Focusing on scRNA-seq-Derived T Cell-Associated Genes to Identify Prognostic Signature and Immune Microenvironment Status in Low-Grade Glioma by Jiayu Wen, Qiaoyi Huang, Jiuxiu Yao, Wei Wei, Zehui Li, Huiqin Zhang, Surui Chang, Hui Pei, Yu Cao, Hao Li

“…The ROC curve showed that the predictive ability of the prognostic model for 1, 3, and 5 years was 0.881, 0.817, and 0.749 in the TCGA cohort, respectively. …”
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ISLR as a Cuproptosis-Related Predictor and Therapeutic Target in Heart Failure: A Multi-Omics and Bioinformatics Approach by Huang K, Ding S, Xu X, Wang C, Han L

“…One hundred and three genes (related to Cuproptosis subtypes and HF phenotype) were put into machine learning algorithms and 6 predictive genes were filtered (HMOX2, MTSS1L, ISLR, GRB14, ARRDC3, and MEIS1). …”
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Methodological Integration of Machine Learning and Geospatial Analysis for PM10 Pollution Mapping by Kalid Hassen Yasin, Muaz Ismael Yasin, Anteneh Derribew Iguala, Tadele Bedo Gelete, Erana Kebede

“…The study contributes valuable insights for implementing scalable pollution prediction systems in resource-constrained urban environments while acknowledging interpretability challenges inherent to complex ML models. • Preprocessing of spatial data from various sources, incorporating the handling of missing/abnormal data, analysis, and normalization • Implementation of the three ML algorithms with rigorous hyperparameter tuning, model validation, and performance assessment • Mapping PM10 Hotspots on the Gradient Direction and Distance from the City Center…”
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An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change by J. D. Jakeman, M. Perego, D. T. Seidl, T. A. Hartland, T. R. Hillebrand, M. J. Hoffman, S. F. Price

“…The MFSE algorithms were able to effectively leverage the high correlation between each model's predictions of mass change, which all responded similarly to perturbations in the model inputs. …”
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Region-wise landmarks-based feature extraction employing SIFT, SURF, and ORB feature descriptors to recognize Monozygotic twins from 2D/3D Facial Images [version 2; peer review: 2... by Srikanth Prabhu, Vinod Nayak, Aparna Jayakala, Krishna Prakasha K, Gangothri Sanil

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Parallel Implicit Runge-Kutta Methods for Stiff ODEs by Bashir Khalaf, Abdulhabib Murshid

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An Optimised CNN Hardware Accelerator Applicable to IoT End Nodes for Disruptive Healthcare by Arfan Ghani, Akinyemi Aina, Chan Hwang See

“…Addressing the challenges posed by constrained dataset sizes, compute-intensive AI algorithms, and hardware limitations, the approach presented in this paper leverages efficient image augmentation and pre-processing techniques to enhance both prediction accuracy and the training efficiency. …”
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Research on RF Intensity Temperature Sensing based on 1D-CNN by DING Meiqi, GUI Lin, WANG Ziyi, SHANG Disen, QIAN Min, LI Qiankun

“…【Results】The experimental results show that the RMSE of the prediction model based on 1D-CNN reaches the order of 10^-3, while the RMSE of the traditional algorithms is usually in the order of 10^-1. …”
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Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP Techniques by Juswin Sajan John, Boppuru Rudra Prathap, Gyanesh Gupta, Jaivanth Melanaturu

“…The best performer of this comparative study was further evaluated against an LLM.The potential of large language models (LLMs), including their language understanding and prediction capabilities, is another key focus. We explore how these models could augment and advance mental health research, offering new perspectives and insights into the characterization of mental stress.Our findings show that the top model, an LSTM with TF-IDF and LDA (class weights assigned) outperformed the PaLM model with a coefficient of variation as low as 0.87% across all labels. …”
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Keypoints-Based Multi-Cue Feature Fusion Network (MF-Net) for Action Recognition of ADHD Children in TOVA Assessment by Wanyu Tang, Chao Shi, Yuanyuan Li, Zhonglan Tang, Gang Yang, Jing Zhang, Ling He

“…Existing video-based action recognition algorithms focus on object or interpersonal interactions, they may overlook ADHD-specific behaviors. …”
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Advancing soil mapping and management using geostatistics and integrated machine learning and remote sensing techniques: a synoptic review by Sunshine A. De Caires, Chaney St Martin, Melissa A. Atwell, Fuat Kaya, Glorious A. Wuddivira, Mark N. Wuddivira

