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Illumination-dependent dielectric and impedance spectroscopy analysis of Schottky-type devices with BOD-Z-EN organic semiconductor interface layer

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Journal of Materials Science: Materials in Electronics 2025年第16期36卷 1-16页

作者： Tuğluoğlu, Nihat Department of Energy Systems Engineering Faculty of Engineering Giresun University Giresun Turkey

This work presents the behavior of adjusting the dielectric and impedance characteristics by varying the voltage and illumination for a metal–organic semiconductor material-semiconductor (MOmS) configuration for optoelectronic applications. We analyzed the Ε′, Ε′′, tan δ, M′, M′′, σac, Z′, Z′′, θ characteristics of the metal-organic semiconductor–semiconductor diodes at a illumination intensity of 0- 120 mW/cm2, bias voltage range of − 4 V;+ 4 V and at 1 MHz. The Cole–Cole plot showed a single semicircle at dark and in the different illumination intensity. The new phenomena that have seemed are the presence of a peak in the Ε′′, M′′, and Z′ characteristics. With increasing voltage ‘and illumination intensity, it was discovered that the dielectric constant Ε′ and dielectric loss Ε′′ increased. The illumination increases from 0 to 120 mW/cm2, increasing the values of Ε′ and Ε′′ from 3.52 to 3.56 and 0.204 to 0.606, respectively. The BOD-Z-EN organic semiconductor layer exhibited a illumination-dependent electrical relaxation phenomenon, as seen by the M′′-M′ graphs. Consequently, this work examined the dielectric characteristics of the BOD-Z-EN-based photocapacitor structure and came to the conclusion that it could be a good dielectric material. Additionally, the material's high modulus, impedance, and phase angle properties suggest that it may find application in photonic devices. This study highlights that the fabricated Au/BOD-Z-En/n-Si device is a photosensitive capacitor. © The Author(s) 2025.

关键词： Varactors

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Staged transfer learning for multi-label half-face emotion recognition

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Journal of engineering and Applied Science 2025年第1期72卷 1-25页

作者： ElMaksoud, Mohamed M. Abd ElGohary, Sherif H. Kandil, Ahmed H. Systems and Biomedical Engineering Department Faculty of Engineering Cairo University Giza Egypt

As fundamental drivers of human behavior, emotions can be expressed through various modalities, including facial expressions. Facial emotion recognition (FER) has emerged as a pivotal area of affective computing, enabling accurate detection of human emotions from visual cues. To enhance the efficiency and maintain accuracy, we propose a novel approach that leverages deep learning and transfer learning techniques to classify emotions based on only half of the human face. We introduce EMOFACE, a comprehensive half-facial imagery dataset annotated with 25 distinct emotion labels, providing a diverse and inclusive resource for multi-label half-facial emotion classification. By combining this dataset with the established FER2013 dataset, we employ a staged transfer learning framework that effectively addresses the challenges of multi-label half facial emotion classification. Our proposed approach, which utilizes a custom convolutional neural network (ConvNet) and five pre-trained deep learning models (VGG16, VGG19, DenseNet, MobileNet, and ResNet), achieves impressive results. We report an average binary accuracy of 0.9244 for training, 0.9152 for validation, and 0.9138 for testing, demonstrating the efficacy of our method. The potential applications of this research extend to various domains, including affective computing, healthcare, robotics, human–computer interaction, and self-driving cars. By advancing the field of half-facial multi-label emotion recognition, our work contributes to the development of more intuitive and empathetic human–machine interactions. © The Author(s) 2025.

关键词： Convolutional neural networks

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New quantum color image watermarking technique (NQCIWT)

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Soft Computing 2024年第17-18期28卷 10177-10186页

作者： Aly, Rasha F. Department of Computer and Systems Engineering Faculty of Engineering Helwan University Cairo Egypt

The crazy, unconscious use of the Internet, and the increase in cybercrime and hacking, which resulted in the loss of a large number of sensitive data, the risk of piracy, etc. were the motivation for protecting rights, securing and hiding essential information such as images, audio, etc. Watermarking was a popular security, copyright and privacy approach and one of the problems in modern quantum digital computing that produced more powerful quantum protection techniques than the classical one that used classical techniques such as digital wavelet transformation and Hungarian algorithms. Recent advances in quantum image representation and processing help to manipulate data hidden. This paper proposes a new watermark technique in which a color watermark image is hidden within another color-carrying image after transformation into the quantum domain using the recently completed quantum color image representation technique. Finally, a comparison is made between the proposed method and the most recent watermarking techniques is establish in terms of PSNR (peak signal-to-noise ratio), which shows the high precision of the proposed technique and improves the imperceptibility compared to the related schemes. © The Author(s) 2024.

