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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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The Simulation of Internet of Things System on the Base of Neural network Approach 8

The Simulation of Internet of Things System on the Base of N...

引用

8th international conference on Information technology and Nanotechnology, ITNT 2022

作者： Lvovich, Igor Lvovich, Yakov Preobrazhenskiy, Andrey Preobrazhenskiy, Yuriy Sakharov, Yuriy Voronezh Institute of High Technologies Faculty of Informatics Voronezh Russia Voronezh State Technical University Faculty of Information Technology and Computer Security Voronezh Russia Dubna International University Dubna Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

The paper deals with the problem associated with the description of the Internet of Things system. The purpose of the paper is to ensure the required efficiency of this system. In the course of describing the problem, it was proposed to use neural networks to divide objects in the Internet of Things system into groups. The illustration of the ART network operation model is given. An appropriate algorithm for dividing objects into groups was developed. The results of modeling on the basis of such an algorithm are given. The dependence of efficiency parameters from variants of system of Internet of Things is obtained. © 2022 IEEE.

关键词： Efficiency

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Data Mining based IBBOA-BP Sintering Endpoint Prediction Model 10

Data Mining based IBBOA-BP Sintering Endpoint Prediction Mod...

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10th IEEE international conference on computer Science and network technology, ICCSNT 2022

作者： Li, Zongping Zhu, Jiang Yi, Lingzhi Liu, Qiankun Huangng, Jianxiong Zhongye Changtian International Engineering Co. Ltd. Hunan China College of Automation and Electronic Information Xiangtan University Hunan China

ISBN: (数字)9781665470612

ISBN: (纸本)9781665470612

In the metallurgical industry, the sintering process is an important and indispensable part of iron making, which is an important means to produce sintered ore. And the stability or otherwise of the sintering endpoint is the key process that affects the quality and yield of sintered ore. In this paper, based on the method of BP neural network modeling, the sintering endpoint prediction model based on IBBOA-optimized BP neural network is proposed. In the IBBOA-BP neural network model, adaptive weights are introduced to coordinate the global and local search ability of BBOA;variation operators are added to improve the diversity of butterfly populations and avoid individual butterflies from falling into local optimum. The weights and thresholds of the BP neural network are optimized by IBBOA to avoid the problem of easy prematureness of the BP neural network and to improve the accuracy of the sintering endpoint prediction model. © 2022 IEEE.

关键词： Sintering

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Training Siamese Neural network Using Triplet Loss with Augmented Facial Alignment Dataset 9

Training Siamese Neural Network Using Triplet Loss with Augm...

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9th NAFOSTED conference on Information and computer Science, NICS 2022

作者： Le-Phan, Anh Nguyen, Xuan-Phuc Phan Ly-Tu, Nga School of Computer Science and Engineering International University VNU-HCM HCM city Viet Nam

ISBN: (纸本)9781665454223

In recent years, deep learning methods, especially CNN, have been gaining huge progress in the development of technologies and humanity. Despite this progress, face recognition challenges are still hindering it. In this paper, we investigate the improvement in the performance of face recognition models by applying a Siamese neural network with triplet loss function and train with an augmented facial dataset. Furthermore, this dataset is collected, cropped, aligned, and augmented with various adjustments in which fill the facial recognition challenges requirements. Moreover, we compare the proposed model with the two best public models using two proposed algorithms. Experimental results display good improvement, and we discuss the possible usage as in checking attendance or biotechnique. © 2022 IEEE.

关键词： Face recognition

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Performance Analysis of Parallel Overlapping Community Detection algorithms in Large-scale Social networks 22

Performance Analysis of Parallel Overlapping Community Detec...

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2nd international conference on Computing Advancements: Age of Computing and Augmented Life, ICCA 2022

作者： Akhter, Shanjidah Asif, Samir Sazzad, Mehedi Hasan Hasan, Md. Manzurul Department of Computer Science American International University-Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (纸本)9781450397346

In today's online social network systems, various communities comprising various unique characteristics and interests play a significant role in complex network analysis. Often such communities comprise nodes which belong to many different communities, resulting rise of parallel overlapping communities. To effectively detect such parallel overlapping communities, various algorithms have been proposed based on node clustering, edge clustering and other approaches. In this paper, these algorithms to detect parallel overlapping communities are studied thoroughly, and their performances are analyzed comparatively. Graph-based approach such as Sequencial Clique Percolation (SCP), intrinsic Longitudinal Community Detection (iLCD), link-based approach such as Top Graph Clusters (TopGC), fuzzy-based approach such as Cluster-Overlap Newman Girvan Algorithm (CONGA), agent and dynamic-based and modularity scoring based approach etc. are comparatively analyzed to evaluate their performance for overlapping community detection using dataset from Zachary karate club and Stanford large network. Analysis conducted on SCP and iLCD using the same datasets show that SCP cannot detect dynamic overlapping communities in a dense network as effectively as iLCD. It is also observed, CONGA can detect the distinctive community clusters in Zachary karate dataset. It is also noted that TopGC is suitable in detecting overlapping clusters from large networks with limited time and memory usage, making it useful for experiments in workstations with lower specs and limited processing power. © 2022 ACM.

