Machine Learning-Based Interpretable Screening for Osteoporosis in Tuberculosis Spondylitis Patients Using Blood Test Data: Development and External Validation of a Novel Web-Based... by Yasin P, Ding L, Mamat M, Guo W, Song X

“…SHAP analysis highlighted the critical roles of HB, eGFR, and CYS_C in osteoporosis prediction. The developed web application facilitates the model’s integration into clinical workflows, enabling healthcare professionals to make informed decisions at the bedside.Conclusion: This study successfully developed and validated an ML-based tool for predicting osteoporosis in TS patients using readily available clinical data. …”
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Theoretical study of the influence of a variable ground resistance by position tracked chassis in motion by Goncharov K.A.

“…The examples show procedures for mathematical prediction of the physical and geometric characteristics of the movement of a tracked vehicle, the use of which in software algorithms of posi-tion control systems can help improve the accuracy of their operation.…”
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Comprehensive multi-omics integration uncovers mitochondrial gene signatures for prognosis and personalized therapy in lung adenocarcinoma by Wenjia Zhang, Lei Zhao, Tiansheng Zheng, Lihong Fan, Kai Wang, Guoshu Li

“…The AIDPS model demonstrated robust predictive power for LUAD patient outcomes, revealing significant differences in responses to immunotherapy and chemotherapy, as well as survival outcomes between risk groups. …”
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Track Classification and Characteristics Analysis of Northeast China Cold Vortex During the Warm Season by Jin Tong, Yueming Yu, Qiuping Wang, Xulin Ma, Zhaorong Zhuang

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Enhancing Neurodegenerative Disease Diagnosis Through Confidence-Driven Dynamic Spatio-Temporal Convolutional Network by Ning Yuan, Donghai Guan, Shengrong Li, Li Zhang, Qi Zhu

“…Second, each window generates an output probability, which quantifies prediction confidence based on the true class probability (TCP). …”
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Integrated GBR–NSGA-II Optimization Framework for Sustainable Utilization of Steel Slag in Road Base Layers by Merve Akbas

“…Among these, GBR demonstrated superior predictive performance (R² > 0.95, RMSE < 7.5), effectively capturing complex nonlinear interactions inherent in slag processing and logistics operations. …”
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Artificial intelligence in nursing: an integrative review of clinical and operational impacts by Salwa Hassanein, Salwa Hassanein, Rabie Adel El Arab, Rabie Adel El Arab, Amany Abdrbo, Mohammad S. Abu-Mahfouz, Mastoura Khames Farag Gaballah, Mohamed Mahmoud Seweid, Mohamed Mahmoud Seweid, Mohammed Almari, Husam Alzghoul

“…Operationally, AI-based automation of routine tasks (e.g., scheduling, administrative documentation, and predictive workload classification) has streamlined resource allocation. …”
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Construction of the miRNA/Pyroptosis-Related Molecular Regulatory Axis in Abdominal Aortic Aneurysm: Evidence From Transcriptome Data Combined With Multiple Machine Learning Approa... by Yongchao Su, Chuangang Lu, Shuchen Chen

“…Conclusion: A predictive model (PRG classifier) incorporating eight PRGs through multiple machine learning algorithms was developed and validated. …”
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Development of standard fuel models in boreal forests of Northeast China through calibration and validation. by Longyan Cai, Hong S He, Zhiwei Wu, Benard L Lewis, Yu Liang

“…Understanding the fire prediction capabilities of fuel models is vital to forest fire management. …”
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Explainable post hoc portfolio management financial policy of a Deep Reinforcement Learning agent. by Alejandra de-la-Rica-Escudero, Eduardo C Garrido-Merchán, María Coronado-Vaca

“…In this work, driven by the motivation of making DRL explainable, we developed a novel Explainable DRL (XDRL) approach for PM, integrating the Proximal Policy Optimization (PPO) DRL algorithm with the model agnostic explainable machine learning techniques of feature importance, SHAP and LIME to enhance transparency in prediction time. …”
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Clinical interpretation of variants identified in RNU4ATAC, a non-coding spliceosomal gene. by Clara Benoit-Pilven, Alicia Besson, Audrey Putoux, Claire Benetollo, Clément Saccaro, Justine Guguin, Gabriel Sala, Audric Cologne, Marion Delous, Gaetan Lesca, Richard A Padgett, Anne-Louise Leutenegger, Vincent Lacroix, Patrick Edery, Sylvie Mazoyer