关键词： Color

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Performance *** Comparative Analysis of Multimodal Bilinear Pooling Fusion Approaches for Deep Learning-Based Visual Arabic-Question Answering systems

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Computer Modeling in engineering & Sciences 2025年第4期143卷 373-411页

作者： Sarah M.Kamel Mai A.Fadel Lamiaa Elrefaei Shimaa I.Hassan Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer and Systems Engineering Faculty of Engineering and TechnologyBadr University in Cairo(BUC)Cairo11829Egypt Communication Systems Engineering Department Faculty of EngineeringBenha National UniversityObour11846QalyubiaEgypt

Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.

关键词： Arabic-VQA deep learning-based VQA deep multimodal information fusion multimodal representation learning VQA of yes/no questions VQA model complexity VQA model performance performance-complexity trade-off

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GY-YOLO: ghost separable YOLO for pedestrian detection

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Neural Computing and Applications 2025年 1-27页

作者： Elhenidy, Ali M. Labib, Labib M. Haikal, Amira Y. Saafan, Mahmoud M. Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Faculty of Engineering Mansoura National University Gamasa Egypt

In recent years, there has been impressive development in human detection. The main challenge in pedestrian detection is the training data. To assess detectors in crowd scenarios more effectively, a novel dataset in this study called the HEP dataset (Hybrid Egyptian Pedestrian dataset) is introduced. The HEP dataset is extensive, has comprehensive annotations, and is highly diverse. The dataset images are collected by two different means. Most of the images are collected from different mobile cameras for people crossing the street in high crowded streets in Egypt, and the rest of the images are collected from the web. That is why the dataset is called hybrid. The collected dataset is more suitable for pedestrian detection as the whole images focus on pedestrian scenarios for people outdoors crossing the street. This outperforms the previous benchmarks such as CrowdHuman and WiderPerson which collect data from the web and surveillance cameras with lots of images for indoor people. GS-YOLO also is proposed to address the real-time performance and the occlusion in the crowd scenes issues. GS-YOLO is a novel pedestrian detection model that utilizes efficient Ghost and depth separable convolution modules. GS-YOLO replaces all the convolution layers in the backbone and the head of the original YOLOv8 with Ghost and depth separable modules, respectively. A deformable to-features module is proposed to enrich features for the different feature pyramid networks. GS-YOLO is trained and tested over the collected dataset and other benchmarks like CrowdHuman and WiderPerson datasets. GS-YOLO achieves competitive results over the state-of-the-art models such as YOLOv5 and YOLOv8. GS-YOLO achieves 92.8% mAp on the HEP dataset, while YOLOv5 achieves 90.3% mAp and YOLOv8 achieves 91.1% mAp. © The Author(s) 2025.

关键词： Computer vision Deep learning Pedestrian detection YOLO

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DMDRDF: diabetes mellitus and retinopathy detection framework using artificial intelligence and feature selection

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Soft Computing 2024年第19期28卷 11393-11420页

