EGRN-YOLO: An Enhanced Multi-View Remote Sensing Detection Algorithm for Onshore Wind Turbines Based on YOLOv7 by Renzheng Xue, Haiqiang Xu, Qianlong Wu

“…However, the challenges posed by complex backgrounds, significant variations in the scale of wind turbine targets, and arbitrary orientations in unmanned aerial vehicle (UAV) remote sensing images have significantly increased the difficulty of real-time wind turbine detection. To address these challenges, this paper introduces an enhanced multi-view onshore wind turbine remote sensing detection algorithm for UAVs based on YOLOv7, termed EGRN-YOLO. …”
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Maize Kernel Broken Rate Prediction Using Machine Vision and Machine Learning Algorithms by Chenlong Fan, Wenjing Wang, Tao Cui, Ying Liu, Mengmeng Qiao

“…The broken rate prediction model based on machine vision and machine learning algorithms is proposed in this manuscript. A new dataset of high moisture content maize kernel phenotypic features was constructed by extracting seven features (geometric and shape features). …”
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ATBHC-YOLO: aggregate transformer and bidirectional hybrid convolution for small object detection by Dandan Liao, Jianxun Zhang, Ye Tao, Xie Jin

“…We propose a new algorithm for detecting small objects in UAV images, called ATBHC-YOLO. …”
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Intelligent Algorithm for Rock Core RQD Based on Object Detection and Image Segmentation to Suppress Noise and Vibration by Feng Xiong, Jintao Wang, Guohua Zhang, Xueming Shi, Hong Zheng, Junjie Hu

“…In the present work, the image treatment process with the aid of the object detection and the image segmentation is adopted to obtain RQD automatically, based on the similarity of features such as color and texture, the segment anything model is adopted to detect the rock cores in the image, then, the YOLOv8 algorithm is adopted to train the model, and the gap features of the rock chip segments are extracted for segmentation of different rock core segments. …”
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A fuzzy track-to-track association algorithm with dynamic time warping for trajectory-level vehicle detection by Siqi Wan, Huaqiao Mu, Ke Han, Taesu Cheong, Chi Xie

“…Aiming to address these issues in an integrated manner, this paper proposes a TTTA algorithm that comprehensively calculates the similarity between trajectories using multiple trajectory features through dynamic time warping (DTW) and Cauchy distribution degree of membership function. …”
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Novel Adaptive Hidden Markov Model Utilizing Expectation–Maximization Algorithm for Advanced Pipeline Leak Detection by Omid Zadehbagheri, Mohammad Reza Salehizadeh, Seyed Vahid Naghavi, Mazda Moattari, Behzad Moshiri

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Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations by Constantinos Halkiopoulos, Evgenia Gkintoni, Anthimos Aroutzidis, Hera Antonopoulou

“…<b>Background/Objectives</b>: The following systematic review integrates neuroimaging techniques with deep learning approaches concerning emotion detection. It, therefore, aims to merge cognitive neuroscience insights with advanced algorithmic methods in pursuit of an enhanced understanding and applications of emotion recognition. …”
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A Plug Seedling Growth-Point Detection Method Based on Differential Evolution Extra-Green Algorithm by Hongmei Xia, Shicheng Zhu, Teng Yang, Runxin Huang, Jianhua Ou, Lingjin Dong, Dewen Tao, Wenbin Zhen

“…It employs an adaptive grayscale processing algorithm based on the differential evolution extra-green algorithm to extract the contour features of seedlings during the early stages of cotyledon emergence. …”
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Detecting Anomalies in CPU Behavior Using Clustering Algorithms from the Scikit-Learn Library in Python Programming Language by Artem Turashev, Vladimir Sukhomlin

“…This article examines the problem of detecting anomalies in central processing unit (CPU) operation using time series clustering algorithms. …”
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Leakage Detection in Subway Tunnels Using 3D Point Cloud Data: Integrating Intensity and Geometric Features with XGBoost Classifier by Anyin Zhang, Junjun Huang, Zexin Sun, Juju Duan, Yuanai Zhang, Yueqian Shen

“…Experimental results demonstrate that the integration of geometric features significantly enhances leakage detection accuracy, achieving an F_1-score of 91.18% and 97.84% on two evaluated datasets, respectively. …”
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An Extrinsic Calibration Method for LiDAR and Camera Based on Feature Matching by CHEN Meilin, JIANG Guotao, PI Zhichao, HE Qian, TAN Bin, LUO Shuo, YANG Hailang

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Multi-Task Perception Algorithm for Rail Transit Scenarios Based on Triplet Attention by GAO Rui, XIONG Yanping, WEI Chenfeng, XIE Guotao, GAO Ming

