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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40757 条记录，以下是4291-4300 订阅

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Balance Relation-Aware Attention Embedding Model for Knowledge Graph Completion 6

Balance Relation-Aware Attention Embedding Model for Knowled...

引用

6th IEEE Pune Section international conference, PuneCon 2023

作者： Khobragade, Anish Ghumbre, Shashikant Pachghare, Vinod Coep Technological University Dept. of Computer Science and Engineering Pune India Government College of Engineering and Research Avasari Khurd Dept. of Computer Science and Engineering Pune India

ISBN: (纸本)9798350324204

Predicting missing links has great significance in knowledge graph completion because it helps improve the accuracy and completeness of the information in the graph. The recent models employ an attention mechanism to capture the graph network. Previous attention-based models treat similar relation semantics for associated pairs of entities, but relations might have similar names but represent different semantics. Thus, models fail to capture the semantic dissimilarity of relations involved within the triplet entity pair. This paper proposes a new graph attention model capable of effectively learning embeddings by combining relationship features with triplet entity embeddings to balance relationship contributions while capturing meaningful graph structure. The model incorporates relational compositional embeddings to determine distinct attention for neighboring nodes during computation and update the entity embedding. This novel approach outperforms the previously established models on the benchmark dataset. © 2023 IEEE.

关键词： Knowledge graph

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Conceptual Architecture for the Provision and Aggregation of Universal Digital Twins within Containerization Environments

Conceptual Architecture for the Provision and Aggregation of...

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2023 international conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Häußermann, Tim Lehmann, Joel Rache, Alessa Reichwald, Julian Mannheim University of Applied Sciences Virtual Engineering Competence Center Mannheim68163 Germany

ISBN: (纸本)9798350322972

Digital Twins (DTs) are technological cutting-edge enablers for future industrial revolutions. Intelligent behavior is nevertheless not often granted to them so that DTs are tied as passive data representations organized in repository-like structures. In order to create intrinsic value, intelligent structures, and approaches must be established leveraging DTs through intelligence and autonomy. The concept can be easily transferred to other application domains, which offers particular advantages for use-case-independent architecture developments. This paper elaborates on a methodology for creating DTs device independently deployed on arbitrary computing nodes with variable configurations tailored to its specific requirements. A learning capability is induced by the migration and adoption of new intelligent code components. After requirements on DTs are derived, a conceptual architecture is outlined. A partial implementation substantiates the feasibility of the presented structure. © 2023 IEEE.

关键词： Cyber Physical System

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Plant Pathology Detection for Massive Heterogeneous Data Using Deep learning 14

Plant Pathology Detection for Massive Heterogeneous Data Usi...

引用

14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Chaudhari, Avinash Jani, Kuntesh Patel, Sunil Modasa Gujarat Technological University Government Engineering College Information Technology Department Modasa India Gujarat Technological University L.D. College of Engineering Information Technology Department Ahmedabad India Gujarat Technological University L.D. College of Engineering Computer Engineering Department Ahmedabad India

ISBN: (纸本)9798350335095

The manual inspection of plant disease is a difficult and time-consuming task that requires expertise in the area of plant pathology which is not always available. Deep learning models have shown significant efficiency in solving image classification problems. They can be used in the field of precision agriculture for disease management tasks. State-of-the-art deep learning models are used in several studies to automatically detect diseases based on the plant leaves symptoms. Most of the studies have been carried out on single or limited plants. In this study large dataset having a total number of 1,02,113 images is created by combining 16 openly available datasets. A comparative performance analysis of three CNN models, ResNet50, MobileNetV2, and InceptionV3 has been carried out after training with the dataset. InceptionV3 has achieved the highest classification accuracy of 0.90. MobileNetV2 has less training time compared to others. © 2023 IEEE.

关键词： Large dataset

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Hybrid of DiffStride and Spectral Pooling in Convolutional Neural Networks 23

Hybrid of DiffStride and Spectral Pooling in Convolutional N...

