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Subspace learning using low-rank latent representation learning and perturbation theorem: Unsupervised gene selection

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Computers in Biology and Medicine 2025年 185卷 109567-109567页

作者： Moslemi, Amir Naeini, Fariborz Baghaei Department of Physics Toronto Metropolitan University ON Canada School of Software Design & Data Science Seneca Polytechnic TorontoONM4N 3M5 Canada Physical Sciences Sunnybrook Health Sciences Centre TorontoONM4N 3M5 Canada Faculty of Engineering Computing and the Environment Kingston University Penrhyn Road Campus Kingston Upon Thames LondonKT1 2EE United Kingdom

In recent years, gene expression data analysis has gained growing significance in the fields of machine learning and computational biology. Typically, microarray gene datasets exhibit a scenario where the number of features exceeds the number of samples, resulting in an ill-posed and underdetermined equation system. The presence of redundant features in high-dimensional data leads to suboptimal performance and increased computational time for learning algorithms. Although feature extraction and feature selection are two approaches that can be employed to deal with this challenge, feature selection has greater interpretability ability which causes it to receive more attention. In this study, we propose an unsupervised feature selection which is based on pseudo label latent representation learning and perturbation theory. In the first step, pseudo labels are extracted and constructed using latent representation learning. In the second step, the least square problem is solved for original data matrix and perturbed data matrix. Features are clustered based on the similarity between the original data matrix and the perturbed data matrix using k-means. In the last step, features in each subcluster are ranked based on information gain criterion. To showcase the efficacy of the proposed approach, numerical experiments were carried out on six benchmark microarray datasets and two RNA-Sequencing benchmark datasets. The outcomes indicate that the proposed technique surpasses eight state-of-the-art unsupervised feature selection methods in both clustering accuracy and normalized mutual information. © 2024 Elsevier Ltd

关键词： Contrastive Learning

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EDCOPA Protocol: Addressing Scalability, High-Density and Load Balancing Challenges in WSN-based IoT

EDCOPA Protocol: Addressing Scalability, High-Density and Lo...

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Electrical, Computer, Telecommunication and Energy Technologies (ECTE-Tech), International Conference on

作者： Foudil Mir Abderrazak Sebaa Abdelkader Laouid Ahcene Bounceur Farid Meziane Faculty of Exact Sciences LIMED Laboratory University of Bejaia Bejaia Algeria Laboratoire LITAN Ecole Supérieure en Sciences et Technologies de l'Informatique et du Numérique Bejaia Algerie LIAP Laboratory University of El Oued El Oued Algeria College of Computing and Informatics University of Sharjah Sharjah UAE Data Science Research Centre University of Derby Derby UK

ISBN: (数字)9798350388480

ISBN: (纸本)9798350388497

Energy optimization in Wireless Sensor Networks for the Internet of Things (WSN-Based IoT) is essential to extend the lifetime of networks while guaranteeing optimum performance. This paper investigates the performance of the EDCOPA protocol, which is an enhancement of the DCOPA protocol, by addressing three key aspects: the impact of scalabil-ity, network density, and load balancing. The DCOPA protocol, while powerful, has limitations in that it relies solely on residual energy and Distance to the Base Station (DistBS) to select cluster heads (CHs). To overcome this limitation, the EDCOPA protocol integrates new criteria, namely the energy consumption in the previous round Epr i and the CH role frequency Nch i of a given node. We study the effect of scalability as well as the impact of density on the Lifetime Parameters (LTP). In addition, we analyze load balancing by evaluating the distribution of CHs and clusters after the clustering process. This analysis examines how the proposed improvements affect energy efficiency and network LTP. This paper provides an in-depth look at the performance of the EDCOPA protocol in terms of properties that are extremely important in WSN-based IoT networks.

