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24th International Conference on Web Information Systems engineering, WISE 2023

作者： Zhou, Guangtong Kwashie, Selasi Zhang, Yidi Bewong, Michael Nofong, Vincent M. Hu, Junwei Cheng, Debo He, Keqing Liu, Shanmei Feng, Zaiwen College of Informatics Huazhong Agricultural University Hubei Wuhan China AI & Cyber Futures Institute Charles Sturt University Bathurst Australia School of Computing Mathematics & Engineering Charles Sturt University Wagga Wagga Australia Department of Computer Science & Engineering University of Mines & Technology Tarkwa Ghana STEM University of South Australia Adelaide Australia School of Computing Science Wuhan University Hubei Wuhan China Hubei Hongshan Laboratory Huazhong Agricultural University Hubei Wuhan China Macro Agricultural Research Institute Huazhong Agricultural University Hubei Wuhan China

ISBN: (纸本)9789819972531

This paper studies the discovery of approximate rules in property graphs. First, we propose a semantically meaningful measure of error for mining graph entity dependencies (GEDs) that almost hold, to tolerate errors and inconsistencies that exist in real-world graphs. Second, we present a new characterisation of GED satisfaction, and devise a depth-first search strategy to traverse the search space of candidate GEDs efficiently. Further, we perform experiments to demonstrate the feasibility and scalability of our solution, FastAGEDs, with three real-world graphs. The results show FastAGEDs is effective and efficient for mining approximate GEDs in noisy and erroneous real-world graphs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： approximate dependency efficient algorithm graph constraints graph entity dependency

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TransRef: Multi-Scale Reference Embedding Transformer for Reference-Guided Image Inpainting

arXiv

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arXiv 2023年

作者： Liu, Taorong Liao, Liang Chen, Delin Xiao, Jing Wang, Zheng Lin, Chia-Wen Satoh, Shin'ichi School of Computer Science Wuhan University Hubei Wuhan430072 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo101-8430 Japan

Image inpainting for completing complicated semantic environments and diverse hole patterns of corrupted images is challenging even for state-of-the-art learning-based inpainting methods trained on large-scale data. A reference image capturing the same scene of a corrupted image offers informative guidance for completing the corrupted image as it shares similar texture and structure priors to that of the holes of the corrupted image. In this work, we propose a Transformer-based encoder-decoder network for Reference-guided image inpainting, named TransRef. Specifically, the guidance is conducted progressively through a reference embedding procedure, in which the referencing features are subsequently aligned and fused with the features of the corrupted image. For precise utilization of the reference features for guidance, a reference-patch alignment (Ref-PA) module is proposed to align the patch features of the reference and corrupted images and harmonize their style differences, while a reference-patch transformer (Ref-PT) module is proposed to refine the embedded reference feature. Moreover, to facilitate the research of reference-guided image restoration tasks, we construct a publicly accessible benchmark dataset containing 50K pairs of input and reference images. Both quantitative and qualitative evaluations demonstrate the efficacy of the reference information and the proposed method over the state-of-the-art methods in completing complex holes. Code and dataset can be accessed at: https://***/Cameltr/TransRef. Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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Mitigation of Straggler in Virtual Machine Stack Using Supervised Learning Methodology

Mitigation of Straggler in Virtual Machine Stack Using Super...

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Emerging Smart Computing and informatics (ESCI), Conference on

作者： Reshma S. Gaykar V. Khanaa S.D. Joshi Department of Computer Science Bharath Institute of Higher Education and Research Chennai India Information Center Bharath Institute of Higher Education and Research Chennai India Faculty of Engineering and Technology Bharati Vidyapeeth University Pune India

