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1st International engineering Conference on electrical, Energy, and Artificial Intelligence, EICEEAI 2022

作者： Khomand, Pouya Sabeti, Malihe Boostani, Reza Moradi, Ehsan Odeh, Mahmoud Al-Mousa, Mohammad Rasmi Islamic Azad University North Tehran Branch Deptartment of Computer Engineering Tehran Iran Shiraz University Faculty of Electrical and Computer Engineering CSE and IT Deptartmnet Shiraz Iran Shahid Beheshti University of Medical Sciences Department of Neurosurgery Tehran Iran Zarqa University Department of Management Information System Jordan Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan

ISBN: (纸本)9798350332742

Skeletal bone age assessment (SBAA) is very important for both sides of parents and physicians to evaluate the irregular growth of children. SBAA process is carried out by radiologists who visually inspect the radiology image of the left hand according to the Greulich and Pyle (GP) or the Tanner-Whitehouse 2 (TW2) methods. However, human eyes have their own limitations and therefore the visual inspection procedure by radiologist involves a degree of error and also intra personal variability. To address these drawbacks, a deep learning-based approach is proposed here to precisely act on these X-ray images. The employed database contains 1391 X-ray left-hand image from Los Angeles children's hospital and 200 left hand x-ray image from different age and gender from Iranian children from Namazi hospital of Shiraz. Our results demonstrate the efficiency of proposed model (mean absolute error of 0.89) in this field. © 2022 IEEE.

关键词： Hospitals

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Nuclei Instance Segmentation in Colon Histology Images with YOLOv7 2nd

Nuclei Instance Segmentation in Colon Histology Images with...

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2nd International Conference on Advanced engineering, Technology and Applications, ICAETA 2023

作者： Yıldız, Serdar Memiş, Abbas Varlı, Songül Department of Computer Engineering Faculty of Electrical and Electronics Engineering Yıldız Technical University İstanbul Turkey Department of Software Engineering Faculty of Engineering and Natural Sciences İstanbul Health and Technology University İstanbul Turkey Health Institutes of Türkiye İstanbul Turkey BİLGEM TÜBİTAK Kocaeli Turkey

ISBN: (纸本)9783031509193

In histology image analysis, instance-based nuclei segmentation is one of the challenging tasks within the segmentation-guided studies since it is quite troublesome to detect each distinct nuclei instance of each nuclei type in images in contrast to the semantic segmentation in which all the image pixels of a nuclei type are labelled with the same mask ID although the segmented region may comprise of multiple instances. In this paper, an instance-based medical image segmentation task is addressed, and in this context, instances of multiple types of nuclei in colon histology images are aimed to be delineated distinctly. For the instance-based segmentation of the nuclei in colon histology images, the YOLOv7 algorithm and its built-in instance segmentation module are utilized. In the experimental studies performed on Colon Nuclei Identification and Counting (CoNIC) Challenge 2022 colon histology image dataset by using a 5-fold cross-validation performance evaluation strategy, nuclei instances belonging to 6 classes as the neutrophil, epithelial, lymphocyte, plasma, eosinophil and connective were segmented. To calculate the overall system accuracy, the quantification metrics of mean average precision (mAP) and mean panoptic quality (mPQ) were measured. In performance evaluations, quite promising accuracy values were obtained. The mAP values of 0.2885 and 0.2903, and mPQ values of 0.1659 and 0.1704 were observed by using the YOLOv7 algorithm. To the best of our knowledge, this is the first nuclei instance segmentation study with YOLOv7. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Colon histology images CoNIC 2022 dataset Nuclei instance segmentation Nuclei segmentation YOLOv7

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An Enhanced Deep Super-Resolution Generative Adversarial Network Approach for Skin Disease Image Regeneration and Assessment

An Enhanced Deep Super-Resolution Generative Adversarial Net...

