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3rd International Conference on Mechatronics and Mechanical Engineering, ICMME 2024

作者： Wu, Jing Yong, Xie Ma, Xiaoguang Zhang, Haotian Li, Shuai Wu, Jianqing Department of Computer and Information Science Qinghai Institute of Technology Qinghai Xining China Foshan Graduate School Northeastern University Guangdong Foshan China Newpearl Group Guangdong Foshan China

ISBN: (纸本)9781510681651

Intelligent manufacturing refers to the use of advanced information technology and modern manufacturing technology to carry out comprehensive intelligent and automated transformation of the whole process and all links of manufacturing and production, as well as to achieve efficient manufacturing production and service-oriented operation and enhance the competitiveness of the entire industry. In the field of intelligent manufacturing, this paper researches on the application of intelligent manufacturing modelling based on cyber-physical system, which takes the auto industry as the background, selects the engine cylinder head deburring production case in the intelligent equipment system from its manufacturing architecture. After analysis of the cyber-physical system, then use UML (unified modeling language) class diagram mode to model the deburring process, and complete the equipment case construction corresponding to the mode, analyze the results and draw relevant conclusions and application to build the actual system in the architecture in component parts selection and factory pipeline design. © 2024 SPIE.

关键词： Smart manufacturing

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface 1

Optical Evanescent Field for Direct Observation of Sub-100nm...

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1st International Conference on Robotics, Engineering, science, and technology, RESTCON 2024

作者： Khajornrungruang, Panart Permpatdechakul, Thitipat Suzuki, Keisuke Blattler, Aran Terayama, Yutaka Goto, Daiki Phaisalpanumas, Pipat Bun-Athuek, Natthaphon Phunopas, Amornphun Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Division Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Iizuka-Shi Mechanical Information Science and Technology Division Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Production and Robotics Engineering Bangkok Thailand Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Course Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Industiral Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Bangsue College of Industrial Technology Dept. of Mechanical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798350348842

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes. © 2024 IEEE.

关键词： Visualization

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Deaf Helper Mobile Application for Interaction of Hearing Disorders Communities 2

Deaf Helper Mobile Application for Interaction of Hearing Di...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Setyawan, Adrian Naphan, Gilbert Xervaxius Dynata, Kevin Friry, Jovano Edmund Warnars, Harco Leslie Hendric Spits Bina Nusantara University School of Information System Information Systems Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

People with hearing loss in this world have not received much serious attention from the authorities. This makes these sufferers confused in choosing learning media to interact with and isolated from their social environment. This application was created to help people with hearing loss to be noticed and understood from the way they communicate using sign language through the mobile application called Assistant for the Deaf, which has many features such as registration, interactive videos, sign language translator, forums, customer service, library, information, history, events, donations, and shops. The application is designed using use case diagrams and class diagrams modeling the database, and the implementation used Android Studio and MySQL database. © 2022 IEEE.

关键词： Audition

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Developing a Cyber-resilience state in Botswana's Energy Industry

Developing a Cyber-resilience state in Botswana's Energy Ind...

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2022 International Conference on Smart Applications, Communications and Networking, SmartNets 2022

作者： Keleba, Habeenzu Tabona, Oteng Maupong, Thabiso Botswana International University of Science and Technology Department of Computer Science and Information Systems Palapye Botswana Higher Colleges of Technology Faculty of Computer and Information Systems Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665487580

- The integration of the internet in industrial control systems in the energy sector has increased the number of cyberattacks on the infrastructure, necessitating cyber resilience. Cyber resilience is the capacity to withstand and recover quickly from unforeseen and severe cyberattack interruptions. In this paper, we investigate the global energy industry's current cyber-resilience rating and Botswana's cyber-resilience developments. We will discuss the timeline of cyberattacks and the weapons used by threat actors. We also examine and recommend the Cybersecurity Capability Maturity Model (C2M2) framework in order to comprehend the current state of a cybersecurity program in Botswana and monitor its development over time. © 2022 IEEE.

关键词： Cybersecurity

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Machine Learning for Intrusion Detection in Vehicular Ad-hoc Networks (VANETs): A Survey

Machine Learning for Intrusion Detection in Vehicular Ad-hoc...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Nesmah A. Al-Khulaidi Ammar T. Zahary Asma A. Al-Shargabi Muneer A. S Hazaa Department of Computer Networks Yemen Academy for Graduate Studies Sana'a Yemen Department of Computer Science Faculty of Computing and IT University of Science and Technology Sana'a Yemen Department of Information Technology College of Computer Qassim University Buraydah Saudi Arabia Department of Computer Science Faculty of Computer and Information System Thamar University Thamar Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Intrusion Detection Systems (IDSs) have become a key security problem due to the increasing number of connected automobiles and the sensitive nature of the data transferred in Vehicular Ad-hoc Networks (VANETs). By keeping an eye on network traffic, spotting questionable activity, and putting countermeasures in place to lessen risks, IDSs protect the integrity and security of VANETs. For VANETs, this study explores the state-of-the-art in machine learning-based IDSs, with a particular emphasis on work released in 2020–2022. We provide a thorough analysis of developments in widely used machine learning methods used for VANET intrusion detection throughout this time. This investigation explores certain machine learning methods that have been recently applied to VANET IDSs. The survey ends with a summary of the current issues and an exploration of potential directions for further investigation.