Hadoop is an inexpensive analytical tool as compared to the other distributed storage in market as it does not need any standalone machines and works on group of commodity hardware. It is a distributed storage system along with this it achieves parallelization of larger data collections. With MapReduce, HDFS (Hadoop distributed file system) provides solution for the system where processing huge datasets is a requirement. Few of the main reasons of stragglers in assorted Hadoop clusters are load inconsistency during storing, resource friction throughout scheduling tasks, hardware downturn due to excessive usage, as well as software configuration issues while managing the cluster. Hadoop's performance lows down in a heterogeneous network due to the technical heterogeneity. We used a supervised machine learning (ML) technique to identify straggler nodes in an eminently distributed network in this article. The suggested technique identifies the proper slow-running job (Straggler) in the network and assign it to other node in the stack to complete the operation with quick succession. Virtual Machine (VM) identifier, network bandwidth consumption, number of processors and its load, memory load and other parameters included in the full data set are utilized for recognition. Various feature extraction methodologies have been utilized to develop its training system. The whole data set was processed for heterogenous features on the dataset. We analyzed our approach using our suggested classifier after doing comprehensive empirical work. As out-turn, the system outperforms using typical machine learning models in classification performance.

关键词： Training Error analysis Supervised learning Machine learning Programming Hardware Virtual machining

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Lung Nodule Analysis in CT Images: Deep Learning for Segmentation and Measurement 24

Lung Nodule Analysis in CT Images: Deep Learning for Segment...

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8th International Conference on Medical and Health informatics, ICMHI 2024

作者： Li, Ai-Hsien Adams Aruperes, Cristian Daniel Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Division of Cardiology Far Eastern Memorial Hospital New Taipei TaiwanGraduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanGraduate Institute of Medicine Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400716874

In the realm of research, the global health challenge posed by lung cancer remains pronounced, contributing substantially to annual cancer-related fatalities. The critical imperative lies in the early identification of pulmonary nodules, frequently indicative of impending lung cancer, to enhance patient outcomes and diminish mortality rates. Computed Tomography (CT) imaging stands out as a pivotal diagnostic instrument for the timely detection of these nodules. The swift proliferation of medical imaging data has underscored the pressing necessity for precise and efficient methodologies dedicated to nodule segmentation and measurement. These approaches are crucial in assisting radiologists in their diagnostic and clinical decision-making endeavors. In this study, we introduced a thorough method for analyzing lung nodules, leveraging dataset from Far Eastern Memorial Hospital (FEMH) comprising original CT images and manually annotated ground truth masks obtained with the assistance of radiologists at FEMH. This dataset is utilized for the segmentation of nodules. We employed advanced deep learning models, specifically the U-Net architecture, identified as the optimal model through our training process. We made substantial progress in nodule segmentation, attaining an Intersection over Union (IoU) score of 0.824 and a Dice Coefficient of 0.903 for the FEMH dataset. Furthermore, our performance improved when utilizing the merged dataset comprising FEMH and Luna16, yielding an IoU score of 0.862 and a Dice Coefficient of 0.926. Luna16 has been extensively utilized in numerous studies related to nodule detection and segmentation. In the next phase of the study, the best-performing model from our segmentation phase was utilized to predict nodule masks on the merged dataset. Subsequently, we measured the size of each predicted nodule by comparing it with the size ground truth mask in millimeters. In detail, this study achieved the Pearson Correlation Coefficient (PCC) at 0.

关键词： computerized tomography

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Breast Cancer Detection in Mammogram Images using Machine Learning Methods and CLAHE Algorithm

Breast Cancer Detection in Mammogram Images using Machine Le...

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International Conference on Contemporary Computing and informatics (IC3I)

作者： Gaurav Srivastav Mamoon Rashid Richa Singh Anita Gehlot Neha Sharma Computer Science & Engineering Datta Meghe Institute of Higher Education & Research Maharastra India Department of Computer Engineering Faculty of Science & Technology Vishwakarma University Pune India Computer Science & Engineering Krishna Institute of Engineering & Technology Gaziabad India Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttrakhand India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India

Breast cancer is one of the most common cancer types. This is the second-leading cause of cancer-related death in women. It ranked 2 nd according to available data lung cancers is the only one causing more causalities. It’s critical to receive a breast cancer diagnosis quickly. The MIAS data set is used in this study to examine machine learning-based categorization approaches used to study breast cancer. Important image data is fetched from mammograms. We choose eight distinct classifiers and assess each one’s precision, recall, accuracy, and F-score. The analysis’s findings were higher than 69.88%.

