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检索条件"机构=Computer System and Information Technology"

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Spearphishing Attack Detection Model Using Machine Learning Approach Based On Network Traffic Analysis

Spearphishing Attack Detection Model Using Machine Learning ...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Yohanes Priyo Atmojo Dandy Pramana Hostiadi I Made Darma Susila Gede Angga Pradipta Made Liandana Putu Desiana Wulaning Ayu Department of Information Technology Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Magister Information Systems Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Computer System Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

In the digital era, people use e-mail as the primary means of communication, as well as credentials and private communication in their work. Sending fake e-mails is usually illegal and aims to steal data or implant malware. Fake e-mails must be prevented and detected as early as possible. This paper proposes a new model for detecting fake e-mail-sending activities using a machine learning classifier approach: NaÏve Bayes, k-NN and Decision Tree. The aim is to see the performance of several classification methods and analyze the best detection performance from using classification methods. This paper contributed to introducing a new extracting features technique and modelling it as a detection model using a machine learning approach. The experiment shows that the decision tree model can detect optimally with an accuracy value of 98.11%, precision of 94.04%, and recall of 99.93%. This model can help system administrators handle appropriate procedures.

关键词： Training Analytical models Phishing Machine learning Telecommunication traffic Malware Electronic mail

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Contactless Respiratory Rate Monitoring Using FMCW Radar

Contactless Respiratory Rate Monitoring Using FMCW Radar

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2022 International Symposium on Electronics and Smart Devices, ISESD 2022

作者： Widiyasari, Diyah Ahmadi, Nur Adiono, Trio Purwarianti, Ayu Purnomo, Ariana Tulus Lin, Ding-Bing Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40132 Indonesia Bandung40132 Indonesia National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei Taiwan Sampoerna University Departement of Information System Jakarta Selatan12780 Indonesia

ISBN: (数字)9798350396607

ISBN: (纸本)9798350396607

Vital sign monitoring plays a very important role in the early diagnosis and prognosis of a disease, as well as for patient health management. Among other vital signs, respiratory rate (RR) is rarely recorded and often forgotten despite the fact that it is an essential predictor of serious events such as cardiac arrest and deteriorating clinical condition. In this paper, we propose a frequency-modulated continuous-wave (FMCW) radar for contactless RR monitoring. We employ FFT to calculate RR in frequency-domain and combine it with Kalman filter to smooth out the estimated RR. We use a public dataset measuring RR from 50 children under 13 years of age recorded with 60 GHz FMCW radar. The experimental results show that the proposed method yields MAE and RMSE of 3.20 and 4.10, respectively, with respect to the reference device. Incorporating Kalman filter reduces the percentage of error by 14.83% and 13.36% compared to without it. The overall results suggest that radar-based technology can be potentially used for contactless RR monitoring which is suitable for those who cannot use contact-based monitoring devices. © 2022 IEEE.

关键词： Kalman filters

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An Area-Efficient Design of Enhanced Space-Time Redundant DFF (IEST_TMR DFF) 7

An Area-Efficient Design of Enhanced Space-Time Redundant DF...

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7th International Conference on computer and Communication systems, ICCCS 2022

作者： Liu, Jiasen Li, Xiaogan Zhang, Jianwei Li, Jinghu Dalian University of Technology School of Microelectronics Dalian China Dalian University of Technology Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System School of Biomeidcal Engineering Dalian China Fujian Agriculture and Forestry University College of Computer and Information Sciences Fuzhou China

ISBN: (数字)9781665450607

ISBN: (纸本)9781665450607

In the Application Specific Integrated Circuit (ASIC) design of spacecraft, anti-radiation performance is a very important factor, and Triple Modular Redundancy (TMR) is a common radiation hardened method, which can well resist the Single Event Upset (SEU) in sequential units. However, conventional TMR cannot eliminate Single Event Transient (SET) pulses that occur in combinational logic. So, varieties of improved schemes have been proposed, including the Space-Time TMR (ST_TMR), the Enhanced Space-Time TMR (EST_TMR), and TMR_5DFF. However, ST_TMR and EST_TMR will cause timing errors in specific cases, and the area cost of TMR_5DFF is too large. Therefore, this paper proposes an area-efficient Improved Enhanced Space-Time TMR (IEST_TMR) scheme based on EST_TMR, which can not only solve the timing errors of EST_TMR, but also reduce the area cost compared with TMR_5DFF. In order to verify the validity of IEST_TMR, this paper uses the Soft Error Rate (SER) analysis method based on double exponential current source and the C17 circuit in ISCAS'85 as the test circuit to analyze the SER of the circuit using IEST_TMR DFF. In addition, we implement a simple anti-radiation Universal Asynchronous Receiver/Transmitter (UART) module by using the method of the Second Synthesis of Modified Netlist (SSMN), and further verify the validity of the IEST_TMR DFF by injecting faults in the back-end simulation. © 2022 IEEE.

