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15th International Conference on Emerging Ubiquitous systems and Pervasive Networks / 14th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2024

作者： Labd, Zakia Bahassine, Said Housni, Khalid Aadi, Fatima Zahrae Aithamou Laboratory of research in informatics LatRI Department of Computer Science Faculty of Sciences Ibn Tofail University Kenitra Morocco Laboratory of Artificial Intelligence and Complex Systems Engineering Department of Computer Science National Higher School of Arts and Crafts Hassan II University Casablanca Morocco

The importance of text classification algorithms has increased due to the growing availability of large-scale data. This has led to a greater demand for efficient classification techniques and encoding algorithms. Word embedding techniques, like Glove, have shown significant success in encoding semantic relationships between words. This research paper aims to reassess the effectiveness of Glove embeddings coupled with deep learning algorithms. The impact of Glove embedding on two widely used deep learning models: Recurrent Neural Networks (RNN) and Recurrent Convolutional Neural Networks (RCNN) is analyzed. The results highlight the impact of Glove embeddings on deep learning models, showcasing significant performance enhancements in some cases while having minimal effects in others. By examining the impact of Glove embeddings on traditional ML algorithms in a previous study, valuable context for understanding the performance differences between the two approaches is obtained. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Biological Influence of Low-Frequency Electromagnetic Field Exposition Based on Selected Cell Death Markers 15

Biological Influence of Low-Frequency Electromagnetic Field ...

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15th International Conference Elektro, ELEKTRO 2024

作者： Cichorowski, Robert Kocikowska, Olga Bajtos, Marek Radil, Roman Janousek, Ladislav De Carrillo, Daria Gendosz Student, Sebastian Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Faculty of Automatic Control Electronics and Computer Science Department of Engineering and Systems Biology Gliwice Poland Silesian University of Medicine Faculty of Medical Sciences Department of Physiology Katowice Poland Faculty of Electrical Engineering and Informational Technology Department of Electromagnetic and Biomedical Engineering Žilina Slovakia Silesian University of Medicine Faculty of Medical Sciences in Zabrze Department of Histology Zabrze Poland Silesian University of Technology Biotechnology Center Gliwice Poland

ISBN: (纸本)9798350372359

This work presents the results of the examination of the HeLa cell line exposure on the ELF-EMF (extremely low-frequency electromagnetic field). In particular, the relationship between ELF-EMF exposition time and cell death. The examination of cell death hallmarks were examined by estimation levels of selected proteins - FACL4 (a protein that is a part of the ferroptosis pathway) and CK18 (Cytokeratin related to necrosis and apoptosis pathways) in the proposed model of workers exposed to ELF-EMF week. © 2024 IEEE.

关键词： Electromagnetic fields

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Iterative Refinement for Variational Quantum Linear Solver 1

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International Conference on Data Analytics and Insights, ICDAI 2023

作者： Saito, Yoshiyuki Lee, Xinwei Cai, Dongsheng Shin, Jungpil Asai, Nobuyoshi Computer Science and Engineering University of Aizu Fukushima Aizu-Wakamatsu965-8580 Japan Faculty of Engineering Information and Systems University of Tsukuba Ibaraki Tsukuba305-8577 Japan

ISBN: (数字)9789819938780

ISBN: (纸本)9789819938773

Variational quantum algorithms require many measurements to train parameters in their circuit to solve a given problem. If we need a highly accurate solution, the further number of measurements increases. However, the increase in the number of measurements may cancel out the usefulness of fast quantum computations. In this paper, we propose a method to reduce the number of measurements that depends on the required accuracy in a variational quantum algorithm for a linear system of equations. We also demonstrate its usefulness through numerical experiments using a quantum simulator from the points of measurement numbers and accuracies obtained. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quantum computers

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Hybrid Malware Variant Detection Model with Extreme Gradient Boosting and Artificial Neural Network Classifiers

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computers, Materials & Continua 2023年第9期76卷 3483-3498页

作者： Asma A.Alhashmi Abdulbasit A.Darem Sultan M.Alanazi Abdullah M.Alashjaee Bader Aldughayfiq Fuad A.Ghaleb Shouki A.Ebad Majed A.Alanazi Department of Computer Science Northern Border UniversityArar9280Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Information Systems College of Computer Science and InformationJouf UniversitySakakaAljoufSaudi Arabia School of Computing University Teknologi Malaysia81310 UTM Johor BahruJohor81310Malaysia Department of Computer and Electronic Engineering Sana’a Community CollegeSana’a5695Yemen

