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A Review of Deep-Learning-Based Models for Afaan Oromo Fake News Detection on Social Media Networks 1

3rd International Conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Arega, Kedir Lemma Tune, Kula Kekeba Beyene, Asrat Mulatu Tariku, Wegderes Bune, Nurhussen Menza ICT Infrastructures and Administrator Team Leader Ambo University Ambo Ethiopia Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Department of Electrical and Computer Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Department of Information Systems Mizan Tepi University Tepi Ethiopia Project Management System Support Expert Ethio Telecom Addis Ababa Ethiopia

ISBN: (数字)9783031862960

ISBN: (纸本)9783031862953

This study explores modern fake news detection techniques in the Afan Oromo language, aiming to contribute to advancing research in this field. It explores deep learning methods for transformers, including Bidirectional Encoder Representations, Attention, and Generative Adversarial Networks, and their applications in various industries such as machine translation, email spam detection, information extraction, summarization, medical, and question-answering. The paper investigates false news identification, its categories, characteristics, and taxonomy, and compares machine learning and deep learning algorithms for fake news, liars, and corpus datasets. It proposes that future studies use picture data and deeper learning network topologies to decrease erroneous outcomes. The research endeavor aims to illuminate and advance the understanding of the complex difficulties and possibilities that exist in this particular sector, thereby acting as a crucial resource for decision-makers, academics, and professionals, while also encouraging the creation of more effective and trustworthy methodologies that can adequately address and alleviate the dissemination of false information shared on various social media channels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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RETRACTED ARTICLE: Toward trustworthy human suspicious activity detection from surveillance videos using deep learning

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Soft Computing 2023年第2期28卷 467-467页

作者： Buttar, Ahmed Mateen Bano, Mahnoor Akbar, Muhammad Azeem Alabrah, Amerah Gumaei, Abdu H. Department of Computer Science University of Agriculture Faisalabad Faisalabad Pakistan Software Engineering Department LUT University Lappeenranta Finland Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

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Multi-Resolution Localization of Individual Logs in Wooden Piles Utilizing YOLO with Tiling on Client/Server Architectures 33

Multi-Resolution Localization of Individual Logs in Wooden P...

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33rd European Modeling and Simulation Symposium, EMSS 2021

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg4785 Austria Faculty of Computer Science and Mathematics University of Passau Innstraße 43 Passau94032 Germany

In industrial domains with time and cost intensive manual or semi-automated inspection the demand for automation is high. Utilizing state of the art deep learning models for localization in vision-based domains such as wood log analysis, the precision can be increased while reducing the demand for manual inspection. In this paper a YOLO network is trained on wood log images to allow for detection of single wood piles in images with hundreds and thousands instances. Due to the high variability in scale and large amount of wood logs within the images, common YOLO architectures are not applicable. Thus, tiling is necessitated to implicitly form a multi-resolution image pyramid. Due to lack in training data, besides common data augmentation modelling of different seasonal and weather conditions is applied. The wood log detection process can be run on a client/server architecture to allow for both, preview and refined results. Evaluation on real-world data sets shows an log detection accuracy of 82,9% utilizing a tiny YOLO model and 94,1% with a fully connected YOLO model, respectively. © 2021 The Authors.

关键词： Deep learning

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T

arXiv

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arXiv 2020年

作者： Aloufi, Khalid S. Alsewari, Abdulrahman A. Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Mala sia26300 Department of Computer Engineering College of Computer Science and Engineering Taibah University Medina Saudi Arabia

This research presents a methodology for trusting the provenance of data on the web. The implication is that data does not change after publication and the source of the data is stable. There are different data that should not change over time, such as published information in books and similar documents as well as news or events reported on the web. If the data change after publication on the web, the web pages that reference the unstable data will lose points of interest or link to different resources. With the current move to linked data and the semantic web, this is becoming a greater obstacle to be solved. This research presents a methodology for establishing trusted information using an encoded reference of the data embedded in its URI, which creates a stable reference of the data and a method for ensuring its provenance stability. After applying the methodology, the results showed that the methodology is highly applicable and has no overhead cost over the loading time. The novel solution can be applied directly to any data portals or web content management systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Websites

