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IoT-Enhanced local attention dual networks for pathological image restoration in healthcare

Measurement: Sensors 2024年 33卷

作者： Hassan, Abdelwahab Said Thakare, Anuradha Bhende, Manisha Prasad, K.D.V. Singh, Pavitar Parkash Byeon, Haewon Department of Mathematics Faculty of Science Suez Canal University Egypt Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune411044 India Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Hyderabad Pune India Department of Management Lovely Professional University India Department of AI and Software Inje University Gimhae50834 Korea Republic of Inje University Medical Big Data Research Center Gimhae50834 Korea Republic of

High-resolution pathological images play a pivotal role in accurate disease diagnosis and are important in precision medicine. However, obtaining real-time high-resolution images faces challenges due to hardware limitations and scanning time constraints. Conventional image super-resolution restoration algorithms struggle to provide satisfactory results for pathological images, resulting in blurred and unrealistic restorations. In response to this challenge, this research proposes a pioneering approach by integrating Internet of Things (IoT) technology with Local Attention Dual Network (IOT-LAT) for super-resolution restoration of pathological images. The enhanced IOT-LATincorporates IoT-based data acquisition and processing, IoT-Enhanced non-local attention mechanisms, Gaussian constraint, and parameter-sharing strategies in up-sampling and down-sampling branches. This integration enables real-time super-resolution restoration of pathological images with improved accuracy. The reconstructed images exhibit a structural similarity of 0.914 and a peak signal-to-noise ratio of 30.84 dB. These results validate the effectiveness of the suggested approach in precisely reconstructing high-frequency details and improving modelling efficiency via a lightweight non-local attention mechanism enhanced by the Internet of Things. This work discusses IoT and non-local attention dual networks to improve pathological image super-resolution restoration. This allows for faster and more accurate disease diagnosis and treatment in precision medicine. © 2024 The Authors

关键词： Internet of things

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Enhancement of Indoor Localization Algorithms in Wireless Sensor Networks: A Survey

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IOP Conference Series: Materials science and engineering 2018年第1期407卷

作者： I D Sumitra S Supatmi R Hou Postgraduate Faculty Universitas Komputer Indonesia Indonesia Computer and Software School of Nanjing University of Information Science and Technology (NUIST) China Computer Engineering of Indonesian Computer University (UNIKOM) Indonesia

The purpose of this study is to enhance the indoor localization Algorithms in wireless sensor. Localization is one of the critical techniques in wireless sensor networks (WSNs). The implementation of localization can be used to determine object's position in the environments that could be applied both indoor and outdoor with several algorithms to estimates distance such as range-based and range-free. In this paper, the purpose of this study was to comprehensive survey of algorithms to optimize the trade-off between localization performance for an indoor area. Additionally, we discuss the application where the location information is crucial to estimate. The results of these localization algorithms comparison depict that the DV-Hop algorithm is more outstanding to detect the object location an indoor due to hop among sensor nodes rather than other algorithms with maximal range location up to 20 m, while the shortest ranges localization is TOA and TDOA with 60 cm and 90 cm, respectively.

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A mobile cloud-based ehealth scheme

arXiv

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arXiv 2020年

作者： Liu, Yihe Abbasi, Aaqif Afzaal Aghaei, Atefeh Abbasi, Almas Mosavi, Amir Shamshirband, Shahab Al-qaness, Mohammed A.A. Data Recovery Key Laboratory of Sichuan Province College of Computer Science and AI Neijiang Normal University Neijiang641100 China Department of Software Engineering Foundation University Islamabad44000 Pakistan Department of Communication Faculty of Social Sciences University of Tehran Tehran*** Iran Department of Computer Science International Islamic University Islamabad46000 Pakistan Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Kalman Kando Faculty of Electrical Engineering Obuda University Budapest1034 Hungary School of the Built Environment Oxford Brookes University OxfordOX3 0BP United Kingdom Department for Management of Science and Technology Development Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam School of Computer Science Wuhan University Wuhan430072 China

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace. Similarly, the field of health informatics is also considered as an extremely important field. This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis. The developed system has two front ends, the first dedicated for the user to perform the photographing of the trace report. Once the photographing is complete, mobile computing is used to extract the signal. Once the signal is extracted, it is uploaded into the server and further analysis is performed on the signal in the cloud. Once this is done, the second interface, intended for the use of the physician, can download and view the trace from the cloud. The data is securely held using a password-based authentication method. The system presented here is one of the first attempts at delivering the total solution, and after further upgrades, it will be possible to deploy the system in a commercial *** Codes 68T05 Copyright © 2020, The Authors. All rights reserved.

