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3rd International Conference on Computing and Applied informatics 2018, ICCAI 2018

作者： Syahputra, M.F. Angkasih, A.P. Purnamawati, S. Andayani, U. Abdullah, D. Sriadhi, S. Iskandar, A. Rosida, T.M. Fitriany, J. Sari, I.N. Utama, M.D. Nanuru, R.F. Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Utara Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Department of Pediatric Universitas Malikussaleh Aceh Utara Indonesia Departement of Social Science Education Program Post Graduate Program Universitas Kanjuruhan Malang Malang Indonesia Faculty of Social and Political Science Universitas Kristen Indonesia Maluku Indonesia Universitas Kristen Indonesia Maluku Ambon Indonesia

Autism Spectrum Disorder is a pervasive developmental disorder that will affect children in terms of interpersonal communication, social interaction, and imaginative levels in play. Many therapies to help the motor neuron performance is one of them Pretend Play. Pretend Play is a therapy that invites children in playing to demonstrate something else and tell how to use objects that are considered in the child's imagination. However, in the era of highly developed technology, many fields have used the Augmented Reality method as a visualization of various aspects. With this method researchers will present the therapeutic visualization of the block to 3D transportation tool that is useful for strengthening motor nerves and visual strength of the child. The system can run well during marker detection, marker movement, and 3D object display with the accuracy of precision angle and distance between virtual world with real world reach 100% with angle 0 at distance 31 cm and the maximum distance from the marker is 46 cm and the maximum angle is 30. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Augmented reality

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ECG-Image-Database: A Dataset of ECG Images with Real-World Imaging and Scanning Artifacts;A Foundation for computerized ECG Image Digitization and Analysis

arXiv

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arXiv 2024年

作者： Reyna, Matthew A. Deepanshi Weigle, James Koscova, Zuzana Campbell, Kiersten Shivashankara, Kshama Kodthalu Saghafi, Soheil Nikookar, Sepideh Motie-Shirazi, Mohsen Kiarashi, Yashar Seyedi, Salman Clifford, Gari D. Sameni, Reza Department of Biomedical Informatics Emory University AtlantaGA30322 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA30332 United States

Objective: We introduce ECG-Image-Database, a large and diverse collection of electrocardiogram (ECG) images generated from ECG time-series data that exhibit real-world scanning, imaging, and physical artifacts. This dataset addresses the need for digitizing paper-based and non-digital ECGs for computerized analysis, providing a foundation for developing robust machine and deep learning models capable of ECG image digitization. Approach: We used ECG-Image-Kit, an open-source Python toolkit, to generate realistic images of 12-lead ECG printouts from raw ECG time-series data. We include images with realistic distortions, including noise, wrinkles, stains, and perspective shifts generated both digitally and physically. The toolkit was applied to 977 12-lead ECG records from the PTB-XL database and 1,000 from Emory Healthcare to create high-fidelity synthetic ECG images. These 1,977 unique images were subjected to both programmatic distortions using ECG-Image-Kit and to physical effects, such as soaking, staining, and mold growth, followed by scanning and photography under various lighting conditions to create real-world artifacts, producing a total of 35,595 images. Main results: The resulting dataset includes 35,595 software-labeled ECG images (generated from 1,977 unique records) from different sources (in Germany and the USA) with a wide range of imaging artifacts and distortions. The dataset provides ground truth time-series data alongside the corresponding images, offering a reference for training and evaluating machine and deep learning models for ECG digitization and classification. The images vary in quality, ranging from clear scans of clean papers to noisy photographs of degraded papers, enabling the development of more generalizable and robust digitization algorithms. Significance: ECG-Image-Database addresses a critical gap in cardiovascular research by supporting the development of machine learning models capable of classifying or converting ECG images int

