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2022 International Seminar on Application for Technology of Information and Communication, iSemantic 2022

作者： Abdussalam Pramudya, Elkaf Rachmawan Setiono, Oki Muslih, Muslih Dian Nuswantoro University Faculty of Computer Science Department of Informatics Engineering Semarang Indonesia

ISBN: (数字)9781665488396

ISBN: (纸本)9781665488396

COVID-19 virus has hit Indonesia since early March 2020. One of the government's efforts to prevent the spread of COVID-19 is to do physical distancing to require people to wear masks when doing activities outside the home. One way to overcome this problem is by detecting mask users to be more obedient and obedient to the rules, then the identification process is carried out for mask users and those who do not use masks. The process is carried out using the Convolutional Neural Network method. CNN is known to be superior and does not require pre-processing so it saves more time. In terms of algorithmic competence, CNN is considered capable of carrying out the data detection process well. Of the 1376 datasets used, 30 epochs, accuracy = 0.988, recall = 0.990, precision = 0.987, and F1 = 0.988 with the required detection time for each image between 4 to 5 seconds. © 2022 IEEE.

关键词： Convolutional neural networks

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Key Lessons from Microservices for Data Mesh Adoption 3

Key Lessons from Microservices for Data Mesh Adoption

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Ashraf, Abdulrahman Hassan, Ahmed Mahdi, Hani Ain Shams University Computer and Systems Engineering Department Cairo Egypt Egypt University of Informatics Ain Shams University Cairo Egypt

ISBN: (纸本)9798350306231

Data Mesh constitutes an architectural blueprint tailored to facilitate the execution of enterprise data platforms within expansive and intricate organizations. Its design aims to propel the broad adoption of analytics beyond a single platform and solitary implementation team. This research delves into the convergence of Microservices and Data Mesh structures, pinpointing vital learnings from Microservices that can inform and ease the transition to Data Mesh architecture. Several critical components like Domain-Driven Design, data consistency, team autonomy, technology selection, and scalability are included in this investigation, each with significant implications for the successful implementation of a Data Mesh. © 2023 IEEE.

关键词： Software engineering

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A Novel Approach to Visual Search in E-commerce Fashion Using Siamese Neural Network and Multi-Scale CNN 25

A Novel Approach to Visual Search in E-commerce Fashion Usin...

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25th International Electronics Symposium, IES 2023

作者： Ma'Rufah, Laila Karlita, Tita Sa'Adah, Umi Fauzi, Willy Achmat Politeknik Elektronika Negeri Surabaya Informatics and Computer Engineering Department Surabaya Indonesia PT. Sinergi Dimensi Informatika Surabaya Indonesia

ISBN: (纸本)9798350314731

The growing number of users on fashion e-retail platforms has highlighted the limitations of the current text-based search system, as users struggle to effectively describe the clothing products they are searching for. To address this challenge, the integration of a visual search system becomes essential. Visual search allows users to find similar products based solely on images. In this research, we propose the utilization of a Siamese Neural Network for implementing visual search in an e-commerce fashion. The Siamese network consists of two identical networks that process different images. By training the network to learn and represent the differences between images as embedding vectors, we enable the generation of similarity scores for pairs of images. To achieve accurate feature extraction, we employ a Multi-Scale CNN approach that captures both high and low-level features. Various experiments are conducted in this study, including the selection of multi-scale CNNs, distance metrics, and diverse data, to determine the network architecture that achieves the highest accuracy. Our findings demonstrate that utilizing the VGG19 model with a shallow layer and fractional distance metrics achieves an impressive accuracy of up to 97%. This indicates the effectiveness of our model in accurately predicting image similarity. © 2023 IEEE.

关键词： Electronic commerce

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Applying Soft System Methodology for a clearer understanding of the future Intensive Care Units 23

Applying Soft System Methodology for a clearer understanding...

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Proceedings of the 27th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Dimitrios Markopoulos Anastasios Tsolakidis Nikitas Karanikolas Aikaterini Marinagi Christos Skourlas Department of Informatics and Computer Engineering University Of West Attica Greece Department of Informatics and Computer Engineering University of West Attica Greece Agricultural University of Athens Greece

ISBN: (纸本)9798400716263

Intensive Care Units (ICUs) generate a large amount of valuable data related to the health status of patients. In addition, ICUs can leverage other sources of big data, such as structured data, text, video, and images. In this work, a framework for the future ICU system is proposed, which is based on the Soft System methodology (SSM) and the use of big data technology. The framework and the related activity models ensure that the ICU can have its particularities and specialties, as well as its core services and functions. The application of the framework also implies that ICU can provide ongoing expertise and training to upgrade its staff, can improve interoperability with the National Health System, and ICU staff intercommunication and remote services.

