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Modulation Assessment With IQD Correction for Two-Way Sequential AAF Relaying Transmissions

IEEE Access 2025年 13卷 72389-72401页

作者： Mostafa, Hala Marey, Mohamed Mohamad Almustafa, Khaled Katrib, Juliano Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia Menoufia University Faculty of Engineering Shebeen El-Kom6132711 Egypt Department of Electrical and Computer Engineering Hawally32093 Kuwait Hawally32093 Kuwait

Modulation assessment (MA) is a fundamental element in the development of adaptive radios that can interact effectively with their context to optimize resource usage. This assessment has been extensively studied in the literature over the past several decades. Nevertheless, there have been a few experiments conducted on relaying systems. Previous explorations of MA for relaying broadcasts have neglected the presence of in-phase and quadrature-phase discrepancies (IQD). However, these discrepancies exist in practical wireless transmissions due to manufacturing faults in the transceiver components, which can lead to substantial performance deterioration. In this paper, we create an original maximum-likelihood (ML) solution to the MA challenge in the circumstance of these discrepancies for amplify-and-forward (AAF) two-way sequential relaying transmissions (TWRT). By assuming that each terminal autonomously creates its own IQD, we depart a stage closer to effectively implementing transmissions in actual scenarios. We deploy an expectation-maximization (EM) tool to execute the indicated solution using an iterative approach. We take use of the data duplication inherent in AAF-TWRT signals to support the offered solution. During each repetition, the soft content provided by the detection procedure is utilized to develop the a posteriori expectations of the broadcast information. These expectations are then included in the assessment process as if they were known symbols. Additionally, the proposed solution involves a supplementary duty, which involves calculating the IQD parameters that are initially triggered at each terminal. The simulation results have validated the viability of the proposed architecture by demonstrating its superior MA performance compared to the standard ones. Based on the results, the offered solution reaches convergence within five cycles and achieves exceptional assessment accuracy across a broad spectrum of signal-to-noise ratios. Moreover, the m

关键词： Signal to noise ratio

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Enhancing Organizational Performance: Synergy of Cyber-Physical Systems, Cloud Services, and Crowdsensing

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International Journal of Crowd Science 2025年第1期9卷 44-55页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Nabot, Ahmad Jebreen, Issam Alauthman, Mohammad Alangari, Someah Aburub, Faisal Aldweesh, Amjad Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan Department of Information Security University of Petra Amman11196 Jordan Department of Computer Science College of Science and Humanities Dawadmi Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia

In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate qualit

关键词： Data quality

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PathDisGene: Discovering Informative Gene Groups for Disease Diagnosis Using Pathway-Disease Associations and a Grouping, Scoring, Modeling-Based Machine Learning Approach

PathDisGene: Discovering Informative Gene Groups for Disease...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Qumsiyeh, Emma Bakir-Gungo, Burcu Yousef, Malik Faculty of Engineering and Information Technology Palestine Ahliya University Bethlehem Palestine Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri Turkey

Recently, machine learning and various feature selection techniques have become popular for understanding the relationship between genes, molecular pathways, and diseases. Integrating existing domain knowledge into biological data analysis has demonstrated considerable potential for finding new biomarkers with translational uses. This paper presents PathDisGene, an innovative machine-learning tool that integrates existing domain knowledge by utilizing a Grouping-Scoring-Modeling (G-S-M) approach to discover associations among gene-pathway-disease. The first step in PathDisGene is the grouping component that associates genes according to their biological associations with diseases and pathways. This component uses the Comparative Toxicogenomics Database (CTD). Subsequently, the scoring component is applied to score each group and the highest-ranked groupings are then used to train the classifier. We test PathDisGene on ten GEO datasets and demonstrate its performance, where most of them are with high accuracy, sensitivity, specificity, and AUC values across various diseases. The tool's capacity to recognize new pathway-disease associations and uncover connections between pathways and diseases along their associated genes underscores its potential as a significant asset in promoting precision medicine and systems biology. © 2025 by SCITEPRESS– Science and technology Publications, Lda.

