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Correction: Web service location‑allocation using discrete NSGA‑II with matrix based genetic operations and a repair mechanism

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Journal of Ambient Intelligence and Humanized computing 2023年第1期15卷 797-798页

作者： Verma, Shanu Pant, Millie Snasel, Vaclav Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Roorkee India Centre for Artificial Intelligence and Data Science Indian Institute of Technology Roorkee Roorkee India Department of Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic

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A Novel Method of Evaluating the Harmonic Power Analyzer's Performance for PC-Based Testing and Measurement Applications Related to Electromagnetic Compatibility

A Novel Method of Evaluating the Harmonic Power Analyzer's P...

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computing and Data Analytics (ICCDA), International Conference on

作者： J Adeline Sneha N. R Wilfred Blessing Suresh Palarimath S Poorana Senthilkumar Manigandansekar G Sutherlin Subitha School of Computing Asia Pacific University of Technology & Innovation Kuala Lumpur Malaysia College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Dr. N. G. P Arts and Science College Coimbatore Tamil Nadu Newyork University Abudhabi United Arab Emirates Department of Computer Science and Engineering Stella Mary's College of Engineering Kanyakumari Dt Tamil Nadu

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This paper discusses the methods for performance analysis of a PC-based harmonic power analyzer used in EMC testing and measuring applications. According to IEC 61000-3-2, this is an easy-to-use, reasonably priced way to assess how well a harmonics emission measurement system is performing in real time. There is no mention of any alternative approach or calibration method to verify the performance analysis in the IEC 61000-3-2 standard. Different vendor equipment is used to create a single harmonic emission monitoring setup. Thus, getting calibrating services is a highly difficult task that also requires a significant financial investment. Using this straightforward, low-cost analysis technique, it is possible to monitor the operation of the measurement setup locally and address all of these problems. Overall, the paper provides a comprehensive overview of the new approach to performance analysis of a PC-based harmonics power analyzer for EMC test and measurement applications, emphasizing the importance of a simple, cost-effective, and reliable method for maintaining measurement system performance.

关键词： Power measurement Power system harmonics Harmonic analysis Electromagnetic compatibility Real-time systems Performance analysis IEC Standards Standards Monitoring Testing

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On the Perception of Small Sub-graphs

arXiv

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arXiv 2023年

作者： Miller, Jacob Ghoniem, Mohammad Wu, Hsiang-Yun Purchase, Helen C. Department of Computer Science University of Arizona United States Luxembourg Institute of Science and Technology Luxembourg St. Pölten University of Applied Sciences Austria Department of Human-Centred Computing University of Monash Australia

Interpreting a node-link graph is enhanced if similar sub-graphs (or ‘motifs’) are depicted in a similar manner – that is, they have the same visual form. Small motifs within graphs may be perceived to be identical when they are structurally dissimilar, or may be perceived to be dissimilar when they are identical. This issue primarily relates to the Gestalt principle of similarity, but may also include an element of quick, low-level pattern-matching. We believe that if motifs are identical, they should be depicted identically;if they are nearly-identical, they should be depicted nearly-identically. This principle is particularly important in domains where motifs hold meaning and where their identification is important. We identified five small motifs: bi-cliques, cliques, cycles, double-cycles, and stars. For each, we defined visual variations on two dimensions – same or different structure, same or different shape. We conducted a crowd-sourced empirical study to test the perception of similarity of these varied motifs, and found that determining whether motifs are identical or similar is affected by both shape and structure. © 2023, CC BY.

关键词： Pattern matching

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On a Survival Gradient Boosting, Neural Network and Cox PH Based Approach to Predicting Dementia Diagnosis Risk on ADNI

On a Survival Gradient Boosting, Neural Network and Cox PH B...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Henry Musto Daniel Stamate Doina Logofatu Lahcen Ouarbya Department of Computing Goldsmiths College University of London United Kingdom Department of Computing Goldsmiths College University of London and School of Health Sciences The University of Manchester United Kingdom Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Germany

ISBN: (数字)9798350337488

ISBN: (纸本)9798350337495

In recent years, attention within the clinical prediction community has turned to the use of survival machine learning as a tool for predicting the risk of developing a disease as a function of time. The current work seeks to contribute to existing literature which demonstrates the utility of these methods when applied to a dementia prediction context. We use the Alzheimer's Disease Neuroimaging Initiative ADNI dataset and model deterioration within two distinct groups, those deemed cognitively normal and those with a formal diagnosis of Mild Cognitive Impairment. In agreement with existing literature we find that survival machine learning outperforms standard survival analysis methods such as Cox PH model, and has very good predictive ability. We propose an innovative approach to predicting dementia diagnosis risk on ADNI, which explores the use of survival neural network and survival extreme gradient boosting techniques that have hitherto seldom been applied to this context. The stability of our models was investigated within a Monte Carlo simulation framework.

