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8th Human-computer Interaction Slovenia Conference, HCI SI 2023

作者： Shishkov, Aleksandr Kolmanič, Simon University of Maribor Faculty of Electrical Engineering and Computer Science Koroška cesta 46 Maribor2000 Slovenia

The landscape of medical education is undergoing a transformative shift driven by rapid technological evolution. In the 21st century, innovative platforms, notably Augmented Reality (AR), play a crucial role in creating immersive and interactive learning environments, especially in medicine. The HoloLens headset, representing a leading AR platform, stands out in revolutionizing medical education by seamlessly merging the physical and digital realms. This article explores the development of an AR application for HoloLens 2, specifically focusing on visualizing human anatomy and highlighting intricate details of implants within the human system. The primary objective is to enrich the learning experience, providing a comprehensive understanding of anatomical structures through the lens of cutting-edge technology. © 2024 Copyright for this paper by its authors.

关键词： Augmented reality Human anatomy Microsoft HoloLens 2

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Arabic Handwritten Document OCR Solution with Binarization and Adaptive Scale Fusion Detection 6

Arabic Handwritten Document OCR Solution with Binarization a...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Waly, Alhossien Tarek, Bassant Feteha, Ali Yehia, Rewan Amr, Gasser Fares, Ahmed Egypt-Japan University of Science and Technology E-JUST Faculty of Engineering Computer Science and Engineering Departement Alexandria21934 Egypt Benha University Faculty of Engineering Electrical Engineering Department Cairo11629 Egypt

ISBN: (纸本)9798350378511

The problem of converting images of text into plain text is a widely researched topic in both academia and industry. Arabic handwritten Text Recognation (AHTR) poses additional challenges due to diverse handwriting styles and limited labeled data. In this paper we present a complete OCR pipeline that starts with line segmentation using Differentiable Binarization and Adaptive Scale Fusion techniques to ensure accurate detection of text lines. Following segmentation, a CNN-BiLSTM-CTC architecture is applied to recognize characters. Our system, trained on the Arabic Multi-Fonts Dataset (AMFDS), achieves a Character Recognition Rate (CRR) of 99.20% and a Word Recognition Rate (WRR) of 93.75% on single-word samples containing 7 to 10 characters, along with a CRR of 83.76% for sentences. These results demonstrate the system's strong performance in handling Arabic scripts, establishing a new benchmark for AHTR systems. © 2024 IEEE.

关键词： Arabic Optical Character Recognition convolutional neural networks line segmentation recurrent neural networks Scene text detection

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Exploring the Usage of Mobile Phones in the Assessment Process: A Case Study 47

Exploring the Usage of Mobile Phones in the Assessment Proce...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Kovačević, Željko Bubnic, Bostjan Kosar, Tomaž Zagreb University Of Applied Sciences Department Of Computer Science And Information Technology Zagreb Croatia University Of Maribor Faculty Of Electrical Engineering And Computer Science Maribor Slovenia

ISBN: (纸本)9798350382495

Technology is changing how students learn in the 21st century significantly. Integrating mobile devices in teaching, learning, and assessment processes has emerged as an important strategy for improving teaching methodologies and students' learning. However, adopting mobile devices presents several challenges, including understanding the students' performance, impact on time, and efficiency in the assessment process. This study explores this area with an empirical study of using mobile devices and compares the results to those using traditional desktop computers. In the educational environment, we investigated students' effective use while interacting with mobile and desktop versions of the Second Introductory programming summative assessment. Multinational/multi-institutional research involved a comparative analysis between two student groups undertaking identical tasks on mobile phones and desktop computers. Our results suggest that the students' results are not affected significantly by using the mobile version of the assessment application. These results highlight the usability of mobile devices as a flexible and effective tool for various educational applications, strengthening their role in the modern learning environment. © 2024 IEEE.

关键词： Object oriented programming

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Incident Detection with Pruned Residual Multilayer Perceptron Networks 18

Incident Detection with Pruned Residual Multilayer Perceptro...