关键词： Adversarial machine learning

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High quality dehazed image and video based on enhanced multi-scale guided filtering dehazing technique

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Cluster Computing 2025年第5期28卷 1-24页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. El-Rabaie, El-Sayed M. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Haze is one of the most common challenges faced by outdoor imaging systems. Hazy images are characterized by low contrast and a white overlay, which make it difficult to produce high-quality results. To address this issue, researchers have developed various haze removal methods aimed at achieving superior image enhancement, efficient processing, and cost-effectiveness. This paper introduces an enhanced Multi-scale Guided Filtering (MGF) technique for dehazing. The proposed technique incorporates several key components: a fast dehazing method, homomorphic processing, and Contrast Limited Adaptive Histogram Equalization (CLAHE) as preprocessing steps, followed by a fuzzy logic approach to enhance contrast. The core of the technique is the MGF dehazing method, which is supported by fast dehazing and fuzzy logic proessing as enhancement strategies to reduce noise and improve the dynamic range of images. The MGF dehazing process gets the transmission map and atmospheric light at the coarsest level, and then guided filtering is employed iteratively to produce a smoothed transmission map, resulting in dehazed images free from artifacts. The haze model is applied to obtain the final dehazed image. This technique effectively combines two traditional approaches—fast dehazing and MGF dehazing—into a unified framework. The performance of the proposed technique is evaluated using visible and Near Infrared (NIR) video frames, as well as real hazy images. To demonstrate its efficacy, its results are compared with those of the standalone MGF dehazing method. Additionally, a comparative analysis is conducted with other dehazing techniques using both visible and NIR frames. Evaluation metrics include both referenced and non-referenced quantitative measures such as Peak Signal-to-Noise Ratio (PSNR), correlation (to measure similarity between hazy and dehazed images), entropy (quantifying the amount of information), Feature Similarity Index (FSIM), Chronic Feature Similarity index (FSI

关键词： Image enhancement

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A Study of Predicting Social Networking Sites(SNS) Media Disorder Survey Data Using a Machine Learning Approach

A Study of Predicting Social Networking Sites(SNS) Media Dis...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Munia, Jerin Akther Tahmiduzzaman, K.B.M. Saad, Izazul Haque Islam, Md Mynul Akash, Abdul Hady Zumma, Md. Thoufiq Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh University of Technology Sydney Faculty of Engineering and Information Technology Sydney Australia University of Asia Pacific Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331509828

This study uses survey data and machine learning algorithms to forecast social media disorder in people. A total of 600 individuals answered questions on their social media usage patterns, internet habits, demographics, and signs of social media disorder. This dataset was used to train a number of machine learning models, such as Gaussian Naive Bayes (GaussianNB), Random Forest, K-Nearest Neighbors (KNN), Decision Tree, and Support Vector Machine (SVM). According to the findings, the GaussianNB model had the greatest accuracy of 97%, followed by SVM (94%), Random Forest (91%), Decision Tree (92%), and KNN (90%). These results demonstrate how well machine learning can identify those who may be at risk for social media disorder. In order to improve prediction performance, future research will concentrate on increasing feature size, diversifying datasets, and refining models. Furthermore, incorporating these models into user-friendly apps and intervention programs may offer useful resources for the early diagnosis and treatment of social media illness. © 2025 IEEE.

关键词： Adversarial machine learning

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Ferroelectrically gated two-dimensional bismuth oxyselenides for strain-invariant flexible synaptic thin-film transistors

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Science China(information Sciences) 2025年第5期68卷 396-403页

作者： Jie WEN Fei XIAO Zheng-Dong LUO Dongxin TAN Xuetao GAN Dawei ZHANG Zhufei CHU Yinshui XIA Yan LIU Genquan HAN Hangzhou Institute of Technology Xidian University State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology School of MicroelectronicsXidian University Key Laboratory of Light Field Manipulation and Information Acquisition Ministry of Industry and Information Technology Shaanxi Key Laboratory of Optical Information Technology School of Physical Science and Technology Northwestern Polytechnical University School of Materials Science and Engineering The University of New South Wales ARC Centre of Excellence in Future Low-Energy Electronics Technologies (FLEET) The University of New South Wales Faculty of Electrical Engineering Computer Science Ningbo University

