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检索条件"任意字段=2022 International Conference on Algorithms, Network and Computer Technology, ICANCT 2022"

共 1025 条记录，以下是481-490 订阅

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HQNet: An Efficient Convolutional Neural network for Cervical Cancer Classification 7

HQNet: An Efficient Convolutional Neural Network for Cervica...

引用

7th international conference on Biomedical Imaging, Signal Processing, ICBSP 2022

作者： Yang, Han Toe, Teoh Teik BNU-HKBU United International College Program of Computer Science and Technology China NTU Business AI Lab Nanyang Technological University China

ISBN: (纸本)9781450397858

With the rapid development of artificial intelligence, cancer cell classification recognition is also gradually intelligent. Although the classification and recognition methods for some common cancers are mature, Cervical Cancer, as a common cancer among middle- aged women, is often neglected in diagnosis because of the low incidence of malignant tumors. In recent years, the incidence of Cervical Cancer has been increasing year by year and has also gained attention. In this paper, an efficient deformable convolu- tional neural network system(HQNet) was constructed to identify Cervical Cancer cells with different degrees of development. © 2022 ACM.

关键词： Convolution

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MCN: Magnitude-Continuity network for Video Anomaly Detection under Weak Supervision 4

MCN: Magnitude-Continuity Network for Video Anomaly Detectio...

引用

4th international conference on Machine Learning and computer Application, ICMLCA 2023

作者： Luan, Xiaohu Zhang, Hong-Min Liangjiang International College Chongqing University of Technology Chongqing4001135 China School of Electrical and Electronic Engineering Chongqing University of Technology Chongqing400054 China

ISBN: (纸本)9798400709449

Video anomaly detection is a complex task that combines anomaly detection and video image processing. Video anomaly detection aims to identify abnormal behavior in video and is widely used in the real world. In existing works, Feature-Magnitude have shown good performance in weakly supervised video anomaly detection. However, Feature Magnitude-based methods is easily affected by the environment, resulting in error detection. Motivated by the fact that the abnormal events in real video are often continuous, we propose a Magnitude-Contiuity network. In addition, in order to further improve the robustness of existing Feature Magnitude-based methods, the dynamic Feature-Magnitude learning loss function is used. Experimental results on UCF-Crime and ShanghaiTech datasets show that the robustness and performance of the proposed method are significantly improved. © 2023 ACM.

关键词： Anomaly detection

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A Novel Power Divider With Tunable Power Ratios and Reconfigurable Output Phase Differences 7

A Novel Power Divider With Tunable Power Ratios and Reconfig...

引用

7th IEEE international conference on Electronic Information and Communication technology, ICEICT 2024

作者： Huang, Jialin Guo, Xulong Xue, Shanshan Xu, Chang Huang, Yong Mao Wang, Xiaolong Jilin University College of Electronic Science and Engineering International Center of Future Science Changchun China Coordination Center of China Inner Mongolia Branch of National Computer Network Emergency Response Technical Team Hohhot China Xihua University School of Electrical and Electronic Information Chengdu China

ISBN: (纸本)9798350384437

In this letter, a novel power divider with a wide tunable range of power ratios and reconfigurable output phase differences is proposed. The tunable power ratio is implemented by replacing the branch transmission line of the conventional Gysel power divider. The branch transmission line equivalent circuit consists of three short-circuited varactors cascaded through two sections of transmission lines. Moreover, by selecting appropriate capacitances of the varactors, the proposed novel power divider can operate either in an in-phase mode or out-of-phase mode. Finally, the electromagnetic (EM) simulation of six examples was conducted and one experimental circuit of the proposed novel power divider was fabricated. The simulation and measurement results verified the feasibility of the proposed novel power divider. © 2024 IEEE.

关键词： Power transmission lines

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MCFFA-Net: Multi-Contextual Feature Fusion and Attention Guided network for Apple Foliar Disease Classification 25

MCFFA-Net: Multi-Contextual Feature Fusion and Attention Gui...

