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20th International Conference on Web information systems and Technologies, WEBIST 2024

作者： Zohra Rennane, Fatma Meziane, Abdelkrim Faculty of Computer Science University of Science and Technologies Houari Boumedien Bab Ezzouar Algiers Algeria Multimedia and Information System Division Research Centre on Scientific and Technical Information Ben Aknoun Algiers Algeria

ISBN: (纸本)9789897587184

The global business environment changes very fast, forcing organizations to seek ways to improve their operational efficiency, cut costs, and enhance their decision-making. Optimizing performance is possible only with a clear understanding of how work gets done. It is here that well-defined business processes become very instrumental. However, for an organization to understand, evaluate, and eventually improve its processes, it is important that they model them in the first place. Traditional modeling techniques, such as BPMN and UML, provide a standard framework of visual and graphical representation for these processes. Most of these methods, however, fall short of capturing domain knowledge. The evolution of semantic web technologies has necessitated ontology-based business process modeling, which provides meaningful representations for business processes through the integration of ontologies. In this paper, an ontology-based business process model (OBPM) of the handicraft domain is presented focusing on the pastry-making field. Copyright © 2024 by SCITEPRESS.

关键词： Ontology

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Automated Autism Spectral Disorder Classification Using Optimal Machine Learning Model

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computers, Materials & Continua 2023年第3期74卷 5251-5265页

作者： Hanan Abdullah Mengash Hamed Alqahtani Mohammed Maray Mohamed K.Nour Radwa Marzouk Mohammed Abdullah Al-Hagery Heba Mohsen Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial Intelligence and Unit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Autism Spectrum Disorder (ASD) refers to a neuro-disorder wherean individual has long-lasting effects on communication and interaction *** information technologywhich employs artificial intelligence(AI) model has assisted in early identify ASD by using pattern *** advances of AI models assist in the automated identification andclassification of ASD, which helps to reduce the severity of the *** study introduces an automated ASD classification using owl searchalgorithm with machine learning (ASDC-OSAML) model. The proposedASDC-OSAML model majorly focuses on the identification and classificationof ASD. To attain this, the presentedASDC-OSAML model follows minmaxnormalization approach as a pre-processing stage. Next, the owl searchalgorithm (OSA)-based feature selection (OSA-FS) model is used to derivefeature subsets. Then, beetle swarm antenna search (BSAS) algorithm withIterative Dichotomiser 3 (ID3) classification method was implied for ASDdetection and classification. The design of BSAS algorithm helps to determinethe parameter values of the ID3 classifier. The performance analysis of theASDC-OSAML model is performed using benchmark dataset. An extensivecomparison study highlighted the supremacy of the ASDC-OSAML modelover recent state of art approaches.

关键词： Autism spectral disorder machine learning owl search algorithm feature selection id3 classifier

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Evaluating the Usability and Career Guidance Functionality of an Academic Performance Tracking Application Using the UEQ 10

Evaluating the Usability and Career Guidance Functionality o...

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10th IEEE information Technology International Seminar, ITIS 2024

作者： Mukaromah, Siti Pratama, Arista Wahanani, Henni Endah Idhom, Mohammad Agussalim Safitri, Eristya Maya University of Pembangunan Nasional "Veteran"Jawa Timur Faculty of Computer Science Department of Information System Surabaya Indonesia University of Pembangunan Nasional "Veteran"Jawa Timur Faculty of Computer Science Department of Informatics Surabaya Indonesia University of Pembangunan Nasional "Veteran"Jawa Timur Faculty of Computer Science Department of Data Science Surabaya Indonesia

ISBN: (纸本)9798331521295

This article evaluates an academic application designed to provide career guidance using the User Experience Questionnaire (UEQ). A survey involving 255 respondents assessed the application across 6 UX dimensions: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. The results reveal the highest scores on Attractiveness 1.473 and Perspicuity 1.642, indicating that the platform is visually appealing and easy to navigate. However, lower scores on Novelty 0.774 and Dependability 1.243 highlight areas requiring improvement, particularly in building trust among users and introducing innovative features. The implications of these findings suggest that while the platform holds significant potential as a tool for academic monitoring and career guidance, enhancing user engagement and reliability will be critical to achieving broader acceptance. Addressing the gaps in Stimulation (1.135) and Novelty could lead to a more engaging and unique user experience, aligning with the evolving needs of students. This study provides actionable insights for the iterative development of user-centered educational tools, emphasizing the importance of UX in supporting informed decision-making and improving academic outcomes. © 2024 IEEE.