关键词： Signal analysis

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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Region-wise generative adversarial image inpainting for large missing areas

arXiv

引用

arXiv 2019年

作者： Ma, Yuqing Liu, Xianglong Bai, Shihao Wang, Lei Liu, Aishan Tao, Dacheng Hancock, Edwin State Key Lab of Software Development Environment Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine UBTECH Sydney Artificial Intelligence Centre School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney DarlingtonNSW2008 Australia Department of Computer Science University of York York United Kingdom

Recently deep neutral networks have achieved promising performance for filling large missing regions in image inpainting tasks. They usually adopted the standard convolutional architecture over the corrupted image, leading to meaningless contents, such as color discrepancy, blur and artifacts. Moreover, most inpainting approaches cannot well handle the large contiguous missing area cases. To address these problems, we propose a generic inpainting framework capable of handling with incomplete images on both contiguous and discontiguous large missing areas, in an adversarial manner. From which, region-wise convolution is deployed in both generator and discriminator to separately handle with the different regions, namely existing regions and missing ones. Moreover, a correlation loss is introduced to capture the non-local correlations between different patches, and thus guides the generator to obtain more information during inference. With the help of our proposed framework, we can restore semantically reasonable and visually realistic images. Extensive experiments on three widely-used datasets for image inpainting tasks have been conducted, and both qualitative and quantitative experimental results demonstrate that the proposed model significantly outperforms the state-of-the-art approaches, both on the large contiguous and discontiguous missing areas. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Kurdish Handwritten Character Recognition using Deep Learning Techniques

arXiv

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arXiv 2022年

作者： Ahmed, Rebin M. Rashid, Tarik A. Fattah, Polla Alsadoon, Abeer Bacanin, Nebojsa Mirjalili, Seyedali Vimal, S. Chhabra, Amit IT Department Faculty of Applied Science Tishk International University Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Software and Informatics Engineering Salahaddin University-Erbil- Erbil Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Sydney Australia Singidunum University Belgrade Serbia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India

Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and converting them into structural text form, etc. Moreover, high accuracy rates have been recorded by handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. Yet there is no such system available for offline Kurdish handwriting recognition. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic\Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The created dataset has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system, the experimental results show an acceptable recognition level. The testing results reported a 96% accuracy rate, and training accuracy reported a 97% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems. © 2022, CC BY.

关键词： Convolutional neural networks

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Making failure the mother of success

Making failure the mother of success

引用

Frontiers in Education (FIE) Conference

作者： Xiang Fu Kai Qian Kent Palmer Boris Peltsverger Brian Campbell Billy Lim Paul Vogt Department of Computer Science Hofstra University USA School of Computing and Software Engineering Southern Polytechnic State University USA Goshen College USA School of Computing and Mathematics Georgia Southwestern State University USA Adjunct Faculty School of Computing and Mathematics Georgia Southwestern State University USA School of Information Technology Illinois State University USA Illinois State University USA

Do students really welcome failure, the mother of success? This paper reports the implementation of a trial-and-failure learning strategy, in both entry and senior level computer science classes. The idea is simple: given a sophisticated course project, let students try project submissions as many times as they want before the project deadline. For each submission, a thorough inspection is performed by an automated grading system called APOGEE. A student project has to accomplish not only the functional requirements but also many desired quality attributes such as robustness and security. In 2009, the trial-and-failure strategy has been adopted by four universities in five class sections. We report some interesting observations from student survey results, e.g., one can find out if factors like students' positive experiences of programming, choice of programming language, years of working experience, and instructors are predictive variables for positive attitudes toward the trial-and-failure learning experience as a whole.

关键词： Graphical user interfaces Educational institutions Servers Testing Engines Conferences

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Circular antenna array design for breast Cancer detection

arXiv

引用

arXiv 2018年

作者： Ouerghi, K. Fadlallah, N. Smida, A. Ghayoula, R. Fattahi, J. Boulejfen, N. Unit of Research in High Frequency Electronic Circuits and Systems Faculty of Mathematical Physical and Natural Sciences of Tunis Tunis El Manar University Tunisia RADIOCOM Institut of Technology saida P.O.Box 813 #36 Lebanon Department of Computer Science and Software Engineering Laval University Quebec Canada Research Center for Microelectronics and Nanotechnology Technopole Sousse Sousse4050 Tunisia

— —Microwave imaging for breast cancer detection is based on the contrast in the electrical properties of healthy fatty breast tissues. This paper presents an industrial, scientific and medical (ISM) bands comparative study of five microstrip patch antennas for microwave imaging at a frequency of 2.45 GHz. The choice of one antenna is made for an antenna array composed of 8 antennas for a microwave breast imaging system. Each antenna element is arranged in a circular configuration so that it can be directly faced to the breast phantom for better tumor detection. This choice is made by putting each antenna alone on the Breast skin to study the electric field, magnetic fields and current density in the healthy tissue of the breast phantom designed and simulated in Ansoft High Frequency Simulation software (HFSS). Copyright © 2018, The Authors. All rights reserved.

