Data-Driven Battery Remaining Life Prediction Based on ResNet with GA Optimization by Jixiang Zhou, Weijian Huang, Haiyan Dai, Chuang Wang, Yuhua Zhong

“…To this end, this paper proposes a data-driven lithium-ion battery life prediction method based on residual network (ResNet) and genetic algorithm (GA) optimization, which is designed to screen the features of the lithium-ion battery training data in order to effectively reduce the redundant features and improve the prediction performance of the model. …”
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M-NIG: mobile network information gain for EEG-based epileptic seizure prediction by Yuting Meng, Yi Liu, Guanglei Wang, Huipeng Song, Yiyu Zhang, Jianbo Lu, Peiluan Li, Xu Ma

“…In this paper, we present the mobile network information gain (M-NIG) method by transforming floating time series datasets into stable network information gain, which reduces the impact of data noise, thereby improving the robustness and effectiveness of the algorithm. The method not only efficiently predicts seizures but also detects their DNB channels. …”
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Near-Infrared Hyperspectral Target Tracking Based on Background Information and Spectral Position Prediction by Li Wu, Mengyuan Wang, Weixiang Zhong, Kunpeng Huang, Wenhao Jiang, Jia Li, Dong Zhao

“…In order to address the problems of in-plane rotation and fast motion during near-infrared (NIR) video target tracking, this study explores the application of capsule networks in NIR video and proposes a capsule network method based on background information and spectral position prediction. First, the history frame background information extraction module is proposed. …”
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SimpleMating: R‐package for prediction and optimization of breeding crosses using genomic selection by Marco Antônio Peixoto, Rodrigo Rampazo Amadeu, Leonardo Lopes Bhering, Luís Felipe V. Ferrão, Patrício R. Munoz, Márcio F. R. Resende Jr.

“…SimpleMating is a flexible and open‐source R package originally designed to predict and optimize breeding crosses in crops with different reproductive systems and breeding designs. …”
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An illustration of multi-class roc analysis for predicting internet addiction among university students. by Nishat Tasnim Thity, Atikur Rahman, Adisha Dulmini, Mst Nilufar Yasmin, Rumana Rois

“…We identified the important features related to IA using the Boruta algorithm. Predictions were made using different machine learning (ML) (decision tree (DT), random forest (RF), support vector machines (SVMs), and logistic regression (LR)) models. …”
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A Cross-Project Defect Prediction Model Based on Deep Learning With Self-Attention by Wanzhi Wen, Ruinian Zhang, Chuyue Wang, Chenqiang Shen, Meng Yu, Suchuan Zhang, Xinxin Gao

“…In particular, we provide semantic extractor named ALC to extract source code semantics based on source code files, and propose classification algorithm based on the semantic information of source project and target project, namely BSL, to build a prediction model. …”
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A nomogram to predict the risk of insulin resistance in Chinese women with polycystic ovary syndrome by Benjie Guo, Yuting Shen, Ziying Dai, Kalibinuer Yimamu, Jianhua Sun, Lixia Pei

“…To address this need, this study developed a predictive nomogram for assessing the risk of IR in women with PCOS, aiming to provide a tool for risk stratification and assist in clinical decision-making.MethodsPatients with untreated PCOS-IR diagnosed in a single-center retrospective cohort study from January 2023 to December 2023 were included for nomogram construction and validation. …”
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Comparison of Cuff Leak Test, Laryngeal Ultrasonography, and Videolaryngoscopy for the prediction of post-extubation stridor by Rubina Khullar Mahajan, Apoorva Gupta, Parshotam Lal Gautam, Gunchan Paul, Vikalp Khatri

“…This study was done to evaluate the sensitivity, specificity, predictive values, and diagnostic accuracy of the cuff leak test (CLT), laryngeal ultrasound (LUS), and videolaryngoscopy (VL) for the prediction of PES. …”
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Nanostring-based multigene assay to predict recurrence for gastric cancer patients after surgery. by Jeeyun Lee, Insuk Sohn, In-Gu Do, Kyoung-Mee Kim, Se Hoon Park, Joon Oh Park, Young Suk Park, Ho Yeong Lim, Tae Sung Sohn, Jae Moon Bae, Min Gew Choi, Do Hoon Lim, Byung Hoon Min, Joon Haeng Lee, Poong Lyul Rhee, Jae J Kim, Dong Il Choi, Iain Beehuat Tan, Kakoli Das, Patrick Tan, Sin Ho Jung, Won Ki Kang, Sung Kim

