Metaheuristic Optimization Review

Wireless signal applications in biomedical science have tremendously developed to improve people’s health care systems and contactless human sensing technologies. This review aims to focus on the current development of wireless microsystems, stressing the application of the wireless microsystem for precise physiological measurement without physical contact. Bio-signals transmitted through wireless telemetry systems help healthcare practitioners cover large numbers of patients continuously without repeated invasive interventions, improving the quality of care. RF systems and data acquisition techniques are critical for constructing wireless biomedical devices with low power consumption, especially in implantable applications. Moreover, depending on the advancements in microtechnology, sensors and actuators are compact and can be combined with communication electronics to produce complex healthchecking systems. The review also presents issues like signal distortion and data processing procedure requirements to obtain correct measurements in complicated surroundings. As technology advances in wireless communication systems, their usage in the health sectors advances, and the upcoming innovations should make healthcare better for the patients and efficient for the clinicians. In this vein, this paper posits wireless technologies as crucial to the advancement and contours of the future of personalized healthcare through monitoring and engagement.

Artificial Intelligence in mental healthcare is a revolution in the delivery, evaluation and administration of mental health services. This review also maps current literature covering the practical application of AI in mental health practice, focusing on the descriptive use and specific integration and case analyses of those technologies in context. AI solutions are as follows: Solutions that assist with the diagnosis of psychiatric patients, as well as solutions enabled by artificial Intelligence that foretell situations of severe mental health emergencies of individual citizens. Some good examples, Like the REACH VET program, show how AI, using EHRs, can identify suicidal veterans’ risk and prevent potential suicides. Nevertheless, numerous challenges exist when using AI in mental health, such as workers’ resistance, ethical questions about patient data, and clinician engagement. Concerning implementation methodology, this review has incorporated ideas from implementation science, showing the need to employ a guided approach when implementing AI technologies in clinical practice. Based on the study results, there is potential for increasing patient outcomes through artificial Intelligence and group-specific treatment options, better tools for diagnosing diseases and practical cooperation, but constant work of technologists, clinicians, and policymakers will be needed to eliminate existing issues and ensure equal access to innovations in the sphere of mental health.

Neurodevelopmental disorders are a broad category that estimates fifteen million people and include autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and intellectual disabilities that, if not found at an early age, present substantial lifelong challenges. Modern technologies in artificial intelligence with generative models mean new possibilities in early diagnostics and prevention. This review aims to review the biomarker potentials of generative models, including GANs, VAEs, and diffusion models, in the early diagnosis of neurodevelopmental disabilities. Having synthesized what is currently known about these models, we explore how the models improve diagnostic precision, minimize the use of invasive procedures, and manage data deficiency. The significant applications discussed involve generative models in analyzing neuroimaging data, modeling speech and behavior, and synthesizing new datasets that are valuable in handling privacy issues and biased datasets. In addition, this paper discusses some of the limitations associated with generative model deployment in clinical practice; these include interpretability, model stability, and the fact that the models rely on extensive and diverse datasets. Finally, we bridge the gap by looking into the future and discussing what future research could bring and ethical concerns regarding generative models and their potential to revolutionize handling cases of early neurodevelopmental disorders and enable early, more effective interventional approaches.

Generative models, including GANs, VAEs, and autoregressive models, have drastically improved neurological and psychological analyses. They allow for the formation of complex data representations that imitate cognitive processes in a human brain and thus help to understand the workings of the brain and mental diseases. Thus, GANs trained with adversarial mechanisms can synthesize nearly photorealistic fake samples, which can mimic neurological disorders or assess the effectiveness of treatment. VAEs are known to give a formidably well founded method of learning the hidden representations of psychological states, therefore allowing researchers to study the potential causes of mental health problems. Autoregressive models, in contrast, are most applicable in time series data, which is highly important when the neurological signal or behavior under investigation needs to be studied over time. This broad survey discusses the assets and liabilities of these generative approaches, emphasizing their usability in simulating elaborate psychological processes and deploying evidence-based observations to understand the assessment and therapy of mental disorders. Therefore, this work aims to reveal the significant connections between these methodologies and their further potential for investigating human cognition and behavior through the coordinated usage of highly effective computational methods.

Artificial intelligence-based monitoring has become an essential technological direction in the development of smart cities, where large-scale sensing, data analytics, and automated decision-support systems are increasingly used to improve urban efficiency, sustainability, safety, and quality of life. As modern cities face growing challenges related to traffic congestion, environmental pollution, energy consumption, public safety, waste management, infrastructure degradation, and rapid population growth, conventional monitoring approaches are no longer sufficient for supporting timely and adaptive urban decision-making. This review examines the role of artificial intelligence in smartcity monitoring by analyzing how machine learning, deep learning, computer vision, Internet of Things sensing, edge computing, and cloud-based analytics contribute to real-time observation, prediction, anomaly detection, and intelligent control across different urban domains. The review highlights major application areas, including traffic-flow monitoring, air-quality prediction, energy management, smart surveillance, waste monitoring, disaster detection, infrastructure inspection, and public-service optimization. It also discusses how artificial intelligence enables cities to move from reactive management toward predictive and preventive governance by identifying hidden patterns in heterogeneous urban data and supporting faster responses to emerging risks. Despite these advantages, the deployment of AI-based monitoring in smart cities remains associated with several challenges, including data privacy, cybersecurity, algorithmic bias, limited interoperability, high infrastructure cost, dependence on reliable sensor networks, and the need for transparent and explainable decision-making. Overall, this review shows that AI-based monitoring can significantly strengthen the operational intelligence of smart cities when it is implemented within ethical, secure, scalable, and citizen-centered governance frameworks.