The exponential growth of digital communications, driven by cloud computing, IoT, 5G, and edge technologies, has significantly expanded the cyber-security threat landscape. Traditional rule-based defense mechanisms are increasingly inadequate against sophisticated attacks such as advanced persistent threats (APTs), and polymorphic malware. In response, machine learning (ML) and artificial intelligence (AI) have emerged as transformative tools for enhancing cyber-security frameworks, enabling adaptive data protection and real-time threat intelligence integration. Research Objective: This study aims to design and evaluate an intelligent cybersecurity framework that leverages machine learning techniques to improve data protection and integrate threat intelligence for securing modern digital communications. Research Methods: A review-based methodology was employed, synthesizing literature published between 2012 and 2024. Sources included peer-reviewed journals, government publications (eg, NIST, CISA), and industry reports. The study applied systematic search strategies using academic databases and threat intelligence platforms. Conclusion:

Autism Spectrum Disorder (ASD) impacts millions worldwide, posing distinct obstacles in schooling, communication, and access to therapy. Conventional educational and therapeutic approaches, while sometimes helpful, frequently lack the adaptability and customization necessary to meet the varied needs of kids with ASD. This research tackles existing gaps by integrating advanced Artificial Intelligence (AI) techniques and data-driven personalization in digital health systems. This study presents a novel, AIdriven adaptive learning and treatment system specifically developed for kids with autism spectrum disorder (ASD). This research assesses the efficacy of AI-driven personalization in improving learning outcomes, communication skills, and therapeutic accessibility through the utilization of simulated experimental data. Adaptive machine learning algorithms, natural language processing, and reinforcement learning techniques were incorporated into digital platforms, resulting in tailored intervention models that dynamically adjust to the cognitive and communicative profiles of each learner. Results from the simulated experiments demonstrate substantial enhancements in tailored adaptive learning pathways, quantifiable progress in communication skills, and heightened therapeutic accessibility and engagement compared to conventional methods. The performance assessment of AI models reveals strong accuracy, responsiveness, and efficiency in customizing instructional and therapeutic content to meet individual learner requirements. This research enhances previous work by providing empirical insights and practical consequences, demonstrating how AI-driven devices can substantially improve educational experiences and treatment outcomes for kids with ASD. Future directions encompass empirical testing, ongoing enhancement of AI models, and additional investigation into scalable application options within educational and healthcare contexts.

The epidemiology of diabetes in the United States is an acute topic of concern in community health and the economy that has some constraints in infrastructure such as the absence of specialists, ineffective glycemic control. A special opportunity, the introduction of Artificial Intelligence (AI) and Machine Learning (ML) into digital health will allow transferring the process of care delivery to the more proactive and personalized intervention and decrease the constantly increasing healthcare expenditures.

Throughout the last couple of years, Artificial Intelligence (AI) has come under consideration as a revolutionizer of numerous sectors in which the non-profit sector is involved is not an exception. AI intervention can be applied to the non-profit techniques in a way that this paper seeks to explain the extent of the success that can be realized. It discusses AI’s role in the non-profit organizations and pinpoints technologies like data analysis, AI-based fundraising solutions, program assessment, and chatbots to engage the donors. The paper also reviews general issues associated with the application of AI solutions including inadequate funds, dearth of specialists in AI and data privacy issues, and come up with measures to mitigate these challenges. It also elaborates on the evaluation indicators of the degree of AI impact in non-profits such as, efficiency increment indicators, fundraising indicators, changes in the programs, and indicators of stakeholders. The conclusions are that it is possible to achieve the positive impact on the function of distinctive non-profit organizations through the successful application of AI.