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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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River Extraction from High Spatial Resolution Remote Sensing Image Using Machine learning Approaches 4

River Extraction from High Spatial Resolution Remote Sensing...

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4th international conference on Digital Data Processing, DDP 2024

作者： Cui, Hanwen School of Computer Science Zhuhai College of Science and Technology Zhuhai519041 China

ISBN: (纸本)9798331515706

the river is a critical freshwater resource for human civilization and plays a pivotal role in sustaining diverse plant and animal species within the ecosystem. Recognizing the paramount importance of rivers, it is imperative to preserve their healthy ecosystems to ensure sustainable utilization. Using machine learning models, this study uses high-resolution remote sensing data from China's Gaofen-2 satellite (GF-2) to promptly and accurately extract real-time river distribution data. the city of Zhuhai, situated downstream of the Pearl River Basin and characterized by an extensive network of rivers, was selected as the study area. this paper employed the Random Forest (RF) and Support Vector Machine (SVM) models for river extraction. the findings revealed superior performance of the RF model over the SVM model, with an overall classification accuracy (OA) of 97.88% and a Kappa coefficient of 0.9577, relative to SVM's 87.55% and 0.7511, respectively. this study demonstrated a substantial enhancement in classification accuracy compared to prior research utilizing SVM with lower-resolution multispectral remote sensing images. Applying machine learning models based on high spatial resolution images holds promise for providing precise information essential for efficient management and preservation of river resources, thereby serving as a powerful instrument to ensure their scientific exploitation and sustainable development. © 2024 IEEE.

关键词： Rivers

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Secure Photo Sharing Across Platforms Using Blockchain Technology 4

Secure Photo Sharing Across Platforms Using Blockchain Techn...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Nivetha, K. Harini, M. Harshinni, V. Nivetha, N. Tamilselvan, P. M Kumarasamy College of Engineering Department of Artificial Intelligence and Data Science Karur India M Kumarasamy College of Engineering Department of Artificial Intelligence and Machine Learning Karur India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

With the rapid expansion of online social networks (OSNs), photo sharing has become increasingly prevalent, raising significant privacy and security concern. Existing approaches, like centralized access control mechanisms and encryption-based privacy solutions, suffer from inconsistent policy enforcement, unauthorized access risk, and lack of transparency. Moreover, traditional OSN platforms afford limited control over the dissemination of content with respect to counteracting illegal reproductions. In order to address these challenges, Photo Chain is designed. the relevance of this name derives from its concept of using a blockchain mechanism to enable photo sharing in social networks securely and in a controlled manner. the system has smart contracts for dynamic access control, PreHash for photo integrity verification, and Gaussian Blur for the protection of sensitive regions. Dissemination control also provides various ways to prevent unauthorized sharing of photos. Ethereum-based smart contracts maintain an immutable log of transactions to guarantee privacy and transparency through user-defined access policies. A functional prototype has also been developed and tested for security, privacy, and controlled dissemination of pictures through OSNs. this prototype has demonstrated the usefulness of Photo Chain. © 2025 IEEE.

关键词： Differential privacy

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smart Solutions for Road Defect Detection: A Deep learning Fusion Approach with CNNs and SVMs 4

Smart Solutions for Road Defect Detection: A Deep Learning F...

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4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Mir, Taifa Ayoub Banerjee, Deepak Upadhyay, Deepak Rawat, Ramesh Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun India

ISBN: (纸本)9798350387490

the purpose of this research is to develop a novel approach to identifying road issues by using a deep learning framework that combines support vector machines (SVMs) with convolutional neural network networks (CNNs). For seven different types of road faults, including transverse cracking, raveling, rutting, alligator cracking, reflection cracking, and potholes, we evaluate the performance of the model. Our model achieves outstanding accuracy, recall, and Fl-Score values as a result of diligent investigation, and strong discriminating capacity is shown by each defect class. the fact that our model consistently achieves a score of 0.99 across the board attests to its high level of accuracy. Integration of CNNs and SVMs enables our framework to identify and classify road defects, which in turn opens the door to enhanced transportation infrastructure management. this capability is made possible by the integration of CNNs and SVMs. A comprehensive assessment of the model's efficacy is also provided by our study, which includes the presentation of a variety of average metrics, including macro, weighted, as well as micro averages, among others. these findings assist in the development of automated transportation systems by drawing attention to the possibility of deep learning technologies to revolutionize the methods by which road faults are identified. © 2024 IEEE.

关键词： Convolutional neural networks

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Availability and Failure Prediction for Telecommunication Equipment in smart Grid 4

Availability and Failure Prediction for Telecommunication Eq...

