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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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Investigating the correlation between smoking and blood pressure via photoplethysmography

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BioMedical engineering Online 2025年第1期24卷 1-22页

作者： Qananwah, Q. Quran, H. Dagamseh, A. Blazek, V. Leonhardt, S. Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid Jordan Department of Electronics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid Jordan Helmholtz-Institute for Biomedical Engineering RWTH Aachen University Aachen Germany

Smoking has been widely identified for its detrimental effects on human health, particularly on the cardiovascular health. The prediction of these effects can be anticipated by monitoring the dynamic changes in vital signs and other physiological signals or parameters such as heart rate, blood pressure (BP), Electrocardiogram (ECG), and Photoplethysmogram (PPG), which subtly encode smoking-related effects. We investigated the influence of different smoking habits—normal cigarettes (NC), electronic cigarettes (EC), and shisha (SH)—on BP through analysis of ECG and PPG signals. The measurements of these physiological signals were taken across three distinct smoking phases: "before", "during", and "after" smoking. The study assessed changes in heart rate, as well as morphological and statistical characteristics of ECG and PPG signals, induced by smoking. A machine learning (ML) model was developed to predict BP values with different smoking habits and smoking phases, while also evaluating the temporal effects of smoking phases. Results show that smoking markedly alters PPG features in such it significantly affects systolic time, heart rate, peak pulse interval variability, and augmentation index. BP variations were evident across all smoking habits and phases. The ML model demonstrated strong accuracy in estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) during and post-smoking, with a mean error of 0.01 ± 0.29 mmHg and a root mean square error (RMSE) of 0.2924 mmHg for DBP and RMSE of 0.0082 mmHg for SBP. Such a study underscores the pronounced effect of smoking on BP and its potential role in cardiovascular system alterations, offering insights into the development of related diseases. © The Author(s) 2025.

关键词： Plasma (human)

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Optimizing crowd evacuation: evaluation of strategies for safety and efficiency

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Journal of Reliable Intelligent Environments 2025年第1期11卷 1-19页

作者： Oliveira, Hugo S. Faculty of Sciences University of Porto Porto Portugal Institute for Systems and Computer Engineering Technology and Science INESC TEC Porto Portugal

Predicting and controlling crowd dynamics in emergencies is one of the main objectives of simulated emergency exercises. However, during emergency exercises, there is often a lack of sense of danger by the actors involved and concerns about exposing real people to potentially dangerous environments. These problems impose limitations in running an emergency drill, harming the collection of valuable information for posterior analysis and decision-making. This work aims to mitigate these problems by using Agent Based Modelling (ABM) simulator to deepen the comprehension of human actions when exposed to a sudden variation in extensive crowded environmental conditions and how evacuation strategies affect evacuation performance. To assess the impact of the evacuation strategy employed, we propose a modified informed leader-flowing approach and compare it with common evacuation strategies in a simulated environment, replicating stadium benches with narrow corridors leading to different exit points. The objective is to determine the impact of each set of configurations and evacuation strategies and compare them against other established ones. Our experiments determined that agents following the crowd generally lead to a higher number of victims due to the rise of herding phenomena near the exits, which was significantly reduced when agents were guided towards the exit via knowing the exit beforehand or following leader agent with real-time information regarding exit location and exit current state, proving that relevant and controlled information in combination with Follow Leader strategies can be crucial in an emergency evacuation scenario with limited evacuation exit capabi and distribution. © The Author(s) 2024.

关键词： Decision making

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Multimodality imaging in prostate cancer diagnosis using artificial intelligence: basic concepts and current state-of-the-art

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Multimedia Tools and Applications 2025年 1-30页

作者： Ayyad, Sarah M. Abdel-Hamid, Nahla B. Ali, H. Arafat Labib, Labib M. Computers and Systems Department Faculty of Engineering Mansoura University Mansoura Egypt Faculty of Engineering Mansoura National University Mansoura Egypt Faulty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt

The early diagnosis of prostate cancer (PCa) is highly recommended, as the tumor will not spread to other organs of the body and the bones. Moreover, a late diagnosis of PCa could lower the survival rate. The growing development of Artificial Intelligence (AI) and Machine Learning (ML) in medical images has led to significant improvement in PCa diagnosis. Multimodality is now commonly applied in medical imaging diagnosis, as it provides comprehensive information about a target (tissue or tumor). It has shown to be useful for advancing the clinical reliability of using medical images and ML for medical diagnostics and analysis. Hence, in this paper, a comprehensive survey is provided to explore the state-of-the-art Computer-Aided Diagnosis systems (CADs) for PCa detection attributed to multimodality imaging, a background of PCa. different types of medical imaging used in PCa diagnosis, related clinical workflows, future perspectives, and some common limitations of related work. The review exhibits an extensive literature review done on multimodality imaging in PCa, highlights that multimodality imaging has the potential of wide applicability in diagnosis systems. It is expected that this study enhances the understanding necessary for developing CAD systems for PCa diagnosis. Additionally, it is expected to establish a great basis for developing multimodal images, the relevant datasets, some of the challenges, and future topics. © The Author(s) 2025.

