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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4501-4510 订阅

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Detection of Breast Cancer from Histopathological Images using Image Processing and Deep-learning 3

Detection of Breast Cancer from Histopathological Images usi...

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3rd international conference on intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Thomas, Anusha Maria Adithya, G. Arunselvan, A.S. Karthik, R. Vellore Institute of Technology Electronics and Computer Engineering Chennai India Vellore Institute of Technology Centre for Cyber Physical Systems School of Electronics Engineering Chennai India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Breast cancer is the most commonly occurring cancer in women. Cancer patients frequently develop metastasis, which is responsible for more than 90% of their deaths. The mortality rate will be significantly reduced if it is identified and treated in an early phase. The categorization of cancer cells is critical for medical diagnosis, tailored therapy, and disease prevention. Classifying various types of these cells with great precision has remained a difficult issue. Deep learning has emerged as a significant tool for such challenging tasks in the fields of biology and medicine. In this research, we propose a novel model that throws light on image processing and deep learning for breast cancer classification from histopathological images. The proposed Vision transformer model outperforms the state-of-the-art convolution neural networks in classifying the breast cancer cell with an accuracy of 96%. © 2022 IEEE.

关键词： Diseases

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Platform of the Web vulnerability detection Based on Natural Language Processing 3

Platform of the Web vulnerability detection Based on Natural...

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3rd international conference on Computer Vision, Image and Deep learning and international conference on Computer engineering and Applications, CVIDL and ICCEA 2022

作者： Ge, Xin Yue, Min-Nan University of Shanghai for Science and Technology Information Office Shanghai China University of Shanghai for Science and Technology School of Energy and Power Engineering Shanghai China

ISBN: (纸本)9781665459112

Rule-based Web vulnerability detection is the most common method, usually based on the analysis of the website code and the feedback on detection of the target. In the process, large amount of contaminated data and network pressure will be generated, the false positive rate is high. This study implements a detection platform on the basis of the crawler and NLP. We use the crawler obtain the HTTP request on the target system firstly, classify the dataset according to whether there is parameter and whether the samples get to interact with a database. then we convert text word vector, carries on the dimensionality of serialized, through train dataset by NLP algorithm, finally obtain a model that can accurately predict Web vulnerabilities. Experimental results show that this method can detect Web vulnerabilities efficiently, greatly reduce invalid attack test parameters, and reduce network pressure. © 2022 IEEE.

关键词： Natural language processing systems

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Optimized Virtual Machine Scheduling for Green Cloud Computing Using MLP-LSTM with Sheep Flock Optimization 3

Optimized Virtual Machine Scheduling for Green Cloud Computi...

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

作者： D'Souza, Mithun Sreenivasa, B.L. Nagalavi, Deepa Ahmad Khan, Shamim Sathyanarayana, S. Dhaaraani, R. St. Joseph's University Department of Computer Science Bangalore India School of It Mangalore India Glocal University Glocal School of Science & Technology U.P India Nie First Grade College Department of Computer Science Mysore India K. Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu Trichy India

ISBN: (纸本)9798331528201

With the growing demand for cloud computing services, it is important to efficiently manage resources using proper strategies in order to save energy consumption while retaining high-level performance. The innovative framework proposed here utilizes the MLP and LSTM models with a bio-inspired optimization algorithm in order to improve workload predictions and resource allocation for Virtual Machine (VM) scheduling. This work has an effective framework for energy-aware virtual machine scheduling in cloud computing environments using MLP-LSTM hybrid models optimized by the Sheep Flock Optimization algorithm, and comprehensive datasets it uses involve essential workload features, such as CPU and memory utilization. The proposed methodology utilizes the min-max normalization during data preprocessing, which ensures uniform scaling of all features. In the integrated MLP-LSTM model, an impressive accuracy of 98.7% was obtained for the task of prediction. Moreover, it was even better than other existing methodologies including Random Forest and Decision Tree. Using the predictions from the MLP-LSTM model, the SFO algorithm optimizes VM placement and migration, reduces energy consumption by a large margin, but at the same time maintains balanced resource distribution across all the physical servers. The results demonstrate the enhancement of both operational efficiency and energy usage, hence the sustainability of cloud computing. The implementation of the framework in Python gives it a realistic solution to situations that pose challenges to resource management in modern cloud infrastructures. This research validates the potential use of an advanced ML approach in conjunction with optimization techniques that can succeed in creating a green and efficient overall ecosystem for cloud computing. © 2024 IEEE.

