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

共 1027 条记录，以下是401-410 订阅

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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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Application of Machine Learning algorithms to Path Loss Modeling: A Review 5

Application of Machine Learning Algorithms to Path Loss Mode...

引用

5th international conference on Information technology for Education and Development, ITED 2022

作者： Abdulkarim, Abubakar Faruk, Nasir Alozie, Emmanuel Sowande, Olugbenga. A. Olayinka, Imam-Fulani Yusuf Usman, Aliyu D. Adewole, Kayode S. Oloyede, Abdulkarim A. Chiroma, Haruna Garba, Salisu Imoize, Agbotiname Lucky Musa, Abdulwaheed Taura, Lawan S. Ahmadu Bello University Department of Electrical Engineering Nigeria Kampala International University Department of Electrical Telecommunications Computer Engineering Uganda Sule Lamido University Department of Physics Nigeria Directorate of ICT Sule Lamido University Nigeria University of Ilorin Department of Telecommunication Science Nigeria Ahmadu Bello University Department of Electronics and Telecommunication Engineering Nigeria University of Ilorin Department of Computer Science Nigeria College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia Sule Lamido University Department of Computer Science Nigeria University of Lagos Department of Electrical and Electronics Engineering Nigeria Kwara State University Department of Electrical and Computer Engineering Nigeria

ISBN: (纸本)9781509064229

The demand for high-speed internet services is increasing due to emerging needs such as e-commerce, e-health, education, and other high-technology applications. Wireless communication networks have now become a necessity, especially with the introduction of the 5G networks which have the potential to provide extraordinary data rates with extremely low latency. The deployment and operation of 5G and beyond networks in built-up environments would require a complex and reliable radio propagation model that guides network engineers to achieve effective coverage estimation and appropriate base station placements. The inefficiency, and sometimes inconsistencies of deterministic and empirical path loss models necessitated the need to integrate machine learning models. Recently, different machine learning-based pathloss models have been developed to overcome drawbacks associated with conventional pathloss models due to their significant learning and prediction abilities. This paper aims to review path loss models relative to machine learning-based algorithms with a focus on models developed in the last 21 years (2000 to 2021) to study their network parameters and architectures, designs, and applicability, and proffer further research directions. © 2022 IEEE.

关键词： 5G mobile communication systems

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To Develop Multi-Object Detection and Recognition Using Improved GP-FRCNN Method 8

To Develop Multi-Object Detection and Recognition Using Impr...

引用

8th international conference on Smart Structures and Systems, ICSSS 2022

作者： Loganathan, Ganesh Babu Fatah, Tooraj Hassan Yasin, Elham Tahsin Hamadamen, Nawroz Ibrahim Tishk International University Faculty of Engineering Department of Mechatronics Kurdistan Region Erbil Iraq Noble Private Institute Information Technology Kurdistan Region Erbil Iraq University of Salahaddin Institute of Higher Education & Scientific Research Software Engineering Department Kurdistan Region Erbil Iraq

ISBN: (纸本)9781665497619

The Region-based Convolutional Neural network (R-CNN), which is more rapid, has been recognized as the best detection algorithm. With our design basis for Faster RCNN (FRCNN), the designers reclassify the proposed object recognition changes with just a rough spatial estimate of the incident. The researchers solve problems such as delicate to complex environments and its principles are derived reductions each time it is -actually confronted with noisier initiatives. Through which designers categorize proposed changes to object recognition using a rough geometric estimate of the scene. This simple expansion requires complex changes of scale, which the authors describe in their entirely throughout this study. Moreover, Geometric Proposals with FRCNN (GP-FRCNN) performed relatively well on small and large things, which has always been a problem for computer vision algorithms. Any use of GP-FRCNN for monitoring film analysis is simple and requires no specific geometric description. We tested the algorithm on the UA-DETRAC database, finding that GP-FRCNN beat the FRCNN by 21%. © 2022 IEEE.

关键词： Training Image recognition Databases Object recognition Proposals Intelligent structures Detection algorithms

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Green Machine Learning Approach for QoS Improvement in Cellular Communications 2

Green Machine Learning Approach for QoS Improvement in Cellu...

