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International Conference on Information technology (INCIT)

作者： Emiya Fefayosa Br Tarigan Rahmat Yasirandi Fahdah Almarshad Isa Mulia Insan Novian Anggis Suwastika School of Computing Telkom University Bandung Indonesia Faculty of Information Techology King Mongkut’s Institute of Technology Ladkrabang Telkom University Bangkok Thailand Departement of Information System Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Departement of Information System Institut Teknologi Sepuluh Nopember Surabaya Indonesia Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur Malaysia

Technological innovation in Indonesia has brought many advantages. Religious activities are no exception. Islam, as Indonesia’s majority religion, is one of those affected by the positive impact. Many technological innovations have helped the activities of Muslims in Indonesia. This includes the Eid Al-Adha event as well as the case study proposed in this study. By using the Design Thinking Approach, this study succeeded in proposing an application related to sacrificial animals. The application prototype is thriving with a proper acceptance level. This study conducted System Usability Scale Testing for 46 participants at the test stage, with SUS’s score of 78.07, means the proposed prototype is included in the “Good” Grade and “Acceptable” category. This means the fulfilment of the needs and functionality of prospective users has been successfully provided.

关键词： COVID-19 Technological innovation Animals Pandemics Government Prototypes Stakeholders

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Medical image denoising on Shearlet-domain using different thresholding methods

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AIP Conference Proceedings 2024年第1期3150卷

作者： Siti Shaliza Mohd Khairi Mohd Aftar Abu Bakar Mohd Almie Alias Sakhinah Abu Bakar School of Mathematical Sciences College of Computing Informatics and Media Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Department of Mathematical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia 43600 Bangi Selangor Malaysia

Medical images are images of the interior body taken using medical imaging techniques. Most of these images are contaminated with noise caused by transmission limitations or storage devices during the process of image collection from MRI, CT scan, or biopsy procedures. To date, many researchers have found that image denoising is a difficult task because they must strike a balance between noise reduction and preserving sharp image edges. Instead of conventional denoising methods, this paper studies the application of Shearlet transform for denoising the medical image. Shearlet transform is able to provide better directional determination during image reconstruction because it has the properties of a multiscale and is able to extract anisotropic features on images. In this study, different thresholding methods such as hard threshold, Otsu threshold and adaptive threshold were considered for denoising the image. In this study, peak signal-to-noise ratio (PSNR), mean square error (MSE) and structural similar index (SSIM) are used for performance evaluation. The experimental results show that the Shearlet transform is suitable to be used for denoising the medical images, where the most suitable thresholding method is the hard threshold, which it effectively reconstructs the images and preserve the edges on medical images compared to the other considered thresholding methods.

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Expression of Concern for: Wireless Sensor Network to Connect Isolated Nodes using Link Assessment Technique

Expression of Concern for: Wireless Sensor Network to Connec...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： B.Barani Sundaram Amit Pandey Aschalew Tirulo Abiko Janga Vijaykumar Adola Haile Genale P. Karthika College of Informatics Bule Hora University Bule hora Ethiopia College of Informatics CSI Executive member CSRSCD BHU Ethiopia School of Computing and Informatics Wachemo University Hosana Ethioipa Dept. Of Information Technology College of Informatics Bule Hora University Bule Hora Ethiopia Department of Information Science Bule Hora University Bule hora Ethiopia Department of Computer Applications Kalasalingam Academy of Research and Education Krishnankoil India

ISBN: (数字)9781665419604

ISBN: (纸本)9781665429986

A simultaneous geography control effort is implemented for full-reachability systems and a genetic algorithm with dynamic transformation to find the breakthrough in a blaze-scene. The proposed scheme efficiently takes care of the issue of separate node for transmission run. The suggested technique makes it possible to improve the display of the genetic algorithm in order to maintain a strategic distance from the possible arrangements which fall into nearby ideal. Different experimental simulations have to be confirmed the performance quality, reliability and timeliness of designed approaches. In the Wireless Sensor established network, can connect at least two sensors in a network sensors and deliver jointly via the obtained transmission extend to form the network of total accessibility. Accordingly, excellent blaze exhaust system with complete network accessibility the proposed approaches in this paper established topology and real-time routing methods. This device supports the protection and security of individuals in a blaze scene.