In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining *** paper presents an innovative Application Programmable Interface(API)-based hybrid model designed to enhance the detection performance of malware *** model integrates eXtreme Gradient Boosting(XGBoost)and an Artificial Neural Network(ANN)classifier,offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware *** model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features,providing a holistic and comprehensive view of malware *** these features,we construct two XGBoost predictors,each of which contributes a valuable perspective on the malicious activities under *** outputs of these predictors,interpreted as malicious scores,are then fed into an ANN-based classifier,which processes this data to derive a final *** strength of the proposed model lies in its capacity to leverage behavioral and signature-based features,and most importantly,in its ability to extract and analyze the hidden relations between these two types of *** efficacy of our proposed APIbased hybrid model is evident in its performance *** outperformed other models in our tests,achieving an impressive accuracy of 95%and an F-measure of 93%.This significantly improved the detection performance of malware variants,underscoring the value and potential of our approach in the challenging field of cybersecurity.

关键词： API-based hybrid malware detection model static and dynamic analysis malware detection

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Low-Cost, High-Performance Algorithm and Hardware Architecture for SAO Mode Decision 2

Low-Cost, High-Performance Algorithm and Hardware Architectu...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Hou, Bingjing Liu, Chang Zhang, Yuejun Huang, Leilei Jing, Minge Fan, Yibo Fudan University State Key Laboratory of Integrated Chips and Systems Shanghai China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China East China Normal University Institute of Microelectronic Circuits and Systems Shanghai China

ISBN: (纸本)9798331515669

Sample adaptive offset (SAO) is applied for reducing sample distortion and attenuating ringing artifacts in both HEVC and VVC standards. The rate-distortion optimization process is used to select the best SAO parameter. It accounts for the majority of SAO complexity and determines the SAO filtering performance. Therefore, the rate-distortion cost calculation should be well handled to achieve both low cost and high coding performance. In this paper, we propose a simplified and optimized rate estimation algorithm based on the characteristics of SAO parameter syntax and entropy coding process. And we propose a hierarchical flow for rate-distortion cost calculation to further reduce the computational cost and storage cost for SAO mode decision hardware. The results show that the proposed algorithm achieves an average YUV loss of -0.10%, and the proposed SAO mode decision hardware architecture can be implemented by only 21.8k gates using TSMC 65nm process under 400 MHz. © 2024 IEEE.

关键词： Electric distortion

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Multi-modal classification of breast cancer lesions in Digital Mammography and contrast enhanced spectral mammography images

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computers in Biology and Medicine 2024年 183卷 109266-109266页

作者： Bouzarjomehri, Narjes Barzegar, Mohammad Rostami, Habib Keshavarz, Ahmad Asghari, Ahmad Navid Azad, Saeed Talatian Department of Computer Engineering Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr*** Iran Department of Electrical Engineering Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr*** Iran

Breast cancer ranks as the second most prevalent cancer in women, recognized as one of the most dangerous types of cancer, and is on the rise globally. Regular screenings are essential for early-stage treatment. Digital mammography (DM) is the most recognized and widely used technique for breast cancer screening. Contrast-Enhanced Spectral Mammography (CESM or CM) is used in conjunction with DM to detect and identify hidden abnormalities, particularly in dense breast tissue where DM alone might not be as effective. In this work, we explore the effectiveness of each modality (CM, DM, or both) in detecting breast cancer lesions using deep learning methods. We introduce an architecture for detecting and classifying breast cancer lesions in DM and CM images in Craniocaudal (CC) and Mediolateral Oblique (MLO) views. The proposed architecture (JointNet) consists of a convolution module for extracting local features, a transformer module for extracting long-range features, and a feature fusion layer to fuse the local features, global features, and global features weighted based on the local ones. This significantly enhances the accuracy of classifying DM and CM images into normal or abnormal categories and lesion classification into benign or malignant. Using our architecture as a backbone, three lesion classification pipelines are introduced that utilize attention mechanisms focused on lesion shape, texture, and overall breast texture, examining the critical features for effective lesion classification. The results demonstrate that our proposed methods outperform their components in classifying images as normal or abnormal and mitigate the limitations of independently using the transformer module or the convolution module. An ensemble model is also introduced to explore the effect of each modality and each view to increase our baseline architecture's accuracy. The results demonstrate superior performance compared with other similar works. The best performance on DM images