“…Although advancements in variogram estimation and kriging techniques have optimized sampling strategies, and improved prediction accuracy, challenges persist in computational efficiency and uncertainty quantification. …”
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Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review by Jade Basem, Racheed Mani, Scott Sun, Kevin Gilotra, Neda Dianati-Maleki, Reza Dashti

“…We found numerous highly accurate and efficient AI/ML products that, when integrated, provided improved efficacy for disease prediction, identification, prognosis, and management within the sphere of stroke and neurocardiology. …”
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Research on the Performance Optimization of Turbulent Self-Noise Suppression and Sound Transmission of Acoustic Windows Made from Functionally Graded Material by Bing LI, Fu-Lin ZHOU, Jun FAN, Bin WANG, Liwen TAN

“…The STL, the turbulent self-noise reduction or a weighting function of the STL and turbulent self-noise reduction of FGM acoustic windows is chosen as the optimization objective. …”
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Design of Gain-Scheduled Lateral Controllers for Autonomous Vehicles by Ákos Bokor, Ádám Szabó, Szilárd Aradi, László Palkovics

“…This study evaluates their effectiveness in real-time navigation scenarios within a simulated environment by applying gain-scheduled linear quadratic regulators and model predictive control. The results show that while traditional controllers, such as Pure Pursuit, perform adequately under constant speed conditions, adaptive model-based algorithms significantly enhance the performance, especially in dynamic driving situations involving speed variations.…”
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A cross-stage features fusion network for building extraction from remote sensing images by Xiaolong Zuo, Zhenfeng Shao, Jiaming Wang, Xiao Huang, Yu Wang

“…To further improve the accuracy of building extraction, we designed the Prediction Enhancement (PE) module, where the last convolutional layer and the feature map generated in the intermediate stage are used for prediction at the same time to enhance the final result. …”
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Imaging-based machine learning to evaluate the severity of ischemic stroke in the middle cerebral artery territory by Gang Xie, Jin Gao, Jian Liu, Xuwei Zhou, Zhengkai Zhao, Wuli Tang, Yue Zhang, Lingfeng Zhang, Kang Li

“…This algorithm was then used to construct a imaging-based prediction model for stroke severity (severe and non-severe). …”
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Detection of the Pigment Distribution of Stacked Matcha During Processing Based on Hyperspectral Imaging Technology by Qinghai He, Zhiyuan Liu, Xiaoli Li, Yong He, Zhi Lin

“…Firstly, a quantitative relationship between HSI data of tea and their pigment contents was developed based on regression analysis, and the results showed that exceptional prediction performance was achieved by the partial least squares regression (PLSR) algorithm combined with the feature band algorithm of competitive adaptive reweighting (CARS), and the R_p² values of detection models of chlorophyll a, chlorophyll b and carotenoids were 0.90465, 0.92068 and 0.62666, respectively. …”
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Metabolic profile changes in patients with rheumatoid arthritis detected using mass spectrometry by Xue Wu, Qi Yang, Shanshan Liu, Peng Yang, Zhengqi Liu, Zhitu Zhu

“…However, the etiology of RA is still unclear, and novel biomarkers are demanded for the early prediction and diagnosis of RA and dissecting disease mechanisms. …”
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Design and validation of a novel multiple sites signal acquisition and analysis system based on pressure stimulation for human cardiovascular information by Gaiqin Liu, Yuan Li, Longcong Chen, Juan Jiang, Jie Tian, Panpan Feng

“…Furthermore, the results suggest that the system can facilitate in-depth research into the relationships between collected signals and CVDs, provide rich raw data for cardiovascular health assessment and disease prediction models based on machine learning algorithms, and offer a new non-invasive method for early diagnosis, evaluation, and prediction of CVDs.…”
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Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis by Chao Tan, Hao Liu, Zhen Zhang, Xinyu Liu, Yinquan Ai, Xiumin Wu, Enlin Jian, Yongyan Song, Jin Yang

“…AlphaFold2-related research received the highest citations, reflecting the transformative role of deep learning in protein structure prediction. Thematic clustering revealed key research foci, including deep learning algorithms, protein–protein interaction prediction, and integrative multi-omics analysis. …”
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