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2nd IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2022

作者： Saeed, Mamoon M. Saeed, Rashid A. Azim, Mohammad Abdul Ali, Elmustafa Sayed Mokhtar, Rania A. Khalifa, Othman Faculty of Engineering Communications and Electronics Engineering Department Yemen Taif University College of Computers and Information Technology Department of Computer Engineering Taif21944 Saudi Arabia Asian University for Women Department of Computer Science Bangladesh Department of Electrical Electronic Engineering Port Sudan Sudan College of Electronics Engineering Khartoum Sudan International Islamic University Malaysia Faculty of Engineering Dept of Electrical and Electronics Engineering Malaysia

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

Green cellular communications are becoming an important approach due to large-scale and complex radio networks. Due to the dynamic cellular network behaviors related to interference distribution, traffic bottlenecks, congestion points, and hotspots, there is a need to evaluate the dynamic processes in cellular systems in addition to ensuring spectrum availability. The delay, loss rate, and SNR are the most issues that may affect cellular communication performance. Artificial intelligent algorithms such as machine learning (ML) enable to detection of the dynamics in cellular networks by analyzing the complex cellular network processes and evaluating the spectrum and links qualities. It enables the extraction of spectrum knowledge from the network autonomously. The extracted information helps to know about every dynamic change in wireless parameters, related to frequency, modulation, route selection, etc. This paper provides details about the use of ML in green cellular networks to efficiently upgrade the communications and enhances different related approaches including quality of services (QoS), signal traffic load, and energy efficiency, which are critical issues of green cellular communication paradigms. The paper also presents the technical concept of green ML approaches to solve significant problems in cellular communications, in addition to future aspects and considerations for energy consumption minimization using the green ML approach in cellular radio communications. © 2022 IEEE.

关键词： Energy efficiency

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Graph Neural network based Agent in Google Research Football 2

Graph Neural Network based Agent in Google Research Football

引用

2nd international conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2022

作者： Liu, Jinglong Niu, Yizhan Shi, Yuhao Zhu, Jiren College of Computer and Information Engineering Central South University of Forestry and Technology Changsha China Department of Computer and Information Science Fordham University New YorkNY10023 United States College of Computer Science and Technologies Zhejiang University Hangzhou China School of International Studies Zhejiang University Hangzhou China

ISBN: (纸本)9781510657717

Deep neural networks (DNN) can approximate value functions or policies for reinforcement learning, which makes the reinforcement learning algorithms more powerful. However, some DNNs, such as convolutional neural networks (CNN), cannot extract enough information or take too long to obtain enough features from the inputs under specific circumstances of reinforcement learning. For example, the input data of Google Research Football, a reinforcement learning environment which trains agents to play football, is the small map of players' locations. The information is contained not only in the coordinates of players, but also in the relationships between different players. CNNs can neither extract enough information nor take too long to train. To address this issue, this paper proposes a deep q-learning network (DQN) with a graph neural network (GNN) as its model. The GNN transforms the input data into a graph which better represents the football players' locations so that it extracts more information of the interactions between different players. With two GNNs to approximate its local and target value functions, this DQN allows players to learn from their experience by using value functions to see the prospective value of each intended action. The proposed model demostrated the power of GNN in the football game by outperforming other DRL models with significantly fewer steps. © 2022 SPIE. All rights reserved.

关键词： Reinforcement learning

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Single Line Outage Analysis on IEEE 39 Bus network 25

Single Line Outage Analysis on IEEE 39 Bus Network

引用

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

作者： Bartend Russell, Chokder Alamgir Khan, Shahriar Seabreeze International School Dhaka Bangladesh Independent University Bangladesh Dept. of Eee Dhaka Bangladesh

ISBN: (纸本)9798350346022

With the recent blackouts in Texas, Manhattan, and NE US, the importance of analyzing outage is greater than ever. Contingency Analysis is helpful to increase the resiliency of power system by analyzing impact of different contingencies. The effect of single transmission line outage in a transmission network has been studied with IEEE 39 bus network. Each of the branches has been disconnected one at a time to find effect on generator constraints, voltage constraints of buses, transmission line loading and possibility of islanding. PSS®E Xplore 34 was used for the simulation. The results show that single transmission line outage may impact generator power factors, increase demand of reactive power from the generators, and overload other transmission lines. Transmission line outage may lead to several violations of system constraints leading to islanding. This study may help research on different impacts of single transmission line outage and improve power system resilience. © 2022 IEEE.

