In recent years, the integration of Multi-Input Multi-Output (MIMO) technology with In-Band Full-Duplex (IBFD) systems has emerged as a promising approach for multi-targets Integrated Sensing and Communication (ISAC),...
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Air pollution has become one of the major problems for human health across the globe. Indoor air pollution poses more risk than outdoor air pollution, since the human body is more exposed to the inside. It refers to t...
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Air pollution has become one of the major problems for human health across the globe. Indoor air pollution poses more risk than outdoor air pollution, since the human body is more exposed to the inside. It refers to the contamination of indoor air that causes harmful health problems. To purify the polluted air, the air purifier is a main necessity. In this Internet of Things (IoT) project, we have created an air purifier that purifies the indoor air by passing it through the High Efficiency Particulate Air (HEPA) filter and Ultraviolet light. The microcontroller used is the NodeMCU based on the ESP8266 WiFi enabled chip. Two MQ135 air quality sensors used to monitor the change and calculate the efficiency of the purified air. IoT customization eases the manual work of changing the purifier manually every time by providing a platform that operates on the mobile phone via Wi-Fi where the user can operate it accordingly. It provides an equivalent grade of services at a very moderate cost that is affordable even for the average person and can provide real time analytics. The proposed system is capable for providing hands-on practical operations, which enables more exciting possibilities in IoT based automation systems.
Pothole detection is crucial for road safety and maintenance, traditionally relying on 2D image segmentation. However, existing 3D Semantic Pothole Segmentation research often overlooks point cloud sparsity, leading t...
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This contribution describes new useful geometric transformations using the tensor product. The geometric transformations are used widely in many applications, especially in CAD/CAM systems, systems for Civil Engineeri...
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Even with a powerful network of signaling and warning systems in the country, there have been many examples of trains crossing the red signal due to various factors, even today. These occurrences, known as Signal Pass...
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The research presents an innovative browser extension that serves as a virtual sales assistant for shoppers across e-commerce platforms. Designed to foster trust and enhance the online shopping experience, this extens...
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ISBN:
(数字)9798350356816
ISBN:
(纸本)9798350356823
The research presents an innovative browser extension that serves as a virtual sales assistant for shoppers across e-commerce platforms. Designed to foster trust and enhance the online shopping experience, this extension enables detailed product inquiries and provides immediate access to comprehensive specifications, empowering customers to make informed purchasing decisions. By seamlessly integrating with browsing sessions and leveraging natural language processing techniques, the virtual assistant generates accurate and informative responses to product-related queries, positioning e-commerce platforms as authoritative and responsive sources. Beyond functional capabilities, the extension aims to transform user engagement by acting as a personalized guide, akin to an in-store representative, thereby heightening satisfaction through tailored service and fostering deeper customer relationships. The research is motivated by factors such as increasing customer engagement, providing round-the-clock support, facilitating product differentiation and comparison, harnessing machine learning for enhanced experiences, building trust through expert guidance, ensuring unique integration with e-commerce platforms, iterating through feedback loops for continuous improvement, and supporting small businesses in enhancing customer interactions.
Log parsing, which involves log template extraction from semi-structured logs to produce structured logs, is the first and the most critical step in automated log analysis. However, current log parsers suffer from lim...
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The crucial field of fruit classification in computer vision and deep learning brings about breakthroughs that have broad applications in the retail and agricultural industries. By automating the identification and so...
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ISBN:
(数字)9798331529765
ISBN:
(纸本)9798331529772
The crucial field of fruit classification in computer vision and deep learning brings about breakthroughs that have broad applications in the retail and agricultural industries. By automating the identification and sorting of various fruit types, these technologies enhance efficiency, accuracy, and scalability, benefiting processes from farm production to market distribution. The integration of sophisticated algorithms and neural networks not only improves the speed and reliability of fruit classification but also offers the potential for real-time quality control and inventory management, revolutionizing traditional methods and paving the way for more innovative, data-driven approaches in these sectors. This study introduces LightNN, a novel and effective paradigm with a lightweight architecture that tackles the complex problems related to computational efficiency. Combining a small classifier with a simplified feature extractor, LightNN achieves the ideal balance between simplicity and accuracy. The feature extractor is capable of extracting hierarchical features from RGB images by using convolutional layers with ReLU activations and max-pooling procedures. The flattened output is then processed by the classifier, which consists of fully connected layers with ReLU activations and dropout regularisation. Remarkably, LightNN, with just 298,835 parameters, manages to get a respectable 96.37% test accuracy in fruit categorization tasks. The results highlight LightNN’s promise as an effective and efficient fruit classifying method, particularly in situations with constrained computational resources.
It is a big challenge in the present urban traffic management system to develop a vehicle detection and traffic surveillance monitoring system through video capture using real time data capture model whereby machine l...
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The area of machine learning that uses deep neural networks (DNNs) is called deep learning. Current developments in device learning with larger and deeper representation strategies are using a "cease-to-cease&quo...
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