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检索条件"任意字段=4th International Conference on Smart Learning Ecosystems and Regional Development"

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Leveraging Enhanced SIFT and ResNet with Multi-Scale Features for Automatic Trash Classification 24

Leveraging Enhanced SIFT and ResNet with Multi-Scale Feature...

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4th international conference on Artificial Intelligence, Automation and Algorithms

作者： Kong, Fanrong Huang, Yan Yu, Zhiguo Shanghai Dev Ctr Comp Software Technol Shanghai Peoples R China

ISBN: (纸本)9798400717840

As urbanization and population density increase, waste management faces significant challenges due to the growing volume of daily trash, which highlights deficiencies in traditional approaches that struggle with collection, sorting, recycling, and disposal. this study proposes an enhanced Scale-Invariant Feature Transform (SIFT) method aimed at improving automatic smart trash sorting by addressing limitations in existing feature extraction techniques. By utilizing a multi-scale pyramid combined with saliency detection for effective keypoint selection, this approach enhances local feature representation through the incorporation of edge intensity and geometric properties while integrating color analysis via normalized RGB histograms. Additionally, leveraging ResNet's deep residual architecture facilitates robust global feature representation and addresses vanishing gradient issues. the implementation of a multi-scale loss function further optimizes learning processes by capturing essential information across different scales. Experimental results demonstrate that the proposed method surpasses current techniques in classification performance for trash materials.

关键词： Waste management Feature extraction SIFT Deep learning Trash classification

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Pipeline Leak Classification using Machine learning Ensemble Methods and Hyperparameter Tuning 4

Pipeline Leak Classification using Machine Learning Ensemble...

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4th international conference of Science and Information Technology in smart Administration, ICSINTESA 2024

作者： Tiquio, Darwin Villanueva, Alonica Technological Institute of the Philippines Quezon City Philippines

ISBN: (纸本)9798350376111

this study focuses on developing and evaluating machine learning models to classify five distinct types of leaks in water distribution systems: Circumferential Crack, Gasket Leak, Longitudinal Crack, No Leak, and Orifice Leak. Its objective is to enhance classification accuracy using the bagging ensemble method and hyperparameter tuning using random search. the dataset comprises '.raw' format audio signals captured via hydrophone sensors, converted to '.wav' format, and segmented into 2-second clips. Pre-processing involves extracting acoustic features like Spectral Centroid, Spectral Bandwidth, Spectral Rolloff, Spectral Flatness, and Mel Frequency Cepstral Coefficients (MFCCs). Four machine learning models - Random Forest, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM) - are employed for classification. Model evaluation employs k-fold cross-validation, assessing accuracy, precision, recall, F1-score, and support metrics. these models achieve accuracy scores, with ANN leading at 92%, closely followed by LSTM at 91%, SVM at 90%, and Random Forest at 86%. © 2024 IEEE.

关键词： Support vector machines

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smart Management of Electric Vehicle Chargers through Reinforcement learning 4

Smart Management of Electric Vehicle Chargers Through Reinfo...

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4th international conference on smart Grid and Renewable Energy, SGRE 2024

作者： Abdullah, Heba M. Gastli, Adel Ben-Brahim, Lazhar Qatar University Department of Electrical Engineering Qatar Qatar University College of Engineering Qatar

ISBN: (纸本)9798350306262

A reinforcement learning (RL) based EV charging management system is developed for the charger coordination problem. RL can handle system uncertainties, requires no historical data, and is not affected by future changes to the system. the EV charging coordination reduced peak demand, required charging infrastructure and increased solar power utilization. the proximal policy optimization (PPO) is used to train the RL to reach targets and the results are compared with other benchmark algorithms. © 2024 IEEE.

关键词： Reinforcement learning

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Leveraging DQN-Based Recommender Systems for E-Commerce in smart Cities 4th

Leveraging DQN-Based Recommender Systems for E-Commerce in S...

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4th international conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Abakarim, Sana Qassimi, Sara Rakrak, Said Cadi Ayyad Univ Fac Sci & Tech L2IS Lab Marrakech Morocco

ISBN: (纸本)9783031821523;9783031821530

In the age of digital urban transformation, smart cities are emerging as ecosystems where technology, infrastructure and data converge to improve quality of life, sustainability and economic prosperity. In this context, e-commerce plays a central role, providing platforms for businesses to thrive by enabling seamless transactions, personalised shopping experiences and greater market reach. However, the dynamic and evolving nature of user preferences presents a significant challenge, requiring more adaptive and smart recommendation systems. this paper presents an approach by integrating Deep Q-Network (DQN), a reinforcement learning technique, into recommendation systems for e-commerce in smart cities. By comparing the proposed DQN model based recommender system with traditional models such as MLP, DeepFM, LSTM and CNN using metrics such as MSE, RMSE and NDCG@5, we demonstrate its superior performance in predicting user preferences and dynamically adapting to changes in user behaviour. the results highlight the potential of DQN models to revolutionise e-commerce recommender systems, delivering more personalised and adaptive user experiences in the interconnected environments of smart cities.

