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5th IEEE Ecuador Technical Chapters Meeting, ETCM 2021

作者： Chimbo, Bryan Ingavélez-Guerra, Paola Otón-Tortosa, Salvador Salesian Polytechnic University Research Group on Artificial Intelligence and Assistive Technologies Cuenca Ecuador University of Alcalá Faculty of Computer Science Information Technology Department of Software Engineering Madrid Spain

ISBN: (纸本)9781665441414

The collaborative approach which are born OER (Open Educational Resource) is the joint search for pedagogical and technological challenges to achieve improved quality reconstruction. It is necessary to demonstrate defined metrics that support processes and provide guidelines or instructions related to design for all. The open feature of the OERs must demonstrate accessibility, guaranteeing their interoperability. The incorporation of intelligent systems based on the efficient publication of accessibility information, contributes to the evaluation of accessible resources, knowledge modeling and future trends based on the feedback of profiles, and personalization from the user experience through. This paper presents the proposal for an expert system based on the ISO 40500, ISO 24751 and UDL standards. © 2021 IEEE.

关键词： Metadata

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Automated Model-Based Test Case Generation Using Uml Activity Diagram: A Review 9th

Automated Model-Based Test Case Generation Using Uml Activit...

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9th International Conference on IT Convergence and Security, ICITCS 2021

作者： Nor, Rozi Nor Haizan Monim, Md Abdul Jusoh, Yusmadi Yah Tajuddin, Nur Ilyana Ismarau Department of Software Engineering and Information System Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Malaysia Tamhidi Centre Universiti Sains Islam Malaysia Nilai Malaysia

ISBN: (纸本)9789811641176

software or application testing is a process of executing a program with the goal of finding defect to make better system. In software testing phase, writing test cases is one of the important activities. Manually writing test cases approach is lengthy of time period and need more effort to accomplish the process. This paper describes test case, test case generation techniques, different types of software testing approaches and comparison of testing tool. Test cases usually writing at the beginning of testing process from the set of software requirements. Test cases are created by using two different approaches. One is manually written test cases and another is automatically generated test cases. Manually written test cases are a very lengthy process and need to give a lot of effort to make good quality test cases. On the other hand, automatically generated test cases are generated by automatically using some software tools and it saves a lot of time and effort. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： software testing

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A GSO-based multi-objective technique for performance optimization of blockchain-based industrial Internet of things

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International Journal of Communication Systems 2024年第15期37卷

作者： Zanbouri, Kouros Darbandi, Mehdi Nassr, Mohammad Heidari, Arash Navimipour, Nima Jafari Yalcın, Senay Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Pôle Universitaire Léonard de Vinci Paris France Communication Technology Engineering Department Tartous University Tartus Syria Department of Mathematics and Natural Sciences Gulf University for Science and Technology Mishref Campus Kuwait Department of Computer Engineering Faculty of Engineering and Natural Science İstanbul Atlas University Turkey Department of Software Engineering Haliç University Istanbul Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey Research Center of High Technologies and Innovative Engineering Western Caspian University Baku Azerbaijan Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Energy System Engineering School of Engineering and Natural Sciences Bahçeşehir University Istanbul Turkey

The latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain-based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain-based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain-based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision-making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain-based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations. © 2024 John Wiley & Sons Ltd.

关键词： Multiobjective optimization

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Fuzzy Logic-Based Temperature and Humidity Measurement for Pharmacy Warehouses

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SSRN 2022年

作者： Avuçlu, Emre Basciftci, Fatih Elen, Abdullah Department of Software Engineering Aksaray University Aksaray Turkey Department of Computer Engineering Technology Faculty Selçuk University Selçuklu Konya42003 Turkey Bandirma Onyedi Eylul University Faculty of Engineering and Naturel Sciences Department of Software Engineering Balikesir Bandirma Turkey

Drugs are dispensed to pharmacies when necessary, by keeping pharmaceutical storage cabinets and the drugs should be kept in suitable environments to preserve the structure of the drug in this process. To create this favourable environment, they should be maintained in a certain level of humidity and temperature. To ensure this, there are two input parameters including temperature and humidity in a designed system and there are two outputs to control these parameters, including coolers and humectants. The temperature and humidity values of real-time running fuzzy expert system-based medicine cabinets are maintained in the appropriate range. The purpose of the application is to keep the environment values which are necessary to preserve the structure of the drugs in the pharmacy warehouse, at the required level and to ensure a longer lifespan. The values are determined according to the instructions for tracking temperature and humidity in pharmacy warehouses by Ministry. © 2022, The Authors. All rights reserved.

