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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Soni, Mukesh Rusho, Maher Ali Byeon, Haewon Alghamdi, Azzah Hannan, Shaikh Abdul Dave, Parth Rameshchandra Dr. D. Y. Patil School of Science & Technology Tathawade Dr. D. Y. Patil Vidyapeeth Marashtra Pune India University of Colorado Department of Lockheed Martin Engineering Management BoulderCO80309 United States Inje University Department of Ai and Software & Medical Big Data Research Centre Gimhae50834 Korea Republic of Imam Abdalrhman Bin Faisal University College of Computer Science and Information Technology Computer Information Systems Department Khobar Saudi Arabia AlBaha University Faculty of Computing and Information AlBaha Saudi Arabia L. D. College of Engineering Department of Computer Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798350350067

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare. © 2024 IEEE.

关键词： Electronic health record

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Simulation-Based Damage Detection for Composite Structures with Machine Learning Techniques 1

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40th IMAC, A Conference and Exposition on Structural Dynamics, 2022

作者： Lang, Alexandre Tavares, André Di Lorenzo, Emilio Cornelis, Bram Peeters, Bart Desmet, Wim Gryllias, Konstantinos Siemens Industry Software NV Leuven Belgium Department of Computer Science KU Leuven Leuven Belgium Department of Mechanical Engineering KU Leuven Leuven Belgium Dynamics of Mechanical and Mechatronic Systems Flanders Make Lommel Belgium

ISBN: (数字)9783031041228

ISBN: (纸本)9783031041211

With the use of composite materials rising steadily in major industries such as the automotive and aerospace, a strong effort is put into structural health monitoring (SHM) and damage detection methods for these components. Nondestructive testing (NDT) techniques such as laser Doppler vibrometry (LDV) provide a valuable experimental setting for making measurements with dense grids of points without mass loading the structure. The use of machine learning (ML) and deep learning (DL) techniques for the subsequent classification of defects has the potential of creating a reliable and automated framework for damage detection. In this work, different ML and DL approaches were assessed for the task of detecting defects on a carbon fiber plate by using frequency response data. The approaches were enriched by considering simulated datasets (created with Finite Element Analysis) in a transfer learning framework. Simulation data is easier to generate than experimental, meaning any added value provided with simulation data is advantageous. A description of the obtained results of damage detection is presented, along with a comparative overview of the different techniques. © 2022, The Society for Experimental Mechanics, Inc.

关键词： Nondestructive examination

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Phase Re-service in Reinforcement Learning Traffic Signal Control

arXiv

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arXiv 2024年

作者： Zhang, Zhiyao Gunter, George Quinones-Grueiro, Marcos Zhang, Yuhang Barbour, William Biswas, Gautam Work, Daniel Department of Civil and Environmental Engineering Vanderbilt University United States Department of Computer Science Vanderbilt University United States Institute for Software Integrated Systems Vanderbilt University United States

This article proposes a novel approach to traffic signal control that combines phase re-service with reinforcement learning (RL). The RL agent directly determines the duration of the next phase in a pre-defined sequence. Before the RL agent’s decision is executed, we use the shock wave theory to estimate queue expansion at the designated movement allowed for re-service and decide if phase re-service is necessary. If necessary, a temporary phase re-service is inserted before the next regular phase. We formulate the RL problem as a semi-Markov decision process (SMDP) and solve it with proximal policy optimization (PPO). We conducted a series of experiments that showed significant improvements thanks to the introduction of phase re-service. Vehicle delays are reduced by up to 29.95% of the average and up to 59.21% of the standard deviation. The number of stops is reduced by 26.05% on average with 45.77% less standard deviation. © 2024, CC BY-NC-ND.

关键词： Traffic signals

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Development of a Model for Discriminating Electroencephalograms of Patients with Mild Cognitive Impairment and Healthy Individuals Using a Support Vector Machine 13

Development of a Model for Discriminating Electroencephalogr...

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Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Tanabe, Akifumi Nakatani, Kyosuke Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Kamae-Shigenobu, Kazue Shinosaki, Kazuhiro Graduate School of Information Science University of Hyogo Kobe Japan School of Life Science and Technology Tokyo Institute of Technology Yokohama Japan Aino University Department of Medical Engineering Ibaraki Japan Institute for Datability Science Osaka University Suita Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Aomori University Faculty of Software and Information Technology Aomori Japan Osaka University Cyber Media Center Suita Japan Asakayama General Hospital Sakai Japan

ISBN: (纸本)9798350373332

We performed machine learning using spectrum analysis and power spectra of electroencephalograms (EEGs) of patients with mild cognitive impairment (MCI). Spectrum analysis was performed on the EEGs of MCI patients and healthy individuals, and the power spectrum values of the two groups were calculated and compared. Based on the power spectrum values of the theta and alpha bands, models for discriminating MCI patients were created using a support vector machine (SVM), and their accuracies were compared. In the spectrum analysis, the theta band showed significantly higher power spectrum values in the MCI group. In the comparison of the accuracies of SVM models, the model using the theta band showed higher values in parameters, such as accuracy rate. These results suggested that the power spectrum values of the theta band are useful for discriminating MCI patients. © 2024 IEEE.

