A natural language processing approach to support biomedical data harmonization: Leveraging large language models. by Zexu Li, Suraj P Prabhu, Zachary T Popp, Shubhi S Jain, Vijetha Balakundi, Ting Fang Alvin Ang, Rhoda Au, Jinying Chen

“…LLM-derived features contributed most to RF's performance. One major cause of errors in automatic variable matching was ambiguous variable definitions.…”
Get full text

Evaluation of the precision and accuracy in the classification of breast histopathology images using the MobileNetV3 model by Kenneth DeVoe, Gary Takahashi, Ebrahim Tarshizi, Allan Sacker

“…Using MobileNetV3, a convolutional architecture designed to achieve high accuracy with a compact parameter footprint, we attempted to classify breast cancer images in the BreakHis_v1 breast pathology dataset to determine the performance of this model out-of-the-box. Using transfer learning to take advantage of ImageNet embeddings without special feature extraction, we were able to correctly classify histopathology images broadly as benign or malignant with 0.98 precision, 0.97 recall, and an F1 score of 0.98. …”
Get full text

Mamba-YOLO-ML: A State-Space Model-Based Approach for Mulberry Leaf Disease Detection by Chang Yuan, Shicheng Li, Ke Wang, Qinghua Liu, Wentao Li, Weiguo Zhao, Guangyou Guo, Lai Wei

“…The improved model achieved superior detection accuracy with 78.2% mAP50 and 59.9% mAP50:95, outperforming YOLO variants and comparable Transformer-based models, establishing new state-of-the-art performance. …”
Get full text

Using machine learning models based on cardiac magnetic resonance parameters to predict the prognostic in children with myocarditis by Dongliang Hu, Manman Cui, Xueke Zhang, Yuanyuan Wu, Yan Liu, Duchang Zhai, Wanliang Guo, Shenghong Ju, Guohua Fan, Wu Cai

“…The performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC). …”
Get full text

IoUT-Oriented an Efficient CNN Model for Modulation Schemes Recognition in Optical Wireless Communication Systems by M. Mokhtar Zayed, Saeed Mohsen, Abdullah Alghuried, Hassan Hijry, Mona Shokair

“…This paper explores the application of Convolutional Neural Networks (CNNs) for modulation recognition in the OWC systems, focusing specifically on 64-QAM (Quadrature Amplitude Modulation) and 32-PSK (Phase Shift Keying). A CNN model-based approach is proposed to automatically extract and classify modulation features from received signals, demonstrating superior performance compared to traditional recognition methods. …”
Get full text

Machine learning model for predicting tertiary lymphoid structures and treatment response in triple-negative breast cancer by Yidan Lin, Yushuai Yu, Qing Wang, Kaiyan Huang, Shukai Guo, Jie Zhang, Yihui He, Xin Yu, Jiwen Zhang, Fan Meng, Shicong Tang, Junhui Yuan, Chuangui Song

“…The rTLS predictive model demonstrated robust predictive performance, including across various patient subgroups. …”
Get full text

A Dual-Source Deep Learning Model for Mapping Wet Anabranches in Braided Rivers on the Tibetan Plateau by Xiaolu Liu, Xiaoyi Ma, Shuai Qin, Tang Liu, Chenghu Zhou

“…The model employs dual encoders to extract features from optical and radar data, combined with a unified decoder and an attention mechanism for efficient feature fusion. …”
Get full text

Large language models for efficient whole-organ MRI score-based reports and categorization in knee osteoarthritis by Yuxue Xie, Zhonghua Hu, Hongyue Tao, Yiwen Hu, Haoyu Liang, Xinmin Lu, Lei Wang, Xiangwen Li, Shuang Chen

“…Abstract Objectives To evaluate the performance of large language models (LLMs) in automatically generating whole-organ MRI score (WORMS)-based structured MRI reports and predicting osteoarthritis (OA) severity for the knee. …”
Get full text

Learning optimal image representations through noise injection for fine-grained search by Vidit Kumar, Vikas Tripathi, Bhaskar Pant, Manoj Diwakar, Prabhishek Singh, Anchit Bijalwan

“…Concurrently, noise injection in the features acts as regularization, facilitating the acquisition of generalized features and mitigating model overfitting. …”
Get full text

