Unraveling Cordia myxa’s anti-malarial potential: integrative insights from network pharmacology, molecular modeling, and machine learning by Yufei Miao, Wenkang Liu, Sarah Mohammed Saeed Alsallameh, Norah A. Albekairi, Ziyad Tariq Muhseen, Christopher J. Butch

“…This study establishes a groundwork for comprehending the function of the anti-malaria action of C. myxa.…”
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Optimizing droplet coalescence dynamics in microchannels: A comprehensive study using response surface methodology and machine learning algorithms by Seyed Morteza Javadpour, Erfan Kadivar, Zienab Heidary Zarneh, Ebrahim Kadivar, Mohammad Gheibi

“…The comparison of different machine learning algorithms indicates that the best ones for predicting DD, VFD, and VBD are function, SMOreg, Lazy-IBK, and Meta-Bagging, respectively.…”
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FldtMatch: Improving Unbalanced Data Classification via Deep Semi-Supervised Learning with Self-Adaptive Dynamic Threshold by Xin Wu, Jingjing Xu, Kuan Li, Jianping Yin, Jian Xiong

“…SDT utilizes a clever mapping function that can solve the problem of differential learning difficulty of various categories in an unbalanced image dataset that adversely affects dynamic thresholding. …”
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Local Back-Propagation for Forward-Forward Networks: Independent Unsupervised Layer-Wise Training by Taewook Hwang, Hyein Seo, Sangkeun Jung

“…To overcome these challenges, we propose Local Back-Propagation (LBP), a method that integrates layer-wise unsupervised learning with standard inputs and conventional loss functions. …”
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Stable coding of aversive associations in medial prefrontal populations by Herry, Cyril, Jercog, Daniel

“…Our data indicated that the presence of threat-predicting cues induces a stable coding dynamics of internally driven representations in the dorsal mPFC, necessary to drive learned defensive behaviours. Moreover, these neural population representations primary reflect learned associations rather than specific defensive behaviours, and the construct of such representations relies on the functional integrity of the amygdala.…”
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CAREC: Continual Wireless Action Recognition with Expansion–Compression Coordination by Tingting Zhang, Qunhang Fu, Han Ding, Ge Wang, Fei Wang

“…In real-world applications, user demands for new functionalities and activities constantly evolve, requiring action recognition systems to incrementally incorporate new action classes without retraining from scratch. …”
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Empirical analysis of control models for different converter topologies from a statistical perspective by Hole Shreyas Rajendra

“…It was observed that bioinspired models and incremental learning techniques assists in improving control performance for efficiency-aware use cases. …”
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Can artificial intelligence improve the diagnosis and prognosis of disorders of consciousness? A scoping review by Mirjam Bonanno, Mirjam Bonanno, Davide Cardile, Davide Cardile, Piergiuseppe Liuzzi, Piergiuseppe Liuzzi, Antonio Celesti, Giuseppe Micali, Francesco Corallo, Angelo Quartarone, Angelo Quartarone, Francesco Tomaiuolo, Rocco Salvatore Calabrò

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Development and validation of a machine learning model based on multiple kernel for predicting the recurrence risk of Budd-Chiari syndrome by Weirong Xue, Bing Xu, Hui Wang, Xiaoxiao Zhu, Jiajia Qin, Guangshuang Zhou, Peilin Yu, Shengli Li, Yingliang Jin

“…This study aims to develop a novel machine learning model based on multiple kernel learning to improve the prediction of 3-year recurrence in BCS patients.MethodsData were collected from BCS patients admitted to the Affiliated Hospital of Xuzhou Medical University between January 2015 and July 2022. …”
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Machine Learning-Based Non-Invasive Prediction of Metabolic Dysfunction-Associated Steatohepatitis in Obese Patients: A Retrospective Study by Jie Chen, Bo Zhang, Yong Cheng, Yuanchen Jia, Biao Zhou

“…<b>Objectives</b>: We aimed to develop and validate machine learning (ML) models that integrate clinical and laboratory data for the non-invasive prediction of metabolic dysfunction-associated steatohepatitis (MASH) in an obese population. …”
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A Machine-Learning-Based Ocean-Current Velocity Inversion Model Using OCN From Sentinel-1 Observations by Yang Bai, Yubin Zhang, Xudong Zhang, Xiaofeng Li