关键词： Random forests

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Research on Privacy Protection based on Network traffic data 3

Research on Privacy Protection based on Network traffic data

引用

3rd international conference on Digital Signal and Computer Communications, DSCC 2023

作者： Liu, Zhiyu Li, Qing Gong, Yue Li, Qiuxiang The First Research Institute of the Ministry of Public Security of P.R.C Beijing China Beijing Municipal Public Security Bureau Beijing China

ISBN: (数字)9781510666566

ISBN: (纸本)9781510666559

This paper discusses the research of privacy protection based on network traffic data. Firstly, the concept and collection method of network traffic data are introduced, and the method of collecting network traffic data by intelligent identification algorithm is proposed, so as to improve the accuracy and efficiency of data collection. Then, aiming at the privacy protection of network traffic data, a privacy protection scheme based on desensitization algorithm is proposed. The scheme uses chaotic cryptography, homomorphic encryption and differential privacy to ensure the privacy and security of data. Finally, the effectiveness and practicability of the scheme are illustrated by the conclusion, which provides a new solution for the privacy protection of network traffic data. © 2023 SPIE.

关键词： data privacy

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Cyber Security Fraud Detection Using Machine learning Approach 3

Cyber Security Fraud Detection Using Machine Learning Approa...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Raman, Ramakrishnan Tiwari, Mohit Buddhi, Dharam Trivedi, Snehal Bothe, Shivaji Ponnusamy, R. Symbiosis Institute of Business Management Maharashtra Pune India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Paschim Vihar Delhi India Uttaranchal University Uttaranchal Institute of Technology India Parul University Department of Mech India Department of IT & Business Analytics India Chennai Institute of Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350399264

It is notoriously harder to identify fraudsters for its fluid nature in terms of recognizable trends. Latest technological breakthroughs were used by scammers. Individuals overcome the protection, incurring millions of dollars of lost revenue. data mining approaches could be employed to crunch numbers and discover out-of-the-ordinary behaviors, allowing it to be pursued to its cause, in this case a fraudulent payment, activities. In this study, we really would like to evaluate and contrast various popular ml algorithms, namely k-nearest peer (KNN), randomized forest (RF), and support vector (SVM), along with popular deep neural networks, including auto - encoder, Classifiers, Multiple solutions, and multi - layer perceptron (DBN). The European Union (EU), Canada, and Netherlands information will be used. The measures utilized for evaluations are the Region That under Fitted Model (AUC), the Matthew R Squared (MCC), and the cost of failing. © 2023 IEEE.

关键词： Multilayer neural networks

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The Development of Advanced Deep learning-Based EoR Signal Separation Techniques 3rd

The Development of Advanced Deep Learning-Based EoR Signal S...

引用

3rd international conference on Computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

作者： Pradeep, S. Prasad, C.V.P.R. Ruchitha, Ch Maisammaguda Telangana Hyderabad India Maisammaguda Telangana Hyderabad India

ISBN: (纸本)9789819966899

The weak EoR signal is submerged in the strong foreground radiation interference. The classic the foreground removal methods assume that the foreground spectrum is smooth, but the complex instrumental effect will affect the spectral structure, resulting in the failure to accurately detect the signal. In this paper, a deep learning network called CNN-LSTM model is proposed to separate the EoR signal to improve computing resource utilization for massive observational data. Based on the simulation data of SKA1-LOW, a CNN-LSTM fusion model is constructed to reconstruct the EoR signal. Experimental results show that compared with the traditional methods including polynomial fitting and continuous wavelet transform, the EoR signals detected by the proposed deep learning model have better quantitative evaluation indexes of SNR and Pearson correlation coefficient. This property provides a new way to explore the research field of EoR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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Research on Abnormal Network Traffic Detection Method based on Machine learning 3

Research on Abnormal Network Traffic Detection Method based ...