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Electronic engineering (ICEEM), International Conference on

作者： Ibrahim Abd El-Fattah Taha E. Taha Walid El-Shafai Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department Electronics and Electrical Communications Engineering Alexandria Higher Institute of Engineering and Technology (AIET) Alexandria Egypt Computer Science Department Security Engineering Lab Prince Sultan University Riyadh Saudi Arabia

Skin cancer is a critical medical concern, posing significant challenges in accurate diagnosis. Algorithmic approaches have seen remarkable advancements across various occupations, including skin disease assessment. This study introduces a distinctive Improved Deep Super-Resolution Generative Adversarial Network (I_DSUR-GAN) methodology for regenerating low-resolution (LOR) skin disease images into super-resolution (SUR) format. Additionally, a modified SUR-dataset inspired by HAM10000 is presented for skin disease applications. The proposed approach incorporates a novel loss function design to provide supplementary information, facilitating the creation of high-quality SUR images. Experimental results demonstrate that our method outperforms existing approaches on the HAM10000 dataset. A comprehensive evaluation and employing sensitive metrics is conducted to assess the effectiveness, training periods, and memory requirements of the proposed framework. The outcomes reveal that the suggested model excels in restoring and identifying hue and texture compared to conventional and earlier models.

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Image Reconstruction Improvement of Variable Coded Aperture Using Deep Learning Method for Gamma and Lensless Imaging Applications

Image Reconstruction Improvement of Variable Coded Aperture ...

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Ariel Schwarz Amir Shemer Eliezer Danan Noa E. Cohen Yossef Danan Department of Electrical and Electronics Engineering Azrieli College of Engineering Jerusalem Israel Faculty of Engineering Bar-Ilan University Ramat-Gan Israel School of Software Engineering and Computer Science Azrieli College of Engineering Jerusalem Israel

ISBN: (纸本)9798350345995

In gamma ray imaging for nuclear medicine, coded aperture is used to improve sensitivity. one of the main reconstructing methods is inverse filtering (deconvolution), where the recorded image is cross-correlated with periodic inverse filter of coded array. The reconstruction is free of coding noise for arbitrary array. However, amplification of quantum noise affect the reconstructed image. Although it is improved by Wiener filtering, the major problem is small terms in the spectral distribution of coded masks. Statistically and experimentally pinhole arrays have at least one term which is zero, resulting unacceptably noisy reconstruction. In our previous research we presented new approach of variable coded aperture (VCA) design for far and near field imaging applications [1–4]. The imaging system is based on time multiplexing method using variable multi pinhole array. The unique variable design enables to overcome the spatial frequencies cutoff and small terms in the Fourier transform exists in static multi pinhole array. The overall pinholes positions are designed to avoid spatial frequencies loss. However, traces of duplications can still be detected in reconstruction using Wiener filtering. Furthermore, since coded aperture blocks most of the photons that enter the detector, the dynamic range of the image is limited, thus leading to low contrast image and inadequate colors gamut.

关键词： Deep learning Fourier transforms Image metrics Imaging systems Quantum noise Spatial frequency

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Unseen Road Type Detection in Road Networks for Intelligent Transportation Systems

Unseen Road Type Detection in Road Networks for Intelligent ...

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International Conference on Intelligent Transportation

作者： Daeho Um Yuneil Yeo Ji Won Yoon Jin Young Choi Department of Electrical and Computer Engineering Automation and Systems Research Institute (ASRI) Seoul National University Seoul South Korea Department of Civil and Environmental Engineering University of California Berkeley USA Department of Artificial Intelligence Chung-Ang University Seoul South Korea

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Existing methods for road type (highway, trunk road, etc.) classification assume that every road in real environments belongs to a road type seen during training. However, there are unseen road types in real-world scenarios. Thus, unreliable classification of an unseen-type road into a seen road type can cause critical safety issues in road-related applications. In this paper, we introduce a new framework to detect unseen road types. To this end, we adopt an out-of-distribution (OOD) detection approach studied in the deep learning field. However, conventional graph-based node-level OOD detection methods cannot be directly applied to the unseen road type detection problem since roads are represented by edges in road networks. To resolve this problem, we establish a new formulation of edge-level OOD detection and propose a novel energy propagation scheme on a line graph transformed from a road network to obtain OOD scores. Experimental results on real-world road networks demonstrate the effectiveness of our method, achieving state-of-the-art performance in unseen road type detection.