关键词： Wireless sensor networks Energy consumption Protocols Scalability Voting Load management Robustness Energy efficiency Internet of Things Optimization

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On the Violation of Honesty in Mobile Apps: Automated Detection and Categories

arXiv

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arXiv 2022年

作者： Obie, Humphrey O. Ilekura, Idowu Du, Hung Shahin, Mojtaba Grundy, John Li, Li Whittle, Jon Turhan, Burak HumaniSE Lab Monash University Melbourne Australia Data Science Nigeria Lagos Nigeria Applied Artificial Intelligence Inst. Deakin University Melbourne Australia School of Computing Technologies RMIT University Melbourne Australia Faculty of IT Monash University Melbourne Australia CSIRO's Data61 Melbourne Australia University of Oulu Oulu Finland

Human values such as integrity, privacy, curiosity, security, and honesty are guiding principles for what people consider important in life. Such human values may be violated by mobile software applications (apps), and the negative effects of such human value violations can be seen in various ways in society. In this work, we focus on the human value of honesty. We present a model to support the automatic identification of violations of the value of honesty from app reviews from an end-user perspective. Beyond the automatic detection of honesty violations by apps, we also aim to better understand different categories of honesty violations expressed by users in their app reviews. The result of our manual analysis of our honesty violations dataset shows that honesty violations can be characterised into ten categories: unfair cancellation and refund policies;false advertisements;delusive subscriptions;cheating systems;inaccurate information;unfair fees;no service;deletion of reviews;impersonation;and fraudulent-looking apps. Based on these results, we argue for a conscious effort in developing more honest software artefacts including mobile apps, and the promotion of honesty as a key value in software development practices. Furthermore, we discuss the role of app distribution platforms as enforcers of ethical systems supporting human values, and highlight some proposed next steps for human values in software engineering (SE) research. © 2022, CC BY.

关键词： Application programs

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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

Automatic detection of lung nodule in CT scan slices using C...

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2022 International Conference on Machine Learning and data Engineering, ICMLDE 2022

作者： Kadry, Seifedine Herrera-Viedma, Enrique Crespo, Rubén González Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Andalusian Granada Spain Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases;(i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： Computerized tomography

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Challenges of implementing industry 4.0 technology in machine-based manufacturing for SMEs

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AIP Conference Proceedings 2023年第1期2680卷

作者： Samuel Ching Xin-Ren Jun-Kit Chaw Yee-Mei Lim Wah-Pheng Lee Department of Mathematical and Data Science Faculty of Computing and Information Technology Tunku Abdul Rahman University College Kuala Lumpur Malaysia

Once German Government had declared their Industry 4.0 strategy, many countries also followed suit to set their aim on promoting the manufacturing computerization in their country. However, there is a gap between the existing machines and the technology advances in Industry 4.0 such as cloud computing, internet of things, system integration and etcetera. The lack of experience to develop the industry 4.0 infrastructure is also one of the major obstacles in the developing countries. The aim of this paper is to address the challenges of implementing industry 4.0 Technology in machine-based manufacturing for small and medium companies. This paper focuses on Malaysia’s Small and Medium Enterprises (SMEs) that are undergoing Industry 4.0 transformation in order to serve as a reference for other developing countries like Malaysia in which a lot of SMEs are facing similar challenges. Based on the literature review and real cases in collaboration with companies in the industry, the challenges of data collection and system interoperability as well as the solutions are presented in this paper.

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Deep and handcrafted feature supported diabetic retinopathy detection: A study

Deep and handcrafted feature supported diabetic retinopathy ...

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2022 International Conference on Machine Learning and data Engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) supported eye screening is one of the ophthalmological practices. This work proposed a Deep-Learning Procedure (DLP) to recognize Diabetic Retinopathy (DR) in FI. The proposed work presents the experimental work with different DLP methods found in the literature. This work is executed with two modes;(i) DR detection using conventional deep-features and (ii) DR discovery using deep ensemble features. To demonstrate this work, 1800 fundus images (900 regular and 900 DR class) are considered for the assessment, and the advantage of proposed plan is confirmed using various performance metrics. The experimental outcome of this study confirms that the AlexNet-based detection provides a better detection (>96%), and the deep ensemble features of AlexNet, VGG16, and ResNet18 provide a detection accuracy of >98% on the chosen FRI database. © 2022 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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Cloud computing in Smart Cities: Privacy, Ethical and Social Issues

Cloud Computing in Smart Cities: Privacy, Ethical and Social...

