Deepfake Face Detection and Adversarial Attack Defense Method Based on Multi-Feature Decision Fusion by Shanzhong Lei, Junfang Song, Feiyang Feng, Zhuyang Yan, Aixin Wang

“…To improve the accuracy of deepfake face detection models and strengthen their resistance to adversarial attacks, this manuscript introduces a method for detecting forged faces and defending against adversarial attacks based on a multi-feature decision fusion. …”
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An Improved YOLOP Lane-Line Detection Utilizing Feature Shift Aggregation for Intelligent Agricultural Machinery by Cundeng Wang, Xiyuan Chen, Zhiyuan Jiao, Shuang Song, Zhen Ma

“…This approach addresses the challenge faced by using embedded devices mounted on AGVs, which are unable to run multiple models for different tasks in parallel due to limited computational resources. For lane-line detection tasks, we also propose an improved YOLOP lane-line detection algorithm based on feature shift aggregation. …”
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Fault Detection in Harmonic Drive Using Multi-Sensor Data Fusion and Gravitational Search Algorithm by Nan-Kai Hsieh, Tsung-Yu Yu

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An Optimized Dynamic Scene Change Detection Algorithm for H.264/AVC Encoded Video Sequences by Giorgio Rascioni, Susanna Spinsante, Ennio Gambi

“…Scene change detection plays an important role in a number of video applications, including video indexing, semantic features extraction, and, in general, pre- and post-processing operations. …”
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Utilization of Stockwell Transform and Random Forest Algorithm for Efficient Detection and Classification of Power Quality Disturbances by T. Ravi, K. Sathish Kumar, C. Dhanamjayulu, Baseem Khan

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A Novel Machine Learning-based Diagnostic Algorithm for Detection of Onychomycosis through Nail Appearance by Serkan Düzayak, Muhammed Kürşad Uçar

“…The best features were selected through feature selection algorithms in the next step to increase the performance and reduce the number of features, and models were created by algorithm classification. …”
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Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms by Ye Liu, Shixin Li, Liming Zhou, Haichen Liu, Zhiyu Li

“…To address these challenges, this paper proposes a low-light object detection algorithm named Dark-YOLO, which dynamically extracts features. …”
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Comprehensive empirical evaluation of feature extractors in computer vision by Murat ISIK

“…Feature detection and matching are fundamental components in computer vision, underpinning a broad spectrum of applications. …”
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Adaptive convolutional neural network-based principal component analysis algorithm for the detection of manufacturing data by Tsun-Kuo Lin

“…This adaptive algorithm is capable of learning from limited image signals or features, enhancing data interpretability and increasing the amount of feature information for detecting manufacturing data.…”
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AS-Faster-RCNN: An Improved Object Detection Algorithm for Airport Scene Based on Faster R-CNN by Zhige He, Yuanqing He

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Detection of Wood Boring Insects’ Larvae Based on the Acoustic Signal Analysis and the Artificial Intelligence Algorithm by Piotr BILSKI, Piotr BOBIŃSKI, Adam KRAJEWSKI, Piotr WITOMSKI

“…An accelerometer was used to record the sound, further analyzed by a computer algorithm extracting features important for artificial-intelligence (AI) based classification employed to detect the old house borer’s (Hylotrupes bajulus L.) activity. …”
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A playback speech detection algorithm based on log inverse Mel-frequency spectral coefficient by Lang LIN, Rangding WANG, Diqun YAN, Can LI

“…The popularity and portability of high-fidelity audio recording equipment and playback equipment poses a serious challenge for speaker recognition systems against playback attacks.Based on the differences between the original speech and the playback speech in high frequency region,the algorithm reversed the Mel-filter bank in Mel-frequency cepstral coefficient (MFCC) calculation,and the coefficients before the DCT were used as the features of the algorithm.SVM was utilized as the classifier.Experimental results show that this algorithm can effectively detect the playback speech.In addition,the algorithm is integrated into the GMM-UBM speaker recognition system,which significantly improves the systems’ capability of resisting the playback attack.…”
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Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior by Huahua CHEN, Zhe CHEN

