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2021 International Research Conference on Smart computing and systems Engineering, SCSE 2021

作者： Kithulwatta, W.M.C.J.T. Jayasena, K.P.N. Kumara, B.T.G.S. Rathnayaka, R.M.K.T. Sabaragamuwa University of Sri Lanka Faculty of Graduate Studies Sri Lanka Sabaragamuwa University of Sri Lanka Fac. of Applied Sciences Dept. of Computing and Information Systems Sri Lanka Sabaragamuwa University of Sri Lanka Fac. of Applied Sciences Department of Physical Sciences and Technology Sri Lanka

ISBN: (纸本)9781665409650

Currently the computing world is getting complex, innovating and maturing with modern technologies. Virtualization is one of the old concepts and currently containerization has arrived as an alternative and innovative technology. Docker is the most famous and trending container management technology. Different other container management technologies and virtualization technologies are respective other corresponding technologies and mechanisms for Docker containerization. This research study aims to identify how Docker incorporation is different from other computer system infrastructure technologies in the perspective of architecture, features and qualities. By considering forty-five existing literatures, this research study was conducted. To deliver a structured review process, a thorough review protocol was conducted. By considering four main research questions, the research study was lined up. Ultimately, Docker architecture and Docker components, Docker features, Docker integration with other computing domains and Docker other computing infrastructures were studied. After synthesizing all the selected research studies, the cream was obtained with plenty of knowledge contribution to the field of computer application deployment and infrastructure. © 2021 IEEE.

关键词： Containers

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Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

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Neural computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

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Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

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Computers, Materials & Continua 2022年第6期71卷 4989-5003页

作者： El-Sayed M.El-kenawy Abdelhameed Ibrahim Seyedali Mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M.Zaki Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial *** Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： Metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

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Operational Level System: Transaction Processing System and Decision Making

Operational Level System: Transaction Processing System and ...

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Computer Science and Emerging Technologies (CSET), International Conference on

作者： Firas Rashed Wahsheh Ziyad Marie Mazen Mohammed Farea Wafa Abdulkarem Al-Haithami Fatma Susilawati Mohamad Neeraj Bisht Department of Management Information Systems Faculty of Business Ajloun National University Ajloun Jordan Public administration Department Faculty of Administrative sciences and Informatics Al-Istiqlal University Jericho 10 Palestine Faculty of Finance & Administrative Sciences Al-Madinah International University Malaysia Faculty of Finance and Administrative Sciences MEDIU Malaysia Faculty of Informatics and Computing UniSZA Malaysia Birla Institute of Applied Sciences Bhimtal India

This research paper investigates the critical role of Operational Level systems (OLS) in facilitating transaction processing and supporting decision-making within modern organizations. In today's fast-paced and data-driven business environment, OLS plays a pivotal role in capturing, processing, and disseminating real-time data to aid in operational efficiency and effective decision-making. This study aims to explore the functionalities and interconnections of Transaction Processing systems (TPS) as integral components of OLS, and their contribution to informed decision-making at the operational level. To achieve this, an extensive literature review is conducted to comprehend the theoretical underpinnings and practical implications of OLS and TPS. Additionally, empirical data are collected through surveys, interviews, and case studies from various industries to gain insights into the real-world implementation and benefits of OLS and its impact on decision-making processes. The findings reveal that OLS, with its robust TPS, provides organizations with streamlined data processing capabilities, enabling seamless handling of day-to-day transactions, such as sales, inventory management, and financial transactions. Moreover, it facilitates the efficient storage and retrieval of vast amounts of transactional data, laying the foundation for data-driven decision-making. Furthermore, the research highlights the significance of the integration between TPS and decision-making systems, emphasizing the pivotal role of OLS in supporting operational managers in making well-informed decisions. The ability to access real-time transactional data empowers decision-makers to identify trends, patterns, and potential bottlenecks, leading to timely adjustments and optimized performance. This research sheds light on the vital role of Operational Level systems, particularly Transaction Processing systems, in today's business landscape. By efficiently processing transactions and providing time

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A Multi-Carrier Deep Learning CSK Communication System

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IEEE communications Letters 2025年

作者： Mobini, Majid Kaddoum, Georges Herceg, Marijan Babol Noshirvani University of Technology Department of ECE Babol Iran University of Québec École de technologie supérieure Département de génie électrique MontréalQC Canada Lebanese American University Cyber Security Systems and Applied AI Research Center Beirut Lebanon University of Osijek Faculty of Electrical EngineeringComputer Science and Information Technology Department of Communications Croatia

A new Multi-Carrier Deep Learning Chaos Shift Keying (MC-DLCSK) modulation is designed in this paper. The proposed receiver includes a Neural Network (NN)-based classifier that recovers MC-DLCSK signals. The results demonstrate that the MC-DLCSK system achieves a considerable Bit Error Rate (BER) gain compared to all existing similar systems under both Additive White Gaussian Noise (AWGN) and Rayleigh fading channels. © 1997-2012 IEEE.

关键词： White noise

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Energy Efficient Cluster Based Clinical Decision Support System in IoT Environment

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Computers, Materials & Continua 2021年第11期69卷 2013-2029页

作者： C.Rajinikanth P.Selvaraj Mohamed Yacin Sikkandar T.Jayasankar Seifedine Kadry Yunyoung Nam Department of Electronics and Communications Engineering Erode Sengunthar Engineering College(Autonomous)Erode638057India Department of Information Technology SRM Institute of Science and TechnologyKattankulathur603203India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Electrical Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Faculty of Applied Computing and Technology(FACT) Noroff University CollegeKristiansand4608Norway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded *** e-healthcare application solely depends on the IoT and cloud computing environment,has provided several characteristics and *** research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing,which led to quick exhaustion of *** this view,this paper introduces a new energy efficient cluster enabled clinical decision support system(EEC-CDSS)for embedded IoT *** presented EECCDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic *** EEC-CDSS model incorporates particle swarm optimization with levy distribution(PSO-L)based clustering technique,which clusters the set of IoT devices and reduces the amount of data *** addition,the IoT devices forward the data to the cloud where the actual classification procedure is *** classification process,variational autoencoder(VAE)is used to determine the existence of disease or *** order to investigate the proficient results analysis of the EEC-CDSS model,a wide range of simulations was carried out on heart disease and diabetes *** obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.