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Computer science and Emerging Technologies (CSET), International Conference on

作者： Abdel Rahman Mohammad Alkhazali Ahmad M. Khasawneh Sharaf Alzoubi Murad Magableh Rajina R. Mohamed Bishwajeet Pandey MIS BI and Data Analysis Department Faculty of Business and Financial Science Irbid National University Irbid Jordan Department of Information Technology College of Computer Science and Informatics Amman Arab University (AAU) Jordan Information Technology department College of Computer Science and Informatics Amman Arab University (AAU) Jordan College of Computing dan Informatics Universiti Tenaga Nasional Malaysia Eurasian National University & Astana IT University Astana Kazaksthan & Jain Deemed To Be University Bangalore India

The rapid development of cloud computing technologies has revolutionized various sectors, and smart cities are no exception. Smart cities leverage cloud computing to optimize urban services, enhance resource management, and improve the overall quality of life for their inhabitants. However, as these technological advancements proliferate, concerns about privacy, ethical considerations, and social implications have emerged. This research paper critically examines the multifaceted challenges associated with the integration of cloud computing in smart cities, shedding light on the potential risks and highlighting the need for comprehensive solutions. The research employs a mixed-methods approach, combining quantitative data analysis and qualitative case studies to offer a comprehensive perspective on the identified issues. The primary focus lies in identifying privacy risks, ethical dilemmas, and social disparities that arise due to the extensive use of cloud-based systems and data in smart cities. Furthermore, the study investigates the role of key stakeholders, including governments, technology providers, and citizens, in mitigating or exacerbating these challenges. Key findings reveal that while cloud computing empowers smart cities with unparalleled capabilities, it also exposes residents' personal information to potential breaches and misuse. Ethical concerns arise from the handling of sensitive data, data ownership, and algorithmic biases that could perpetuate discrimination. Moreover, social issues like the digital divide and access disparities may further exacerbate existing inequalities in smart city implementation. This research paper concludes by proposing a comprehensive framework of guidelines and best practices to address the identified issues effectively. These recommendations encompass enhanced data privacy measures, transparent and accountable data governance, the promotion of ethical data usage, and inclusive strategies to bridge social disparities. B

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Transformers in medical image analysis

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Intelligent Medicine 2023年第1期3卷 59-78页

作者： Kelei He Chen Gan Zhuoyuan Li Islem Rekik Zihao Yin Wen Ji Yang Gao Qian Wang Junfeng Zhang Dinggang Shen Medical School of Nanjing University NanjingJiangsu 210093China National Institute of Healthcare Data Science at Nanjing University NanjingJiangsu 210093China BASIRA Laboratory Faculty of Computer and Informatics EngineeringIstanbul Technical UniversityIstanbulTurkey School of Science and Engineering ComputingUniversity of DundeeUK State Key Laboratory for Novel Software Technology Nanjing UniversityNanjingJiangsu 210093China School of Biomedical Engineering ShanghaiTech UniversityShanghai 201210China Department of Research and Development Shanghai United Imaging Intelligence Co.Ltd.Shanghai 200030China Shanghai Clinical Research and Trial Center Shanghai 201703China

Transformers have dominated the field of natural language processing and have recently made an impact in the area of computer *** the field of medical image analysis,transformers have also been successfully used in to full-stack clinical applications,including image synthesis/reconstruction,registration,segmentation,detection,and *** paper aimed to promote awareness of the applications of transformers in medical image ***,we first provided an overview of the core concepts of the attention mechanism built into transformers and other basic ***,we reviewed various transformer architectures tailored for medical image applications and discuss their *** this review,we investigated key challenges including the use of transformers in different learning paradigms,improving model efficiency,and coupling with other *** hope this review would provide a comprehensive picture of transformers to readers with an interest in medical image analysis.