引用

8th international conference on Sustainable Information engineering and Technology, SIET 2023

作者： Rafif, Sulthan Azhar, Mohammad Faris Wahyu Pratama, Mochamad Arfan Ravy Ibad, Ahmad Mustafidul Yudistira, Novanto Muflikhah, Lailil Faculty of Computer Science University of Brawijaya Malang East Java Indonesia

ISBN: (纸本)9798400708503

Stride determines the distance between adjacent filter positions as the filter moves across the input. A fixed stride causes important information contained in the image can not be captured, so that important information is not classified. Therefore, in previous research, the DiffStride Method was applied, namely the Strided Convolution Method with which it can learn its own stride value. Severe Quantization and a constraining lower bound on preserved information are arises with Max Pooling Downsampling Method. Spectral Pooling reduce the constraint lower bound on preserved information by cutting off the representation in the frequency domain. In this research a CNN Model is proposed with the Downsampling Learnable Stride Technique performed by Backpropagation combined with the Spectral Pooling Technique. Diffstride and Spectral Pooling techniques are expected to maintain most of the information contained in the image. In this study, we compare the Hybrid Method, which is a combined implementation of Spectral Pooling and DiffStride against the Baseline Method, which is the DiffStride implementation on ResNet 18. The accuracy result of the DiffStride combination with Spectral Pooling improves over DiffStride which is baseline method by 0.0094. This shows that the Hybrid Method can maintain most of the information by cutting of the representation in the frequency domain and determine the stride of the learning result through Backpropagation. © 2023 ACM.

关键词： Backpropagation

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Design and Implementation of Crime Prediction Using Machine learning Algorithm 5

Design and Implementation of Crime Prediction Using Machine ...

引用

5th international conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Bhardwaj, Shivani Kaur, Sandeep Lovely Professional University Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9781665475242

A common approach to reducing crime in a city is to encourage citizen-police cooperation. In the United States and around the world, Twitter is a popular social media platform used by millions of people. Predictive analytics based on Twitter can aid decision support systems. All messages are tagged in real time with their location and time of transmission, which is completely free. Machine learning techniques were used to create an intelligent method of effective criminal detection. In order to find the most accurate classifier, six different classification methods were devised. The learning model parameters of the classifier had to be carefully chosen in order to accurately predict the model's performance. The SVM (2.697) and XG-Boost (2.561) performed better than the other six classifiers in the overall log loss test. © 2023 IEEE.

关键词： Crime

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Image segmentation algorithm based on multi-granularity clustering 7

Image segmentation algorithm based on multi-granularity clus...

引用

7th international conference on Mechatronics and Intelligent Robotics, ICMIR 2023

作者： Xia, Shuyin Xia, Cenjun Dai, Dawei Ni, Jinyuan School of Computer Science and Technology Chongqing University of Posts and Telecommunication ChongQing400065 China School of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

This article proposes an image processing algorithm based on multi-scale clustering, which can improve the probability of a classifier correctly classifying an image even after it has been subjected to noise attacks. The algorithm mainly involves performing multi-scale clustering on the pixels of the image based on features such as color space, distance, texture, etc., and then incorporating the clustering results into the training of the classifier. By directly extracting the main feature information from the image for training, the algorithm prevents large classification errors due to changes in certain pixel values, thus reducing the impact of attacks on other pixels in the image and greatly improving the robustness of the image. Therefore, this algorithm is a robust and efficient denoising method. © 2023 SPIE. All rights reserved.

关键词： Pixels

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An Efficient Technique of Predicting Toxicity on Music Lyrics Machine learning 3

An Efficient Technique of Predicting Toxicity on Music Lyric...

引用

3rd international conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Bin Noor, Nahiyan Ahmed, Ishraq University of Arkansas at Little Rock Information Science 72204 United States Chittagong University of Engineering & Technology Electrical and Electronic Engineering Bangladesh

