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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4311-4320 订阅

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A Fully Integrated Deep learning Framework for Semantic Segmentation of Vegetation Classification Based on Active learning Strategies and UAV Remote Sensing 15th

A Fully Integrated Deep Learning Framework for Semantic Segm...

引用

15th international conference on computer Science and its Applications, CSA 2023 and 17th KIPS international conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Liu, Yufeng Kong, Guangze Shen, Xiang Miao, Sheng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China Department of Statistic The George Washington University 1918 F Street NW WashingtonDC20052 United States

ISBN: (纸本)9789819724468

With the development of urbanization and the increasing use of land for construction, there is a growing focus on detecting different types of surface vegetation cover. Unmanned Aerial Vehicle (UAV) remote sensing is an important method for acquiring high-resolution multispectral images of surface vegetation, and its data provides reliable support for deep learning. However, labeling training samples requires researchers to conduct field surveys, which is a time-consuming and expensive process. In order to reduce the cost of labeling, this study proposes a deep learning framework for fully fusing semantic segmentation for vegetation classification, which is built based on an active learning strategy. The framework utilizes the Gated Full Fusion module (GFF) and the attention mechanism to enhance the model’s learning ability. It dynamically extracts more important samples to be labeled by the Oracle, reducing the number of sample markers while ensuring accuracy. We conducted several experiments in areas with real urban vegetation coverage, and the results demonstrate that the framework proposed in this study surpasses the current state-of-the-art baselines. Additionally, it ensures model stability while reducing the cost of labeling a large number of samples. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Active learning

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Spatio-Temporal Graph Neural Networks for Accurate Crime Prediction 13

Spatio-Temporal Graph Neural Networks for Accurate Crime Pre...

引用

13th international conference on computer and Knowledge engineering, ICCKE 2023

作者： Roshankar, Rojan Keyvanpour, Mohammad Reza Alzahra University Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Tehran Iran Alzahra University Department of Computer Engineering Faculty of Engineering Tehran Iran

ISBN: (纸本)9798350330151

As a matter of public safety and resource allocation, crime prediction is of paramount importance. As a result of applying data preprocessing techniques and a graph-based approach, this paper presents a crime prediction model. This study shows a comprehensive analysis of crime incidents reported in Chicago over five years, utilizing several preprocessing steps in preparing the dataset. Irrelevant features are eliminated, missing values are handled, and a new feature is extracted. An algorithm based on the k-nearest neighbors method is proposed for representing crime incidents by constructing a graph representation. Each crime incident is connected to its k nearest neighbors based on spatial coordinates. This study compares graph-based machine learning algorithms and conventional approaches, including logistic regression, decision trees, and the recently related work, Spatial-Temporal Meta-path Guided Explainable Crime Prediction (STMEC). The results reveal that the graph-based model demonstrates superior accuracy, recall, and F1 scores compared to traditional models, notably outperforming the recently related work, STMEC. Based on the current study, the graph-based approach effectively captures crime data's spatial dependencies and patterns. Incorporating additional features and fine-tuning hyperparameters of the model can provide valuable insights into crime prevention strategies and urban planning applications. © 2023 IEEE.

关键词： Forecasting

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Circular Arc Length-Based Kernel Matrix For Protein Sequence Classification

Circular Arc Length-Based Kernel Matrix For Protein Sequence...

引用

2023 IEEE international conference on Big Data, BigData 2023

作者： Murad, Taslim Ali, Sarwan Chourasia, Prakash Mansoor, Haris Patterson, Murray Georgia State University Department of Computer Science AtlantaGA United States Lahore University of Management Sciences Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350324457

Biological sequence analysis is crucial in understanding the sequence structure, function, and evolutionary relationships. In traditional methods, using Euclidean distance metrics is common in measuring the similarity between sequence embeddings. However, they fail to capture sequence space's inherent curvature and spherical nature. Therefore, we explore the application of spherical geometry and distance metric, circular arc length (CAL) based distance for comparing biological sequence embeddings. Spherical geometry is a non-Euclidean geometry that models the surface of a sphere, accounting for its curvature. By leveraging CAL, we can more accurately measure the pairwise distances between bio-sequence embeddings. In this study, we propose the utilization of CAL for comparing bio-sequence embeddings. We develop a function to compute the CAL distance between two sequence embeddings, enabling researchers to accurately measure the similarity between sequences while considering the underlying spherical geometry. Incorporating spherical geometry enables a more comprehensive understanding of the relationships and similarities between biological sequences, improving various downstream tasks such as classification, clustering, and evolutionary analysis. Our experimental evaluation demonstrates the advantages of using a spherical distance metric over Euclidean metrics for bio-sequence analysis. Our proposed CAL-based approach outperforms the Euclidean geometry-based baselines by depicting a huge performance improvement for the protein subcellular location classification task. In our experiments, accuracy is improved by 52.5% and 60. 3% compared to the PWM2Vec and Autoencoder methods, respectively, corresponding to the DT classifier. © 2023 IEEE.