关键词： Photography

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Federated Learning for computer Vision

arXiv

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arXiv 2023年

作者： Himeur, Yassine Varlamis, Iraklis Kheddar, Hamza Amira, Abbes Atalla, Shadi Singh, Yashbir Bensaali, Faycal Mansoor, Wathiq College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Informatics and Telematics Harokopio University of Athens Greece LSEA Laboratory Electrical Engineering Department University of Medea Algeria Department of Computer Science University of Sharjah United Arab Emirates Department of Radiology Mayo Clinic RochesterMN United States Electrical Engineering Department Qatar University Qatar

computer Vision (CV) is playing a significant role in transforming society by utilizing machine learning (ML) tools for a wide range of tasks. However, the need for large-scale datasets to train ML models creates challenges for centralized ML algorithms. The massive computation loads required for processing and the potential privacy risks associated with storing and processing data on central cloud servers put these algorithms under severe strain. To address these issues, federated learning (FL) has emerged as a promising solution, allowing privacy preservation by training models locally and exchanging them to improve overall performance. Additionally, the computational load is distributed across multiple clients, reducing the burden on central servers. This paper presents, to the best of the authors’ knowledge, the first review discussing recent advancements of FL in CV applications, comparing them to conventional centralized training paradigms. It provides an overview of current FL applications in various CV tasks, emphasizing the advantages of FL and the challenges of implementing it in CV. To facilitate this, the paper proposes a taxonomy of FL techniques in CV, outlining their applications and security threats. It also discusses privacy concerns related to implementing blockchain in FL schemes for CV tasks and summarizes existing privacy preservation methods. Moving on, the paper identifies open research challenges and potential future research directions to further exploit the potential of FL and blockchain in CV applications. © 2023, CC BY.

关键词： computer vision

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A Multi-phase Deep Learning Approach for Predicting HCC Response to TACE using Complete Computed Tomography

A Multi-phase Deep Learning Approach for Predicting HCC Resp...

引用

Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Shun Cheng Chang Chien Fu Liu Marta Misztal Yi Hsien Hsieh Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taiwan School of Electronic Engineering and Computer Science Queen Mary University of London UK Department of Electrical Engineering NTU Taiwan Center for Computational and Systems Biology and Center for Biotechnology NTU Taiwan Smart Medicine and Health Informatics Program NTU Taiwan

In this research study, we compare the predictive performance of two advanced deep learning-based models in order to provide a solution to TACE (Transarterial Chemoembolization) response prediction in HCC (Hepatocellular Carcinoma) patients. Using entire abdominal CT scans enabled a broader perspective available for the model, eliminating the need for segmentation during the preprocessing. Making use of both single-phase and multi-phase CT imaging, we have used DenseNet121 and have obtained an accuracy of 80% for the multi-phase *** Relevance: The ability to predict the effectiveness of TACE treatment prior to its administration makes it possible to provide a better decision-making aid for physicians and patients.

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Colorectal Polyp Localization: From Image Restoration to Real-time Detection with Deep Learning

Colorectal Polyp Localization: From Image Restoration to Rea...

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International Symposium on Applied Computational Intelligence and informatics ( SACI)

作者： Mahsa Dehghan Manshadi Milad Mousavi Arian Golzarian Madjid Soltani Amir Mosavi Department of Mechanical Engineering K.N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering University of Waterloo Ontario Canada John von Neumann Faculty of Informatics Obuda University Budapest Hungary University of Public Service Budapest Hungary

Increasing demands for artificial intelligence tools in the medical sector show the huge interest of physicians. In recent years, one of the most effective assistant systems in this field is the real-time detection of early-stage colorectal polyps during colonoscopy. Since even experienced physicians may miss polyps during colonoscopy, the real-time assistance system is designed to prevent this and hence contributes to diminish the number of missed critical cases. One challenge in this field is the detection of false positives. These systems are prone to mistake artifacts for colorectal polyps. This review provides an overview over current quality assessment and restoration techniques to make a high-quality training dataset for training deep neural network algorithms. Furthermore, four of the latest, fastest, and most accurate methods are introduced and analyzed in the rest of the review. Our main contribution is to provide an analysis of current methods used to detect colorectal polyps. We present a list of available datasets and present a range of challenges colorectal cancer detection systems face.