关键词： Decision making

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Covert Channel Detection and Generation Techniques: A Survey 3

Covert Channel Detection and Generation Techniques: A Survey

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Al-Khulaidi, Nesmah A. Zahary, Ammar T. Hazaa, Muneer A.S Nasser, Adel A. Yemen Academy for Graduate Studies Department of Computer Networks Sana'a Yemen University of Science and Technology Faculty of Computing and It Department of Computer Science Sana'a Yemen Thamar University Faculty of Computer and Information System Department of Computer Science Thamar Yemen Sa'adah University Faculty of Sciences Department of Information Systems and Computer Science Sa'adah Yemen

ISBN: (纸本)9798350305333

The use of covert communications has become more widespread recently as a solution to the information security issue. information security can be partially solved by the discovery and creation of covert routes. Covert channel use is still in its early phases, even though information security concerns are growing as a result of quick technology advancements. As a result, several problems and concerns need to be resolved, and the future plan is foggy. This paper seeks to give a thorough comparison of earlier and more recent attempts in this field. It offers a survey of the literature on the drawbacks and gaps in conventional detection techniques, emphasizing the most recent techniques for discovering and creating these covert channels while carefully considering machine learning and deep learning-based detection techniques. Additionally, it broadens the topic to cover the crucial function that these channels play in technology. Timing channels, storage channels, and hybrid channels are just a few of the ways covert channels may be utilized to transfer data. They can be used to get around security measures like intrusion detection systems and firewalls. Additionally, they may be used to steal confidential data from a system, including passwords, credit card numbers, and intellectual property. system security is seriously threatened by covert channels, and more study on covert channel production and detection is required to stay up with the changing threat environment. © 2023 IEEE.

关键词： Intrusion detection

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Exploring Deep Insights from Vast Data: An Overview of Deep Learning Techniques for Big Data 4

Exploring Deep Insights from Vast Data: An Overview of Deep ...

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4th International Conference on Electrical Engineering and computer science, ICECOS 2024

作者： Baez Hasan, Sarhad Kareem, Shahab Wahhab Ameen, Didar Dlshad Hamad Soran University Faculty of Science Computer Science Department Soran Iraq Erbil Polytechnic University College of Engineering Knowledge University Department of Information System Engineering Department of Computer Engineering Erbil Iraq

ISBN: (纸本)9798350368253

Big data has emerged very fast, and this has brought both opportunities and problems that are related to the application of deep learning. This paper explores how deep learning can be implemented using big data and in particular the Apache Mahout library. Unlike most other machine learning tools, Mahout can perform distributed computation, especially while using the MapReduce frameworks including Apache Hadoop and Apache Spark. However, this study does not only focus on the explanation of the need for Mahout's integration and employment but also carries out a high-fidelity argument on how deep learning algorithms can be incorporated in the Mahout framework to optimize the manipulation of large and complicated datasets. Mahout-based contributions include the comparative analysis of key deep learning paradigms including distributed processing, parallel processing and transfer learning. This paper also includes examples of the Mahout deep learning integration use and its application in real contexts. This article describes the challenges that currently organizations face in handling big data and indeed, the potential threats these challenges pose to the success of deep learning techniques when used in organizations' big data processing and analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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On a new fractional integral operator convoluted with a fractional k-symbol Raina function: stability

On a new fractional integral operator convoluted with a frac...

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2023 International Conference on Fractional Differentiation and Its Applications, ICFDA 2023

作者： Obaiys, Suzan J. Ibrahim, Rabha W. University of Malaya Faculty of Computer Science and Information Technology Department of Computer System & Technology KL Malaysia Lebanese American University Department of Computer Science and Mathematics Beirut Lebanon

ISBN: (纸本)9798350321685

The fractional k-symbol Raina function (k-FRF) is convoluted with a class of analytic functions in a complex domain to build a new fractional integral operator (FIO) in this attempt. We research the polynomials produced by the suggested FIO in terms of their geometry and stability. On the basis of the Hadamard product's characteristics, sufficient criteria are shown (convolution product). © 2023 IEEE.

关键词： Convolution

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A Holistic Anti-Counterfeiting Platform Using NFC and Blockchain Technologies

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computers, Materials and Continua 2025年第3期83卷 4257-4280页

作者： Rajendren Subramaniam Saaidal Razalli Azzuhri Teh Ying Wah Atif Mahmood Vimala Balakrishnan Department of Information System Faculty of Computer Science & Information Technology Universiti Malaya Kuala Lumpur 50603 Malaysia Department of Computer System & Technology Faculty of Computer Science & Information Technology Universiti Malaya Kuala Lumpur 50603 Malaysia Faculty of Data Science and Information Technology INTI International University Nilai 71800 Malaysia

