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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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High Accurate PMSM Computation Model Based on Strongly Coupled Magnetic Field and Multi-Turn Electric Winding Circuits Using the Time-Stepping Finite Element

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Progress in Electromagnetics Research C 2025年 153卷 13-23页

作者： Merzouki, Tarik Rachek, M’Hemed Department of Electrotechnics Faculty of Electrical and Computer Engineering Mouloud Mammeri University Algeria

The work presented in this paper has great significance in improving electromagnetic models based on the strong coupling between the magnetic and electric fields transient equations while considering a realistic random multi-turn stranded winding where eddy currents, proximity and skin effects occur. The space-time partial differential equations of electromagnetic field expressed in terms of magnetic vector potential under nonlinear (B-H) magnetic materials curves handled by the iterative Newton-Raphson (NR) algorithm are simultaneously coupled with the voltage fed multi-turns electric circuits equations based on Kirchhoff’s voltage law for each turn coil current loop. The magnetic field-multi-turn electric circuit coupled model solved using the time-stepping finite element method (FEM) formulation is dedicated to highly accurate computation of electromagnetic-mechanical devices. The developed FEM tools implemented under Matlab software are used to the modeling of the permanent magnet synchronous motor (PMSM) behavior through the physical quantities such as magnetic flux density, electric current, electromagnetic torque, and angular velocity. © 2025, Electromagnetics Academy. All rights reserved.

关键词： MATLAB

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Impact of machine learning-based imputation techniques on medical datasets- a comparative analysis

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Multimedia Tools and Applications 2025年第9期84卷 5905-5925页

作者： Tiwaskar, Shweta Rashid, Mamoon Gokhale, Prasad Department of Computer Engineering Faculty of Science and Technology Vishwakarma University Pune India

In the realm of medical datasets, particularly when considering diabetes, the occurrence of data incompleteness is a prevalent issue. Unveiling valuable patterns through medical data analysis is crucial for early and precise medical predictions. However, the quality of data and the proper handling of missing data hold significant significance. To address this challenge, imputation stands as a robust approach. The main goal of this paper aims to provide a comprehensive investigation into the effects brought about by Machine Learning (ML) based imputation techniques, specifically K Nearest Neighbor Imputation (KNNI), Multiple Imputation by Chained Equations (MICE), and MissForest. Results of all three techniques are compared with the complete dataset for five missing rates (10% to 50%), and evaluated using four categories of evaluation criteria i.e. (1) model performance, (2) imputation error rate (Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Coefficient of Determination (R^2) values), (3) Pearson correlation analysis and, (4) model selection basis (Bayesian information criterion (BIC), Akaike information criterion (AIC), values). Model performance includes accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (Mcoff) score of four ML classifiers viz. (a) Random Forest (RF), (b) Support vector machine (SVM), (c) AdaBoost, (d) XGBoost (XGB). For all missing rate cases, the MissForest technique is better than the KNNI and MICE in accuracy and Mcoff in 80% of cases, precision in 40% of cases, recall in 60% of cases, F1 score, MAE, RMSE, R^2 in 100% of cases, AIC in 80% of cases, and BIC values in 100% of cases. Also, the correlation analysis confirms that the MissForest imputation preserves association between the variables, like the complete dataset. Overall, our findings suggest that MissForest is a better machine learning-based imputation technique for handling missing data in diabetes research. © The Author(s), under exclusive lice

关键词： Adaptive boosting

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Neutrosophic Fuzzy Power Management (NFPM): Tackling Uncertainty in Energy Harvesting for Sensor Networks

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Neutrosophic Sets and Systems 2025年 76卷 99-114页

作者： Alzubi, Musallam M. Mohamed, Mohamed A. Amer, Hanan M. Salama, A.A. Electronics and Communications Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt

