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Deep learning-based method for detection of copy-move forgery in videos

Neural Computing and Applications 2025年第14期37卷 8451-8464页

作者： Elbarougy, Reda Abdelfatah, Osama Behery, G.M. El-Badry, Noha M. Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Ha’il Ha’il81481 Saudi Arabia Department of Information Technology Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Computer Science Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Mathematics Faculty of Science Damietta University New Damietta Kafr Saad Egypt

Video forgery is one of the most serious problems affecting the credibility and reliability of video content. Therefore, detecting video forgery presents a major challenge for researchers due to the diversity of forgery types, the modernity of the programs used in forgery operations, and the abundance of information and content present in videos. The seriousness of this issue arises from the widespread use of videos in vital fields that require very high accuracy with no room for doubt or error, such as courtrooms, journalism, and others. Copy-move forgery is one of the most common and dangerous types of video forgery because of the difficulty in identifying it with the naked eye and the great diversity of forgery techniques involved in this particular type. One of the biggest challenges researchers have faced in the past is the complexity of the steps required to detect forgery in videos, leading to significant computational complexity and time consumption. The proposed method also aims to achieve better results than previous methods while reducing computational operations. Ultimately, forgery is detected with great efficiency. Compared to previous methods used for detecting copy-move forgery in the Rewind dataset, the proposed method achieves the highest F1, reaching 0.86, with a significant difference of 0.13 compared to the best result of the previous methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Deep learning

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Proposing a digital twin-based sustainable water governance system for rural Indian villages

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1777-1783页

作者： Singh, Dineshkumar Sharma, Vishnu Department of Computer Science and Engineering Faculty of Computer Science & Engineering (FCE) Poornima University Jaipur India Department of Computer Science and Application Faculty of Computer Science & Engineering (FCE) Poornima University Jaipur India

A sustainably governed water-ecosystem at village-level is crucial for the community's well-being. It requires understanding natures’ limits to store and yield water and balance it with the stakeholders’ needs, in the context of population growth, climate change, and unsustainable agricultural practices. Maharashtra villages like Randullabad and Karanjkhop are impacted by limited groundwater availability due to the geological structure of the Deccan Traps basaltic rock formation and are recharged by the most rainfall during the monsoon season. Traditional water management practices are inadequate, with old wells causing conflicts among villagers due to fluctuating water levels. In order to achieve a sustainable water system for rural Indian villages, this paper proposes a Digital Twin-Based Water Governance System (DToWGS) with the aim of balancing nature's boundaries with stakeholders' interests to provide real-time water insights, encourage community engagement, transparency in water allocation and decision-making, and promote sustainable practices. These measured indicators can potentially link local governance outcomes to global goals like Viksit Bharat 2047 and UN Sustainable Development Goal 6 (SDG 6). © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Digital twin Empathy map Remote sensing SDG6 Stakeholders Village water environment

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Evolutionary optimization of Yagi–Uda antenna design using grey wolf optimizer

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Neural Computing and Applications 2025年第6期37卷 4155-4183页

作者： Braik, Malik Sheta, Alaa Aljahdali, Sultan El-Hefnawi, Fatma Al-Hiary, Heba Elashmawi, Walaa H. Computer Science Department Al-Balqa Applied University Salt Jordan Department of Computer Science Southern Connecticut State University New HavenCT United States Computer Science Department Taif University Taif Saudi Arabia Microwave Department Electronics Research Institute Giza Egypt Department of Computer Information System Al-Balqa Applied University Salt Jordan Department of Computer Science Faculty of Computers & Informatics Suez Canal University Ismailia Egypt Department of Computer Science Faculty of Computer Science Misr International University Cairo Egypt

Meta-heuristic optimization algorithms have become widely used due to their outstanding features, such as gradient-free mechanisms, high flexibility, and great potential for avoiding local optimal solutions. This research explored the grey wolf optimizer (GWO) to find the ideal configuration for a six-element Yagi–Uda antenna. The GWO algorithm adjusted the lengths of the antenna wires and the spacings between them. The goal was to maximize the antenna’s ability to transmit signals (throughput gain). Optimal antenna selection relies on various parameters, including gain, bandwidth, impedance matching, frequency, side-lobe levels, etc. The optimization of a six-element Yagi–Uda antenna presents a challenging engineering design problem due to its multimodal and nonlinear nature. Achieving optimal performance hinges on the intricate interplay between the lengths of the constituent elements and the spacing configurations. To this end, a multiobjective function was adopted to design this antenna. The performance of several meta-heuristic algorithms, including genetic algorithms, biogeography-based optimization, simulated annealing, and grey wolf optimizer, was compared. The GWO-based approach has performed better than its competitors. This optimized antenna design based on GWO reported a gain of 14.21 decibel. Therefore, the GWO-based method optimizes antennas that can be further investigated for other antenna design problems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Simulated annealing