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Markov Chain-Based Model of Blockchain Radio Access Networks

Markov Chain-Based Model of Blockchain Radio Access Networks

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IEEE International Workshop on computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Vasileios Kouvakis Stylianos E. Trevlakis Alexandros-Apostolos A. Boulogeorgos Hongwu Liu Theodoros A. Tsiftsis Octavia A. Dobre Department of Research and Development InnoCube P.C. Thessaloniki Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece School of Information Science and Electrical Engineering Shandong Jiaotong University Jinan China Department of Informatics & Telecommunications University of Thessaly Lamia Greece Faculty of Engineering and Applied Science Memorial University St. John's NL Canada

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

Security has always been a priority, for re- searchers, service providers and network operators when it comes to radio access networks (RAN). One wireless access approach that has captured attention is blockchain enabled RAN (B-RAN) due to its secure nature. This research introduces a framework that integrates blockchain technology into RAN while also addressing the limitations of state-of-the-art models. The proposed framework utilizes queuing and Markov chain theory to model the aspects of B-RAN. An extensive evaluation of the models performance is provided, including an analysis of timing factors and a focused assessment of its security as- pects. The results demonstrate reduced latency and comparable security making the presented framework suitable for diverse application scenarios.

关键词： Wireless communication Technological innovation Privacy Computational modeling Blockchains Timing History Usability Queueing analysis Radio access networks

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Multiple Teachers-Meticulous Student: A Domain Adaptive Meta-Knowledge Distillation Model for Medical Image Classification

arXiv

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arXiv 2024年

作者： Nabavi, Shahabedin Hamedani, Kian Anvari Moghaddam, Mohsen Ebrahimi Abin, Ahmad Ali Frangi, Alejandro F. Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran Division of Informatics Imaging and Data Sciences Schools of Computer Science and Health Sciences The University of Manchester Manchester United Kingdom Electrical Engineering and Cardiovascular Sciences Departments KU Leuven Leuven Belgium Alan Turing Institute London United Kingdom

Background: Image classification can be considered one of the key pillars of medical image analysis. Deep learning (DL) faces challenges that prevent its practical applications despite the remarkable improvement in medical image classification. The data distribution differences can lead to a drop in the efficiency of DL, known as the domain shift problem. Besides, requiring bulk annotated data for model training, the large size of models, and the privacy-preserving of patients are other challenges of using DL in medical image classification. This study presents a strategy that can address the mentioned issues simultaneously. Method: The proposed domain adaptive model based on knowledge distillation can classify images by receiving limited annotated data of different distributions. The designed multiple teachers-meticulous student model trains a student network that tries to solve the challenges by receiving the parameters of several teacher networks. The proposed model was evaluated using six available datasets of different distributions by defining the respiratory motion artefact detection task. Results: The results of extensive experiments using several datasets show the superiority of the proposed model in addressing the domain shift problem and lack of access to bulk annotated data. Besides, the privacy preservation of patients by receiving only the teacher network parameters instead of the original data and consolidating the knowledge of several DL models into a model with almost similar performance are other advantages of the proposed model. Conclusions: The proposed model can pave the way for practical clinical applications of deep classification methods by achieving the mentioned objectives simultaneously. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Encrypted Data Caching and Learning Framework for Robust Federated Learning-based Mobile Edge Computing

TechRxiv

引用

TechRxiv 2023年

作者： Nguyen, Chi-Hieu Saputra, Yuris Mulya Hoang, Dinh Thai Nguyen, Diep N. Nguyen, Van-Dinh Xiao, Yong Dutkiewicz, Eryk The School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 Australia Department of Electrical Engineering and Informatics Vocational College Universitas Gadjah Mada Yogyakarta55281 Indonesia The College of Engineering and Computer Science VinUniversity Vinhomes Ocean Park Hanoi100000 Viet Nam The School of Electronic Information and Communications The Huazhong University of Science and Technology Wuhan430074 China

Federated Learning (FL) plays a pivotal role in enabling artificial intelligence (AI)-based mobile applications in mobile edge computing (MEC). However, due to the resource heterogeneity among participating mobile users (MUs), delayed updates from slow MUs may deteriorate the learning speed of the MEC-based FL system, commonly referred to as the straggling problem. To tackle the problem, this work proposes a novel privacy-preserving FL framework that utilizes homomorphic encryption (HE) based solutions to enable MUs, particularly resource-constrained MUs, to securely offload part of their training tasks to the cloud server (CS) and mobile edge nodes (MENs). Our framework first develops an efficient method for packing batches of training data into HE ciphertexts to reduce the complexity of HE-encrypted training at the MENs/CS. On that basis, the mobile service provider (MSP) can incentivize straggling MUs to encrypt part of their local datasets that are uploaded to certain MENs or the CS for caching and remote training. However, caching a large amount of encrypted data at the MENs and CS for FL may not only overburden those nodes but also incur a prohibitive cost of remote training, which ultimately reduces the MSP’s overall profit. To optimize the portion of MUs’ data to be encrypted, cached, and trained at the MENs/CS, we formulate an MSP’s profit maximization problem, considering all MUs’ and MENs’ resource capabilities and data handling costs (including encryption, caching, and training) as well as the MSP’s incentive budget. We then show that the problem is convex and can be efficiently solved using an interior point method. Extensive simulations on a real-world human activity recognition dataset show that our proposed framework can achieve much higher model accuracy (improving up to 24.29%) and faster convergence rate (by 2.86 times) than those of the conventional FedAvg approach when the straggling probability varies between 20% and 80%. Moreover, the proposed

