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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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Artificial intelligence-inspired comprehensive framework for desertification prediction

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International Journal of Cognitive Computing in engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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A Learning-Based Ensemble Algorithm with Optimal Selection for Outlier Detection

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 171-192页

作者： Ginni, Girish Reddy Lade, Srinivasa Chakravarthi Computer Science Engineering GITAM University Gandhi Nagar Rushikonda Andhra Pradesh Visakhapatnam530 045 India Department of Computer Science and Engineering College GITAM University Gandhi Nagar Rushikonda Andhra Pradesh Visakhapatnam530 045 India

In this paper, we propose a Learning-based Ensemble Method with Optimal selection strategy (LbEM-OSS), which presents a new outlier detection algorithm that captures only outstanding ones of constituent models. Using KNN to define local regions and Pearson correlation to evaluate the detectors makes the ensemble robust. Our method can adapt and generalize better across different high-dimensional datasets by generating pseudo-ground truths with average and maximum aggregation strategies. On a wide range of benchmark datasets, LbEM-OSS outperformed both statistics-based and neural ensemble methods, which achieved state-of-the-art ROC-AUC as high as 97.78% in the best-case and 4-8% AUC improvements over existing methods on average. These results portray its potential for noise, different dimensionality, and heterogeneous data nature. Moreover, it is highly scalable and accurate, which makes it an essential application in practical fields like fraud detection, network security, and healthcare. This research highlights the need for dynamic selection approaches within ensemble methods, providing the groundwork for future developments in sound outlier detection. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Optimal detection

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CNN-Based Machine Learning Model for Disease Prediction in Crops

CNN-Based Machine Learning Model for Disease Prediction in C...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Choudhary, Vijay Thakur, Archana Bhalse, Nisha IPS Academy Institute of Engineering & Science Department of Computer Science & Engineering Indore India School of Computer Science & IT Devi Ahilya University Indore India

ISBN: (纸本)9798331512965

Potato crops are vital to global food security, but they are susceptible to several diseases that hinder growth and yield. Traditional methods of detecting these diseases rely on labor-intensive lab tests and human observation. This research presents an enhanced machine learning model for detecting and categorizing potato leaf diseases, specifically early blight and late blight, using Convolutional Neural Networks (CNN). The model is trained on various datasets of potato leaf images, utilizing techniques such as hyper-parameter tuning, data augmentation, and transfer learning. The proposed model attains an accuracy of 96.11%, significantly improving upon existing methods. Image processing techniques, Segmentation is performed using K-means technique and feature extraction is done by utilizing gray-level co-occurrence matrix, along with multi-class support vector machines for classification, are employed. Additionally, the study shows that CNN performs better than Artificial Neural Networks (ANN) in image classification. The study demonstrates how AI and deep learning technology can be used in agriculture to improve crop management and enable early disease prediction. Experiments have been conducted using the real image dataset. © 2025 IEEE.

关键词： Adversarial machine learning

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Building Reliable IoT Ecosystems: A Generative AI-Enabled Federated Learning-Based Trust Management Approach

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IEEE Internet of Things Journal 2025年第10期12卷 13353-13366页

作者： Ud Din, Ikram Almogren, Ahmad Han, Zhu Guizani, Mohsen University of Haripur Department of Information Technology Haripur22620 Pakistan College of Computer and Information Sciences King Saud University Department of Computer Science Riyadh11633 Saudi Arabia University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of Mohamed Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates

In the rapidly evolving domain of the Internet of Vehicles (IoV), ensuring robust trust management, privacy, and security presents significant challenges. This article proposes a novel approach integrating generative AI (GAI) and federated learning (FL) to address these challenges. FL allows distributed learning across vehicles without the need to share data, enhancing privacy compared to centralized methods. Our approach enhances trust management by raising the level of accuracy in detecting anomalies and preserving data privacy. As a result, the effectiveness of the proposed approach in practical real-world urban settings is illustrated by comprehensive evaluations using the CityPulse dataset. The results show a 20% improvement in trust scores under normal conditions, a 92% anomaly detection accuracy, and acceptable latency despite the added security measures. Additionally, 3-D visualizations illustrate the system’s robustness and scalability. This solution aligns with the objectives of 6G wireless communications, laying the groundwork for future intelligent, ultrareliable, and secure vehicular networks. Future research will focus on expanding the application of GAI and FL for real-time decision-making in large-scale IoV networks and optimizing cryptographic protocols. © 2014 IEEE.