关键词： Urology

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Classification of human-written and AI-generated sentences using a hybrid CNN-GRU model optimized by the spotted hyena algorithm

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Alexandria engineering Journal 2025年 126卷 116-130页

作者： Ragab, Mahmoud Ashary, Ehab Bahaudien Kateb, Faris Hakeem, Abeer Mosli, Rayan Albogami, Nasser N. Nooh, Sameer Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Electrical and Computer Engineering Department Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Information Systems Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

The rapid advancement of artificial intelligence (AI) in generating human-like text poses significant challenges in distinguishing between human-written and AI-generated content. Recent advancements in natural language generation have significantly enhanced the quality and variety of AI-generated text, making it almost indistinguishable from human-written content. ChatGPT, a popular AI model, belongs to the generative pre-trained transformer family. While human content is created with a clear intent to convey meaning, AI-generated text aims to replicate human-like language. Classifying human-written and AI-generated sentences is crucial for addressing issues like fake news, plagiarism, and spamming. AI text often follows repetitive patterns, while human writing is more creative and original, making detection significant for combating misinformation. Therefore, this study proposes to classify human-written and AI-generated sentences using a hybrid CNN-GRU model optimized by the Spotted Hyena Algorithm (CHWAIG-DLSHO) approach. The approach involves preprocessing text data through tokenization, lemmatization, and data splitting, followed by word embedding using Latent Dirichlet Allocation (LDA). A hybrid convolutional neural network (CNN) and gated recurrent unit (GRU) model is employed for sentence classification. The spotted hyena optimizer (SHO) model is utilized to fine-tune the hyperparameters of the CNN-GRU model, enhancing its performance. The analysis of the CHWAIG-DLSHO method takes place utilizing AI vs. human text dataset. The performance validation of the CHWAIG-DLSHO method portrayed a superior accuracy value of 99.17 % over existing techniques. © 2025 The Authors

关键词： Convolutional neural networks

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Collision avoidance and routing based on location access (CARLA) of mobile robots

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Neural Computing and Applications 2025年第9期37卷 6401-6430页

作者： ElSayyad, Shimaa Ezzat Saleh, Ahmed I. Ali, Hesham A. Saraya, M.S. Rabie, Asmaa H. Abdelsalam, Mohamed M. Computers Engineering Department MISR Higher Institute for Engineering and Technology Mansoura Round Road Mansoura City35516 Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Dean of Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa Mansoura Egypt Faculty of Engineering Mansoura National University Gamasa Mansoura Egypt

The paper introduces a new path-planning robotic system methodology called Collision Avoidance and Routing based on Location Access (CARLA) for use in critical environments such as hospitals and crises where quick action and saving human lives are vital. The main focus of our framework is on accuracy and fast responses, such as delivering tools or items in a specific area while avoiding collisions with other robots and obstacles. CARLA is designed to provide quick responses during emergencies, unlike most existing algorithms that are integrated into site control units or distributed among mobile robots on-site. By being loaded onto a remote server node rather than individual robots, CARLA helps to conserve the robots' capabilities, hardware resources, and power consumption. Additionally, our system utilizes cloud computing and Fog servers technology to improve data transmission times between the cloud and smart devices, especially for applications with strict timing requirements like emergency response. The Fog platform is also leveraged to enhance on-site access to real-time interaction and location-based services by bringing processing power closer to the robots from far-off Cloud servers. CARLA has various applications, such as in factories and warehouses, where mobile robots need to be selected and directed by a central control system remotely. The proposed framework consists of three main modules: Robot Knowledge Module, Robot Selection Module, and Route Reservation Module, which will all be discussed in detail in this paper. The results of simulations using this framework show that the robots have improved flexibility and efficiency in terms of computing paths and successfully fulfiling requests without colliding, compared to traditional methods used in similar scenarios. © The Author(s) 2025.

关键词： Mobile robots

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and Technology Department of Electrical and Computer Engineering University of Alberta Systems Research Institute Polish Academy of Sciences Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University

Changes in the Atmospheric Electric Field Signal(AEFS) are highly correlated with weather changes, especially with thunderstorm activities. However, little attention has been paid to the ambiguous weather information implicit in AEFS changes. In this paper, a Fuzzy C-Means(FCM) clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS. First, a time series dataset is created in the time domain using AEFS attributes. The AEFS-based weather is evaluated according to the time-series Membership Degree(MD) changes obtained by inputting this dataset into the FCM. Second, thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus. Thus, a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain. Finally, the rationality and reliability of the proposed method are verified by combining radar charts and expert experience. The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS, and a negative distance-MD correlation is obtained for the first time. The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field (AEF) Thunderstorm Fuzzy C-means (FCM) Attribute

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Observer-based Implementation of Discrete Gabor Transform

Periodica Polytechnica Electrical Engineering and Computer S...

