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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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DTDG-Net: A Few-shot Data Augmentation Method for X-ray Security Inspection Images 3

DTDG-Net: A Few-shot Data Augmentation Method for X-ray Secu...

引用

3rd international conference on Image Processing, computer Vision and machine learning, ICICML 2024

作者： Jing, Bing Du, Yanhui First Research Institute of the Ministry of Public Security of PRC Testing Center for Quality of Security and Police Electronic Product Under the Ministry of Public Security Beijing China People's Public Security University of China School of Information and Network Security Beijing China

ISBN: (纸本)9798350355413

Since the detection of prohibited items is a low-probability event, the target detection model based on deep learning often suffers from issues of low recognition accuracy due to limited training data. To ad-dress this challenge, this article proposes an enhanced Domain Transformation Data Generation Network (DTDG-Net), aiming to solve the problems brought by the scarcity of training samples. Initially, we designed a Color Consistency Cycle Generative Adversarial Network (CC-CycleGAN) based on a Dynamic Color Attention Module (DCAM), which can effectively ensure the color consistency between the original and generated images. Secondly, we developed a Data Fusion Generator (DFG), which aligns the transparency features of the generated and original images through a blending Poisson fusion method based on hue transformation, ultimately achieving data augmentation with limited samples. Finally, to objectively evaluate the effectiveness of our approach, we introduce a style transfer quality evaluation co-efficient, specifically designed for the performance evaluation of few-shot sample data augmentation for object detection. Through this metric, we can comprehensively and accurately measure the contribution of data augmentation to the object detection task. This unique framework design holds potential for widespread applications in the field of prohibited item detection in X-ray images. Through a series of experiments, we demonstrate that our approach, exhibits stronger robustness and generalization, effectively alleviating the challenges posed by few-shot sample problems, providing an innovative solution for pro-hibited item detection in X-ray images. © 2024 IEEE.

关键词： Data augmentation Deep learning Few-shot detection of prohibited items Poisson fusion Self-attention mechanism

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Detecting Distributed DoS attacks in autonomous vehicles external environment using machine learning Techniques 3

Detecting Distributed DoS attacks in autonomous vehicles ext...

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3rd international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2022

作者： El-Ghamry, Amir Elhoseny, Mohamed Faculty of Computers and Information Mansoura University Mansoura Egypt University of Sharjah Sharjah United Arab Emirates School of Engineering and Computer Science University of Hertfordshire Global Academic Foundation Egypt Faculty of Computer Science and Engineering New Mansoura University Egypt

ISBN: (纸本)9781839538186

The use of autonomous vehicles rapidly growing, especially iu smart cities. Autonomous vehicles exchange information with an external network through wireless communication, and hence security and privacy challenges become a major challenge for all user of autonomous vehicles. Attackers try to exploit the vulnerabilities in the wireless network that connect autonomous vehicles these by performing different types of attacks such as denial of service attack (DDOS) and injection attacks. To mitigate the catastrophic effect of these attacks, intrusion detection process is a must. In this paper, A novel intrusion-detection method is proposed to detect distributed denial of service attack (DDOS) in the wireless network of autonomous vehicles. In this method, the wireless network intrusion detection system (WNIDS) is applied on "CICDDoS2019" dataset for training and testing our model. First the dataset is cleaned and the most important features are selected using recursive feature elimination method which decrease the number of features to only 8 features. The reduced dataset is tested by two machine learning classifiers (Random Forest (RF) and Stochastic Gradient Descent (SGD)). The evaluation results showed that both RF and SGD classifiers can be used to detect DDOS attack with an accuracy of 99% using only 8 features, which were selected using the proposed feature selection method. Relying only on 8 features only make the proposed method to be applicable to any IoT application such as internet of autonomous vehicles. © IET conference Proceedings 2022.

关键词： Denial-of-service attack

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Detection of Alzheimer's Disease Using machine learning Models: The Impact of Pre-processing 3

Detection of Alzheimer's Disease Using Machine Learning Mode...

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3rd international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2022

作者： Al Zoubi, Rouaa Elnagar, Ashraf Dept. of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781839538186

Alzheimer's disease is a brain ailment that causes memory loss, disorientation, personality changes, and a loss of independence over time. People with this disease initially experience minor memory loss and confusion. This is referred to as cognitive deterioration. However, as time passes, these symptoms get more acute. On the other hand, as there is no cure for this disease, medical professionals concentrate on detecting dementia in its early stages, this helps them determine the best treatments and allows patients to take precautionary measures before any damage happens to their brain which is irreversible. As a result, researchers focus on using machine learning models that can give significant results in identifying symptoms of dementia in the early stages. In this paper, we employed machine learning methods to identify the various phases of dementia using brain MRI scans. We used multiple traditional algorithms such as XGBoost, AdaBoost, Gradient Boosting, Decision trees, SVM, Logistic regression, Random forest, and KNearest Neighbor. We compared the performance of these algorithms with and without the use of preprocessing techniques. Our best model was the SVM model with an accuracy of 100% after applying preprocessing. In addition, we used a transfer learning model that achieves an accuracy of 90%. Thus, our work demonstrated the effect of preprocessing on enhancing the results of the traditional algorithms and transfer learning models in identifying the stages of dementia. © IET conference Proceedings 2022.

