Navigating the Complexities of Cyber Threats, Sentiment, and Health with AI/ML

Abstract

When it comes to interactions that occur in the contemporary global environment characterized by increased use of computers and information technology, cybersecurity, sentiment analysis, and healthcare problems become much more challenging. Using AI and machine learning, we are now able to approach these domains with powerful tools for the discovery of cyber threats, sentiment from unstructured text and healthcare service optimization. The following paper evaluates how AI / ML propound these problems by discussing on their capability of predictive analysis, threat detection, natural language processing, besides personalized healthcare. In cybersecurity, the advantages of AI/ML are enhancing threat patterns or signatures with an ability to review big data sets for anomalous behavior, the implementation of AI decision-making for responses to attacks and attacks, and the ability of these systems to adapt to new threats as time goes on. Information containing sentiment analysis involves the use of NLP to evaluate the sentiments of the population and the customers in particular, hence making it possible for the business and organizations to take action with reference to the results gathered from various platforms such as social media and customer reviews, among others. In healthcare, AI/ML models upgrade patients’ clinical benefits via accurate diagnosis suggestions, optimized methods of treatment, and precision medicines caused by big medical records and genomics data analysis. It outlines the essential information of AI/ML in each of these three core areas and discusses potential developments encountered within the last couple of years and how they inform practice. Supervised and unsupervised learning methods enforce artificial intelligence and machine learning utilities used in cyber threat investigation, data outlier identification, and response to security breaches. It is the use of deep learning models for the purpose of analyzing text data for the characterization of trends and moods in and amongst consumers and the public. On the other hand, consumer AI/ML is learning from large datasets and displaying predictive analytics for healthcare that is used in decision-making, resource utilization and patient care benefits. On this basis, we can advance decision-making, cut operation expenses, and advance security, emotion recognition, and healthcare. The ethical issues related to AI and ML, such as data privacy, model prejudice, model interpretability, and explainability, are also discussed in this paper to present the importance of the ethical principles and guidelines to facilitate the integration of AI/ML systems. Further, this paper examines the issues of interpretability, explainability, and trust regarding AI and ML solutions. Using the detailed approach, numerous case studies and real-world examples are discussed throughout the paper to explain how the AI/ML systems are developed and trained in these domains. Consequently, the fields, concerns, and directions for future research and development of AI /ML applications in security, sentiment analysis and healthcare related areas are incorporated in the paper’s conclusion.