Progress and current trends in prediction models for the occurrence and prognosis of cancer and cancer-related complications: a bibliometric and visualization analysis by Siyu Li, Wenrui Li, Xiaoxiao Wang, Wanyi Chen

“…Emerging modeling techniques, such as neural networks and deep learning algorithms, are likely to play a pivotal role in current and future cancer-related prediction model research. …”
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Identifying Precipitation Types From Surface Meteorological Variables With Machine Learning by Adrienn VARGA-BALOGH, Ádám LEELŐSSY, Róbert MÉSZÁROS

“…Known precipitation types in the training set allowed us to fit classifying algorithms. We evaluated several classification models. …”
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Identification and validation of biomarkers associated with glycolysis in polycystic ovarian syndrome by Rongyan Zhu, Xiao Yu, Yulan Li

“…These findings suggest that TXNIP and TGFBI may serve as potential biomarkers associated with glycolytic dysregulation in PCOS, offering insights into the interplay between metabolic dysfunction and disease mechanisms.…”
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Estimating river water level using satellite altimetry and water surface temperature data in a regulated subtropical river by Qi Wang, Hailong Wang

“…Temperature retrieved for narrow rivers may contain mixed information of water and non-water surfaces, weakening the water level-temperature relationship. …”
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Temporal causal inference with stochastic audiovisual sequences. by Shannon M Locke, Michael S Landy

“…A simulation analysis revealed that the stimulus-generation algorithms of previous studies is likely responsible for the observed integration of temporally independent sequences. …”
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Physical education and sport activity assessment tool-based machine learning predictive analysis for planification of training sessions by Mohamed Rebbouj, Said Lotfi

“…Conclusions Incorporating the Machine Learning techniques may encourage the change in the way we teach physical education and sport activities; otherwise, the assessment based on ML techniques will give a different overview on how to start a learning cycle and follow it up. …”
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Sex hormones correlate with heart rate variability in healthy women and this correlation is conserved in women with well-controlled type 2 diabetes mellitus. by Adriana Robles-Cabrera, Claudia Lerma, Silvia Ruiz-Velasco Acosta, Iván Pérez-Díaz, Ruben Fossion

“…However, in regression models adjusted for age and glucose levels, only estradiol was found to be significant, and should be considered an important variable related to cardiovascular and autonomic balance in T2DM women and may provide crucial information to improve cardiovascular risk algorithms.…”
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Freedom of Speech and Expression in the Digital Age: Philosophical Analysis and Social Security Challenges by Tatiana Yu. Lifanova, Sergey A. Lifanov, Alexey V. Verevkin

“…The results of the study show that traditional legal measures to regulate information space are not always effective and may contradict the principle of individual autonomy. …”
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Optimization of multi-objective feature regression models for designing performance assessment methods in college and university educational reform by Fengjun Qi, Zhenping Liu, Wenzheng Zhang, Zhenjie Sun

“…However, conventional regression models often rely on a shared input space for all targets, neglecting the fact that distinct and complex feature sets may influence each target. This study introduces a novel Multi-Objective Feature Regression model under Label-Specific Features (MOFR-LSF), which integrates target-specific features and inter-target correlations to address this limitation. …”
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Optimization of Traffic Flow by Allowing Private Cars to Merge into the Bus Lane at Specific Locations by Zhentao Yin, Kai Yuan, Shenshen Zheng

“…Previous work focuses on allowing private cars to use bus lanes which may cause bus delays. To strike a balance, some approaches involve mandating private cars to leave bus lanes and limiting the number of private cars entering bus lanes. …”
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The evolutionary analysis of emerging low frequency HIV-1 CXCR4 using variants through time--an ultra-deep approach. by John Archer, Andrew Rambaut, Bruce E Taillon, P Richard Harrigan, Marilyn Lewis, David L Robertson

“…Then, in conjunction with coreceptor prediction algorithms that infer HIV tropism, our software was used to quantify the viral population structure pre- and post-treatment. …”
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Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures by Yiqun Chen, Mingxuan Ma, Dandan Qu, Chunxiang Xu

“…By adopting appropriate health management practices, the risk of developing postoperative pneumonia in this patient population may be reduced.…”
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Integrative multi-omics analysis and machine learning reveal the unique role of ASCC3 in combination with various immune-related genes in rectal adenocarcinoma by Chuang Miao, Fei Qian

“…Prognostic models were constructed using ssGSEA analysis and various machine learning algorithms. We explored the signaling pathways regulated by ASCC3 using the CancerSEA and TCPA databases. …”
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Debris Flow Susceptibility Prediction Using Transfer Learning: A Case Study in Western Sichuan, China by Tiezhu Li, Qidi Huang, Qigang Chen

“…In this study, debris flow susceptibility models were developed using three machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). …”
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Computational identification of phospho-tyrosine sub-networks related to acanthocyte generation in neuroacanthocytosis. by Lucia De Franceschi, Giovanni Scardoni, Carlo Tomelleri, Adrian Danek, Ruth H Walker, Hans H Jung, Benedikt Bader, Sara Mazzucco, Maria Teresa Dotti, Angela Siciliano, Antonella Pantaleo, Carlo Laudanna

“…Since RBCs are easily accessible, the study of acanthocytes in NA may provide insights into potential mechanisms of neurodegeneration. …”
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Assessing the Effects of Climate Change and Land‐Use Changes on Extreme Discharge in the Western Watershed of Lake Urmia, Iran by Ghasem Farahmand, Behzad Hessari, Hossein Salehi

“…Employing machine learning algorithms (e.g., SVM), stochastic models (e.g., CA‐MARKOV), ERA5 reanalysis climate data, and the large‐scale hydrological VIC model, we assessed these effects across multiple sub‐basins. …”
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Enhancing Streamflow Prediction in Ungauged Basins Using a Nonlinear Knowledge‐Based Framework and Deep Learning by Parnian Ghaneei, Ehsan Foroumandi, Hamid Moradkhani

“…The results reveal that the combination of clustering algorithms with the PCA method may lead to loss of information while the nonlinear method (UMAP) can extract more informative features. …”
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Quantum annealing feature selection on light-weight medical image datasets by Merlin A. Nau, Luca A. Nutricati, Bruno Camino, Paul A. Warburton, Andreas K. Maier

“…Abstract We investigate the use of quantum computing algorithms on real quantum hardware to tackle the computationally intensive task of feature selection for light-weight medical image datasets. …”
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Impact of EU Laws on AI Adoption in Smart Grids: A Review of Regulatory Barriers, Technological Challenges, and Stakeholder Benefits by Bo Nørregaard Jørgensen, Saraswathy Shamini Gunasekaran, Zheng Grace Ma

“…The analysis delves into regulatory barriers such as data protection requirements, algorithmic transparency mandates, and liability concerns that can limit the scope and scale of AI deployment. …”
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Self-calibrated acceleration and detail preserving for semantic segmentation of lactating sows and piglets under low-light conditions by Aqing Yang, Yueju Xue, Na Han, Jiabi Zheng, Lei Zhang, Yizhi Luo

“…However, semantic segmentation algorithms do not yield satisfactory results under low-light conditions because these methods are highly dependent on image quality. …”
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