关键词： Transformer Medical image analysis Deep learning Diagnosis Registration Segmentation Image synthesis Multi-task learning Multi-modal learning Weakly-supervised learning

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Two Birds With One Stone: Enhancing Communication and Sensing via Multi-Functional RIS

arXiv

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arXiv 2024年

作者： Ni, Wanli Wang, Wen Zheng, Ailing Wang, Peng You, Changsheng Eldar, Yonina C. Niyato, Dusit Schober, Robert The Department of Electronic Engineering Tsinghua University Beijing100084 China The Pervasive Communications Center Purple Mountain Laboratories Nanjing211111 China The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The School of Electrical and Electronic Engineering North China Electric Power University Beijing102206 China The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China The Faculty of Mathematics and Computer Science Weizmann Institute of Science Israel The College of Computing and Data Science Nanyang Technological University Singapore Erlangen91054 Germany

In this article, we propose new network architectures that integrate multi-functional reconfigurable intelligent surfaces (MF-RISs) into 6G networks to enhance both communication and sensing capabilities. Firstly, we elaborate how to leverage MF-RISs for improving communication performance in different communication modes including unicast, mulitcast, and broadcast and for different multi-access schemes. Next, we emphasize synergistic benefits of integrating MF-RISs with wireless sensing, enabling more accurate and efficient target detection in 6G networks. Furthermore, we present two schemes that utilize MF-RISs to enhance the performance of integrated sensing and communication (ISAC). We also study multi-objective optimization to achieve the optimal trade-off between communication and sensing performance. Finally, we present numerical results to show the performance improvements offered by MF-RISs compared to conventional RISs in ISAC. We also outline key research directions for MF-RIS under the ambition of 6G. Copyright © 2024, The Authors. All rights reserved.

关键词： Reconfigurable architectures

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FAST Observations of Four Comets to Search for the Molecular Line Emissions between 1.0 and 1.5 GHz Frequencies

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Research in Astronomy and Astrophysics 2024年第10期24卷 130-140页

作者： Long-Fei Chen Chao-Wei Tsai Jian-Yang Li Bin Yang Di Li Yan Duan Chih-Hao Hsia Zhichen Pan Lei Qian Donghui Quan Xue-Jian Jiang Xiaohu Li Ruining Zhao Pei Zuo School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550025China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guiyang 550025China Research Center for Astronomical Computing Zhejiang LaboratoryHangzhou 311100China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China Key Laboratory of Radio Astronomy and Technology Chinese Academy of SciencesBeijing 100101China School of Atmospheric Sciences Sun Yat-sen UniversityZhuhai 519082China Núcleo de Astronomía Facultad de Ingenieríay CienciasUniversidad Diego PortalesChile Department of Astronomy College of Physics and Electronic EngineeringQilu Normal UniversityJinan 250200China Department of Electronic and Optical Engineering Space Engineering UniversityBeijing 101416China Laboratory for Space Research Faculty of ScienceThe University of Hong KongHong Kong(SAR)China Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China CAS Key Laboratory of Optical Astronomy National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China School of Astronomy and Space Sciences University of Chinese Academy of SciencesBeijing 100049China

We used the Five-hundred-meter Aperture Spherical radio Telescope(FAST)to search for the molecular emissions in the L-band between 1.0 and 1.5 GHz toward four comets,C/2020 F3(NEOWISE),C/2020 R4(ATLAS),C/2021 A1(Leonard),and 67P/Churyumov-Gerasimenko during or after their perihelion *** of molecular transition lines fall in this low-frequency range,many attributed to complex organic or prebiotic *** conducted a blind search for the possible molecular lines in this frequency range in those comets and could not identify clear signals of molecular emissions in the *** several molecules have been detected at high frequencies of greater than100 GHz in comets,our results confirm that it is challenging to detect molecular transitions in the L-band frequency *** non-detection of L-band molecular lines in the cometary environment could rule out the possibility of unusually strong lines,which could be caused by the masers or non-LTE *** the line strengths are predicted to be weak,for FAST,using the ultra-wide bandwidth receiver and improving the radio frequency interference environments would enhance the detectability of those molecular transitions at low frequencies in the future.

关键词： astrochemistry ISM:molecules comets:general line:identification

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