Enhancing Power Grid Reliability With Machine Learning and Auxiliary Classifier Generative Adversarial Networks: A Study on Fault Detection Using the Georgia Electric System Load D... by Hafeez Ur Rehman Siddiqui, Robert Brown, Adil Ali Saleem, Muhammad Amjad Raza, Sandra Dudley

“…Unlike traditional approaches, the research utilizes an Auxiliary Classifier Generative Adversarial Network (ACGAN) to generate synthetic data representative of underrepresented fault types, enhancing model training and performance. By extracting both spectral and statistical features from the Grid Event Signature Library (GESL) dataset, a comprehensive representation of power system signals is achieved. …”
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Quantitative assessment of brain glymphatic imaging features using deep learning-based EPVS segmentation and DTI-ALPS analysis in Alzheimer’s disease by Fenyang Chen, Tiantian Heng, Qi Feng, Rui Hua, Jiaojiao Wu, Feng Shi, Zhengluan Liao, Keyin Qiao, Zhiliang Zhang, Jianliang Miao

“…EPVS were automatically segmented from T1WI and T2WI images using a VB-Net-based model. Quantitative metrics, including total EPVS volume, number, and regional volume fractions were extracted, and segmentation performance was evaluated using the Dice similarity coefficient. …”
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Integrative radiomics of intra- and peri-tumoral features for enhanced risk prediction in thymic tumors: a multimodal analysis of tumor microenvironment contributions by Liang zhu, Jiamin Li, Xuefeng Wang, Yan He, Siyuan Li, Shuyan He, Biao Deng

“…Subsequently, hierarchical clustering and the LASSO algorithm were applied to identify the most predictive features. These selected features were then used to train machine learning models, which were optimized on the training dataset and assessed for predictive performance. …”
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A Neural Network with Multiscale Convolution and Feature Attention Based on an Electronic Nose for Rapid Detection of Common Bunt Disease in Wheat Plants by Zhizhou Ren, Kun Liang, Yihe Liu, Xiaoxiao Wu, Chi Zhang, Xiuming Mei, Yi Zhang

“…It significantly improves the deep learning performance of the model in extracting key gas features. …”
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Rolling Bearing Degradation Identification Method Based on Improved Monopulse Feature Extraction and 1D Dilated Residual Convolutional Neural Network by Chang Liu, Haiyang Wu, Gang Cheng, Hui Zhou, Yusong Pang

“…The established 1D-DRCNN model integrates the advantages of dilated convolution and residual connections and can deeply mine sensitive features and accurately identify different bearing degradation states. …”
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Estimation of elbow flexion torque using equilibrium optimizer on feature selection of NMES MMG signals and hyperparameter tuning of random forest regression by Raphael Uwamahoro, Raphael Uwamahoro, Kenneth Sundaraj, Farah Shahnaz Feroz

“…The performance of the GLEO-coupled with the RFR model was compared with the standard Equilibrium Optimizer (EO) and other state-of-the-art algorithms in physical and physiological function estimation using biological signals.ResultsExperimental results showed that selected features and tuned hyperparameters demonstrated a significant improvement in root mean square error (RMSE), coefficient of determination (R2) and slope with values improving from 0.1330 to 0.1174, 0.7228 to 0.7853 and 0.6946 to 0.7414, respectively for the test dataset. …”
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Tilting Pad Thrust Bearing Fault Diagnosis Based on Acoustic Emission Signal and Modified Multi-Feature Fusion Convolutional Neural Network by Meijiao Mao, Zhiwen Jiang, Zhifei Tan, Wenqiang Xiao, Guangchao Du

“…Defects in their shaft tiles directly impact lubrication characteristics, thereby influencing the overall safety performance of the entire unit. To address this issue, this paper presents a fault diagnosis method for tilting pad thrust bearings using a modified multi-feature fused convolutional neural network (MMFCNN). …”
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LDAGM: prediction lncRNA-disease asociations by graph convolutional auto-encoder and multilayer perceptron based on multi-view heterogeneous networks by Bing Zhang, Haoyu Wang, Chao Ma, Hai Huang, Zhou Fang, Jiaxing Qu

“…Prediction of the lncRNA-disease association relationship is performed using the Multilayer Perceptron model. To enhance the performance and stability of the Multilayer Perceptron model, we introduce a hidden layer called the aggregation layer in the Multilayer Perceptron model. …”
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Assessment of MGMT promoter methylation status in glioblastoma using deep learning features from multi-sequence MRI of intratumoral and peritumoral regions by Xuan Yu, Jing Zhou, Yaping Wu, Yan Bai, Nan Meng, Qingxia Wu, Shuting Jin, Huanhuan Liu, Panlong Li, Meiyun Wang