“…Anomaly detection remains to be an essential and extensive research branch in data mining due to its widespread use in a wide range of applications.It helps researchers to obtain vital information and make better decisions about data by detecting abnormal data.Considering that sparse coding can get more powerful features and improve the performance of other tasks, an anomaly detection model based on sparse variational autoencoder was proposed.Firstly, the discrete mixed modelspike and slab distribution was used as the prior of variational autoencoder, simulated the sparsity of the space where the hidden variables were located, and obtained the sparse representation of data characteristics.Secondly, combined with the deep support vector network, the feature space was compressed, and the optimal hypersphere was found to discriminate normal data and abnormal data.And then, the abnormal fraction of the data was measured by the Euclidean distance from the data feature to the center of the hypersphere, and then the abnormal detection was carried out.Finally, the algorithm was evaluated on the benchmark datasets MNIST and Fashion-MNIST, and the experimental results show that the proposed algorithm achieves better effects than the state-of-the-art methods.…”
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Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior by Huahua CHEN, Zhe CHEN

“…Anomaly detection remains to be an essential and extensive research branch in data mining due to its widespread use in a wide range of applications.It helps researchers to obtain vital information and make better decisions about data by detecting abnormal data.Considering that sparse coding can get more powerful features and improve the performance of other tasks, an anomaly detection model based on sparse variational autoencoder was proposed.Firstly, the discrete mixed modelspike and slab distribution was used as the prior of variational autoencoder, simulated the sparsity of the space where the hidden variables were located, and obtained the sparse representation of data characteristics.Secondly, combined with the deep support vector network, the feature space was compressed, and the optimal hypersphere was found to discriminate normal data and abnormal data.And then, the abnormal fraction of the data was measured by the Euclidean distance from the data feature to the center of the hypersphere, and then the abnormal detection was carried out.Finally, the algorithm was evaluated on the benchmark datasets MNIST and Fashion-MNIST, and the experimental results show that the proposed algorithm achieves better effects than the state-of-the-art methods.…”
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Securing IoT Communications via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method by Behnam Seyedi, Octavian Postolache

“…The second phase focuses on optimal feature selection using a Genetic Algorithm enhanced with eagle-inspired search strategies. …”
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TCE-YOLOv5: Lightweight Automatic Driving Object Detection Algorithm Based on YOLOv5 by Han Wang, Zhenwei Yang, Qiaoshou Liu, Qiang Zhang, Honggang Wang

“…Therefore, a lightweight object detection algorithm based on YOLOv5 is proposed to solve the problem of excessive network parameters in automatic driving scenarios. …”
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YOLO-SRW: An Enhanced YOLO Algorithm for Detecting Prohibited Items in X-Ray Security Images by Minwei Chen, Zhixian Zhang, Nian Jiang, Xingxing Li, Xin Zhang

“…To address the challenges of false positives and false negatives in prohibited item detection within X-ray security images, caused by complex backgrounds, poor image quality, and varying scales, this paper proposes an improved algorithm based on YOLOv8, named YOLO-SRW, to improve the accuracy of detecting prohibited items. …”
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CSCP-YOLO: A Lightweight and Efficient Algorithm for Real-Time Steel Surface Defect Detection by Chenglong Wang, Heng Wang, Yimin Jiang, Lei Yu, Xueting Wang

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YOLOv10-LGDA: An Improved Algorithm for Defect Detection in Citrus Fruits Across Diverse Backgrounds by Lun Wang, Rong Ye, Youqing Chen, Tong Li

“…We propose an improved YOLOv10-based disease detection method that replaces the traditional convolutional layers in the Backbone network with LDConv to enhance feature extraction capabilities. …”
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Investigation of an Efficient Multi-Class Cotton Leaf Disease Detection Algorithm That Leverages YOLOv11 by Fangyu Hu, Mairheba Abula, Di Wang, Xuan Li, Ning Yan, Qu Xie, Xuedong Zhang

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