The increasing data volume given by the exponential growth of digital devices, cloud platforms, and the Internet of Things (IOT) had become an attractive target for attackers. This makes the search for innovative defe...
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Maintaining pH, conductivity, and water tank levels is essential for water quality in industrial heat transfer applications. Traditional Proportional-Integral-Derivative (PID) controllers may struggle to manage nonlin...
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The traditional waste management system, prevalent in many regions, relies heavily on manual sorting processes carried out by human workers. These processes are often labor- intensive and time-consuming, leading to in...
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ISBN:
(纸本)9798331529635
The traditional waste management system, prevalent in many regions, relies heavily on manual sorting processes carried out by human workers. These processes are often labor- intensive and time-consuming, leading to inefficiencies in waste management operations. Manual sorting methods struggle to cope with the increasing volume and complexity of waste generated, resulting in challenges in accurately categorizing and classifying different types of waste materials. This limitation not only hampers the effectiveness of waste sorting but also impedes efforts to maximize recycling rates and minimize environmental impact. In response to the limitations of traditional waste management approaches, the proposed Eco Detect Advanced Waste Sorting system presents a transformative solution. Leveraging the advancements in artificial intelligence and computer vision, this system introduces the utilization of the YOLOv7 algorithm for real-time interference. YOLOv7, renowned for its exceptional accuracy and speed in object detection, is integrated into the waste sorting process to enable rapid and precise identification and classification of diverse waste materials. By employing deep learning methodologies, the system is capable of recognizing a wide array of waste categories, including plastics, paper, glass, metals, and organic materials, with remarkable efficiency. The integration of the YOLOv7 algorithm into the proposed waste sorting system represents a significant advancement in waste management technology. By enabling swift and accurate identification of various waste types, the system facilitates the optimization of recycling practices, promotes the establishment of a circular economy, and contributes to the overall sustainability agenda. Its adaptability and scalability make it well-suited for widespread implementation, addressing the pressing need for sustainable waste management for clean energy solutions on a global scale. Ultimately, the paper envisions a future where te
The proliferation of deep fake technology presents a substantial peril to both individuals and society, underscoring the urgent requirement for the development of effective deep fake detection techniques. This study i...
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The utilization of the Internet of Things (IOT) has shown significant potential in various aspects of daily life, yet its application in addressing social issues remains underdeveloped. India, with a substantial numbe...
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ensorFlow and Theano were popular deep learning frameworks for building and training neural networks, including image classification and NLP models. TensorFlow's evolution from a static graph-based approach to inc...
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In the recent years, Internet of Things (IoT) technology has become widespread and is commonly used by organizations for many different purposes. With the increase in the demand for this technology, new security chall...
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Real-time applications based on Wireless Sensor Network(WSN)tech-nologies are quickly increasing due to intelligent *** the most significant resources in the WSN are battery power and *** stra-tegies improve the power ...
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Real-time applications based on Wireless Sensor Network(WSN)tech-nologies are quickly increasing due to intelligent *** the most significant resources in the WSN are battery power and *** stra-tegies improve the power factor and secure the WSN *** takes more electricity to forward data in a *** numerous clustering methods have been developed to provide energy consumption,there is indeed a risk of unequal load balancing,resulting in a decrease in the network’s lifetime due to network inequalities and less *** possibilities arise due to the cluster head’s limited life *** cluster heads(CH)are in charge of all activities and con-trol intra-cluster and inter-cluster *** proposed method uses Lifetime centric load balancing mechanisms(LCLBM)and Cluster-based energy optimiza-tion using a mobile sink algorithm(CEOMS).LCLBM emphasizes the selection of CH,system architectures,and optimal distribution of *** addition,the LCLBM was added with an assistant cluster head(ACH)for load *** consumption,communications latency,the frequency of failing nodes,high security,and one-way delay are essential variables to consider while evaluating *** will choose a cluster leader based on the influence of the fol-lowing parameters on the energy balance of *** to simulatedfind-ings,the suggested LCLBM-CEOMS method increases cluster head selection self-adaptability,improves the network’s lifetime,decreases data latency,and bal-ances network capacity.
It comprehensively discusses the effect of deep mastering on data technology practices. Deep studying has advanced to become a powerful tool for information mining, pattern recognition, and feature engineering. It is ...
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We present a system for efficient privacy-preserving multi-party querying (PPMQ) over federated graph databases. This framework offers a customisable and adaptable approach to privacy preservation using two different ...
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