M18K: A Multi-Purpose Real-World Dataset for Mushroom Detection, 3D Pose Estimation, and Growth Monitoring by Abdollah Zakeri, Mulham Fawakherji, Jiming Kang, Bikram Koirala, Venkatesh Balan, Weihang Zhu, Driss Benhaddou, Fatima A. Merchant

“…The dataset, featuring realistic growth environment scenarios with comprehensive 2D and 3D annotations, is assessed using advanced detection and instance segmentation algorithms. …”
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GreyWolfLSM: an accurate oil spill detection method based on level set method from synthetic aperture radar imagery by Nastaran Aghaei, Gholamreza Akbarizadeh, Abdolnabi Kosarian

“…In this paper, a new oil spill detection algorithm based on level set method (LSM) is presented. …”
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A Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using Internet of Things-Based Monitoring and Explainable Artificial Intelligence by Emrah Aslan, Yildirim Ozupak, Feyyaz Alpsalaz, Zakaria M. S. Elbarbary

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Evaluating multitemporal vegetation indices from Zhuhai-1 hyperspectral images for detecting a rapidly spreading invasive species - Spartina alterniflora by Yuanhui Zhu, Soe W. Myint, Jingjing Cao, Kai Liu, Mei Zeng, Chenxi Diao

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Sticky Trap-Embedded Machine Vision for Tea Pest Monitoring: A Cross-Domain Transfer Learning Framework Addressing Few-Shot Small Target Detection by Kunhong Li, Yi Li, Xuan Wen, Jingsha Shi, Linsi Yang, Yuyang Xiao, Xiaosong Lu, Jiong Mu

“…Currently, there is a notable absence of machine vision devices capable of real-time monitoring for small-sized tea pests in the market, and the scarcity of open-source datasets available for tea pest detection remains a critical limitation. This manuscript proposes a YOLOv8-FasterTea pest detection algorithm based on cross-domain transfer learning, which was successfully deployed in a novel tea pest monitoring device. …”
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MS3OSD: A Novel Deep Learning Approach for Oil Spills Detection Using Optical Satellite Multisensor Spatial-Spectral Fusion Images by Kai Du, Yi Ma, Zhongwei Li, Rongjie Liu, Zongchen Jiang, Junfang Yang

“…Furthermore, existing oil spill detection algorithms often prioritize surrounding spatial features while neglecting the contribution of central spectral features, resulting in reduced detection accuracy. …”
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Automated detection of hard exudates in retinal fundus images for diabetic retinopathy screening using textural-based radon transform and morphology reconstruction by Esmat Ramezanzadeh, Naser Shoeibi, Akram Feizabadi, Touka Banaee, Mohammad Hossein Bahreyni Tussi, Meysam Tavakoli

“…We employed Kirsch edge detection to distinguish HEs based on edge sharpness and utilized Top-Hat transformation to highlight small-scale features. …”
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MRI based early Temporal Lobe Epilepsy detection using DGWO based optimized HAETN and Fuzzy-AAL Segmentation Framework (FASF). by Hasim Khan, Ahmed Ibrahim Alutaibi, Ghanshyam G Tejani, Sunil Kumar Sharma, Ahmad Raza Khan, Fuzail Ahmad, Seyed Jalaleddin Mousavirad

“…Furthermore, an effective feature selection method is proposed using the Dipper- grey wolf optimization (DGWO) algorithm to improve the performance of the proposed model. …”
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Optimized Two-Stage Anomaly Detection and Recovery in Smart Grid Data Using Enhanced DeBERTa-v3 Verification System by Xiao Liao, Wei Cui, Min Zhang, Aiwu Zhang, Pan Hu

“…The first stage employs an optimized increment-based detection algorithm achieving 95.0% for recall and 54.8% for precision through multidimensional analysis. …”
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Preliminary Development of a Database for Detecting Active Mounting Behaviors Using Signals Acquired from IoT Collars in Free-Grazing Cattle by Miguel Guarda-Vera, Carlos Muñoz-Poblete

“…The algorithm achieves an average F1 Score of 88.6% for the World Frame of reference, showing a significant improvement compared to the algorithm trained with Body Frame (78.6%) when both are trained with the same 112 features. …”
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Investigating and Optimizing MINDWALC Node Classification to Extract Interpretable Decision Trees from Knowledge Graphs by Maximilian Legnar, Joern-Helge Heinrich Siemoneit, Gilles Vandewiele, Jürgen Hesser, Zoran Popovic, Stefan Porubsky, Cleo-Aron Weis

