Detection of water surface targets based on improved Deformable DETR by Pengjiu WANG, Junbin Gong, Wei LUO, Xiao HUANG, Junjie GUO

“…However, conventional detection methods encounter several challenges, and existing deep-learning-based algorithms have limitations in this field, including limited datasets and insufficient detection speed even after improvement. …”
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Optical Fiber Vibration Signal Recognition Based on the EMD Algorithm and CNN-LSTM by Kun Li, Yao Zhen, Peng Li, Xinyue Hu, Lixia Yang

“…CNN is used to extract time-series features from the vibration signal and LSTM is employed to classify the reconstructed signal. …”
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Deepfake detection method based on patch-wise lighting inconsistency by Wenxuan WU, Wenbo ZHOU, Weiming ZHANG, Nenghai YU

“…The rapid development and widespread dissemination of deepfake techniques has caused increased concern.The malicious application of deepfake techniques also poses a potential threat to the society.Therefore, how to detect deepfake content has become a popular research topic.Most of the previous deepfake detection algorithms focused on capturing subtle forgery traces at pixel level and have achieved some results.However, most of the deepfake algorithms ignore the lighting information before and after generation, resulting in some lighting inconsistency between the original face and the forged face, which provided the possibility of using lighting inconsistency to detect deepfake.A corresponding algorithm was designed from two perspectives: introducing lighting inconsistency information and designing a network structure module for a specific task.For the introduction of lighting task, a new network structure was derived by designing the corresponding channel fusion method to provide more lighting inconsistency information to the network feature extraction layer.In order to ensure the portability of the network structure, the process of feature channel fusion was placed before the network extraction information, so that the proposed method can be fully planted to common deepfake detection networks.For the design of the network structure, a deepfake detection method was proposed for lighting inconsistency based on patch-similarity from two perspectives: network structure and loss function design.For the network structure, based on the characteristic of inconsistency between the forged image tampering region and the background region, the extracted features were chunked in the network feature layer and the feature layer similarity matrix was obtained by comparing the patch-wise cosine similarity to make the network focus more on the lighting inconsistency.On this basis, based on the feature layer similarity matching scheme, an independent ground truth and loss function was designed for this task in a targeted manner by comparing the input image with the untampered image of this image for patch-wise authenticity.It is demonstrated experimentally that the accuracy of the proposed method is significantly improved for deepfake detection compared with the baseline method.…”
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Prognostic algorithm for early diagnosis of subcritical conditions as predictors of sudden cardiac death by A. V. Bykov, P. S. Azarova, S. A. Parkhomenko, A. V. Bykov, A. V. Polyakova, M. V. Alymova, A. V. Vinnikov

“…Based on the informative features proposed by specialized experts using multivariate statistics methods (discriminant analysis), two condition classes were formed. …”
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Analysis of the Possibility of Using Selected Artificial Intelligence Algorithms for the Assessment of the Microstructure of Vermicular Cast Iron by U. Janiszewska, Ł. Marcjan, S. Gajoch, K. Jaśkowiec, A. Bitka, M. Małysza, D. Wilk-Kołodziejczyk

“…Distinctive image features need to be distinguished in order to detect these places of interest. …”
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Deep learning and hyperspectral features for seedling stage identification of barnyard grass in paddy field by Siqiao Tan, Qiang Xie, Wenshuai Zhu, Yangjun Deng, Lei Zhu, Xiaoqiao Yu, Zheming Yuan, Zheming Yuan, Yuan Chen, Yuan Chen

“…Notably, this surpasses the capabilities of other models that rely on amalgamations of machine learning algorithms and feature dimensionality reduction methods. …”
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Research progress of abnormal user detection technology in social network by Qiang QU, Hongtao YU, Ruiyang HUANG

“…In social networks,the problem of anomalous users detection is one of the key problems in network security research.The anomalous users conduct false comments,cyberbullying or cyberattacks by creating multiple vests,which seriously threaten the information security of normal users and the credit system of social networks ,so a large number of researchers conducted in-depth study of the issue.The research results of the issue in recent years were reviewed and an overall structure was summarized.The data collection layer introduces the data acquisition methods and related data sets,and the feature presentation layer expounds attribute features,content features,network features,activity features and auxiliary features.The algorithm selection layer introduces supervised algorithms,unsupervised algorithms and graph algorithms.The result evaluation layer elaborates the method of data annotation method and index.Finally,the future research direction in this field was looked forward.…”
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The Line Pressure Detection for Autonomous Vehicles Based on Deep Learning by Xuexi Zhang, Ying Li, Ruidian Zhan, Jiayang Chen, Junxian Li