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Explosion resistance evaluation and damage prediction of middle partition walls in prefabricated frame tunnels by Zhen Huang, Yuzhu Zhou, Ziming Xiong, Hao Lu, Minqian Sun, Maojiang Qin

“…The damage level of the middle partition wall was predicted by employing the deflection-span ratio damage assessment criterion and machine learning. …”
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Unraveling the role of gut microbiome in predicting adverse events in neoadjuvant therapy for rectal cancer by Jingxin Ma, Shengbo Sun, Xin Cheng, Cong Meng, Hanzheng Zhao, Wentao Fu, Yan Gao, Liyan Ma, Zhengyang Yang, Hongwei Yao, Jianrong Su

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Optimizing electric vehicle energy consumption prediction through machine learning and ensemble approaches by Izhar Hussain, Kok Boon Ching, Chessda Uttraphan, Kim Gaik Tay, Adeeb Noor, Sufyan Ali Memon

“…Abstract Accurately predicting energy consumption in electric vehicles (EVs) is essential for enhancing energy efficiency and improving infrastructure planning. …”
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Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model. by Li Wang, Yu Zhang, Feng Li, Caiyun Li, Hongzeng Xu

“…Accurate mortality prediction of inpatient is crucial for clinical decision-making of non-ST-segment elevation myocardial infarction (NSTEMI) patients.…”
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Comparative Analysis of Resampling Techniques for Class Imbalance in Financial Distress Prediction Using XGBoost by Guodong Hou, Dong Ling Tong, Soung Yue Liew, Peng Yin Choo

“…This study examines eight resampling techniques for improving distress prediction using the XGBoost algorithm. The study was performed on a dataset acquired from the CSMAR database, containing 26,383 firm-quarter samples from 639 Chinese A-share listed companies (2007–2024), with only 12.1% of the cases being distressed. …”
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SGO enhanced random forest and extreme gradient boosting framework for heart disease prediction by Anima Naik, Ghanshyam G. Tejani, Seyed Jalaleddin Mousavirad

“…This study proposes a heart disease prediction (HDP) model employing Random Forest (RF) and eXtreme Gradient Boosting (XGB) classifiers. …”
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Rapid Damage Assessment and Bayesian-Based Debris Prediction for Building Clusters Against Earthquakes by Xiaowei Zheng, Yaozu Hou, Jie Cheng, Shuai Xu, Wenming Wang

“…Finally, with the structural response data of maximum floor displacement, a surrogate model for rapidly calculating seismic responses of structures is developed based on the XGBoost algorithm, achieving R² > 0.99 for floor displacements and R² = 0.989 for maximum inter-story drift ratio (MIDR) predictions. …”
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Artificial Intelligence-Based Prediction of Bloodstream Infections Using Standard Hematological and Biochemical Markers by Ferhat DEMİRCİ, Murat AKŞİT, Aylin DEMİRCİ

“…Basophil count, while ranked highest by SHAP, showed low sensitivity, highlighting the difference between algorithmic weight and bedside utility. Conclusion: These findings support the integration of routine, readily available laboratory data into an explainable AI framework to accurately predict culture positivity. …”
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Artificial intelligence models utilize lifestyle factors to predict dry eye related outcomes by Andrew D. Graham, Jiayun Wang, Tejasvi Kothapalli, Jennifer E. Ding, Helen Tasho, Alisa Molina, Vivien Tse, Sarah M. Chang, Stella X. Yu, Meng C. Lin

“…Abstract The purpose of this study is to examine and interpret machine learning models that predict dry eye (DE)-related clinical signs, subjective symptoms, and clinician diagnoses by heavily weighting lifestyle factors in the predictions. …”
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Building a machine learning-based risk prediction model for second-trimester miscarriage by Sangsang Qi, Shi Zheng, Mengdan Lu, Aner Chen, Yanbo Chen, Xianhu Fu

“…Currently, there is a scarcity of research on predictive models for the risk of second-trimester miscarriage. …”
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Prediction of Electrotactile Stimulus Threshold in Real Time Using Voltage Waveforms Between Electrodes by Vibol Yem, Yasushi Ikei, Hiroyuki Kajimoto

“…In this study, we explored four methods to predict the electrotactile sensation threshold across all five fingers. …”
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