作者： Balaha, Hossam Magdy El-Gendy, Eman M. Saafan, Mahmoud M. Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Diabetes mellitus is one of the most common diseases affecting patients of different ages. Diabetes can be controlled if diagnosed as early as possible. One of the serious complications of diabetes affecting the retina is diabetic retinopathy. If not diagnosed early, it can lead to blindness. Our purpose is to propose a novel framework, named DMDRDF, for early and accurate diagnosis of diabetes and diabetic retinopathy. The framework consists of two phases, one for diabetes mellitus detection (DMD) and the other for diabetic retinopathy detection (DRD). The novelty of DMD phase is concerned in two contributions. Firstly, a novel feature selection approach called Advanced Aquila Optimizer Feature Selection (A2OFS) is introduced to choose the most promising features for diagnosing diabetes. This approach extracts the required features from the results of laboratory tests while ignoring the useless features. Secondly, a novel classification approach (CA) using five modified machine learning (ML) algorithms is used. This modification of the ML algorithms is proposed to automatically select the parameters of these algorithms using Grid Search (GS) algorithm. The novelty of DRD phase lies in the modification of 7 CNNs using Aquila Optimizer for the classification of diabetic retinopathy. The reported results concerning the DMD datasets shows that AO reports best performance metrics in the feature selection process with the help of modified ML classifiers. The best achieved accuracy is 98.65% with the GS-ERTC model and max-absolute scaling on the "Early Stage Diabetes Risk Prediction Dataset" dataset. Also, from the reported results concerning the DRD datasets, the AOMobileNet is considered a suitable model for this problem as it outperforms the other modified CNN models with accuracy of 95.80% on the "The SUSTech-SYSU dataset" dataset. © The Author(s) 2024.

关键词： Feature Selection

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MCHIAO: a modified coronavirus herd immunity-Aquila optimization algorithm based on chaotic behavior for solving engineering problems

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Neural Computing and Applications 2024年第22期36卷 13381-13465页

作者： Selim, Heba Haikal, Amira Y. Labib, Labib M. Saafan, Mahmoud M. Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

This paper proposes a hybrid Modified Coronavirus Herd Immunity Aquila Optimization Algorithm (MCHIAO) that compiles the Enhanced Coronavirus Herd Immunity Optimizer (ECHIO) algorithm and Aquila Optimizer (AO). As one of the competitive human-based optimization algorithms, the Coronavirus Herd Immunity Optimizer (CHIO) exceeds some other biological-inspired algorithms. Compared to other optimization algorithms, CHIO showed good results. However, CHIO gets confined to local optima, and the accuracy of large-scale global optimization problems is decreased. On the other hand, although AO has significant local exploitation capabilities, its global exploration capabilities are insufficient. Subsequently, a novel metaheuristic optimizer, Modified Coronavirus Herd Immunity Aquila Optimizer (MCHIAO), is presented to overcome these restrictions and adapt it to solve feature selection challenges. In this paper, MCHIAO is proposed with three main enhancements to overcome these issues and reach higher optimal results which are cases categorizing, enhancing the new genes’ value equation using the chaotic system as inspired by the chaotic behavior of the coronavirus and generating a new formula to switch between expanded and narrowed exploitation. MCHIAO demonstrates it’s worth contra ten well-known state-of-the-art optimization algorithms (GOA, MFO, MPA, GWO, HHO, SSA, WOA, IAO, NOA, NGO) in addition to AO and CHIO. Friedman average rank and Wilcoxon statistical analysis (p-value) are conducted on all state-of-the-art algorithms testing 23 benchmark functions. Wilcoxon test and Friedman are conducted as well on the 29 CEC2017 functions. Moreover, some statistical tests are conducted on the 10 CEC2019 benchmark functions. Six real-world problems are used to validate the proposed MCHIAO against the same twelve state-of-the-art algorithms. On classical functions, including 24 unimodal and 44 multimodal functions, respectively, the exploitative and explorative behavior of the hybrid

关键词： Coronavirus

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IHHO: an improved Harris Hawks optimization algorithm for solving engineering problems

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Neural Computing and Applications 2024年第20期36卷 12185-12298页

