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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20256 条记录，以下是641-650 订阅

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Joint Extraction of Entity Relationships in Walnut Disease and Pest Based on Chinese NLP Models 3

Joint Extraction of Entity Relationships in Walnut Disease a...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Zhu, Ruiliang Song, Xiangshuai Zhang, Hao Cai, Xuli School of Computer and Electronic Information Guangxi University Nanning Guangxi China

ISBN: (纸本)9798350380989

This study addresses the limited application of deep learning techniques in the field of walnut disease and pest and the challenges posed by complex relationships and diverse entity types in this domain. We propose a deep learning-based method for constructing a knowledge graph in the walnut disease and pest domain, incorporating ontologies to establish a conceptual model for the disease and pest knowledge graph. To overcome issues such as relationship overlap (e.g., one-to-many, many-to-many) and loss of relationship chains, we introduce a novel labeling scheme called 'based on ontology binding BIESO (Begin-Inside-End-Single-Other)' that directly models triplets. By employing a label matching algorithm, we obtain triplet data. We train and predict on the dataset using an end-to-end model consisting of Bidirectional Encoder Representations from Transformers (BERT), Bi-directional Gate Recurrent Unit (BiGRU), and Conditional Random Field (CRF). Experimental results show an F1 score of 75.79%, outperforming models such as BERT-BiLSTM-CRF and word2vec-BiGRU-CRF. We semiautomatically extract unstructured knowledge and store the extracted triplets in a Neo4j graph database, enabling visualization of the knowledge. The research methodology of this knowledge graph can serve as a reference for constructing knowledge graphs in walnut agriculture and developing intelligent question-answering systems for walnut disease and pest. © 2024 IEEE.

关键词： BERTBiGRU-CRF deep learning knowledge graph ontology Walnut disease and pest

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Application Research on Automatic Mapping of Distribution Networks Based on Big data Technology 3

Application Research on Automatic Mapping of Distribution Ne...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Fan, Litao Bi, Yunchuan Jiang, Jigang Yunnan Power Grid Co. Ltd. Electric Power Science Research Institute Kunming Yunnan China Dehong Power Supply Bureau of Yunnan Power Grid Co. Ltd. Kunming Yunnan China

ISBN: (纸本)9798350380989

To solve the problem of automatic mapping of single line diagrams in distribution networks, a large amount of manual adjustment is required to meet practical requirements. This article is based on automatic mapping of a distribution network in Yunnan. The system introduces machine learning technology from the big data Spark framework to the single line graph layout algorithm, and conducts application research. Firstly, this paper introduces the automatic mapping system's Technical architecture, explaining the application environment of machine learning technology, proposing the construction of a decision tree machine learning model based on the iterative binary tree 3rd generation (ID3) algorithm. The distribution network data is used as training samples to describe the construction process of the decision tree model. Furthermore, the graph adjusted sample data is used as the training set to train the layout algorithm library. The algorithm for the layout angle of the child devices is related to the parent device. Finally, it validates the machine learning model and layout algorithm using a single line diagram data instance of the distribution network. As a result, after machine learning, the generated single line diagram runs horizontally and vertically, with balanced layering of the station building and a reasonable and beautiful layout effect, which proves big data and machine learning technology and shows the effectiveness of application in automatic layout of single line diagrams. © 2024 IEEE.

关键词： Big data Decision Tree Layout algorithm Machine learning Single line diagram

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Environmental Monitoring using LoRa and NodeMCU 3

Environmental Monitoring using LoRa and NodeMCU

引用

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

作者： Mihir, D.V.S. Pavan, Baramkula Vishnu Datta Aralaguppi, Yogen Avinash Nair, Pranav Biju Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Monitoring of environmental parameters in areas with a sparsely populated and less infrastructure is quite challenging due to issues such as high power consumption, limited range for transmission, and lack of scalability. The innovation of the system proposed here deals with these problems using an IoT-based monitoring system, using LoRa technology and NodeMCU microcontrollers. It can collect and transmit real-time data on temperature, humidity, air quality, and sound levels efficiently. Key outcomes of this research work are reliable range communication up to 2 km under line-of-sight conditions, minimal power consumption, and robust data integrity through sensor calibration and error-checking mechanisms. The solution, which is scalable and low-cost, is proven feasible in real-world applications, particularly in remote and underserved areas, and promises a great future enhancement, including range extension and energy harvesting. © 2025 IEEE.

