A novel and efficient personalized stress detection technique using a deep learning model by Ulligaddala Srinivasarao, Gopisetty Rathnamma, M. Satish Kumar, Lakshmipathi Anantha, Rakesh Kumar Donthi, T. Jhansi Rani

“…Several techniques were introduced to detect the stress level through text from social media using both machine learning (ML) and deep learning (DL) models. Several limitations occurred, such as higher training time, high time consumption, and limited features utilized to train the model. …”
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Estimating Subsurface Thermohaline Structure in the Tropical Western Pacific Using DO-ResNet Model by Xianmei Zhou, Shanliang Zhu, Wentao Jia, Hengkai Yao

“…Under the data conditions considered in this study, DO-ResNet demonstrates superior performance relative to the extreme gradient boosting model, random forest model, and artificial neural network model. …”
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Interpretable web-based machine learning model for predicting intravenous immunoglobulin resistance in Kawasaki disease by Ying He, Fan Lin, Xin Zheng, Qiaobin Chen, Meng Xiao, Xiaoting Lin, Hongbiao Huang

“…Thirteen ML algorithms were evaluated via cross-validation, with performance assessed by AUC and other metrics. Feature importance was determined using SHapley Additive exPlanations (SHAP), and risk of bias was evaluated using the Prediction Model Risk of Bias Assessment Tool. …”
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Dark Side of the Web: Dark Web Classification Based on TextCNN and Topic Modeling Weight by Gun-Yoon Shin, Younghoan Jang, Dong-Wook Kim, Sungjin Park, A-Ran Park, Younghwan Kim, Myung-Mook Han

“…To further improve classification performance, we integrated TextCNN with topic modeling weights. …”
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Object based Markov random field model for hierarchical semantic segmentation of remote sensing imagery by Jun Wang, Chen Zheng, Haoyu Fu, Yili Zhao, Qinling Dai, Xin Huang, Junfeng Xie, Leiguang Wang

“…In comparison to 15 state-of-the-art methods, our model achieved optimal performance in 9 instances and suboptimal performance in 2 instances.…”
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BiLSTM-Based Parallel CNN Models With Attention and Ensemble Mechanism for Twitter Sentiment Analysis by Anas W. Abulfaraj

“…Sentiment analysis has been found to have positive results when using deep learning. When used together, models like the Convolutional Neural Networks (CNN) and LSTM networks have significant high-performance results for text feature extraction and semantic relationship of the word. …”
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The response of hydraulic conductivity to air-trapped saturation in a dissolution process of trapped-air in quasi-saturated fine sands media by Donghui CHENG, Yingbo LAN, Jing YUAN, Lin XIANG, Xiaoting YANG, Xiaoying QIAO, Lin DENG, Zilin WANG, Qing WANG

“…At high air-trapped saturation, the performance of van Genuchten model is comparable to the traditional model. …”
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A Novel Extended Power-Lomax Distribution for Modeling Real-Life Data: Properties and Inference by Maha E. Qura, Mohammed Alqawba, Mashail M. Al Sobhi, Ahmed Z. Afify

“…One of the important features of generalized distribution is its ability and flexibility to model real-life data in several applied fields such as medicine, engineering, and survival analysis, among others. …”
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A machine learning approach for corrosion rate modeling in Patna water distribution network of Bihar by Saurabh Kumar, Uruya Weesakul, Divesh Ranjan Kumar, Pradeep Thangavel, Warit Wipulanusat, Jirapon Sunkpho

“…On the basis of the performance of the developed models, the proposed MARS model is the most accurate model, with R2 = 0.9872 for training and R2 = 0.9741 for the testing phase, followed by the GMDH and MPMR models. …”
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An interpretable machine learning model for predicting early liver metastasis after pancreatic cancer surgery by Hao Zhu, Yiyan Zhou, Danyang Shen, Kejia Wu, Xiaojie Gan, Xiaofeng Xue, Weigang Zhang, Xiaohua Yang, Junyi Qiu, Ding Sun