“…As RNU4ATAC has a single non-coding exon, the bioinformatic prediction algorithms assessing the effect of sequence variants on splicing or protein function are irrelevant, which makes variant interpretation challenging to molecular diagnostic laboratories. …”
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An Analysis of Semi-Supervised Machine Learning in Electrical Machines by V. Raju Arvind, S. Shyamsharan, Poorvajaa Gurunathan, Krishna Kumba, Nawin Ra

“…The research investigates important SSML algorithms such as self-training, co-training, generative models, and graph-based methods, highlighting their particular uses in fault diagnosis, condition monitoring, and predictive maintenance of electrical machines. …”
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Empirical Evaluation on GPU, Overclocking, and LoRA for Deep Learning on Embedded Systems by Evandro Raphaloski, Mariana Caravanti De Souza, Edson Takashi Matsubara

“…Regarding LoRA, we observed an average reduction in training time of 70% in Jetson Nano, but an average accuracy loss ranging from 21% to 40% in all models. …”
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Study on debris flow vulnerability of ensemble learning model based on spy technology A case study of upper Minjiang river basin by Yutao Chen, Ning Li, Fucheng Xing, Han Xiang, Zilong Chen

“…Since it is challenging to predict debris flows with precision using traditional methods, machine learning algorithms have been used more and more in this field in recent years. …”
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Exercise-related immune gene signature for hepatocellular carcinoma: machine learning and multi-omics analysis by Cheng Pu, Lei Pu, Xiaoyan Zhang, Qian He, Jiacheng Zhou, Jianyue Li

“…However, its prognostic value in hepatocellular carcinoma (HCC) remains largely unknown.ObjectiveThis study aims to construct a machine learning-based prognostic signature using exercise-related immune genes (EIGs) to predict prognosis in HCC.MethodsWe obtained mRNA-seq and scRNA of HCC from GeneCards, GEO, TCGA and ICGC. …”
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Metabolic pathway activation and immune microenvironment features in non-small cell lung cancer: insights from single-cell transcriptomics by Yanru Liu, Yanru Liu, Yanru Liu, Hanmin Liu, Hanmin Liu, Ying Xiong, Ying Xiong

“…Given that lung cancer is a leading cause of cancer-related deaths globally and NSCLC accounts for the majority of lung cancer cases, understanding the relationship between TME and metabolic pathways in NSCLC is crucial for developing new treatment strategies.MethodsFinally, machine learning algorithms were employed to construct a risk signature with strong predictive power across multiple independent cohorts. …”
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Machine Learning Classification of First-Onset Drug-Naive MDD Using Structural MRI by Donghwa Kim, Pilsung Kang, Junhong Kim, Czang Yeob Kim, Jong-Ha Lee, Sangil Suh, Moon-Soo Lee

“…We aimed to determine whether it is possible to reliably train a highly accurate predictive classification algorithm, even with the first onset of drug-naive adolescent MDD, solely using structural magnetic resonance imaging and without using any other clinical data from the patients. …”
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Theoretical and computational investigations on estimation of viscosity of ionic liquids for green adsorbent: Effect of temperature and composition by Zhaoxiong Han

“…The models are optimized using the Whale Optimization Algorithm (WOA) to fine-tune hyperparameters, enhancing their predictive performance. …”
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A computational approach and software package RNAexploreR for grouping RNA molecules of human genes by exon features by M. M. Yatskou, V. V. Skakun, V. V. Grinev

“…A certain part of the research is aimed at developing reliable prediction models for global exon combinatorics during the formation of mature RNA. …”
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Automatic screening for posttraumatic stress disorder in early adolescents following the Ya’an earthquake using text mining techniques by Yuzhuo Yuan, Yuzhuo Yuan, Zhiyuan Liu, Wei Miao, Xuetao Tian

“…Text classification models were constructed using three supervised learning algorithms (BERT, SVM, and KNN) to identify PTSD symptoms and their corresponding behavioral indicators in each sentence of the self-narratives.ResultsThe prediction accuracy for symptom-level classification reached 73.2%, and 67.2% for behavioral indicator classification, with the BERT performing the best.ConclusionsThese findings demonstrate that self-narratives combined with text mining techniques provide a promising approach for automated, rapid, and accurate PTSD screening. …”
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