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

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Evaluation Method of English Teaching Quality Based on SOFM Neural network 2nd

Evaluation Method of English Teaching Quality Based on SOFM ...

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2nd EAI international conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2022

作者： Sheng, Jing Wuchang University of Technology Hubei Wuhan430070 China

ISBN: (纸本)9783031239465

With the globalization of world economy, culture, science and technology, international cooperation and exchanges are becoming more and more frequent, and English will occupy a more and more important position. As a public compulsory basic course for Non-English Majors in Colleges and universities in China, College English plays a very important role in Expanding College Students’ knowledge, improving foreign language comprehensive quality and cultivating language application ability. Therefore, the quality of College English teaching is also included in one of the important indicators of college curriculum construction evaluation. This paper analyzes four problems existing in College English teaching, and points out that emphasizing summative assessment and neglecting formative assessment are the main reasons affecting the quality of English teaching. Therefore, this paper proposes an English teaching quality evaluation method based on SOFM neural network to strengthen the monitoring of the formation process of teaching quality, so as to improve teaching quality. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： international cooperation

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1st international conference on Artificial Intelligence and Internet of Things, ICAII 2022

1st International Conference on Artificial Intelligence and ...

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1st international conference on Artificial Intelligence and Internet of Things, ICAII 2022

ISBN: (纸本)9783031373022

The proceedings contain 23 papers. The special focus in this conference is on Artificial Intelligence and Internet of Things. The topics include: On Assaying the T-score Value for the Detection and Classification of Osteoporosis Using AI Learning Techniques;evaluation of Healthcare Data in Machine Learning Model Used in Fraud Detection;a New Approach to Heart Disease Prediction Using Clustering and Classification algorithms;skin Lesion Classification: Scrutiny of Learning-Based Methods;Lung Disease Classification Using CNN-Based Trained Models from CXR Image;A Novel Framework for Satellite Image Denoising and Super Resolution Using CNN-GAN;deep Learning Based Enhanced Text Recognition System;meta-transfer Learning for Contextual Emotion Detection in Face Affirmation;hand-Written Devanagri Character Recognition Using Convolutional Neural network in Python with Tensorflow;the Design and Development of computerized Geogebra Mathematical Model for Painting Color Learning System;bisimulations for Fuzzy Geometric Models;research Study: Data Preprocessing Using Machine Learning for Prediction of Booking Cancellations;Advanced MRI Segmentation Algorithm for the Detection of Brain Tumor Using U-Net Architecture with Transfer Learning EfficientNet-B7;audio Analysis for Early Diagnosis of Alzheimer’s Disease: A Study on Fisher’s Linear Discriminant and Mel-Frequency Cepstral Coefficients;automation in Agriculture Using Deep Machine Learning: A Survey;A Survey on Security Threats in VANET and Its Solutions;An Analysis of Advanced Computations and Semantic Reasoning to Create an Intelligence System Using WSN;ioT & Smart City Viability: An Empirical Study;design of a Contextual IoT Framework for the Improved User Experience and Services;human Motion Detection Using Ultra-Wide Band Radar.

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Automated Depression Diagnosis Based on Facial Expression and Deep Dual-Stream Convolutional Neural networks 3

Automated Depression Diagnosis Based on Facial Expression an...

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3rd international conference on computer Vision and Pattern Analysis, ICCPA 2023

作者： Chen, Yangyang Chen, Yingyu Li, Mi Department of Automation Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

ISBN: (纸本)9781510667563

Depression is a common mental illness characterized by symptoms such as low mood, pessimism, and insomnia. In this study, we developed a deep Dual-Stream CNN to automatically diagnose and classify depression in expression video sequences. The network has two branches that extract static features and dynamic features from static and dynamic expressions, respectively, which are then fused for depression classification. The experiments were performed on the AVEC2014 database, and the results showed that the Dual-Stream model significantly improved the classification performance of depression, achieving an accuracy of 69.08% in depression categorization. © 2023 SPIE.

关键词： Convolutional neural networks

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Performance Improvement Strategy for Differentiated Services in Multi-rate IEEE 802.11 Wireless networks 12

Performance Improvement Strategy for Differentiated Services...

