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共 20256 条记录，以下是331-340 订阅

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intelligent Identification Method for Intermittent Electricity Theft Behavior of Users Based on Multitask learning 3

Intelligent Identification Method for Intermittent Electrici...

引用

3rd international conference on Energy engineering and Power Systems, EEPS 2023

作者： Gao, Sheng State Grid Beijing Tongzhou Electric Power Supply Company Beijing101000 China

ISBN: (纸本)9798350313857

In order to improve the detection rate and AUC value of electricity theft behavior recognition results, and reduce the false detection rate, a user intermittent electricity theft behavior intelligent recognition method based on multi task learning is proposed. By collecting and analyzing user electricity consumption data, identify the characteristics of electricity theft behavior, obtain electricity theft feature data, and use factor analysis to dimensionally regulate the electricity theft feature data. Based on the results of data processing, train a multi task learning model to gain a more comprehensive understanding of users' electricity consumption and identify intermittent electricity theft behavior. The experimental results show that the detection rate and AUC value of the proposed method are higher, and the false detection rate is significantly reduced, indicating that the recognition effect of this method is good. © 2023 IEEE.

关键词： Factor analysis

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Tailoring Content with Keyword-Based Recommendation Engine Using Machine learning 3

Tailoring Content with Keyword-Based Recommendation Engine U...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Prabha, M. Vijay Ananth, K.S. Vinoth Kannan, S. Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

It is the information age thus;it is very important to understand how to personalize content in accordance with what appeals to a user. This work is conceptualized to be labeled 'Tailoring Content with Keyword-Based Recommendation Engine Using Machine learning'. This work provides the applicative system catering for the experience of users by proposing personalization in terms of content. The core of the work is keyword-based recommendation engine chosen by the user to recommend posts as well as other contents believed to be of the user's interest. A recommendation system uses various types of algorithms related to machine learning and a combination of data analysis technologies while considering the interaction of the user with posts, created, liked, or participated in. From this, it will then understand keen interests by a user and make recommendations based on those grounds. This will keep users interesting about the relevant content and help develop skills in areas they are interested in. Some scope includes designing a recommendation engine, developing it into an application, and measuring performance. This would be in the ability of doing so, with accuracies at all high degrees, when recommending this content, scalability, and also privacy for the users. It will be used to prove how machine learning techniques can be applied towards developing personalized user experience and, as a by-product, contribute to this field known as content recommendation systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Research and Application of Deep learning Algorithm Based on Multi-Source data Fusion of Power Grid and Communication Network 3

Research and Application of Deep Learning Algorithm Based on...

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Li, Li Zhou, Wei Ma, Liang Jiang, Yingjie Sun, Chao Zhang, Pu Information Telecommunications Company State Grid Shandong Electric Power Company Jinan250001 China State Grid Shandong Electric Power Company 250001 China

ISBN: (纸本)9798350354171

Multi-source data fusion plays a very important role in improving the intelligent level of power grid operation, but there is a problem that multi-source data fusion is not ideal. The previous operation mode could not solve the problem of data fusion in the intelligent operation of the power grid, and the processing was inaccurate. Therefore, this paper proposes a deep learning algorithm for multi-source data fusion analysis. Firstly, the signal processing and estimation theory are used to integrate the power grid communication data, and the indicators are carried out according to the requirements of multi-source data fusion Partitioning to reduce interference factors in multi-source data fusion. Then, signal processing and estimation theory intelligently process power grid communication to form an intelligent processing scheme and fuse multi-source data The results were comprehensively analyzed. MATLAB simulation shows that under the condition of certain processing standards, deep learning algorithms intelligently process power grid communication accuracy and multi-source data fusion time are better than previous operating modes. © 2024 IEEE.

关键词： data fusion deep learning algorithms Improve the intelligent level of power grid operation multi-source data fusion signal processing and estimation theory

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An Overview Study of automated Inland Waterway Topographic Contour Mapping 24

An Overview Study of Automated Inland Waterway Topographic C...

引用

3rd international conference on Frontiers of Artificial Intelligence and Machine learning (FAIML)

作者： Liu, Hua Liu, Xuehao Zhang, Jing Huang, Lingli Changjiang Yichang Waterway Engn Bur Yichang Peoples R China Henan Vocat Coll Water Conservancy & Environm Zhengzhou Peoples R China China Three Gorges Univ Hubei Key Lab Intelligent Vis Based Monitoring Hy Yichang Peoples R China China Three Gorges Univ Coll Comp & Informat Technol Yichang Peoples R China