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

作者： Mecheter, Imene De Santana, Eros John Santos, Daniel Lucas Dos Santana, Wesley De Oliveira Garcia, Joao Vyctor Schneider, Jens Iberdrola Innovation Middle East Doha Qatar Neoenergia Bahia Brazil Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9798350306262

Telecommunication, with its recent advancement, has become an essential and crucial part of smart grids to enable reliable, efficient, and safe operations. Telecommunication predictive maintenance plays a key role in transforming the way in which the electricity grid is monitored. By predicting the availability status and failure possibility of telecommunication equipment, the operators in the smart grid control center can make proper decisions and efficient maintenance scheduling. In this work, a monitoring system for telecommunication equipment in the smart grid is proposed. the system includes three main components: availability prediction, failure prediction, and visualization. Time series analysis is performed using K-means clustering to classify the availability time series into clusters with similar behaviors. For each cluster, an ensemble boosting algorithm is applied to predict the availability value of each piece of equipment in the next hour, 12 hours, and 24 hours. Fully convolutional network (FCN) and Residual network (ResNet) are employed to predict the failures as a classification task. With availability prediction, the Extreme Gradient Boosting (XGBoost) model shows accurate prediction results with a mean absolute error between 1 - 9%. For failure prediction, deep learning outperforms the ensemble models with an F1 score of 91%. the prediction results are visualized in a Grafana dashboard to facilitate the smart grid monitoring services. Telecommunication automation coupled with appropriate visualization tools enables the advancement in the smart grid field. © 2024 IEEE.

关键词： smart power grids

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Real-time monitoring and fault detection in smart grids using IoT sensors and deep learning algorithm 4

Real-time monitoring and fault detection in smart grids usin...

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4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Arnika Tyagi, Reshu Kumar, Pawan Sagar, Pramod Kumar Saraswat, Manish Bansal, Abhinav Manav Rachna University Department of Computer Science and Technology Haryana Faridabad India Abes Institute of Technology Department of Information Technology UP Ghaziabad India Hi-Tech Institute of Engineering and Technology Computer Science & Engineering UP Ghaziabad India Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Lloyd Institute of Engineering & Technology Department of Mechanical Engineering UP Greater Noida India Raj Kumar Goel Institute of Technology Electronics & Communication Engineering UP Ghaziabad India

ISBN: (纸本)9798350364729

the concept of smart grids was released a long time ago, and since then the development in clever grids have changed our conventional energy machine with new advanced features like real-time monitoring power distribution control. this increased complexity brings with it the danger of errors and failures which can destabilize the system, leading to massive financial losses as well as social problems. In order to address this problem, we propose a real-time monitoring and fault detection system for smart grids via IoT sensors along with deep learning algorithms. this is able to encompass a network of Iot sensors which might be deployed in the course of the grid for real time facts on numerous parameters together with voltage, current and power glide. that number is then input into a deep getting to know set of rules, capable of detecting abnormal behavior and anomalies in the grid that can highlight faults or outages. In other words, the device is studying and learning about these facts all the time so it can be used as warning signs for grid operators who might still act proactively by avoiding any kind of matrix breakages. In addition the device can provide actual time color coded grid performance maps providing operators with the ability to track health of their own particular targeted area. Having green third-party maintenance will make your life and environment easier, decreasing downtime as well cost. Using an autonomous and durable strength supply, our suggested device is environmentally friendly along with a dependable answer for real-time tracking etc. fault detection in smart grids. © 2024 IEEE.

关键词： smart power grids

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Integrating IoT in Transportation for smart City development for Secured Data Processing 4

Integrating IoT in Transportation for Smart City Development...

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4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Priya, R. Lakshmi, R. Anandha Sudha, C. Pal, Souvik Ali, Ghassan Ahmed Alajmani, Samah H. Vels Institute of Science Technology and Advanced Studies Dept. of Computer Applications Chennai India Anna Adarsh College for Women Dept. of Computer Applications Chennai India SSKV College of Arts and Science for Women Dept. of Computer Applications Kanchipuram India Saveetha College of Liberal Arts & Sciences Saveetha Institute of Medical & Technical Sciences Chennai India Universiti Islam Sultan Sharif Ali Faculty of Islamic Technology Brunei College of Computer & IT Taif University Department of Information Technology Saudi Arabia

ISBN: (纸本)9798350353266

In this study, we propose the architecture and implementation of an IoT and machine learning powered Integrated Transport Management (ITM-SCD)for smart City development to mitigate the issues described above. the design takes all potential transportation system difficulties into account by utilizing many scenarios. the model integrates the various city systems into a single, cohesive whole, with an emphasis on secured data. People's movements can be monitored that smart transportation, healthcare, and entertainment can be provided. We discuss the challenges of transit security and smart city applications, as well as possible solutions based on AI and ML. then, for smart health, transportation, and energy, some solutions to these privacy and security issues are offered. © 2024 IEEE.

关键词： Traffic congestion

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Real-Time Fault Detection and Classification in smart Grids Using Support Vector Machines 4

Real-Time Fault Detection and Classification in Smart Grids ...