关键词： Support vector machines Machine learning algorithms Supervised learning Lung cancer Machine learning Muscles Feature extraction

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An Automatic CNN-based Face Mask Detection Algorithm Tested During the COVID-19 Pandemics

An Automatic CNN-based Face Mask Detection Algorithm Tested ...

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2022 International Conference of Yearly Reports on informatics, Mathematics, and engineering, ICYRIME 2022

作者： De Magistris, Giorgio Iacobelli, Emanuele Brociek, Rafal Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The ongoing COVID-19 pandemic has highlighted the importance of wearing face masks as a preventive measure to reduce the spread of the virus. In medical settings, such as hospitals and clinics, healthcare professionals and patients are required to wear surgical masks for infection control. However, the use of masks can hinder facial recognition technology, which is commonly used for identity verification and security purposes. In this paper, we propose a convolutional neural network (CNN) based approach to detect faces covered by surgical masks in medical settings. We evaluated the proposed CNN model on a test set comprising of masked and unmasked faces. The results showed that our model achieved an accuracy of over 96% in detecting masked faces. Furthermore, our model demonstrated robustness to different mask types and fit variations commonly encountered in medical settings. Our approaches reaches state of the art results in terms of accuracy and generalization. © 2022 Copyright for this paper by its authors.

关键词： COVID-19

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Observation of Su-Schrieffer-Heeger Topological Model Band Structure in Integrated LNOI Coupled Ring Cavities

Observation of Su-Schrieffer-Heeger Topological Model Band S...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Hiep X. Dinh Armandas Balcytis Tomoki Ozawa Yasutomo Ota Toshihiko Baba Satoshi Iwamoto Arnan Mitchell Thach G. Nguyen Integrated Photonics and Applications Centre RMIT University Melbourne VIC Australia Advanced Institute for Materials Research Tohoku University Sendai Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama Japan Department of Electrical and Computer Engineering Yokohama National University Yokohama Japan Research Center for Advanced Science and Technology The University of Tokyo Tokyo Japan

We report synthetic frequency dimension Su-Schrieffer-Heeger model and its band structure observation using coupled ring cavities on an integrated photonic chip. Intra-cell and inter-cell couplings between hybridized ... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Structural rings Band structures Couplings Frequency modulation Scalability Electro-optic modulators Ring lasers

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Federated Learning for Privacy-Preserving Healthcare Data Analysis in the Age of Cybersecurity Threats 1

Federated Learning for Privacy-Preserving Healthcare Data An...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Sravan, P. Saranya, S. Deepika, N.M. Nijhawan, Ginni Alawadi, Ahmed Hussien M.v, Suresh Kumar School of Cs & Ai Sr University Telanagan Warangal India Dr. N.G.P. Institute of Technology Department of Computer Science and Engineering Coimbatore India Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India The Islamic University Najaf Iraq Research Scholar College of Law Koneru Lakshmaiah Education Foundation Green Fields Andhra Pradesh Vaddeswaram Guntur522302 India