关键词： Microstrip antennas

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SusTrainable: Promoting Sustainability as a Fundamental Driver in software Development Training and Education Teacher Training November 1–5, Nijmegen, The Netherlands Revised lecture notes

arXiv

引用

arXiv 2022年

作者： Koopman, Pieter Lubbers, Mart Fernandes, João Paulo Korečko, Štefan Paquete, Luís Porkoláb, Zoltán Szalay, Richárd Mauša, Goran Oprescu, Ana Koedijk, Lukas van Oostveen, Sander Kok, Stephan Li, Jianhao Zsók, Viktória Grbac, Tihana Galinac Grbac, Neven Valkanov, Vladimir Petrov, Mihail Radboud University Nijmegen Netherlands University of Porto Portugal Department of Computers and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Slovakia CISUC Department of Informatics Engineering University of Coimbra Portugal Department of Programming Languages and Compilers Institute of Computer Science Faculty of Informatics Eötvös Lorád University Budapest Hungary University of Rijeka Faculty of Engineering Vukovarska 58 Rijeka51000 Croatia Complex Cyber Infrastructure University of Amsterdam Netherlands KPMG Software Engineering Master University of Amsterdam Netherlands Informatics Institute University of Amsterdam Netherlands Juraj Dobrila Univeristy of Pula Zagrebačka 30 PulaHR-52100 Croatia University Of Plovdiv "Paisii Hilendarski" Faculty of Mathematics and Informatics 24 Bulgaria Blvd. Plovdiv4027 Bulgaria

These are the proceedings of the first teacher training of the Erasmus+ project No. 2020-1-PT01-KA203-078646 -- Sustrainable. The full title of this project is Promoting Sustainability as a Fundamental Driver in software Development Training and Education and the interested reader may know more about it at: https://*** The flagship contribution is the organization of two summer schools on sustainable software production. The first summer school is moved due to the Corona pandemic. It is now scheduled for July 2022 in Rijeka, Croatia. This summer school will consist of lectures and practical work for master and PhD students in the area of computing science and closely related fields. There will be contributions from Plovdiv University, University of Ko\v{s}ice, University of Coimbra, University of Minho, E\"otv\"os Lor\'and University, University of Rijeka, Radboud University Nijmegen, University of Pula, University of Amsterdam and Babe\c{s}-Bolyai University. To prepare and streamline this summer school, the consortium organized a teacher training in Nijmegen, the Netherlands. This was an event of five full days on November 1-5, 2022 organized by Ingrid Berenbroek, Mart Lubbers and Pieter Koopman on the campus of the Radboud University Nijmegen, The Netherlands. Sustainability is one of the themes within the strategy of the Radboud University called `A Significant Impact', see https://***/sustainability/organisation/radboud-sustainable/. Sustainability plays an important role in education, research, and management. The contributions were reviewed and give a good overview of what can be expected from the summer school. Based on these papers and the very positive and constructive atmosphere, we expect a high quality and pleasant summer school. We are looking forward to contributing to a sustainable future. © 2022, CC BY-NC-ND.

关键词： Personnel training

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Resource-aware network topology management framework

arXiv

引用

arXiv 2020年

作者： Abbasi, Aaqif Afzaal Shamshirband, Shahaboddin Al-qaness, Mohammed A.A. Abbasi, Almas ALJallad, Nashat T. Mosavi, Amir Department of Software Engineering Foundation University Islamabad44000 Pakistan Department for Management of Science and Technology Development Ton Duc Thang University Ho Chi Minh758307 Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh758307 Viet Nam School of Computer Science Wuhan University Bayi Road No. 299 Wuhan430072 China Department of Computer Science International Islamic University Islamabad44000 Pakistan School of Computer Science and Technology Wuhan University of Technology Wuhan430079 China School of Computer Science Palestine Technical University Tulkarem44864 Palestine School of the Built Environment Oxford Brookes University No.5 Jack Straws Lane OxfordOX3 0BP United Kingdom Kalman Kando Faculty of Electrical Engineering Obuda University Becsi ut 94-96 Budapest1034 Hungary Institute of Structural Mechanics Bauhaus University Weimar Marienstraße 15 WeimarD-99423 Germany

Cloud infrastructure provides computing services where computing resources can be adjusted on-demand. However, the adoption of cloud infrastructures brings concerns like reliance on the service provider network, reliability, compliance for service level agreements (SLAs), etc. software-defined networking (SDN) is a networking concept that suggests the segregation of a network’s data plane from the control plane. This concept improves networking behavior. In this paper, we present an SDN-enabled resource-aware topology framework. The proposed framework employs SLA compliance, Path Computation Element (PCE) and shares fair loading to achieve better topology features. We also present an evaluation, showcasing the potential of our *** Codes 68T05 Copyright © 2020, The Authors. All rights reserved.

关键词： Information management

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