关键词： Training Deep learning Roads Image edge detection Energy resolution Safety Intelligent transportation systems

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Modulation and Estimation with a Helper

arXiv

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arXiv 2023年

作者： Khina, Anatoly Merhav, Neri The Department of Electrical Engineering–Systems Tel Aviv University Tel Aviv6997801 Israel The Viterby Faculty of Electrical and Computer Engineering Technion–Israel Institute of Technology Haifa3200004 Israel

The problem of transmitting a parameter value over an additive white Gaussian noise (AWGN) channel is considered, where, in addition to the transmitter and the receiver, there is a helper that observes the noise non-causally and provides a description of limited rate Rh to the transmitter and/or the receiver. We derive upper and lower bounds on the optimal achievable α-th moment of the estimation error and show that they coincide for small values of α and for high values of Rh. The upper bound relies on a recently proposed channel-coding scheme that effectively conveys Rh bits essentially error-free and the rest of the rate—over the same AWGN channel without help, with the error-free bits being allocated to the most significant bits of the quantized parameter. We then concentrate on the setting with a total transmit energy constraint, for which we derive achievability results for both channel coding and parameter modulation for several scenarios: when the helper assists only the transmitter or only the receiver and knows the noise, and when the helper assists the transmitter and/or the receiver and knows both the noise and the message. In particular, for the message-informed helper that assists both the receiver and the transmitter, it is shown that the error probability in the channel-coding task decays doubly exponentially. Finally, we translate these results to those for continuous-time power-limited AWGN channels with unconstrained bandwidth. As a byproduct, we show that the capacity with a message-informed helper that is available only at the transmitter can exceed the sum of the capacity without help and the help rate Rh, when the helper knows only the noise but not the message. © 2023, CC BY-SA.

关键词： Transmitters

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On the Issue of Determining the Design Parameters of Magnetic Systems with Concentrators for Extracting Ferromagnetic Particles from Dispersed Materials

On the Issue of Determining the Design Parameters of Magneti...

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International Conference on Modern electrical and Energy Systems (MEES)

作者： Vadym Hulevskyi Yulia Postol Department of Power Engineering and Electrical Technologies Faculty of Energy and Computer Technology Dmytro Motornyi Tavria State Agrotechnologica University Melitopol Ukraine Department of Power Engineering and Electrical Technologies Faculty of Energy and Computer Technology Dmytro Motornyi Tavria State Agrotechnological University Melitopol Ukraine

The article discusses the problem of improving calculations when developing technical means for magnetic purification of dispersed agricultural raw materials. To solve this problem, the authors offer a scientifically based analysis of the design of a magnetic system with concentrators in order to develop mathematical methods for calculating working areas with spherical (or other shaped) bodies of certain sizes placed in them. Theoretical studies of particle dynamics in the working zone of field concentrators have been carried out. A design has been proposed for a separator for purifying dispersed materials from ferromagnetic impurities

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Execution of smart electric vehicle charging station driven by RE technology using soft computing approach 3

Execution of smart electric vehicle charging station driven ...

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3rd IEEE International Virtual Conference on Innovations in Power and Advanced Computing Technologies, i-PACT 2021

作者： Kumar, K. Vinoth Sujitha, S. Stalin, B. Buvana, D. Josh, F.T. Vinodha, K. Ramya, K.C. Saravanakumar, R. Logeswaran, D. New Horizon College of Engineering Department of Electrical and Electronics Engineering Bengaluru India Dr. MGR Polytechnic College Department of Electrical and Electronics Engineering Arni India Dhanalakshmi College of Engineering Department of Electrical and Electronics Engineering Chennai India Karunya Institute of Technology and Sciences Department of Electrical and Electronics Engineering Coimbatore India PES University Electronic City Campus Department of Computer Science Engineering Bengaluru India Sri Krishna College of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India School of Electrical Engineering Vellore Institute of Technology Department of Control and Automation Vellore India Kongu Engineering College Department of Electrical and Electronics Engineering Perundurai India

