A PCC-Ensemble-TCN model for wind turbine icing detection using class-imbalanced and label-missing SCADA data by Shenyi Ding, Zhijie Wang, Jue Zhang, Fang Han, Xiaochun Gu, Guangxiao Song

“…Data-driven indirect icing detection methods based on supervisory control and data acquisition system have shown strong potential recently. …”
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Detection of anthropogenic noise pollution as a possible chronic stressor in Antarctic Specially Protected Area N°150, Ardley Island by Maximiliano Anzibar Fialho, Martín Rocamora, Lucía Ziegler

“…We used Audiomoth recorders to hourly monitor the soundscape in Ardley Island and create a simple yet effective detection method based on spectral features of the source. …”
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Precision Oral Medicine: A DPR Segmentation and Transfer Learning Approach for Detecting Third Molar Compress Inferior Alveolar Nerve by Yuan-Jin Lin, Shih-Lun Chen, Yi-Cheng Mao, Tsung-Yi Chen, Cheng-Hao Peng, Tzu-Hsiang Tsai, Kuo-Chen Li, Chiung-An Chen, Wei-Chen Tu, Patricia Angela R. Abu

“…Moreover, a transformer-based image detection model to consider whether the third molar compresses the inferior alveolar nerve. …”
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Tiny dLIF: a dendritic spiking neural network enabling a time-domain energy-efficient seizure detection system by Luis Fernando Herbozo Contreras, Leping Yu, Zhaojing Huang, Ziyao Zhang, Armin Nikpour, Omid Kavehei

“…However, these techniques often rely on feature extraction techniques such as short time Fourier transform (STFT) for efficiency in seizure detection. …”
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ACD-ML: Advanced CKD detection using machine learning: A tri-phase ensemble and multi-layered stacking and blending approach by Mir Faiyaz Hossain, Shajreen Tabassum Diya, Riasat Khan

“…This study aims to leverage machine learning algorithms and ensemble models for early detection of CKD using the “Chronic Kidney Disease (CKD15)” and “Risk Factor Prediction of Chronic Kidney Disease (CKD21)” datasets from the UCI Machine Learning Repository. …”
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DRR-YOLO: A Study of Small Target Multi-Modal Defect Detection for Multiple Types of Insulators Based on Large Convolution Kernel by Mingming Hu, Jun Liu, Junfu Liu

“…The existing insulator defect detection algorithms are mainly characterized by their ability to identify only a single type of defect, accompanied by relatively low accuracy, a Dilated Re-parameterized Residual-YOLO (DRR-YOLO) algorithm is proposed, which is capable of identifying four defects of each of the four insulator types. …”
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A Novel Shape Classification Approach Based on Branch Length Similarity Entropy by Sang-Hee Lee, Cheol-Min Park

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Impact of Temporal Resolution on Autocorrelative Features of Cerebral Physiology from Invasive and Non-Invasive Sensors in Acute Traumatic Neural Injury: Insights from the CAHR-TBI... by Nuray Vakitbilir, Rahul Raj, Donald E. G. Griesdale, Mypinder Sekhon, Francis Bernard, Clare Gallagher, Eric P. Thelin, Logan Froese, Kevin Y. Stein, Andreas H. Kramer, Marcel J. H. Aries, Frederick A. Zeiler

“…Therapeutic management during the acute phase of traumatic brain injury (TBI) relies on continuous multimodal cerebral physiologic monitoring to detect and prevent secondary injury. These high-resolution data streams come from various invasive/non-invasive sensor technologies and challenge clinicians, as they are difficult to integrate into management algorithms and prognostic models. …”
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Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection by Ruo-Fei Xu, Zhen-Jing Liu, Shunan Ouyang, Qin Dong, Wen-Jing Yan, Dong-Wu Xu

“…Model performance was systematically evaluated through discrimination (ROC analysis), calibration (Brier score), and clinical utility analyses (decision curve analysis), with additional validation using random forest and support vector machine algorithms across independent samples. Results The resulting stratified screening system consists of an initial four-item rapid screening layer (encompassing emotional, cognitive, and interpersonal dimensions) for detecting probable depression (AUC = 0.982, sensitivity = 0.945, specificity = 0.926), followed by an enhanced assessment layer with five additional items. …”
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Precision Weed Management for Straw-Mulched Maize Field: Advanced Weed Detection and Targeted Spraying Based on Enhanced YOLO v5s by Xiuhong Wang, Qingjie Wang, Yichen Qiao, Xinyue Zhang, Caiyun Lu, Chao Wang