关键词： Social networking (online)

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Robotics Architectures Based Machine Learning and Deep Learning Approaches 8

Robotics Architectures Based Machine Learning and Deep Learn...

引用

8th international conference on Mechatronics Engineering, ICOM 2022

作者： Alatabani, Lina E. Ali, E.S. Mokhtar, R.A. Khalifa, Othman O. Saeed, Rashid A. Department of Data Communications & Network Engineering Faculty of Telecommunications The Future University Khartoum Sudan port Sudan Sudan Khartoum Sudan Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electrical and Electronics Engineering Faculty of Engineering International Islamic University Malaysia Malaysia The Libyan Center for Engineering Research and Information Technology Bani Walid Libya

ISBN: (数字)9781839538063

ISBN: (纸本)9781839538063

Robotics has been playing a vital role in our daily lives with a wide range of applications to improve the quality of life. With a variety of usable applications in the medical, manufacturing, and transportation industries, there is a continuous need for improving the performance of robotics for the importance of precision in executing commands and tasks. The implementation of precise commands has led to intense research on approaches to improve the performance of robotics. Machine Learning (ML) and Deep Learning (DL) have been drawing attention to applying architectures and algorithms to robotics which imposed a positive impact on the field of robotics. ML and DL applications in robotics include areas of computer vision, imitation learning, self-supervised learning, assistive and medical technologies, multi-agent learning, and manufacturing. This paper provides a comprehensive review of autonomous vs automatic robotics, robotic applications, extreme learning machine methods, and ML for soft robotics applications, in addition, to discussing the challenges, and future trends for AI applications in robotics applications. © 2022 IET conference Proceedings. All rights reserved.

关键词： Robotics

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Intelligent Wardrobe Clothing Recommendation System Based on ZigBee network and CLO Value Measurement 4

Intelligent Wardrobe Clothing Recommendation System Based on...

引用

4th international conference on Emerging Research in Electronics, computer Science and technology, ICERECT 2022

作者： Liu, Kun Chen, Danhong Zhao, Jidong Liu, Xin Gao, Luya School of Economics and Management Shenyang Aerospace University Shenyang China School of Humanities Jiangxi University of Finance and Economics Nanchang China School of International Business Shenyang Normal University Shenyang China

ISBN: (纸本)9781665456357

At present, smart home can improve people's production and life style. In order to intelligent wardrobe, this paper designs and develops an intelligent clothing recommendation system based on ZigBee network and CLO value algorithm, clothing thermal resistance meter and memory technology. This system can realize the identification and recommendation of clothing, recent record of user's wear and manufacture, improve user satisfaction. © 2022 IEEE.

关键词： Recommender systems

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Stroke Probability Prediction and Data Visualization Based on Multiple Machine Learning Models 5

Stroke Probability Prediction and Data Visualization Based o...

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5th international conference on computer Information Science and Application technology, CISAT 2022

作者： Huang, Xinyue Qi, Meng Wang, Yihan Yang, Juntian Computer Science and Software Engineering Maynooth International Engineering College Fuzhou University Fuzhou350108 China Department of Food Science & Technology School of Agriculture and Biology Shanghai Jiao Tong University Shanghai200240 China College of Chemistry and Molecular Engineering Peking University Beijing100081 China Depu Foreign Language School Chongqing500000 China

ISBN: (纸本)9781510660076

The issue of stroke happening in world range acting a significant problem for human health. Until 2019, there were about 411 death cases per day in America which is 3 years earlier than the appearance of e-cigarettes in 2022. However, the judgement between different doctors could be variable and a slightly human error in complex situations could lead to a bad estimation result. To deal with this, the prediction of stroke probability of patients could provide an objective reference for doctors. This study made a data treatment of factors choosing and translation then calculating 4 accuracy of models and finally an application. To be more specific, an online stroke dataset was employed for factor analysis firstly and three machine learning algorithms were utilized to select most important stroke-related factors. 6 most influential factors were further passed into four machine learning models for training. The work gave the result of the highest accuracy performance of Random Forest between the four models which could be assistance of the prediction in stroke. © 2022 SPIE.