关键词： Biological Sequence Analysis Circular Arc Length Embedding Generation Non-Euclidean Representation learning

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An Improved learning Experience for English Language Using Big Data Analytics and AAC 2

An Improved Learning Experience for English Language Using B...

引用

2nd international conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Kumar, P.Vinoth Balamurugan, D. Suriya, K. Reshma, R. Faculty of Engineering Karpagam Academy of Higher Education Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9781665491990

Teachers and students in the 21st century are shifting their attention from the classroom to digital education, aiming for meaningful, high-quality, and dynamic learning. With the introduction of digital learning, the educational system has undergone significant transformations. Some teachers/instructors, on the other hand, are still experiencing difficulties. For this reason, this research finds a solution named AAC integrated Big Data Analytics Framework (AACBDAF) for people with neural disorders in the English language. AAC-BDAF gathers all the linguistic data and categorizes it based on how frequently it is used. To input data, the AAC system uses eyeball tracking to display symbols and characters on a screen. To help the listener follow along, the selected option/word is converted into a text-to-speech paradigm. The detection algorithms of machine learning and deep learning are evaluated for word detection and suggest the convolutional neural network (CNN) support AACBDAF. According to simulation findings, the proposed AAC-BDAF recognizes phrases and predicts the word from their eye-gazing with higher accuracy of 98.63%. © 2023 IEEE.

关键词： Students

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Research on Error Evaluation of Capacitive Voltage Transformer Based on EMD-LSTM 5

Research on Error Evaluation of Capacitive Voltage Transform...

引用

5th international conference on Communications, Information System and computer engineering, CISCE 2023

作者： Fang, Huanhuan School of Information Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350326796

Capacitive voltage transformer (CVT) is a kind of electrical isolation and metering equipment. At present, the most widely used type of gateway voltage transformer in high-voltage and ultra-high voltage power grids is capacitive voltage transformer. The evaluation of its error determines the fairness and quality of the electric energy market. The safety of the power system. In this paper, by analyzing the error characteristics of the gateway voltage transformer, the error formation mechanism is analyzed and determined, and the deep learning algorithm is used to evaluate the error. Before the evaluation, the empirical mode decomposition method is used to decompose the characteristics of the monitoring data of the voltage transformer, and the components containing different time scale information are extracted. Each component is predicted using the long-short-term memory (LSTM) network, and finally the prediction result is obtained. At the same time, to avoid sudden changes caused by fluctuations and noise, multi-step averaging is used to reduce the possibility of misjudgment. The final false alarm rate is reduced to 0.1790%, improving the performance of the prediction. © 2023 IEEE.

关键词： Errors

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A large input-space-margin approach for adversarial training 13

A large input-space-margin approach for adversarial training

引用

13th international conference on computer and Knowledge engineering, ICCKE 2023

作者： Nikouei, Reihaneh Taheri, Mohammad Shiraz University Department of Computer Science and Engineering Shiraz Iran

ISBN: (纸本)9798350330151

It is shown that machine learning models are vulnerable to adversarial attacks. Therefore, different defense methods such as adversarial training have been proposed to improve models' robustness against these attacks. Some recent approaches proposed to structurally improve the robustness of the models. For example, large margin methods try to increase a margin, empty of instances, along decision boundaries that structurally increase necessary change to modify a training instance to an adversarial one. However, nonlinear large-margin models, maximize the margin in a high dimensional space although adversarial examples are generated with a little change in the original space. In this paper, a novel mixed approach is proposed, called LIM (Large Input Margin) to improve the robustness of the model by minimizing both structural and empirical risks. Specifically, both training and adversarial example generation are done based on a loss function to maximize the margin in the original feature space even in a non-linear model. The proposed method is evaluated with FGSM and PGD attacks on MNIST and CIFAR10 datasets. The experimental results show that LIM method outperforms the state-of-the-art defense methods significantly and improves adversarial robustness against FGSM and PGD attacks on both datasets. © 2023 IEEE.