关键词： Deep Q-Network Recommender Systems Reinforcement learning smart Cities E-Commerce Machine learning Deep Reinforcement learning

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A Hybrid Physics-Deep learning Load-Altering Attack Detection and Localization Mechanism 4

A Hybrid Physics-Deep Learning Load-Altering Attack Detectio...

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4th international conference on smart Grid and Renewable Energy, SGRE 2024

作者： Sayed, Mohammad Ali Arfaoui, Mohamed Amine Atallah, Ribal Debbabi, Mourad Assi, Chadi

ISBN: (纸本)9798350306262

Due to their growing popularity, smart high-wattage devices have rapidly proliferated in the modern power grid. Additionally, the grid has witnessed an increasing integration of Information and Communication Technologies (ICTs) to facilitate the shift towards a more efficient smart grid. this integration, however, has made the smart grid and its subsystems prone to cyber-attacks. One especially dangerous family of attacks is the family of Load-Altering (LA) Attacks that manipulate the demand side of the power grid and cause dangerous instabilities and blackouts. To this end, we develop a Hybrid Physics-Deep learning (DL) LA attack detection and localization mechanism. In this mechanism, the utility collects the generator frequencies and feeds them into the mathematical equations representing the physical behavior of the grid to estimate the load variation patterns on the load buses. these patterns are then fed to a DL algorithm to identify the attacks. the performance of the proposed mechanism is evaluated against several Machine learning and DL algorithms and detection models in the literature. Our proposed mechanism proved to be more accurate with an accuracy of 99.75%. © 2024 IEEE.

关键词： smart power grids

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the Socio-legal Aspect of the Profile of 'Forest Cadets' as a Strategy of international Cooperation 4

The Socio-legal Aspect of the Profile of 'Forest Cadets' as ...

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4th international conference on Technology Enhanced learning in Higher Education, TELE 2024

作者： Kuzovleva, Natalia Barabanshchkova, Ekaterina Samoilov, Alexander Municipal Autonomous Educational Institution 'Secondary School No. 12 Named after the Hero of the Russian Federation V.A. Dorokhin' Elets Russia Municipal Autonomous Educational Institution 'Secondary School No. 12 Named after the Hero of the Russian Federation V.A. Dorokhin' Elets Russia

ISBN: (纸本)9798350353532

the current school education system does not take into account modern trends in the development of the forestry industry. the article examines the socio-legal aspect of the profile of 'Forest Cadets' and the dissemination of the practice of introducing a smart model of the professional educational trajectory of the target stream of school-university-employer in the MAEI 'Secondary School No. 12 named after the Hero of the Russian Federation V.A. Dorokhin'. Attention is drawn to the commonality of the socio-Iegal framework of the Russian Federation and the Republic of Belarus concerning the education of future forestry specialists. the proposed aspects of international cooperation in the modular trajectory of interaction through effective learning activities, innovations, methodologies and practices are outlined. © 2024 IEEE.

关键词： Forest ecology

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smart Sensors in Industry 4.0 4

Smart Sensors in Industry 4.0

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4th international conference for Emerging Technology, INCET 2023

作者： Dodia, Ayush Shah, Pritesh Sekhar, Ravi Murugesan, D. Pune India Paavai Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Namakkal637018 India

ISBN: (纸本)9798350335750

smart sensors are critical in industry 4.0 because they enable manufacturing processes to be more intelligent. the development and advances in smart sensor technology serves as the backbone of successful industry 4.0 implementation across various sectors. this paper presents various aspects of smart sensors from an industry 4.0 perspective, such as their functions, characteristics, components, working principle, fabrication, types, applications, selection criteria, advantages and limitations. Current and upcoming development technological trends in this field are also highlighted. the paper also briefly discusses some of the latest machine learning based 'smart soft sensor' applications in diverse areas. © 2023 IEEE.

关键词： Industry 4.0

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Privacy-Preserving Prediction of Electricity Consumption in smart Grids 4

Privacy-Preserving Prediction of Electricity Consumption in ...

