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Effectiveness of generative adversarial networks in denoising medical imaging (CT/MRI images)

Multimedia Tools and Applications 2024年 1-25页

作者： Ramadass, Sudhir Narayanan, Sreekumar Kumar, Rajiv K, Thilagavathi Data Analytics Sterck Systems Private Limited Chennai Nungambakkam India Network and Infrastructure Management Faculty of Engineering and Technology Botho University Gaborone Botswana School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Department of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India

For clinical analysis or medical intrusion, imaging technology is a potent tool for producing visual images of a body's inside. However, several existing methods include restricted access to high-quality and diverse datasets, overfitting, a lack of interpretability, high computational costs, varying medical imaging data, and ethical issues concerning patient privacy, consent, and bias. To overcome these issues, the effectiveness of generative adversarial networks in denoising medical imaging (CT/MRI images) is proposed. The input CT images are first gathered and preprocessed to reduce undesired noise using Altered Phase Preserving Dynamic Range Compression (APPDRC). The General Adversarial network (GAN) is then given these preprocessed images to cope with the noisy areas of CT/MRI while also complying with maintaining image structures such as boundary details while preventing over-smoothing, and the weight parameters for the GAN are optimized using the Golden Eagle Optimization (GEO) technique. General Adversarial network (GAN) has piqued the interest of academics studying medical image-denoising mechanisms and developing sophisticated medical image-denoising algorithms based on deep learning. In light of reducing the difficulty in feature learning and improving final diagnosis accuracy, data preprocessing is necessary and crucial in gan-based medical image denoising methods. This work focuses on GAN-based effective denoising approaches in CT/MRI. The potential challenges and research objectives are in the context of data preprocessing in medical image denoising. Because of how CT/MRI images are acquired, it was observed that noise in CT/MRI data follows the distribution. Generative Adversarial networks (GANs) for denoising medical imaging, mainly CT and MRI images, have yielded promising results of 99% accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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The Effectiveness of SPBE Application and Infrastructure Audit Tools in Enhancing Information Technology Governance Using COBIT 2019

The Effectiveness of SPBE Application and Infrastructure Aud...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Irzavika, Nindy Mahda, Friyogi Resvy Universitas Pembangunan Nasional Veteran Jakarta Faculty of Computer Science Jakarta Indonesia Badan Riset Dan Inovasi Nasional Research Center for Data and Information Sciences Jakarta Indonesia

ISBN: (纸本)9798350307306

Information Technology (IT) has become the core of nearly every aspect of business and organizations. Effective IT governance is vital for achieving organizational goals and managing risks. This study uses COBIT 2019 to assess the SPBE application and infrastructure audit tool effectiveness. SPBE Application and Infrastructure Audit Tools are used in Indonesian central and local government agencies. SPBE aims to create a clean, efficient, transparent government system and trusted public services. The SPBE Audit Tools assist in auditing SPBE applications and infrastructures. SPBE Audit tools play a crucial role in monitoring and evaluating SPBE applications and infrastructure in Indonesia, contributing to improved IT Governance. However, effective governance requires risk management to address potential threats and vulnerabilities. COBIT 2019 is designed to support digital transformation and adapt to the dynamic changes in the IT landscape. This study uses COBIT 2019 as a framework for assessing the effectiveness of SPBE application and infrastructure audit tools in enhancing information technology governance because COBIT has more advantages compared to other frameworks. Based on the data, applications that conduct audits using SPBE Audit Tools show an improvement in the maturity level acquired each year. This suggests that SPBE Audit Tools contribute to achieving SPBE monitoring and evaluation objectives, including measuring progress, enhancing SPBE quality, and improving public services at IPPD. © 2023 IEEE.