关键词： Surveys Machine learning algorithms Intrusion detection Vehicular ad hoc networks Machine learning Telecommunication traffic Safety

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Enterprise Architecture Trends in the Digital Transformation Era

Enterprise Architecture Trends in the Digital Transformation...

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2023 International Seminar on Application for technology of information and Communication, iSemantic 2023

作者： Mahendra, Irfan Gaol, Ford Lumban Supangkat, Suhono Harso Ranti, Benny Bina Nusantara University Computer Science Jakarta11480 Indonesia Nusa Mandiri University Faculty of Information Technology Jakarta11450 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40116 Indonesia University of Indonesia Faculty of Computer Science Jakarta16424 Indonesia

ISBN: (纸本)9798350339215

Enterprise Architecture (EA) is a solution to build alignment between business strategy and information technology in dealing with digital transformation that causes fundamental changes for companies and businesses. The purpose of this study is to examine the trends and progress of EA in responding to the challenges of the digital transformation era, which is carried out through SLRs using the PRISMA method. According to the research findings, implementing EA in businesses serves the following purposes: establishing clear objectives and strategies for the development of business and technology, achieving harmony between business and information technology, time and cost efficiency, and effectiveness in business development processes and information technology. Then, it was concluded that there are nine components of EA, namely, vision, mission, and strategic objectives;business architecture;data & information architecture;information system architecture;infrastructure and network architecture;security architecture;human resources architecture;governance, legal, and compliance;and standards & regulations. © 2023 IEEE.

关键词： Network architecture

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The Analysis of Fuzzy C-Means and Pearson Correlation Methods for Data Reduction in kNN Algorithm

The Analysis of Fuzzy C-Means and Pearson Correlation Method...

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2021 International Conference on Data science, Artificial Intelligence, and Business Analytics, DATABIA 2021

作者： Arisandi, Cut Desy Zarlis, Muhammad Efendi, Syahril Universitas Sumatera Utara Graduate School of Computer Science Faculty of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9781665426800

The kNN algorithm, a reasonably popular method in data mining, is used for the data classification process. The traditional kNN method experiences several disadvantages, like slow computation in case the dimensions of the training data are significant and a strong dependence on the amount of training data. Therefore, before the data classification process was carried out, this study tested data reduction on the kNN algorithm using the FCM and Pearson correlation method on the training data. Simultaneously, the FCM algorithm is implemented in the data clustering process. It is operated to get the center point of the cluster and selects the training data with the lowest correlation level using the Pearson correlation equation for further reduction. The results of the two methods are in the form of reduced data, which will then be used as training data with more limited dimensions for the classification process in the kNN algorithm. Based on the research conducted, it can be concluded that the accuracy of data classification in the kNN algorithm by data reduction using the FCM algorithm and Person correlation achieved an average accuracy of 92.22% and an increase of 2.7%, compared to the classification results in kNN algorithm without data reduction with an average accuracy gain of 89.52%. In addition, the use of the data reduction method proposed in the kNN algorithm can reduce the training data into smaller dimensions with an average reduction percentage of 19.22%. © 2021 IEEE.

关键词： Clustering algorithms

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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A Low Complexity ML-Based Methods for Malware Classification

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computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Classification 4

Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Cl...

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4th International Conference on Informatics, Multimedia, Cyber and information System, ICIMCIS 2022

作者： Fitrianah, Devi Suryaningrum, Kristien Margi Sagala, Noviyanti Tri Maretta Ayumi, Vina Lim, Siew Mooi School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Mathematics Statistics Department Jakarta Indonesia Universitas Mercu Buana Informatics Engineering Program Faculty of Computer Science Jakarta Indonesia Tunku Abdul Rahman University College Faculty of Computing and Information Technology Computer Science Department Kuala Lumpur Malaysia

ISBN: (纸本)9781665473279

Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations and ecosystems in a particular area. Furthermore, this research helps monitor fish that are considered threatened or endangered so that it makes iteasier to map prohibited areas for fishing. This research aims to know performance of MobileNetV2 and VGG16 with parameter tuning process by identifying the value of batch size, epoch, learning rate, and optimizer for fish image dataset. The proposed research phase consists of five main stages, including experimental setup, dataset construction, dataset preprocessing, dataset training and modelling and evaluation. As the result, VGG16 obtained the highest accuracy value. For VGG16 without fine-tuning, the testing accuracy is 98.07%. For VGG16 with fine-tuning, the testing accuracy is 96.56%. © 2022 IEEE.

关键词： Fish

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