关键词： Electric lines

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A Study of Smart Presence System by using Machine Learning Approach

A Study of Smart Presence System by using Machine Learning A...

引用

2022 IEEE international conference on Current Development in Engineering and technology, CCET 2022

作者： Rahman, Mahmudur Tahmiduzzaman, K.B.M. Sharma, Pranto Snigdha, Shouvik Roy Mondal, Tanmoy Zumma, Md. Thoufiq Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665454155

In large organizations, the time and effort spent verifying whether or not employees have met their attendance quotas may become a substantial burden. In order to solve this issue, a presence mechanism that is both automated and efficient is being built. However, this technique relies heavily on verification. Smart Presence System deployments often make use of real-time facial recognition and identification technologies. In this investigation, we use two different kinds of algorithms. It uses the Convolutional Neural network (CNN) technique as well as the Haar Cascade Classifier. The Haar Cascade Classifier technique was used during development of this function. We used the CNN algorithm to compare the results. When a user's face is detected, the system will generate a new spreadsheet for each day of the week. This real-time face recognition and identification technology is restricted to authorized corporate personnel only. Those who have not yet registered may have their information confirmed using a QR code verification mechanism. This system might make use of selected pieces of the user's data. Users with and without accounts may cohabit without incident within the parameters of this system. We got 99% accuracy after using the Haar Cascade Classifier. © 2022 IEEE.

关键词： Face recognition

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Application of using R language to call SNNS neural network learning platform function in econometric prediction——Take the price fluctuation prediction of gold and bitcoin as an example

Application of using R language to call SNNS neural network ...

引用

2022 international conference on Advanced algorithms and Neural networks, AANN 2022

作者： Wang, Yuan Zhang, Ziyue Li, Zihao International Business School Jinan University Guangdong Zhuhai519070 China

ISBN: (纸本)9781510655751

Maximizing returns is a constant pursuit of the financial market. In this paper, we show the relevant models we designed to realize the optimal allocation and combination of gold, bitcoin and cash. Advanced forecasting technology can promote the maturity of transactions. Our model takes the lead in introducing the rolling learning prediction method of feedforward multilayer neural network (MLP), which provides a method for accurately predicting market fluctuations from another way. We improved the original algorithm and changed the unified learning method to the rolling learning method based on the existing data before each day. Our practice has proved that our technology has better prediction effect for assets with less market volatility. For example, for gold, a safe haven asset, our prediction accuracy is 99.5%;For products with large market fluctuations, the prediction accuracy is relatively low. For example, for speculative assets with large market fluctuations such as bitcoin, our prediction accuracy is also as high as 97%. © 2022 SPIE.

关键词： Forecasting

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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...

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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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Logistics Efficiency Optimization of Cross-Border e-Commerce based on Remote Sensing network Optimization Algorithm 3

Logistics Efficiency Optimization of Cross-Border e-Commerce...

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3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Hu, Yuting Zhang, Ling Shanghai Urban Construction Vocational College International Business Shanghai201415 China Jiangxi Shangrao Normal University Fine Arts Jiangxi Shangrao334099 China

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Logistics efficiency optimization of cross-border e-commerce based on the remote sensing network optimization algorithm is studied in the paper. The popularization of 5G mobile communication technology in my country has become an inevitable trend. With the further development of my country's network information technology, it is not a long time to popularize 5G mobile communication technology nationwide. Hence, the theoretical background is selected in this paper. The output can be viewed through the console or store it through the logging system. Write the converted data into the receiving queue of the logistics tracking system DB, and the database background processing code will complete the specific processing of the message, generate the relevant result data, and store it in the corresponding data table according to the rules. Parsed real-time data. Then the integration of the remote sensing algorithms are studied and the system is implemented. © 2022 IEEE.

关键词： Remote sensing

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