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12th international conference on Information technology in Medicine and Education, ITME 2022

作者： Xu, Haihong Lu, Shejie Hu, Siping Gao, Jinfeng Liu, Fang Wu, Haitao Hubei University of Science and Technology Information Center Institute of Engineering and Technology Xianning437100 China Huanghuai University International Joint Laboratory of Behavior Optimization Control for Smart Robots College of Computer and Artificial Intelligence Zhumadian463000 China

ISBN: (纸本)9798350310153

With the wide application of wireless LAN, the real-time and differentiated services of IEEE 802.11 MAC are becoming more and more important. IEEE 802.11 uses CSMA/CA mechanism in MAC layer to coordinate access, but it does not meet the different requirements of different users for QoS. Considering the different requirements of different priority users for QoS, a solution is proposed for this problem. The core idea of this strategy is: in a multi rate network, according to the priority of nodes in the network, through adaptive optimization of relevant parameters, the probability of high priority nodes accessing the channel can be improved, and its throughput can be improved accordingly;The channel access probability of nodes with lower priority is reduced accordingly, and the throughput is reduced accordingly. Therefore, this policy can expand the throughput of each priority and realize the service differentiation function. © 2022 IEEE.

关键词： IEEE Standards

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international Workshops which were held in conjunction with 27th European Symposium on Research in computer Security, ESORICS 2022

International Workshops which were held in conjunction with ...

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8th Workshop on the Security of Industrial Control Systems and of Cyber-Physical Systems, CyberICPS 2022, 6th international Workshop on Security and Privacy Requirements Engineering, SECPRE 2022, 4th Workshop on Security, Privacy, Organizations, and Systems Engineering, SPOSE 2022, 3rd Cyber-Physical Security for Critical Infrastructures Protection, CPS4CIP 2022, 2nd international Workshop on Cyber Defence Technologies and Secure Communications at the network Edge, CDT and SECOMANE 2022, 1st international Workshop on Election Infrastructure Security, EIS 2022 and 1st international Workshop on System Security Assurance, SecAssure 2022 held in conjunction with 27th European Symposium on Research in computer Security, ESORICS 2022

ISBN: (纸本)9783031254598

The proceedings contain 39 papers. The special focus in this conference is on Research in computer Security. The topics include: Towards a Security Impact Analysis Framework: A Risk-Based and MITRE Attack Approach;data Protection Officers’ Perspectives on Privacy Challenges in Digital Ecosystems;Rebooting IT Security Awareness – How Organisations Can Encourage and Sustain Secure Behaviours;towards Reverse Engineering of Industrial Physical Processes;solutions for Protecting the Space Ground Segments: From Risk Assessment to Emergency Response;modelling and Simulation of Railway networks for Resilience Analysis;honeyChart: Automated Honeypot Management over Kubernetes;ComSEC: Secure Communications for Baggage Handling Systems;Coordinated network Attacks on Microgrid Dispatch Function: An EPIC Case Study;methodology for Resilience Assessment for Rail Infrastructure Considering Cyber-Physical Threats;coverage-Guided Fuzzing of Embedded Systems Leveraging Hardware Tracing;Challenges and Pitfalls in Generating Representative ICS Datasets in Cyber Security Research;securing Cyber-Physical Spaces with Hybrid Analytics: Vision and Reference Architecture;a Precision Cybersecurity Workflow for Cyber-physical Systems: The IoT Healthcare Use Case;a Revisitation of Clausewitz’s Thinking from the Cyber Situational Awareness Perspective;examining 5G technology-Based Applications for Military Communications;design of a Validation Model of the Cognitive State in Military Operations in Cyberspace;design and Validation of a Threat Model Based on Cyber Kill Chain Applied to Human Factors;the Cloud Continuum for Military Deployable networks: Challenges and Opportunities;adversarial Attacks and Mitigations on Scene Segmentation of Autonomous Vehicles;ballot-Polling Audits of Instant-Runoff Voting Elections with a Dirichlet-Tree Model;non(c)esuch Ballot-Level Comparison Risk-Limiting Audits;why Is Online Voting Still Largely a Black Box?;connecting Incident Reporting Infrastructure to Ele

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Traffic Big Data Intelligent Object Tracking technology under Noise Conditions 2

Traffic Big Data Intelligent Object Tracking Technology unde...

