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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Singh, J.N. Solanki, Brijraj Singh Galgotias University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Jaipur National University School of Computer & System Sciences Department of Computer Sciences Jaipur India

ISBN: (纸本)9781665450461

Technology advancement makes significant contributions to the healthcare system. By automating the patient monitoring task, smart health monitoring will reduce human effort while improving patient work flow management. Computational intelligence plays a vital part in intelligent failure diagnosis and prediction since it is one of the most potent data processing methods for resolving challenging nonlinear issues. The continued use of computational intelligence in machinery condition monitoring and problem diagnosis has, however, only been briefly covered in a few thorough papers. This paper describes the creation of an induction machine health monitoring system. It is briefly explained how artificial neural networks work and how they might be used for machine diagnostics. It is covered the general situation of designing intelligent systems based on neural networks. The effectiveness and accuracy of the proposed intelligent system for identifying machine health are demonstrated. © 2022 IEEE.

关键词： Intelligent systems

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Ev-IDID: Enhancing Solutions to Interactive Dynamic Influence Diagrams through Evolutionary Algorithms 21

Ev-IDID: Enhancing Solutions to Interactive Dynamic Influenc...

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21st International Conference on Autonomous Agents and Multiagent systems, AAMAS 2022

作者： Ma, Biyang Pan, Yinghui Zeng, Yifeng Ming, Zhong School of Computer Science and Engineering Minnan Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University United Kingdom College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781713854333

Interactive dynamic influence diagrams (I-DIDs) are a general framework for multiagent sequential decision making under uncertainty. Due to the model complexity, a significant amount of research has been invested into solving the model through various types of either exact or approximate algorithms. However, there is no tool that allows users to specify the algorithm parameters and visualise the model solutions. In this demo, we develop an interactive I-DID system that implements most of the state-of-art I-DID algorithms and develops a new type of algorithms based on evolutionary computation. In particular, we propose a multi-population genetic algorithm for solving the I-DID models and automate the generation of behavioural models in the solutions. This demo will facilitate the I-DID research development and practical applications, and elicit a new wave of I-DID solutions based on evolutionary algorithms. © 2022 International Foundation for Autonomous Agents and Multiagent systems (***). All rights reserved.

关键词： Decision making

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Efficient Coefficient Bit-Width Reduction Method for Ising Machines

Efficient Coefficient Bit-Width Reduction Method for Ising M...

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Yachi, Yuta Mukasa, Yousuke Tawada, Masashi Togawa, Nozomu Waseda University Department of Computer Science and Communications Engineering Japan Green Computing System Research Organization Waseda University Japan

ISBN: (纸本)9781665441544

Ising machines such as quantum annealing machines and semiconductor-based annealing machines can solve various combinatorial optimization problems very efficiently by transforming it into a data structure called an Ising model. At that time, the bit-widths of the coefficients of the Ising model have to be kept within the range that an Ising machine can deal with. However, by reducing the Ising-model bit-widths, its minimum energy state, or ground state, may become different from that of the original one and hence the targeted combinatorial optimization problem cannot be well solved. This paper proposes an effective method for reducing Ising-model bit-widths. The proposed method is composed of the two processes: First, given an Ising model with large coefficient bit-widths, the shift method is applied to reduce its bit-widths roughly. Second, the spin-adding method is applied to further reduce its bit-widths to those that Ising machines can deal with. Without adding too many extra spins, we efficiently reduce the coefficient bit-widths of the original Ising model. Furthermore, the ground state before and after reducing the coefficient bit-widths is not much changed in most of the practical cases. Experimental evaluations demonstrate the effectiveness of the proposed method, compared to existing methods. © 2022 IEEE.

关键词： Ising model

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Moodle Plugin for Real-time Face Recognition in Online Exams for systems engineering and computer science Students at Universidad Continental, Huancayo, Peru.

Moodle Plugin for Real-time Face Recognition in Online Exams...

