optimizing memory, performing feature engineering, adjusting model parameters for both LGBM. Evaluation metrics, including RMSE, MAE, WMAPE, WRMSEE, demonstrate that LGBM consistently outperforms in capturing predicting patterns. analysis favors as the preferred demand forecasting, emphasizing importance of selection. contributes to practical machine learning applications highlighting effective choice.

Language: Английский

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Revolutionizing Banking Decision-Making: A Deep Learning Approach to Predicting Customer Behavior

Journal of Business and Management Studies, Journal Year: 2024, Volume and Issue: 6(3), P. 21 - 27

Published: May 7, 2024

This article explores a machine learning approach focused on predicting bank customer behavior, emphasizing deep methods. Various architectures, including CNNs like VGG16, ResNet50, and InceptionV3, are compared with traditional algorithms such as Random Forest SVM. Results show models, particularly outperform ones, an accuracy of 86.66%. A structured methodology ensures ethical data use. Investing in infrastructure expertise is crucial for successful integration, offering competitive edge banking decision-making.

Language: Английский

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Credit Risk Prediction Using Explainable AI

Journal of Business and Management Studies, Journal Year: 2024, Volume and Issue: 6(2), P. 61 - 66

Published: March 18, 2024

Despite advancements in machine-learning prediction techniques, the majority of lenders continue to rely on conventional methods for predicting credit defaults, largely due their lack transparency and explainability. This reluctance embrace newer approaches persists as there is a compelling need default models be explainable. study introduces employing several tree-based ensemble methods, with most effective model, XGBoost, being further utilized enhance We implement SHapley Additive exPlanations (SHAP) ML-based scoring using data from US-based P2P Lending Platform, Club. Detailed discussions results, along explanations SHAP values, are also provided. The model explainability generated by Shapely values enables its applicability broad spectrum industry applications.

Language: Английский

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Improving Cardiovascular Disease Prediction through Comparative Analysis of Machine Learning Models

Journal of Computer Science and Technology Studies, Journal Year: 2024, Volume and Issue: 6(2), P. 62 - 70

Published: April 20, 2024

Cardiovascular diseases, including myocardial infarction, present significant challenges in modern healthcare, necessitating accurate prediction models for early intervention. This study explores the efficacy of machine learning algorithms predicting leveraging a dataset comprising various clinical attributes sourced from patients with heart failure. Six models, Logistic Regression, Support Vector Machine, XGBoost, LightGBM, Decision Tree, and Bagging, are evaluated based on key performance metrics such as accuracy, precision, recall, F1 Score, AUC. The results reveal XGBoost top performer, achieving an accuracy 94.80% AUC 90.0%. LightGBM closely follows 92.50% 92.00%. Regression emerges reliable option 85.0%. underscores potential enhancing infarction prediction, offering valuable insights decision-making healthcare intervention strategies.

Language: Английский

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Deep Learning for Enterprise Decision-Making: A Comprehensive Study in Stock Market Analytics

Journal of Business and Management Studies, Journal Year: 2024, Volume and Issue: 6(2), P. 153 - 160

Published: April 20, 2024

This study explores the transformative impact of deep learning, specifically Convolutional Neural Networks (CNNs), on organizational decision-making in stock market. Utilizing CNN architectures like VGG16, ResNet50, and InceptionV3, research emphasizes significance leveraging learning for improved business intelligence management. It highlights superiority models over traditional algorithms, with VGG16 achieving an accuracy rate 90.45%. The underscores potential extracting valuable insights from complex data, leading to a shift optimizing processes. Additionally, it stresses importance investing infrastructure expertise successful integration, alongside addressing ethical privacy concerns. Through dive into real-time mathematical concepts, provides functionality offers comparisons between different architectures, aiding specialized applications such as market trends.

Language: Английский

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A Comprehensive Review of Text Mining Approaches for Predicting Human Behavior using Deep Learning Method

Journal of Computer Science and Technology Studies, Journal Year: 2024, Volume and Issue: 6(1), P. 170 - 178

Published: Feb. 12, 2024

This article presents a systematic review of research on predicting human behavior through unstructured textual data, employing comprehensive selection process illustrated in flow diagram. The categorizes 82 selected papers into three primary behavioral domains: emotional, social, and cognitive. Each paper undergoes meticulous examination, identifying objectives, algorithms, computational models, applications. Natural language processing (NLP) emerges as dominant text mining approach, utilized over half the literature, followed by data extraction, report arrangement, clusterization. study further employs VOSviewer to visualize co-occurrence term "text mining," revealing prevalent associations emphasizing challenges analyzing efficiently. contributes understanding evolving landscape analysis mining, addressing need for automated methods evaluating individuals' attitudes, emotions, or performance.

Language: Английский

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