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Transfer Learning-based Edge AI Resource Allocation in NGEPON & B5G X-haul Network for Immersive Media

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IEEE Access 2025年 13卷 84881-84896页

作者： Kharga, Razat Nikoukar, Aliakbar Hwang, I-Shyan Liem, Andrew Tanny Goudarzi, Hamidreza Yuan Ze University Department of Computer Science and Engineering Taoyuan City320 Taiwan College of Science Yasouj University Department of Mathematics Iran Universitas Klabat Department of Computer Science Manado95371 Indonesia

This study introduces an EdgeAI-based framework for the dynamic bandwidth allocation and traffic priority prediction in 25G-Next-Generation Ethernet Passive Optical Network (NGEPON) and Beyond 5G (B5G) X-haul networks, targeting the requirements of immersive media services such as Virtual Reality (VR) and Augmented Reality (AR). The proposed framework employs a Software-Defined Networking (SDN) Cloud-Edge Service Orchestrator with an LSTM-transfer learning-based mechanism (EdgeAIDBA) to estimate network traffic and optimize content caching at the network edge while dynamically prioritizing bandwidth allocation based on real-time traffic demands for the Fixed wireless NGEPON & B5G X-haul access network. Simulation results under realistic network conditions indicate that EdgeAIDBA significantly improves packet delay, drop probability, and system throughput. These results emphasize that our EdgeAIDBA mechanism can deliver high-capacity, low-latency services critical for future immersive media delivery. © 2013 IEEE.

关键词： Augmented reality

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CNN-HOG based hybrid feature mining for classification of coffee bean varieties using image processing

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Multimedia Tools and Applications 2025年第2期84卷 749-764页

作者： Molla, Yirga Kene Mitiku, Emebet Abeje Department of Computer Science Gafat Institute of Technology Debre Tabor University Debre Tabor Ethiopia Department of Computer Science Faculty of Natural and Computational Science Debark University Debark Ethiopia

Ethiopia, known as the birthplace of coffee, relies on coffee exports as a major source of foreign currency. This research paper focuses on developing a hybrid feature mining technique to automatically classify Ethiopian coffee beans based on their provenance: Harrar, Jimma, Limu, Sidama, and Wellega, which correspond to their botanical origins. A dataset of coffee bean images is collected from various regions through the Ethiopian Commodity Exchange (ECX) in Addis Ababa. The proposed system incorporates preprocessing phases including image resizing, filtering, contrast enhancement, noise removal, grayscale conversion, and segmentation using a combined thresholding and K-means approach for grayscale and RGB images, respectively. Classification is performed using a radial basis function (RBF) kernel function of support vector machine (SVM). To address the color-feature similarity challenge, the study explores merging color and texture features using the histogram of oriented gradients (HOG) local feature descriptor. Performance evaluation is conducted for HOG feature extraction, CNN feature extraction, and a hybrid feature vector (HOG-CNN) using a multi-class SVM classifier, achieving accuracies of 74.17%, 85.83%, and 97.5%, respectively. The deep-shallow-based feature (CNN-HOG) combination demonstrates the highest accuracy of 97.5% in this study. The findings highlight the effectiveness of the proposed hybrid feature mining approach in automatically classifying Ethiopian coffee bean varieties, with potential applications in quality control and traceability within the coffee industry. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Support vector machines

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Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

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Semi-analytic solution of the nonlinear Sharma-Tasso-Olver equation via ramadan group integral transform and accelerated adomian decomposition

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Journal of Umm Al-Qura University for Applied sciences 2025年 1-14页

作者： Ramadan, Mohamed A. Mansour, Mariam M. A. El-Shazly, Naglaa M. Mathematics and Computer Science Department Faculty of Science Menoufia University Menoufia Shibin El-Koom Egypt Department of Basic Science Modern Academy of Computer Science and Management Technology Cairo Maadi Egypt

This work focuses on finding an approximate solution to the Sharma-Tasso-Olive (STO) equation. The approach combines accelerated Adomian decomposition (ADM) with the Ramadan group integral transform (RGT) to tackle these nonlinear partial differential equations. Two analytical solution forms are analyzed to evaluate the accuracy and effectiveness of the proposed method. The results demonstrate that the suggested technique yields a rapidly convergent series solution for the STO equation. The paper emphasizes that these semi-analytic methods are powerful tools applicable in engineering and physics for approximating solutions to such nonlinear equations. The resulting series can offer a closed-form solution. Ultimately, the accelerated RGT-ADM variant outperforms the traditional RGT combined with standard Adomian decomposition due to its faster convergence and the elimination of the need for complex Adomian polynomials. © The Author(s) 2025.