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Image-to-Image Translation Based Face Photo De-Meshing Using Gans

SSRN

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SSRN 2023年

作者： Jabbar, Abdul Assam, Muhammad Shloul, Tamara Al Hameed, Hala Abdel Al-Kahtani, Noor Alkahtani, Hend Khalid Ghamry, Nivin A. Tag El Din Bro, Wahii Elsayed M. Amin, Muhammad Shoib College of Computer Science and Technology Zhejiang University Hangzhou310027 China School of Software Engineering East China Normal University Shanghai200050 China Liwa College of Technology College of Business administration United Arab Emirates Khaybar Applied College Taibah University Saudi Arabia Department of Health Information Management and Technology College of Public Health Imam Abdulrahman Bin Faisal University Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Faculty of Computers and Artificial intelligence Cairo University Giza Egypt Faculty of Engineering and Technology Future University New Cairo11835 Egypt

Most of the existing face photo de-meshing methods have accomplished promising results;there are certain quality problems with these methods like the inpainted regions would appear blurry and unpleasant boundaries becoming visible. Such artifacts cause generated face photos unreal. Therefore, we propose an effective image-to-image translation framework called Face De-meshing Using Generative Adversarial Networks (De-mesh GANs). The De-mesh GANs is a two-stage model: (i) binary mask generating module, is a three convolution layers-based encoder-decoder network architecture that automatically generates a binary mask for the meshed region, and (ii) face photo de-meshing module, is a GANs-based network that eliminates the mesh mask and synthesizes the meshed area. An arrangement of careful losses (reconstruction loss, adversarial loss, and perceptual loss) is used to reassure the better quality of the de-mesh face photos. To facilitate the training of the proposed model, we have designed a dataset of clean/corrupted photo pairs using the CelebA dataset. Qualitative and quantitative evaluations of the De-mesh GANs on real-world corrupted face photo images show better performance than the previously proposed face photo de-meshing models. Furthermore, we also offer the ablation study for performance assessment of the additional network i.e., perceptual network. © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Spatio-temporal split learning for privacy-preserving medical platforms: Case studies with COVID-19 CT, X-Ray, and cholesterol data

arXiv

引用

arXiv 2021年

作者： Miss, Yoo Jeong Ha Yoo, Minjae Lee, Gusang Jung, Soyi Choi, Sae Won Kim, Joongheon Yoo, Seehwan Department of Electrical and Computer Engineering Korea University Seoul Korea Republic of School of Software Hallym University Chuncheon Korea Republic of Office of Hospital Information Seoul National University Hospital Seoul Korea Republic of Department of Mobile Systems Engineering Dankook University Yongin Korea Republic of

Machine learning requires a large volume of sample data, especially when it is used in high-accuracy medical applications. However, patient records are one of the most sensitive private information that is not usually shared among institutes. This paper presents spatio-temporal split learning, a distributed deep neural network framework, which is a turning point in allowing collaboration among privacy-sensitive organizations. Our spatio-temporal split learning presents how distributed machine learning can be efficiently conducted with minimal privacy concerns. The proposed split learning consists of a number of clients and a centralized server. Each client has only has one hidden layer, which acts as the privacy-preserving layer, and the centralized server comprises the other hidden layers and the output layer. Since the centralized server does not need to access the training data and trains the deep neural network with parameters received from the privacy-preserving layer, privacy of original data is guaranteed. We have coined the term, spatio-temporal split learning, as multiple clients are spatially distributed to cover diverse datasets from different participants, and we can temporally split the learning process, detaching the privacy preserving layer from the rest of the learning process to minimize privacy breaches. This paper shows how we can analyze the medical data whilst ensuring privacy using our proposed multi-site spatio-temporal split learning algorithm on Coronavirus Disease-19 (COVID-19) chest Computed Tomography (CT) scans, MUsculoskeletal RAdiographs (MURA) X-ray images, and cholesterol levels. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

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Corrections to “An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification”

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IEEE Access 2025年 13卷 31659-31659页

作者： Khaoula Taji Ali Sohail Tariq Shahzad Bilal Shoaib Khan Muhammad Adnan Khan Khmaies Ouahada Electronic Systems Information Processing Mechanics and Energy Laboratory Kenitra Faculty of Sciences Ibn Tofail University Kenitra Morocco Department of Computer Science Minhaj University Lahore Lahore Pakistan Department of Electrical and Electronic Engineering Science University of Johannesburg Johannesburg South Africa Department of Computer Science Green International University Lahore Pakistan Department of Software Faculty of Artificial Intelligence and Software Gachon University Seongnam-si Republic of Korea

Presents corrections to the paper, Corrections to “An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification”.