ISBN: (纸本)9798350345360

It is widely accepted that music is humanity's universal language since it can spread happiness and excitement throughout people's lives. Music is a form of art that is highly regarded worldwide. There are many ways that music lyrics affect our daily lives. In the music industry, it is crucial to prevent the reproduction of songs whose lyrics are toxic or unsuitable for children. Our mood might be impacted by listening to particularly toxic or non-toxic music. The listener's experience might be enhanced if the recommendation method eliminates toxicity. In this study, we use machine learning (ML) algorithms to classify lyrics from various musical genres and performers as toxic or non-toxic. Utilizing the Detoxify model, the toxicity score was generated and labelled the songs as toxic and non-toxic based on the scores. The study demonstrates that the configuration using the lyric data set along with TF-IDF vectorization and Ensemble of Logistic Regression, Support Vector Machine and Decision Tree as an algorithm surpasses all other designs with 94% accuracy. This classification will help the authority and policymakers of music industries to categorize the song based on the label and mention in the song description which is not appropriate for the children and set guidelines to prevent toxicity via songs. © 2023 IEEE.

关键词： Music

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An analysis of Intrusion detection systems in IIoT 8

An analysis of Intrusion detection systems in IIoT

引用

8th IEEE international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Latha, R. Bommi, R.M. Simats Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350347791

Enhancing manufacturing and industrial processes are achieved using smart sensors and actuators known as the industrial internet or things or IIoT. By implementing IIoT, machines transform into smart devices, communicating important information that enables driving business decisions faster and more accurately. Thus, IIoT enables the improvement of business processes that are agile and dependable. However, the widespread deployment of IoT networks across different industrial sectors has resulted in numerous security gaps, posing threats to the integrity of IIoT systems. An intrusion detection system (IDS) is a typical software that monitors and stimulates security explanations for computer networks. Deployment of the solutions aims to identify malicious activity and take measures that facilitate risk aversion. Due to IIoT's peculiarities, standard IDS-based solutions are complicated to implement. This includes limited resources, sensitive data, and heterogeneous architecture. Researchers are implementing Fog/Edge computing, Machine learning (ML), and Deep learning for effective and flexible IDS deployments in different IIoT scenarios. This research focuses on how IDS emerges in specific manufacturing settings. To do this, we present an in-depth analysis of IDS deployment strategies, detection strategies, evaluation approaches, and data sources. © 2023 IEEE.

关键词： Internet of things

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Research on Small Target Railway Foreign Object Intrusion Detection Based on YOLOX- Tiny 3

Research on Small Target Railway Foreign Object Intrusion De...

引用

3rd international conference on computer Science, Electronic Information engineering and Intelligent Control Technology, CEI 2023

作者： Zhu, Kaiyuan Gao, Deyong Wang, Song Wang, Anzheng School of Electronic Information and Engineering Lanzhou Jiaotong University Lanzhou Gansu China

ISBN: (纸本)9798350372052

Based on the YOLOX- Tiny detection model, a multi-scale fusion and attention-enhanced anchor-less deep learning model for foreign object detection was proposed, which solved the problems of insufficient feature extraction, low detection accuracy, and low real-time performance in railway foreign object intrusion detection. Based on the YOLOX- Tiny model multi-scale FPN pyramid network, a small target detection layer is added to propose a multi-scale weighted bidirectional feature fusion module, which enhances the utilization of feature maps and improves detection accuracy. At the same time, an index attention mechanism is introduced to improve the network structure of YOLOX- Tiny, increase the focus on target regions, and highlight target features in small target scenarios. This approach effectively improved the detection accuracy for small-scale targets and enhanced the convergence speed. © 2023 IEEE.

关键词： Intrusion detection

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Review on Non-Invasive Electromagnetic Approaches for Blood Glucose Monitoring Using Machine learning 11

Review on Non-Invasive Electromagnetic Approaches for Blood ...

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11th international Japan-Africa conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Mansour, Esraa Shahin, Mohamed Ashraf, Abdelrahman Atta, AbdelAzim Allam, Ahmed Zewail, Rami Abdel-Rahman, Adel B. School of Electronics Egypt-Japan University of Science and Technology Communications and Computer Engineering Alexandria Egypt

ISBN: (纸本)9798350369328

This paper particularly explores the microwave technique, reviewing current research and its limitations in non-invasive blood glucose monitoring applications. It also examines how using artificial intelligence can make predicting blood sugar levels more precise. Combining machine learning with current technology could transform diabetes care, making it easier and more effective for patients worldwide. © 2023 IEEE.

关键词： Glucose

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