关键词： Electroencephalography

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Enhancing Energy Sector Resilience: Integrating Security by Design Principles

arXiv

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arXiv 2024年

作者： Shirtz, Dov Koberman, Inna Elyashar, Aviad Puzis, Rami Elovici, Yuval Cyber@BGU Cyber Labs Ben-Gurion University Israel Department of Software and Information Systems Engineering Ben-Gurion University Israel Department of Computer Science Shamoon College of Engineering Beer-Sheva Israel

Security by design (SbD) is a concept for developing and maintaining systems that are, to the greatest extent possible, free from security vulnerabilities and impervious to security attacks. In addition to technical aspects, such as how to develop a robust Industrial control system (ICS), hardware (HW), software (SW), firmware, communication product, etc., SbD also includes soft aspects, such as organizational management's attitude and behavior, and employee awareness. Under the SbD concept, systems, i.e., in our context ICS, will be considered more trustworthy by users;users' trust in the systems will be derived from meticulous adherence to SbD processes and policies. In accordance with the SbD concept, security is considered. Security measures are implemented, at every stage of the product and system development lifecycle, rather than afterward, which is beneficial, enabling the identification of vulnerabilities early in the development process;when vulnerabilities are identified at an early stage, the ability to address them is improved, and the cost of resolving them is reduced, saving time, money, and effort. As mentioned above, the SbD approach focuses on making systems as secure and immune to attack as possible through the use of measures such as continuous testing, authentication, safeguards, and adherence to best programming practices. As such, SbD enables an organization to formalize the design of its infrastructure and applications and build and integrate security into its IT and OT processes. This document presents the security requirements for the implementation of SbD in industrial control systems. The information presented does not negate any existing security and cyber security standards, quality assurance standards, best practices, or management methods such as COBIT. Instead, we strongly recommend that organizations should implement and comply with those standards and best practices. Security by design is not a one-time process. It starts at the ve

关键词： Cybersecurity

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AI-Powered Conceptual Model for Scrum Framework

AI-Powered Conceptual Model for Scrum Framework

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Rana Mdallal Dania Alsawalmeh Dania Jarad Ammar Odeh Department of Software Engineering/Enterprise Systems Engineering (ESE) Princess Sumaya University for Technology (PSUT) Department of Computer Science/Cybersecurity Princess Sumaya University for Technology (PSUT)

Scrum is an incremental, interactive agile framework widely used to develop practical problems; the Scrum team focuses on achieving a common goal each cycle, "Sprint Goal" instead of the traditional sequential technique for delivering products. Although many organizations are adopting Scrum, it is still following the traditional manual approaches. Even though many tools are available in the market to facilitate project management and Scrum master tasks, the support is still limited and lacks the utilization of Artificial Intelligence science. This study focuses on integrating the Scrum framework with Artificial Intelligence to build a predicting model that can assist project management "in particular the Scrum master" to predict the achievement of the Scrum Team per Sprint using Story Points; this prediction is built based on several factors that can affect the burndown charts per Sprint.

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Product Recommendation System using Deep Learning based Recurrent Neural Network

Product Recommendation System using Deep Learning based Recu...

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International Conference on computer Communication and Informatics (ICCCI)

作者： S. Shanthi K. Nirmaladevi N. Mathiarasi As. Krithika Tharani A. Monisha R. Kuppuchamy N. Krishnamoorthy Department of Computer Science & Engineering Kongu Engineering College Perundurai India Department of Computer Applications PSNA College of Engineering and Technology Dindigul Tamilnadu India Department of Software Systems and Engineering Vellore Campus Vellore Institute of Technology India

Today’s marketing strategies place a high priority on comprehending customer sentiments. It will not only give businesses a better understanding of how their clients view their goods and/or services, but it will also give them suggestions on how to enhance their offerings. We make an effort to comprehend the relationship between various factors in customer reviews on an online store for women’s clothing. The proposed system also classifies each review, according to, whether it recommends the product in question or not, and whether it has a positive, negative, or neutral sentiment. With the exception of review titles and review texts, we used univariate and multivariate analyses on datasets features to accomplish these goals. Additionally, we have implemented a bidirectional recurrent neural network (RNN) with a long-short term memory unit (LSTM) for sentiment and recommendation.

关键词： Deep learning Recurrent neural networks Filtering Clothing Informatics Recommender systems Business

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A Novel Method for Pneumothorax Diagnosis and Segmentation Using Deep Convolution Neural Network

A Novel Method for Pneumothorax Diagnosis and Segmentation U...

