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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Sitompul, Taufiq Hendric Spits Warnars, Harco Leslie Meyliana Hidayanto, Achmad Nizar Prabowo, Harjanto Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Universitas Indonesia Faculty of Computer Science Depok16424 Indonesia Binus Business School Undergraduate Program Bina Nusantara University Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

Modern healthcare systems demand comprehensive information systems but face obstacles during adoption. Organizational and structural complexity, especially decentralized systems, challenges the integrated management and sharing of data across information systems. Interoperability is critical to address these issues effectively in heterogeneous and distributed environments. However, different medical terminology hinders interoperability within health information systems. To address this, a systematic literature review examined various medical terminology standards for national interoperability. From 790 articles, 12 were selected according to PRISMA guidelines with inclusion and exclusion criteria. Standard health terminology is critical to achieving interoperability, and many countries have adopted it, although some have required adaptation to accommodate local contexts. Data models, governance, and maintenance of standards are equally important in achieving national interoperability. In addition, standardized health terminology promotes consistency and uniformity in terminology, which is beneficial for decision-making by stakeholders in the health system. © 2023 IEEE.

关键词： Interoperability

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Understanding the Definitions of Microcredentials in Higher Education: Systematic Literature Review

Understanding the Definitions of Microcredentials in Higher ...

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2024 International Conference on TVET Excellence and Development, ICTeD 2024

作者： Wihendro Warnars, Harco Leslie Hendric Spits Prabowo, Harjanto Sfenrianto Bina Nusantara University BINUS Graduate Program- Computer Science Information Systems Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Business School Undergraduate Program Management Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program-Master of Information Systems Management Information Systems Management Department Jakarta Indonesia

ISBN: (纸本)9798331516413

The growing focus on microcredentials emphasizes the urgent need for precise and widely accepted definitions, as existing uncertainties hinder their effective implementation. This research aims to investigate the comprehension of microcredentials definitions in the context of higher education by conducting a systematic literature review. The goal is to identify current definitions of microcredentials to facilitate standardization initiatives. Following The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology to systematically search in Scopus database within 2019-2024 resulting 940 articles, by removing the duplicates, 280 articles were initially reviewed, with 29 selected for detailed thematic analysis based on specific inclusion and exclusion criteria. By adapting the six phases of thematic analysis, selected articles were identified analyzed, organized, described, and themes found the data set were reported. Key findings reveal that, while a universally accepted definition is lacking, common themes emerge: microcredentials are competency-based, modular, portable, stackable, and often include quality assurance. These attributes highlight microcredentials as valuable for upskilling and reskilling in a flexible manner. The study concludes that establishing standard definitions can increase the recognition and utility of microcredentials across educational and professional sectors and recommends further research to strengthen core elements for consistency. © 2024 IEEE.

关键词： Competency-based Higher Education Microcredentials Modular Reskilling Upskilling

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Bengali News Abstractive Summarization: T5 Transformer and Hybrid Approach

Bengali News Abstractive Summarization: T5 Transformer and H...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Hasib, Khan Md Rahman, Md. Atiqur Masum, Mustavi Ibne Boer, Friso De Azam, Sami Karim, Asif Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh Charles Darwin University Faculty of Science and Technology NT Australia

ISBN: (纸本)9798350382204

In today's fast-paced world, everyone wants things to happen quickly. Thanks to the internet, news spreads super fast. But not all news is important. News summarization helps by giving a short version of each news story, so readers can easily figure out what type of news they want to read. There are two main types of summarization: Abstractive Text Summarization and Extractive Text Summarization. The process of abstractive text summarization is much more complex than that of extractive text summarization. This study proposes a model for generating extractive summaries, which are then utilized as input to generate abstractive summaries. The model uses the Bengali Text Summarization (BenSumm) model for extractive summarization and the Bangla Text-to-Text Transfer Transformer (BanlaT5) for abstractive summarization. The research also compares summarization acquired straight from the BanglaT5 model with summarization obtained via the proposed model. Abstractive summarization in the Bengali language has been accomplished using the Text-to-Text Transfer Transformer(T5) in this research. Although abstractive summarization of the Bengali language has been accomplished over the years using a variety of techniques, the field of using T5 in this field has only recently been discovered, and there is still a wide range of opportunities to be explored. The study has achieved promising results. © 2023 IEEE.

关键词： Text processing

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Comparative Study between KNN and CNN's Techniques for Kidney Stone Detection

Comparative Study between KNN and CNN's Techniques for Kidne...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Al-Tbenawey, Fahdah M. Alotaibi, Shoayee Salem, Marwa Mir, Shamaila Mir, Zahida Computer Science and Engineering College University of Ha'il Department of Computer Science and Information Hail Saudi Arabia Faculty of Engineering Department of Electrical Communication and Electronics Systems Engineering Cairo Egypt

ISBN: (纸本)9798350347050

This paper aims to determine the better technique for kidney stone detection between K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNNs). As well known, the presence of kidney stones is an important topic that deserves discussion regarding expected medical outcomes. Most of the detection measures comprise CT scans, ultrasounds, and abdominal X-rays. These measures have varying effectiveness levels, hence used in the detection to prepare patients for specific treatment options. Patients' treatment options comprise percutaneous nephrolithotomy (PCNL), extracorporeal shockwave lithotripsy (ESWL), and ureteroscopy. The different detection techniques carried out for the patients are essential to test whether the patient has small or large kidney stones. One essential technique is using conventional neural networks (CNNs) technology. In this paper kidney, stone detection will be carried out using both of K-Nearest Neighbor (KNN) and CNN's techniques. A comparison between both techniques will be carried out to figure out the better. All of the simulations are carried out using MATLAB. © 2023 IEEE.

