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smart Retail Store Surveillance and Security with Cloud-Powered Video analytics and Transfer Learning Algorithms 2

Smart Retail Store Surveillance and Security with Cloud-Powe...

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2nd international conference on intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Sivalakshmi, P. Kavitha, U. Usha, R. Pattanaik, Ojasvi Maniraj, S. P. Srinivasan, C. RMK Coll Engn & Technol Dept Elect & Commun Engn Chennai Tamil Nadu India Veltech Rangarajan Dr Sagunthala R&D Inst Sci & T Sch Commerce Chennai Tamil Nadu India SA Engn Coll Dept Math Chennai Tamil Nadu India JNTU Dept Comp Sci & Engn Vignan Inst Management & Technol Women Hyderabad India SRM Inst Sci & Technol Fac Engn & Technol Dept Data Sci & Business Syst Chennai Tamil Nadu India Saveetha Univ Saveetha Sch Engn Saveetha Inst Med & Tech Sci Dept Comp Sci & Engn Chennai Tamil Nadu India

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.

关键词： Real-time monitoring Anomaly detection Proactive alerting Crowd analysis Customer behavior Retail optimization

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Integration and Implementation of intelligent Circuit System in Internet of Things Equipment 3

Integration and Implementation of Intelligent Circuit System...

引用

3rd international conference on data analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Sang, Yike Han, Xintong Zhao, Songling Chang, Mingsheng Weifang Engineering Vocational College Shandong Qingzhou262500 China

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

关键词： Decision making

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IoTPHM: Experimental Evaluation of Internet of Things Powered Patient Health Monitoring System using smart Sensors

IoTPHM: Experimental Evaluation of Internet of Things Powere...

引用

2024 international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2024

作者： Prince, P. Rajabhushanam, C. Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331543617

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 continuously monitors vital health parameters such as heart rate, blood oxygen saturation (SpO2), body temperature, blood pressure, respiratory rate, and physical activity. Utilizing an ESP32 microcontroller, the system efficiently collects, preprocesses, and transmits data to cloud servers or mobile applications using MQTT and Bluetooth protocols. Preprocessing techniques, such as noise filtering and data normalization, ensure accurate and reliable analysis. The system's sustainability is enhanced through a rechargeable battery with a solar charging option. Evaluation results demonstrate high sensor accuracy, low latency in data transmission, and robust user feedback for comfort and usability. The IoTPHM system supports edge computing for real-time alerts and cloud-based analytics for predictive insights, enabling proactive healthcare management. Its compact, wearable design ensures convenience for continuous monitoring, catering to both clinical and home environments. © 2024 IEEE.

关键词： Electronic health record

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A Comparative Study on Online Book Recommendation System 2

A Comparative Study on Online Book Recommendation System

引用

2nd international conference on Advancements in smart, Secure and intelligent computing, ASSIC 2024

作者： Roy, Prabahan Singha Sarkar, Debjoy Saha, Lewlisa Chakrabarty, Mayuri Techno Main Salt Lake Kolkata India Techno Main Salt Lake Kolkata India

ISBN: (纸本)9798350370188

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 this work employs a few recommenders' system-based strategies to predict customers' behavioral patterns based on demographic data and previous behavioral patterns. After investigating many recommenders' system-based machine learning techniques, the model was created. On our dataset model-based collaborative gave a better result than the other techniques used with an accuracy of 83.3% and precision of 90.1 %. The major goal has been met with the recommended model's ability to outperform the existing works stated in the related work section. © 2024 IEEE.

关键词： data analytics

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A smart Home Design with intelligent Patient Monitoring Features by Using Internet of Things (IoT) Enabled Communication Technology

A Smart Home Design with Intelligent Patient Monitoring Feat...

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2024 international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2024

作者： Prince, P. Rajabhushanam, C. Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331543617

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 on designing and evaluating a smart home system with intelligent patient monitoring features, aimed at enhancing patient safety, comfort, and healthcare delivery. The system employs a network of IoT-enabled devices, including wearable health sensors, environmental monitors, and smart cameras, to track vital signs such as ECG, SPO2, and body temperature, along with environmental parameters like air quality, humidity, and temperature. The system achieved a high accuracy rate, with vital sign sensors demonstrating over 98% reliability across various metrics. A communication framework using Wi-Fi and MQTT protocols ensures seamless data transfer and real-time responsiveness. Edge computing enables low-latency data processing, while cloud analytics provide predictive insights for early health issue detection. Key findings demonstrate the system's efficiency in emergency response, with an average detection-to-alert time of 1.4 seconds, and its ability to adapt the environment based on health conditions with over 95% precision. Automated features such as temperature control, fall detection, and personalized lighting enhance patient care and well-being. This work underscores the transformative potential of smart home technologies in healthcare, paving the way for future advancements in personalized health monitoring systems. © 2024 IEEE.

关键词： Electronic health record

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Integrating Predictive analytics and Deep Learning for Vehicle Safety Incident Forecasting

Integrating Predictive Analytics and Deep Learning for Vehic...