关键词： Analytical models Biological system modeling Neural networks Predictive models Boosting Stability analysis Alzheimer's disease

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Enhancing Safety and Independence: A Smart Switch Solution for Elderly Individuals Living Independently

Enhancing Safety and Independence: A Smart Switch Solution f...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Prakash Babu Nazia Ahmad Sajithunisa Hussain Remya P. George Ravi Shankar Shukla Vishal Jain Dept of IT Acharya Institute of Technology Bangalore India Computer Science Department The Applied College Imam Abdulrahman Bin Faisal University Saudi Arabia Department of Computer Science College of Computing and Informatics Saudi Electronic University Saudi Arabia Sharda University India

As the world's population ages, more and more people will need assistance with daily tasks, such as making sure their homes are safe and secure for themselves and their loved ones. This study presents a new smart switch technology that aims to make houses safer, more independent, and more comfortable for older people. The smart switch uses the Internet of Things (IoT) and an intuitive user interface to make it easy for the elderly to control their household appliances, which in turn reduces the likelihood of accidents and allows caregivers to remotely monitor their well-being. This switch has been meticulously designed to meet the unique physical and cognitive requirements of older users by actively involving those users in the design process. Initial research shows that users' home environment management skills significantly increase, which has a favorable effect on their everyday lives and overall health. By improving the lives of elderly people who live independently, the study does double duty: it advances smart home technology and meets a pressing social need.

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Computational Model Based on CNN to Identify Masses from Liver Images 2

Computational Model Based on CNN to Identify Masses from Liv...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Jyothirmai, Ch. N. V. Hemalakshmi, G.R. Prakash, N.B. Upadhye, Vijay Jagdish Patil, Harshal Taqui, Syed Noeman Potti Sreeramulu Chalavadhi Mallikarjuna Rao College of Engineering and Technology Department of CSE Andhra Pradesh 521 225 India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh 466114 India National Engineering College Department of Electrical and Electronics Engineering Kovilpatti 628503 India Parul Institute of Applied Sciences Parul University Research and Development Cell Department of Microbiology Gujarat 391760 India Department of Computer Science and Engineering Maharashtra 412 115 India Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Saveetha School of Engineering Chennai602105 India

ISBN: (纸本)9798350327533

Therefore, Convolutional Neural Networks (CNNs) are capable of predicting or extrapolating from identified patterns to unknown data, enabling the analysis and extraction of knowledge from large images, in order to produce consistent outputs for inputs not previously presented. On the downside, the training process takes a long time as it is computationally expensive. Aiming to reduce the computational time to perform the training of the neural network (NN), and the analysis of images during the testing process, as well as taking advantage of the natural distributed characteristic of ANN, it was decided to transfer the processing-intensive parts of the tool from CPU (Central Processing Unit) to a GPU (Graphics Processing Unit). Finally, simulator tests were carried out, with consequent verification of the effectiveness of diagnoses on ultrasound images of the liver. The accuracy and total processing time for carrying out the analyses proposed during this research were then evaluated, making a comparison between the network training and testing times obtained on the CPU and GPU. It can also be seen-in both Tr&Tt cases-that GPU had a higher gain than CPU (about 10.6 times faster), even when dealing with a small database, with 120 images of 202500 pixels each. © 2024 IEEE.

关键词： Central Processing Unit Convolutional Neural Networks Graphics Processing Unit Masses Ultrasound liver images

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An Educational Tool for Learning about Social Media Tracking, Profiling, and Recommendation

arXiv

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arXiv 2024年

作者： Pope, Nicolas Kahila, Juho Laru, Jari Vartiainen, Henriikka Roos, Teemu Tedre, Matti School of Computing University of Eastern Finland Joensuu Finland School of Applied Educational Science and Teacher Education University of Eastern Finland Joensuu Finland University of Oulu Faculty of Education and Psychology Finland Department of Computer Science University of Helsinki Finland

This paper introduces an educational tool for classroom use, based on explainable AI (XAI), designed to demystify key social media mechanisms—tracking, profiling, and content recommendation—for novice learners. The tool provides a familiar, interactive interface that resonates with learners’ experiences with popular social media platforms, while also offering the means to "peek under the hood" and exposing basic mechanisms of datafication. Learners gain first-hand experience of how even the slightest actions, such as pausing to view content, are captured and recorded in their digital footprint, and further distilled into a personal profile. The tool uses real-time visualizations and verbal explanations to create a sense of immediacy: each time the user acts, the resulting changes in their engagement history and their profile are displayed in a visually engaging and understandable manner. This paper discusses the potential of XAI and educational technology in transforming data and digital literacy education and in fostering the growth of children’s privacy and security mindsets. Copyright © 2024, The Authors. All rights reserved.

关键词： Metadata

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Network-based methods with heterogeneous data to identify severe COVID immune-related genes 26

Network-based methods with heterogeneous data to identify se...