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Measuring the performances of covariates using exponential survival analysis with partly-interval censored simulation data

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AIP Conference Proceedings 2024年第1期2895卷

作者： Siti Afiqah Muhamad Jamil Jessintha Lai Mohd Asrul Affendi Abdullah School of Mathematical Sciences College of Computing Informatics and Media Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia 84600 Pagoh Johor Malaysia

In many fields of science, modelling and analyzing survival rates has shown to be a valuable element of statistical study. This paper aims at proposing the partly-interval censored data into the fixed and time-varying covariates and measure the performances of Exponential survival distribution using mean square error (MSE), mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and standard error. As a result, when dealing the data without censored observations, the exponential distribution significantly fit the simulation data since low values of error measurements appeared when the data included the exact and complete types of simulation. Thus, this study proposed that the uncensored data could be applicable towards the Exponential survival distribution compared to other distributions of survival analysis.

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ECA-RetinaNet: A Novel Self-Attention RetinaNet for Environmental Microorganism Image Object Detection

ECA-RetinaNet: A Novel Self-Attention RetinaNet for Environm...

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IEEE International Conference on Big Data

作者： Hechen Yang Jinzhu Yang Tao Jiang Xin Zhao Jinghua Zhang Peng Zhao Ao Chen Qianqing Nie Marcin Grzegorzek Chen Li Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang Liaoning China School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information Technology Chengdu China Institute of Medical Informatics University of Luebeck Luebeck Germany

The detection of environmental microorganisms is always a difficult task, e specially when the multi-scale environment is complex. For tiny objects in microscopic images, current detection methods face the challenge of accurate identification and localization. In contrast, we propose a convolutional neural network (ECA-RetinaNet) for microscopic object detection of which underlying dataset is a high-quality EMDS-7 dataset. The accuracy of ECA-RetinaNet is high, with a high mean Average Precision (mAP) value of 81.42% in the Environmental Microorganisms (EMS) detection task. Its accuracy has been higher than that of the two-stage object detection network.

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Deep Reinforcement Learning for Computation Offloading and Resource Allocation in Blockchain-Based Multi-UAV-Enabled Mobile Edge computing

Deep Reinforcement Learning for Computation Offloading and R...

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International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： Abegaz Mohammed Hayla Nahom Ayall Tewodros Yasin Habtamu Gebrye Hayelom School of Informatics and Computing Dilla University Dilla Ethiopia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781665405058

ISBN: (纸本)9781665405065

In the current fifth-generation (5G) and Beyond 5G (B5G) era, the Unmanned Aerial Vehicles (UAVs) have been playing a vital role and attracting interest in different application areas in the military, and civil applications such as communications, disaster management, search and rescue, security, control, agriculture, Internet of things (IoT), etc. In these networks, ultra-heterogeneous IoT devices generate time-sensitive traffic. However, those devices have limited resources to compute tasks. Recently, Mobile Edge Computation Offloading (MECO) has been considered as an encouraging model to enable the computation tasks of IoT devices to be performed by MEC servers and support ultra-low latency IoT applications to ensure Quality of services (QoS). However, terrestrial network failure due to natural and human-made disasters has been increasing, and difficult to provide reliable computation offloading and resource allocation services to IoT networks. Nowadays, UAVs have been promising technology to quickly deploy and recover the system to provide efficient services to edge nodes. The offloading and resource allocation problems in current network technology are complex, and offloading task to edge server is vulnerable to security risks. Hence, we utilize a deep reinforcement learning method to handle a complex problem for computation offloading and resource allocations in a dynamic environment. And also, we explore a blockchain-based multi-UAV-assisted MEC architecture in securing and optimizing the offloading problems.

关键词： Internet of Things Resource management Cloud computing Blockchain Task analysis Edge computing Computer architecture

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

arXiv

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arXiv 2023年

作者： Nghiem, Truong X. Drgoňa, Ján Jones, Colin Nagy, Zoltan Schwan, Roland Dey, Biswadip Chakrabarty, Ankush Cairano, Stefano Di Paulson, Joel A. Carron, Andrea Zeilinger, Melanie N. Cortez, Wenceslao Shaw Vrabie, Draguna L. School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ86011 United States Pacific Northwest National Laboratory RichlandWA United States EPFL Switzerland The University of Texas Austin United States Siemens Corporation Technology PrincetonNJ08540 United States Mitsubishi Electric Research Laboratories CambridgeMA02139 United States The Ohio State University ColumbusOH43210 United States ETH Zurich Switzerland

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to purely data-driven methods, PIML models can be trained from additional information obtained by enforcing physical laws such as energy and mass conservation. More broadly, PIML models can include abstract properties and conditions such as stability, convexity, or invariance. The basic premise of PIML is that the integration of ML and physics can yield more effective, physically consistent, and data-efficient models. This paper aims to provide a tutorial-like overview of the recent advances in PIML for dynamical system modeling and control. Specifically, the paper covers an overview of the theory, fundamental concepts and methods, tools, and applications on topics of: 1) physics-informed learning for system identification;2) physics-informed learning for control;3) analysis and verification of PIML models;and 4) physics-informed digital twins. The paper is concluded with a perspective on open challenges and future research opportunities. © 2023, CC BY-SA.