“…This work deals with the investigation and optimization of the MINDWALC node classification algorithm with a focus on its ability to learn human-interpretable decision trees from knowledge graph databases. …”
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QPPLab: A generally applicable software package for detecting, analyzing, and visualizing large-scale quasiperiodic spatiotemporal patterns (QPPs) of brain activity by Nan Xu, Behnaz Yousefi, Nmachi Anumba, Theodore J. LaGrow, Xiaodi Zhang, Shella Keilholz

“…QPPLab integrates correlation-based iterative algorithms, supports customizable parameter settings, and features automated workflows to simplify analysis. …”
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Centrality nearest-neighbor projected-distance regression (C-NPDR) feature selection for correlation-based predictors with application to resting-state fMRI study of major depressi... by Elizabeth Kresock, Bryan Dawkins, Henry Luttbeg, Yijie Jamie Li, Rayus Kuplicki, B A McKinney

“…<h4>Background</h4>Nearest-neighbor projected-distance regression (NPDR) is a metric-based machine learning feature selection algorithm that uses distances between samples and projected differences between variables to identify variables or features that may interact to affect the prediction of complex outcomes. …”
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Fast Monocular Measurement via Deep Learning-Based Object Detection for Real-Time Gas-Insulated Transmission Line Deformation Monitoring by Guiyun Yang, Wengang Yang, Entuo Li, Qinglong Wang, Huilong Han, Jie Sun, Meng Wang

“…Within these ROIs, grayscale data is used to dynamically set thresholds for FAST corner detection, while the Shi–Tomasi algorithm filters redundant corners to extract unique feature points for precise tracking. …”
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Authenticity Detection of Egg White Powder Using Near-Infrared Spectroscopy Based on Improved One-Dimensional Convolutional Neural Network Model by ZHU Zhihui, LI Wolin, HAN Yutong, JIN Yongtao, YE Wenjie, WANG Qiaohua, MA Meihu

“…The average time spent (AATS) for the detection was 0.004 4 seconds. Compared with traditional 1D-CNN network structure and other improved algorithms, the EG-1D-CNN model exhibited higher accuracy, faster detection speed, and smaller model footprint, thus making it more suitable for deployment on embedded devices. …”
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PermQRDroid: Android malware detection with novel attention layered mini-ResNet architecture over effective permission information image by Kazım Kılıç, İbrahim Alper Doğru, Sinan Toklu

“…Methods In this study, an attention-layered mini-ResNet model is proposed, which can detect QR code-like images created using the 100 most effective defined permission information of Android applications. …”
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Individual Tree Segmentation From Airborne LiDAR Data Based on Automatic Treetop Detection and Simulated Stem-Branch Points Compensation by Qingjun Zhang, Jiale Chen, Hanwen Qi, Xu Wang, Xinlian Liang

“…Therefore, we propose a novel individual tree segmentation method based on treetop detection and point compensation. First, a center shift algorithm is proposed to detect treetop candidates, which are then refined to identify reliable treetops through geometric-feature analysis of these candidates&#x2019; neighborhoods. …”
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Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned by Marketa Ciharova, Khadicha Amarti, Ward van Breda, Ward van Breda, Martin J. Gevonden, Sina Ghassemi, Annet Kleiboer, Christiaan H. Vinkers, Christiaan H. Vinkers, Christiaan H. Vinkers, Christiaan H. Vinkers, Milou S. C. Sep, Milou S. C. Sep, Milou S. C. Sep, Milou S. C. Sep, Sophia Trofimova, Alexander C. Cooper, Xianhua Peng, Xianhua Peng, Mieke Schulte, Mieke Schulte, Eirini Karyotaki, Eirini Karyotaki, Eirini Karyotaki, Pim Cuijpers, Pim Cuijpers, Pim Cuijpers, Heleen Riper, Heleen Riper

“…Use of multimodal data, meaning data coming from multiple sources, might contribute to machine-learning stress severity detection. We aimed to detect laboratory-induced stress using multimodal data and identify challenges researchers may encounter when conducting a similar study.MethodsWe conducted a preliminary exploration of performance of a machine-learning algorithm trained on multimodal data, namely visual, acoustic, verbal, and physiological features, in its ability to detect stress severity following a partially automated online version of the Trier Social Stress Test. …”
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Intelligent Recognition Method for Ferrography Wear Debris Images Using Improved Mask R-CNN Methods by Xiangwen Xiao, Weixuan Zhang, Qing Wang, Yuan Liu, Yishou Wang

“…The accurate characterization of wear debris is crucial for assessing the health of rotating engine components and for conducting simulation experiments in debris detection. This study proposed an intelligent recognition method for ferrography wear debris images, leveraging several improved Mask Region-based Convolutional Neural Network (Mask R-CNN) algorithms to quantitatively calculate both the number of debris particles and their coverage areas. …”
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South China Sea SST Fronts, 2015–2022 by Igor M. Belkin, Yi-Tao Zang

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