“…Aiming at the challenges of insufficient object detection accuracy and low detection speeds, and the pursuit of an accuracy-speed balance in environmental perception within rail transit scenarios, this paper proposes a multi-task perception model that features simultaneous detection and segmentation. …”
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Malware detection approach based on improved SOINN by Bin ZHANG, Lixun LI, Shuqin DONG

“…To deal with the problems of dynamic update of detection model and high computation costs in malware detection model based on batch learning,a novel malware detection approach is proposed by combing SOINN and supervised classifiers,to reduce computation costs and enable the detection model to update dynamically with the assistance of SOINN′s incremental learning characteristic.Firstly,the improved SOINN was given.According to the whole alignment algorithm,search the adjusted weights of neurons under all input sequences in the learning cycle and then calculate the average value of all adjusted weights as the final result,to avoid SOINN′s stability under different input sequences and representativeness of original data,therefore improve malware detection accuracy.Then a data preprocessing algorithm was proposed based on nonnegative matrix factor and Z-score normalization to transfer the malware behavior feature vector from high dimension and high order to low dimension and low order,to speed up and avoid overfitting and further improve detection accuracy.The results of experiments show that proposed approach supports dynamic updating of detection model and has a significantly higher accuracy of detecting unknown new samples and lower computation costs than tradition methods.…”
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Toward lightweight intrusion detection systems using the optimal and efficient feature pairs of the Bot-IoT 2018 dataset by Erman Özer, Murat İskefiyeli, Jahongir Azimjonov

“…Next, 10 full-feature-based intrusion detection systems were developed by training the 10 machine learning algorithms with the 12 full features. …”
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Automated Quality Control of Candle Jars via Anomaly Detection Using OCSVM and CNN-Based Feature Extraction by Azeddine Mjahad, Alfredo Rosado-Muñoz

“…Two anomaly detection strategies are explored: (1) a baseline model using convolutional neural networks (CNNs) as an end-to-end classifier and (2) a hybrid approach where features extracted by CNNs are fed into One-Class classification (OCC) algorithms, including One-Class SVM (OCSVM), One-Class Isolation Forest (OCIF), One-Class Local Outlier Factor (OCLOF), One-Class Elliptic Envelope (OCEE), One-Class Autoencoder (OCAutoencoder), and Support Vector Data Description (SVDD). …”
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Application of 4PCS and KD-ICP Alignment Methods Based on ISS Feature Points for Rail Wear Detection by Jie Shan, Hao Shi, Zhi Niu

“…The experimental results show that when the number of ISS feature points extracted is 4496, the 4PCS coarse alignment algorithm based on ISS feature points is higher than the original 4PCS algorithm as well as the other algorithms in terms of alignment accuracy; the ICP fine alignment algorithm based on the kd-tree is less than the original ICP algorithm as well as the other algorithms in terms of the time consumed. …”
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A comparative study of convolutional neural networks and traditional feature extraction techniques for adulteration detection in ground beef by Leila Bahmani, Saied Minaei, Alireza Mahdavian, Ahmad Banakar, Mahmoud Soltani Firouz

“…In order to identify the most appropriate feature extraction algorithm and classify samples having various levels of adulteration, Local Binary Pattern (LBP), Gray Level Co-occurrence Matrixes (GLCM) and Gabor filter were compared. …”
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Combining Transfer Learning and Ensemble Algorithms for Improved Citrus Leaf Disease Classification by Hongyan Zhu, Dani Wang, Yuzhen Wei, Xuran Zhang, Lin Li

“…These findings conclusively illustrate that deep learning model fusion networks combining transfer learning and integration algorithms can automatically extract image features, enhance the automation and accuracy of disease recognition, demonstrate the significant potential and application value in citrus leaf disease classification, and potentially drive the development of smart agriculture.…”
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On the generalisation capabilities of Fisher vector‐based face presentation attack detection by Lázaro J. González‐Soler, Marta Gomez‐Barrero, Christoph Busch

“…A feature space based on Fisher Vectors computed from compact binarised statistical image features histograms, which allows discovering semantic feature subsets from known samples to enhance the detection of unknown attacks is presented. …”
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BOLT LOOSENING ANGLE DETECTION METHOD BASED ON COLOR SEGMENTATION by KANG Jingjie, ZHANG Lijun, SUN Yuandong, YANG Xiaoyu, WANG Ruolan, ZHAO Tianhao

“…To achieve quantitative detection of bolt loosening angles through single frame images, a method based on color segmentation and connected domain feature processing was designed. …”
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