ISBN: (纸本)9798350330915

This examination explores joined picking up gathering appraisals, unequivocally United Averaging (FedAvg), Weighted Consolidated Averaging (FedAvg-W), Bound together Learning with Adaptable Learning Rate (FedAdapt), and Secure Combination for Brought Learning (SecAgg), inside the space of assertion saving clinical benefits data assessment. The reason for the organized assessments was to assess their performance in terms of accuracy, evidence coverage and communication speed. This article provides a comparative evaluation to help practitioners select the most appropriate algorithm for clinical reasoning applications. The results show that FedAvg-W achieves much higher accuracy than other algorithms especially when used in locations with varying data attributes implying that it can adapt to the changes. In relation to this, a method called FedAdapt mixes quickly while maintaining high accuracy by way of dynamically changing learning rate limits with respect to particular instances of distribution information. A secure aggregation framework based on homomorphic encryption guarantees exact compliance. The review provides subtle experiences into space-related works, such as health informatics and federated learning. On one hand, SecAgg fulfills a basic requirement for ensuring and preserving medical benefits data while on the other side, FedAdapt's flexibility concerns the anticipated scalability of clinical evaluation applications. © 2023 IEEE.

关键词： Federated Learning Healthcare Data Analysis Homomorphic Encryption Privacy Preservation

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Illuminating the x-Axis by α-Floodlights 32

Illuminating the x-Axis by α-Floodlights

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32nd International Symposium on Algorithms and Computation, ISAAC 2021

作者： Nilsson, Bengt J. Orden, David Palios, Leonidas Seara, Carlos Żyliński, Pawel Department of Computer Science and Media Technology Malmö University Sweden Physics and Mathematics Department Universidad Alcalá Spain Department of Computer Science and Engineering University of Ioannina Greece Departament of Mathematics Universitat Politècnica de Catalunya Barcelona Spain Institute of Informatics University of Gdańsk Poland

ISBN: (纸本)9783959772143

Given a set S of regions with piece-wise linear boundary and a positive angle α ◦, we consider the problem of computing the locations and orientations of the minimum number of α-floodlights positioned at points in S which suffice to illuminate the entire x-axis. We show that the problem can be solved in O(n log n) time and O(n) space, where n is the number of vertices of the set S. © Bengt J. Nilsson, David Orden, Leonidas Palios, Carlos Seara, and Pawel Żyliński.

关键词： Floods

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Internet of Things Assisted Remote Health and Safety Monitoring Scheme Using Intelligent Sensors

Internet of Things Assisted Remote Health and Safety Monitor...

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Advances in Computing, Communication and Applied informatics (ACCAI), International Conference on

作者： V. RamKumar S. Shanthi K. Suresh Kumar S. Kanageswari S. Mahalakshmi R. Anusuya Department of ECE R.M.K. Engineering College Chennai Department of ECE CARE College of Engineering Tiruchirappalli Department of MBA Panimalar Engineering College Chennai Department of Computer Science Loyola College Chennai Department of Computer Science and Design R.M.K Engineering College Chennai Department of CSE Modern Institute of Technology and Research Centre Alwar Rajasthan

The Internet of Things (IoT) in telemedicine creates an enormous data volume that must be processed, analyzed, and stored. Risk to one's health is no longer mitigated by one's chronological age, thanks to today's hectic schedules and unexpected ways of living. The Internet of Things has made it easier to provide continuous remote health checks on patients outside of the hospital or clinic, or even in their own homes. Detectors record data on a variety of patient health factors, and the Internet of Things saves and displays this data on a website that also allows for remote monitoring. When the potential of IoT technology is taken into consideration, the challenges that patients with physical instability have while trying to regularly contact a doctor in person can be circumvented. The microcontroller's role in this project is that of a data relay. This system presents a new method for health service activities by using sensors to monitor a person's vitals and the network to alert concerned family members or physicians in the case of an *** attached buzzer in the controller will sound when there is a change in detector output, alerting the career. A microcontroller is used to monitor the patient's condition; this controller communicates with an LCD display and a wireless LAN to send out alert messages. The patient's heart rate, blood pressure, and temperature will all be displayed in the cloud in instantaneously, and a warning will be sent to the physician via the Internet of Things if the sensor recognizes an abnormality. In order to comprehend how the sensors worked, a comprehensive analysis of the data was collected with regard to fluctuations in physical and environmental activity.

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