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4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Gupta, Sarita Gard, Gopal Krishan Varshney, Neeraj Rao, A.L.N. Srivastava, Amit Sharma, Deepti Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Chitkara University Centre of Research Impact and Outreach Chitkara Business School Punjab India GLA University Department of Computer Engineering and Applications Mathura India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India Uttaranchal University Uttaranchal Institute of Management Department of Management Dehradun248007 India

ISBN: (纸本)9798350307757

this research investigates real-time fault detection and classification in smart grids using five machine learning algorithms: Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN' Decision Trees, Random Forest and Gradient Boosting. Extensive experiments were conducted to compare them performance-wise in terms of accuracy, computational efficiency and robustness. the findings showed that Gradient Boosting attained the highest accuracy of 95.2%, which surpassed other algorithms. Second, the RF was illustrated with competitive accuracy (94.6%) less overfitting providing a reliable option for use. Interpretable Decision Trees were challenged by issues surrounding overfitting. 92.5% accuracy was found in the case of SVMs;however, these turned out to be computationally complex procedures as well. K-NN reported competitive accuracy (89.3%), but was computationally expensive. Comparison to related works from all fields underscored the cross-domain relevance of fault detection in critical systems. the study provides useful observations on the choice of fault detection algorithms to be used in smart grids, which recommend a compromise based on accuracy performance and computational efficiency while also being adapted for specific grid features. © 2024 IEEE.

关键词： smart power grids

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A Novel Industrial Load Data Augmentation Method Based on Deep Ensemble learning 4

A Novel Industrial Load Data Augmentation Method Based on De...

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4th international conference on New Energy and Power Engineering, ICNEPE 2024

作者： Xue, Jiguang Tang, Ye Feng, Zhibo Cheng, Wenyu Liu, Gang Wang, Jiahan State Grid Liaoning Marketing Service Center Shenyang110164 China

ISBN: (纸本)9798331516086

Renewables performance data is important in assessing the development and deployment of renewables. this paper is an analysis of the state of the art of research in the field of renewable energy, both at home and abroad. Firstly, an algorithm based on deep ensembles and probability theory was developed with the objective of optimizing the forecasting of load data. the result is an improvement in the accuracy of load forecasts for the power grid. the second stage of the process is the construction of the group load information base. the correlation of data within the information pool has been enhanced by the implementation of a multi-task learning strategy, which allows for the simultaneous processing of multiple tasks. this algorithmic approach enhances predictive accuracy while concomitantly reducing the consumption of computational resources. thirdly, the integration encompasses a multitude of strategies pertaining to neural networks. the incorporation of modeling optimization and quantile aggregation has enhanced both the predictive performance and the capacity for generalization. In conclusion, a new approach to augmenting industrial load data based on deep ensemble learning has been put forth. the numerical results indicate that the proposed methodology demonstrates superior precision in comparison to the traditional approach. © 2024 IEEE.

关键词： load data augmentation machine learning microgrid energy management smart grid

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Deep learning-based short-term load forecasting for power grids 4

Deep learning-based short-term load forecasting for power gr...

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4th international conference on Energy, Power and Electrical Engineering, EPEE 2024

作者： Jia, Lifeng Li, Genbang Zhang, Zhihao Wang, Yingnan Sun, Yuwen Li, Shuang State Grid Jilin Electric Power Company Baishan Power Supply Branch Jilin Baishan China Changchun University of Technology Jilin Changchun China

ISBN: (纸本)9798331518066

With the development of smart grids, the accuracy of short-term load forecasting is of great significance for the stable operation and optimal scheduling of power grids. this paper proposes a short-term load forecasting model for power grids based on deep learning technology. the paper first reviews the current state of research on power load forecasting, including traditional methods and modern deep learning approaches. It then introduces the theoretical foundations of deep learning, with a particular focus on the application of Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM) in time series prediction. Next, the paper designs a load forecasting model that integrates CNN and LSTM, and its effectiveness is verified through experimental validation. In the section on model implementation and training, detailed information is provided on data preprocessing, model training processes, and hyperparameter tuning. Additionally, the paper explores methods for model optimization and proposes improvement strategies to further enhance forecasting accuracy. A case study is presented to demonstrate the application effect of the model in real power grid load forecasting. Finally, the paper summarizes the research findings and prospects future research directions. © 2024 IEEE.

关键词： Convolutional neural networks

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WEBARIO: A Web based Portal with Revolutionary Virtual Classroom and Augmented Reality Integration for Enhanced learning 4

WEBARIO: A Web based Portal with Revolutionary Virtual Class...

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4th international conference on smart Electronics and Communication, ICOSEC 2023

作者： Sooraj, Nived N Manoj, Sidharth Mahalakshmi, P. Srm Institute of Science and Technology Department of Networking and Communication Kattankulathur India

ISBN: (纸本)9798350300888

webARio is a web-based portal designed to enhance the process of learning by streamlining visualization using a revolutionary virtual classroom setup with 3D models and augmented reality integration. In this study, the authors present the development of the webARio platform, its architecture, and the implementation of its key features. Proposed system offers a user-friendly interface that allows users to access the platform from any device with internet access. the revolutionary virtual classroom setup uses 3D models to facilitate learning, enabling users to visualize complex concepts in a more engaging and interactive way. Additionally, the integration of augmented reality enhances the user experience, allowing users to visualize 3D models in the real world. the webARio platform has the potential to revolutionize the way students learn by offering an innovative and effective way to visualize and understand complex concepts. this study presents a detailed analysis of the webARio platform, its features, and its potential impact on the education sector. © 2023 IEEE.

关键词： Augmented reality

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