“…Built on a fully connected neural network, the OCN-CIM features a custom loss function focused on high ocean-current velocities. …”
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Machine learning models for reinjury risk prediction using cardiopulmonary exercise testing (CPET) data: optimizing athlete recovery by Arezoo Abasi, Ahmad Nazari, Azar Moezy, Seyed Ali Fatemi Aghda

“…Abstract Background Cardiopulmonary Exercise Testing (CPET) provides detailed insights into athletes’ cardiovascular and pulmonary function, making it a valuable tool in assessing recovery and injury risks. …”
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Machine learning-driven integration of time-series InSAR and multiple surface factors for landslide identification and susceptibility assessment by Qianyu Wang, Wen Zhang, Jinglin Li, Ziyang Li, Zhi Luo, Qiang Zhao, Jianbo Jian, Fang Shangguan, Yuanxing Yang, Yangyang Ma, Zhen Zhang, Shuangming Zhao, Linyi Li, Lingkui Meng

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Application of the U-Net Deep Learning Model for Segmenting Single-Photon Emission Computed Tomography Myocardial Perfusion Images by Ahmad Alenezi, Ali Mayya, Mahdi Alajmi, Wegdan Almutairi, Dana Alaradah, Hamad Alhamad

“…Methods: In this study, a deep learning (DL) algorithm, U-Net, was employed to enhance segmentation accuracy for image segmentation in MPI. …”
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Research Progress of Intelligent Evaluation and Virtual Reality Based Training in Upper Limb Rehabilitation afrer Stroke by XIE Qiurong, LIN Wanqi, ZHANG Qi, SHENG Bo, ZHANG Yanxin, HUANG Jia

“…Automated assessment of upper extremity motor function based on machine learning algorithms with markerless sensing techniques has focused on the Fugl-Meyer assessment of upper extremity (FMA-UE), Brunnstrom stages, and Wolf motor function test (WMFT) scales and has been proved with high-scoring accuracy and time efficiency. …”
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Aminooxyacetic acid ameliorates alcohol-induced learning and memory deficits through BDNF-TrkB pathway and calcium homeostasis by Zhen Sun, Meinan Nie, Xunling Wang, Bolin Jiao, Jiayi Fu, Ailin Du

“…However, its potential in maintaining learning and memory functions by regulating Ca2+ and mitochondrial functional status remains uncertain. …”
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Development and validation of a biomarker-based prediction model for metastasis in patients with colorectal cancer: Application of machine learning algorithms by Erfan Ayubi, Sajjad Farashi, Leili Tapak, Saeid Afshar

“…Subsequently, the prediction model was developed and internally validated using five machine learning (ML) algorithms including lasso and elastic-net regularized generalized linear model (glmnet), k-nearest neighbors (kNN), support vector machine (SVM) with Radial Basis Function Kernel, random forest (RF), and eXtreme Gradient Boosting (XGBoost). …”
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Longitudinal markers of cognitive procedural learning in fronto-striatal circuits and putative effects of a BDNF plasticity-related variant by Lena S. Geiger, Torsten Wüstenberg, Zhenxiang Zang, Mirjam Melzer, Stephanie H. Witt, Marcella Rietschel, Markus M. Nöthen, Stefan Herms, Franziska Degenhardt, Andreas Meyer-Lindenberg, Carolin Moessnang

“…We used linear and exponential modeling to characterize procedural learning by means of learning curves on the behavioral and brain functional level. …”
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Developing a machine learning-based predictive model for levothyroxine dosage estimation in hypothyroid patients: a retrospective study by Tran Thi Ngan, Tran Thi Ngan, Dang Huong Tra, Ngo Thi Quynh Mai, Hoang Van Dung, Nguyen Van Khai, Pham Van Linh, Nguyen Thi Thu Phuong, Nguyen Thi Thu Phuong

“…The findings underscore the potential of machine learning in refining LT4 dose estimation by incorporating diverse clinical factors beyond traditional weight-based approaches. …”
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Protein interactions, network pharmacology, and machine learning work together to predict genes linked to mitochondrial dysfunction in hypertrophic cardiomyopathy by Jia-lin Chen, Di Xiao, Yi-jiang Liu, Zhan Wang, Zhi-huang Chen, Rui Li, Li Li, Rong-hai He, Shu-yan Jiang, Xin Chen, Lin-xi Xu, Feng-chun Lu, Jia-mao Wang, Zhong-gui Shan

“…We employed six machine learning techniques and two protein–protein interaction (PPI) network gene selection approaches to search for the most characteristic gene (MCG). …”
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