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2nd IEEE international conference on Data Science and computer Application, ICDSCA 2022

作者： Mingze, Tang Yu, Xinyao Telecommunications Engineering with Management Beijing University of Posts and Telecommunications Beijing China International School Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

Intelligent transportation systems make extensive use of image processing techniques such as object detection and tracking. Real-world traffic conditions require specialized processing of image noise, typically caused by weather conditions or occlusions. This paper proposes a contextual community attention graph neural network structure, which uses the attention mechanism representation of different community nodes to associate important recognition features. This structure can effectively track objects in noisy videos. Algorithm comparison experiments on the dataset show that the method performs better in rainy and night conditions. © 2022 IEEE.

关键词： Big data

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Investigation of Credit Card Fraud detection under the lens of Comparative Machine Learning models

Investigation of Credit Card Fraud detection under the lens ...

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2022 international conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2022

作者： Tahsin, Mohammad Sadman Firoz, Neda Chowdhury, Shah Abul Hasnat Wasei, S.M.A. Karim, Musaddiq Al Ara, Mst Yeasmin Brac University Department of Computer Science and Engineering Dhaka Bangladesh Tomsk State University Institute of Applied Mathematics and Computer Science Tomsk Russia American International University-Bangladesh Dept. of Electrical and Electronics Engineering Dhaka Bangladesh American International University-Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (数字)9781665451932

ISBN: (纸本)9781665451932

Fraudulent use of credit cards is common and may result in significant financial losses. Using Trojans or Phishing, criminals may get the credit card numbers of innocent victims for their criminal acts. As a result, it is vital to have fraud detection technology that can catch a thief in the act of purchasing using a stolen credit card. One solution is to utilize machine learning algorithms to construct normal/fraud behavior characteristics from all historical transaction data, including legitimate and fraudulent ones, and then use these features to identify whether a transaction is fraudulent or not. In this paper, SVC, Multinomial Naïve Bayes, K-Neighbors Classifier, Logistic Regression, Random Forest, Bernoulli Naïve Bayes, and SVM algorithms were used for credit card fraud detection. The detailed results of all seven algorithms were studied. Then a comparison between these seven algorithms with their precise accuracy, ROC curve, train-test, validation scores, and the learning curve was discussed. Logistic Regression, Random Forest, Bernoulli Naïve Bayes, and SVM showed 100% accuracy in training dataset. While Random Forest algorithm outdid every other algorithm by scoring 100% for both train and test data, the result of the remaining algorithms for the train and test data were also satisfactorily ranging from 75.3% to 99.6%. Logistic Regression, Random Forest, Bernoulli Naïve Bayes, and SVM all 4 scored 100% in both train and test data, and Random Forest scored 100% leaving behind all the algorithms. Logistic Regression, Bernoulli Naïve Bayes and SVM scored 99.6% in test. The best-performing algorithm was selected according to its performance and discussed its AUC score and confusion matrix. © 2022 IEEE.

关键词： Crime

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Performance Analysis of Classification algorithms for Outcome Prediction of T20 Cricket Tournament Matches 12

Performance Analysis of Classification Algorithms for Outcom...

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12th international conference on computer Communication and Informatics, ICCCI 2022

作者： Pramanik, Md. Aktaruzzaman Suzan, Md. Mahmudul Hasan Biswas, Al Amin Rahman, Mohammad Zahidur Kalaiarasi, A. Daffodil International University Dept. of CSE Dhaka Bangladesh Daffodil International University Dept. of Cis Dhaka Bangladesh Jahangirnagar University Dept. of CSE Dhaka Bangladesh Sri Shakthi Institute of Engineering and Technology Dept. of Eee Tamil Nadu India