关键词： Big data Intensive Care MIMIC III Soft System Methodology

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The Basics of Neutrosophic Simulation for Converting Random Numbers Associated with a Uniform Probability Distribution into Random Variables Follow an Exponential Distribution

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Neutrosophic Sets and Systems 2023年 53卷 358-366页

作者： Jdid, Maissam Alhabib, Rafif Salama, A.A. Faculty of Informatics Engineering Al-Sham Private University Damascus Syria Department of Mathematical Statistics Faculty of Science Albaath University Homs Syria Department of Mathematics and Computer Science Faculty of Science Port Said University Port Said Egypt

When performing the simulation process, we encounter many systems that do not follow by their nature the uniform distribution adopted in the process of generating the random numbers necessary for the simulation process. Therefore, it was necessary to find a mechanism to convert the random numbers that follow the regular distribution over the period [0, 1] to random variables that follow the probability distribution that works on the system to be simulated. In classical logic, we use many techniques in the transformation process that results in random variables that follow irregular probability distributions. In this research, we used the inverse transformation technique, which is one of the most widely used techniques, especially for the probability distributions for which the inverse function of the cumulative distribution function can found. We applied this technique to generate neutrosophic random variables that follow an exponential distribution or a neutrosophic exponential distribution. This based on classical or neutrosophic random numbers that follow a regular distribution. We distinguished three cases according to the logic that each of the random numbers or the exponential distribution follows. We arrived at neutrosophic random variables that, when we use them in systems that operate according to an exponential distribution, such as queues and others, will provide us with a high degree of accuracy of results, and the reason for this is due to the indeterminacy provided by neutrosophic logic © 2023, Neutrosophic Sets and *** Rights Reserved.

关键词： Random variables

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Predicting Citation Counts with Machine Learning: A Citation Function Approach 11

Predicting Citation Counts with Machine Learning: A Citation...

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11th International Conference on Advanced informatics: Concept, Theory and Application, ICAICTA 2024

作者： Basuki, Setio Sari, Zamah Indrabayu, Rizky Fauzan, Reza Wardhana, Aulia Arif Tsuchiya, Masatoshi Universitas Muhammadiyah Malang Informatics Engineering Malang Indonesia Politeknik Negeri Banjarmasin Department of Electical Engineering Banjarmasin Indonesia Doctoral School Wroclaw University of Science and Technology Wroclaw Poland Toyohashi University of Technology Department of Computer Science and Engineering Toyohashi Japan

ISBN: (纸本)9798331520311

This paper develops a Machine Learning model to estimate the citation counts of research papers. The model uses citation functions, representing the intentions of the paper's author when making citations of previous works, to estimate the number of citations. These intentions include introducing a research topic, comparing, and criticizing previous works, etc. Three predictors have been developed based on citation functions: citing sentence, regular sentence, and reference. The prediction is seen as a regression and classification problem by pre-grouping the number of citations into high-count, medium-count, and low-count. The dataset was obtained from the International Conference on Learning Representations (ICLR) 2017-2020 containing 5,156 accepted and rejected papers. This paper uses only the accepted papers since the main task is to predict the number of citations of accepted/published papers. To obtain the number of citations one year after publication, this paper uses the API provided by Semantic Scholar. According to experiments, the best results in classification reach 98.33% accuracy, and in regression, the results reach 0.3 on both RMSE and MAE. The feature labeled 'citing paper dominant,' which reflects the superiority of the citing paper over the cited paper, has proven highly effective in delivering the best prediction results, even though it is sparsely represented in the dataset. In conclusion, citation function-based predictors are effective in estimating the future impact of a paper. © 2024 IEEE.

关键词： Contrastive Learning

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AVIG: A Real-Time Visual Inspection for Guava Grading System Using computer Vision and XGBoost 23

AVIG: A Real-Time Visual Inspection for Guava Grading System...

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8th International Conference on Sustainable Information engineering and Technology, SIET 2023

作者： Setyawan, Raden Arief Perdana, Rizal Setya Harilegawa, Made Wena Balantimuhe, Alan Stevrie Basuki, Achmad Department of Electrical Engineering Faculty of Engineering Brawijaya University Malang Indonesia Department of Informatics Engineering Faculty of Computer Science Brawijaya University Malang Indonesia Directorate of Information Technology Brawijaya University Malang Indonesia

