International Journal of Computer (IJC)

Data Science and Machine Learning for Network Management in Telecommunication Systems: Trends and Opportunities

2024-08-17T22:58:38+00:00

This paper examines the transformative impact of data science, machine learning (ML), and artificial intelligence (AI) on network management in telecommunications, focusing on techniques such as network monitoring, predictive maintenance, anomaly detection, automated network configuration, and self-healing mechanisms. We analyze specific methodologies, including deep learning for anomaly detection and federated learning for predictive maintenance, and address current challenges such as data quality, system integration, and model interpretability. Emerging technologies like edge computing, federated learning, and quantum computing are explored for their potential to enhance predictive maintenance and network management. The paper provides an overview of how AI-driven solutions are revolutionizing telecom networks, offering unprecedented efficiency, reliability, and performance while highlighting the need for ongoing research to tackle complex issues of explainability and privacy.

The Impact of Machine Learning Algorithms on Improving the User Experience in E-Commerce

2024-11-08T06:06:40+00:00

This article explores the transformative impact of machine learning algorithms on improving the user experience in e-commerce. As e-commerce develops, it is becoming a key sector that uses advanced technologies to meet the changing needs of consumers. Machine learning plays a crucial role in personalizing user interactions, optimizing inventory management through predictive analytics, and improving recommendation systems. The article examines the various methodologies used, including collaborative filtering and contextual networks, and highlights the benefits of artificial intelligence-based chatbots to improve customer interaction. It should be noted that potentially in the future it will be possible to use machine learning in e-commerce, which will lead to solving problems such as data privacy and algorithm bias. Ultimately, the article highlights the need to adapt and innovate in the field of e-commerce to maintain user loyalty and satisfaction in a growing competitive market.

Typing in JavaScript API SDK development: Benefits and Implementation Techniques Using TypeScript

2024-10-04T14:40:13+00:00

This article aims to explore the development of a scalable and maintainable API SDK using TypeScript, with a focus on the practical implementation of modern programming techniques. The study presents a detailed methodology, including the selection of TypeScript for strict type enforcement, the use of Rollup and microbundle for optimized bundling, and the application of modular design principles through TypeScript mixins. The results highlight the advantages of these approaches in creating a lightweight, cross-platform SDK that works seamlessly in both browser and Node.js environments. Testing strategies, including the use of Nock for HTTP request simulation, are also discussed to ensure reliability and stability. The conclusions emphasize the significance of these modern practices in enhancing code quality, maintainability, and scalability. The novelty of this work lies in its comprehensive integration of these methodologies, providing a robust framework for API SDK development in contemporary software engineering.

Leveraging AI Techniques to Enhance Data Security in Cloud Environments: Challenges and Future Prospects

2024-08-08T11:30:08+00:00

This paper explores the application of Artificial Intelligence (AI) techniques to enhance data security in cloud computing environments. As organizations increasingly migrate to the cloud, the need for robust security measures has become paramount. Traditional security approaches often struggle to keep pace with the dynamic nature of cloud environments and sophisticated cyber threats. This research examines how AI can address these challenges and improve cloud security. The study analyzes the current state of AI applications in cloud security, evaluates key AI techniques applicable to various cloud security challenges, and identifies future directions for AI integration in cloud security. Machine learning, natural language processing, and other AI methods are discussed in the context of threat detection, anomaly identification, and adaptive security measures. While highlighting the potential of AI in cloud security, the paper also addresses significant challenges, including data quality issues, model interpretability, adversarial attacks on AI systems, privacy concerns, integration with legacy systems, and the cybersecurity skills gap. The research concludes by proposing future directions, such as quantum-resistant AI, federated learning for collaborative security, AI-driven autonomous security systems, and the development of explainable AI for security applications. This comprehensive analysis provides valuable insights for cloud service providers, enterprise customers, cybersecurity professionals, and policymakers navigating the rapidly evolving landscape of AI-driven cloud security.

Low Dimension Medical Images and Generative Deep Learning Models Can Help to Reduce X-Ray Radiation Exposure of Patients

2024-10-29T16:42:37+00:00

Background: X-ray and other radiation-based diagnosis form a critical step in many clinical investigations, including early detection of diseases. Deep learning based methods to derive diagnosis from medical images have been shown to be highly accurate in this regard. However, the radiological images collected for this purpose continue to be guided by what is suitable for clinical practitioners to visually interpret them, ignoring the possibility that machines can detect patterns better than the human eye, making the high dimension images unnecessary. On the other hand, image analysis studies have primarily focused on classification accuracy, ignoring the diagnostic tradeoffs with radiation exposure.

Methods: Chest X-ray images from medical datasets have been modeled using EfficientNetB0 deep learning model by reducing the images to different pixel sizes: 1 x 1, 2 x 2, 4 x 4, 8 x 8, 16 x 16, 32 x 32, 64 x 64, 128 x 128, 224 x 224, 256 x 256 and 300 x 300 pixels. The effect of increasing image size on the predictive power of the model has been studied viz-a-viz the radiation exposure of the patient for collecting a chest X-ray image of that size.

Results: In this work, we show that reduced image sizes from the original X-ray images are capable of accurate diagnosis of medical conditions with little loss in predictive power and propose that potentially lower dimensions than what is needed for visual inspection may be sufficient for the purpose, thereby substantially reducing the risks associated with high radiation dosage, currently practiced for use of images by human interpretation. We also demonstrate how reduced images can be used to generate high dimension versions suitable for visual inspection with the help of generative super-resolution techniques (SRGAN) based on deep learning.

Conclusions: In summary this paper makes a case for low dimension collection of X-ray images, with accurate clinical outcomes and thereby addresses the issue of resolution versus diagnostic accuracy.