Differential gray matter correlates and machine learning prediction of abuse and internalizing psychopathology in adolescent females by Sara A. Heyn, Taylor J. Keding, Josh Cisler, Katie McLaughlin, Ryan J. Herringa

“…After extensive training, model tuning, and model evaluation, the neural networks performed above chance when predicting IP (63% accuracy) and abuse experiences (71% accuracy) at the level of the individual. …”
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Leveraging Synthetic Data to Develop a Machine Learning Model for Voiding Flow Rate Prediction From Audio Signals by Marcos Lazaro Alvarez, Alfonso Bahillo, Laura Arjona, Diogo Marcelo Nogueira, Elsa Ferreira Gomes, Alipio M. Jorge

“…To extract the audio features, our analysis showed that segmenting the audio signals into 1000 ms segments with frequencies up to 16 kHz provided the best results. …”
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An Improved U-Net-Based Framework for Estimating River Surface Flow Velocity by 周继威, 安国成, 王根一

“…This study aimed to address these limitations by developing a robust framework that integrates improved data labeling strategies, optimized training methods, and a novel neural network architecture to achieve high-precision velocity estimation. …”
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Regression machine learning methods for isolation prediction and massive gain broadband MIMO antenna design for 28 GHz applications by Md.Ashraful Haque, Redwan A. Ananta, Jun-Jiat Tiang, Mouaaz Nahas, Md Afzalur Rahman, Narinderjit Singh Sawaran Singh

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Application of Machine Learning Models for the Early Detection of Metritis in Dairy Cows Based on Physiological, Behavioural and Milk Quality Indicators by Karina Džermeikaitė, Justina Krištolaitytė, Ramūnas Antanaitis

“…Five classification models—partial least squares discriminant analysis (PLS-DA), random forest (RF), support vector machine (SVM), neural network (NN), and an Ensemble model—were developed using standardised features and stratified 80/20 training/test splits. …”
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Comprehensive Analysis of N6-Methyladenosine (m6A) RNA Methylation Regulators and Tumour Microenvironment Cell Infiltration Involving Prognosis and Immunotherapy in Gastroesophagea... by Duanrui Liu, Mingjie Yuan, Zongming Wang, Liping Sun, Yusong Fang, Xiaoli Ma, Lulu Zhang, Yuanxin Xing, Jingyu Zhu, Yunyun Liu, Wenshuai Zhu, Shuqin Bao, Yanfei Jia, Yunshan Wang

“…In addition, PPI and WGCNA network analysis were integrated to identify key module genes closely related to immune infiltration of GEA to find immunotherapy markers. …”
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Breaking Diagnostic Barriers: Vision Transformers Redefine Monkeypox Detection by Gelan Ayana, Beshatu Debela Wako, So-yun Park, Jude Kong, Sahng Min Han, Soon-Do Yoon, Se-woon Choe

“…Performance was assessed relative to convolutional neural network (CNN)-based TL models and ViT models trained from scratch across key metrics: accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). …”
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Synergistic hyperspectral and SAR imagery retrieval of mangrove leaf area index using adaptive ensemble learning and deep learning algorithms by Jun Sun, Weiguo Jiang, Bolin Fu, Hang Yao, Huajian Li

“…This study proposes a new approach to the retrieval of the mangrove LAI by combining a one-dimensional convolutional neural network (1D-CNN) with adaptive ensemble learning regression (AELR) and deep learning regression (DNNR) algorithms. …”
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Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities by Mahmoud Ragab, Ehab Bahaudien Ashary, Bandar M. Alghamdi, Rania Aboalela, Naif Alsaadi, Louai A. Maghrabi, Khalid H. Allehaibi

“…Abstract With the fast growth of artificial intelligence (AI) and a novel generation of network technology, the Internet of Things (IoT) has become global. …”
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Efficient Anomaly Detection for Edge Clouds: Mitigating Data and Resource Constraints by Javad Forough, Hamed Haddadi, Monowar Bhuyan, Erik Elmroth

“…This enables the model to benefit from the learned features and patterns from related tasks such as network intrusion detection, resulting in improved detection accuracy. …”
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Pine wood nematode disease area identification based on multi-temporal multi-source remote sensing images and BIT model by Degao Wang, Zhangyu Sun, Xinxia Huang, Mingzhong Liu, Qingqing Zheng, Huailiang Zhang, Guanghe Zhang