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18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Soubra, Mohamad Dorohinicki, Marek-Kisiel Kurdziel, Marcin Zachara, Marek Agh University Faculty of Computer Science Electronics and Telecommunications Krakow Poland Agh University Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Krakow Poland

ISBN: (纸本)9788396744784

Internet of things (IoT) has opened new horizons in connecting all sorts of devices to the internet. However, continuous demand for connectivity increases the cybersecurity risks, rendering IoT devices more prone to cyberattacks. At the same time, rapid advances in Deep Learning (DL)-based algorithms provide state-of-the-art results in many classification tasks, including classification of network traffic or system logs. That said, deep learning algorithms are considered computationally expensive as they require substantial processing and storage capacity. Sadly, IoT devices have limited resources, making renowned DL models hard to implement in this environment. In this paper we present a Residual Neural Network inspired DL-based Intrusion Detection System (IDS) that incorporates weight pruning to make the model more compact in size and resource consumption. Additionally, the proposed system leverages feature selection algorithms to reduce the feature-space size. The model was trained on the NSL-KDD dataset benchmark. Experimental results show that the proposed system is effective, being able to classify network traffic with an F1 score of up to 98.9% before the pruning and an F1 score of up to 97.5% after pruning 90% of network weights. © 2023 Polish Information Processing Society.

关键词： Internet of things

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Analyzing Data Characteristics for Learning OPFs

Analyzing Data Characteristics for Learning OPFs

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Lugmania, Hugo Luna, Jocellyn Rodriguez-Veliz, Xavier Cordova-Garcia, Jose Escuela Superior Politécnica Del Litoral Espol Faculty of Electrical Engineering and Computer Science Guayaquil Ecuador

ISBN: (纸本)9789531842976

Renewable energy sources have altered the generation and loading characteristics of modern electrical power systems, which is why finding the solution to the Power Flow Problem (OPF) has increased in complexity and the need to solve it more frequently. Despite increasing complexity, the OPF has a known structure presenting features that allow the use of Machine Learning (ML) to find solutions. This paper sets out to investigate the effects of varying specific characteristics of the input data used to train ML models for solving the OPF problem, particularly the load. We examine how the sample size, load correlation coefficient and maximum load deviation impact the performance of a neural network trained to predict OPF solutions. The results of this research demonstrate a clear correlation between these variations in the training dataset and the performance of different ML models. © 2024 IEEE.

关键词： Feasibility metrics Machine Learning Neural Networks OPF Training Data Variability

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Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

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computers, Materials & Continua 2024年第7期80卷 139-161页

作者： Roseline Oluwaseun Ogundokun Joseph Bamidele Awotunde Hakeem Babalola Akande Cheng-Chi Lee Agbotiname Lucky Imoize Department of Multimedia Engineering Kaunas University of TechnologyKaunas44249Lithuania Department of Computer Science Landmark UniversityOmu Aran251103Nigeria Department of Computer Science Faculty of Information and Communication SciencesUniversity of IlorinIlorin240003Nigeria Department of Telecommunication Science University of IlorinIlorin230003Nigeria Department of Library and Information Science Fu Jen Catholic UniversityNew Taipei City24205TaiwanChina Department of Computer Science and Information Engineering Fintech and Blockchain Research CenterAsia UniversityTaichung City41354TaiwanChina Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr UniversityBochum44801Germany

Mobile technology is developing *** phone technologies have been integrated into the healthcare industry to help medical ***,computer vision models focus on image detection and classification ***2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to *** leads to increased *** biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational ***,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is *** pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory *** proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and *** contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable *** model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class *** the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is *** testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,a

关键词： Retinal images ocular disorder deep transfer learning disease identification mobile device

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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MQTT based monitoring and control system for remote stations in automotive industry

MQTT based monitoring and control system for remote stations...

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2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： ivanovic, Denis Job, Josip Puntaric, Marko Herceg, Marijan TTTech-Auto CX Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Department of Software Engineering Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Department of Communications Osijek Croatia