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Robotics and computer Vision (ICRCV), International Conference on

作者： Miguel Torres Vargas Luis Fernando Rojas Valle Diego Porras Vera Daniel Gamarra Moreno School of System Engineering and Computer Science Universidad Continental Huancayo Peru

Facial recognition systems on learning platforms like Moodle enable fast and efficient evaluation, automating the detection of impersonation in online exams. This project implements a Moodle plugin that performs facial recognition on the student attempting to take a quiz. It consists of two parts for authentication through a webcam: the first part occurs before accessing the quiz, and the second part involves surveillance through a camera during the quiz. The plugin was developed using the PHP and JavaScript programming languages, as well as Face-api. The JavaScript API detects and recognizes faces in the web browser using Tiny Yolo v2 and FaceRecognizerNet. It compares a student’s photo or image with the image from their webcam while they are taking the quiz. The Moodle plugin achieved an average error probability of 39.39% for face detection similarity distance.

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QUBO Matrix Distorting Method for Consumer Applications

QUBO Matrix Distorting Method for Consumer Applications

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Yoshimura, Tomokazu Shirai, Tatsuhiko Tawada, Masashi Togawa, Nozomu Waseda University Department of Computer Science and Communications Engineering Japan Green Computing System Research Organization Waseda University Japan

ISBN: (纸本)9781665441544

We propose a Quadratic Unconstrained Binary Optimization (QUBO) matrix distorting method to solve combinatorial optimization problems, which are very often seen in consumer applications, with high speed and high accuracy. Combinatorial optimization problems are formulated as the problems of finding the ground state of the energy function determined by the QUBO matrix. The proposed method consists of the local optimization and the distortion of the energy function by adding a constant to each QUBO matrix element with a certain probability. The probability is initially large, decreased during the method linearly, and zero at the end. The distortion process aims to make a local optimal solution to be no longer a local optimum and avoid staying in the local optimal solution with a probability. We apply the proposed method to the graph partitioning problem as a typical combinatorial optimization problem with many local optimal solutions and large energy barriers among them. We verify the effectiveness of the proposed method against the iterative improvement method and the simulated annealing. © 2022 IEEE.

关键词： Ising model

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Enhancing IT Risk Management in XYZ Vocational School: Leveraging COBIT 5 Framework with the APO12 Domain

Enhancing IT Risk Management in XYZ Vocational School: Lever...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Winanti Fernando, Erick Gate, Jullend Universitas Insan Pembangunan Department of Information Systems Indonesia Universitas Multimedia Nusantara Information System Faculty of Engineering and Informatics Tangerang Indonesia Bina Nusantara University Binus Online Learning Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350372229

This article explores the importance of IT risk management, especially at XYZ Vocational School. The agency is equipped to manage business risks, emphasizing the need to improve decision-making and risk mitigation by identifying potential hazards and evaluating internal and external threats, including hacking risks. The research investigates IT risk management at XYZ Vocational School using a qualitative and quantitative method with a focus on the APO12 COBIT 5 domain: Align, plan, and organize. It assesses subdomains such as data collection, risk analysis, profile maintenance, risk articulation, portfolio management, and response strategies. Conclusively, the research reveals institutional IT capabilities at level 1 (Performed Process) in the APO12 subdomain. Recommendations include developing COBIT-compliant SOPs for processes at this level, highlighting the need for further improvements in IT governance. This study provides valuable insights into IT risk management practices, offering recommendations for improving IT governance in educational settings. © 2024 IEEE.

关键词： APO12 Domain COBIT 5 Information technology Risk Management Vocational School

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Intelligent Contract Timestamp Vulnerability Detection Based on Key Control Flow Graph 36

Intelligent Contract Timestamp Vulnerability Detection Based...

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36th International Conference on Software engineering and Knowledge engineering, SEKE 2024

作者： Quan, Qing Yu Zeng, Cheng Wang, Yi Feng Wang, Jiao Ran School of Computer Science and Information Engineering Hubei University Wuhan China School of Artificial Intelligence Hubei University Wuhan China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Wuhan China

ISBN: (纸本)1891706594

The extensive application of smart contract technology in the blockchain domain has positioned it as a key component of the digital economy. However, as the application scope of smart contracts expands, security issues have become increasingly evident, resulting in substantial economic losses. Current vulnerability detection methods largely depend on analyzing complete smart contract source code, which struggles to filter out extensive redundant information when identifying individual vulnerabilities. This leads to an insufficient exploration of the characteristics of smart contract code, which in turn results in lower overall accuracy of detection methods. In this context, the study introduces a method that transforms smart contract source code into a control flow graph, filtering out the main basic blocks based on key information about timestamp vulnerabilities. Subsequently, individual operation audits are conducted on these basic blocks, combined with the graphical structure of the control flow graph, utilizing gated graph neural networks for detecting timestamp vulnerabilities in smart contracts. Comparative experiments were performed on two public datasets, and the results indicate that compared to state-of-the-art detection methods, the proposed approach has improved the accuracy and F1 score for detecting timestamp vulnerabilities in smart contracts by 1.65% and 6.51%, providing valuable application prospects for the security of smart contracts. © 2024 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Graph neural networks