关键词： Network security

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Blockchain-based federated learning for internet of vehicles 8

Blockchain-based federated learning for internet of vehicles

引用

8th international conference on Traffic engineering and Transportation System, ICTETS 2024

作者： Wang, Yibo Fang, Chen Wang, Mingshang Di, Wenkai Information Engineering University Zhengzhou China

ISBN: (数字)9781510686298

ISBN: (纸本)9781510686281

The purpose of this study is to construct a block chain encryption federation learning model for vehicle networking scenario, in order to solve the problems of data security, privacy protection and model training efficiency in the pre-test scenario of vehicle networking traffic. The model integrates federation learning, block chain and homomorphic encryption technology, and designs a roadside unit federation learning framework based on block chain, which ensures the integrity and non-repudiation of data transmission and solves the problem of data tampering in the process of communication. At the same time, homomorphic encryption algorithm is introduced to ensure the safe transmission of model parameters and avoid gradient leakage. Finally, the use of small batch gradient descent algorithm reduces the communication overhead and improves the training efficiency. The experimental results show that the model improves the security and efficiency of the data sharing scheme, and successfully achieves the purpose of traffic prediction. © 2024 SPIE.

关键词： Data modeling Education and training Blockchain Data privacy Data communications Internet Roads computer security Instrument modeling Telecommunications

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Study of Multi-class Arrhythmia Detection and Classification from ECG data using Deep learning 26

Study of Multi-class Arrhythmia Detection and Classification...

引用

26th international conference on computer and Information Technology, ICCIT 2023

作者： Akash, Ahsan Habib Ahamed, Rizwan Bangladesh University of Engineering and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Cardiovascular diseases are responsible for a significant number of fatalities worldwide each year and the death rate from these diseases has increased in recent years. ECG signals are one of the most important sources of information about the heart's electrical activity which can be used to detect abnormalities in the heart. Detection of heart diseases using ECG signals still largely depends on the physicians. A well-trained automated system can go a long way to give assistance to the physician and reduce the time and cost of the detection process. Deep learning models can be trained on large datasets of ECG signals to learn to identify patterns that are associated with different heart conditions. In this paper, we have presented a comparative study of different 1D convolutional deep-learning model architectures with attention mechanisms for electrocardiogram (ECG) signal classification. The models are trained on five large publicly available datasets of ECG signals, which is a diverse collection of ECG signals from patients suffering from various cardiac diseases. In our work, we have shown how different deep learning techniques perform in a large collection of ECG datasets from various sources. We have also proposed an ensemble technique that achieved a significantly higher score in terms of both AUC-ROC(98%) and F1-score(66.9%) than previously suggested techniques. © 2023 IEEE.

关键词： Attention Network Classification Deep learning: Biomedical Signal Processing ECG signal

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Review of Abnormalities Detection in IoT Using Evolutionary Deep learning 5

Review of Abnormalities Detection in IoT Using Evolutionary ...

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5th international conference on Information Technology, Applied Mathematics and Statistics, ICITAMS 2023

作者： Kokaz, Sameer Rasim Salih, Alaa Taima Albu Ghandour, Ahmad Islamic University Of Lebanon Faculty Of Engineering Beirut Lebanon University Of Al-Qadisiyah College Of Computer Science And Information Technology Al-Qadisiyah Iraq

ISBN: (纸本)9798350335118

Today, the Internet is used for various applications such as sending or receiving emails, access to multimedia services, social networks, etc. Moreover, the population of people who use the Internet is increasing. Therefore, the issue of Internet of Things (IoT) security has become a major problem today. Recently, Deep learning has become one of the most influential and valuable methods for detecting abnormalities in IoT Networks. Further, Deep learning models produced more effective performance than traditional techniques in abnormalities detection. Moreover, the structure and parameters of ML and DL models can be optimized using evolutionary algorithms (EA). The survey aims to overview recent research on anomaly detection in the IoT-based Deep learning model that EA has enhanced. © 2023 IEEE.

关键词： Internet of things

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Spam Article Detection on Social Media Platform Using Deep learning: Enhancing Content Integrity and User Experience 23

Spam Article Detection on Social Media Platform Using Deep L...

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13th international conference on Advances in Information Technology, IAIT 2023

作者： Chiawchansilp, Panuwat Kantavat, Pittipol Department of Computer Engineering Faculty of Engineering Chulalongkorn University Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9798400708497

In the digital age, the widespread use of online social media corresponds with a rise in spam. These messages often appear as forceful ads, sales offer, gambling promotions, or repetitive chain messages, and they compromise users' experiences and the overall communication quality on these platforms. Various strategies have been developed to tackle this problem. Some strategies involve human reviewers, which is a straightforward yet time-consuming method. Others utilize advanced techniques like machine and deep learning. Even though a substantial amount of research has applied these methods to English-based spam, studies focusing on Thai language spam are limited. Given this context, our study seeks to develop tools based on machine and deep learning techniques to identify and classify Thai spam content effectively. The central goal of our research is to compare these techniques and determine the most effective model for classifying Thai spam content. © 2023 Owner/Author.

关键词： Deep learning Machine learning Natural Language Processing Social Media Text Classification

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