ISBN: (纸本)9798400709777

With the continuous optimization of methods for acquiring high-resolution and high-precision elevation data, the efficiency of traditional manual contouring plotting has become increasingly difficult to satisfy the rapid growth of practical demands. Automatic contouring technology, leveraging its ability to automatically generate contour lines from discrete point elevation data, has significantly reduced labor and resource costs while ensuring consistent results. This has led to its widespread application in terrain analysis. The capability of current general algorithms and software to capture subtle geomorphological features when dealing with the complexity of inland water terrains remains limited. This paper reviews some of the mainstream algorithms in automatic contouring, including interpolation methods based on regular grids and irregular triangular meshes, as well as line-tracing methods. Additionally, it elucidates several major automatic contouring software systems, such as the EPS software system developed by Tsinghua Shanwei Company and the ArcGIS platform, and emphasizes the application prospects of hybrid programming techniques combining VC++ and MATLAB in this field. Future research should focus on developing specialized contouring algorithms tailored for the unique geomorphology of inland water areas, designing integrated and efficient hybrid algorithm systems, and developing professional integrated software. Automation technologies should be incorporated throughout the entire mapping workflow to further enhance the accuracy and applicability of inland water contouring.

关键词： Automatic contour mapping Inland water topography Interpolation algorithms

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Forecasting Gilt Mutual Fund Performance Using data-Driven Machine learning Models 3

Forecasting Gilt Mutual Fund Performance Using Data-Driven M...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Chinnasamy, Gopalakrishnan Vinoth, S. Chandran, Preetha Jain Deemed-to-be University Faculty of Management Cms Business School Bengaluru India Welingkar Institute of Management Research and Development Bengaluru India

ISBN: (纸本)9798331515911

This study investigates the performance of gilt mutual funds by employing data-driven machine learning algorithms to forecast returns and classify funds based on performance indicators. It predicts year-to-date (YTD) returns using linear regression (MSE 48.24, and R2 of 0.59), K-means clustering, and Random Forest classification, as well as categorizing funds as 'Good' or 'Average' based on performance criteria such as assets under management (AUM) and risk profiles. The analysis focuses on the top five gilt funds, utilizing historical returns data to forecast performance and volatility. Key findings identify Kotak Gilt - Investment, HDFC Gilt Fund, and Bandhan Government Securities Fund as top performers, giving practical insights for investors. However, model limitations, such as limited fit and precision for lower-performing funds, highlight possibilities for improvement. The findings help investors make better decisions by improving their understanding of fund performance trends. © 2025 IEEE.

关键词： Adversarial machine learning

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Improving Text Summarization with Transformer Networks and Reinforcement learning for English 3

Improving Text Summarization with Transformer Networks and R...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Vijaya Durga, Chinni Bala Arunadevi, V. Pathak, Punit Prema, S. Nair, Hari Nagaraj, P. Potti Sreeramulu Chalavadhi Mallikarjuna Rao College of Engineering & Technology A.P. Vijayawada India Dr.Mahalingam College of Engineering and Technology Department of Science and Humanities India Auro University Department of School of Liberal Arts and Human Sciences Surat India Panimalar Engineering College Department of English Chennai India Department of English and Foreign Languages Andhra Pradesh Bhimavaram India K.Ramakrishnan College of Engineering Department of Ai & Ml Tamil Nadu Trichy India

ISBN: (纸本)9798331528201

As the volume of text data continues to grow rapidly, efficient and accurate text summarization has become crucial for managing and processing this information. Traditional summarization methods, including extractive and abstractive approaches, face challenges in maintaining relevance, fluency, and factual consistency, particularly in complex domains such as legal documents. In this research, we propose an advanced summarization framework leveraging transformer networks, specifically designed to enhance the quality of English text summaries. The framework begins with the preprocessing of text data, including lowercasing, elimination of punctuation, HTML tags, stopwords, and stemming/lemmatization to standardize the input. Text feature extraction is then performed using the Term Frequency-Inverse Document Frequency (TF-IDF) method, converting textual data into numerical representations that highlight the significance of terms within documents. The core of the framework employs a transformer model that eschews traditional RNNs in favor of multiple attention layers and positional encoding to capture global dependencies and enable parallel processing. The model's encoder-decoder structure uses self-attention mechanisms and multi-head attention layers to effectively encode the sequence and context of words, leading to high-quality abstractive summaries. The proposed architecture's effectiveness is evaluated using the BillSum dataset, which includes over 22,000 U.S. Congressional bills and human-written summaries. The results highlight the superior performance of the proposed Transformer model, which achieved the highest ROUGE-1, ROUGE-2, and ROUGE-L scores, with values of 37.03, 16.89, and 28.34, respectively and the Proposed method is implemented using python software. Our approach demonstrates significant improvements in summary accuracy and relevance compared to existing methods, making it a promising solution for automated summarization tasks across various domains.

关键词： Reinforcement learning

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Personalized learning Paths Based on Big data Analysis in Digital English Teaching 3

Personalized Learning Paths Based on Big Data Analysis in Di...