关键词： Federated learning

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Advancing Alzheimer’s detection: integrative approaches in MRI analysis with traditional and deep learning models

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Neural Computing and Applications 2025年第14期37卷 8527-8546页

作者： Vanaja, T. Shanmugavadivel, Kogilavani Subramanian, Malliga Kanimozhiselvi, C.S. Department of Artificial Intelligence Kongu Engineering College Tamil Nadu Erode India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Erode India

Alzheimer’s dementia (AD) poses a significant global health challenge, characterized by progressive cognitive decline, memory impairment, and behavioral changes. The critical need for early detection to enable timely intervention and personalized care is emphasized by the current lack of effective treatments. This study aims to develop precise diagnostic models for AD by employing machine learning and a customized deep-convolutional neural network (cDCNN) with three convolution layers, utilizing Magnetic Resonance Imaging (MRI) data. Methods involve analyzing two distinct datasets—Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Kaggle—to explore diverse cohorts and imaging features associated with AD pathology. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied across both datasets. A range of traditional machine learning methods, including support vector machine, k-nearest neighbor, random forest, decision trees, and XGBoost classifier, are evaluated alongside the cDCNN model, which leverages key MRI biomarkers of AD for both datasets. Results show the cDCNN model achieved a specific accuracy of 87% on the ADNI dataset, despite challenges in converting ADNI’s Digital Imaging and Communications in Medicine (DICOM) files to JPEG, impacting image clarity. Conclusions suggest that this research provides critical diagnostic tools for clinicians, offering insights into AD pathology and contributing to the alleviation of AD’s societal impact. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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A semantic driven model for extraction of text using TF-IDF, SFLA and XGBoost

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International Journal of Information Technology (Singapore) 2025年 1-6页

作者： Madhumala, R.B. Vineetha, B. Shree, M. Rajani Sanjesh, Riya Charanraj, B.R. Department of Computer Science and Engineering Dayanand Sagar Academy of Technology and Management (DSATM) Bengaluru India Computer Science and Engineering Presidency University Bangalore India Department of CSE BNM Institute of Technology Bengaluru India Computer Science PES University BANGALORE URBAN India

As people increasingly express opinions, offer feedback, and share suggestions on websites, e-forums, and blogs, consumers have come to rely heavily on online product reviews before making purchases or using services. Some spammers deliberately manipulate reviews to either enhance or discredit products, leading consumers to make misguided decisions based on these misleading reviews. To address this issue, a hybrid approach combining TF-IDF (term frequency-inverse document frequency) with the shuffled frog leaping algorithm (SFLA) is proposed. This approach aims to reduce the high dimensionality of feature sets and select optimized subsets of features. Finally, the sentiments from online reviews are classified into genuine and misleading by using Extreme Gradient Boosting (XGBoost) classifier. By enhancing review credibility, the proposed approach helps customers identify authentic feedback and improves classification accuracy. The performance of the proposed approach is assessed and validated with existing studies based on accuracy (%), precision (%) and recall (%). The results indicate that this hybrid feature selection approach achieves an optimized subset of features, leading to superior classification accuracy. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Classification SFLA Text extraction TF-IDF XGBoost

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Metamaterial inspired axe-shaped terahertz patch antenna design: a tool for early skin cancer detection

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Optical and Quantum Electronics 2025年第2期57卷 1-15页

作者： Sharan, Bhagwati Raja, Manjula Department of Computer Science and Engineering SRM University AP Andhra Pradesh Amaravati522240 India