This paper presents a novel approach known as Neutrosophic Fuzzy Power Management (NFPM) aimed at addressing the critical challenge of uncertain energy availability in Energy Harvesting Sensor Networks (EHWSNs). The main objective of this research is to enhance the management of energy resources within these networks, which traditional fuzzy logic methods often fail to do, leading to power failures and reduced reliability. NFPM utilizes neutrosophic logic to effectively model uncertainty by representing the degrees of truth, indeterminacy, and falsity of both harvested and residual energy levels. Through a fuzzy inference system, NFPM dynamically allocates energy budgets for each time slot based on these neutrosophic sets, resulting in more adaptive and conservative energy distribution. The results are validated through numerical examples and extensive simulations, demonstrating NFPM's superiority over traditional fuzzy logic, with significant improvements such as a 25% reduction in power failures, 95% enhanced network connectivity, a 15% increase in data transmission success rates, and overall improvements in energy efficiency and robustness to fluctuations and noise. This research establishes NFPM as a promising solution to the uncertainties inherent in EHWSNs. Future research directions include exploring the integration of NFPM with machine learning algorithms for predictive energy management, assessing its scalability in larger networks, and examining its applicability in other domains requiring energy management under uncertainty. © 2025, University of New Mexico. All rights reserved.

关键词： Budget control

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Deep learning and supervised learning techniques for modeling and prediction of strength of ground granulated blast furnace slag based sustainable mortar

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Neural Computing and Applications 2025年第3期37卷 1709-1726页

作者： Gürsoy-Demir, Handan Ozturk, Murat Department of Computer Engineering Iskenderun Technical University Hatay Iskenderun31200 Turkey Faculty of Engineering Department of Civil Engineering Bursa Uludag University Bursa Turkey

This paper presents an analysis and synthesis of deep learning and supervised learning techniques for predicting the flexural tensile strength and compressive strength of mortars containing ground granulated blast furnace slag (GGBFS). The goal is to save time and reduce the costs associated with experimental testing. Initially, mechanical tests were conducted experimentally, using GGBFS as a partial replacement for cement in the mortar, with replacement levels ranging from 0 wt% to 80 wt% in 1% intervals. The results indicated a nearly linear decrease in flexural tensile strength and a non-linear change in compressive strength with increasing GGBFS content. To address the limitations of traditional experimental methods and improve processes, deep learning and supervised learning approaches were explored and compared. Evaluation metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared demonstrated that the deep learning model achieved higher sensitivity and efficiency for both 7-day and 28-day flexural tensile strength, as well as 7-day and 28-day compressive strength. Quantitatively, the deep learning model outperformed traditional models for 28-day flexural tensile strength, with an RMSE of 0.6779, an R-squared value of 0.90, an MSE of 0.4596, and an MAE of 0.6045, and for 28-day compressive strength, with an RMSE of 0.6808, an R-squared value of 0.99, an MSE of 0.4636, and an MAE of 0.4891. These findings suggest that deep learning is a promising method for accurately modeling and predicting the mechanical properties of GGBFS-based mortars, particularly for the 28-day strengths. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Mortar

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Enhancing Evapotranspiration Estimation: A Bibliometric and Systematic Review of Hybrid Neural Networks in Water Resource Management

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computer Modeling in engineering & Sciences 2025年第2期142卷 1109-1154页

作者： Moein Tosan Mohammad Reza Gharib Nasrin Fathollahzadeh Attar Ali Maroosi Department of Water Science and Engineering Faculty of AgricultureUniversity of BirjandBirjand***Iran Department of Mechanical Engineering University of Torbat HeydariehTorbat Heydarieh***Iran Department of Water Engineering Faculty of AgricultureUrmia UniversityUrmia***Iran Department of Computer Engineering University of Torbat HeydariehTorbat Heydarieh***Iran

Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water *** study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers,selected according to PRISMA guidelines,to evaluate the performance of Hybrid Artificial Neural Networks(HANNs)in ET *** findings demonstrate that HANNs,particularly those combining Multilayer Perceptrons(MLPs),Recurrent Neural Networks(RNNs),and Convolutional Neural Networks(CNNs),are highly effective in capturing the complex nonlinear relationships and tem-poral dependencies characteristic of hydrological *** hybrid models,often integrated with optimization algorithms and fuzzy logic frameworks,significantly improve the predictive accuracy and generalization capabilities of ET *** growing adoption of advanced evaluation metrics,such as Kling-Gupta Efficiency(KGE)and Taylor Diagrams,highlights the increasing demand for more robust performance assessments beyond traditional *** the promising results,challenges remain,particularly regarding model interpretability,computational efficiency,and data *** research should prioritize the integration of interpretability techniques,such as attention mechanisms,Local Interpretable Model-Agnostic Explanations(LIME),and feature importance analysis,to enhance model transparency and foster stakeholder ***,improving HANN models’scalability and computational efficiency is crucial,especially for large-scale,real-world *** such as transfer learning,parallel processing,and hyperparameter optimization will be essential in overcoming these *** study underscores the transformative potential of HANN models for precise ET estimation,particularly in water-scarce and climate-vulnerable *** integrating CNNs for automatic feature extraction and leveraging hybr

关键词： Artificial neural networks bibliometric analysis evapotranspiration hybrid models research trends systematic literature review water resources management

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A Density-Based Fuzzy Clustering Algorithm Using Multi-Representatives Points Per Cluster Based on a New Distance Measure Using KNN Algorithm

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International Journal of engineering, Transactions B: Applications 2025年第12期38卷 2865-2876页

作者： Klidbary, S. Haghzad Javadian, M. Faculty of Engineering Department of Electrical and Computer Engineering University of Zanjan Zanjan Iran School of Electrical Engineering Shahid Beheshti University Tehran Iran

In analyzing phenomena around us, clustering is among the most commonly used techniques in machine learning for comparing, and categorizing them into different groups based on intrinsic features. One of the main challenges facing clustering algorithms is selecting a suitable representative for each cluster. Existing algorithms often choose a single representative, which can lead to suboptimal performance on many datasets (especially asymmetric datasets). This process is completely dependent on the type of internal distribution of the clusters, and that single point may not be a suitable representative for that cluster. The proposed algorithm for dealing with datasets, inspired by the fuzzy ALM method and avoiding complex formulas, and calculations, initially breaks the system down into simpler (two-dimensional) systems. After spreading ink drops, by finding the vertical Narrow path and the horizontal narrow path, it selects a set of points as the representation of each cluster. The proposed algorithm, unlike many conventional algorithms, provides a representative set for each cluster and also enhances the algorithm's performance in dealing with datasets that have an asymmetric structure by introducing a new distance measure based on the KNN method and utilizing the set of prime numbers. The Accuracy, F1-Score, and AMI achieved when working with many low-dimensional, and high-dimensional datasets has been higher compared to algorithms such as FUALM, HiDUALM, K-Means, DBSCAN, DENCLUE and IRFLLRR and in some cases, the achieved accuracy has been equal to 100 percent. ©2025 The author(s)

关键词： K-means clustering

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An Improved Algorithm for Accelerating Modular Exponentiation with Periodic Bit Pattern Exponents

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KSII Transactions on Internet and Information Systems 2025年第4期19卷 1267-1285页

作者： Somsuk, Kritsanapong Panit, Nirundon Phromloungsri, Ravee Department of Computer and Communication Engineering Faculty of Technology and Engineering Udon Thani Rajabhat University UDRU Udon Thani Thailand

This paper presents a novel approach to improve the efficiency of modular exponentiation computation. In fact, modular exponentiation is essential in public key cryptography, as it can be applied to many algorithms, including RSA, which has been selected for the implementation in this study. In addition, Periodic Left-to-Right Binary Exponentiation (PLR-BE) is introduced, utilizing exponents characterized by periodic bit patterns to reduce the modular multiplication operations. In fact, the proposed method improves the conventional binary exponentiation algorithm by leveraging the repetitive nature of periodic bit patterns, enabling the reutilization of results and thus reducing computational overhead. The implementation of PLR-BE is demonstrated in conjunction with the Chinese Remainder Theorem (CRT) in RSA. This algorithm is called Modified RSA with Periodic Patterns of Sub-private Keys (MRSA-PP). The experimental results show that PLR-BE outperforms the compared techniques, including Left-to-Right Binary Exponentiation (LR-BE) and Right-to-Left Binary Exponentiation (RL-BE), particularly for exponents with high Hamming weights. The efficiency improvements are especially pronounced in the decryption phase of the MRSA-PP. Although, the proposed method does not reduce key sizes, it substantially decreases the time to complete the computation. Furthermore, the proposed method is adaptable and can be applied to other algorithms requiring modular exponentiation for the computation to decrease computation resources. Copyright © 2025 KSII.