关键词： Mobile edge computing

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An Efficient Learning Weight of Elman Neural Network with Chicken Swarm Optimization Algorithm

引用

Procedia computer Science 2021年 192卷 3060-3069页

作者： Barkat Ali Saima Anwar Lashari Wareesa Sharif Abdullah Khan Kamran ullah Dzati Athiar Ramli Institute of Computer Science and Information Technology The University of Agriculture Peshawar Pakistan College of Computing and Informatics Saudi Electronic University Riyadh KSA Faculty of Computing The Islamia University of Bahawalpur Pakistan Intelligent Biometrics Group (IBG) School of Electrical and Electronic Engineering USM Engineering Campus University Sains Malaysia 14300 Nibong Tebal Pulau Pinang

Data classification is one of the most frequently used tasks carried out to label information into predefined classes. The most commonly used models for data classification are Feed Forward Artificial Neural Networks (FFANN), and recurrent neural networks. The connection paths of these two structures are different from each other. Generally, the trained algorithm for these structures is back propagation (BP) algorithm which has many defects. For instance, due to the uncertain number of hidden layer neuron and fixed learning rate, it is easy to fall into local minimum, and it will never reach global minimum error function, it may stay at local minimum. Therefore, to make the slow learning process faster, it is necessary to carefully select the initial weight value. Therefore Meta heuristic search techniques play an important role for selection initial weights for the network. Chicken Swarm Optimization algorithm is one of the Meta heuristic technique effectively for selecting the initial weights values to converge to the optimal solution. However, this study proposed the Chicken Swarm Optimization for efficiently learn the initial weights value of Elman Neural Network algorithm. To validate the proposed algorithm, it is compared with existing algorithms such as Back Propagation Neural Network, Artificial Bee Colony Back Propagation and Genetic Algorithm Neural Network, and verified by two classification problems namely: IRIS and 7-bit parity. Simulation results shows that proposed algorithm outperforms with existing algorithms in terms of accuracy and mean square error.

关键词： Classification ANN RNN ENN BPNN ABCBP GANN CSENN Local minima Global minima

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Middle-East Ionospheric Signatures Related to the 2023 Turkey Earthquake

Middle-East Ionospheric Signatures Related to the 2023 Turke...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Mefe Moses Haris Haralambous Christina Oikonomou Muhammad Mubasshir Shaikh Department of Geomatics Ahmadu Bello University Zaria Nigeria Frederick Research Center Nicosia Cyprus Department of Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus Cloudwater Ltd Nicosia Cyprus Department of Electrical Engineering University of Sharjah Sharjah UAE Space Weather and Ionosphere Laboratory Sharjah Academy for Astronomy Space Sciences & Technology University of Sharjah Sharjah UAE

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This paper presents co-seismic earthquake-related ionospheric signatures observed by remote sensing instrumentation situated in the eastern Mediterranean and Middle-East and in particular in Cyprus, Iraq and UAE by exploiting Total Electron Content measurements from calibrated 1-30 sec RINEX files NICO and ISBA IGS stations and ionograms from Nicosia and Sharjah ionosonde stations. Based on the analysis of measurements from these instruments we observe daytime acoustic ionospheric waves after the second (10:24 UTC) 7.6 (Mw) earthquake shock of the 6 February 2023 Turkey earthquake. Although the GNSS ionospheric signatures are only detectable in the near-field, multiple-cusp ionogram signatures generated by Rayleigh surface waves, generating acoustic waves propagating up to the ionosphere, appear as far as Sharjah.

关键词： Seismic measurements Global navigation satellite system Instruments Electric shock Earthquakes Surface acoustic waves Acoustic measurements

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