Research on Wind Turbine Unbalance Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network by Zhenling Li, Yukun Gao

“…Finally, a new data set is built in the highly fidelity simulation model, and the trained model is loaded for test and verification. …”
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Elevator fault precursor prediction based on improved LSTM-AE algorithm and TSO-VMD denoising technique. by Hao Cao, Xiaoyan Du

“…These performance metrics indicate a significant improvement over traditional and other deep learning models, confirming the model's superiority in predictive maintenance of elevators. …”
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Employing convolutional neural networks and explainable artificial intelligence for the detection of seizures from electroencephalogram signal by Tamilarasi Kathirvel Murugan, Anush Kameswaran

“…Model visualization and XAI approaches like feature importance analysis from SHAP values offer clear insights into the model's decision-making process. …”
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Deep hashing and attention mechanism-based image retrieval of osteosarcoma scans for diagnosis of bone cancer by Taisheng Zeng, Yuguang Ye, Yusi Chen, Daxin Zhu, Yifeng Huang, Ying Huang, Yijie Chen, Jianshe Shi, Bijiao Ding, Jianlong Huang, Mengde Ling

“…Image preprocessing includes adaptive histogram equalization and dataset augmentation to enhance quality and diversity. Feature extraction employs the WRN-AM model to map high-dimensional features to a low-dimensional hash code space, improving retrieval efficiency. …”
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Forecasting Hospitalization for Adult Asthma Patients in Emergency Departments Based on Multiple Environmental and Clinical Factors by Xi H, Zhang Y, Li W, Zhang C, Sun Y, Ji H, He Z, Chang C

“…After integrating ambient air pollutant and meteorological features, the RF model consistently outperformed the other models, achieving an AUC of 0.8555. …”
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Exploring the relationship between sepsis and Golgi apparatus dysfunction: bioinformatics insights and diagnostic marker discovery by Wanli Ma, Xinyi Liu, Ran Yu, Jiannan Song, Lina Hou, Ying Guo, Hongwei Wu, Dandan Feng, Qi Zhou, Haibo Li

“…A diagnostic model was developed using logistic regression, and its performance was validated using ROC curve analysis. …”
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Early Yield Prediction of Oilseed Rape Using UAV-Based Hyperspectral Imaging Combined with Machine Learning Algorithms by Hongyan Zhu, Chengzhi Lin, Zhihao Dong, Jun-Li Xu, Yong He

“…(ii) The ELM model (R_pre = 0.7674 and RMSE_P = 187.6 kg/hm²), using 14 combined VIs, showed excellent performance. …”
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Neuroevolutionary Convolutional Neural Network Design for Low-Resolution Face Recognition by Jhon I. Pilataxi, Juan P. Perez, Claudio A. Perez, Kevin W. Bowyer

“…The classifier and performance predictor are trained using the CNN architectures evaluated from previous generations, with the architecture encoding used as a feature vector. …”
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Prognostic factors associated with non-remission and low disease activity status af ter one year of psoriatic arthritis patients treatment in real practice by E. Yu. Loginova, T. V. Korotaeva, E. E. Gubar, Yu. L. Korsakova, S. I. Glukhova, E. L. Nasonov

“…By 1 year of therapy the proportion of pts who had not reached remission or LDA were calculated. The one-factor model of logistic regression was used to identify a group of features that are associated with remission or LDA  nonachievement. …”
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Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation by Zongqi Xia, Prerna Chikersal, Shruthi Venkatesh, Elizabeth Walker, Anind K Dey, Mayank Goel

“…Further, we examined the impact of incorporating behavioral context features and ecological momentary assessments on prediction performance. …”
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Amismart an advanced metering infrastructure for power consumption monitoring and forecasting in smart buildings by Sarah Hadri, Mehdi Najib, Mohamed Bakhouya, Youssef Fakhri, Mohamed El aroussi, Zaradatcht Taifour, Jaafar Gaber