“…At present, the line pressure detection algorithms mainly include algorithms based on traditional features and models and algorithms based on deep learning. …”
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SOD-YOLO: A lightweight small object detection framework by Yunze Xiao, Nan Di

“…Abstract Currently, lightweight small object detection algorithms for unmanned aerial vehicles (UAVs) often employ group convolutions, resulting in high Memory Access Cost (MAC) and rendering them unsuitable for edge devices that rely on parallel computing. …”
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Optical fiber eavesdropping detection method based on machine learning by Xiaolian CHEN, Yi QIN, Jie ZHANG, Yajie LI, Haokun SONG, Huibin ZHANG

“…Optical fiber eavesdropping is one of the major hidden dangers of power grid information security,but detection is difficult due to its high concealment.Aiming at the eavesdropping problems faced by communication networks,an optical fiber eavesdropping detection method based on machine learning was proposed.Firstly,seven-dimensions feature vector extraction method was designed based on the influence of eavesdropping on the physical layer of transmission.Then eavesdropping was simulated and experimental feature vectors were collected.Finally,two machine learning algorithms were used for classification detection and model optimization.Experiments show that the performance of the neural network classification is better than the K-nearest neighbor classification,and it can achieve 98.1% eavesdropping recognition rate in 10% splitting ratio eavesdropping.…”
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An Innovative Approach for Fake News Detection using Machine Learning by Maya Hisham, Raza Hasan, Saqib Hussain

“…The project uses an open-source online dataset of fake and real news to determine the credibility of news. Various text feature extraction techniques and classification algorithms are reviewed, with the Support Vector Machine (SVM) linear classification algorithm using TF-IDF feature extraction achieving the highest accuracy of 99.36%. …”
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Detection of axonal synapses in 3D two-photon images. by Cher Bass, Pyry Helkkula, Vincenzo De Paola, Claudia Clopath, Anil Anthony Bharath

“…To find the most appropriate techniques for this task, we compared several well-known algorithms for interest point detection and feature descriptor generation. …”
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Final weight prediction from body measurements in Kıvırcık lambs using data mining algorithms by Ö. Şengül, Ş. Çelik

“…<p>This study was carried out to determine the final weight estimation of Kıvırcık lambs using body measurements via Chi-square automatic interaction detection (CHAID), exhaustive CHAID, classification and regression tree (CART), random forest (RF), multivariate adaptive regression spline (MARS), and bootstrap-aggregating multivariate adaptive regression spline (Bagging MARS) algorithms. …”
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Cross-Modal Fake News Detection Method Based on Multi-Level Fusion Without Evidence by Ping He, Hanxue Zhang, Shufu Cao, Yali Wu

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Remote Sensing Change Detection by Pyramid Sequential Processing With Mamba by Jiancong Ma, Bo Li, Hanxi Li, Siying Meng, Ruitao Lu, Shaohui Mei

“…Change detection (CD) in remote sensing imagery is crucial for monitoring environmental variations over time. …”
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Uncertainty Quantification in Data Fusion Classifier for Ship-Wake Detection by Maice Costa, Daniel Sobien, Ria Garg, Winnie Cheung, Justin Krometis, Justin A. Kauffman

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Siamese comparative transformer-based network for unsupervised landmark detection. by Can Zhao, Tao Wu, Jianlin Zhang, Zhiyong Xu, Meihui Li, Dongxu Liu

“…Current landmark detection algorithms often train a sophisticated image pose encoder by reconstructing the source image to identify landmarks. …”
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Detection of Fake Instagram Accounts via Machine Learning Techniques by Stefanos Chelas, George Routis, Ioanna Roussaki

“…After making the necessary feature additions to and removals from these data, they are fed into machine learning algorithms with the aim of detecting fake Instagram accounts. …”
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White Blood Cell Detection Based on FBDM-YOLOv8s by Borui Sun, Xiangsuo Fan, Jie Meng, Jinfeng Wang, Huajin Chen, Lei Liu

“…The FBDM-YOLOv8s significantly outperforms other advanced object detection algorithms in terms of performance, with a 2.1% increase in mAP compared to the baseline YOLOv8s.We will release the source code on <uri>https://github.com/SSRR-LLL/FBDM-YOLOv8.git</uri>.…”
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Study on the Lightweighting Strategy of Target Detection Model with Deep Learning by Junli Hu

“…Aiming at the high miss detection and false detection rate of traditional SSD (single shot multibox detector) target detection algorithm in target detection, this paper proposes a lightweight detection algorithm for deep learning target detection model in order to improve the detection accuracy. …”
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