GrainNet: efficient detection and counting of wheat grains based on an improved YOLOv7 modeling by Xin Wang, Changchun Li, Chenyi Zhao, Yinghua Jiao, Hengmao Xiang, Xifang Wu, Huabin Chai

“…We propose a wheat grain detection and counting model called GrainNet, which significantly improves the counting performance and detection speed across diverse conditions and adhesion levels by incorporating lightweight and efficient feature fusion modules. …”
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Detection of Oil Mineral Pollution in Tigris River from Aldora Refined using Absorbance Spectroscopy by Thamer Mahmood Mohammed, Ahmed K. Ahmed

“…It is fast, accurate data analysis, and a lower cost compared with the other chemical analysis and conventional methods. …”
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Optical detection of beetle-related indicators and stem quality in roundwood using convolutional neural networks by Julia Achatz, Mark Schubert

“…However, current models focus primarily on cross-sectional images, limiting their ability to detect important features like knots and beetle infestations. …”
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A novel machine learning model for perimeter intrusion detection using intrusion image dataset. by Shahneela Pitafi, Toni Anwar, I Dewa Made Widia, Zubair Sharif, Boonsit Yimwadsana

“…Perimeter Intrusion Detection Systems (PIDS) are crucial for protecting any physical locations by detecting and responding to intrusions around its perimeter. …”
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Void Detection of Airport Concrete Pavement Slabs Based on Vibration Response Under Moving Load by Xiang Wang, Ziliang Ma, Xing Hu, Xinyuan Cao, Qiao Dong

“…The results revealed that the RF model achieved strong predictive performance, with a high correlation between key features and void characteristics. This work demonstrates the feasibility of integrating simulation analysis, signal feature extraction, and machine learning to support intelligent diagnostics of concrete pavement health.…”
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Research and Application of a Multitarget Detection Algorithm Based on Improved YOLOv8 for Indoor Objects by Yanzhen Wang, Wei Wang, Xiaolong Zhou, Xubin Dong, Jianyong Li, Qi Zhao, Xinyu Yang, Yao Wang

“…A comparison with other popular models shows that YOLOv8 - CBW3 has strong generalizability, localization performance, detection ability and robustness. To verify that YOLOv8-CBW3 has strong generalizability, a comparative analysis is conducted on the VOC2007 public dataset, which shows through the data that its detection ability is still optimal, and it provides a reference solution for subsequent similar problems.…”
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Nonlinear Model for Condition Monitoring and Fault Detection Based on Nonlocal Kernel Orthogonal Preserving Embedding by Bo She, Fuqing Tian, Weige Liang, Gang Zhang

“…Compared with KONPE and KPCA, NLKOPE combines both the advantages of KONPE and KPCA, and NLKOPE is also more powerful in extracting potential useful features in nonlinear data set than NLOPE. For the purpose of condition monitoring and fault detection, monitoring statistics are constructed in feature space. …”
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Conventional KPCA Approach Applied to Detect Simulated Faults in PV Systems Using Simulated Data by Charlène Bernadette Lema, Steve Perabi Ngoffe, Francelin Edgar Ndi, Grégoire Abessolo Ondoua, Salomé Ndjakomo Essiane

“…This study addresses the challenge of maintaining reliability in PV systems by proposing a method to detect and identify simultaneous faults, using kernel principal component analysis (KPCA) and statistical metrics. …”
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High-Throughput 3D Rice Chalkiness Detection Based on Micro-CT and VSE-UNet by Zhiqi Cai, Yangjun Deng, Xinghui Zhu, Bo Li, Chenglin Xu, Donghui Li

“…This framework overcomes the limitations of single-grain analysis, enabling simultaneous multi-grain detection. …”
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Future of Alzheimer's detection: Advancing diagnostic accuracy through the integration of qEEG and artificial intelligence by Sahar Rezaei, Farzan Asadirad, Alireza Motamedi, Mohammadsadegh Kamran, Farzaneh Parsa, Haniyeh Samimi, Parna Ghannadikhosh, Mahdi Zahmatyar, Seyed Ali Hosseinzadeh, Hossein Arabi

“…Through systematic analysis of 11 key studies across multiple international databases, we evaluated various AI architectures, including machine learning algorithms and deep learning networks, applied to qEEG data for AD detection. …”
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A Hybrid Convolutional–Transformer Approach for Accurate Electroencephalography (EEG)-Based Parkinson’s Disease Detection by Chayut Bunterngchit, Laith H. Baniata, Hayder Albayati, Mohammad H. Baniata, Khalid Alharbi, Fanar Hamad Alshammari, Sangwoo Kang