关键词： Energy efficiency intelligent models decision support system IoT e-healthcare machine learning

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Exploiting Circular Shifts for Efficient Chaotic Image Encryption

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IEEE Access 2025年 13卷 92997-93016页

作者： Lazaros Moysis Marcin Lawnik Wassim Alexan Sotirios K. Goudos Murilo S. Baptista George F. Fragulis Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Mechanical Engineering University of Western Macedonia Kozani Greece Department of Mathematical Methods in Technics and Informatics Faculty of Applied Mathematics Silesian University of Technology Gliwice Poland Communications Department Faculty of Information Engineering and Technology German University in Cairo Cairo Egypt ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Physics Institute for Complex Systems and Mathematical Biology SUPA The University of Aberdeen Aberdeen U.K.

This work presents a chaotic image encryption algorithm that acts on the binary level of the image for effective permutation, and on the byte level for substitution. The permutation step combines actions of bit level rearranging, and bit shuffling. Circular shift operations are used for bit shuffling, to reduce the execution time. Circular shifts are also applied to shuffle the permutation and substitution rules, effectively increasing security at a very low execution cost. The encryption keys are plaintext dependent, and a key mixing step in included, so that each part of the key affects all the subsequent encryption operations. For the encryption, a new Soboleva hyperbolic tangent based map is first designed. The dynamical behavior of the map is studied and shows robust chaos, and an absence of equilibria, meaning that it can generate hidden attractors. This map is used as an entropy source, along with a chaotic pseudo random number generator (PRNG). The encryption process shows a good performance under a collection of tests, and has also a low execution time, 0.0134 sec for a $256\times 256$ image, 0.0530 sec for a $512\times 512$ image, and 0.2050 sec for a $1024\times 1024$ image. So the proposed algorithm is a viable option for fast, efficient, and secure data encryption.

关键词： Encryption Security Chaotic communication Generators Robustness Matrices Costs Standards Real-time systems Computational efficiency

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Regression Models for Predicting Gamified Educational Outcomes

Regression Models for Predicting Gamified Educational Outcom...

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Telecommunications Conference (ITC-Egypt), International

作者： Abdelhameed Ibrahim Christos Gatzoulis Marwa M. Eid Faris H. Rizk Laith Abualigah El-Sayed M. El-kenawy Faculty of Engineering Design and Information & Communications Technology (EDICT) School of ICT Bahrain Polytechnic Isa Town Bahrain Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Computer Science and Intelligent Systems Research Center Blacksburg Virginia USA Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Computer Science Department Al al-Bayt University Mafraq Jordan MEU Research Unit Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper focuses on improving learning and involvement in studies by employing game elements and considering online learning platforms to deliver statistics curriculum. According to the problem statement, the research examines gamification methods to transform studying from a routine to having enjoyment while improving academic results. This area of investigation is being extended to predict academic outcomes by applying different machine learning models to student performance data received from gamified. The strategy entails using a pedagogy based on collecting data from students who participated in a gamified educational platform; the data comprises indicators like pre-and post-platform exam grades, grades on quizzes, and indicators of platform usage. Multiple regression algorithms, including Random Forest and Gradient Boosting, are applied to student performance. In the experiments, the output results indicate that the model of Random Forest Regressor outperforms other models with the least Mean Squared Error (MSE) of 0.052. This model performs the best in terms of prediction accuracy with competence level in predicting students' results in gamified environments. The findings can support decision-making in designing educational activities. The Random Forest Regressor model can be identified as the best predictive model and may help train gamified learning platforms, which can encourage student engagement that correlates to performance.

关键词： Machine learning algorithms Transforms Predictive models Nearest neighbor methods Prediction algorithms Boosting Data models

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Breast-Cancer Detection using Thermal Images with Marine-Predators-Algorithm Selected Features 7

Breast-Cancer Detection using Thermal Images with Marine-Pre...

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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Rajinikanth, Venkatesan Kadry, Seifedine Taniar, David Damasevicius, Robertas Rauf, Hafiz Tayyab St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Monash University Faculty of Information Technology ClaytonVIC3800 Australia Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland Staffordshire University Centre for Smart Systems Ai and Cybersecurity Stoke-on-Trent United Kingdom

ISBN: (纸本)9781665441261

Breast-cancer (BC) is one of the major diseases in women group and the early diagnosis and treatment is necessary to cure the disease. Early detection of BC is very essential to implement appropriate treatment and the proposed research aims to develop an automated BC detection system using Breast-Thermal-Images (BTI). The executed approach is as follows;(i) Recording the image for various breast orientation, (ii) Extracting the healthy/DCIS image patches, (iii) Treating the patches with image processing scheme, (iv) Feature extraction, (v) Feature optimization with Marine-Predators-Algorithm (MPA), and (vi) Two-class classification and validation. In this work, the essential image features, such as GLCM and LBP with varied weights are considered to classify the clinically collected BTI into healthy/DCIS class using a chosen two-class classifier. The result of this study confirms that the Decision-Tree (DT) classifier helps to achieve enhanced accuracy (>92%) compared to other methods adopted in this research. © 2021 IEEE.

关键词： Diseases

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