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Periodica Polytechnica Electrical engineering and Computer Science 2025年第1期69卷 79-88页

作者： Ország, Bence Sujbert, László Department of Artificial Intelligence and Systems Engineering Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary

The most commonly used technique in time-frequency analysis is the short-time Fourier transform. It can be used to determine the spectral components as they change over time by computing the Fourier transform of a windowed segment of the signal. A fundamental constraint of this method is that the frequency resolution of the representation in the time-frequency domain will be linear by design. The frequency adaptive nonstationary discrete Gabor transform offers an alternative that does not have this limitation. A Luenberger observer is capable of the implementation of the short-time Fourier transform, and its numerical advantages are already established based on the work of Hostetter and Péceli. Here, we introduce the family of discrete Gabor transforms and its properties along with a constructive method to define such transforms. Furthermore, we show that they are realizable by Luenberger observers which are capable of the error-free reconstruction of the observed signal in finite steps. The dead-beat property is derived for the state variables as well which estimate the transform of the windowed signal. © 2025 Budapest University of technology and Economics. All rights reserved.

关键词： Frequency domain analysis

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Methanol Operation in Heavy-Duty DICI Dual-Fuel Engines: Investigating Charge Cooling Effects Using Engine Combustion Network Spray D Data

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Journal of engineering for Gas Turbines and Power 2025年第10期147卷 101007页

作者： Zoumpourlos, Konstantinos Geertsma, Rinze Van De Ketterij, Robert Coraddu, Andrea Sustainable Drive & Energy Systems Faculty of Mechanical Engineering Department Maritime & Transport Technology Delft University of Technology Delft2628 CD Netherlands Faculty of Military Sciences Netherlands Defence Academy Den Helder4811 XC Netherlands

Methanol is a promising alternative fuel, which can assist in reducing emissions in heavy-duty (HD) dual-fuel (DF) compression ignition (CI) engines. In medium and large bore marine engines, DF operation is achieved through either direct injection (DI) or port fuel injection (PFI) of methanol with diesel acting as a DI pilot fuel for ignition. However, the injection of methanol presents a significant challenge due to its high latent heat of vaporization and decreased lower heating value (LHV) compared to diesel. Therefore, for the same energy content operation, methanol requires around eight times the amount of heat to evaporate completely in comparison to diesel, which results in lower in-cylinder temperatures. This charge cooling effect leads to a strong negative temperature gradient influencing ignition and flame propagation. This paper aims to quantify the cooling effect of methanol in a heavy-duty dual-fuel direct injection compression ignition (DICI) engine environment. The presented methodology uses computational fluid dynamics (CFD) simulations to model methanol sprays with validation originating from the engine combustion network (ECN) Spray D experimental data. The CFD models operate within the Lagrangian-Eulerian framework in CONVERGE-CFD using the Reynolds Averaged Navier Stokes (RANS) turbulence modeling. Compared to diesel, injecting methanol with the same energy content exhibited up to 100 K more decreased temperature within the mixture. Consequently, this cooled mixture may pose challenges to combustion stability due to the intense temperature gradients. Nonetheless, lower mixture temperature decreases NOx emissions, which can prove beneficial for high methanol energy fractions in dual-fuel DICI engines. © 2025 by ASME.

关键词： Thermal gradients

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Optimization of Stiffness and Damping Coefficient Using Genetic Algorithm in an Electric Bus with Air Suspension System 6th

Optimization of Stiffness and Damping Coefficient Using Gene...

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6th International Conference on Mechanical engineering, ICOME 2023

作者： Hendrowati, Wiwiek Sutantra, INyoman Yuniarto, M. Nur Solichin, Moch. Nurlita, Cony Mechanical Engineering Department Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Jl AriefRahmanHakim Surabaya60111 Indonesia Graduate of the Department of Mechanical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9789819778973

Riding comfort is the minimum vibration transferred by the road surface to a passenger, where it is characterized by maximum sprung mass acceleration. International standards determine riding comfort, specifically ISO 2631. This research utilizes a genetic algorithm to obtain optimal stiffness and damping coefficients for the vehicle suspension system. The system modeling is simulated using MATLAB software. Based on the genetic algorithm optimization performed, the optimal stiffness and damping coefficients obtained for the system are 203,800 N/s and 12,771 Ns/m for the front suspension stiffness and damping coefficient, respectively. The rear suspension stiffness and damping coefficient are 310,750 N/s and 10,702 Ns/m, respectively. With the optimized parameters, the dynamic responses of acceleration and displacement in vertical and pitch motions yield more comfortable results. The conclusions are drawn based on the root mean square (RMS) acceleration value for drivers who meet ISO 2631 comfort standards. The driver's system operates comfortably without complaints at low speeds, with only minor complaints at high rates. With the optimized suspension, the driver can sustain over 16 h of operation, whether subjected to sinusoidal road. The optimized parameters result in an improved transient vehicle response, characterized by reduced impact forces and quicker attainment of settling time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Damping

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