作者： Akl, Dalia T. Saafan, Mahmoud M. Haikal, Amira Y. El-Gendy, Eman M. Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Harris Hawks optimization (HHO) algorithm was a powerful metaheuristic algorithm for solving complex problems. However, HHO could easily fall within the local minimum. In this paper, we proposed an improved Harris Hawks optimization (IHHO) algorithm for solving different engineering tasks. The proposed algorithm focused on random location-based habitats during the exploration phase and on strategies 1, 3, and 4 during the exploitation phase. The proposed modified Harris hawks in the wild would change their perch strategy and chasing pattern according to updates in both the exploration and exploitation phases. To avoid being stuck in a local solution, random values were generated using logarithms and exponentials to explore new regions more quickly and locations. To evaluate the performance of the proposed algorithm, IHHO was compared to other five recent algorithms [grey wolf optimization, BAT algorithm, teaching–learning-based optimization, moth-flame optimization, and whale optimization algorithm] as well as three other modifications of HHO (BHHO, LogHHO, and MHHO). These optimizers had been applied to different benchmarks, namely standard benchmarks, CEC2017, CEC2019, CEC2020, and other 52 standard benchmark functions. Moreover, six classical real-world engineering problems were tested against the IHHO to prove the efficiency of the proposed algorithm. The numerical results showed the superiority of the proposed algorithm IHHO against other algorithms, which was proved visually using different convergence curves. Friedman's mean rank statistical test was also inducted to calculate the rank of IHHO against other algorithms. The results of the Friedman test indicated that the proposed algorithm was ranked first as compared to the other algorithms as well as three other modifications of HHO. © The Author(s) 2024.

关键词： Optimization

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Employing of machine learning and wearable devices in healthcare system: tasks and challenges

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Neural Computing and Applications 2024年第29期36卷 17829-17849页

作者： Saad, Hend S. Zaki, John F. W. Abdelsalam, Mohamed M. Computers Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Disease outbreaks are nowadays a critical issue despite the development and rapid growth of technology. One of the major challenges facing healthcare professionals and healthcare industries is disease prevention and control by technology. Due to busy work schedules, maintaining a healthy lifestyle has become impossible, so the solution to these previous challenges is the intelligent health monitoring system. Over the past few years, a lot of research has been done on the use of Machine learning (ML) techniques in healthcare applications. With wearable devices, ML greatly helps in tracking human activities and vital signs as well as helping to monitor and diagnose patients’ health so it plays a huge role in elderly care. Research and development of more applications for wearable devices has been underway in the past five years, due to significant technological advances in medical sensors. Despite the widespread use of wearable devices, there is little research on machine learning applications of these devices. This paper presents a review of the different areas of recent ML research for healthcare wearable devices. It also discusses the different challenges facing ML applications on wearable devices. © The Author(s) 2024.

关键词： Machine learning

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Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression Filtering Method for SEM Images

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IEEE Access 2025年 13卷 93574-93592页

作者： Ong, Dominic Chee Yong Bukhori, Iksan Sim, Kok Swee Gan, Kok Beng Multimedia University Faculty of Engineering and Technology Melaka75450 Malaysia President University Faculty of Engineering Department of Electrical Engineering 17550 Indonesia Universiti Kebangsaan Malaysia Department of Electrical Electronic & Systems Engineering Faculty of Engineering and Built Environment Malaysia

Scanning Electron Microscopy (SEM) images often suffer from noise contamination, which degrades image quality and affects further analysis. This research presents a complete approach to estimate their Signal-to-Noise Ratio (SNR) and noise variance (NV), and enhance image quality using NV-guided Wiener filter. The main idea of this study is to use a good SNR estimation technique and infuse a machine learning model to estimate NV of the SEM image, which then guides the wiener filter to remove the noise, providing a more robust and accurate SEM image filtering pipeline. First, we investigate five different SNR estimation techniques, namely Nearest Neighbourhood (NN) method, First-Order Linear Interpolation (FOL) method, Nearest Neighbourhood with First-Order Linear Interpolation (NN+FOL) method, Non-Linear Least Squares Regression (NLLSR) method, and Linear Least Squares Regression (LSR) method. It is shown that LSR method to perform better than the rest. Then, Support Vector Machines (SVM) and Gaussian Process Regression (GPR) are tested by pairing it with LSR. In this test, the Optimizable GPR model shows the highest accuracy and it stands as the most effective solution for NV estimation. Combining these results lead to the proposed Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression (AO-GPRLLSR) Filtering pipeline. The AO-GPRLLSR method generated an estimated noise variance which served as input to NV-guided Wiener filter for improving the quality of SEM images. The proposed method is shown to achieve notable success in estimating SNR and NV of SEM images and leads to lower Mean Squared Error (MSE) after the filtering process. © 2013 IEEE.

关键词： Support vector machines

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