Editorial

Emergence of data communication networks and the evolution of global data communication via the Internet have provided a potential platform for researchers around the globe to disseminate their research findings to the global community

Enhancing Orthodontic Pain Management: A vision for Improved Patient Comfort

Pain is a common concern in orthodontic treatment, resulting from inflammatory responses triggered by force application. This review explores the characteristics, mechanisms, causes, and management strategies for orthodontic pain. Patient-specific factors, including age, gender, and anxiety, contribute to pain perception. Pain typically peaks shortly after orthodontic procedures and diminishes gradually. Orthodontic Pain management encompasses pharmacological interventions (NSAIDs, analgesics), mechanical methods (chewing gum, laser therapy), and behavioral approaches (CBT, physical activity). Modifications in orthodontic procedures, such as using Ni-Ti wires and alternatives to traditional appliances, have been introduced to alleviate pain. These advances have transformed the orthodontic experience, making it more tolerable and enhancing treatment outcomes. Overall, this review provides insights into orthodontic pain and its management, benefiting both patients and practitioners in achieving successful orthodontic treatmen

Challenges and Solution for Identification of Plant Disease Using Machine Learning & IoT

Internet of Thing (IoT) is a groundbreaking technology that has been introduced in the field of agriculture to improve the quality and quantity of food production. As agriculture plays a vital role in feeding most of the world\u27s population, the increasing demand for food has led to a rise in food grain production. The identification of plant diseases is a critical task for farmers and agronomists as it enables them to take proactive measures to prevent the spread of diseases, protect crops, and maximize yields. Traditional methods of plant disease detection involve visual inspections by experts, which can be time-consuming and often subject to human error. However, with technological advancements, IoT and Machine Learning (ML) has emerged as promising solution for automating and improving plant disease identification. This paper explores the challenges and solutions for identifying plant diseases using IoT and ML. The challenges discussed include data collection, quality, scalability, and interpretability. The proposed solutions include using sensor networks, data pre-processing techniques, transfer learning, and explainable AI

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