Counterfeiting is still a pervasive global issue, affecting multiple industries and hindering industrial innovation, while causing substantial financial losses, reputational damage, and risks to consumer safety. From luxury goods and pharmaceuticals to electronics and automotive parts, counterfeit products infiltrate supply chains, leading to a loss of revenue for legitimate businesses and undermining consumer trust. Traditional anti-counterfeiting measures, such as holograms, serial numbers, and barcodes, have proven to be insufficient as counterfeiters continuously develop more sophisticated replication techniques. As a result, there is a growing need for more advanced, secure, and reliable methods to prevent counterfeiting. This paper presents a novel, holistic anti-counterfeiting platform that integrates Near Field Communication (NFC)-enabled mobile applications with blockchain technology to provide an innovative, secure, and consumer-friendly authentication mechanism. Our approach addresses key gaps in existing solutions by incorporating dynamic product identifiers, which make replication significantly more difficult. The system enables consumers to verify the authenticity of products instantly using their smartphones, enhancing transparency and trust in the supply chain. Blockchain technology plays a crucial role in our proposed solution by providing an immutable, decentralized ledger that records product authentication data. This ensures that product verification records cannot be tampered with or altered, adding a layer of security that is absent in conventional systems. Additionally, NFC technology enhances security by offering unique identification capabilities, enabling real-time product verification. To validate the effectiveness of the proposed system, real-world testing was conducted across different industries. The results demonstrated the platform’s ability to significantly reduce counterfeit products in the supply chain, offering businesses and cons

关键词： Blockchain near field communication (NFC) anti-counterfeiting

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A novel U-net model for brain tumor segmentation from MRI images

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Alexandria Engineering Journal 2025年 126卷 220-230页

作者： Obayya, Marwa Alshuhail, Asma Mahmood, Khalid Alanazi, Meshari H. Alqahtani, Mohammed Aljehane, Nojood O. Almansour, Hamad Al-Hagery, Mohammed Abdullah Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal university Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Applied Collage Najran University Najran Saudi Arabia Department of Computer Science College of Computer Qassim University Saudi Arabia

Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis. © 2025 The Authors

关键词： Convolutional neural networks

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Combination of CRITIC and PROMETHEE II Methods as An Early Detection of Potential Dropout Students 6

Combination of CRITIC and PROMETHEE II Methods as An Early D...

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6th International Conference on information and Communications Technology, ICOIACT 2023

作者： Yusa, Mochammad Coastera, Funny Farady Rahmadani, Deni Sari, Julia Purnama Kusrini, Kusrini Hartanto, Anggit Dwi University of Bengkulu Faculty of Engineering Department of Informatics Bengkulu Indonesia University of Bengkulu Faculty of Engineering Department of Information System Bengkulu Indonesia Universitas Amikom Yogyakarta Magister of Informatics Engineering Faculty of Computer Science Yogyakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Department of Information System Yogyakarta Indonesia

ISBN: (纸本)9798350315639

Dropout (DO) has negative implications for individuals and educational institutions. The field of education data mining (EDM) offers valuable contributions to prevent dropout cases and improve student retention. This study focuses on implementing EDM to identify students at risk of dropping out by integrating academic activity criteria with real-time data sources. Previous studies have encountered limitations in classification and clustering techniques, with classification relying on labeled training data (supervised learning) and clustering constrained by attribute or criteria weight (unsupervised learning). This study aims to address these limitations by combining the PROMETHEE II ranking approach and the CRITIC weighting method, producing a list of students with dropout potential. The results can aid stakeholders in implementing preventive measures. The study involves several steps: 1) Collecting data through integration with academic portals;2) Filtering and selecting criteria for student academic activities;3) Transforming and normalizing the data;4) Applying automatic weighting using CRITIC;5) Calculating criteria rankings using the PROMETHEE II technique;and 6) Validating the ranking outcomes. © 2023 IEEE.

关键词： Decision support systems

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Long Short-Term Memory and Word Embedding For Sentiment Analysis of User Review 1

Long Short-Term Memory and Word Embedding For Sentiment Anal...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Silviana, Lia Nababan, Erna Budhiarti Zarlis, Muhammad Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia Bina Nusantara University Department of Information System Jakarta Indonesia

ISBN: (纸本)9798350306156

Accuracy in training sentiment analysis models for large number of review datasets is affected by the correct classification of sentiment labels. Improving the accuracy of sentiment labels, and text representation also affects the performance of sentiment analysis models. Deep learning methods have been widely used to solve various sentiment analysis problems. To improve the performance of deep learning in sentiment analysis, it is necessary to use the right labeling method and good text representation to be used as an embedding layer. This study proposes sentiment labeling using Lexicon and Long Short-Term Memory (LSTM) as well as used FastText as embedding words in sentiment classification. As a corpus, the InSet Lexicon Dictionary is employed for feature extraction. The sentiment data used is the reviews given by users on several applications provided on Google Play. The results showed that the LSTM network using Word-embedded FastText with a dimension of 300 words received a small error value of 0.111 with an accuracy of 95.55% for data labeled based on Lexicon. © 2023 IEEE.

关键词： Long short-term memory

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