MEViT: Generalization of Deepfake Detection With Meta-Learning EfficientNet Vision Transformer by Van-Nhan Tran, Hoanh-Su Le, Piljoo Choi, Suk-Hwan Lee, Ki-Ryong Kwon

“…With the rapid advances in deep generative models, the accessibility and sophistication of such manipulation technologies are increasing, making it more challenging to detect fake content. Different facial forgery techniques result in complex data distributions, and most existing deepfake detection approaches rely on convolutional neural networks (CNNs) that treat the task as a binary classification problem. …”
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BACAD: AI-based framework for detecting vertical broken access control attacks by Ahmed Anas, Ayman A. Alhelbawy, Salwa El Gamal, Basheer Youssef

“…Furthermore, it shows that BACAD addresses the VBAC problem by presenting an applicable, dynamic, flexible, and technology-independent solution to counter VBAC vulnerability risks. …”
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Detection of Leaf Miner Infestation in Chickpea Plants Using Hyperspectral Imaging in Morocco by Mohamed Arame, Issam Meftah Kadmiri, Francois Bourzeix, Yahya Zennayi, Rachid Boulamtat, Abdelghani Chehbouni

“…This study addresses the problem of early detection of leaf miner infestations in chickpea crops, a significant agricultural challenge. …”
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Lightweight remote sensing change detection with progressive multi scale difference aggregation by Yinghua Fu, Haifeng Peng, Tingting Zhao, Yize Li, Jiansheng Peng, Dawei Zhang

“…Deep learning is becoming increasingly popular for change detection tasks in remote sensing due to its significant advantages in deep feature representation and nonlinear problem modeling. …”
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Proteomics and machine learning: Leveraging domain knowledge for feature selection in a skeletal muscle tissue meta-analysis by Alireza Shahin-Shamsabadi, John Cappuccitti

“…Typically, proteomics research focuses narrowly on using a limited number of datasets, hindering cross-study comparisons, a problem that can potentially be addressed by machine learning. …”
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Detecting subsurface diseases on airport road surface based on an improved SSD algorithm. by Mengmeng Pan, Huiguang Chen, Lipeng Yang, XianRong Jiang

“…Facing the radar data of underground hidden diseases of airport roads with low recognition and high noise intensity, it is inefficient to recognize the diseases by manual identification, and it is difficult to achieve accurate differentiation and localization of the diseases by the existing detection methods. We analyze and propose an improved algorithm EFA-SSD (Enhanced Feature Aggregation SSD) for automatic detection of airport road subsurface diseases, which solves the problems of strong noise background conditions, severe interference of morphological features of different types of subsurface diseases, and low target recognition. …”
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Lightweight anomaly detection model for UAV networks based on memory-enhanced autoencoders by HU Tianzhu, SHEN Yulong, REN Baoquan, HE Ji, LIU Chengliang, LI Hongjun

“…In order to solve the problems of high energy consumption and high reliance on manual annotation data of traditional intelligent attack detection methods in UAV networks, a lightweight UAV network online anomaly detection model based on a double-layer memory-enhanced autoencoder integrated architecture was proposed. …”
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A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder by Hoyeon Lee, Jaehong Yu

“…Finally, we construct the fault detection model by applying the one-class classification algorithm to the latent feature vectors of training signals. …”
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Toward Better Accuracy-Efficiency Tradeoffs for Oriented SAR Ship Object Detection by Moran Ju, Buniu Niu, Mulin Li, Tengkai Mao, Si-nian Jin

“…In this article, we propose a novel neural architecture search based lightweight detector in polar coordinate system with DETR as search space for oriented SAR ship detection, where oriented bounding boxes are encoded and decoded in polar coordinate system to cope with boundary discontinuity problems, and the weight entanglement strategy is adopted to realize automatic and lightweight design of DETR. …”
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Advancing Ton-Bag Detection in Seaport Logistics with an Enhanced YOLOv8 Algorithm by Xiulin Qiu, Haozhi Zhang, Chang Yuan, Qinghua Liu, Hongzhi Yao