ISBN: (纸本)9781665480352

Cricket is the most popular sport in south Asian countries and the second most popular sport globally. And T20 cricket is the most attractive version of this game. Businesses have grown enormously based on cricketing sports events from the last decade. A large number of research have been done to predict the winner of cricket matches or to analyze game statistics. These studies are helping investors and franchisees to decide on which team they can invest in to gain more profit. Also, coaches, sports analysts, and technicians get game facts and ideas about other teams, which help them make decisions and change plans accordingly. In this study, we have shown a comparative analysis of different non-ensemble and ensemble machine learning classifiers. We have collected data of all sea-sons of the Bangladesh Premier League(BPL) T20 tournament. We have prepared two datasets: one contains only pre-match information, and the second includes post-match information. We have applied five base classifiers and five ensemble classifiers for the analysis. We found that K-Nearest Neighbor(KNN) performed best compared to other algorithms while predicting match results before starting the game. The Gradient Boosting classifier seemed more robust than the different classifiers for predicting match outcomes considering all features. © 2022 IEEE.

关键词： Sports

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Multi-class Motor Imagery Classification Method Using One-dimensional Convolutional Neural networks 4

Multi-class Motor Imagery Classification Method Using One-di...

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4th international conference on Applied Machine Learning, ICAML 2022

作者： Li, Dezhao Ma, Yanni Hu, Shang Su, Yan Ruan, Yangtao Lin, Qiang College of Science Zhejiang University of Technology Hangzhou310023 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China College of Education Science and Technology Zhejiang University of Technology Hangzhou China School of Art Zhejiang International Studies University Hangzhou China

ISBN: (纸本)9781665462655

Motor Imagery-based Brain-computer interface (MI-BCI) enables communications between the human brain and external machines, which is considered a valuable frontier technology. Motor imagery (MI) identification is a fundamental problem for these applications. However, because of the non-stationary and nonlinear properties of EEG motor imagery signals, it is very hard to get a high accuracy multi-class method. In this study, a multi-class classification method of MI EEG signals using wavelet packets and a one-dimensional convolutional neural network is presented. Data normalization and wavelet threshold denoising were performed for signal preprocessing. Then EEG signals were decomposed and reconstructed by wavelet packets transform. According to the features between different channels of EEG signal, a multilayer one-dimensional convolutional neural network model was adopted to get a multi-class classification model for MI. This model was validated on the public datasets BCI competition IV 2a. The result shows that the mean accuracy of this model is 93.05%. This study is beneficial to MI-BCI research applications in the future. © 2022 IEEE.

关键词： Convolutional neural networks

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The Impact Value of Smart network Marketing Communication in the New Media Era 2

The Impact Value of Smart Network Marketing Communication in...

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2nd IEEE international conference on Artificial Intelligence in Everything, AIE 2022

作者： Zhang, Wei Hunan University of Information Technology School of International Business Changsha410151 China

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

With the growth of social economy and the continuous improvement of people's daily life in the new media era(NME) at this stage, the value of National Energy's communication through smart network marketing(SMM) will follow the development of the times, and the economic benefits it brings bigger. The research purpose of this article is to use the Internet to actively explore new media network marketing strategies through the value of SMM communication in the NME. In this paper, based on the new smart digital network marketing model based on SMM technology, big data technology(BDT), artificial intelligence technology and computer network technology are used as the core analysis basis of SMM. The growth of the SMM communication model has not only made great contributions to the growth of the social economy, but also has produced various problems in the development of the new type of smart network communication marketing. The most common of them is the current stage of my country. The data and models used by large companies in Internet marketing are not accurate enough. Many new and emerging companies can only establish relatively direct communication channels with consumers, and accurately locate each consumer's current corporate products and services and needs, in order to find the true purpose of smart marketing, and then use each of these BDT conducts communication impact value analysis and uses smart terminal technology to conduct intelligent marketing model analysis to accurately promote marketing information to the public. Finally, we can start from the experimental analysis results of intelligent network marketing communication under new media technology and intelligent technology, we are clearly aware that network marketing after person location service is not only about finding the right person at the right time, and pushing the most correct one. © 2022 IEEE.

关键词： Value engineering

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