关键词： MATLAB

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SQL Injection Detection in Cloud Computing with Machine Learning Algorithms 23

SQL Injection Detection in Cloud Computing with Machine Lear...

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6th International Conference on Networking, Intelligent systems and Security, NISS 2023

作者： Hadji, Abdelhakim Raouyane, Brahim Rachdi, Mohamed Department of Mathematics and Computer Science Faculty of Sciences Ain Chock Computer Sciences and Systems Laboratory Hassan Ii University of Casablanca Casablanca Morocco Isga Casablanca Morocco

ISBN: (纸本)9798400700194

Due to the coronavirus crisis, a lot of companies all over the world started a fast digitalization of their business and became more comfortable with the digital world. In this way, a lot of people in the digital domain, including net developers and system engineers, are exposed to different types of risks that they may not completely know or understand. SQL injection is one such vulnerability that can be exploited by attackers to inject malicious data into a database via an application programming interface (API) call request that uses SQL syntax. Cloud computing (IaaS) and network as a service (Naas) provide companies with a wide range of advantages. However, the same advantages can also pose certain vulnerabilities to the organization. In this paper we will focus on SQL injection attacks and make a comparison between different Machine learning algorithms that can be used to detect SQL injection attacks, such as "Naive Bayes"and "K-NN", SVN, and decision tree, we will also use a deep learning algorithm CNN, this comparison will show the potential that machine learning algorithms can offer in terms of cloud security. © 2023 ACM.

关键词： Cloud computing

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Survey semantic Arabic language in deep learning based recommendation system 17

Survey semantic Arabic language in deep learning based recom...

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17th International Conference on INnovations in Intelligent systems and Applications, INISTA 2023

作者： Kadhem, Rasha Mallat, Souheyl Zrigu, Mounir University of ALQadisiyah Department of Computer Science Iraq Research Laboratory in Algebra Numbers Theory and Intelligent Systems Monastir Tunisia University of Monastir Faculty of Sciences of Monastir Department of Computer Science Tunisia

ISBN: (纸本)9798350338904

Due to problems, Arabic-speaking internet users have surged, although nothing is done on it. It is challenging to develop a repliable recognition system (RS) for cursive languages such as Arabic. Variations in text size, fonts, word semantics, user Arabic region, etc. complicate these issues. Deep learning models can model big datasets and handle them. Good features can be selected and learned consecutively by both CNNs and RNNs. In numerous studies, both of these neural networks have proven to be superior than their counterparts. This is true for text recognition, voice recognition, and several NLP tasks (NLP), but they are not qualified to deal with the semantics of the text, so we decided to find the best DL technique for semantic Arabic language from a lot of research to be a survey to other searchers. Our paper compared different algorithms and their accuracy. © 2023 IEEE.

关键词： Semantics

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Electric Bike Security and Protection System 2

Electric Bike Security and Protection System

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Maniraj, P. Murugan, A. Durai Gokul, R. Shalini, A.S. Kesavan, K. Kavin, P. M. Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India M. Kumarasamy College of Engineering Computer Science and Business Systems Karur India M. Kumarasamy College of Engineering Electrical and Electronics Engineering Karur India

ISBN: (纸本)9798350349221

With the developing prevalence of electric bicycles (e-bicycles) as a feasible and proficient method of transportation, guaranteeing their security has turned into a foremost concern. Electric bicycles are important resources helpless against robbery and harm, to conquer this issue the venture proposes the improvement of an Electric Vehicle Burglary Location and Warning Framework utilizing IOT innovation. The framework integrates sensors to screen electric vehicles for unapproved access and utilizes Radio Recurrence ID innovation to improve security. After recognizing an unfair RFID section or any dubious movement, the framework sets off a caution to tell the proprietor and naturally locks the back tire, forestalling robbery. This framework could be an instrument for deflecting the robbery of electric vehicles and keeping proprietors from monetary misfortunes. In a period of developing IOT-based security arrangements, this framework offers an important expansion to safeguard electric vehicle and their venture. © 2024 IEEE.