关键词： Image enhancement

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Advancing Text-to-Image Generation: A Comparative Study of StyleGAN-T and Stable Diffusion 3 under Neutrosophic Sets

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Neutrosophic Sets and systems 2025年 85卷 784-800页

作者： Sadek, Mohamed G. Hassan, A.Y. Diab, Tamer O. Abdelhafeez, Ahmed Benha Engineering Faculty Egypt Electrical Engineering Department Benha Engineering Faculty Benha University 13511 Egypt Computer Science Department Faculty of Information Systems and Computer Science October 6th University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan

Recent advances in generative models have revolutionized the technology employed for image synthesis quite significantly, and two paradigms—GANs and diffusion-based models—are leading the pack of innovation. This paper outlines an extensive comparison and analysis of some of the best models across both paradigms, namely StyleGAN-T, DF-GAN, AttnGAN, and BigGAN on the GAN side and Stable Diffusion 3 (SD3), DALL·E 3, Midjourney v6, and Imagen 2 on the diffusion side. We systematically inspect the architectural design, training protocols, text-conditioning processes, and domain adaptability of each model, highlighting how they address text-to-image generation challenges differently. Through qualitative and quantitative measurements—such as FID, CLIP Score, human preference surveys, and compositional accuracy, the work reveals performance tradeoffs concerning speed, control, creativity, semantic alignment, and photorealism. We use the Neutrosophic Set model to select the best model based on these evaluation matrices. We have different scores for each model based on evaluation matrices. So, the neutrosophic set is used to overcome the uncertainty information. We use the COPRAS method to rank the models and select the best one based on the evaluation matrix weights. © 2025, University of New Mexico. All rights reserved.

关键词： Photointerpretation

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

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Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

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Digital Communications and Networks 2023年第2期9卷 367-382页

作者： Qusay M.Salih Md Arafatur Rahman A.Taufiq Asyhari Muhammad Kamran Naeem Mohammad Patwary Ryan Alturki Mohammed Abdulaziz Ikram Faculty of Computing Universiti Malaysia Pahang26300GambangPahangMalaysia School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonUK Data Science Program Monash UniversityIndonesiaand Birmingham City UniversityB47XGUK Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Computer Science Department Al Jumoum University CollegeUmm Al-Qura UniversitySaudi Arabia

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed ***,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel ***,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing *** study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT ***,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability ***,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation *** protocol combines lower layer(Physical layer and Data Link layer)sensing that is derived from the channel estimation ***,it periodically updates and stores the routing table for optimal route ***,in order to achieve higher throughput and lower delay,a new routing metric is *** evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a *** simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achiev

关键词： Channel selection Cross-layer design Mobile cognitive radio networks Routing protocol IIoT applications

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Magnetic Domain Observation of Milled Nanocrystalline Alloy Powder

Magnetic Domain Observation of Milled Nanocrystalline Alloy ...

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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Takezawa, Masaaki Nagaki, Tomoya Motozuka, Satoshi Sasaki, Iwao Ando, Yoshito Narahara, Hiroyuki Mori, Naoki Shikayama, Toru Mukai, Shinichiro Maeda, Shuhei Sakima, Shuhei Ishii, Takaaki Kyushu Institute of Technology Faculty of Engineering Kitakyushu804-8550 Japan Kyushu Institute of Technology Graduate School of Life Science and Systems Engineering Kitakyushu808-0196 Japan Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering Iizuka820-8502 Japan Yaskawa Electric Corporation Kitakyushu806-0004 Japan

ISBN: (纸本)9798350338362

Fe-based nanocrystalline alloys have high magnetic flux density and low loss. So, in this study, the magnetic domain structure of ball-milled magnetic powders of these nanocrystalline alloys was observed using a Kerr effect microscope. The effect of strain removal heat treatment on the magnetic properties of the powder was also investigated by observing and comparing the magnetic domain structure of the milled powder before and after heat treatment during the preparation process of the nanocrystalline milled powder. Our findings showed that the strain removal heat treatment changed the size and shape of the magnetic domains to a simple structure. In addition, the magnetic wall motion was found to occur quickly, resulting in soft magnetic properties. © 2023 IEEE.

关键词： Nanocrystals

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