关键词： Integrated circuit design

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AI Bot to Increase the Accuracy and Efficiency of Hiring Process of Business Organizations

AI Bot to Increase the Accuracy and Efficiency of Hiring Pro...

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Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： Dilusha N.H.A.S Weerasinghe E.J.K.D Chamuditha S.M.A Wijekoon K.W.M Samantha Thelijjagoda Nishantha Giguruwa Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Computer System Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka College of Sustainability and Tourism Ritsumeikan Asia Pacific University Beppu Oita Prefecture Japan

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Recruitment in most organizations is normally a rather inefficient process where it takes quite a long time to match the right candidate to a given job. In this research, AI-based hiring system is recommended to improve the efficiency and accuracy of the hiring process with the help of NLP and machine learning algorithms. Employment enabling processes such as CV filtering, skill evaluation and personality profiling as well as job matching are achieved by the system. An NLP-based tool is used for short listing of CVs, assessment quizzes are used to evaluate different skills and qualifications of the candidates and chatbot enables communication between candidates and HRs, while an AI bot is used to complete different document verifications' processes. Recruitment operation is made easier and efficient, and matching of candidates to the available jobs is done better since it is less time-consuming and energy-demanding, giving a solution that may be implemented on a large scale. Further improvements are to be made in the fine tuning of algorithms, enlargement of comprehensiveness of systems and the implementation of actual sample cases before the introduction into the marketplace.

关键词： Accuracy Machine learning algorithms Filtering Organizations Machine learning Chatbots Organizational aspects Tuning Recruitment Qualifications

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Parallel Optimization of Computational Fluid Dynamics Application code_saturne Based on Next Generation Sunway Supercomputer 14

Parallel Optimization of Computational Fluid Dynamics Applic...

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14th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2023

作者： Liu, Anjun Liao, Yin Xu, Xihao Chen, YanYan Zhong, Hongjun Wu, Jianming Kong, Bo Raikov, Alexander Guo, Meng Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Industrial Software Jinan China Zhejiang Yuansuan Technology Co. Ltd Jinan Supercomputing-Zhejiang Yuansuan Industrial Simulation System Joint Laboratory Hangzhou China Qilu University of Technology Shandong Academy of Sciences Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Key Laboratory of Computing Power Network and Information Security China Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Shandong Jinan China

ISBN: (纸本)9798350371024

code_saturne is an open-source general large-scale computational fluid dynamics software independently developed by Electricite de France since 1997. Due to its high computing performance, code_saturne has been widely used in the nuclear industry, heavy fuel and biomass combustion, particle tracking and other computational fluid dynamics fields. In this work, we implemented code_saturne on the new generation Sunway supercomputer (SW26010pro) at the first time. By applying the data value-taking technology of double cache, combined with Sunway shared memory technology, we have adapted the data structure and Modular programming of code_saturne conducively to the new generation Sunway supercomputer (SW26010pro) and accelerated the software hotspots the software hotspots by 1-2 times. After optimizing and porting, this software has successfully served in the numerical simulation of the lateral flow of fuel assemblies in the nuclear industry. © 2023 IEEE.

关键词： Supercomputers

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DNPS: A Robust Aggregation Method for Heterogeneous Distributed Learning Based on Gradient Direction and Norm Probability Screening

DNPS: A Robust Aggregation Method for Heterogeneous Distribu...

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International Conference on computer Supported Cooperative Work in Design

作者： Ming Yang Caiyun Li Yunpeng He Xin Wang Xiaoming Wu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Heterogeneous distributed machine learning systems are vulnerable to Byzantine attacks, where malicious worker nodes disrupt global model convergence by submitting incorrect model updates, leading to degraded system performance. Current aggregation methods that filter based on gradient norm or direction often underperform in heterogeneous environments and struggle to defend effectively against Byzantine attacks. To address this issue, we propose the Direction-Norm Probability Screening (DNPS) algorithm-a novel approach that harmonizes gradient norm and direction to blunt these attacks. DNPS integrates gradient norm and directional information to establish a probability screening mechanism for identifying and filtering Byzantine worker nodes, thereby fortifying the system's defen-sive capabilities and enhancing its robustness against Byzantine attacks. Experimental results show the DNPS algorithm provides more effective protection under both non-attack and Byzantine attack scenarios than existing aggregation methods.

关键词： computer aided instruction Machine learning algorithms Distance learning Federated learning system performance Filtering algorithms information filters Robustness Protection Optimization

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification 19

Deep Learning in Smart Healthcare: A GAN-based Approach for ...

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19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy information technology, SEIT 2024

作者： Tufail, Hina Ahad, Abdul Puspitasari, Ira Shayea, Ibraheem Coelho, Paulo Jorge Pires, Ivan Miguel Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga Indonesia School of Software Northwestern Polytechnical University Shaanxi Xian China Turkey Polytechnic University of Leiria Leiria Portugal Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which affects patients' lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD. © 2024 The Authors. Published by Elsevier B.V.