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26th International computer science and Engineering Conference, ICSEC 2022

作者： Sagulkoo, Pakorn Suratanee, Apichat Plaimas, Kitiporn Chiang Mai University Center of Biomedical Informatics Department of Family Medicine Faculty of Medicine Chiang Mai50200 Thailand King Mongkut's University of Technology North Bangkok Department of Mathematics Faculty of Applied Science Bangkok10800 Thailand Faculty of Science Chulalongkorn University Advance Virtual and Intelligent Computing Center Department of Mathematics and Computer Science Bangkok10330 Thailand

ISBN: (纸本)9781665491983

Bioinformatics and systems biology play a vital role in the computational prediction of disease-associated genes using multi-omics data. The network-based approach is one of the most potent tools in disease-associated gene prediction. The two commonly used methods are neighborhood-based and network diffusion techniques. However, there is still a lack of studies comparing the performance of these methods, especially in terms of functional pathway discovery. Thus, this study demonstrated the performance comparison of these two techniques in both numerical accuracies based on the area under the receiver operating characteristic curve (AUROC) and biological meaning efficiency based on functional pathway enrichment. In this study, we analyzed data of severe COVID-19 immune-related genes using heterogeneous data. The prediction results of the COVID-19 immune-related genes in the human protein-protein interaction (PPI) network showed that the network diffusion had better performance in both AUROC and pathway enrichment even though it provided a longer computational time than the neighborhood method. © 2022 IEEE.

关键词： COVID-19

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A Survey on Decentralized Identifiers and Verifiable Credentials

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IEEE Communications Surveys and Tutorials 2025年

作者： Mazzocca, Carlo Acar, Abbas Uluagac, Selcuk Montanari, Rebecca Bellavista, Paolo Conti, Mauro University of Salerno Department of Information and Electrical Engineering and Applied Mathematics Fisciano84084 Italy Florida International University Cyber-Physical Systems Security Lab School of Computing and Information Science MiamiFL33174 United States University of Bologna Department of Computer Science and Engineering Bologna40136 Italy University of Padua Department of Mathematics Padua35131 Italy

Digital identity has always been one of the keystones for implementing secure and trustworthy communications among parties. The ever-evolving digital landscape has undergone numerous technological transformations that have profoundly reshaped digital identity management, leading to a major shift from centralized to decentralized identity models. The latest stage of this evolution is represented by the emerging paradigm of Self-Sovereign Identity (SSI), which gives identity owners full control over their data. SSI leverages Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), which have been recently standardized by theWorldWideWeb Consortium (W3C). These technologies have the potential to build more secure and decentralized digital identity systems, significantly strengthening communication security in scenarios involving many distributed participants. It is worth noting that use of DIDs and VCs is not limited to individuals but extends to a wide range of entities including cloud, edge, and Internet of Things (IoT) resources. However, due to their novelty, existing literature lacks a comprehensive survey on DIDs and VCs beyond the scope of SSI. This paper fills this gap by providing a comprehensive overview of DIDs and VCs from multiple perspectives. It identifies key security threats and mitigation strategies, analyzes available implementations to guide practitioners in making informed decisions, and reviews the adoption of these technologies across various application domains. Moreover, it also examines related regulations, projects, and consortiums emerging worldwide. Finally, it discusses the primary challenges hindering their real-world adoption and outlines future research directions. © 1998-2012 IEEE.

关键词： Decentralized control

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Advancing Brain Image Segmentation: A Comprehensive Exploration of Enhanced VGG16 Architecture for Precise Neuroanatomical Mapping

Advancing Brain Image Segmentation: A Comprehensive Explorat...

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computing and Data Analytics (ICCDA), International Conference on

作者： Sheeja Kumari V Wilfred Blessing N. R Suresh Palarimath Hemalatha Gunasekaran Poorana Senthilkumar S Sutherlin Subitha G Department of Computational Intelligence Saveetha School of Engineering SIMATS University Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Dr. N. G. P Arts and Science College Coimbatore Tamil Nadu India Department of Computer Science and Engineering Stella Mary's College of Engineering Kanyakumari Dt Tamil Nadu India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This study introduces a novel approach to brain image segmentation by enhancing the VGG16 architecture. Neuroanatomical mapping is a critical aspect of understanding brain structures, and our research focuses on refining the VGG16 model to achieve more accurate segmentation results. We address the intricacies of brain image analysis, optimizing the architecture for the nuanced structures inherent in neuroimaging data. Our enhanced VGG16 model is thoroughly evaluated across diverse datasets, demonstrating superior performance compared to state-of-the-art segmentation methods. The framework proves robust and efficient in accurately delineating complex brain regions. We also delve into the interpretability of the segmentation results, shedding light on the learned features that contribute to the model's efficacy. The significance of our work extends beyond improved segmentation, offering valuable insights into the interpretability of deep learning models in neuroimaging. This research contributes to the broader field of biomedical image analysis and holds promise for applications in neuroscience and medical diagnostics. The proposed enhanced VGG16 framework emerges as a potent tool for advancing our understanding of brain structures and enhancing the capabilities of neuroimaging technologies.

关键词： Neuroimaging Deep learning Measurement Image segmentation Neuroscience Biological system modeling Refining computer architecture Brain modeling Medical diagnosis

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