EEG-based brain age prediction in infants–toddlers: Implications for early detection of neurodevelopmental disorders by Winko W. An, Aprotim C. Bhowmik, Charles A. Nelson, Carol L. Wilkinson

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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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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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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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Enhanced RT-DETR with Dynamic Cropping and Legendre Polynomial Decomposition Rockfall Detection on the Moon and Mars by Panpan Zang, Jinxin He, Yongbin Yang, Yu Li, Hanya Zhang

“…Our coordinated optimization strategy integrates dynamic cropping optimization with architectural innovations: Kolmogorov–Arnold Network based C3 module (KANC3) replaces RepC3 through Legendre polynomial decomposition to strengthen feature representation, while our dynamic cropping strategy significantly improves small-target detection in low-contrast grayscale imagery by mitigating background and target imbalance. …”
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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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Explainable artificial intelligence with temporal convolutional networks for adverse weather condition detection in driverless vehicles by Samah Alzanin

“…Therefore, this paper proposes a Complex Data Analysis for Adverse Weather Detection in Autonomous Vehicles Using Explainable Artificial Intelligence (CDAAWD-AVXAI) approach. …”
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High-Precision Defect Detection in Solar Cells Using YOLOv10 Deep Learning Model by Lotfi Aktouf, Yathin Shivanna, Mahmoud Dhimish

“…Detailed analysis of the model’s performance revealed exceptional precision and recall rates for most defect classes, notably achieving 100% accuracy in detecting black core, corner, fragment, scratch, and short circuit defects. …”
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The MacqD deep-learning-based model for automatic detection of socially housed laboratory macaques by Genevieve Jiawei Moat, Maxime Gaudet-Trafit, Julian Paul, Jaume Bacardit, Suliann Ben Hamed, Colline Poirier

“…Abstract Despite advancements in video-based behaviour analysis and detection models for various species, existing methods are suboptimal to detect macaques in complex laboratory environments. …”
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Evaluating the Performance of a Fake News Model on A Domain-Specific and Heterogeneous Dataset to Improve Detection by Georgina Obuandike, Emmy Danny Ajik, Faith Oluwatosin Echobu

“…These findings suggest that dynamic and robust fake news detection systems should integrate both heterogeneous datasets and domain-specific features to enhance effectiveness. …”
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Two-stage augmentation for detecting malignancy of BI-RADS 3 lesions in early breast cancer by Huanhuan Tian, Li Cai, Yu Gui, Zhigang Cai, Xianfeng Han, Jianwei Liao, Li Chen, Yi Wang

“…We conducted a comparative analysis between our model and four radiologists in breast imaging diagnosis. …”
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Boosting Cyberattack Detection Using Binary Metaheuristics With Deep Learning on Cyber-Physical System Environment by Alanoud Al Mazroa, Fahad R. Albogamy, Mohamad Khairi Ishak, Samih M. Mostafa

“…In addition, the binary grey wolf optimizer (BGWO) model is utilized to choose an optimal feature subset. Moreover, the Enhanced Elman Spike Neural Network (EESNN) model detects cyber-attacks. …”
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A Transductive Zero-Shot Learning Framework for Ransomware Detection Using Malware Knowledge Graphs by Ping Wang, Hao-Cyuan Li, Hsiao-Chung Lin, Wen-Hui Lin, Nian-Zu Xie

“…As a result, these conventional approaches frequently fail to detect newly emerging malware variants in a timely manner. …”
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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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PAB-Mamba-YOLO: VSSM assists in YOLO for aggressive behavior detection among weaned piglets by Xue Xia, Ning Zhang, Zhibin Guan, Xin Chai, Shixin Ma, Xiujuan Chai, Tan Sun

“…The mean average precision (mAP) of 0.985 reflected the model's overall effectiveness in detecting all classes of aggressive behaviors. The model achieved a detection speed FPS of 69 f/s, with model complexity measured by 7.2 G floating-point operations (GFLOPs) and parameters (Params) of 2.63 million. …”
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A Multi-Strategy Active Learning Framework for Enhanced Peripheral Blood Cell Image Detection by Yuheng Feng, Jiangtao He, Linjin Wang, Wuchen Yang, Sihan Deng, Lanlin Li, Xinwei Li

“…The process begins with entropy-based uncertainty selection to identify the most uncertain samples, followed by clustering analysis to capture diverse samples from the feature space, and concludes with density-based selection using the k-nearest neighbors algorithm to prioritize samples from high-density regions. …”
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A Comparative Crash-Test of Manual and Semi-Automated Methods for Detecting Complex Submarine Morphologies by Vasiliki Lioupa, Panagiotis Karsiotis, Riccardo Arosio, Thomas Hasiotis, Andrew J. Wheeler

“…Multibeam echosounders provide ideal data for the semi-automated seabed feature extraction and accurate morphometric measurements. …”
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Integrated pixel-level crack detection and quantification using an ensemble of advanced U-Net architectures by Rakshitha R, Srinath S, N Vinay Kumar, Rashmi S, Poornima B V

“…This framework provides a scalable and efficient solution for automated pavement crack analysis. It addresses critical challenges in accuracy, adaptability, and reliability under diverse operational conditions, marking significant progress in crack detection technology.…”
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Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning by Muhammad Umair, Jawad Ahmad, Nada Alasbali, Oumaima Saidani, Muhammad Hanif, Aizaz Ahmad Khattak, Muhammad Shahbaz Khan

“…IntroductionMajor Depressive Disorder (MDD) remains a critical mental health concern, necessitating accurate detection. Traditional approaches to diagnosing MDD often rely on manual Electroencephalography (EEG) analysis to identify potential disorders. …”
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Pancreatic cancer in liquid-based cytology: cytological features and cell block utility from 254 fine-needle aspiration samples by Jaeyong Min, Wookjin Oh, Baek-hui Kim

“…In cases of conventional pancreatic ductal adenocarcinoma, the palliative treatment subgroup showed a higher incidence of necrotic background than the resection subgroup. In the cell block analysis, tumor cells not identified in LBC slides were detected in 16 FNAs. …”
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