关键词： Deterioration

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Defect detection method for key components of transmission line based on incremental learning 3

Defect detection method for key components of transmission l...

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3rd international conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022

作者： Yang, Wei Hao, Jingchang Yu, Jinhe Customer Service Center of State Grid Company Limited Tianjin China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

With the continuous development of deep learning, how to use computer vision technology to accurately locate key components of transmission lines and identify defects in complex natural backgrounds has become a common problem faced by both academia and industry. Aiming at the problems of slow detection speed and high false detection rate in the existing defect detection methods of key components of transmission lines, a method for defect detection of key components of transmission lines based on incremental learning is proposed. First, a 34-layer ResNet network is used, two 3×3 convolutional networks are concatenated to form a residual network module, and Pytorch is used to build the ResNet network. Second, when training the network, the parameters of the pre-trained model ResNet34 are used as the initialization parameters of the classification network model. Then, after the training is completed, the network model is trained with a new set of data to obtain a model with incremental learning ability. Finally, the application verification is carried out in the inspection scheme of a power company's transmission line above 110kV. The test results show that the method proposed in this paper can assist inspectors to quickly screening and locating defective bolts in high-resolution pictures to a certain extent, thereby significantly improving the work efficiency of the inspection process. © 2022 IEEE.

关键词： Electric lines

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application of Nonstationary Time Series Prediction to Shanghai Stock Index Based on SVM 22

Application of Nonstationary Time Series Prediction to Shang...

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3rd Asia-Pacific conference on Image Processing, Electronics and computers, IPEC 2022

作者： Yang, Chun Ou, Kaiman Hong, Shaoyong School of Accounting Guangzhou Huashang College Guangzhou China School of Data Science Guangzhou Huashang College Guangzhou China

ISBN: (纸本)9781450395786

With the development of computer software and hardware system, machine learning methods are more and more used in various industries of social development. In the aspect of stock index prediction, the current prediction method has gradually changed from the traditional statistical analysis method to the artificial intelligence analysis method. Based on the original sample data, this paper uses support vector machine regression (SVR) model to predict the opening price of Shanghai stock index. The parameters of SVR model are optimized and debugged by grid search method (grid), particle swarm optimization (PSO) and genetic algorithm (GA). The analysis results show that the three types of support vector machine prediction models based on the original sample data can fully reflect the time-varying law of stock index and have high prediction accuracy. Among them, genetic algorithm support vector machine regression (GA-SVR) model shows that the minimum root mean square error (RMSE) is 14.730 and the minimum average absolute percentage error (MAPE) is 0.375%. GA-SVR model has good prediction effect and has certain significance for the prediction of stock price. © 2022 ACM.

关键词： Genetic algorithms

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FedSimRep: A Federated learning Method Based on Data Representation Selection 25

FedSimRep: A Federated Learning Method Based on Data Represe...

引用

Proceedings of the 2025 3rd international conference on Communication Networks and machine learning

作者： Falu Zhai Lu Wu Chunxiao Wang Tongyang Hao Shucang Zang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan Shandong China

ISBN: (纸本)9798400713231

As an emerging distributed technology, Federated learning enables model training while protecting privacy without sharing local data. However, the heterogeneity of geographic locations and devices leads to response delays from stragglers. Client grouping in federated learning can tolerate stragglers from a global perspective, but differences in data distribution within groups may result in local performance bias. To address this issue, we propose a federated learning method called FedSimRep, which selects representative clients based on data distribution similarity. FedSimRep performs group training by collecting readiness signals from topological communications and maintains synchronization tables and graphs to track training progress. Additionally, it selects representative clients by leveraging the relationship between local optimization similarity and data distribution similarity to optimize the global model. Experiments and theoretical analyses on public datasets such as MNIST, CIFAR-10, and UEU-Fed demonstrate that FedSimRep achieves significant advantages in terms of accuracy and efficiency.