关键词： Environmental monitoring

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Kannada Continuous Speech Recognition Using Deep learning 3rd

Kannada Continuous Speech Recognition Using Deep Learning

引用

3rd international conference on Advanced Network Technologies and intelligent Computing (ANTIC)

作者： Paul, Shubhojeet Bhattacharjee, Vandana Saha, Sujan Kumar Birla Inst Technol Ranchi Mesra India Natl Inst Technol Durgapur India

ISBN: (纸本)9783031640667;9783031640674

Automatic Speech Recognition (ASR) is a prevalent approach for attaining human-machine interaction by enabling machines to transcribe speech data. We propose a Continuous Speech Recognition model in the Kannada language using deep learning techniques such as Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (Bi-GRU). The model was trained and validated using 100 and 20 h of data, respectively. The experiment has generated encouraging results with a Character Error Rate (CER) of 15.62% and a Word Error Rate of 34.47% (WER).

关键词： Kannada Automatic Speech Recognition Continuous Speech Recognition Deep learning

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Strategic Innovations in Network Security: A Machine learning Approach to Detect and Mitigate Backdoor Attacks 3

Strategic Innovations in Network Security: A Machine Learnin...

引用

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

作者： Khan, Aadil Gupta, Deepali Dutta, Monica Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331515911

A backdoor attack is a malicious act in which a hacker takes advantage of holes in the system to enter without authorization. This requires changing the network and authentication processes in order to insert malicious code and provide an entry point for unwanted activities. Early detection of backdoor attacks is essential to preventing significant damage. Proactive defence searches for irregularities in network traffic before dubious behaviours become widespread. Prompt innovation boosts the robustness of a cybersecurity system, protecting information and infrastructure against illegal access and possible breaches. This paper emphasizes proactive security measures and offers a methodical approach to identifying backdoor attacks. Strong early detection might be achieved at the network-database interface with ensemble machine learning. This helps to ensure that SQL queries are corrected correctly and prevents unwanted access. Our work improves cybersecurity resilience by deepening our knowledge of how to successfully defend against backdoor attacks. Producing datasets for machine learning algorithms of superior quality is essential to the backdoor incursion detection procedure. The variety of conditions it offers makes accurate testing of detecting systems possible. The use of ensemble machine learning on datasets like UNSW-NB15 improves the accuracy of backdoor attack detection, hence supporting the development of an all-encompassing and efficient cybersecurity plan. © 2025 IEEE.

关键词： Adversarial machine learning

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SVM at Edge: Low Cost Caching Prediction for Connected Edge Intelligence in Federated Machine learning 3

SVM at Edge: Low Cost Caching Prediction for Connected Edge ...

引用

3rd international conference on Applied Artificial Intelligence (ICAPAI) - Human-Centric AI for the Digital Society

作者： Jain, Sanyam Ostfold Univ Coll N-1783 Halden Norway

ISBN: (纸本)9798350328929

This research work targets a problem in edge intelligent devices where caching becomes challenging task. We propose a Federated learning (FL) technique that uses local model as Support Vector Machines (SVM) and global model as a Neural Network. Each participant in one FL run would train a SVM on its local data and use it to make predictions. Further, local aggregated predictions and learned SVM boundary used to train a global NN model. SVM is a simple and fast algorithm that can work well on small, local datasets, while Neural Networks are capable of modeling more complex relationships in data and can achieve higher accuracy on large, global datasets. The combination of these two models balances the trade-off between local accuracy and global accuracy, while preserving privacy by only sharing predictions. Results of this approach demonstrate its potential to provide a more accurate representation of data across multiple participants in a federated learning setup to predict cache and increase cache hit.

关键词： Federated learning Support Vector Machines Cache Prediction

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Plant Diseases Detection Using Deep learning and Machine Vision 3

Plant Diseases Detection Using Deep Learning and Machine Vis...