关键词： Risk management

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ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural networks

arXiv

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arXiv 2024年

作者： Chen, Zhaoliang Wu, Zhihao Sadikaj, Ylli Plant, Claudia Dai, Hong-Ning Wang, Shiping Cheung, Yiu-Ming Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Department of Computer Science Hong Kong Baptist University Hong Kong Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Faculty of Computer Science and with the research network Data Science @ Uni Vienna University of Vienna Vienna1090 Austria

Although Graph Neural networks (GNNs) have exhibited the powerful ability to gather graph-structured information from neighborhood nodes via various message-passing mechanisms, the performance of GNNs is limited by poor generalization and fragile robustness caused by noisy and redundant graph data. As a prominent solution, Graph Augmentation Learning (GAL) has recently received increasing attention in the literature. Among the existing GAL approaches, edge-dropping methods that randomly remove edges from a graph during training are effective techniques to improve the robustness of GNNs. However, randomly dropping edges often results in bypassing critical edges. Consequently, the effectiveness of message passing is weakened. In this paper, we propose a novel adversarial edgedropping method (ADEdgeDrop) that leverages an adversarial edge predictor guiding the removal of edges, which can be flexibly incorporated into diverse GNN backbones. Employing an adversarial training framework, the edge predictor utilizes the line graph transformed from the original graph to estimate the edges to be dropped, which improves the interpretability of the edge-dropping method. The proposed ADEdgeDrop is optimized alternately by stochastic gradient descent and projected gradient descent. Comprehensive experiments on eight graph benchmark datasets demonstrate that the proposed ADEdgeDrop outperforms state-of-the-art baselines across various GNN backbones, demonstrating improved generalization and robustness. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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BlueVR: Design and Evaluation of a Virtual Reality Serious Game for Promoting Understanding towards People with Color Vision Deficiency

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Proceedings of the ACM on Human-computer Interaction 2023年第CHIPLAY期7卷 289–318页

作者： You, Ruoxin Zhou, Yihao Zheng, Weicheng Zuo, Yiran Barrera Machuca, Mayra Donaji Tong, Xin University College London Gower Street LondonWC1E 6BT United Kingdom School of Computer Science and Technology East China Normal University Shanghai China Duke Kunshan University Kunshan China Shanghai Yue Kong Pao Senior Secondary School Shanghai China Faculty of Computer Science Dalhousie University 6050 University Avenue HalifaxNS Canada Data Science Research Center Duke Kunshan University Kunshan China

People with color vision deficiency (CVD) often encounter color-related challenges in their daily life, which are difficult for those with non-CVD to comprehend fully. Therefore, we designed a Virtual Reality (VR) serious game, BlueVR, to simulate challenging scenarios encountered by people with CVD and facilitate understanding from people with non-CVD. We conducted an empirical study with thirty participants with non-CVD and six participants with CVD to evaluate the opportunities and challenges of BlueVR. Our findings suggest that BlueVR increased people with non-CVD's understanding, awareness, and perspective-taking abilities towards people with CVD. Moreover, interviews with participants with CVD revealed that BlueVR accurately depicts their real-life discomforts and meets their expectations to improve potential social awareness. This research contributes valuable insights into the mechanisms underlying the effectiveness of VR serious games in promoting understanding and design implications for future game development. © 2023 ACM.

关键词： Virtual reality

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Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

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SN computer science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities 24

On the Effectiveness of Function-Level Vulnerability Detecto...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hong Kong Jin YinHu Laboratory Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Colorado Colorado Springs Department of Computer Science Colorado Springs Colorado United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400702174

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. © 2024 ACM.

关键词： Open source software

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Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

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computer Modeling in Engineering & sciences 2024年第6期139卷 3353-3385页

作者： Murugan Palanikumar Nasreen Kausar Dragan Pamucar Seifedine Kadry Chomyong Kim Yunyoung Nam Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Mathematics Faculty of Arts and ScienceYildiz Technical UniversityEsenlerIstanbul34220Turkey Department of Operations Research and Statistics Faculty of Organizational SciencesUniversity of BelgradeBelgrade11000Serbia College of Engineering Yuan Ze UniversityTaoyuan City320315TaiwanChina Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos1102-2801Lebanon ICT Convergence Research Center Soonchunhyang UniversityAsan31538South Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538South Korea

