The Influence of Implementing Process-Oriented Guided Inquiry Learning (POGIL) Model With Website Assistance To Improve Students' Logical Thinking by Alya Arthamevia Solehuddin, Munir, Rasim

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DeepMiRBP: a hybrid model for predicting microRNA-protein interactions based on transfer learning and cosine similarity by Sasan Azizian, Juan Cui

“…Advanced bioinformatics tools are urgently needed to facilitate this research. Methods We present DeepMiRBP, a novel hybrid deep learning model specifically designed to predict microRNA-binding proteins by modeling molecular interactions. …”
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A Lightweight TA-YOLOv8 Method for the Spot Weld Surface Anomaly Detection of Body in White by Weijie Liu, Miao Jia, Shuo Zhang, Siyu Zhu, Jin Qi, Jie Hu

“…In this paper, we propose a lightweight YOLOv8-based method for the quality inspection of car body welding spots. …”
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Automatic detecting multiple bone metastases in breast cancer using deep learning based on low-resolution bone scan images by Jialin Shi, Ruolin Zhang, Zongyao Yang, Zhixian Chen, Zhixin Hao, Li Huo, Ji Wu, Qiang Sun, Yali Xu

“…WBS images with breast cancer bone metastasis have the characteristics of low resolution, small foreground, and multiple lesions, hindering the widespread application of deep learning-based models. Automatically detecting a large number of densely small lesions on low-resolution WBS images remains a challenge. …”
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Novel Deepfake Image Detection with PV-ISM: Patch-Based Vision Transformer for Identifying Synthetic Media by Orkun Çınar, Yunus Doğan

“…Following extensive hyperparameter tuning, an accuracy of 96.60% was achieved, surpassing the performance of ResNet50 transfer learning approaches (93.32%) and other comparable methods reported in the literature. …”
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LRMAHpan: a novel tool for multi-allelic HLA presentation prediction using Resnet-based and LSTM-based neural networks by Xue Mi, Shaohao Li, Zheng Ye, Zhu Dai, Bo Ding, Bo Sun, Yang Shen, Yang Shen, Zhongdang Xiao, Zhongdang Xiao

“…We trained and tested the LRMAHpan BA (binding affinity) and the LRMAHpan AP (antigen processing) models using mass spectrometry data, subsequently combined them into the LRMAHpan PS (presentation score) model. Our approach is based on a novel pHLA encoding method that enables the integration of neoantigen prediction tasks into computer vision methods. …”
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A robust automated segmentation method for white matter hyperintensity of vascular-origin by Haoying He, Jiu Jiang, Sisi Peng, Chu He, Tianqi Sun, Fan Fan, Hao Song, Dong Sun, Zhipeng Xu, Shenjia Wu, Dongwei Lu, Junjian Zhang

“…The aims of this study are to develop and validate a robust deep learning segmentation method for WMH of vascular-origin. …”
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Exploring a learning-to-rank approach to enhance the Retrieval Augmented Generation (RAG)-based electronic medical records search engines by Cheng Ye

“…Background: This study addresses the challenge of enhancing Retrieval Augmented Generation (RAG) search engines for electronic medical records (EMR) by learning users' distinct search semantics. The specific aim is to develop a learning-to-rank system that improves the accuracy and relevance of search results to support RAG-based search engines. …”
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Option Pricing Based on Modular Neural Network by Moslem Peymany Foroushani, mohamad ali dehghan dehnavi, Milad Kouhkan

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A novel system applying artificial intelligence in the identification of air leak sitesCentral MessagePerspective by Yuka Kadomatsu, MD, PhD, Megumi Nakao, PhD, Harushi Ueno, MD, Shota Nakamura, MD, PhD, Toyofumi Fengshi Chen-Yoshikawa, MD, PhD

“…In the training phase, deep learning-based air leak detection software was developed using leak-positive endoscopic images. …”
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Composition Design and Property Prediction for AlCoCrCuFeNi High-Entropy Alloy Based on Machine Learning by Cuixia Liu, Meng Meng, Xian Luo

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Joint image reconstruction and segmentation of real-time cardiovascular magnetic resonance imaging in free-breathing using a model based on disentangled representation learning by Tobias Wech, Oliver Schad, Simon Sauer, Jonas Kleineisel, Nils Petri, Peter Nordbeck, Thorsten A. Bley, Bettina Baeßler, Bernhard Petritsch, Julius F. Heidenreich

“…Methods: A multi-tasking neural network architecture, incorporating disentangled representation learning, was trained using simulated examinations based on data from a public repository along with cardiovascular magnetic resonance (CMR) scans specifically acquired for model development. …”
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Automatic assessment of root numbers of vertical mandibular third molar using a deep learning model based on attention mechanism by SUN Chunsheng, DAI Xiubin, ZHOU Manting, JING Qiuping, ZHANG Chi, YANG Shengjun, WANG Dongmiao

“…Objective To develop a deep learning network based on attention mechanism to identify the number of the vertical mandibular third molar(MTM) roots(single or double) on panoramic radiographs in an automatic way. …”
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Uncovering subtype-specific metabolic signatures in breast cancer through multimodal integration, attention-based deep learning, and self-organizing maps by Parisa Shahnazari, Kaveh Kavousi, Hamid Reza Khorram Khorshid, Zarrin Minuchehr, Bahram Goliaei, Reza M. Salek

“…A feedforward attention-based deep learning model effectively selected 99 significant metabolites, outperforming traditional static methods in classification performance and biomarker consistency. …”
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Secure internet of medical things based electronic health records scheme in trust decentralized loop federated learning consensus blockchain by Megha Kuliha, Sunita Verma

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Machine Learning-Based Prediction Model for Predicting the Effect of the Serum γKlotho Level on Susceptibility to Coronary Heart Disease by Guo ZT, Yu XL, Cheng H, Naman T

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Understanding the Engagement and Interaction of Superusers and Regular Users in UK Respiratory Online Health Communities: Deep Learning–Based Sentiment Analysis by Xiancheng Li, Emanuela Vaghi, Gabriella Pasi, Neil S Coulson, Anna De Simoni, Marco Viviani

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Role of necroptosis and immune infiltration in essential thrombocytosis by Guangming Li, Ying Guo, Yuanyuan Zhang

“…GSE57793 and GSE26049 datasets were recruited to identify necroptosis differentially expressed genes based on differential gene identification, necroptosis gene sets and data machine learning. …”
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A few-shot learning-based dual-input neural network for complex spectrogram recognition system with millimeter-wave radar by Kaiyu Chen, Shaoxi Wang, Wei Li, Yucheng Wang, Cunqian Feng, Yannian Zhou, Jian Cao, Binfeng Zong, Minming Gu

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Identifying emphysema risk using brominated flame retardants exposure: a machine learning predictive model based on the SHAP methodology by Qihang Xie, Haoran Qu, Jianfeng Li, Rui Zeng, Wenhao Li, Rui Ouyang, Chengxiang Zhang, Siyu Xie, Siyu Xie, Ming Du

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