引用

3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Luo, Yali School of General Education Guangxi Vocational College of Water Resources and Electric Power Nanning Guangxi530023 China

ISBN: (纸本)9798350376173

With the deep integration of artificial intelligence and education, the change and innovation of education is facing an unprecedented historic opportunity, and in the form of classroom organisation, it is gradually moving towards a large-scale, individualised teaching model. Individualised teaching is premised on the understanding of individual differences in students, both at the cognitive level and at the emotional level of obvious differences. Under the support of intelligent technology, based on personalised learning platform, adaptive learning analytics, predictive artificial intelligence and data-driven means, intelligent identification and intervention of learners' individual cognition is carried out at both scientific research and practical application levels. In this paper, data collection and analysis methods are used to conduct the research. Through data analysis, this paper reveals the advantages of big data in digital English teaching. The study shows that big data analytics has great potential in digital English language teaching to improve student learning and engagement. © 2024 IEEE.

关键词： Federated learning

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Design and Development of an AI Big Model for Elderly Education 3

Design and Development of an AI Big Model for Elderly Educat...

引用

3rd international conference on Artificial Intelligence and Computer Information Technology, AICIT 2024

作者： Tang, Jian Yiyang Open University Yiyang Education College No. 5 Kangfu North Road Hunan Province Yiyang China

ISBN: (纸本)9798350365443

With the development of technology and social progress, artificial intelligence (AI) has gradually penetrated into various fields of our lives. In the field of education, the application of AI is becoming increasingly widespread, especially in elderly education, where the application of AI is of great significance. Design and develop an AI model for elderly education, aiming to provide personalized, intelligent, and convenient learning experiences for the elderly. Utilizing artificial intelligence technology to provide customized learning content and methods by analyzing the learning needs and interests of the elderly;Through real-time language communication and intelligent tutoring functions, help elderly people solve problems and confusions in learning;Through data analysis and mining, provide support and services for elderly people in areas such as health management and social activities. In the future, technologies such as virtual reality and augmented reality can be combined to create a more diverse and interactive learning experience. © 2024 IEEE.

关键词： Contrastive learning

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Fire Detection and Autonomous Evacuation Guidance System 3

Fire Detection and Autonomous Evacuation Guidance System

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vadlamudi, Sandyarani Roja Ramani, D. Preetham, S. Dagadi, Ganesh Goyal, Nipun Mantri, Harshavardhan V. Department of AIML New Horizon College of Engineering Bengaluru India Department of CSE New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798331527549

Effective fire emergency responses are critical to ensuring safety and minimizing damage. This research exhibits an innovative fire detection and evacuation system that utilizes deep-learning and real-time video analysis to improve response times and efficiency. Utilizing convolution neural nets (CNNs), the system is able to detect fires with high precision and minimal delay, while dynamically generating evacuation plans depending on the fire's location. The system integrates advanced machine learning techniques to analyse building layouts and determine the safest, shortest evacuation routes. This information is conveyed to occupants via a public address system, enabling a streamlined and well-coordinated evacuation process. By combining real-time fire detection with intelligent route optimization, the system offers a significant advancement over traditional fire safety technologies. The paper also addresses key challenges such as processing latency and the system's ability to operate effectively in real-world conditions. Experimental results demonstrate the reliability of the CNN model—Inception V3 for fire recognition and Dijkstra algorithm for route planning—in both accuracy and speed aspects. The system’s ability to handle complex visual data while maintaining operational efficiency is a highlight of this research. Along with technical details, the research explores the practical implications of implementing this system in various environments. Key challenges include maintaining low latency in video streaming and fire detection, as well as ensuring the effectiveness of the escape route analysis in complex environments. Future improvements focus on scalability, with plans to expand the system’s capabilities to support multi-camera setups and larger buildings, making it adaptable for widespread use. © 2025 IEEE.

关键词： Video streaming

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Deep learning-Based Transfer learning with MobileNetV2 for Crop Disease Detection 3

Deep Learning-Based Transfer Learning with MobileNetV2 for C...

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3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Jesupriya, J. Mageswari, P. Uma Alli, A. Bengaluru India Bengaluru India Bengaluru India

ISBN: (纸本)9798331515911

Early diagnosis of such maladies can avoid crop diseases and increase agricultural yield. We attempt to propose deep learning-based approach for the classification of diseases in an apple crop using the New Plant illnesses dataset sourced from Kaggle. A pre-trained feature extractor called Mobile NetV2,with global average pooling and denser layers and dropout is included for achieving better regularization in the proposed model along with transfer learning. The increase in augmented image data increases generalization and robustness at training. Our approach is distinctive in terms of achieving excellent accuracy based on 97% training accuracy with a validation accuracy of 97% over20 epochs. In its pursuit to precision agriculture, this research allows quick interventions for crop health management through early disease detection-an efficient and lightweight choice. © 2025 IEEE.

关键词： Fruits

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