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this *** is provided of the scoring function,accuracy function and operational laws of the log q-rung *** algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming ***,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify *** chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile *** numbers q are used to express the results of the *** demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

关键词： Vague set aggregating operators euclidean distance hamming distance decision making

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ARSlice:Head-Mounted Display Augmented with Dynamic Tracking and Projection

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Journal of computer science & Technology 2022年第3期37卷 666-679页

作者： Yu-Ping Wang Sen-Wei Xie Li-Hui Wang Hongjin Xu Satoshi Tabata Masatoshi Ishikawa Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Institute of Semiconductors Guangdong Academy of SciencesGuangzhou 510651China Data Science Research Division Information Technology CenterThe University of TokyoTokyo 113-8656Japan Core Network Department Advanced Operations Development DivisionKDDI CooperationTokyo 206-0034Japan Tokyo University of Science Tokyo 113-8601Japan

Computed tomography(CT)generates cross-sectional images of the *** CT images has been a challenging *** emergence of the augmented and virtual reality technology has provided promising ***,existing solutions suffer from tethered display or wireless transmission *** this paper,we present ARSlice,a proof-of-concept prototype that can visualize CT images in an untethered manner without wireless transmission *** ARSlice prototype consists of two parts,the user end and the projector *** employing dynamic tracking and projection,the projector end can track the user-end equipment and project CT images onto it in real *** user-end equipment is responsible for displaying these CT images into the 3D *** main feature is that the user-end equipment is a pure optical device with light weight,low cost,and no energy *** experiments demonstrate that our ARSlice prototype provides part of six degrees of freedom for the user,and a high frame *** interactively visualizing CT images into the 3D space,our ARSlice prototype can help untrained users better understand that CT images are slices of a body.

关键词： augmented and virtual reality computed tomography(CT)image visualization interactive visualization

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Twin Transition or Competing Interests? Validation of the Artificial Intelligence and Sustainability Perceptions Inventory (AISPI)

arXiv

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arXiv 2025年

作者： Bush, Annika Research Center Trustworthy Data Science and Security University Alliance Ruhr Faculty of Computer Science Technical University Dortmund Dortmund Germany

As artificial intelligence (AI) and sustainability initiatives increasingly intersect, understanding public perceptions of their relationship becomes crucial for successful implementation. However, no validated instrument exists to measure these specific perceptions. This paper presents the development and validation of the Artificial Intelligence and Sustainability Perceptions Inventory (AISPI), a novel 13-item instrument measuring how individuals view the relationship between AI advancement and environmental sustainability. Through factor analysis (N = 105), we identified two distinct dimensions: ‘Twin Transition’ and ‘Competing Interests’. The instrument demonstrated strong reliability (α = .89) and construct validity through correlations with established measures of AI and sustainability attitudes. Our findings suggest that individuals can simultaneously recognize both synergies and tensions in the AI-sustainability relationship, offering important implications for researchers and practitioners working at this critical intersection. This work provides a foundational tool for future research on public perceptions of AI’s role in sustainable development. © 2025, CC BY-NC-ND.

关键词： Sustainable development goals

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Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

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Journal of Bionic Engineering 2023年第3期20卷 1263-1295页

作者： Jeffrey O.Agushaka Absalom E.Ezugwu Oyelade N.Olaide Olatunji Akinola Raed Abu Zitar Laith Abualigah School of Mathematics Statisticsand Computer ScienceUniversity of KwaZulu-NatalKing Edward AvenuePietermaritzburg CampusPietermaritzburg3201KwaZulu-NatalSouth Africa Department of Computer Science Federal University of LafiaLafia950101Nigeria Unit for Data Science and Computing North-West University11 Hoffman StreetPotchefstroom2520South Africa Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044Abu DhabiUnited Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia

This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.

关键词： Improved dwarf mongoose Nature-inspired algorithms Constrained optimization Unconstrained optimization Engineering design problems

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