This study investigates the use of artificial intelligence (AI) and machine learning (ML) models to predict, detect, and mitigate cybersecurity threats, including zero-day attacks, ransomware, and insider threats. Using a comprehensive dataset of network logs and attack signatures, we evaluated models such as Logistic Regression, Random Forest, XGBoost, CNN, and LSTMOur results demonstrate that deep learning models, particularly CNN (97.3% AUC-ROC) and LSTM (96.8% AUC-ROC), significantly outperform traditional methods, excelling in real-time threat detection and minimizing false positives. This study highlights the practical applicability of AI and ML in enhancing cybersecurity frameworks, paving the way for more efficient and scalable solutions against evolving threats.

This study evaluated multiple machine learning models to predict customer lifetime value (CLV), including ensemble methods, linear regression, and deep learning architectures. Our comparative analysis demonstrated that ensemble models, such as Random Forest and Gradient Boosting, provided a robust balance of accuracy and interpretability, while deep learning approaches excelled in capturing complex data patterns but required higher computational resources. The results highlight the trade-offs businesses must consider in selecting models based on data availability, scalability, and interpretability. The insights from this research enable businesses to identify high-value customers effectively, optimize marketing strategies, and drive sustainable profitability.

This study investigates the application of machine learning algorithms for fraud detection in the banking sector, addressing the increasing sophistication of fraudulent activities in digital banking. A comparative analysis was conducted on various models, including logistic regression, decision trees, random forests, support vector machines, neural networks, and ensemble methods. Performance metrics such as precision, recall, F1-score, and AUC-ROC were used to evaluate model effectiveness. Results indicate that ensemble models, specifically the stacked ensemble, outperformed other algorithms in balancing precision and recall, thus minimizing false positives and false negatives. These models demonstrated superior accuracy and adaptability to complex fraud patterns, making them particularly suitable for real-time fraud detection. However, challenges related to model interpretability and data quality highlight the need for further research on explainable AI and unsupervised learning approaches. This study underscores the promise of machine learning as a strategic solution for enhancing fraud detection in banking, offering a path to more robust and responsive financial security measures.

This study addresses the challenge of detecting Advanced Persistent Threats (APTs) in corporate networks by developing a hybrid multi-modal detection framework. We combine traditional machine learning models, deep learning architectures, and transformer-based models to improve the detection of sophisticated and stealthy cyber threats. A comprehensive dataset, consisting of network traffic and event logs, was processed through rigorous data preprocessing, feature engineering, and model development. The results show that the hybrid ensemble model, integrating Gradient Boosting and Transformer-based architectures, outperforms all other models, achieving 98.7% accuracy, 98.3% precision, and 97.9% recall, while maintaining a false positive rate below 1%. The model demonstrated exceptional performance in real-world simulations, detecting over 98% of malicious activities. Our findings highlight the importance of combining the strengths of classical and advanced machine learning techniques for effective APT detection and mitigation, providing a reliable, scalable solution for real-time cybersecurity.

Banking fraud has become a pervasive challenge, necessitating innovative solutions to protect financial institutions and their customers. This study investigates the effectiveness of supervised machine learning algorithms in detecting fraudulent activities within the banking sector. We conducted a comparative analysis of five widely used algorithms: Logistic Regression, Random Forest, Support Vector Machines, Gradient Boosting, and Neural Networks. Using a real-world banking dataset, we employed robust preprocessing and fine-tuning techniques to address class imbalances and optimize model performance. The evaluation metrics, including accuracy, precision, recall, F1-score, and area under the ROC curve (AUC), revealed that Gradient Boosting and Neural Networks consistently outperformed other models, achieving high precision and recall rates. The results highlight the potential of machine learning to detect subtle patterns of fraud while minimizing false positives and negatives. Furthermore, we discuss the implications of these findings for real-time fraud prevention systems and emphasize the importance of algorithm selection and scalability in operational environments.

This study explores how machine learning algorithms can optimize the performance of US fashion brands by analyzing the relationship between digital transformation, social media analytics, and customer engagement. Using a Kaggle dataset, models including linear regression, random forest, gradient boosting, and neural networks were evaluated to predict brand performance. Neural networks achieved the highest accuracy (R-squared: 0.92), while gradient boosting balanced performance and interpretability (R-squared: 0.88). Results highlight the critical role of customer engagement in driving brand success and demonstrate the potential of machine learning for actionable insights. This research provides a robust framework for data-driven strategies in the fashion industry.