Shopping convenience and automation have gained much priority in the retail industry, especially with the advent of RFID and IoT. This paper presents a Human-Following Smart Trolley using RFID, together with automated...
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Noise pollution poses a significant barrier to effective learning and teaching, impacting concentration, retention, and overall academic performance. This paper explores the implementation of a comprehensive Noise Pol...
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ISBN:
(数字)9798331523893
ISBN:
(纸本)9798331523893
Noise pollution poses a significant barrier to effective learning and teaching, impacting concentration, retention, and overall academic performance. This paper explores the implementation of a comprehensive Noise Pollution Control System (NPCS) designed to mitigate disruptive auditory distractions in educational environments. The NPCS integrates sound insulation technologies, real-time noise monitoring, and adaptive acoustic treatments to create a conducive learning atmosphere. Through a combination of quantitative measurements and qualitative assessments, the system aims to identify noise sources, evaluate their impact on students and educators, and implement targeted interventions. Initial findings suggest that reduced noise levels correlate with improved student engagement and academic outcomes. By fostering a quieter and more focused learning environment, the NPCS seeks to enhance the overall educational experience, emphasizing the critical relationship between ambient sound conditions and effective teaching methodologies. This paper highlights best practices, challenges, and future directions for noise management in schools and universities, aiming to provide actionable insights for educators, administrators, and policymakers. It develops an integrated classroom noise monitoring system that combines real-time noise level detection with automated fan control and mobile application notifications. The system employs a noise sensor connected to a microcontroller to continuously monitor the classroom's ambient noise levels. When the noise exceeds a predefined threshold, the system turns off the classroom fan to help reduce the noise. Additionally, it sends real-time notifications to a mobile application, providing updates on the noise levels and alerting teachers and school administrators to disturbances. This dual approach - automated fan control and mobile notifications - ensures an optimal learning environment by minimizing noise disruptions and keeping relevant
Cervical cancer is a serious health issue and a leading reason of cancer-related deaths among women, particularly in less economically developed countries (LEDCs) where there is limited availability of screening. Earl...
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Recognizing architectural styles is essential for preserving & understanding cultural heritage, as it helps categorize & document diverse structures, highlighting their historical, cultural & artistic valu...
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In this paper a work on Federated learning based Machine Translation is performed using English-Assamese Language pair. Federated learning is the concept of machine learning where data are distributed on different sys...
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Pediatric pneumonia is a significant cause of morbidity and mortality in children worldwide. Traditional methods for detecting the disease are time-consuming, thereby necessitating automated detection methods. Hence, ...
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This research is interested in the design of an autonomous plant care system for optimal growth and it controls the supply of water and lighting in real time. Its application is, of course, best suited to indoor plant...
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The increasing demand for energy-efficient solutions in air conditioning has prompted the development of smart monitoring systems that leverage the Internet of Things (IoT). This study introduces an IoT-enabled embedd...
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Knowledge graphs provide concept visualization and context information across many applications. However, the process of building a knowledge graph by transforming large and intricate unstructured data into a new doma...
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Uterine cancer is a serious worry for women all over the world, and we have used multi-omics datasets to present a model that predicts the survival rate of uterine cancer patients by combining machine learning approac...
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