关键词： Electric vehicles

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AraDS: Arabic Datasets for Text Mining Approaches

AraDS: Arabic Datasets for Text Mining Approaches

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Yafooz, Wael M.S. Alsaeedi, Abdullah Emara, Abdel-Hamid M. College of Computer Science and Engineering Taibah University Computer Science Department Medina Saudi Arabia Al-Azhar University Faculty of Engineering Department of Computers and Systems Engineering Cairo11884 Egypt

ISBN: (纸本)9798350347050

With the digital transformation, life started depending on the digital world. Hence, there is a massive amount of unstructured textual data produced and accumulated faster. Such data used in many applications such sentiment analysis, topic modeling, summarization, classifications, and clustering. However, researcher's wastes time and effort in collecting data and constructing a dataset to examine and evaluate their models or algorithms. In the Universal Language of English, there are many benchmark datasets. The Arabic language lacks datasets in many domains. This paper introduces an Arabic dataset called AraDS. AraDS consist of three Arabic datasets namely;Arabic Dataset on Herbal Treatments for Diabetes (ADHTD), Arabic Multi-Classification Dataset (AMCD), and the Arabic Sentiment Dataset-Khat (ASDK). These datasets were collected from social media platforms such as YouTube. It contains user-generated comments and video metadata. AraDS publicly available dataset. The assessment of the annotation process has been carried out and evaluated using four methods. They are Cohen's kappa, three Arabic native speaker annotators, accuracy, and F-measure. AMCD and ASDK datasets are balanced, whereas ADHTD is an imbalanced dataset. Two datasets are sided with binary classes and the third one is a multi-class dataset. These datasets can be beneficial for many data, text mining, and sentiment analysis researchers to apply methods and algorithms. © 2023 IEEE.

关键词： Sentiment analysis

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The Impact of Tutor Quality on E-Learning Acceptance: A Mediating Effect of E-Assessment in Public Universities of Malaysia 13

The Impact of Tutor Quality on E-Learning Acceptance: A Medi...

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13th International Symposium on computer science and Educational Technology, ISCSET 2024

作者： Imran, Muhammad Haque, Adnan Ul Kozlovski, Eugene School of Business Management Universiti Utara Malaysia Sintok Malaysia Yorkville University Business Management Faculty Concord Canada The University of Exeter Department of Computer Science Exeter United Kingdom

ISBN: (纸本)9798350390803

The present study seeks to examine the extent to which tutor quality affects the acceptance level of students toward e-learning at public universities in Malaysia, while also considering the mediating role of e-Assessment. The structured questionnaires were adopted to collect 450 responses from students in public universities. The outcomes demonstrate a significant and positive relationship between tutor quality and students' e-learning adoption rate. Additionally, the relationship between tutor quality and acceptance level of students toward e-learning is significantly mediated by e-Assessment. The key gap that the present study has filled is the inclusion of the above-stated mediating variable. Several important recommendations and insights are provided to enhance the acceptance and effectiveness of e-learning in this specific educational environment. © 2024 IEEE.

关键词： Contrastive Learning

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GA-Stacking:A New Stacking-Based Ensemble Learning Method to Forecast the COVID-19 Outbreak

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computers, Materials & Continua 2023年第2期74卷 3945-3976页

作者： Walaa N.Ismail Hessah A.Alsalamah Ebtesam Mohamed Department of Management Information Systems College of Business AdministrationAl Yamamah UniversityRiyadh11512Saudi Arabia Faculty of Computers and Information Minia UniversityMinia61519Egypt Information Systems Department College of Computer and Information SciencesKing Saud UniversityRiyadh4545Saudi Arabia Computer Engineering Department College of Engineering and ArchitectureAl Yamamah UniversityRiyadh11512Saudi Arabia

As a result of the increased number of COVID-19 cases,Ensemble Machine Learning(EML)would be an effective tool for combatting this pandemic *** ensemble of classifiers can improve the performance of single machine learning(ML)classifiers,especially stacking-based ensemble *** utilizes heterogeneous-base learners trained in parallel and combines their predictions using a meta-model to determine the final prediction ***,building an ensemble often causes the model performance to decrease due to the increasing number of learners that are not being properly ***,the goal of this paper is to develop and evaluate a generic,data-independent predictive method using stacked-based ensemble learning(GA-Stacking)optimized by aGenetic Algorithm(GA)for outbreak prediction and health decision aided ***-Stacking utilizes five well-known classifiers,including Decision Tree(DT),Random Forest(RF),RIGID regression,Least Absolute Shrinkage and Selection Operator(LASSO),and eXtreme Gradient Boosting(XGBoost),at its first *** also introduces GA to identify comparisons to forecast the number,combination,and trust of these base classifiers based on theMean Squared Error(MSE)as a fitness *** the second level of the stacked ensemblemodel,a Linear Regression(LR)classifier is used to produce the final *** performance of the model was evaluated using a publicly available dataset from the Center for systems science and Engineering,Johns Hopkins University,which consisted of 10,722 data *** experimental results indicated that the GA-Stacking model achieved outstanding performance with an overall accuracy of 99.99%for the three selected ***,the proposed model achieved good performance when compared with existing baggingbased *** proposed model can be used to predict the pandemic outbreak correctly and may be applied as a generic data-independent model 3946 CMC,2023,vol.74,no.2 to pre

关键词： COVID-19 ensemble machine learning genetic algorithm machine learning stacking ensemble unbalanced dataset vaccine

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