关键词： Public key cryptography

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CSRWA:Covert and Severe Attacks Resistant Watermarking Algorithm

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computers, Materials & Continua 2025年第1期82卷 1027-1047页

作者： Balsam Dhyia Majeed Amir Hossein Taherinia Hadi Sadoghi Yazdi Ahad Harati Faculty of Engineering Ferdowsi University of MashhadMashhad***Iran Department of Computer Techniques Engineering Imam Al-Kadhum CollegeBaghdad10087Iraq

Watermarking is embedding visible or invisible data within media to verify its authenticity or protect *** watermark is embedded in significant spatial or frequency features of the media to make it more resistant to intentional or unintentional *** of these features are important perceptual features according to the human visual system(HVS),which means that the embedded watermark should be imperceptible in these ***,both the designers of watermarking algorithms and potential attackers must consider these perceptual features when carrying out their *** two roles will be considered in this paper when designing a robust watermarking algorithm against the most harmful attacks,like volumetric scaling,histogram equalization,and non-conventional watermarking attacks like the Denoising Convolution Neural Network(DnCNN),which must be considered in watermarking algorithm design due to its rising role in the state-of-the-art *** DnCNN is initialized and trained using watermarked image samples created by our proposed Covert and Severe Attacks Resistant Watermarking Algorithm(CSRWA)to prove its *** this algorithm to satisfy the robustness and imperceptibility tradeoff,implementing the Dither Modulation(DM)algorithm is boosted by utilizing the Just Noticeable Distortion(JND)principle to get an improved performance in this ***,luminance,inter and intra-block contrast are used to adjust the JND values.

关键词： Covert attack digital watermarking DnCNN JND perceptual model robustness

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Optimized COVID-19 detection using sparse deep learning models from multimodal imaging data

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Multimedia Tools and Applications 2025年第13期84卷 11451-11474页

作者： Moradi, MohammadMahdi Hassanzadeh, Alireza Haghanifar, Arman Ko, Seok Bum Faculty of Computer Science and Software Engineering Concordia University MontrealQC Canada Faculty of Electrical Engineering Shahid Beheshti University Tehran Tehran Iran Department of Electrical & Computer Engineering University of Saskatchewan SaskatoonSK Canada

This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease diagnosis has demonstrated commendable effectiveness in promptly diagnosing patients and curbing infection transmission. The study introduces a deep learning-based model tailored for COVID-19 detection, leveraging three prevalent medical imaging modalities: computed tomography (CT), chest X-ray (CXR), and Ultrasound. Various deep Transfer Learning Convolutional Neural Network-based (CNN) models have undergone assessment for each imaging modality. For each imaging modality, this study has selected the two most accurate models based on evaluation metrics such as accuracy and loss. Additionally, efforts have been made to prune unnecessary weights from these models to obtain more efficient and sparse models. By fusing these pruned models, enhanced performance has been achieved. The models have undergone rigorous training and testing using publicly available real-world medical datasets, focusing on classifying these datasets into three distinct categories: Normal, COVID-19 Pneumonia, and non-COVID-19 Pneumonia. The primary objective is to develop an optimized and swift model through strategies like Transfer Learning, Ensemble Learning, and reducing network complexity, making it easier for storage and transfer. The results of the trained network on test data exhibit promising outcomes. The accuracy of these models on the CT scan, X-ray, and ultrasound datasets stands at 99.4%, 98.9%, and 99.3%, respectively. Moreover, these models’ sizes have been substantially reduced and optimized by 51.93%, 38.00%, and 69.07%, respectively. This study proposes a computer-aided-coronavirus-detection system based on three standard medical imaging techniques. The intention is to assist radiologists in accurately and swiftly diagnosing the disease, especially during the screen

关键词： COVID-19

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