ISBN: (纸本)9798400708503

The conventional approach to manual inspection can be described by its considerable time and labor demands and the unavoidable occurrence of human error when faced with the inspection of a large number of products. Therefore, the application of an automated visual inspection system is frequently employed. The camera(s) can easily capture visual features for automatic visual inspection. However, it is essential to note that there are limitations when using this method to estimate the size-based grading system. The volume measurement is an essential feature in the field of automatic image analysis for fruit and vegetable grading, specifically guava. In this study, we propose a novel image analysis approach that utilizes a dual-view camera system to enhance the accuracy of volume approximation, particularly in scenarios involving non-perfect spherical objects. Hence, this research presents the training process of a comprehensive model for estimating the volume of objects with irregular shapes, named AVIG (Automatic Visual Inspection for Grading). The model utilizes the ellipsoid approximation technique, a dual-view camera, and XGBoost algorithm. The results from the accuracy comparison demonstrate that XGBoost shows the highest degree of accuracy, specifically 86%, thereby outperforming the performance of other algorithms. The comprehensive experiment conducted in this study has shown that the proposed architecture, AVIG, exhibits superior performance compared to previous research in terms of automatic evaluation of moving objects. © 2023 ACM.

关键词： computer vision

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Classification of Bird Species using K-Nearest Neighbor Algorithm 10

Classification of Bird Species using K-Nearest Neighbor Algo...

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10th International Conference on Cyber and IT Service Management, CITSM 2022

作者： Budiman, Ichsan Ramdania, Diena Rauda Gerhana, Yana Aditia Putra, Alif Rakasha Pratama Faizah, Nisairrizqy Nabilah Harika, Maisevli Uin Sunan Gunung Djati Bandung Department of Informatics Bandung Indonesia Politeknik Negeri Bandung Computer Engineering and Informatics Department Bandung Indonesia

ISBN: (数字)9781665460743

ISBN: (纸本)9781665460743

Birds play an essential role in the functioning of the world's ecosystems by directly impacting human health, economy, and food production and benefiting millions of other species. The diversity of bird habitats shows that there are many types of bird species. This study aims to provide a new treasure for the ontology field by applying a machine learning algorithm to classify bird species based on K-Nearest Neighbor (KNN). A total of 400 species of bird images with a total of 58388 images of data were tested in this study. The test scenario was carried out in 3 stages: 400, 200, and 100 species. The results of testing the accuracy of the K-Nearest Neighbor model applied to the bird image dataset are 26.846%, with a value of K=1. The comparison of training data with test data is 95:5 percent (%). This result shows that the KNN algorithm can classify bird species. © 2022 IEEE.

关键词： Birds

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Estimating Uncertainty in Deep Learning Methods and Applications

Estimating Uncertainty in Deep Learning Methods and Applicat...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Alqarafi, Abdulrahman Batool, Hina Abbas, Tahir Janjua, Jamshaid Iqbal Ramay, Sadaqat Ali Ahmed, Munir College of Computer Science and Engineering Taibah University Medina42353 Saudi Arabia Times Institute Department of Computer Science Multan Pakistan Lahore Pakistan College of Informatics Korea University Seoul02841 Korea Republic of

ISBN: (纸本)9798350367560

Uncertainty estimation in deep learning has emerged as a crucial area of research due to its significance in enhancing model reliability and decision-making in critical applications. This article explores various methods and applications of uncertainty estimation in deep learning, aiming to provide insights into its importance, methods, and potential impact. Through a comprehensive literature review and analysis, the study identifies key findings regarding the effectiveness, limitations, and ethical considerations associated with uncertainty estimation techniques. The results reveal the diverse range of methodologies employed, including Bayesian approaches, ensemble methods, and Monte Carlo sampling, each with its strengths and drawbacks. Furthermore, the article discusses the implications of uncertainty estimation in deep learning for fields such as healthcare, autonomous systems, and safety-critical domains. Overall, this study underscores the significance of uncertainty estimation in deep learning and provides valuable insights for researchers and practitioners in the field. © 2024 IEEE.

关键词： Adversarial machine learning

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A survey of disaster management datasets

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Journal of Information and Telecommunication 2025年

作者： Abbas, Sohail Talib, Manar Abu Nasir, Qasim Belal, Omar Al-Haidary, Mohammed Emad Ahmed, Ibtihal Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

Natural disasters are characterized as a combination of natural hazards and vulnerabilities that endanger communities and result in significant financial and human losses. The uncertain occurrence of these events and the limited resources in affected regions constitute their core attributes. Leveraging information technology to assess, forecast, and depict these occurrences can improve handling such disasters. Artificial Intelligence (AI) technology has recently been employed practically in various disciplines for various applications, and disaster management applications are among the most critical areas. The dataset is essential to any AI system and must be corrected to deliver reliable results. Disaster datasets are presented and described in literature. This study examines the datasets available for disaster evaluation and detection frameworks. A general framework consisting of five main dataset properties is designed for the presentation of the datasets. Some features include general information, data volume, recording environment, data nature, and evaluation. There are two or three sub-properties under each of the five main properties. Each dataset is examined in comparison to the listed attributes. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Artificial Intelligence datasets properties image dataset Natural disasters social media dataset

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