Journal of Artificial Intelligence and Machine Learning in Management

Elevating Security Operations: The Role of AI-Driven Automation in Enhancing SOC Efficiency and Efficacy

Wei Chen and Jing Zhang — Tue, 06 Feb 2024 00:00:00 -0700

Security operations centers (SOCs) are under increasing pressure to detect and respond to cyber threats in real-time amidst an ever-expanding attack surface and talent shortage. Artificial intelligence (AI) and automation offer immense potential to augment human analysts and boost SOC performance and productivity. This paper examines the evolution of SOCs, key challenges, and the role AI-driven automation can play in elevating security operations. An overview of core AI capabilities for security use cases across major SOC functions is provided. Critical factors for successful AI adoption, including workflow integration, transparent AI, and continuous ML model validation, are discussed. Recommendations are presented to guide security leaders in leveraging AI-driven automation to enhance the efficiency, efficacy, and resilience of SOCs against modern cyber threats.

The Evolving Role of Healthcare Professionals in the Age of AI: Impacts on Employment, Skill Requirements, and Professional Development

Tarek Ibrahim, Hala Rashad — Fri, 09 Feb 2024 00:00:00 -0700

This study explores the transformative impact of artificial intelligence (AI) on the healthcare industry, focusing on its implications for employment, skill requirements, and professional development of healthcare professionals. In the context of employment, we find that AI is reshaping rather than replacing healthcare roles. It fosters the emergence of new specialties and increases efficiency in existing jobs by automating routine tasks. Regarding skill requirements, there is a growing necessity for healthcare professionals to acquire basic technical competencies and data literacy. These skills enable effective interaction with AI systems and informed decision-making based on AI-generated data. Furthermore, the rapid evolution of AI technologies necessitates a commitment to continual learning and adaptation among healthcare workers. In terms of professional development, our study highlights the importance of specialized training programs that incorporate AI-related skills and knowledge. These programs are crucial for preparing both current and future healthcare professionals to work alongside AI technologies. Additionally, we emphasize the need for interdisciplinary collaboration and a thorough understanding of the ethical and legal aspects of AI in healthcare. This study concludes that AI, rather than being a disruptive force, is a catalyst for empowerment and efficiency in the healthcare sector. It calls for healthcare professionals to embrace these technological advancements and adapt their skills and practices accordingly, ensuring they remain integral and effective in the AI-augmented healthcare landscape.

AI Techniques for Decentralized Data Processing: Advanced Methods for Enhancing Scalability, Efficiency, and Real-Time Decision-Making in Distributed Architectures

Daniela Torres, Julián Castillo — Mon, 12 Feb 2024 00:00:00 -0700

This paper explores advanced AI techniques tailored for decentralized data processing, addressing the limitations and challenges of traditional centralized systems. The study emphasizes the evolution of AI from symbolic reasoning to deep learning, highlighting the critical role of data processing in modern applications such as healthcare, finance, and autonomous systems. Decentralized data processing, leveraging distributed networks and edge computing, offers solutions to scalability, privacy, and latency issues inherent in centralized architectures. Key methods investigated include federated learning, which enhances privacy by training models locally on devices without sharing raw data, and edge AI, which deploys lightweight models on edge devices for real-time processing. The integration of blockchain technology further secures data sharing across decentralized networks. Empirical evaluations demonstrate the efficacy of these techniques in enhancing data privacy, reducing latency, and improving the resilience of AI systems. The study concludes that decentralized AI holds significant potential for various applications, such as smart cities, IoT, and personalized healthcare, by providing robust, efficient, and scalable data processing solutions.

Pioneering Testing Technologies: Advancing Software Quality Through Innovative Methodologies and Frameworks

Syafiq Rahman, Farah Nadia — Fri, 16 Feb 2024 00:00:00 -0700

Ensuring software quality is paramount in the development process, influencing reliability, efficiency, maintainability, and user satisfaction. Traditional testing methods, though foundational, exhibit limitations such as time-consuming manual testing, resource-intensive automated testing, and challenges in scaling and human error. This research explores emerging testing technologies, including AI and ML-based testing, crowd testing, and exploratory testing, to address these limitations. AI-driven test automation and predictive analytics are highlighted for their potential to enhance efficiency, accuracy, and coverage by automating and optimizing test processes. The study involves a comprehensive literature review and comparative analysis of traditional and innovative testing methods, assessing their impact on defect detection, test coverage, and overall software quality. The findings illustrate that leveraging advanced testing technologies can significantly improve software development practices, ensuring higher quality and faster time-to-market.

Real-Time Decision Making with Edge AI Technologies: Advanced Techniques for Optimizing Performance, Scalability, and Low-Latency Processing in Distributed Computing Environments

Tariq Al-Momani, Maysa Al-Hussein — Tue, 20 Feb 2024 00:00:00 -0700

This paper explores the transformative potential of Edge AI technologies in enhancing real-time decision-making across various industries. Edge AI refers to the deployment of artificial intelligence algorithms on localized hardware devices, such as smartphones, IoT devices, and autonomous vehicles, enabling immediate data processing at the edge of the network. This approach mitigates latency, enhances privacy, and reduces bandwidth usage, addressing the limitations of traditional cloud-based AI models. The paper examines the evolution and core concepts of Edge AI, the benefits of reduced latency, enhanced data privacy and security, and scalability. It delves into the synergistic relationship between Edge AI and emerging technologies like 5G, while also considering ethical and privacy implications. By analyzing case studies and specific applications in sectors such as healthcare, manufacturing, and smart cities, the paper highlights the practical benefits and future trajectory of Edge AI. The research aims to provide comprehensive insights, equipping stakeholders with the knowledge necessary to leverage Edge AI effectively for real-time decision-making.