关键词： Machine learning

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Survival analysis using censored lung cancer data: A preliminary study

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AIP Conference Proceedings 2024年第1期3123卷

作者： Siti Afiqah Muhamad Jamil Nurul Izzah Ali Mahayaudin M. Mansor Ahmad Zia Ul-Saufi Muhammad Ammar Shafi School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia 84600 Pagoh Johor Malaysia

The primary goal of Exploratory Data Analysis (EDA) is exploring and understanding data in order to gain insights and guide for further analysis. It allows for data cleaning which involves removing redundancies, handling missing values, correcting errors and transforming data if necessary as it is an important part of overall data preparation process. The aim of this study is to conduct a preliminary study before applying the survival method of analysis to censored lung cancer observations. The Kaplan-Meier survival curve, proportional hazard assumption, time varying covariate assumption via Scaled Schoenfeld residuals, Cox-Snell residuals for overall goodness of fit of the model assumption, and normality assumption via quantile-quantile (Q-Q) plot were all used in this study. The study discovered that the lung cancer censored observations were not violated with the parametric assumption among semi-parametric, and non-parametric assumptions. Thus, future work is recommended to include a comparison of the parametric method of survivals using the lung cancer data. The application of survival analysis would be ambiguous and mislead the researcher if the fundamental part of survival being left out. As a result, this study may aid in identifying appropriate assumptions for prior application on the survival analysis of censored observations.

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NetMoST: A network-based machine learning approach for subtyping schizophrenia using polygenic SNP allele biomarkers

arXiv

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arXiv 2023年

作者： Wei, Xinru Dong, Shuai Su, Zhao Tang, Lili Zhao, Pengfei Pan, Chunyu Wang, Fei Tang, Yanqing Zhang, Weixiong Zhang, Xizhe Early Intervention Unit Department of Psychiatry The Affiliated Brain Hospital of Nanjing Medical University Jiangsu Nanjing210029 China School of Biomedical Engineering and Informatics Nanjing Medical University Jiangsu Nanjing210001 China School of Computer Science and Engineering Northeastern University Shenyang China Department of Health Technology and Informatics Department of Computing The Hong Kong Polytechnic University Hong Kong Functional Brain Imaging Institute of Nanjing Medical University Nanjing China Department of Psychiatry The First Affiliated Hospital of China Medical University Shenyang China Brain Function Research Section The First Affiliated Hospital of China Medical University Shenyang China Department of Gerontology The First Affiliated Hospital of China Medical University Shenyang China

Subtyping neuropsychiatric disorders like schizophrenia is essential for improving the diagnosis and treatment of complex diseases. Subtyping schizophrenia is challenging because it is polygenic and genetically heterogeneous, rendering the standard symptom-based diagnosis often unreliable and unrepeatable. We developed a novel network-based machine-learning approach, netMoST, to subtyping psychiatric disorders. NetMoST identifies polygenic risk SNP-allele modules from genome-wide genotyping data as polygenic haplotype biomarkers (PHBs) for disease subtyping. We applied netMoST to subtype a cohort of schizophrenia subjects into three distinct biotypes with differentiable genetic, neuroimaging and functional characteristics. The PHBs of the first biotype (36.9% of all patients) were related to neurodevelopment and cognition, the PHBs of the second biotype (28.4%) were enriched for neuroimmune functions, and the PHBs of the third biotype (34.7%) were associated with the transport of calcium ions and neurotransmitters. Neuroimaging patterns provided additional support to the new biotypes, with unique regional homogeneity (ReHo) patterns observed in the brains of each biotype compared with healthy controls. Our findings demonstrated netMoST’s capability for uncovering novel biotypes of complex diseases such as schizophrenia. The results also showed the power of exploring polygenic allelic patterns that transcend the conventional GWAS approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

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