Skin cancer involves abnormal growth of skin cells, typically caused by ultraviolet radiation exposure. Timely and accurate detection is essential to mitigate significant health risks and ensure effective treatment. This paper proposes a nanoantenna to enhance diagnostic and therapeutic capabilities for skin cancer detection. These antennas, emitting electromagnetic waves in the terahertz band (0.1–10 THz), improve integration for miniaturized wireless systems and serve as a foundation for the Internet of Medical Things (IoMT). We design a miniaturized, metamaterial-inspired gold-patch axe-shaped nanoantenna (121.97×110×17μm3), implemented in CST Studio Software. The antenna resonates at 1.152 THz, with a very low return loss ( © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Diseases

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Data augmentation for blurred-text image deblurring using non-euclidean extrapolations

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International Journal of Information Technology (Singapore) 2025年第4期17卷 2309-2320页

作者： Ranjan, Arti Ravinder, M. Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for Women (IGDTUW) New Delhi India Department of Computer Science and Engineering SSET Sharda University Greater Noida India

The deblurring of blurred-text images is a critical task in computer vision, especially in applications such as document digitization, OCR (Optical Character Recognition), and image restoration. Traditional deblurring methods often struggle with text images due to the complex structures and high-frequency details inherent in textual content. However, existing datasets are often insufficient to train robust deblurring models due to their limited variety in blur patterns, text formats, and environmental conditions. This insufficiency poses significant challenges in data augmentation, where traditional techniques may distort text structures or fail to simulate the complexity of real-world blurs, leading to models that struggle to generalize effectively. To address these issues, we propose a novel data augmentation method using existing dataset SROIE along with Non-Euclidean extrapolation, designed to create more realistic and varied training examples. It consists the combinations of existing Riemannian mixup (R-Mixup), Hyperbolic Spaces for generating a new dataset SROIE-RH and the combination of Hyperbolic Spaces, and Gaussian mix-up for creation another dataset SROIE-HG to improve the quality and the sources of the augmented dataset using. The validity of these methods is proven through various tests and shows enhanced increases in the model’s resilience and precision leading to sharper and more accurate deblurring of blurred-text images. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Blurred-text image deblurring non-euclidean extrapolation Data augmentation Dataset generation Gaussian mixup Hyperbolic spaces Riemannian manifolds

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Diversified deep hierarchical kernel ensemble regression

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Multimedia Tools and Applications 2025年第15期84卷 15553-15573页

作者： Liu, Zhifeng Xu, Zhengqin Ebhohimhen Abhadiomhen, Stanley Qian, Xiaoqin Shen, Xiang-Jun School of Computer Science and Communication Engineering JiangSu University Zhenjiang212013 China Department of Computer Science University of Nigeria Nsukka410105 Nigeria Department of Ultrasound Affiliated People’s Hospital of Jiangsu University Zhenjiang212002 China

Deep ensemble learning models that combine multiple independent deep learning models with multi-layer processing architectures have proven to be effective techniques for improving the accuracy and robustness of deep learning models. In this paper, we propose a diversified kernel ensemble regression method, which is developed from the well-known kernel ridge regression methods. Motivated by multi-view data modeling ideas, we treat each individual kernel as one view of original data in kernel representation space. Therefore, we develop a deep kernel ensemble ridge regression method in Neural Tangent Kernel (NTK) to address the problem faced by traditional kernel ridge regression methods in finding appropriate types of kernels and their parameters. Specifically, as multiple deep kernel regressors share common information from the multi-view kernel representations, our proposed method is built through a hierarchical modeling method, where deep kernel regressors share a common parameter and also have model-specific parameters in individual regressors, which is further helpful in improving our model’s performance. Furthermore, to achieve better diversified deep kernel representations in our proposed method, the Hilbert-Schmidt Independence Criterion (HSIC) is used to regularize our proposed model. In this way, we can find more diversified kernel representations among multiple kernel ensemble regressors to achieve better generalization performance. Experiments on several classification and regression datasets, such as MNIST, TinyImageNet-200, ORL, Credit and YearPredictionMSD demonstrate that our proposed method can achieve best regression performance than other state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification (of information)

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