引用

25th international conference on computer and Information technology, ICCIT 2022

作者： Ahmed, Md. Rayhan Ferdous Ashrafi, Adnan Ahmed, Raihan Uddin Ahmed, Tanveer United International University Department of Computer Science and Engineering Bangladesh Stamford University Department of Computer Science and Engineering Bangladesh Stamford University Department of Electrical and Electronics Engineering Bangladesh

ISBN: (纸本)9798350346022

Numerous diseases cause severe economic loss in the apple production-based industry. Early disease identification in apple leaves can help to stop the spread of infections and provide better productivity. Therefore, it is crucial to study the identification and classification of different apple foliar diseases. Various traditional machine learning and deep learning methods have addressed and investigated this issue. However, it is still challenging to classify these diseases because of their complex background, variation in the diseased spot in the images, and the presence of several symptoms of multiple diseases on the same leaf. This paper proposes a novel transfer learning-based stacked ensemble architecture named Multi-Contextual Feature Fusion network (MCFFA-Net). MCFFA-Net incorporates three pre-trained architectures: MobileNetV2, DenseNet201, and In-ceptionResNetV2 as backbone networks for effective feature extraction and shorter training time. We integrate a novel multi-scale dilated residual convolution module in the network that captures multi-scale contextual information with several dilated receptive fields from the extracted features. Channel-based attention mechanism is provided through squeeze and excitation networks which provides improved channel dependency and access to global contextual information. The proposed MCFFA-Net achieves a classification accuracy of 90.86% and outperforms several previous architectures. © 2022 IEEE.

关键词： Convolutional neural networks

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Vision Anomaly Detection Using Self-Gated Rectified Linear Unit 4

Vision Anomaly Detection Using Self-Gated Rectified Linear U...

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4th international conference on Artificial Intelligence in Information and Communication, ICAIIC 2022

作者： Jahan, Israt Ali, Md. Osman Rahman, Md. Habibur Chung, Byungdeok Jang, Yeong Min Kookmin University Department of Electronics Engineering Seoul02707 Korea Republic of Department of Electrical and Electronic Engineering Daffodil International University Dhaka1341 Bangladesh Deptment of Electrical and Electronic Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh ENS. Co. Ltd. Ansan15655 Korea Republic of

ISBN: (纸本)9781665458184

In the area of image processing and computer vision, visual anomaly detection is a critical and difficult task. For anomaly detection in surface image data, a customized neural network incorporating self-gated rectified linear unit (SGReLU) was designed, and the SGReLU-based model excelled other activation function-based models with a top-20 average test accuracy of 99.84%. The computational time needed for the operation is 10533 s for 20 epochs and the top-20 average test loss is 0.0125 using SGReLU, both of them were comparatively less than other activation functions. © 2022 IEEE.

关键词： computer vision

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Optimized Intelligent Systems for Predicting Rainfall 2

Optimized Intelligent Systems for Predicting Rainfall

引用

2nd international conference on Innovative Practices in technology and Management, ICIPTM 2022

作者： Rayavarapu, Neela Hudnurkar, Shilpa Symbiosis Institute Of Technology Symbiosis International Deemed University Maharashtra Pune India

ISBN: (纸本)9781665466431

Water is essential for all human activities, and rainfall is one of the critical sources of this precious commodity. Prediction of how much rainfall and when it is most likely to occur will assist concerned officials in planning for its storage and subsequent distribution. Meteorological agencies predict rainfall using statistical or dynamic models. Because of the complexity involved in rainfall prediction and limitations of existing techniques, prediction skill improvement is necessary. Recently, intelligent systems such as Artificial Neural networks, Fuzzy Inference Systems, Support Vector Machines, and Genetic algorithms are being used by researchers for prediction. Many network parameters are required to be selected for the use of these systems, and the choice of the parameters affects the accuracy of the model. Experimental discovery of the parameters is one way, and the other way is to use optimization algorithms. In this paper, various optimization techniques used in computationally intelligent systems are surveyed for rainfall prediction. The optimization techniques mainly used for this purpose are Particle Swarm Optimization, Genetic Algorithm, and Ant Colony Optimization. In all the research articles, case study of a certain geographical area for rainfall prediction with a different set of inputs and different forecasting lead times is presented, and hence comparison between the models is difficult. For rainfall prediction, model input selection is equally important to the selection of model parameters as a set of predictors change with increasing or decreasing geographical area and forecast lead time. This paper attempts to identify optimization techniques suitable for medium-range rainfall forecasting over a small geographical area. © 2022 IEEE.

关键词： Intelligent systems

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Role of Zero-Knowledge Proof in Blockchain Security

Role of Zero-Knowledge Proof in Blockchain Security

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2022 international conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Tyagi, Shobha Kathuria, Madhumita Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

Distributed Ledger technology is also known as blockchain technology. It brings the characteristics like transparency, decentralization, immutability, and the distributed ledger in one package. The user identity of the public blockchain is anonymous on the network and cannot control the confidentiality and privacy of their data. The Anonymity of a user does not mean privacy. The importance of user and data privacy and confidentiality in blockchain technology is realized and is yet to be addressed by blockchain protocols. Zero-Knowledge-Proof can be the best solution to this problem. It is relatively new and is developing very rapidly. Zero-Knowledge-Proof can provide strong privacy if the security model is well understood and used carefully. Zero-Knowledge-Proof makes sure that no one accesses the secured data except the user. Paper discusses the types and the various applications of the Zero-Knowledge-Proof in the blockchain. © 2022 IEEE.