关键词： Plant diseases Metaheuristics

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A survey of static formal methods for building dependable industrial automation systems

arXiv

引用

arXiv 2021年

作者： Sinha, Roopak Patil, Sandeep Gomes, Luis Vyatkin, Valeriy Department of IT and Software Engineering Auckland University of Technology Auckland New Zealand Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå97187 Sweden Electrical and Computer Engineering Department NOVA School of Science and Technology NOVA University of Lisbon Portugal Centre of Technology and Systems UNINOVA Caparica2829-517 Portugal Computer Technologies International Laboratory ITMO University Saint Petersburg197101 Russia Department of Electrical Engineering in Automation Aalto University Aalto00076 Finland

Industrial automation systems (IAS) need to be highly dependable;they should not merely function as expected but also do so in a reliable, safe, and secure manner. Formal methods are mathematical techniques that can greatly aid in developing dependable systems and can be used across all phases of the system development life cycle (SDLC), including requirements engineering, system design and implementation, verification and validation (testing), maintenance, and even documentation. This state-of-the-art survey reports existing formal approaches for creating more dependable IAS, focusing on static formal methods that are used before a system is completely implemented. We categorize surveyed works based on the phases of the SDLC, allowing us to identify research gaps and promising future directions for each phase. Copyright © 2021, The Authors. All rights reserved.

关键词： Formal verification

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A kNN approach based on ICP metrics for 3D scans matching: an application to the sawing process ⁎

引用

IFAC-PapersOnLine 2021年第1期54卷 396-401页

作者： Sylvain Chabanet Philippe Thomas Hind Bril El-Haouzi Michael Morin Jonathan Gaudreault Université de Lorraine CNRS CRAN F-88000 Epinal France FORAC Research Consortium Université Laval Québec QC Canada Department of Operations and Decision Systems Université Laval Québec QC Canada Department of Computer Science and Software Engineering Université Laval Québec QC Canada

The Canadian wood industry use sawing simulators to digitally break a log into a basket of lumbers. However, those simulators tend to be computationally intensive. In some cases, this renders them impractical as decision support tools. Such a use case is the problem of dispatching large volume of wood to several sawmills in order to maximise total yield in dollars. Fast machine learning metamodels were recently proposed to address this issue. However, the approach needs a feature extraction step which could result in a loss of information. Conversely, it was proposed to directly make use of the raw information, available in the 3D scans of the logs typically used by a recent sawmill simulator, in order to retain that information. Here, we improve upon that method by reducing the computational cost incidental with the processing of those raw scans.

关键词： Sawing Simulation Iterative Closest Point Nearest Neighbours Machine Learning Application

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Analysis of modern security plugins for wordpress

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AIP Conference Proceedings 2024年第1期3084卷

作者： Georgi Stoyanov Adelina Aleksieva-Petrova Milen Petrov Department of Software Engineering Sofia University “St. Kl. Ohridski” James Baucher 5 Blvd Sofia 1000 Bulgaria Department of Computer Systems Technical University of Sofia 8 Kliment Ohridski Blvd Sofia 1000 Bulgaria

WordPress is the world’s most popular content management system. In recent years, websites using WordPress have been a popular target for hackers due to the platform’s popularity. To solve these problems, users turn to security plugins to help them deal with security threats. However, there is no in-depth security analysis of the WordPress plugin system. In this regard, the paper aims to analyze the work of the main security plugins, including code analysis, testing with different types of threats, checking the data that they collect, and evaluating their strengths and weaknesses. The working principles and implementations of modern security plugins have been described and the criteria for selecting the studied plugins have been defined. The results have been summarized and the approaches used by the different plugins have been compared.

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