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engineering Education Using Serious Games: The Current State, Trends and Future Using a Bibliometric Study 1

international Symposium on Automatic Control and Emerging Technologies, ACET 2023

作者： Sabri, Zineb Remaida, Ahmed Benyoussef, Abdellaoui Qostal, Aniss Sellamy, Khadija Fakhri, Youssef Moumen, Aniss Laboratory of Computer Science Faculty of Science Ibn Tofail University Kenitra Morocco Engineering Sciences Laboratory National School of Applied Science Ibn Tofail University Kenitra Morocco S.I.G.E.R Laboratory Faculty of Science and Technology Fez Morocco

ISBN: (数字)9789819701261

ISBN: (纸本)9789819701254

There is a global concern about using serious games in multiple fields with numerous publications, especially in Education. Conducting a bibliometric analysis can be insightful to depict and analyze the current state, trends, and methods most used in serious games for engineering education to provide knowledge upgrades in this research area. Therefore, this bibliometric study used the Web of science database to select the most relevant documents, VOSviewer and Bibliometrix to conduct the analysis. The results obtained in this bibliometric study highlight that researchers are currently interested in exploiting serious games worldwide. On the one hand, the most advanced countries like USA, England, Germany, and others are the top countries focusing on using games in educational fields. On the other hand, the trend topics related to serious games are indeed applied in the educational engineering area and computer science. Based on the network and thematic map, these two pillars are fundamental and must be developed more. Finally, the top relevant articles reviewed indicate that computer engineering is the most used discipline in serious games. Most of the articles used quantitative or mixed methods as research design. The most used game genres are Simulation, and web-based platforms are reported commonly in serious games in engineering education. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Serious games

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Go vs. Java: A Detailed Performance Analysis in Concurrent Programming

Go vs. Java: A Detailed Performance Analysis in Concurrent P...

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Advances in Computing, Communication and Networking (iCAC2N), international Conference on

作者： Md. Neaz Morshed Prince Roy Computer Science and Engineering (With Specialization in Software Development) MMDU Haryana India Student of Applied Computer Science Fairleigh Dickinson University Vancouver Canada

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Go, a programming language developed by Google, is relatively new but offers modern and powerful features, including similarities to Java and an emphasis on rapid development and concurrency. This paper examines Go's performance compared to Java, focusing on compile time and concurrency. As applications increasingly require concurrent data transmission, there is a strong demand for languages optimized for such tasks. Although many studies compare programming languages, few specifically investigate Go, and some of these evaluations are based on its experimental release. in this paper, we analyze the concurrency features of Go and Java through a comprehensive performance analysis, considering their runtime, memory usage, and unique concurrency *** analyze concurrency features, we implemented CRUD operations on a single MySQL table using both Go and Java programs. Go utilizes its built-in lightweight thread, Goroutine, while Java uses Java Thread. We conducted our experiments by running the programs a thousand times and analyzed the resulting data using various statistical parameters and graphs.

关键词： Concurrent computing Java computer languages Runtime instruction sets Focusing Performance analysis internet Data communication

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Enhancing malware detection performance: leveraging K-Nearest Neighbors with Firefly Optimization Algorithm

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Multimedia Tools and Applications 2025年第12期84卷 10071-10094页

作者： Al Saaidah, Adeeb Abualhaj, Mosleh M. Shambour, Qusai Y. Abu-Shareha, Ahmad Adel Abualigah, Laith Al-Khatib, Sumaya N. Alraba’nah, Yousef H Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Software Engineering Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon College of Engineering Yuan Ze University Taoyuan32003 Taiwan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

Malware detection plays a crucial role in ensuring robust cybersecurity amidst the ever-evolving cyber threats. This research paper delves into the realm of machine learning (ML) algorithms for malware detection, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm, utilizing tailored parameter settings and the Firefly Optimization Algorithm (FOA). The study leverages the MalMem-2022 dataset to assess the efficacy of KNN and KNN with FOA in malware detection. The impact of parameter tuning and feature selection on classification is elucidated by comparing the performance of both approaches. Encouragingly, the results reveal promising advancements in one of the multiclass classification scenarios when employing KNN with FOA, yielding noteworthy enhancements in Accuracy, Recall, Precision, Matthews Correlation Coefficient, TNR, and F1-score by 2.65%, 2.65%, 2.47%, 3.59%, 0.17%, and 2.61%, respectively. These findings accentuate the significance of optimizing KNN’s parameters and implementing FOA for feature selection, culminating in heightened accuracy in malware detection. The symbiotic fusion of KNN and FOA exhibits remarkable effectiveness in augmenting the classification model’s performance, thus offering valuable insights for bolstering cybersecurity measures. Adopting ML algorithms, particularly KNN with specific parameter settings and FOA, presents a promising avenue for enhancing malware detection capabilities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Nearest neighbor search

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Blockchain-Assisted Secure Fine-Grained Searchable Encryption for a Cloud-Based Healthcare Cyber-Physical System