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3rd IEEE international conference on intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Singh, Nidhi School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9798350361186

This research delves into deep learning and machine vision applications for plant leaf disease detection in agricultural settings, focusing on farm village datasets. Utilizing a blend of authentic farm village data and synthetic data from Generative Adversarial Networks (GANs), three advanced convolutional neural network (CNN) models VGG16, ResNet50, and InceptionNet V3 are employed with transfer learning. Leveraging transfer learning enhances model performance through fine-tuning pre-trained networks. The study systematically evaluates models based on key metrics like accuracy, precision, recall, and F1 score. Results showcase the methodology's robustness, with ResNet50 emerging as the leading performer at 83.23%, contributing to precision agriculture's advancements with promising implications for sustainable farming and crop yield optimization. © 2024 IEEE.

关键词： Generative adversarial networks

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Component-based Approach to Software engineering of Machine learning-enabled Systems 24

Component-based Approach to Software Engineering of Machine ...

引用

IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： Indykov, Vladislav Univ Gothenburg Chalmers Gothenburg Sweden

ISBN: (纸本)9798400705915

Machine learning (ML) - enabled systems capture new frontiers of industrial use. The development of such systems is becoming a priority course for many vendors due to the unique capabilities of Artificial Intelligence (AI) techniques. The current trend today is to integrate ML functionality into complex systems as architectural components. There are a lot of relevant challenges associated with this strategy in terms of the overall system architecture and in the context of development workflow (MLOps). The probabilistic nature, crucial dependency on data, and work in an environment of high uncertainty do not allow software engineers to apply traditional software development methodologies. As a result, there is a community request to systematize the most relevant experience in building software architectures with ML components, to create new approaches to organizing the process of developing ML-enabled systems, and to build new models for assessing the system quality. Our research contributes to all mentioned directions and aims to create a methodology for the efficient implementation of ML-enabled software and AI components. The results of the research can be used in the design and development in industrial settings, as well as a basis for further studies in the research field, which is of both practical and scientific value.

关键词： machine learning software architecture software quality

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Noise Tolerance and Robustness Ranking in Machine learning Models 25th

Noise Tolerance and Robustness Ranking in Machine Learning M...

引用

25th international conference on intelligent data engineering and automated learning

作者： Padro-Ferragut, Cristina Ramirez-Quintana, Maria Jose Martinez-Plumed, Fernando Univ Politecn Valencia Valencia Spain

ISBN: (纸本)9783031777370;9783031777387

Machine learning models often excel in controlled environments but may struggle with noisy, incomplete, or shifted real-world data. Ensuring that these models maintain high performance despite these imperfections is crucial for practical applications, such as medical diagnosis or autonomous driving. This paper introduces a novel framework to systematically analyse the robustness of Machine learning models against noisy data. We propose two empirical methods: (1) Noise Tolerance Estimation, which calculates the noise level a model can withstand without significant degradation in performance, and (2) Robustness Ranking, which ranks Machine learning models by their robustness at specific noise levels. Utilizing Cohen's kappa statistic, we measure the consistency between a model's predictions on original and perturbed datasets. Our methods are demonstrated using various datasets and Machine learning techniques, identifying models that maintain reliability under noisy conditions.

关键词： Model robustness Noise in data Machine learning Cohen's kappa Performanceevaluation

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automated Object Detection and Grasp Form Classification 3

Automated Object Detection and Grasp Form Classification

引用

3rd IEEE international conference on Electronic engineering, ICEEM 2023

作者： Yacoub, Kerolous M. Elbialy, Ahmed El Shorouk Academy Higher Institute of Engineering Systems and Biomedical Engineering Department Cairo Egypt

ISBN: (纸本)9798350323511

Intricacy is one of the challenges associated with robotic hand systems. By offering simple and efficient systems, the chance of utilizing them being rejected is reduced. The study aims to develop a deep learning-based model for automated detection of the suitable grasp form of objects. The methodology was developed using the U-Net model and image processing method, in addition to five distinct classification models. The data source is a data collection referred to as ALOI for coloured small objects. By using ground-truth labelling in four grasp forms and substituting different backgrounds, the issue of there being only black backgrounds was overcome. Two experiments have been conducted on the dataset. The accuracy and intersection over union of the segmentation algorithm, as well as the accuracy, sensitivity, specificity, and precision of the classification models, were measured to evaluate their performance. The proposed system paradigm has yielded reliable results. In particular, employing the proposed segmentation algorithm produced a significant improvement in the performance and efficacy of all five models. © 2023 IEEE.

关键词： Object detection

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