Two-dimensional van der Waals（2D vdW） semiconductors have proven to be of great importance for flexible thinfilm transistors（TFTs） owing to their intrinsic mechanical flexibility and superior electronic *** particular,bismuth oxyselenide（Bi2O2Se）,featuring ultrahigh electron mobility along with facile scalable thin-film growth methods,could offer a new option to deliver massively enhanced potential for flexible ***,it has remained a challenge to achieve nonvolatile flexible memory devices based on Bi2O2Se TFTs,thereby hindering the extension of Bi2O2Se TFTs to storage and emerging neuromorphic computing ***,a flexible synaptic TFT is demonstrated through the creation of a Bi2O2Se-based ferroelectric field-effect transistor（FeFET） structure on the flexible mica *** proposed device exhibits excellent nonvolatile memory characteristics,including a large memory window,excellent current modulation ratio,great retention,and strong ***,the Bi2O2Se-based FeFET can be operated as a synaptic device with analog conductance-modulating *** to the superior mechanical flexibility of the component materials and the mica substrate,the Bi2O2Se-based FeFETs can retain their performance against various bending states,showing a straininvariant electrical *** study marks the advancement of Bi2O2Se-based TFTs toward flexible nonvolatile memories and synaptic devices.

关键词： Bi$_{2}$O$_{2}$Se FeFET nonvolatile memory flexible device synaptic transistor

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to Data Clustering

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computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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RobustFace: a novel image restoration technique for face adversarial robustness improvement

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Multimedia Tools and Applications 2025年第11期84卷 8965-8993页

作者： Sadu, Chiranjeevi Das, Pradip K. Yannam, V Ramanjaneyulu Nayyar, Anand Department of Computer Science and Engineering RGUKT Nuzvid Andhra Pradesh Nuzvid India Department of Computer Science and Engineering IIT Guwahati Assam Guwahati India School of Technology Woxsen University Telangana Hyderabad India Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam

Machine Learning (ML) models, particularly Deep Learning (DL), have made rapid progress and achieved significant milestones across various applications, including numerous safety-critical contexts. However, these models have recently been discovered to be susceptible to adversarial attacks, which are well-crafted input images. The primary objective of this paper is to propose a novel methodology titled "RobustFace", which is based on deep image restoration networks, that significantly improves the facial adversarial robustness of various image-classification models. Adversarial images are created using the Private Fast Gradient Sign Method (P-FGSM), StyleGAN and Fast Landmark Manipulation (FLM) methods. The adversarial images are then enhanced using deep image restoration networks to bring back them into the original space. The encoded weighted local magnitude patterns (WLMP) are extracted and provided to different types of classifiers to detect facial adversarial images from the clean images. The effectiveness of RobustFace has been demonstrated on two real-world datasets and experimental outcomes show that it significantly improves facial adversarial robustness on all evaluating classifiers. It improves the highest classification accuracy from 98.75% to 99.00% on P-FGSM attacks, from 77.94% to 85.25% on adversarial attacks generated by StyleGAN and from 65.52% to 69.50% for FLM attacks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image reconstruction

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A novel U-net model for brain tumor segmentation from MRI images

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Alexandria engineering Journal 2025年 126卷 220-230页

作者： Obayya, Marwa Alshuhail, Asma Mahmood, Khalid Alanazi, Meshari H. Alqahtani, Mohammed Aljehane, Nojood O. Almansour, Hamad Al-Hagery, Mohammed Abdullah Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal university Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Applied Collage Najran University Najran Saudi Arabia Department of Computer Science College of Computer Qassim University Saudi Arabia

Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis. © 2025 The Authors

关键词： Convolutional neural networks

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Parameters Estimation of Modified Triple Diode Model of PSCs Considering Charge Accumulations and Electric Field Effects Using Puma Optimizer

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computer Modeling in engineering & Sciences 2025年第4期143卷 723-745页

作者： Amlak Abaza Ragab A.El-Sehiemy Mona Gafar Ahmed Bayoumi Electrical Engineering Department Faculty of EngineeringKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Computer Engineering and Information College of Engineering inWadi AlddawasirPrince Sattam Bin Abdulaziz UniversityKharj16278Saudi Arabia Machine Learning and Information Retrieval Department Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Electrical Department Faculty of Engineering and TechnologyBadr University in CairoBadr11829Egypt Physics and Mathematics Department Faculty of EngineeringKafrelsheikh UniversityKafrelsheikh33516Egypt College of Engineering Al-Ayen UniversityThi-Qar64001Iraq

Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution *** study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on *** models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs *** PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis *** suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its *** results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the *** statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing *** convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive ***,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.

关键词： Dynamic model of PSCs puma optimizer parameter estimation triple diode model

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