Characterization of immune microenvironment associated with medulloblastoma metastasis based on explainable machine learning by Fengmao Zhao, Xiangjun Liu, Jingang Gui, Hailang Sun, Nan Zhang, Yun Peng, Ming Ge, Wei Wang

“…Eight algorithms were evaluated, and the optimal model was selected. …”
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MoNetViT: an efficient fusion of CNN and transformer technologies for visual navigation assistance with multi query attention by Liliek Triyono, Liliek Triyono, Rahmat Gernowo, Prayitno

“…Experiments show MoNetViT outperforms other semantic segmentation algorithms in efficiency and effectiveness, particularly in detecting Aruco markers, making it a promising tool to improve navigation aids for the visually impaired.…”
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GaussianMix: Rethinking Receptive Field for Efficient Data Augmentation by A. F. M. Shahab Uddin, Maryam Qamar, Jueun Mun, Yuje Lee, Sung-Ho Bae

“…Mixed Sample Data Augmentation (MSDA) enhances deep learning model generalization by blending a source patch into a target image. …”
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MoCoUTRL: a momentum contrastive framework for unsupervised text representation learning by Ao Zou, Wenning Hao, Dawei Jin, Gang Chen, Feiyan Sun

“…This paper presents MoCoUTRL: a Momentum Contrastive Framework for Unsupervised Text Representation Learning. This model improves two aspects of recently popular contrastive learning algorithms in natural language processing (NLP). …”
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Application of Feedforward Artificial Neural Networks to Predict the Hydraulic State of a Water Distribution Network by Leandro Evangelista, Débora Móller, Bruno Brentan, Gustavo Meirelles

“…Improving the operational efficiency of water distribution networks (WDNs) is a subject that has been widely explored in the literature. …”
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Study of methods for extracting contours of objects on raster images of amber samples by Yu.O. Podchashynskyi, A.V. Ryzhuk

“…Effective methods and algorithms for detecting objects and their contours on raster images significantly improve the performance of TVS in applied tasks. …”
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Development and Training of a Neural Network Filter for Satellite Images Processing by N. Andriyanov, A. Kim

“…As part of the study, the architecture of the autoencoder was developed, optimal hyperparameters were selected and the resulting neural network model was trained. In addition, the paper compares the effectiveness of the proposed approach with traditional filtering algorithms such as Kalman filter and Wiener filter. …”
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Investigating the Role of Code Smells in Preventive Maintenance by Junaid Ali Reshi, Satwinder Singh

“…Further, different code smells, derived subjectively through iPlasma, are taken into conjugation and three efficient, but subjective models are developed to detect code smells on each of Random Forest, J48 and SVM machine learning algorithms. …”
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A Combined Approach Using T2*-Weighted Dynamic Susceptibility Contrast MRI Perfusion Parameters and Radiomics to Differentiate Between Radionecrosis and Glioma Progression: A Proof... by José Pablo Martínez Barbero, Francisco Javier Pérez García, David López Cornejo, Marta García Cerezo, Paula María Jiménez Gutiérrez, Luis Balderas, Miguel Lastra, Antonio Arauzo-Azofra, José M. Benítez, Antonio Jesús Láinez Ramos-Bossini

“…This study aimed to develop and evaluate a machine learning model that integrates radiomics features and T2*-weighted Dynamic Susceptibility Contrast MRI perfusion (DSC MRI) parameters to improve diagnostic accuracy in distinguishing these entities. …”
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Age and Saving Lives in Crisis Standards of Care: A Multicenter Cohort Study of Triage Score Prognostic Accuracy by Michael Hermsen, MD, MS, Patrick G. Lyons, MD, MS, Govind Persad, JD, PhD, Alice F. Bewley, MS, Chengsheng Mao, PhD, Kaveri Chhikara, MS, Anoop Mayampurath, PhD, Matthew Churpek, MD, PhD, Monica E. Peek, MD, MPH, MSc, Yuan Luo, PhD, William F. Parker, MD, PhD

“…Incorporating age into prioritization algorithms could save more lives in a crisis scenario.…”
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Pertinence of contact duration as edge feature for epidemic spread analysis by Ramya D. Shetty, Shrutilipi Bhattacharjee

“…Existing studies consider the edges mostly equally while designing the algorithms for the unweighted contact networks, where each connection explicitly shows whether the individuals are in contact or not. …”
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Dual-stage gated segmented multimodal emotion recognition method by MA Fei, LI Shuzhi, YANG Feixia, XU Guangxian

“…In the interaction stage, the OAGL fusion strategy was employed to model global-local cross-modal interactions, improving the efficiency of feature fusion. …”
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PROTAX-Sound: A probabilistic framework for automated animal sound identification. by Ulisses Moliterno de Camargo, Panu Somervuo, Otso Ovaskainen

“…PROTAX-Sound is based on a multinomial regression model, and it can utilize as predictors any kind of sound features or classifications produced by other existing algorithms. …”
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What factors enhance students' achievement? A machine learning and interpretable methods approach. by Hui Mao, Ribesh Khanal, ChengZhang Qu, HuaFeng Kong, TingYao Jiang

“…This study addresses these limitations by employing an ensemble of five machine learning algorithms (SVM, DT, ANN, RF, and XGBoost) to model multivariate relationships between four behavioral and six instructional predictors, using final exam performance as our outcome variable. …”
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Metric-Based Few-Shot Transfer Learning Approach for Voice Pathology Detection by Jong-Ho Won, Deok-Hwan Kim

“…The study employs a metric-based FSL approach to enhance model generalization with limited data and validates this method using both voice and electroglottographic (EGG) signals, thereby improving the diagnostic accuracy and clinical applicability. …”
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Advancements in Remote Sensing for Monitoring and Risk Assessment of Glacial Lake Outburst Floods by Serik Nurakynov, Nurmakhambet Sydyk, Zhaksybek Baygurin, Larissa Balakay

“…Central to this review is the exploration of multi-sensor data fusion for high-resolution lake dynamics mapping, machine learning algorithms for predictive risk modelling, and hydrodynamic simulations for flood propagation analysis. …”
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AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK by LAWRENCE OMOTOSHO, IBRAHIM OGUNDOYIN, OLAJIDE ADEBAYO, JOSHUA OYENIYI

“…Convolutional Neural Network is based on deep supervised learning model and was employed for training, classification, and testing of the system. …”
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Generalizable Solar Irradiance Prediction for Battery Operation Optimization in IoT-Based Microgrid Environments by Ray Colucci, Imad Mahgoub

“…Using satellite data from weather sensors, we trained machine learning models to enhance solar irradiance predictions. We evaluated five popular machine learning algorithms and applied ensemble methods, achieving a substantial improvement in predictive accuracy. …”
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Revolutionizing Supply Chain Management With AI: A Path to Efficiency and Sustainability by Kassem Danach, Ali El Dirani, Hassan Rkein

“…Additionally, a case study is provided to validate the proposed AI-driven strategies, demonstrating significant improvements in accuracy and operational performance. …”
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Preset Conditional Generative Adversarial Network for Massive MIMO Detection by Yongzhi Yu, Shiqi Zhang, Jiadong Shang, Ping Wang

“…The detection performance of NR-PC-GAN is far superior to the other algorithms in low-SNR scenarios.…”
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