The phrase "Internet of Things"(loT) describes a wide range of actual physical things or gadgets that collect, measure, and wirelessly transmit data about physical properties over the Internet. loT systems h...
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In the pandemic situations, when multiple strains of coronavirus are still a concern, RT-PCR is a lengthy manual testing technique that can take anywhere from a few hours to two days to complete. The system aims to de...
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Unwanted plants called weeds commonly appear among crops. These weeds have the potential to drastically lower farm output yield and quality. Unfortunately, most of the time, site-specific weed management is not implem...
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Unwanted plants called weeds commonly appear among crops. These weeds have the potential to drastically lower farm output yield and quality. Unfortunately, most of the time, site-specific weed management is not implemented. This means that a field is treated with a broadcast herbicide spray rather than a specific type of herbicide. Herbicide-resistant weeds have developed as a result of this herbicide's widespread use, which has various negative effects on the environment. This has led to numerous research investigations looking for the best weed control methods. computer vision-based automatic weed detection and identification is one such method. With the help of this method, weeds may be located and identified, and farmers can be advised to use a certain herbicide. Consequently, it's crucial that the correctly recognise and categorise the crops and weeds from the digital photos using a computer vision technology. Deep learning, a type of artificial intelligence, is a rapidly expanding research area at the moment. Its many uses, which incorporate computer vision, include object recognition. The goal of this effort is achieved by combining these two technologies. As an alternative to the system used in the literature, a system for the identification of various crops and weeds has been devised in this research. Digital pictures of the crops and weeds growing in the fields were taken using three separate cameras that were mounted at varied heights from the ground. Digital image properties like texture, colour, and shape were retrieved after the backdrop was removed and used for classification. To accomplish this, access computer vision is utilised to process images, and artificial intelligence is employed to apply transfer learning to an RCNN that automatically recognises plants. The method has an accuracy of 78.10% for the main crop and 53.12% and 44.76% for the two weeds under consideration, according to the results. The coordinates of the weeds are also included in
Speech analysis has emerged as a crucial tool in bridging the gap between the real and virtual worlds as the amount of human contact with machines increases. One subfield that has long been investigated in both psychi...
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This research study intends to explore various challenges that humans have encountered as well as any that may arise in the near future. Poor sanitation facility is one of the main causes of malnutrition and it leads ...
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In the realm of he althc are, precise intravenous therapy (IV) involving saline solutions is paramount for patient well-being. Accurate monitoring of saline levels is essential to ensure uninterrupted treatment, as de...
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Smoking is a destructive and addictive practice of inhaling burning plant material. It produces nicotine in the bloodstream that creates a negative impact on the bones, hormones, DNA, eyes, and the immune system. This...
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In recent years wireless sensor networks have helped with automation in many industry domains. Wireless technology cuts cable costs, deployment time and information to transfer and process from source to destination. ...
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The penetration of electric vehicles (EV s) entails the deployment of more charging station (CS) infrastructure to realize the charging requirement issues of the EV s. But, limited installation of charging infrastruct...
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Advanced Double Input Layered Neural Network has the potential to revolutionize medical diagnostics by solving pressing problems. It improves diagnostic precision by providing a single, unified platform for the examin...
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ISBN:
(纸本)9798350359756
Advanced Double Input Layered Neural Network has the potential to revolutionize medical diagnostics by solving pressing problems. It improves diagnostic precision by providing a single, unified platform for the examination of both organized and unstructured medical data. It provides real-time decision assistance and data management by utilizing the scalable, secure, and efficient data processing capabilities made possible by cloud computing. Improved patient care, more effective therapy, and higher-quality healthcare are some areas where Advanced Double Input Layered Neural Network can make a difference. Rapid, precise, and secure data analysis;the administration of multiple data sources;and realtime decision support are a few of the difficulties inherent in medical diagnosis. This research proposes an Advanced Double Input Layered Neural Network (ADILNN) with a double input layered neural community, enabling it to examine prepared and unstructured scientific data. This novel technique improves the community's mastering functionality from various data kinds. By centralizing statistics garage, processing, and analysis on the cloud, computing sources may be extra reliably accessed whilst wanted. The network's diagnostic precision and flexibility are each advanced through way of tool gaining knowledge of strategies (MLM). Because of its adaptability, ADILNN can be utilized in diverse medical fields, which include radiology, pathology, cardiology, and genetics. It permits examine genomic information, making recovery alternatives, and analyzing x-ray photos. The technique has numerous ability makes use of in healthcare, improving prognosis accuracy in diverse settings. Simulation analysis is used to gauge ADILNN's capacity by way of gauging its diagnostic accuracy, processing speed, scalability, and records protection. These research validate ADILNN's potential to improve clinical analysis, streamline facts management, and guarantee healthcare records's safe and effectiv
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