关键词： Fuzzy logic

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Enhancing Federated Learning by One-Shot Transferring of Intermediate Features from Clients

Enhancing Federated Learning by One-Shot Transferring of Int...

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International Conference on Data Science and Advanced Analytics (DSAA)

作者： Youxingzhu Deng Yipeng Zhou Gang Liu Jessie Hui Wang Yu Shui College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Institute for Network Sciences and Cyberspace Tsinghua University Peking China School of Computer Science University of Technology Sydney Sydney Australia

Federated learning (FL) is an emerging paradigm using a parameter server (PS) to coordinate multiple decentralized clients for training a common model without exposing their raw data. Despite its amazing capability in preserving data privacy, FL confronts two significant challenges that have not been sufficiently addressed by existing works, which are: 1) heterogeneous data distributed on clients given that the PS cannot alter data locations. 2) limited computation resources of FL clients who may conduct model training with mobile devices. To tackle these challenges, we propose a novel federated one-shot transferring of intermediate features (FedOTF) algorithm. More specifically, FedOTF consists of two stages: feature extraction and model reconstruction. To overcome challenge 1, clients in stage 1 only collaboratively train a small model in a federated manner, with the objective to extract features. In stage 2, intermediate features (generated by the small model trained by stage 1) are transferred from clients to the PS so that a large model can be trained to overcome challenge 2. In addition, the design of FedOTF is robust, which can flexibly diminish the number of communication rounds of stage 1 when network capacity is limited, and reduce the amount of exposed features when privacy is concerned. To verify the superiority of FedOTF, we conduct comprehensive experiments with real datasets. The experiment results demonstrate that FedOTF can significantly improve the model utility of FL because a better model can be finally obtained by the PS without incurring heavy computational load on clients. Besides, we conduct robustness evaluation of FedOTF which can achieve stable performance when varying network capacity and privacy requirement.

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A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

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computers, Materials & Continua 2021年第3期66卷 3289-3310页

作者： Mazin Abed Mohammed Karrar Hameed Abdulkareem Begonya Garcia-Zapirain Salama A.Mostafa Mashael S.Maashi Alaa S.Al-Waisy Mohammed Ahmed Subhi Ammar Awad Mutlag Dac-Nhuong Le College of Computer Science and Information Technology University of AnbarRamadi31001Iraq College of Agriculture Al-Muthanna UniversitySamawah66001Iraq eVIDA Lab University of DeustoBilbao48007Spain Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia Faculty of Engineering and Built Environment Universiti Kebangsaan MalaysiaBangi43600Malaysia Pure Science Department Ministry of EducationGeneral Directorate of CurriculaBaghdad10Iraq Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global *** biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 *** this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 ***(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection *** on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 *** comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

关键词： Coronavirus disease COVID-19 diagnosis machine learning convolutional neural networks resnet50 artificial neural network support vector machine X-ray images feature transfer learning

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Effects of term weighting approach with and without stop words removing on Arabic text classification

Effects of term weighting approach with and without stop wor...

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International Conference on Optimization and Applications (ICOA)

作者： Esra’a Alhenawi Ruba Abu Khurma Pedro A. Castillo Maribel G. Arenas Ayat Mahmoud Al-Hinawi Department of Software Engineering Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Computer Science MEU Research Unit Faculty of Information Technology Middle East University Amman Jordan University of Granada Granada Spain Department of Allied Engineering Sciences Hashemite University Zarqa Jordan

Text classification is a technique for grouping articles into pre-defined categories. Prior to categorization, text documents have to be set up and organised in a form that is compatible with the data mining techniques. That has produced many term weighting techniques have been developed in the literature to improve the functionality of text categorization systems. This paper compares techniques for text classification based on when stop words are eliminated from the documents once and when they are not. We used an Arabic data set of 322 papers grouped into six subjects (health, politics, science, education, agriculture and finance) to research which is the influence of prior weighting of features approaches on classification outcomes with regard to accuracy, recall, precision, and F-measure values. Each document subset includes 50 documents, except the health one that includes 61. We carried out four experiments and checked results getting that only for precision, the term frequency feature weighting approach with stop word removal are more or less the same than for binary method. Moreover the conclusion for accuracy, recall and F-measure is that Feature weighting approach with stop word outperforms the Binary one. Additionally, if the phrase for weighting is the same for both methods, to remove stop word increases classification accuracy for all the experiments.