关键词： Client Grouping

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applications of MLOps in the Cognitive Cloud Continuum 23rd

Applications of MLOps in the Cognitive Cloud Continuum

引用

23rd international conference on Product-Focused Software Process Improvement (PROFES)

作者： Moreschini, Sergio Tampere Univ Tampere Finland

ISBN: (纸本)9783031213878;9783031213885

Background. Since the rise of machine learning, the automation of software development has been a desired feature. MLOps is targeted to have the same impact on software development as DevOps had in the last decade. Objectives. The goal of the research is threefold: (RQ1) to analyze which MLOps tools and platforms can be used in the Cognitive Cloud Continuum, (RQ2) to investigate which combination of such tools and platforms is more beneficial, and (RQ3) to define how to distribute MLOps to nodes across the Cognitive Cloud Continuum. Methods. The work can be divided into three main blocks: analysis, proposal and identification, and application. The first part builds the foundations of the work, the second proposes a vision on the evolution of MLOps then identifies the key concepts while the third validates the previous steps through practical applications. Contribution. The thesis's contribution is a set of MLOps pipelines that practitioners could adopt in different contexts and a practical implementation of an MLOps system in the Cognitive Cloud Continuum.

关键词： Software engineering machine learning Mlops

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Migration Techniques for Virtual machine Distribution Using machine learning Heuristics application in Cloud Computing

Migration Techniques for Virtual Machine Distribution Using ...

引用

Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Gurpreet Singh Panesar Raman Chadha Computer Science and Engineering Chandigarh University Chandigarh India

Cloud computing has revolutionized the way IT resources are provisioned, deployed, and managed. Virtual machine (VM) migration is a crucial aspect of cloud computing that facilitates resource optimization, load balancing, and fault tolerance. In recent years, machine learning (ML) heuristics have emerged as effective tools for VM distribution, allowing dynamic and intelligent allocation of resources. This research review paper presents a comprehensive analysis of various migration techniques that leverage ML heuristics for VM distribution in cloud computing environments. The paper reviews relevant literature, compares different methodologies, and discusses their advantages, limitations, and potential applications.

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Video Based Human Head and Shoulders Detection Using machine learning 3

Video Based Human Head and Shoulders Detection Using Machine...

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3rd international conference on Industrial Artificial Intelligence, IAI 2021

作者： Jiang, Tian Kong, Delin Southeast University School of Automation Nanjing China College of Computer Science and Technology Heilongjiang Institute of Technology Harbin China

ISBN: (纸本)9781665435178

With the development of artificial intelligence, the application of computer technology for analyzing the objects in images and videos has been more and more widely used. This paper describes the human head and shoulders based on Haar-like features, and realizes the detection of the head and shoulders in the video using machine learning. Firstly, this paper selects the ViBe background model method to extract the moving area in the video. Then, Haar-like features are selected to describe the human head and shoulders. Finally, the head and shoulders in the video are detected by the soft cascade classifier. The algorithm is programmed using C++ language, and selects traffic intersection videos for experiment. Experimental results demonstrate that above 90% detection rate is achieved, which meets the design requirements. © 2021 IEEE.

关键词： machine learning

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Duplicate Detection Using Unsupervised Random Forests: A Preliminary Analysis 3

Duplicate Detection Using Unsupervised Random Forests: A Pre...

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3rd international conference on Artificial Intelligence and Data Sciences, AiDAS 2022

作者： Omar, Zaturrawiah Ali Abu Bakar, Mohd Aftar Zamzuri, Zamira Hasanah Ariff, Noratiqah Mohd Universiti Malaysia Sabah Mathematics with Computer Graphics Programme Faculty of Science and Natural Resources Sabah Malaysia Universiti Kebangsaan Malaysia Faculty of Science and Technology Department of Mathematical Sciences Selangor Malaysia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

Detecting duplicate records is an essential step in data preparation for producing high-quality data for analysis. In statistics, it is commonly referred to as record linkage, but it goes by other names depending on the field of study. This study attempted to conduct duplicate detection on RLdata500 data using unsupervised Random Forests. In general, unsupervised machine learning algorithms are inferior to supervised learning algorithms. However, supervised learning requires labelled training data, which is absent from the majority of real-world datasets. Therefore, unsupervised learning is still applicable, and the performance of unsupervised learning algorithms can be enhanced through proper data conditioning. This paper reports the outcome of such conditioning by using bins and string encoding (Onca and Soundex). The similarity weight produced by the pairwise proximity matrix was used as the matching measure, and an optimal threshold was decided to be the cut-off point for classifying the record pairs as a match (duplicate detected) or non-match. Since the duplicate records in this dataset are known, the performances of the conditions can be compared using recall, precision, and F-measure, and the optimal threshold was determined using the maximum F -measure. Additional precision-recall graphs, as well as an F -measure graph that was transformed for a fair comparison, were used. The results then determined that the optimal conditions (2, 6 and 7) were the ones where the number values were treated as continuous. The performance of the employed encoding methods was equal when applied to the dataset and, together with binning, those conditions were able to detect more perfect matches. In the future, more testing and comparing will be done with different datasets and methods. © 2022 IEEE.

关键词： Supervised learning

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