Method for Determining Igneous Rock Mineral Content Using Element Logging Data Based on Variational AutoEncoder by JIA Ruilong, PAN Baozhi, WANG Qinghui, LI Yan, GUAN Yao, WANG Xinru

“…The results demonstrate the superiority of the proposed model over the typical algorithms while maintaining good applicability.…”
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Machine learning-based detection of DDoS attacks on IoT devices in multi-energy systems by Hesham A. Sakr, Mostafa M. Fouda, Ahmed F. Ashour, Ahmed Abdelhafeez, Magda I. El-Afifi, Mohamed Refaat Abdellah

“…This study aims to address these challenges by evaluating the effectiveness of various supervised machine learning (ML) algorithms in predicting DDoS attacks targeting EH systems through IoT devices. …”
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From molecules to data: the emerging impact of chemoinformatics in chemistry by Anup Basnet Chetry, Keisuke Ohto

“…Additionally, it outlines future challenges and opportunities, emphasizing the need for improved algorithms, data standardization, and interdisciplinary collaboration. …”
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Features of Breast Cancer Progression after Comprehensive Treatment, Prognosis of Metastatic Spread by Yu. Chuprovska, V. Bodiaka, O. Ivashchuk, Ch. Tsagkaris

“…Still, there are no clear criteria and algorithms for predicting the occurrence of this complication. …”
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Contrastive Disentangled Variational Autoencoder for Collaborative Filtering by Woo-Seong Yun, Seong-Min Kang, Yoon-Sik Cho

“…Recommender systems aim to accurately predict user preferences in order to provide potential items of interests. …”
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An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion by Kavita Rawat, Trapti Sharma

“…The model achieved promising results with high accuracy (98.54%) and sensitivity (97.77%) in predicting mental problems, including neurological and psychiatric conditions. …”
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Spatial Evaluation of <i>Salurnis marginella</i> Occurrence According to Climate Change Using Multiple Species Distribution Models by Jae-Woo Song, Jaho Seo, Wang-Hee Lee

“…This distribution currently covers approximately 9.53% of the global land area; however, the model predicted this distribution would decrease to 6.85%. …”
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Clinically validated graphical approaches identify hepatosplenic multimorbidity in individuals at risk of schistosomiasis by Yin-Cong Zhi, Simon Mpooya, Narcis B. Kabatereine, Betty Nabatte, Christopher K. Opio, Goylette F. Chami

“…Co-occurrence graphs were clinically uninformative with low predictive capacity. Graph learning algorithms with statistical assumptions, e.g. graphical lasso, enabled accurate and clinically valid multimorbidity representations. …”
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Financing Mechanisms and Preferences of Technology-Driven Small- and Medium-Sized Enterprises in the Digitalization Context by Jing Hu, Lianming Huang, Weifu Li, Hongyi Xu

“…Additionally, six machine learning (ML) algorithms were employed to predict financing preferences. …”
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Evaluating the performance of herd-specific long short-term memory models to identify automated health alerts associated with a ketosis diagnosis in early-lactation cows by N. Taechachokevivat, B. Kou, T. Zhang, M.E. Montes, J.P. Boerman, J.S. Doucette, R.C. Neves

“…Currently, various commercial systems offer built-in alert algorithms to identify cows requiring attention. To our knowledge, no work has been done to compare the use of models accounting for herd-level variability on their predictive ability against automated systems. …”
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Biomarker and clinical data–based predictor tool (MAUXI) for ultrafiltration failure and cardiovascular outcome in peritoneal dialysis patients: a retrospective and longitudinal st... by Eva María Arriero-País, María Auxiliadora Bajo-Rubio, Roberto Arrojo-García, Pilar Sandoval, Guadalupe Tirma González-Mateo, Patricia Albar-Vizcaíno, Gloria del Peso-Gilsanz, Marta Ossorio-González, Pedro Majano, Manuel López-Cabrera, Gloria del Peso-Gilsanz