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ResNet Model to Forecast Plant Leaf Disease

ResNet Model to Forecast Plant Leaf Disease

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2022 International Conference on Computing, Communication and Power Technology, IC3P 2022

作者： Godi, Brahmaji Muttipati, Appala Srinuvasu Rao, M Purnachandra Satyanarayana, G.V. Padmaja, G.M. Gadiraju, Sasi Rekha Raghu Institute of Technology Computer Science & Engineering Visakhapatnam India Kl Deemed to Be University Computer Science & Application Guntur India Assistant System Engineer Tata Consultancy Services Hyderabad India

ISBN: (纸本)9781665425254

Agriculture is a significant part of a developing country like India, and agricultural intervention is essential in rural livelihood. As per the statistical measurement, 65% of the Indian economy is dependent on agriculture and farming. On the other hand, various plant diseases hugely affect most crops. In this concern, the farmer needs an effective plant leaf disease analysis model to detect plant illness in the early stages to moderate the criticality and prevent severe losses in crop growth, quantity and production etc. In this regard, it is possible to mention that the plant leaf disease identification methods play a major role in protecting plants. Researchers are working to analyze the plant diseases by using recognized classifying techniques. Supportive, deep learning and Image processing are the leading cutting-edge technologies used to address these challenges. This work uses the Resnet 152 v2 model on a data set containing 7000 plant leaf pictures. The model achieved 95% accuracy which is better than many previous models discussed in the literature. © 2022 IEEE.

关键词： Crops

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A Hybrid Model Combined with SVM and CNN for Community Content Classification 23

A Hybrid Model Combined with SVM and CNN for Community Conte...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Ye, Yukui Xie, Xia Jin, Hai Wang, Duoqiang National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9781665494571

Community websites bring many conveniences to people, and the classification of community content is playing an important role in website management and information searching. As the carrier of community content, posts are difficult to classify manually. According to the characteristics of community content, a hybrid classification model for machine learning is proposed. This model consists of three steps. Firstly, aiming at the problem of fewer features of posts, a weighted word vector is proposed to enrich the features of posts. Secondly, since the single kernel function of SVM can not completely match all data distributions, a mixed kernel function is employed to improve the model. Finally, in order to fully utilize the powerful feature extraction ability of Convolutional Neural Network as well as the classification ability of SVM, a hybrid model is designed and implemented by replacing softmax layer with SVM classifier. The corresponding experiment results indicate that compared with traditional Convolutional Neural Network, the proposed hybrid model has better performance and stability with classification accuracy improved from 0.9% to 1.4% in general. © 2021 IEEE.

关键词： Support vector machines Smart cities Support vector machine classification Machine learning Feature extraction Stability analysis Data models

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An Efficient Intrusion Detection Framework in Software-Defined Networking for Cybersecurity Applications

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computers, Materials & Continua 2022年第8期72卷 3529-3548页

作者： Ghalib H.Alshammri Amani K.Samha Ezz El-Din Hemdan Mohammed Amoon Walid El-Shafai Department of Computer Science Community CollegeKing Saud UniversityRiyadh28095Saudi Arabia Deanship of Scientific Research Saudi Electronic UniversityRiyadhSaudi Arabia Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh28095Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Electronics and Electrical Communications Eng.Depart. Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic *** recent times,the most complex task in Software Defined Network(SDN)is security,which is based on a centralized,programmable ***,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN ***,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five ***,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model with the KDD(Knowledge Discovery in Databases)***,the utilized dataset is clustered into normal and four major attack categories via the clustering ***,a deep learning method is projected for building an efficient SDN-based intrusion detection *** results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD ***,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing *** example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.

关键词： Deep neural network DL WEKA network traffic intrusion and anomaly detection SDN clustering and classification KDD dataset

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