With the rise in motor vehicles, driving safety is a major concern, and autonomous driving technology plays a key role in enhancing safety. Vision-based lane departure warning systems are essential for accurate navigation, focusing on lane line detection. This paper reviews the development of such systems and highlights the limitations of traditional image processing. To improve lane line detection, a dataset from Roboflow Universe will be used, incorporating techniques like priority pixels, least squares fitting for positioning, and a Kalman filter for tracking. YOLOv5 will be enhanced with a di-versified branch block (DBB) for better multi-scale feature extraction and an improved segmentation head inspired by YOLACT (You Only Look At CoefficienTs) for precise lane line segmentation. A multi-scale feature fusion mechanism with self-attention will be introduced to improve robustness. Experiments will demonstrate that the improved YOLOv5 outperforms other models in accuracy, recall, and mAP@0.5. Future work will focus on optimizing the model structure and enhancing the fusion mechanism for better performance.

Diabetes, a chronic metabolic disease marked by elevated blood glucose levels, affects millions of people globally. A lower quality of life and a markedly higher chance of potentially deadly consequences, such as heart disease, renal failure, and other organ dysfunctions, are closely linked to it. In order to effectively manage diabetes and avoid serious consequences, early detection and ongoing monitoring are essential. Remote health monitoring has emerged as a viable and promising option for proactive healthcare due to the development of contemporary technology, particularly in the areas of wearables and mobile computing. In this work, we suggest a thorough and sophisticated framework for remote monitoring that is intended to automatically predict, identify, and manage diabetes risks. To facilitate real-time data collection analysis and tailored feedback, the system makes use of the integration of smartphones, wearable sensors, and specialized medical equipment. In addition to enhancing patient engagement and lowering the strain on conventional healthcare infrastructures, our suggested model aims to assist patients and healthcare providers in maintaining improved glycemic control. We employed a tenfold stratified cross-validation approach to assess the efficacy of our framework and the results showed remarkable performance metrics. A score of 79.00 percent for clarity (specificity) 87.20 percent for sensitivity, and 83.20 percent for accuracy were all attained by the system. The outcomes show how our framework can be a dependable and scalable remote diabetes management solution, opening the door to more intelligent and easily accessible healthcare systems around the world.

Hydroponic agriculture based on the Internet of Things (IoT) is an innovative solution to face the challenges of land limitations and climate uncertainty. This study aims to analyze the role of IoT in accelerating the growth of hydroponic plants through monitoring and automation of the planting environment, as well as evaluating its impact on productivity, especially for the planting process in land with minimal sunlight. The system integrates sensors to monitor environmental parameters such as pH, temperature, and humidity, which are then processed in real-time to optimize nutrient delivery and irrigation. The results show that the use of IoT in hydroponic systems is able to significantly improve the quality and quantity of crop yields compared to conventional methods. However, there are several challenges in implementation, such as high initial costs, limited infrastructure in certain areas, and potential cybersecurity threats. Nonetheless, innovation and collaboration opportunities between the public and private sectors can accelerate the adoption of these technologies in sustainable agriculture.

This study explores the bibliometric analysis of research on smart agriculture and IoT-based irrigation management from 2019 to 2024, focusing on trends, topics, and publication patterns. Smart agriculture integrates advanced technologies such as IoT, artificial intelligence (AI), and big data analytics to enhance productivity and sustainability in farming systems. The analysis identifies a steady growth in publications, with a significant spike in 2023, reflecting the increasing global interest in utilizing IoT technologies to address challenges like water scarcity, climate change, and food security. Key topics include IoT-based sensor technologies for soil and water management, precision agriculture, and sustainable farming practices.The study also highlights the role of international collaboration, with countries like India, China, and the United States emerging as central hubs in fostering interdisciplinary research. Major sources such as Lecture Notes in Networks and Systems, AIP Conference Proceedings, and IEEE Access significantly contribute to the dissemination of research findings. Furthermore, the co-occurrence network analysis reveals the dominance of IoT as a central theme, with strong links to related topics like irrigation, soil moisture, and crop management.This research provides valuable insights for researchers, policymakers, and practitioners by identifying critical trends and research gaps, emphasizing the importance of IoT in optimizing irrigation systems, improving efficiency, and promoting sustainable agricultural innovations. These findings support the development of smart agriculture systems that are adaptive to global challenges and resource constraints.

Water quality monitoring is an important step in maintaining environmental sustainability and public health. Water turbidity is one of the main parameters in assessing water quality, because a high level of turbidity can indicate pollution that is harmful to aquatic ecosystems and humans. In the digital era, Internet of Things (IoT) technology has been applied to improve the effectiveness of real-time monitoring of water turbidity. This study aims to examine IoT-based water turbidity monitoring strategies and technologies using the Systematic Literature Review (SLR) method with the PRISMA protocol. In the process of searching for literature, this study identified 222 articles from the Scopus database, which, after going through the screening stage based on relevance, document type, and accessibility, resulted in seven main articles for further analysis. The results of the review show that the utilization of IoT sensors and wireless communication enables real-time monitoring of water turbidity, improves early detection of pollution, and improves effectiveness in water monitoring. However, challenges such as data security, sensor reliability, and communication network stability still need to be overcome to ensure the system works optimally. This study confirms that IoT can be a more efficient and sustainable solution in monitoring water turbidity.