“…The combined model of intratumoral and peritumoral regions exhibited superior diagnostic performance relative to individual models, achieving an AUC of 0.923 (95% confidence interval [CI]: 0.890 – 0.948) in stratified cross-validation, with sensitivity and specificity of 86.45% and 87.62%, respectively. …”
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A novel two-stage feature selection method based on random forest and improved genetic algorithm for enhancing classification in machine learning by Junyao Ding, Jianchao Du, Hejie Wang, Song Xiao

“…Abstract The data acquisition methods are becoming increasingly diverse and advanced, leading to higher data dimensions, blurred classification boundaries, and overfitting datasets, affecting machine learning models’ accuracy. Many studies have sought to improve model performance through feature selection. …”
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Stroke prediction in elderly patients with atrial fibrillation using machine learning combined clinical and left atrial appendage imaging phenotypic features by Hao Huang, Yan Xiong, Yuan Yao, Jie Zeng

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FIDC-YOLO: Improved YOLO for Detecting Pine Wilt Disease in UAV Remote Sensing Images via Feature Interaction and Dependency Capturing by Zekun Xu, Yipeng Zhou, Shiting Wen, Weipeng Jing

“…However, directly applying these generic detectors to detect PWD results in suboptimal performance due to insufficient utilization of local features. …”
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Smart crop disease monitoring system in IoT using optimization enabled deep residual network by Ashish Saini, Nasib Singh Gill, Preeti Gulia, Anoop Kumar Tiwari, Priti Maratha, Mohd Asif Shah

“…In the current study, a new model is developed to categorize plant diseases within an IoT network. …”
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A Novel Framework for Improving Soil Organic Carbon Mapping Accuracy by Mining Temporal Features of Time-Series Sentinel-1 Data by Zhibo Cui, Bifeng Hu, Songchao Chen, Nan Wang, Defang Luo, Jie Peng

“…Therefore, integrating the optimal monitoring period, feature selection, and deep learning model offers significant potential for enhancing the accuracy of digital SOC mapping.…”
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Prediction of methylphenidate treatment response for ADHD using conventional and radiomics T1 and DTI features: Secondary analysis of a randomized clinical trial by Mingshi Chen, Zarah van der Pal, Maarten G. Poirot, Anouk Schrantee, Marco Bottelier, Sandra J.J. Kooij, Henk A. Marquering, Liesbeth Reneman, Matthan W.A. Caan

“…At post-treatment, performance was markedly reduced. Conclusion: While conventional and radiomics models performed equally well in predicting clinical improvement across children and adults during treatment, radiomics features offered enhanced structural information beyond conventional region-based volume and FA averages in children. …”
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XTNSR: Xception-based transformer network for single image super resolution by Jagrati Talreja, Supavadee Aramvith, Takao Onoye

“…This paper presents a Deep Learning model for single-image super-resolution. In this paper, we present the XTNSR model, a novel multi-path network architecture that combines Local feature window transformers (LWFT) with Xception blocks for single-image super-resolution. …”
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A Multi-Scale Adaptive Fusion Network: End-to-End Interpretable Small-Sample Classifier for Motor Imagery EEG by Qiulei Han, Yan Sun, Ze Song, Hongbiao Ye, Tingwei Chen, Jian Zhao

“…Existing studies often struggle with feature extraction, dynamic feature selection, and temporal modeling, failing to capture critical EEG patterns effectively. …”
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Using partially shared radiomics features to simultaneously identify isocitrate dehydrogenase mutation status and epilepsy in glioma patients from MRI images by Yida Wang, Ankang Gao, Hongxi Yang, Jie Bai, Guohua Zhao, Huiting Zhang, Yang Song, Chenglong Wang, Yong Zhang, Jingliang Cheng, Guang Yang

“…We proposed an iterative approach derived from LASSO to select features shared by two tasks and features specific to each task, before using them to construct the final models. …”
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Design of upper limb muscle strength assessment system based on surface electromyography signals and joint motion by Siqi Wang, Wei Lai, Yipeng Zhang, Junyu Yao, Xingyue Gou, Hui Ye, Jun Yi, Dong Cao

“…The RF model, with its feature importance capabilities, provides valuable insights that can assist therapists in the muscle strength assessment process.…”
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Predicting the Toxicity of Drug Molecules with Selecting Effective Descriptors Using a Binary Ant Colony Optimization (BACO) Feature Selection Approach by Yuanyuan Dan, Junhao Ruan, Zhenghua Zhu, Hualong Yu

“…Only those high-frequency features are used to train a support vector machine (SVM) and construct the structure–activity relationship (SAR) prediction model. …”
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