“…Finally, transformer decoder is utilized to refine the original features. To evaluate the model’s generalization ability, the best-trained model for different regions affected by pine nematode disease is identified using a transfer learning mechanism. …”
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Leveraging Prior Knowledge in Semi-Supervised Learning for Precise Target Recognition by Guohao Xie, Zhe Chen, Yaan Li, Mingsong Chen, Feng Chen, Yuxin Zhang, Hongyan Jiang, Hongbing Qiu

“…Underwater acoustic target recognition (UATR) is challenged by complex marine noise, scarce labeled data, and inadequate multi-scale feature extraction in conventional methods. This study proposes DART-MT, a semi-supervised framework that integrates a Dual Attention Parallel Residual Network Transformer with a mean teacher paradigm, enhanced by domain-specific prior knowledge. …”
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Comparison of the in-hospital mortality risk predictive models among patients with ischemic stroke combined by dysphagia based on interpretable machine learning by TAI Yaoyong, WU Shengyong, LUO Xiao, ZHU Ronghui, HE Qian, WU Cheng

“…In addition, the calibration curve and Brier score were used to evaluate the calibration degree of the model, and the decision curve was drawn to reflect the clinical net benefit. …”
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The Depth Estimation and Visualization of Dermatological Lesions: Development and Usability Study by Pranav Parekh, Richard Oyeleke, Tejas Vishwanath

“…MethodsWe started by performing classification using a convolutional neural network (CNN), followed by using explainable artificial intelligence to localize the image features responsible for the CNN output. …”
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Roughness prediction of end milling surface for behavior mapping of digital twined machine tools [version 2; peer review: 2 approved, 1 approved with reservations] by Gedong Jiang, Suiyan Shang, Wenwen Tian, Zheng Sun, Jun Xu, Dawei Zhang, Chi Fai Cheung

“…Conclusions Compared with the state-of-the-art convolutional neural network method with automatic feature extraction ability and other commonly used traditional machine learning methods, the proposed data preprocessing procedure integrated with a three-layer backpropagation neural network has a minimum prediction error.…”
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Development and validation of an explainable machine learning model for predicting the risk of sleep disorders in older adults with multimorbidity: a cross-sectional study by Xia Wang, Dan Zhang, Liu Lu, Shujie Meng, Yong Li, Rong Zhang, Jingjie Zhou, Qian Yu, Li Zeng, Jiang Zhao, Yu Zeng, Ru Gao

“…We employed six machine learning (ML) methods, namely logistic regression (LR), neural network (NN), support vector machine (SVM), gradient boosting machine (GBM), K-Nearest Neighbors (KNN), and light gradient boosting machine (LightGBM), to predict the risk of sleep disorders based on their sociodemographic data, health behavior factors, mental health, and disease-related data. …”
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Enhancing Hajj and Umrah Services Through Predictive Social Media Classification by Samia Allaoua Chelloug, Mohammed Saleh Ali Muthanna, Faisal Jamil, Mehdhar S. A. M. Al-Gaashani, Soha Alhelaly, Ahmed Aziz, Ammar Muthanna

“…The model’s performance is evaluated using standard metrics, including F-measure, Precision, and Recall. …”
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CWMS-GAN: A small-sample bearing fault diagnosis method based on continuous wavelet transform and multi-size kernel attention mechanism. by Shun Yu, Zi Li, Jialin Gu, Runpu Wang, Xiaoyu Liu, Lin Li, Fusen Guo, Yuheng Ren

“…Many recent studies have shown that data augmentation using generative adversarial networks (GAN) can effectively alleviate this problem. …”
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Small Object Detection Algorithm Based on Partial Convolution and Attention Fusion Detection Head by Peng Sheng, Zhu Fenghua, Zhou Jin, Zhu Gaofeng, Wang Yingxu, Chen Yuehui

“…The feature extraction network is optimized to enhance the detection effectiveness for small-sized targets. …”
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An Interpretable Method for Anomaly Detection in Multivariate Time Series Predictions by Shijie Tang, Yong Ding, Huiyong Wang

“…Anomaly detection methods for industrial control networks using multivariate time series usually adopt deep learning-based prediction models. …”
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