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4th international conference on smart Grid and Renewable Energy, SGRE 2024

作者： Akin, Murat Canbay, Yavuz Canbay, Pelin Gazi University Faculty of Engineering Ankara Turkey Sutcu Imam University Faculty of Engineering and Architecture Computer Engineering Kahramanmaraş Turkey

ISBN: (纸本)9798350306262

smart grid is a large-scale electrical network that allows the network administrator to efficiently manage the entire system using data-driven methods. smart meters serve as a crucial data source in a smart grid system. they accurately measure the electricity use of households and transfer this data to the control center. Periodically, specialists utilize smart meter data to predict electricity demand. this prediction empowers smart grid operators to assign the necessary quantity of electricity and effectively operate the system. Although, as electricity usage is considered as personal data, it is imperative to consider implementing protective measures. this research presents a novel method for predicting electricity consumption while taking data privacy into account. We ensured data privacy by implementing differential privacy. Besides, prediction based experiments are conducted with two mostly used RNN based deep learning methods, LSTM and GRU. Experimental results have shown that the model proposed in this study produces effective results for utility, privacy and prediction. © 2024 IEEE.

关键词： Forecasting

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A Detailed Analysis of Speculative Based Algorithm Implementation for Modish House 4

A Detailed Analysis of Speculative Based Algorithm Implement...

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4th IEEE international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Shah, Sanjeev Kumar Kumar, N. Siva Gurumoorthi, E. Shnain, Ammar Hameed Kumar, Ashok Nandhini, T.J. Uttaranchal University Uttaranchal Institute of Technology Department of Electronics & Communication Engineering Dehradun248007 India Aditya Engineering College Department of Computer Science and Engineering Surampalem India Cmr College of Engineering & Technology Department of Cse India The Islamic University Najaf Iraq Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Department of Computer Science and Engineering Chennai Tamil Nadu Thandalam602105 India

ISBN: (纸本)9798350360165

In the abruptly evolving vicinity of clever homes, the integration of quick-variety devices through the Internet of things (IoT) affords an exceptional opportunity and a massive task. the variety of tool communique protocols and the absence of popular production requirements have historically impeded seamless integration, necessitating a sturdy answer. this paper introduces the smart home framework, a progressive IoT-based designed to bridge the gap amongst a massive range of brief-variety devices and man or integration platforms. Leveraging the OSGi ultra-modern, smart allows key protocols along with Zigbee, EnOcean, KNX, X10, Z-Wave for tool verbal exchange, and CoAP (Constrained Application Protocol), MQTT (Message Queuing Telemetry Transport) and XAMPP ((CrossPlatform, Apache, MySQL, PHP, Perl) for application interaction. this framework stands out for its adaptability, extensibility, and the potential to significantly streamline and enhance smart domestic ecosystems. © 2024 IEEE.

关键词： smart homes

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development and Implementation of a smart Parking System in Coimbatore, Tamil Nadu: Enhancing Urban Mobility and Efficiency 4

Development and Implementation of a Smart Parking System in ...

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4th international conference on Artificial Intelligence and Signal Processing

作者： Aswin, S. Ruthra, L. Jashwanth, Sudheep G. Anand, R. Amrita Vishwa Vidyapeetham Dept Elect & Elect Engn Amrita Sch Engn Coimbatore Tamil Nadu India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661;9798350350654

this paper discusses the creation of a detailed smart parking app designed to improve the effectiveness and ease of use of city parking systems. the application includes many advanced features and technologies to streamline the parking process for users and optimize parking lot *** early stages of development focused on creating a user-friendly interface, including a sign-up page built using the Flutter framework. the integration of webcam functionality enabled real-time detection of the number of cars within the camera's focus, providing users with accurate information about parking availability. Payment integration was seamlessly integrated into the application, allowing users to securely pay for parking services. Leveraging machine learning techniques, specifically random forest detection, further increased the accuracy of car detection within the camera feed. Additionally, analogic modeling was used to simulate traffic flow at intersections and junctions, helping to understand vehicle traffic patterns and optimize parking slot. the application fetches and stores data on user activity and parking availability using Firebase, ensuring efficient data management and real-time updates. the culmination of these efforts is a robust smart parking application capable of providing users with real-time parking information, seamless payment options and customized parking management solutions. the work demonstrates the potential of integrating different technologies to solve complex urban challenges and pave the way for smarter and more efficient parking solutions.

关键词： Flutter framework Real-time detection Payment integration Car detection Traffic flow

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