“…Finally, the C2f-ORECZ block based on a linear scaling layer is designed for the neck, which reduces the training overhead and strengthens the feature learning of the feature extraction network for the targets in the complex background of the harbor and adds the 160 × 160 scale detection head to strengthen small target detection abilities. …”
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FB-YOLOv8s: A fire detection algorithm based on YOLOv8s by Yuhang Liu, Chunjuan Bo, Chong Feng

“…However, there exist some problems in traditional detection algorithms of fire, such as low accuracy, high miss rate, and low detection rate of small targets. …”
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Transfer learning and single-polarized SAR image preprocessing for oil spill detection by Nataliia Kussul, Yevhenii Salii, Volodymyr Kuzin, Bohdan Yailymov, Andrii Shelestov

“…We investigated the effectiveness of various neural network architectures and encoders for this task, focusing on scenarios with limited training data. The research problem centered on enhancing feature extraction from single-channel SAR data to improve oil spill detection performance.Our methodology involved developing a novel preprocessing pipeline that converts single-channel SAR data into a three-channel RGB representation. …”
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Abnormal noise detection of electric machines based on HPSS-CIS and CNN-CBAM by Zhao Qingsong, Wang Xiufeng, Luo Kun, He Dan, Liu Xiang

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Harnessing self-supervised learning to boost malicious traffic detection with enhanced attention by SUN Jianwen, ZHANG Bin, LI Hongyu, CHANG Heyu

“…The existing deep learning-based malicious traffic detection methods generally suffered from three main problems: labeled sample scarcity, inadequate representation of malicious behavior traffic features, and a high false positive rate due to ineffective integration of behavioral association patterns during detection. …”
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Pear Fruit Detection Model in Natural Environment Based on Lightweight Transformer Architecture by Zheng Huang, Xiuhua Zhang, Hongsen Wang, Huajie Wei, Yi Zhang, Guihong Zhou

“…Aiming at the problems of low precision, slow speed and difficult detection of small target pear fruit in a real environment, this paper designs a pear fruit detection model in a natural environment based on a lightweight Transformer architecture based on the RT-DETR model. …”
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Ultrasound super resolution imaging for accurate uterus tumor detection and malignancy prediction by Ashwini Sawant, Sujata Kulkarni, Milind Sawant

“…Improving patient outcomes requires the early detection of malignant tumors. Despite its lower resolution and noise problems, ultrasound, which is frequently used to diagnose uterine tumors, is safer and more economical than magnetic resonance imaging (MRI) scans and biopsies. …”
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Artificial Neural Networks Based Decision Support System for the Detection of Diabetic Retinopathy by Zehra Karapınar Şentürk

“…Artificial Neural Networks (ANN) method has been applied to the problem using Rapid Miner, a data mining tool. Some other methods have also adapted to the problem, but ANN based detection approach gave the best results. 88.52% sensitivity has been obtained using the features of Messidor dataset. …”
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Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with Leukemia by Mahwish Ilyas, Muhammad Bilal, Nadia Malik, Hikmat Ullah Khan, Muhammad Ramzan, Anam Naz

“…Early and accurate diagnosis is essential for detecting medical problems and then prescribing the most effective treatment strategies, especially in life-threatening diseases such as leukemia. …”
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Low scaling factor Seam Carving tamper detection algorithm with hybrid attention by ZHAO Jie, CHANG Haochan, WU Bin

“…The existing seam carving tamper detection algorithms have the problems of low detection accuracy and weak robustness for the case of low scaling factor. …”
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Small target detection of bearing surface defects based on improved YOLOv5s. by Haisong Xu, Xiaolin Shi, Han Zhang, Fan Yang, Yun Chen

“…However, traditional detection methods have problems such as insufficient feature extraction for small targets and sensitivity to background interference. …”
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