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2024 international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2024

作者： Adike, Anil Kumar Priscila, S. Silvia Bharath Institute of Higher Education and Research Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798331543617

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 high false positive rates and poor accuracy. It presents an integrated predictive system that combines with Deep Learning using CNN and LSTM analysis of multimodal data channels (Traffic images, Weather reports, Vehicle sensor signals). The hybrid architecture incorporates spatial and temporal dynamics, achieving a mean prediction accuracy of 92 % and lowering the false positive rate to 10%. It is important to highlight that results show significant improvements over state-of-the-art systems with 89% precision and 85% recall. The model allows safety management in real-time so that authorities can intervene before an accident happens. By creating a shift from reactive to proactive, the new way of forecasting incidents also makes for improved road safety and ultimately saves lives through timely interventions. © 2024 IEEE.

关键词： Incident Prevention Predictive analytics Proactive Safety Management Road Safety Vehicle Safety Incident Forecasting

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AI-Powered data-Driven Approaches to Fraud Detection in Finance

AI-Powered Data-Driven Approaches to Fraud Detection in Fina...

引用

2024 international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2024

作者： Murugan, P. Sundara Bala Satish, R. Paranthaman, Prabakaran Muzhumathi, R. St. Joseph's Institute of Technology Department of Management Studies Tamil Nadu Chennai India Velammal Engineering College Department of Management Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798331543617

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 their rigidity and vulnerability to changing fraud tactics. To improve fraud detection in finance, the study suggests a data-driven, AI-powered method. The system continuously adapts to new fraud tendencies by dynamically analyzing transactional data and utilizing machine learning (ML) algorithms for anomaly identification and predictive analytics. When it comes to real-time monitoring, essential elements include feature engineering, data preprocessing, data collecting, and model training. Its effectiveness in reducing financial risks is demonstrated by testing it against Existing systems and finding superior performance metrics in precision (0.95), recall (0.92), F1-score (0.93), and AUC-ROC (0.97). With the ever-changing financial landscape, the scalable and real-time fraud detection capabilities of the proposed system are essential for preserving transaction security and integrity © 2024 IEEE.

关键词： Predictive analytics

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Task Offloading in Edge computing System with Deep Reinforcement Learning and Reward Shaping 9

Task Offloading in Edge Computing System with Deep Reinforce...

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9th international conference on Cloud computing and Big data analytics, ICCCBDA 2024

作者： Zhao, Huishi Liu, Yitong Liu, Xingcheng Sun Yat-sen University Guangzhou China Waterloo Canada School of Information and Intelligent Engineering Guangzhou Xinhua University Guangzhou China

ISBN: (纸本)9798350373554

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 solution, in which the smart devices are enabled to offload tasks to nearby servers, thus facilitating the necessary computations with reduced latency and energy consumption. This study focuses on the challenge of task offloading. In our proposed scheme, edge computing is considered as a Markov decision process (MDP). Hence, the deep Q-networks with reward shaping (DQN-RS) can be utilized to improve training effectiveness for sparse reward conditions. Experimental results show that this method DQN-RS significantly enhances DRL algorithm performance compared to a well hand-crafted greedy algorithm, demonstrating the efficacy of reinforcement learning in discovering optimal offloading strategies. © 2024 IEEE.

关键词： Reinforcement learning

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In-depth Exploration of the Present Research Status on smart Cities 10

In-depth Exploration of the Present Research Status on Smart...

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10th international conference on Ubiquitous Networking

作者： Bakir, Doha Chiba, Zouhair Abghour, Noreddine Moussaid, Khalid Miyara, Mounia Ouaguid, Abdellah Hassan II Univ Fac Sci Ain Chock LIS Labs Math & Comp Dept Casablanca Morocco Univ Hassan 2 ENSET Mohammedia 2IACS Lab Casablanca Morocco

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.

关键词： smart cities challenges health energy mobility agriculture

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Unlocking the Potential of Competition-Based Learning: A Case Study of Kaggle in Big data analytics Education 10

Unlocking the Potential of Competition-Based Learning: A Cas...

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10th international conference on smart computing and Communication, ICSCC 2024

作者： Fan, Pengfei Purwanto, Erick Li, Na Wang, Jia Tay, Ting Ting Wang, Ling Wang, Qiufeng School of Advanced Technology Xi'an Jiaotong-Liverpool University Department of Intelligent Science Suzhou China School of Electronic Engineering and Computer Science School of Physical and Chemical Sciences Queen Mary University of London London United Kingdom School of Advanced Technology Xi'an Jiaotong-Liverpool University Department of Computing Suzhou China Xi'an Jiaotong-Liverpool University Department of Educational Studies Academy of Future Education Suzhou China Xi'an Jiaotong-Liverpool University Educational Development Unit Academy of Future Education Suzhou215123 China

ISBN: (纸本)9798350363104

This study investigates the effectiveness of Competition-Based Learning (CBL) using Kaggle in teaching Big data analytics. Through a quasi-mixed research design, integrating qualitative and quantitative methods, data was collected from students who completed the Big data analytics module. Results indicate high satisfaction levels among participants, with the majority expressing willingness to recommend CBL using Kaggle to future students. Moreover, the CBL approach significantly enhanced student engagement and improved their understanding of Big data analytics concepts and techniques. The matic analysis revealed positive aspects such as real-time scoring and leaderboards, but also highlighted challenges including the lack of structured learning and guidance. Solutions proposed by students include additional support mechanisms, a balance between competition and learning, and diverse competition themes. Overall, this study underscores the potential of CBL using Kaggle to transform Big data analytics education, while also emphasizing the need for tailored support and refinement of course design. © 2024 IEEE.

关键词： Contrastive Learning

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