“…Through model test and spraying experiments, the results demonstrated that while the model exhibited a 0.9% decrease in average detection accuracy for weeds, it achieved an 8.46% increase in detection speed, with model memory and computational load reduced by 50.36% and 53.16%, respectively. …”
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Advancing Alzheimer’s disease detection: a novel convolutional neural network based framework leveraging EEG data and segment length analysis by Md Nurul Ahad Tawhid, Siuly Siuly, Enamul Kabir, Yan Li

“…While no cure currently exists, recent advancements in preventive drug trials and therapeutic management have increased interest in developing clinical algorithms for early detection and biomarker identification. …”
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Distinguishing Difficulty Imbalances in Strawberry Ripeness Instances in a Complex Farmland Environment by Yang Gan, Xuefeng Ren, Huan Liu, Yongming Chen, Ping Lin

“…The existing strawberry ripeness detection algorithm has the problems of a low precision and a high missing rate in real complex scenes. …”
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Automatic Detection for Mining Subsidence Areas Using the CBAM-Enhanced VGG-UNet Model With Long Time Series InSAR Interferograms by Kegui Jiang, Keming Yang, Mengting Gao, Liuguo Zhu, Chuang Jiang

“…First, this study designs a VGG-UNet model enhanced by an attention mechanism module to learn and detect mining subsidence areas. This enhancement improves the feature representation and perception capabilities of the model. …”
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LogiTriBlend: A Novel Hybrid Stacking Approach for Enhanced Phishing Email Detection Using ML Models and Vectorization Approach by Aqsa Khalid, Maria Hanif, Abdul Hameed, Zeeshan Ashraf, Mrim M. Alnfiai, Salma M. Mohsen Alnefaie

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Smoke and Fire-You Only Look Once: A Lightweight Deep Learning Model for Video Smoke and Flame Detection in Natural Scenes by Chenmeng Zhao, Like Zhao, Ka Zhang, Yinghua Ren, Hui Chen, Yehua Sheng

“…Owing to the demand for smoke and flame detection in natural scenes, this paper proposes a lightweight deep learning model, SF-YOLO (Smoke and Fire-YOLO), for video smoke and flame detection in such environments. …”
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Research and implementation of intelligent terminal lightweight face recognition system by Guo-jun MA, Hai-dong ZHOU

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Weak Fault Detection for Rolling Bearings in Varying Working Conditions through the Second-Order Stochastic Resonance Method with Barrier Height Optimization by Huaitao Shi, Yangyang Li, Peng Zhou, Shenghao Tong, Liang Guo, Baicheng Li

“…The stochastic resonance (SR) method is widely applied to fault feature extraction of rotary machines, which is capable of improving the weak fault detection performance by energy transformation through the potential well function. …”
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Efficient deep learning based rail fastener screw detection method for fastener screw maintenance robot under complex lighting conditions by Yijie Cai, Ming He, Bin Chen

“…These complex lighting conditions (CLCs) interfere with the fastener recognition ability of the fastener screw detection algorithm since it can hardly maintain fixed and optimized lighting conditions of the fastener screw. …”
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A Study of Potential Applications of Student Emotion Recognition in Primary and Secondary Classrooms by Yimei Huang, Wei Deng, Taojie Xu

“…This network fuses skeletal, environmental, and facial information, and introduces a central loss function and an attention module AAM to enhance the feature extraction capability. The experimental results show that MultiEmoNet achieves a classification accuracy of 91.4% on a homemade classroom student emotion dataset, which is a 10% improvement over the single-channel classification algorithm. …”
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RDW-YOLO: A Deep Learning Framework for Scalable Agricultural Pest Monitoring and Control by Jiaxin Song, Ke Cheng, Fei Chen, Xuecheng Hua

“…This study introduces RDW-YOLO, an improved pest detection algorithm based on YOLO11, featuring three key innovations. …”
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