关键词： Blockchain

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The Russian Segment (RU-VRF) in WLCG Infrastructure: High Performance Computing network 4

The Russian Segment (RU-VRF) in WLCG Infrastructure: High Pe...

引用

4th international Science and technology conference "Modern network Technologies", MoNeTec 2022

作者： Velikhov, Vasily E. Korenkov, Vladimir V. Ryabinkin, Eygene A. Dolbilov, Andrey G. Gugel, Yury V. Strizh, Tatiana A. Joint Computing Cluster National Research Center 'Kurchatov Institute' Moscow Russia International Intergovernmental Organization Joint Institute for Nuclear Research Meshcheryakov Laboratory of Information Technologies Dubna Russia

ISBN: (数字)9781665472463

ISBN: (纸本)9781665472463

The paper describes a high-performance computer network optimized for the transmission of experimental data generated by CERN for potential use by the Large Hadron Collider (LHC) network community, including the Russian segment of this network. The Worldwide LHC Computing Grid (WLCG) is a global computing infrastructure whose mission is to provide computing resources for storing, distributing and analyzing data generated by the LHC at CERN, making the data equally accessible to all participants, regardless of their physical location. WLCG is a multi-layer distributed computing infrastructure. It provides a community of over 12,200 scientists of 110 nationalities from institutions in over 40 countries with near real-time access to LHC data. WLCG is the world's largest computer network. It is supported by many related national and international networks and projects around the world. Since the beginning of the WLCG Project the Russian computing centers in NRC 'Kurchatov Institute' an JINR have participated with reliable and stable resources as well as personnel for the LHC. Contribution to the ALICE, ATLAS, CMS and LHCb Tier 1 and Tier 2 computing resources (disk and CPUs) in the last 10 years has been substantial. In this paper, we would like to show the evolution and technical updates in the Russian segment Tier1 and Tier 2. The Russian segment is an infrastructure fully integrated into the WLCG. © 2022 IEEE.

关键词： Program processors

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Hybrid Ensemble Model for Handling Class Imbalance Problem in Big Data Analytics

Hybrid Ensemble Model for Handling Class Imbalance Problem i...

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2022 IEEE international conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Mwangi, Peter Irungu Nderu, Lawrence Mutanu, Leah Mwigereri, Dorcas Gicuku Jomo Kenyatta University of Agriculture and Technology Pure and Applied Sciences Nairobi Kenya United States International University-Africa School of Science and Technology Nairobi Kenya

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

In big data analytics, class imbalance problem adversely affects the performance measure of machine learning classifiers as they tend to achieve higher accuracy by giving more attention to the instances from the majority class, thus ignoring those from the minority class, which are often the class of interest and of value. Techniques for handling class imbalance include;data level, algorithm level, and ensemble techniques. The ensemble models are seen to always perform better as compared to the other techniques. In our work we propose an ensemble model based on random oversampling and bagging models for handling class imbalance. Additionally, the genetic algorithm technique has been adopted for feature selection, and the random forest and decision trees are used as machine learning classifiers. The experimental results showed that our ROS-Bagging ensemble model outperformed ROS and bagging separately to handle class imbalance. There was steady rise in precision value for all the classifiers used when trained using a dataset balanced using ROSBagging ensemble model. © 2022 IEEE.

关键词： Genetic algorithms

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Collaboration enhancing between industry staff and university researchers in international scientific communications system

Collaboration enhancing between industry staff and universit...

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2022 international conference on Engineering Management of Communication and technology, EMCTECH 2022

作者： Dymkova, Svetlana Vienna Austria Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (数字)9798350397680

ISBN: (纸本)9798350397680

The article considers the global network space as a set of interacting information systems and as one of the processes in computer technologies development, the content of which is media resources, which are characterized by convergence, digitalization and interactivity. Information and bibliographic environment represent the highest level of international relations integration due to modern technologies. The article proposes a methodology for publication efficiency calculating. As part of the collaboration enhancing between industry staff and university researchers in international scientific communications system considered: role of the organization publication activity, main methods used in scientometrics, as well as topical issues of researchers, such as open identifiers, bibliometric software, new processes, methods and means of scientific communication are described. The main scientific tasks that can be solved with the help of bibliometric indices are highlighted. © 2022 IEEE.

关键词： Information systems

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