“…Linear discriminant analysis (LDA) discerns among transfer to haemodialysis or death, predicts whether the cause of PD end is ultrafiltration failure (UFF) or cardiovascular disease (CVD) and anticipates the type of CVD (receiver operating characteristic curve under the area&gt;0.71).Discussion Our combination of longitudinal PD datasets, attribute shrinkage and gold-standard algorithms with overfitting testing and class imbalance ensures robust predictions in PD. …”
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Radiomics analysis of thoracic vertebral bone marrow microenvironment changes before bone metastasis of breast cancer based on chest CT by Hao-Nan Zhu, Yi-Fan Guo, YingMin Lin, Zhi-Chao Sun, Xi Zhu, YuanZhe Li

“…Multiple machine learning algorithms were utilized to construct various radiomics models for predicting the risk of bone metastasis, and the model with optimal performance was integrated with clinical features to develop a nomogram. …”
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MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models by Devlin C. Moyer, Justin Reimertz, Daniel Segrè, Juan I. Fuxman Bass

“…Abstract Genome-scale metabolic models (GSMMs) are used to predict metabolic fluxes, with applications ranging from identifying novel drug targets to engineering microbial metabolism. …”
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Machine learning approaches for modelling of molecular polarizability in gold nanoclusters by Abhishek Ojha, Satya S. Bulusu, Arup Banerjee

“…Our results demonstrate the efficacy of machine-learning in accurately predicting the polarizability of gold nanoclusters. …”
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Using random forests to forecast daily extreme sea level occurrences at the Baltic Coast by K. Bellinghausen, B. Hünicke, E. Zorita

“…<p>We have designed a machine learning method to predict the occurrence of daily extreme sea level at the Baltic Sea coast with lead times of a few days. …”
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A Monocyte-Driven Prognostic Model for Multiple Myeloma: Multi-Omics and Machine Learning Insights by Xie L, Gao M, Tan S, Zhou Y, Liu J, Wang L, Li X

“…Subsequently, based on 482 prognostic moDEGs, we developed and validated an optimal model, termed the Monocyte-related Gene Prognostic Signature (MGPS), by integrating 101 predictive models generated from 10 machine learning algorithms across multiple transcriptome sequencing datasets. …”
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Transcriptomics-based exploration of ubiquitination-related biomarkers and potential molecular mechanisms in laryngeal squamous cell carcinoma by Qiu Chen, Zhimin Wu, Yifei Ma

“…Meanwhile, the drugs garcinol, cocaine, and triazolam, among others, used for LSCC treatment were predicted. Finally, transcription factors (TFs) (BRD4, MYC, AR, and CTCF) were predicted to regulate the biomarkers. …”
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Simultaneous Instance and Attribute Selection for Noise Filtering by Yenny Villuendas-Rey, Claudia C. Tusell-Rey, Oscar Camacho-Nieto

“…Removing or reducing noise can help classification algorithms focus on relevant patterns, preventing them from being affected by irrelevant or incorrect information. …”
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Enhancing accuracy through ensemble based machine learning for intrusion detection and privacy preservation over the network of smart cities by Mudita Uppal, Yonis Gulzar, Deepali Gupta, Jayant Uppal, Mukesh Kumar, Shilpa Saini

“…The dataset utilized for anomaly-based detection techniques is KDDCup99 dataset, on which the different algorithms have been applied. The goal is to gain knowledge about data integrity and improve the predictive power of data. …”
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AI‐Driven TENGs for Self‐Powered Smart Sensors and Intelligent Devices by Aiswarya Baburaj, Syamini Jayadevan, Akshaya Kumar Aliyana, Naveen Kumar SK, George K Stylios

“…This review explores the synergistic potential of AI‐driven TENG systems, from optimizing materials and fabrication to embedding machine learning and deep learning algorithms for intelligent real‐time sensing. These advancements enable improved energy harvesting, predictive maintenance, and dynamic performance optimization, making TENGs more practical across industries. …”
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