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iEEE/CAA Journal of Automatica Sinica 2021年第12期8卷 1877-1890页

作者： Mamta Brij B.Gupta Kuan-Ching Li Victor C.M.Leun Kostas E.Psannis Shingo Yamaguchi IEEE the Department of Computer Engineering National Institute of Technology the Asia University the Macquarie University the Department of Computer Science and Information Engineering Providence University the Computer Science and Software Engineering Shenzhen University the University of British Columbia the Department of Applied Informatics University of Macedonia the Graduate School of Sciences and Technology for Innovation Yamaguchi University

The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access *** privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective ***-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud ***,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious *** a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE *** the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain ***,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical *** the assistance of blockchain technology,the proposed scheme offers two main ***,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of ***,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain ***,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus *** eliminates the need of the trusted authority and reduces the burden of data users,***,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret

关键词： Cloud-based cyber-physical systems(CCPS) data encryption healthcare information search and retrieval keyword search public-key cryptosystems searchable encryption

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Cloud Security Service for identifying Unauthorized User Behaviour

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computers, Materials & Continua 2022年第2期70卷 2581-2600页

作者： D.Stalin David Mamoona Anam Chandraprabha Kaliappan S.Arun Mozhi Selvi Dilip Kumar Sharma Pankaj Dadheech Sudhakar Sengan Department of Computer Science and Engineering IFET College of EngineeringVillupuram605108Tamil NaduIndia Department of Computer Sciences and Software Engineering Faculty of Basic and Applied SciencesInternational Islamic UniversityIslamabadPakistan Department of Information Technology Bannari Amman Institute of TechnologySathyamangalam638401Tamil NaduIndia Department of Computer Science Engineering DMI St.John the Baptist UniversityMangochiLilongweMalawi Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226Madhya PradeshIndia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement and Gramothan(SKIT)Jaipur302017RajasthanIndia Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia

Recently,an innovative trend like cloud computing has progressed quickly in *** a background of distributed networks,the extensive sprawl of internet resources on the Web and the increasing number of service providers helped cloud computing technologies grow into a substantial scaled information Technology service *** cloud computing environment extracts the execution details of services and systems from end-users and ***,through the system’s virtualization accomplished using resource pooling,cloud computing resources become more *** attempt to design and develop a solution that assures reliable and protected authentication and authorization service in such cloud environments is described in this *** the help of multi-agents,we attempt to represent Open-identity(iD)design to find a solution that would offer trustworthy and secured authentication and authorization services to software services based on the *** research aims to determine how authentication and authorization services were provided in an agreeable and preventive *** on attack-oriented threat model security,the evaluation *** considering security for both authentication and authorization systems,possible security threats are analyzed by the proposed security systems.

关键词： Cloud computing user behaviour access control security model

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FGRL-Net: Fine-Grained Personalized Patient Representation Learning for Clinical Risk Prediction Based on EHRs

FGRL-Net: Fine-Grained Personalized Patient Representation L...

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2023 iEEE international Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Chio, KaKit Zhu, Wenhao He, Lihua Zhang, Dian Yang, Xu Luo, Wuman Macao Polytechnic University Faculty of Applied Sciences S.A.R Macao China School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Province China Shenzhen University Department of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350337020

Personalized patient representation learning (PPRL) is a critical element in clinical risk prediction. it aims to obtain a complete portrait of each patient based on Electronic Health Records (EHR). Although existing works have achieved remarkable progress in healthcare prediction, there are still three major issues. First, feature correlation is crucial for risk prediction, but it has not yet been fully exploited by existing works. Second, variation pattern of dynamic feature contains useful information about patient's physical status, but adaptive pattern recognition is still a challenge. Third, existing works usually adopt a two-stage embedding process to process each dimension of the EHR data. However, some useful low-level information for PPRL will be lost. To address these issues, in this paper, we propose a fine-grained PPRL architecture named FG RL- N et for clinical risk prediction based on EHR. Specifically, we propose a Medical Feature Correlation Detection Module (FCM) to effectively learn the feature correlations for each patient and a Temporal Variation Pattern Recognition Module (TVM) to effectively detect the variation patterns of each dynamic feature. Moreover, we design a Fine-Grained Representation Mechanism (FGRM) to preserve the low-level information (from both feature and visit dimensions) useful for risk prediction. in addition, in the stage of data preprocessing, We utilize generic medical classification knowledge to classify numerical dynamic data. We conduct the in-hospital mortality experiment and the decompensation experiment on a real-world dataset. The experiment results show that the FGRL-Net outperforms state-of-the-art approaches. The source code is provided in github https://***/JackyChio/FGRL-Net. © 2023 iEEE.