关键词： Forecasting

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Improved YOLOv5 light source positioning method in multi-medium 2

Improved YOLOv5 light source positioning method in multi-med...

引用

2nd international conference on Optics and Communication technology, ICOCT 2022

作者： Wei, Ruitong Yang, Lei computer Dongguan University of Technology Dongguan China International Institute of Microelectronics Dongguan University of Technology Dongguan China

ISBN: (纸本)9781510660533

Object spatial positioning in multi-medium is always a difficult problem to overcome. It is not only necessary to realize object detection in complex environment, but also need to overcome camera distortion to achieve spatial positioning, and at the same time ensure the accuracy and speed of detection. In the current space positioning field, there are some problems such as low detection accuracy, many detection restrictions and single environment. In this paper, the light source is taken as the detection target. Firstly, OpenCV is used to correct the light source data set collected by CCD camera to reduce the influence of distortion. Then, based on YOLO series algorithms, an improved YOLOV5 network model is proposed to train the light source training set. The experimental results show that the improved YOLOV5 model can accurately detect the light source after distortion correction with an average accuracy of 96.2%, a transmission rate of 135 f/s and a spatial position error of 7.3526mm. © 2022 SPIE.

关键词： computer vision

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Analysis of N-Way K-Shot Malware Detection Using Few-Shot Learning 1

引用

international conference on Cyber Security, Privacy and networking, ICSPN 2022

作者： Chui, Kwok Tai Gupta, Brij B. Lee, Lap-Kei Torres-Ruiz, Miguel Kowloon Hong Kong Department of Computer Science and Information Engineering International Center for AI and Cyber Security Research and Innovations Asia University Taichung41354 Taiwan Lebanese American University Beirut1102 Lebanon Department of Electronic Engineering and Computer Science School of Science and Technology Hong Kong Metropolitan University Kowloon Hong Kong Instituto Politécnico Nacional Centro de Investigacion en Computacion UPALM-Zacatenco Mexico City07320 Mexico

ISBN: (数字)9783031220180

ISBN: (纸本)9783031220173

Solving machine learning problems with small-scale training datasets becomes an emergent research area to fill the opposite end of big data applications. Attention is drawn to malware detection using few-shot learning, which is typically formulated as N-way K-shot problems. The aims are to reduce the effort in data collection, learn the rare cases, reduce the model complexity, and increase the accuracy of the detection model. The performance of the malware detection model is analyzed with the variation in the number of ways and the number of shots. This facilitates the understanding on the design and formulation of three algorithms namely relation network, prototypical network, and relation network for N-way K-shot problems. Two benchmark datasets are selected for the performance evaluation. Results reveal the general characteristics of the performance of malware detection model with fixed ways and varying shots, and varying ways and fixed shots based on the trends of results of 30 scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Benchmarking

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A Dynamic Time Slot Algorithm Based on Throughput Optimization in IEEE 802.11 Wireless networks 12

A Dynamic Time Slot Algorithm Based on Throughput Optimizati...

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

作者： Jin, Guonian Lu, Shejie Hu, Siping Xu, Haihong Gao, Jinfeng Liu, Fang Wu, Haitao Hubei University of Science and Technology School of Computer Science and Technology 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

In IEEE 802.11 wireless LAN, as the number of nodes increases, collisions will increase correspondingly, channel utilization will decline, and the total throughput will decline instead of increasing, leading to system performance degradation. Through in-depth analysis of the root cause of this problem, this paper designs a simple and effective algorithm, which uses ARMA filtering algorithm to measure the conditional collision probability and then calculate the number of nodes in the network. The AP tells all nodes in the network in the form of broadcast when the number of nodes in the network changes, after all nodes receive the broadcast sent by the AP, they dynamically adjust the network parameters accordingly. Finally, NS2 simulation software is used to conduct simulation experiments in a variety of network scenarios, and the experimental results verify that the algorithm in this paper is simple and effective, with less changes to IEEE 802.11, and is suitable for use and promotion in wireless networks. It can optimize the network system performance based on the number of nodes, improve the system throughput, and significantly improve the network performance. According to the number and density of nodes, the algorithm reduces the collision probability by dynamically adjusting the timeslot, accordingly increases the total network throughput, and achieves the purpose of effectively improving the network performance. © 2022 IEEE.

关键词： IEEE Standards

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