A Comprehensive Framework for Strengthening USA Financial Cybersecurity: Integrating Machine Learning and AI in Fraud Detection Systems (Published)
Financial cybersecurity is of paramount importance in today’s digital age, particularly in the United States, where the financial sector plays a crucial role in the global economy. With the increasing frequency and sophistication of cyber threats, traditional fraud detection systems are facing significant challenges in keeping pace with evolving risks. This paper presents a comprehensive framework for strengthening US financial cybersecurity by integrating machine learning (ML) and artificial intelligence (AI) techniques into fraud detection systems. The framework begins with an exploration of the fundamental concepts of financial cybersecurity, highlighting key threats and regulatory considerations. It then delves into the fundamentals of ML and AI, discussing their applications in fraud detection and the associated benefits and limitations. The design of the framework encompasses data collection, preprocessing, feature engineering, model selection, and integration with existing systems, emphasizing scalability and adaptability. Through case studies and best practices, the paper illustrates successful implementations of ML/AI in financial cybersecurity and draws lessons from real-world applications. Ethical and privacy considerations are addressed, emphasizing the importance of ethical guidelines, privacy protection, and regulatory compliance. Looking to the future, the paper discusses emerging trends in cyber threats and advancements in ML/AI technologies, while also acknowledging anticipated challenges. In conclusion, the framework outlined in this paper offers a holistic approach to enhancing US financial cybersecurity, emphasizing the critical role of ML and AI in mitigating cyber risks and safeguarding financial institutions and their customers. Recommendations for future research and implementation efforts are provided to further strengthen the resilience of financial systems against evolving cyber threats.
Keywords: AI, Framework, US financial cybersecurity, fraud detection systems., integrating machine learning, strengthening
Application of Expert System for Diagnosing Medical Conditions: A Methodological Review (Published)
Naturally, human diseases should be treated on time; otherwise the patients might die if there is delay in attending to such patient or scarcity of medical practitioners’ or experts. Several attempts have been made through studies to design and built software based medical expert systems for probing and prognosis of several medical conditions using artificial and non-artificial based approaches for patients and medical facilities. This paper represents a comprehensive methodological review of existing medical expert systems used for diagnosis of various diseases based on the increasing demand of expert systems to support the human experts. The study provides a concise evaluation of the various techniques used such as rule-based, fuzzy, artificial neural networks and intelligent hybrid models. The rule-based techniques is not too efficient based on its inability to learn and require powerful search strategies for its knowledge-base; while the fuzzy or ANN models are less efficient when compared to the hybrid models that can give a more accurate results.
Keywords: AI, ANN, Expert System, Fuzzy Logic, Intelligent hybrid model, Rule-based