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Application of Machine Learning for Online Reputation Systems

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international Journal of Automation and Computing 2021年

作者： Ahmad Alqwadri Mohammad Azzeh Fadi Almasalha Department of Computer Science Applied Science Private University Department of Software Engineering Applied Science Private University

Usersontheinter*** reputation system that processes ratings to provide recommendations. The rating aggregation process is a main part of reputation systems to produce global opinions about the product quality. Naive methods that are frequently used do not consider consumer profiles in their calculations and cannot discover unfair ratings and trends emerging in new ratings. Other sophisticated rating aggregation methods that use a weighted average technique focus on one or a few aspects of consumers′profile data. This paper proposes a new reputation system using machine learning to predict reliability of consumers from their profile. in particular, we construct a new consumer profile dataset by extracting a set of factors that have a great impact on consumer reliability, which serve as an input to machine learning algorithms. The predicted weight is then integrated with a weighted average method to compute product reputation score. The proposed model has been evaluated over three MovieLens benchmarking datasets, using 10-folds cross validation. Furthermore, the performance of the proposed model has been compared to previous published rating aggregation models. The obtained results were promising which suggest that the proposed approach could be a potential solution for reputation systems. The results of the comparison demonstrated the accuracy of our models. Finally, the proposed approach can be integrated with online recommendation systems to provide better purchasing recommendations and facilitate user experience on online shopping markets.

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Efficient Models for Detecting Monkeypox using Skin Lesion images 3

Efficient Models for Detecting Monkeypox using Skin Lesion I...

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3rd international Conference on innovative Mechanisms for industry Applications, iCiMiA 2023

作者： Sunda, Neelam Sharma, Divya Battu, Sujith Banoth, Rewanth Nayak Rahul Monika Kanoria PG Mahila Mahavidyalaya Department of Computer Science Jaipur India Kanoria PG Mahila Mahavidhyalaya Department of Computer Science jaipur India Delhi Technological University Dept. of Software Engineering New Delhi India Delhi Technological University Department of Applied Physics New Delhi India University of Delhi Shaheed Rajguru College of Applied Sciences for Women Department of Computer Science India

ISBN: (纸本)9798350343632

Monkeypox is predicted to be the next pandemic and is spreading over African countries like water under a mat. The study basically investigates three deep learning imaging models for the identification of monkeypox from skin lesion photos from the MSiD dataset. The motto is to define a computer-based technique to identify monkeypox from just its image, making it cheaper and faster with accurate results. The study achieved the highest accuracy of 99.37% as compared to previous works related to Resnet 50. The limits and complexity of the procedures in real-world situations are also clearly addressed. © 2023 iEEE.

关键词： Convolutional Neural Network image preprocessing Monkeypox Performance Evaluation Metrics Transfer learning

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Streamlining Attendance with Voice Recognition via Gaussian Mixture Model

Streamlining Attendance with Voice Recognition via Gaussian ...

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2024 international Conference on iT and industrial Technologies, iCiT 2024

作者： Ali, Noor Ali, Khadija Ali, Fatimah Ali, Aaminah Ali, Nisar Ali, Raja Hashim A-levels Lahore Grammer School 55 Main Gulberg Lahore54660 Pakistan Department of Software Engineering NUST College of Electrical and Mechanical Engineering Islamabad43701 Pakistan Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

Voice recognition systems are crucial because they allow seamless human-computer interaction and improve accessibility for users of all abilities. The use of these technologies in hands-free control, language translation, virtual assistants, transcription services, and hands-free control is revolutionising how we engage with technology and enhancing convenience and productivity in general. Several attendance systems based on voice recognition exist, but we wanted to deploy an attendance system with a good graphical user interface specifically for students of GiK institute. For this purpose, we wanted to make a user-friendly and accurate voice recognition system based and trained on self-provided data of ten students. This study introduces an Ai-driven attendance system, which demonstrates high efficiency and accuracy in identifying students’ daily class attendance. To achieve this, the Gaussian Mixture Model approach was employed. The paper also delves into the utilization of libraries and methods, encompassing the training and validation of well-known machine learning models. Additionally, the system’s performance, its strengths, weaknesses and potential areas for improvement are also discussed in the study. © 2024 iEEE.

关键词： Students

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A Comprehensive Review of Active EV Battery Cell Voltage Balancing Systems: Current issues and Prospective Solutions 1

A Comprehensive Review of Active EV Battery Cell Voltage Bal...

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1st iEEE industrial Electronics Society Annual On-Line Conference, ONCON 2022

作者： Chetri, Chandan Huynh, Alvin Williamson, Sheldon S. Laboratory Faculty of Engineering and Applied Science Department of Electrical Computer and Software Engineering OshawaON L1G 0C5 Canada

ISBN: (纸本)9798350398069

The performance of a battery pack is greatly affected by an imbalance between the cells. Cell balancing is a very important criterion for Battery Management System (BMS) to operate properly. This paper presents the various cell balancing topologies which have been presented by various researchers in the past years. A novel topology has also been discussed, which improves the overall efficiency of the components being used. Various comparison parameters such as Control, Speed, Efficiency, Power Effects, Manufacturability, Size, and Cost have been considered. © 2022 iEEE.

关键词： Topology

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