“…To overcome these challenges, this study proposes a convolutional transformer enhanced sequential model (CTESM), which integrates convolutional neural networks, transformer attention blocks, and long short-term memory layers to capture spatial, temporal, and sequential EEG features. Enhanced by biologically informed feature extraction techniques, including spectral power analysis, frequency band ratios, wavelet transforms, and statistical measures, the model was trained and evaluated on a publicly available EEG dataset comprising 31 participants (15 with PD and 16 healthy controls), recorded using 40 channels at a 500 Hz sampling rate. …”
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Efficient automated detection of power quality disturbances using nonsubsampled contourlet transform & PCA-SVM by Pampa Sinha, Kaushik Paul, Asit Mohanty, IM Elzein, Chandra Sekhar Mishra, Mohamed Metwally Mahmoud, Daniel Eutyche Mbadjoun Wapet, Abdulrahman Al Ayidh, Ahmed Althobaiti, Hany S Hussein, Thamer AH Alghamdi, Ahmed M Ewais

“…Principal component analysis (PCA) is applied to the extracted features to reduce dimensionality and improve feature separability. …”
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A Systematic Review of Surface Electromyography in Sarcopenia: Muscles Involved, Signal Processing Techniques, Significant Features, and Artificial Intelligence Approaches by Alessandro Leone, Anna Maria Carluccio, Andrea Caroppo, Andrea Manni, Gabriele Rescio

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Multimodal AI-driven object detection with uncertainty quantification for cardiovascular risk assessment in autistic patients by Ling Tang, Chengchao Shen

“…Conventional methods, relying heavily on manually extracted features and rule-based analysis, often fail to capture subtle cardiovascular abnormalities, leading to suboptimal clinical outcomes.MethodsTo address these limitations, we propose an AI-driven object detection framework that leverages advanced deep learning techniques for automated, accurate cardiovascular risk assessment in autistic patients. …”
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Machine learning techniques in ultrasonics-based defect detection and material characterization: A comprehensive review by Boris I, Kseniia Barashok, Yongjoon Choi, Yeongil Choi, Mohammed Aslam, Jaesun Lee

“…This review provides a comprehensive overview of ML techniques applied to ultrasonic-based damage detection and material characterization, including key processes such as data preprocessing and feature engineering. …”
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A comprehensive study of non-destructive localization of structural features in metal plates using single and multimodal Lamb wave excitations by Silitonga Dicky J., Declercq Nico F., Walaszek Henri, Vu Quang A., Saidoun Abdelkrim, Samet Naim, Ndiaye Elhadji Barra, Thabourey Jérôme

“…They provide critical insights into the method’s ability to deliver precise and efficient detection of structural anomalies despite inherent challenges in signal interpretation and analysis.…”
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Hyperspectral Imaging Study of Wheat Grains Infected with Several Fusarium Fungal Species and Their Identification with PCA-Based Approach by Anastasia Povolotckaia, Dmitrii Pankin, Mikhail Gareev, Dmitrii Serebrjakov, Anatoliy Gulyaev, Evgenii Borisov, Andrey Boyko, Sergey Borzenko, Sergey Belousov, Oleg Noy, Maxim Moskovskiy

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Rice-SVBDete: a detection algorithm for small vascular bundles in rice stem’s cross-sections by Xiaoying Zhu, Weiyu Zhou, Jianguo Li, Jianguo Li, Mingchong Yang, Mingchong Yang, Haiyu Zhou, Haiyu Zhou, Jiada Huang, Jiada Huang, Jiahua Shi, Jun Shen, Guangyao Pang, Lingqiang Wang, Lingqiang Wang, Lingqiang Wang

“…Compared to the baseline YOLOv8, Rice-SVBDete improves precision by 0.179, recall by 0.201, and mAP@.5 by 0.227, demonstrating its effectiveness in small object detection.DiscussionThese results highlight Rice-SVBDete's potential for accurately identifying small vascular bundles in complex backgrounds, providing a valuable tool for rice anatomical analysis and supporting advancements in precision agriculture and plant science research.…”
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Detection and Classification of Abnormal Power Load Data by Combining One-Hot Encoding and GAN–Transformer by Ting Yang, Hongyi Yu, Danhong Lu, Shengkui Bai, Yan Li, Wenyao Fan, Ketian Liu

“…To provide the model with a suitable feature dataset, One-hot encoding is introduced to label different categories of abnormal power load data, enabling staged mapping and training of the model with the labeled dataset. …”
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Star-YOLO: A Lightweight Real-Time Wheat Grain Detection Model for Embedded Deployment by Zhihang Qu, Xiao Liang, Sicheng Liang, Xiumei Guo

“…To this end, this paper introduces Star-YOLO, a lightweight wheat grain detection model built upon YOLOv11n. The model employs StarNet to refine the C3k2 structure, reducing computational complexity without compromising detection accuracy, and integrates the MBConv module into the detection head to boost feature extraction while further minimizing computational load. …”
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