ISBN: (纸本)9781665426916

This paper outlines potential demand for electric vehicles, charging station infrastructure and its challenges for the India scenario, as well as the increasing liberalization and expansion of production, transmission and distribution. Distributed and renewable electricity generation and distribution, as well as market processes related to energy allocation and energy mix. is the subject of evolutionary development with improved performance and reliability. Structural analysis of respective parameters was performed for the commercial scope of electric vehicles in the existing energy market and regulatory concerns are taken into account to describe a scenario in which a company global control of electric vehicle charging and service provision of ancillary networks. Experimental results show confirmatory results. © 2021 IEEE.

关键词： Commercial vehicles

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Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Deep Learning Technologies

Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Dee...

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Telecommunications Conference (ITC-Egypt), International

作者： Mohamed M. El-Sheikh Aya Bayoumy Nada M. Ahmed Amany Mohamed Eldeeb Fatma I. Rabea Amira A. Nawar Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper introduces an alternative technique for diagnosing Acute Ischemic Stroke within the IoMT environment. In the proposed approach, the collected data is transmitted to a cloud-based center where the technique utilizes EfficientNet, a deep learning model, designed to extract features from MRI images thereby enhancing the detection of acute ischemic infarctions. The performance of EfficientNet is compared against two other models, CNN and MobileNet, demonstrating its superior efficacy through metrics such as accuracy, precision, recall, and F1-score, which stand at 92.31%, 92.28%, 92.33%, and 92.30%, respectively.

关键词： Deep learning Accuracy Magnetic resonance imaging Soft sensors Medical services Feature extraction Regulation

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Empowering Software engineering Students: The Role of Gamification in Promoting Learning Independence

Empowering Software Engineering Students: The Role of Gamifi...

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Vocational Education and electrical engineering, International Conference on

作者： Wahyu Nur Hidayat Achmad Hamdan Muhammad Aris Ichwanto Muslihati Gulsun Kurubacak Cakir Rahajeng Kartika Sari Nur Afni Mochammad Raffi Fadhil Hisyam Department of Electrical Engineering and Informatics Faculty of Engineering Universitas Negeri Malang Malang Indonesia Department of Electronic Systems Engineering Technology Faculty of Vocational Universitas Negeri Malang Malang Indonesia Department of Civil Engineering and Planning Faculty of Engineering Universitas Negeri Malang Malang Indonesia Department of Guidance and Counseling Faculty of Education Science Universitas Negeri Malang Malang Indonesia Department of Journalism College of Communication Ankara Haci Bayram Veli University Ankara Turkey Department of Computer Network and Telecommunication Engineering SMK Negeri 10 Malang Malang Indonesia

ISBN: (数字)9798331505103

ISBN: (纸本)9798331505110

The role of learning media greatly supports the learning process, increasing motivation and interest in the process. Learning media can also be a place to improve competence optimally through bootcamp training menus and the like with more specific training objectives. The main objective of this study is to develop a gamification-based website to enhance students’ preparedness for their Final Competency Test by tackling challenges in comprehending programming absent practical experience. The learning method implemented in this website is case-based learning that integrates dynamic coding activities. The research method uses the Plomp approach with 3 main stages, namely preliminary research, prototyping, and assessment stage. The trial of the gamification website began with media and content validation followed by small group testing and large group testing. Validation by media and material experts led to the execution of small group trials (95.7%) and large group trials (91.7%), producing exceptionally positive outcomes. The results of the small group test with 12 respondents produced a very suitable value with a score of 95.7 and the large group test with 27 respondents produced a score of $\mathbf{9 1. 7 \%}$ with very suitable criteria. This indicates that the web-based platform is highly viable for assisting vocational students in acquiring software development skills, enhancing their preparedness for practical IT positions such as software developers and system analysts. Subsequent study involved the incorporation of gamification, such as computer-supported collaborative learning.

关键词： Training Federated learning Media Programming Encoding Software Research and development Testing Software engineering Software development management

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