引用

3rd international conference on Machine learning and Computer Application, ICMLCA 2022

作者： Tan, Xiaobo Jia, Yumei College of Information Science and Engineering Ligong University Liaoning Shenyang China Department of Computer Science and Technology Shenyang Ligong University Liaoning Shenyang China

ISBN: (纸本)9781510664814

Aiming at the problem of high false detection rate and low detection rate of abnormal network traffic detection methods due to uneven data distribution of current network traffic datasets, a network abnormal traffic detection method based on CNN-GRU is proposed. The method firstly performs local feature learning on the data according to the local learning ability unique to the convolutional neural network (CNN), and replaces the fully connected layer with a global average pooling layer to reduce the feature dimension and parameters;secondly, it uses a gated recurrent unit (GRU) learns the time series features of the data to improve the nonlinear representation ability of the method;finally, for the imbalance between the data, the cross entropy loss function is optimized, and the penalty for abnormal class sample detection and normal class sample detection is passed. The adjustment of the weight increases the attention of the model to the abnormal samples, thereby improving the accuracy of the model detection. The simulation results show that the method can effectively improve the classification and detection performance. © 2023 SPIE.

关键词： Convolutional neural networks

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Deep learning and Machine learning Algorithms Methods in Cyber Security 3rd

Deep Learning and Machine Learning Algorithms Methods in Cyb...

引用

3rd international conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Al-Absi, Mohammed Abdulhakim R’Bigui, Hind Al-Absi, Ahmed A. Department of Computer Engineering Dongseo University 47 Jurye-ro Sasang-gu Busan47011 Korea Republic of Digital Enterprise Department Nsoft CO. LTD No. 407 14 Tekeunosaneop-go 55beon gil Nam-gu Ulsan44776 Korea Republic of Department of Smart Computing Kyungdong University 46 4-gil Bongpo Gangwon-do Goseong-gun24764 Korea Republic of

ISBN: (纸本)9789819705726

This study investigates the use of Machine learning (ML) and Deep learning (DL) algorithms in cyber security. By leveraging ML and DL technologies, organizations can enhance their ability to detect and prevent cyber threats, protecting sensitive data and critical systems. In this study, we explored different machine learning and deep learning techniques used in cyber security. We discussed the advantages and disadvantages of each algorithm. Additionally, we identified the top 12 cybersecurity datasets that can be valuable for your future machine learning and deep learning projects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cybersecurity

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A model parameter update strategy for enhanced asynchronous federated learning algorithm 3

A model parameter update strategy for enhanced asynchronous ...

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3rd IEEE international conference on Computer Systems, ICCS 2023

作者： Sun, Yu Gao, Xin Shen, Ying Xie, Jianfei Yang, Jiandong Si, Nong Beijing University of Technology Faculty of Information Technology Beijing China North China Institute of Science & Technology Department of Internet of Things Engineering Hebei China Capinfo Technology Development Co. Ltd. Beijing China

ISBN: (纸本)9798350313666

Federated learning addresses the 'data islands' issue resulting from privacy concerns, enabling multiple users to train a model collaboratively without sharing their private datasets. However, in the model training process of federated learning, effectively coordinating the communication between participants and server is essential to improve the training efficiency. Traditional synchronous and asynchronous federated learning algorithms cannot meet the requirements of convergence efficiency and precision stability at the same time. In order to balance algorithm efficiency and model stability, this paper proposes a model parameter update strategy for an enhanced asynchronous federated learning algorithm (EAFL). This strategy requires the server to store the latest global model parameters, the last round of global model parameters, and the update part of both, and judge the issued model type according to the sensitivity difference of the client. The client executes different local update processes according to different model types to improve the training efficiency and maintain the stability of the training accuracy. The experimental results show that the EAFL algorithm achieves better training results on MNIST and CIFAR-10 data sets than the traditional federated learning parameter update algorithm. This improvement in training results can provide new thoughts to solve computer vision tasks such as object detection with scattered devices. © 2023 IEEE.

关键词： learning algorithms

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A method of generating abstract ink paintings based on machine learning 3

A method of generating abstract ink paintings based on machi...

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3rd international conference on Computer Vision and data Mining, ICCVDM 2022

作者： Huang, Yike Sichuan University Chuanda Road Shuangliu County Chengdu610207 China

ISBN: (纸本)9781510661363

The advancement of computer technology in the area of image generation opens up opportunities for the creation of art. However, owing to the complexity of artistic expression and the difficulty of data collection, generating abstract ink paintings from photos remains a big challenge. This paper proposed a method of machine learning to generate abstract ink paintings. First, a dataset consisting of photos and Chinese abstract ink paintings is collected. Then, the statistical information of photos is obtained by using color segmentation to draw abstract images. On the unpaired dataset, an imageto- image translation network with contrastive learning is trained to learn a mapping from abstract images to abstract ink paintings. Experiments showed that this method can effectively realize the process of generating abstract ink paintings from photos. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Machine learning

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