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Transformative Applications and Key Challenges of Generative AI

Transformative Applications and Key Challenges of Generative...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Attila Kovari Jozsef Katona Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Department of Institute of Engineering University of Dunaújváros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The paper discusses the integration and use of generative artificial intelligence technologies in education. Generative AI, such as OpenAI, in particular the GPT model, has fundamentally changed the way humans interact with machines. Advanced technologies in the field of generative AI in education are mostly used to personalize learning experiences by dynamically customizing learning materials according to the needs of learners, thus enabling the improvement of outcomes. This article explores the use of generative AI tools with the aim of assessing how useful students perceive these tools to be. The findings show mixed receptivity from users, highlighting the critical balance in using innovative AI capabilities through AI.

关键词： Power engineering Accuracy Generative AI Education Chatbots Market research Problem-solving Reliability

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Experimental Evaluation of Cross-Platform Recognition of Identical and Unknown Users over Social Networking Environment

Experimental Evaluation of Cross-Platform Recognition of Ide...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Kancharla Nagababu S. Ananthi Neeraj Sunheriya Venkata Siva Prasad Ch J. Jayanthi Hamed Fawareh Department of Computer Applications Aditya University Department of CSE St. Joseph's College of Engineering Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of ECE Jeppiaar Engineering College Chennai-119 Department of CSE Sona College of Technology Salem Software Engineering Dept Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The capacity to individually identify and categorize people across sites and apps is vital for trustworthiness, fraud prevention, and personalized experiences in online social networking. In this study, strategies for platform identification among similar and unknown social networking users are evaluated. We study strategies that employ user attributes, behavior history, and inter-network connections to match profiles on multiple platforms and uncover new, previously connected people. This cross-validation on the proposed method uses Social Linkage Identification Strategy (SLIS) and Friend Relationship Evaluator. According to quantitative experiment results, our approach improves search people across multiple social networks more than FRE. The extensive social connection web in many online social networks is used to enhance user identification and verification via the Friend Relationship Evaluator (FRE) system. Our process uses advanced machine learning and data fusion to handle a wide variety of natural ventilation phenomena from different sensors and platform formats. We evaluate attribute-based matching, graph approaches, and behavior analytics for accuracy, scalability, and computing cost. This study advances user identification systems across digital platforms and helps service providers and end-users establish more sophisticated online identity management strategies.

关键词： Couplings Accuracy Social networking (online) Scalability Sensor phenomena and characterization Sensor fusion Ventilation User experience Protection Standards

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CoATR: Integrating Model and Data-Driven for Learning Sparse Spatio-Temporal Traffic Data

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SSRN 2023年

作者： Liao, Tianchi Zhang, Lei Yang, Jinghua Chen, Chuan Zheng, Zibin School of Software Engineering Sun Yat-sen University Zhuhai519000 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Faculty of Innovation Engineering Macau University of Science and Technology Taipa999078 China

Spatio-temporal traffic data collected by various sensing systems have chronic issues of missing and corruption, thus accurate data imputation and prediction have been extensively researched. Previous methods can be divided into two categories: model-driven methods with physical explanations and data-driven deep learning methods. These methods have some limitations: model-driven methods are often linear and may not accurately model the spatio-temporal complexity, while deep learning methods often lack the physical reality making them probe to over-fitting. Inspired by both, we propose a new Convolutional-based generalized Autoregressive Tensor-Ring decomposition method (CoATR) for the completion of spatiotemporal data. CoATR not only retains the advantages of the tensor-ring (TR) decomposition model for global modeling of spatio-temporal data but also exploits the ability of deep networks to model nonlinear features. To be specific, we introduce TR decomposition to capture the global low-rankness and employ multilayer convolutional neural networks to model the global complex interactions among the TR factors. Moreover, we design a new autoregressive network to further explore the local temporal variation in the data. Extensive experiments on a variety of common traffic datasets have validated the effectiveness and superiority of the CoATR over classical model-driven methods and other state-of-the-art data-driven deep learning methods. © 2023, The Authors. All rights reserved.

关键词： Tensors

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