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International Conference on computer Communication and Informatics (ICCCI)

A pneumothorax is the accumulation of air in the pleural space between the chest and lungs. Air that is in the lungs during breathing can leak out of the lungs and become trapped between the chest and lungs in the thoracic cavity, causing chest pain, difficulty breathing, and ultimately the complete collapse of the taut lungs. pneumothorax. Therefore, early diagnosis and treatment should be started. Neural networks have proven effective in image classification tasks, and many researchers believe that training neural network models can detect radiologist-diagnosed chest abnormalities such as pneumonia and pneumothorax. is proving. Moreover, we utilize various techniques, including Stochastic Weight Averaging (SWA), aggressive data augmentation, and learning rate schedule. We use the chest x-ray dataset provided by the 2019 SIIM-ACR In this study, a ResNet-34 neural network is trained on a data set of chest x-rays containing over 10,675 images to create a model that predicts the presence of pneumothorax from chest *** suggested system receives a Dice Similarity Coefficient (DSC) of 0.8410

关键词： Training Image segmentation Schedules Pulmonary diseases Neural networks Lung Stochastic processes

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Instructors' Perspectives on Capstone Courses in Computing Fields: A Mixed-Methods Study 2024

Instructors' Perspectives on Capstone Courses in Computing F...

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29th Working Group Reports on Innovation and Technology in computer science Education, ITiCSE WGR 2024

作者： Hooshangi, Sara Shakil, Asma Dasgupta, Subhasish Davis, Karen C.C. Farghally, Mohammed Fitzpatrick, KellyAnn Gutica, Mirela Hardt, Ryan Riddle, Steve Seyam, Mohammed Department of Computer Science Virginia Tech BlacksburgVA United States School of Computer Science University of Auckland Auckland New Zealand Info Systems and Tech Mgmt Dept George Washington University WashingtonDC United States Computer Science and Software Engineering Dept. Miami University OxfordOH United States School of Computing Instruction Georgia Institute of Technology AtlantaGA United States School of Computing & Academic Studies British Columbia Institute of Technology BurnabyBC Canada Computer & Information Science University of St. Thomas Saint PaulVA United States School of Computing Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798400712081

Team-based capstone courses are integral to many undergraduate and postgraduate degree programs in the computing field. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self-reflection as they transition into the real world. Prior research on capstone courses has focused primarily on the experiences of students. The perspectives of instructors who teach capstone courses have not been explored comprehensively. However, an instructor's experience, motivation, and expectancy can have a significant impact on the quality of a capstone course. In this working group, we used a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation were examined using a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across different continents. Our findings show that there are more similarities than differences across various capstone course structures. Similarities include team size, team formation methodologies, duration of the capstone course, and project sourcing. Differences in capstone courses include class sizes and institutional support. Some instructors felt that capstone courses require more time and effort than regular lecture-based courses. These instructors cited that the additional time and effort is related to class size and liaising with external stakeholders, including industry partners. Some instructors felt that their contributions were not recognized enough by the leadership at their institutions. Others acknowledged institutional support and the value that the capstone brought to their department. Overall, we found that capstone instructors were highly intrinsically motivated and enjoyed teaching the capstone course. Most of them agree that the course contributes to their professional de

关键词： Students

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Enhancing e-KYC Security and Privacy: Harnessing Quantum Computing and Blockchain in Web 3.0

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Distributed Ledger Technologies: Research and Practice 2024年第4期3卷 1-23页

作者： G. M. Faruk Ahmed Imran Hasan Md Soumike Hassan Rahul Khan Abu Jor Al Gefari Zain Buksh Joseph Liu A B M Shawkat Ali Cybersecurity Cell Rupali Bank Limited Dhaka Bangladesh Department of Computer Science and Engineering Islamic University Kushtia Bangladesh Department of Statistics Jahangirnagar University Dhaka Bangladesh Department of Pharmacy Bangladesh University Dhaka Bangladesh Rajshahi Science and Technology University Natore Bangladesh Department of Computer Science and Mathematics The University of Fiji Lautoka Fiji Department of Software Systems and Cybersecurity Monash University Melbourne Australia

By implementing a successful and effective Electronic-Know Your Customer (e-KYC) infrastructure, conventional defects and expenses may be greatly reduced. Strong authentication procedures are used by existing e-KYC solutions to guarantee safety and confidentiality. However, these technologies have shortcomings like as key administration expenses and dependency on encryption. We suggest a revolutionary strategy that integrates blockchain technology, Web 3.0, and quantum computing to get around these restrictions. In this research, we show how current e-KYC processes could possibly be improved, making them faster and more reliable, by integrating Web 3.0 with quantum computing. Our solution makes it simple to quickly verify several consumers, increasing productivity. We provide improved safety for online requests, transcending the weaknesses inherent in traditional encryption techniques by utilizing quantum computing technology and applying the concept of quantum encryption key methodology. We contribute to the development of safe and private solutions by addressing the shortcomings of the present e-KYC systems. The results of our research show how Web 3.0 and quantum computer technology have the power to completely alter the e-KYC environment.

关键词： e-KYC Quantum Computing Blockchain Web 3.0 Privacy Security

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