“…In online mode, the proposed stacking model achieved the best forecasting performance with a sMAPE of 1.15%, followed by RF (1.22%), XGBoost (1.23%), CNN-LSTM (1.97%) and LSTM (2.20%). …”
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Accurate and lightweight oral cancer detection using SE-MobileViT on clinically validated image dataset by Md Firoz Kabir, Md Yousuf Ahmad, Roise Uddin, Martin Cordero, Shashi Kant

“…This study proposes an advanced deep learning-based diagnostic model, LightSE-MobileViT, specifically designed to classify oral cancer using medical imaging. …”
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D₂X: Depression Detection System Through X Using Hybrid Machine Learning by Thara Angskun, Suda Tipprasert, Atitthan Thippongtorn, Jitimon Angskun

“…The study showed that combining text with emoticons resulted in the highest performance. Additionally, the research revealed that the most crucial feature for predicting levels of depression is the text tweets. …”
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Predicting the interfacial tension of CO2 and NaCl aqueous solution with machine learning by Kashif Liaqat, Daniel J. Preston, Laura Schaefer

“…The linear regression model demonstrated the worst performance with a MAPE of 4.25% and an MAE of 1.7 mN/m. …”
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An integrated deep convolutional neural networks framework for the automatic segmentation and grading of glioma tumors using multimodal MRI scans by Otung John Peter Odong, Mohammed Abo-Zahhad, Moataz Abdelwahab

“…A thorough performance evaluation of CNN architectures based on pre-trained (ResNet50, EfficientNetB0, DensNet121, EfficientNetB2, and ResNet152V2) was conducted on a three-class dataset of glioma, meningioma, and pituitary tumors to determine the best model for tumor grading. …”
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Enhancing Frailty Assessments for Transcatheter Aortic Valve Replacement Patients Using Structured and Unstructured Data: Real-World Evidence Study by Mamoun T Mardini, Chen Bai, Anthony A Bavry, Ahmed Zaghloul, R David Anderson, Catherine E Crenshaw Price, Mohammad A Z Al-Ani

“…We also used least absolute shrinkage and selection operator regression for feature selection. Model performance was rigorously evaluated using nested cross-validation, ensuring the generalizability of the findings. …”
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A novel network-level fused deep learning architecture with shallow neural network classifier for gastrointestinal cancer classification from wireless capsule endoscopy images by Muhammad Attique Khan, Usama Shafiq, Ameer Hamza, Anwar M. Mirza, Jamel Baili, Dina Abdulaziz AlHammadi, Hee-Chan Cho, Byoungchol Chang

“…These features are classified using a Shallow Wide Neural Network (SWNN) and traditional classifiers. …”
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Application and Analysis of Random Forest and Support Vector Classification in Risk Prediction of Childhood Obesity and Hyperuricemia by Wang Y, Shi S, Wei X, Wu Y, Shi Y, Cai J

“…SHAP (Shapley Additive Explanations) analysis was conducted to interpret feature contributions.Results: Both models demonstrated strong predictive performance, with AUCs reaching 0.96. …”
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Multi-omic and machine learning analysis of mitochondrial RNA modification genes in lung adenocarcinoma for prognostic and therapeutic implications by Xiao Zhang, Jiatao Liu, Yaolin Cao, Wei Wang, Haoran Lin, Yue Yu

“…The MRMS demonstrates superior predictive performance for LUAD survival compared to previously reported models. …”
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A non-invasive wearable microwave sensor for Alzheimer's Stage differentiation using realistic 3D human head phantoms by Jinan N. Shehab, Malik Jasim Farhan, Saad Al-Azawi

“…The proposed system features a compact antenna design that achieves an optimal balance between gain and size, along with a realistic head phantom that enhances testing accuracy.While not yet validated for clinical deployment, its performance was evaluated using a vector network analyzer (VNA), and the results confirmed its high compatibility with biological tissues, demonstrating its effectiveness in accurately detecting Alzheimer's-related dielectric changes through microwave technology.…”
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