关键词： Accelerated variant of Adomain Accuracy Approximate solutions Computational efficiency Convergence rate Nonlinear partial differential equations Numerical methods Polynomial calculations Ramdan group transform Semi-analytic methods Series solutions Sharma-Tasso-Olive equation

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Efficient and lightweight CNN model for COVID-19 diagnosis from CT and X-ray images using customized pruning and quantization techniques

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Neural Computing and Applications 2025年 1-20页

作者： Sriwiboon, Nattavut Department of Computer Science and Information Technology Faculty of Science and Health Technology Kalasin University Kalasin Thailand

The coronavirus disease 2019 (COVID-19) has posed significant challenges globally, with image classification becoming a critical tool for detecting COVID-19 from chest X-ray and CT images. Convolutional neural network (CNN) architectures are widely employed for this purpose, yet many previous models have struggled to balance high accuracy with low storage requirements, limiting their applicability in resource-constrained environments. This paper introduces an efficient and lightweight CNN model optimized for COVID-19 diagnosis through pruning and quantization techniques, addressing these limitations. Our approach significantly reduces storage size and parameter count while maintaining high accuracy. We customized several CNNs, particularly EfficientNetV2 and MobileNetV2, to achieve these goals. We specifically selected EfficientNetV2 and MobileNetV2 due to their superior balance of accuracy and efficiency, making them well-suited for lightweight medical applications. EfficientNetV2’s storage size has been reduced from 568 to 142 KB through customized pruning, achieving 98.9% accuracy for 2-class and 98.28% for 3-class X-ray classification, as well as 98.3% and 98.05% for 2-class and 3-class CT classification, respectively. MobileNetV2, after hybrid pruning and quantization, achieved a remarkably low storage size of 70 KB, making it highly effective for deployment in environments with limited resources. Despite a slight accuracy decrease of 0.9 to 1.58%, both models have maintained robust performance suitable for COVID-19 diagnosis. The results demonstrate that our pruning and quantization techniques are effective in developing lightweight, high-performance CNN models, making them suitable for broader applications in medical imaging where both efficiency and accuracy are critical. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： COVID-19

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Teaching Coordination to Selfish Learning Agents in Resource-Constrained Partially Observable Markov Games

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IEEE Transactions on Automatic Control 2025年第5期70卷 3449-3455页

作者： Tsaousoglou, Georgios Technical University of Denmark Department of Applied Mathematics and Computer Science Lyngby2800 Denmark

Of increasing relevance to engineering systems are problems that include online resource allocation to agents that feature adaptation and learning capabilities. This article considers the case where a coordinator gets to design a resource allocation mechanism (i.e., a bidding-allocation-rewards protocol) to efficiently allocate a resource to selfish agents that try to gain access by learning to communicate strategically. Toward aligning the agents' incentives with the social objective, a critical-value-based mechanism is proposed. Analytic results are presented for a simple, stylized setting, whereas simulation results for a use case with reinforcement learning agents controlling flexible loads in the smart grid demonstrate the mechanism's ability to teach coordinated behavior to the distributed learners. © 1963-2012 IEEE.

关键词： Adversarial machine learning

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Fuzzy N-Bipolar Soft Sets for Multi-Criteria Decision-Making:Theory and Application

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computer Modeling in Engineering & sciences 2025年第4期143卷 911-943页

作者： Sagvan Y.Musa Baravan A.Asaad Hanan Alohali Zanyar A.Ameen Mesfer H.Alqahtani Department of Mathematics College of EducationUniversity of ZakhoZakho42002Iraq Department of Computer Science College of ScienceKnowledge UniversityErbil44001Iraq Department of Mathematics College of ScienceUniversity of ZakhoZakho42002Iraq Department of Computer Science College of ScienceCihan University-DuhokDuhok42001Iraq Department of Mathematics College of ScienceKing Saud UniversityRiyadh11451Saudi Arabia Department of Mathematics College of ScienceUniversity of DuhokDuhok42001Iraq Department of Mathematics University College of UmlujUniversity of TabukTabuk48322Saudi Arabia

This paper introduces fuzzy N-bipolar soft(FN-BS)sets,a novel mathematical framework designed to enhance multi-criteria decision-making(MCDM)processes under *** study addresses a significant limitation in existing models by unifying fuzzy logic,the consideration of bipolarity,and the ability to evaluate attributes on a multinary *** specific contributions of the FN-BS framework include:(1)a formal definition and settheoretic foundation,(2)the development of two innovative algorithms for solving decision-making(DM)problems,and(3)a comparative analysis demonstrating its superiority over established *** proposed framework is applied to a real-world case study on selecting vaccination programs across multiple countries,showcasing consistent DM outcomes and exceptional adaptability to complex and uncertain *** results position FN-BS sets as a versatile and powerful tool for addressing dynamic DM challenges.

关键词： Fuzzy N-bipolar soft sets N-bipolar soft sets N-soft sets MCDM algorithms

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