ISBN: (纸本)9798350347722

In this paper is presented implementation of a lightweight supervisory control and data acquisition system for remote test stations as a part of a larger test house in the automotive industry. It is necessary to allow remote set up and monitoring of the test stations, which means defining the structure of a system with numerous test stations as endpoints. The test stations are meant to be virtual machines but can be both virtual and physical machines. A communication protocol must be used that enables this within the organization's network, regardless of actual distance and location. The parameters of the test stations to be monitored can be defined according to the needs and purpose. Even when creating your own tool, this function is not unique, i.e. on one test station parameters of operating system resources such as memory usage can be monitored, while on another test station logged-in users or running programs, reported system errors and others must be monitored. Data storage is one of the mandatory functions of this system. This process allows viewing data throughout history and more detailed analysis of events. There are numerous benefits that can be drawn and used from the data stored in the database. It is necessary to pay attention to the data stored as well as the scope and retention throughout history. The correct and secure setup of a database is one of the prerequisites for any serious system that processes data. Appropriate views should be created for users involved in the data monitoring process using all the elements mentioned so far. Creating a visually acceptable and easy-To-use interface that serves the main purpose is the final presentation of the entire system to users. A complete system leads to higher productivity, profitability and better organization of the company. It also allows project managers to better plan the use and occupancy of test stations. At any time, they have insight into the status of individual test stations, their activitie

关键词： Automotive industry

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High-Boost-Gain Split-Source Inverter With Shared-Ground and No Shoot-Through Issue

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IEEE Transactions on Industrial Informatics 2024年第3期20卷 3252-3265页

作者： Ahmed, Hafiz Furqan Moursi, Mohamed Shawky El Hosani, Khalifa Al Alzaabi, Omar Zahawi, Bashar National Sun Yat-sen University Department of Electrical Engineering Kaohsiung804201 Taiwan Khalifa University Advanced Power and Energy Center Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates Mansoura University Faculty of Engineering Mansoura35516 Egypt

In this article, we propose an improved high-boost-gain split-source inverter (SSI) for renewable energy generation. The proposed inverter retains the features of the existing SSIs, such as single-stage boost inversion with a reduced number of passive components and elimination of the inverse modulation-duty relationship constraint of the impedance-source inverters. It also provides additional features that include higher boost inversion ability than existing SSIs to connect low-voltage renewable energy sources to the grid, no voltage-source short-circuit issue, which increases the robustness of the proposed inverter and pulsewidth modulation dead times can be relieved, and the negative input dc-terminal and ac-neutral are electrically connected in the proposed inverter, resulting in a constant common-mode voltage and elimination of leakage ground currents for grid-interfaced PV power systems. A detailed circuit operation and analysis is provided, and experiments are performed on a 400-VA prototype inverter to confirm the working of the proposed topology. © 2005-2012 IEEE.

关键词： Pulse width modulation

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A Comprehensive Two-Step Methodology for Automated Segmentation of Architectural Distortion in Mammograms 6

A Comprehensive Two-Step Methodology for Automated Segmentat...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Ibrahim, Sameh E. Elnokrashy, Ahmed F. Mohamed, Wael A. Benha University Benha Faculty of Engineering Department of Electrical Engineering Benha Egypt Benha University Electrical Engineering Benha Egypt Nile University Computer Science Giza Egypt

ISBN: (纸本)9798350378511

Breast cancer is one of the most prevalent forms of cancer worldwide and a leading cause of mortality among women. Early detection of breast cancer is crucial for effective treatment. Architectural distortion (AD) is an early sign of breast cancer, characterized by a subtle contraction of breast tissue that often goes unnoticed. Traditional methods of detection heavily rely on the expertise of radiologists, making the process more difficult. To address this, we propose a deep learning approach to automate precise and efficient AD segmentation. Our approach involves a two-step process. In the first step, we utilize a Mask R-CNN Detectron2 model to perform AD segmentation across the entire set of mammography images. This initial segmentation provides a baseline for identifying AD regions. The ResNet-18 patch model is incorporated into the Mask R-CNN model's segmentation pipeline in the second stage. The purpose of this combination is to improve AD area localization and precision. By combining the strengths of both models, we achieve improved accuracy in AD segmentation. The evaluation of our fully automated method yielded remarkable outcomes on a diverse test set consisting of private datasets, including the Baheya dataset and the NCI dataset, as well as the publicly available Digital Database for Screening Mammography (DDSM). The results showed a Segmentation Accuracy of 0.852, Classification Accuracy of 0.915, and Mean Average Precision (mAP) of 0.894. These findings demonstrate potential to enhance the efficiency and accuracy of AD detection and segmentation in mammogram images, contributing to early diagnosis and treatment planning for patients at risk of breast cancer. © 2024 IEEE.

关键词： Mammography

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