Deep neural networks (DNNs) have achieved outstanding performance in areas such as computer vision, speech recognition, natural language processing, and autonomous systems. However, their high computational cost, memory usage, and energy consumption limit deployment in resource-constrained environments like mobile and edge devices. Model compression has emerged as a crucial solution to improve efficiency while maintaining accuracy. This paper provides a comprehensive study of neural network compression techniques, including pruning, quantization, low-rank factorization, knowledge distillation, and neural architecture optimization. These methods are analyzed based on compression ratio, latency, memory efficiency, and accuracy trade-offs. The study also explores hybrid compression approaches and proposes a systematic workflow from model training to deployment on constrained hardware. Experimental results demonstrate that effective compression significantly reduces model size and computational cost with minimal performance loss. The paper highlights the importance of compression-aware design and concludes as a valuable reference for building efficient and scalable AI systems.
Daniel Rodríguez· International Journal of App...· 0 citations
Financial risk assessment is essential in handling uncertainties and complexities in modern financial systems. Traditional statistical models often struggle with nonlinear relationships and dynamic market conditions. This paper explores the application of data science techniques—such as machine learning, deep learning, and big data analytics—in evaluating various financial risks, including credit, market, operational, and systemic risks. The proposed framework integrates data preprocessing, feature engineering, predictive modeling, and explainability to enhance decision-making and regulatory compliance. Experimental insights show that models like random forests, gradient boosting, SVMs, and neural networks provide better predictive accuracy and early warning capabilities compared to traditional methods. However, challenges related to data quality, interpretability, and ethical concerns remain. Overall, data science is identified as a transformative approach to financial risk assessment when supported by proper governance and validation practices.
Daniel Rodríguez· International Journal of App...· 0 citations