Preventing the Drug Consumptions among the Growing Generation using AI Technology

Abstract

An international public health concern is the rising prevalence of drug misuse among the younger population, especially among students and young people. Addiction to drugs has far-reaching consequences that affect not only the addict but also their loved ones and the community. Responding to drug use rather than proactively preventing it is a common goal of conventional approaches. A major challenge in drug misuse is the lack of empirical evidence supporting the new technological approaches effectiveness, including Artificial Intelligence (AI) and Internet of Things (IoT). Current monitoring systems, do not leverage advanced technologies and often fail to detect early indicators in time. The proposed solution aims to fill this gap, although further empirical data is required to fully validate the effectiveness of AI and IoT technologies. This research investigates a novel approach to monitor and prevent drug misuse by using AI and IoT technology. The goal is to create a system that can identify the first physical signs of drug use and notify parents or guardians so they can intervene quickly. The proposed approach utilizes an IoT device with sensors to track vital signs. AI algorithms process and analyse this data to identify anomalies that may indicate drug usage. The technology notifies the parent or guardian's mobile app in real-time if it detects unusual symptoms. Continuous learning from data enhances the accuracy of the system's predictions. The AI-driven IoT system successfully identifies irregularities in the tracked physiological data, including unusual temperature swings indicative of drug use. The system's ability to generate and transmit alerts to guardians enabled early intervention. Impressive precision, accuracy and recall accompanied the system's ability to detect possible drug intake. This study shows that AI and the IoT have the ability to identify drug misuse in its early stages. However, the effectiveness of the proposed solution remains to be fully verified through empirical testing and data collection. The technology may provide a preventative measure against drug addiction by sending real-time notifications.