“…Out of 22 disease characteristics, nine key features were selected for the development of the model. …”
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The Optimal Design of an Inclined Porous Plate Wave Absorber Using an Artificial Neural Network Model by Senthil Kumar Natarajan, Seokkyu Cho, Il-Hyoung Cho

“…These features have been optimized to minimize the averaged reflection coefficient and the installation space (spatial footprint) with the application of a trained ANN model. …”
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Machine Learning-Based Pathomics Model to Predict the Prognosis in Clear Cell Renal Cell Carcinoma by Xiangyun Li MD, MS, Xiaoqun Yang MD, PhD, Xianwei Yang BSc, Xin Xie MD, PhD, Wenbin Rui MD, PhD, Hongchao He MD, PhD

“…The model's predictive performance was assessed through Kaplan-Meier (KM) survival curves and Cox regression analysis. …”
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Quantifying the Impact of Multiple Factors on Air Quality Model Simulation Biases Using Machine Learning by Chunying Fan, Ruilin Wang, Ge Song, Mengfan Teng, Maolin Zhang, Huangchuan Liu, Zhujun Li, Siwei Li, Jia Xing

“…When trained on WRF-CMAQ inputs, XGBoost still improved performance but revealed biases linked to both model inputs (10–60%) and mechanisms (1–30%). …”
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Risk factors associated with severe progression of Parkinson’s disease: random forest and logistic regression models by Jie Tan, E. Huang, Yang Hao, Hongping Wan, Qian Zhang

“…This study evaluates the performance of Random Forest (RF) and Logistic Regression (LR) models in forecasting the major risk factors associated with severe PD progression.MethodsWe performed a retrospective analysis of 378 PD patients (aged 40–75 years) with at 2 years of follow-up. …”
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Scale effects of physically based TOPKAPI model in reservoir inflow flood forecasting for ungauged basins by Yihua Sheng, Zhijia Li, Zhiyu Liu, Yalei Han, Jie Wang, Junfu Gong, Ning Xu

“…Then, intensive multi-scale modeling is conducted across resolutions from 2000 m to 100 m to understand how scale influences model performance. …”
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Random forest-based model for the recurrence prediction of borderline ovarian tumor: clinical development and validation by Liheng Yan, Qiulin Ye, Baole Shi, Juanjuan Liu, Yuexin Hu, Ouxuan Liu, Xiao Li, Bei Lin, Yue Qi

“…The calibration curve further illustrates the reliability of the model. Then, the optimal ML-based model determined the importance of features using SHAP values. …”
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Model-Based Electroencephalogram Instantaneous Frequency Tracking: Application in Automated Sleep–Wake Stage Classification by Masoud Nateghi, Mahdi Rahbar Alam, Hossein Amiri, Samaneh Nasiri, Reza Sameni

“…These transitions offer valuable insights into sleep quality and quantity, essential for diagnosing sleep disorders, designing effective interventions, enhancing overall health and well-being, and studying sleep’s effects on cognitive function, mood, and physical performance. This study presents a novel EEG feature extraction pipeline for the accurate classification of various wake and sleep stages. …”
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A non-invasive prediction model for coronary artery stenosis severity based on multimodal data by Jiyu Zhang, Jiatuo Xu, Liping Tu, Tao Jiang, Yu Wang, Jijie Xu

“…The integration of multimodal data and advanced architectural components significantly enhanced the model’s performance.ConclusionThis study developed a non-invasive, transformer-based multimodal prediction model for assessing coronary artery stenosis severity. …”
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Channel Coding QoS Analysis in 5G V2X Dynamic Propagation Models by Dimitrios Chatzoulis, Costas Chaikalis, Apostolos Xenakis, Dimitrios Kosmanos, Kostas E. Anagnostou

“…Our analysis introduces novel large-scale V2X fading models and ultimately proves that turbo-based coding schemes have superior performance for small-frame 5G V2X communication.…”
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Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model by Gang Wei, Yuqi Miao, Zhicheng Wang

“…Recent progress in foundational vision models, like the Segment Anything Model (SAM), has led to significant improvements in feature extraction. …”
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