Retail security and surveillance systems have transformed into data-driven smart solutions thanks to the fast development of technology. This paper presents a new method for improving retail shop security and surveill...
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ISBN:
(纸本)9798331540661;9798331540678
Retail security and surveillance systems have transformed into data-driven smart solutions thanks to the fast development of technology. This paper presents a new method for improving retail shop security and surveillance by combining transfer learning (TL) algorithms with cloud-powered video analytics. It is now possible to remotely interpret, analyze, and store massive amounts of video data produced by security cameras using cloud computing capabilities. In addition, TL methods are used to customize pre-trained deep learning models for retail settings, allowing for precise event and anomaly identification. This method drastically reduces the computing load on local devices and makes surveillance systems more accurate and responsive. One of the main advantages of the proposed system is that it can monitor the store premises in real time. It can also automatically identify suspicious actions like stealing or damage and inform staff or security about possible risks. By using cloud infrastructure, the system can also adapt to changing security needs and expand dynamically to handle increasing data volumes. Retailers may find useful information for refining shop layouts, product placements, and marketing tactics by integrating cloud-powered video analytics, which allows advanced functions like crowd analysis, heat mapping, and customer behavior analysis. A thorough plan for smart retail store security and surveillance, using cloud computing, video analytics, and TL algorithms to build a strong and effective system for protecting digital retail spaces.
The integration and implementation of intelligent circuit system in Internet of Things (IoT) devices is one of the key technologies in the current digital era. With the continuous improvement of the intelligence of Io...
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ISBN:
(纸本)9798331519032
The integration and implementation of intelligent circuit system in Internet of Things (IoT) devices is one of the key technologies in the current digital era. With the continuous improvement of the intelligence of IoT equipment, intelligent circuit system, as its core component, undertakes the heavy responsibility of data acquisition, processing and transmission, and gives the equipment the ability of independent learning and decision-making. However, there are still many challenges to effectively integrate the intelligent circuit system into IoT devices and achieve efficient and stable operation. In this paper, the integration method and implementation technology of intelligent circuit system in IoT equipment are deeply discussed. In the aspect of hardware integration, the research emphasizes the importance of standardization and universal interface design, and how to reduce energy consumption through intelligent sleep technology and energy management strategy. Software integration involves selecting the appropriate operating system, communication protocol and data processing strategy to ensure that the intelligent circuit system can seamlessly interface with IoT devices and realize efficient data processing. In addition, the paper also discusses the selection and application of embedded system and microcontroller, as well as the selection and optimization of communication technology to meet the needs of different IoT devices. Through case analysis, this paper shows the integration and realization process of intelligent circuit system in smart home system, including hardware selection, software development, application of communication technology and application of data analysis and processing technology. The analysis of performance, stability and security shows that the smart home system performs well in real-time control, data processing and personalized experience. At the same time, it also points out the potential risks of the system in extreme cases and puts
The Internet of Things Powered Patient Health Monitoring (IoTPHM) system represents a significant advancement in remote healthcare by integrating smart sensors, microcontrollers, and IoT technology. This system contin...
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The most influencing factors in recommending a suitable book can be the language it is written in or the author the book has been written by and majorly, the genre that book belong in. The research model presented in ...
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The integration of Internet of Things (IoT) technology into smart home systems has revolutionized patient monitoring by providing real-time health insights and automated environmental adjustments. This study focuses o...
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Forecasting vehicle safety incidents is an important step towards improving road safety and reducing accidents. Rule-based systems, which often rely on historical data, can be inflexible and reactive, resulting in hig...
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Despite detection techniques have advanced, financial fraud is still a serious problem. Conventional rule-based approaches frequently suffer from high false positive rates and missed fraudulent activity because of the...
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The evolution of mobile internet has brought challenges to smart devices such as smartphones and VR headsets, only having limited computational power to handle intensive tasks. The advent of edge computing provides a ...
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smart cities harness pervasive technology and big data to cultivate more efficient, sustainable, and innovative urban environments. They leverage ubiquitous computing and analytics to monitor and optimize services, re...
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ISBN:
(纸本)9798350356151;9798350356144
smart cities harness pervasive technology and big data to cultivate more efficient, sustainable, and innovative urban environments. They leverage ubiquitous computing and analytics to monitor and optimize services, resource management, and economic growth. Additionally, smart cities tackle urban challenges like climate change, overcrowding, and resource scarcity through intelligent systems and solutions. In domains such as health, energy, mobility, and agriculture, smart cities utilize sensors, micro-grids, smart vehicles, and other advanced technologies to evaluate conditions, impacts, programs, and strategies. However, these advancements introduce challenges, including data security, privacy concerns, interoperability issues, and infrastructure limitations. By examining research and case studies across these domains, this paper evaluates potential limitations and future directions for smart city development. It aims to contribute to a deeper understanding of smart cities' complexities and provide valuable insights for new research avenues for future scholars.
This study investigates the effectiveness of Competition-Based Learning (CBL) using Kaggle in teaching Big dataanalytics. Through a quasi-mixed research design, integrating qualitative and quantitative methods, data ...
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