关键词： Generative adversarial networks

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Design Thinking and Innovative Tool Use in Business Model

Design Thinking and Innovative Tool Use in Business Model

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Intelligent systems for Cybersecurity (ISCS), International Conference on

作者： Nitika Singhi Ankush Verma Atul Agrawal Irfan Khan Computer Science Engineering Shri Vaishnav Vidhyapeeth Vishwavidyalava Indore India Data Analytics and Information System Prestige Institute of Management & Research Indore India Computer Science Engineering Sushila Devi bansal College of Technology Indore India Computer Science Engineering Bansal Institute of Science and Technology Bhopal India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

In past few year designs thinking has worked on several new effective problem and has generated innovative solution for new business model. For the scenario of today's demand, to better understand how design thinking can work on different perspectives in business models and how it can bring new innovative solutions for any problem is essential However, studies of existing literature explore the application, potential, and challenges of Design Thinking towards the comprehensive aim of this paper, which regards business model innovation as a fundamental subject of Design Thinking. The paper provides practical tools for exploring Design Thinking along with various critical aspects for the researchers to explore how Design Thinking can be applied to innovative business models. The main focus of this paper is to explain the basics of Design Thinking, how to frame the mind through different techniques used, the Design Techniques used for a business model, the methodology of a design thinker, and different tools like BPM, Blue Work Live, Mind Mapping, Brainstorming, and RICPS.

关键词： Technological innovation Solution design Heuristic algorithms Design methodology Brain modeling Real-time systems Iterative methods

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Deep Learning-Based Dimension Measurement system for Industrial Part Inspection

Deep Learning-Based Dimension Measurement System for Industr...

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2024 China Automation Congress, CAC 2024

作者： Li, Xiao-Di Fan, Xing-Rong Zhang, Xian-Ming Zhang, Han-Zhi Huang, Chun-Jie Xu, Cheng Chongqing Engineering Laboratory for Detection Control and Integrated System School of Artificial Intelligence Chongqing Technology and Business University Chongqing China Engineering Research Centre for Waste Oil Recovery Technology and Equipment Ministry of Education Chongqing Key Laboratory Chongqing Technology and Business University Chongqing China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China Chongqing Intelligent Supply Chain Engineering & Technology Research Center Chongqing Jiaotong University Chongqing China Chongqing Aerospace Bashan Motorcycle Manufacturing Co. Ltd. Chongqing China

ISBN: (纸本)9798350368604

This paper develops a deep learning-based system for the dimension measurement of industrial parts. The system employs a general self-supervised image segmentation network to perform dichotomous image segmentation (DIS) on the parts, effectively extracting binary images of the target components from complex backgrounds without requiring preprocessing steps such as filtering or denoising. Following segmentation, contour detection, and dimension measurement are executed. The system utilizes a CMOS area scan camera and was tested on a computer equipped with a GTX1050 graphics card. The tests, conducted on two typical thrust ball bearing components (i.e., the ball retainer and the shaft ring) ü yielded ten measurements per part. Comparison with a vernier caliper as the reference standard reveals that under normal operating conditions, the maximum standard deviation is 0.007 mm and the maximum absolute error is 0.017 mm. © 2024 IEEE.

关键词： Ball bearings

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GMD-YOLOv5s: An Improved Steel Surface Defect Detection Algorithm Based on YOLOv5s

GMD-YOLOv5s: An Improved Steel Surface Defect Detection Algo...

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International Conference on computer Supported Cooperative Work in Design

作者： Zhike Su Aimin Li Zheqi Yang Mian Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Current steel surface defect detection methods have the shortcomings of low accuracy, poor real-time performance and limitation to detect small targets. To solve those problems, we proposed a lightweight detection model named GMD-YOLOv5s. Firstly, we introduce the C3 Global Ghost (C3-GGhost) module, an enhancement of the original C3 module, which improves both detection speed and accuracy for industrial applications. Secondly, Multi-Scale Dilated Attention (MSDA) is integrated into the neck network to capture target at different scales. Because MSDA can expand the model's receptive field and feature representation capabilities. Finally, in order to enhance the model's ability to detect small targets, we replace the conventional detection head with a Distributed Pixel-level Feature Refinement Head (DPFRHead). Experimental results show that GMD-YOLOv5s achieves a mean Average Precision (mAP) of 82.9% on the NEU-DET dataset. Our method improves by 30% compared to the baseline YOLOv5, while reducing the number of parameters by 60%. Ablation and comparative experiments validate the model's effectiveness and accuracy in detecting defects on steel surfaces.

关键词： YOLO Accuracy Federated learning Computational modeling Heuristic algorithms Feature extraction Real-time systems Neck Steel Defect detection

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