A Hybrid Machine Learning Approach for Detecting and Assessing <i>Zyginidia pullula</i> Damage in Maize Leaves by Havva Esra Bakbak, Caner Balım, Aydogan Savran

“…Furthermore, integrating multiple feature representations significantly enhances the model’s ability to differentiate between varying levels of pest damage. …”
Get full text

Differentiation of multiple adrenal adenoma subtypes based on a radiomics and clinico-radiological model: a dual-center study by Xinzhang Zhang, Yapeng Si, Xin Shi, Yiwen Zhang, Liuyang Yang, Junfeng Yang, Ye Zhang, Jinjun Leng, Pingping Hu, Hao Liu, Jiaqi Chen, Wenliang Li, Wei Song, Jianping Zhu, Maolin Yang, Wei Li, Junfeng Wang

“…This model exhibited superior predictive performance in differentiating between the three adrenal adenoma subtypes. …”
Get full text

PDGrad: Guiding Diffusion Model for Reference-Based Blind Face Restoration with Pivot Direction Gradient Guidance by Geon Min, Tae Bok Lee, Yong Seok Heo

“…Recently, guided diffusion models have demonstrated exceptional performance in this task without requiring training. …”
Get full text

Research on the Classification of Sun-Dried Wild Ginseng Based on an Improved ResNeXt50 Model by Dongming Li, Zhenkun Zhao, Yingying Yin, Chunxi Zhao

“…In contrast, deep learning methods can objectively learn the features of ginseng, saving both labor and time. This experiment proposes a ginseng-grade classification model based on an improved ResNeXt50 model. …”
Get full text

A Variational-Mode-Decomposition-Cascaded Long Short-Term Memory with Attention Model for VIX Prediction by Do-Hyeon Kim, Dong-Jun Kim, Sun-Yong Choi

“…VMD is employed as a preprocessing step to decompose financial time-series data into multiple Intrinsic Mode Functions (IMFs), effectively isolating short-term fluctuations from long-term trends. These decomposed features serve as inputs to a Cascaded LSTM model with an Attention mechanism, which enables the model to capture critical temporal dependencies, thereby improving forecasting performance. …”
Get full text

A Data Resource Trading Price Prediction Method Based on Improved LightGBM Ensemble Model by Wan Nie, Bingliang Shen, Desheng Li

“…The experimental results demonstrate that the proposed model achieves approximately a 10% improvement in predictive performance compared to conventional LightGBM models, with the coefficient of determination (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) exceeding 99%, reaching a high level of prediction accuracy. …”
Get full text

A novel ensemble model for fall detection: leveraging CNN and BiLSTM with channel and temporal attention by Sarita Sahni, Sweta Jain, Sri Khetwat Saritha

“…Meanwhile, the temporal attention module captures associations within the sensor data’s temporal dimension, allowing the model to focus on critical features and enhance performance. …”
Get full text

Enhancing Multi-Class Classification of Non-Functional Requirements Using a BERT-DBN Hybrid Model by Badzliana Aqmar Suris, Aris Thobirin, Sugiyarto Surono, Mohamed Naeem Antharathara Abdulnazar

“…The model is tested on the PROMISE dataset consisting of 625 data. …”
Get full text

Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data by Quan Zhou, Lihua Zhang, Nan Xiang, Lele Zhang, Fuqiang Ma, Fengchang Yu, Shenhui Lv, Zhilin Lu, He-Rong Mao

“…FT4, TPOAB and FT3 were validated as the top three features in the Gradient Boosting model.Conclusions This study innovatively developed a predictive model for benign and malignant TNs based on the Gradient Boosting Decision Tree algorithm. …”
Get full text

Advanced multi-label brain hemorrhage segmentation using an attention-based residual U-Net model by Xinxin Lin, Enmiao Zou, Wenci Chen, Xinxin Chen, Le Lin

“…Results The ResUNet model showed excellent performance during both training and testing. …”
Get full text

Development of Hybrid Optimization Model Using Grey-ANFIS-Jaya Algorithm for CNC Drilling of Aluminium Alloy by Katta Lakshmi Narasimhamu, Manikandan Natarajan, Pasupuleti Thejasree, Emad Makki, Jayant Giri, Neeraj Sunheriya, Rajkumar Chadge, Chetan Mahatme, Pallavi Giri, T. Sathish

“…Statistical error analysis is used to estimate the performance of the established optimization model. Based on the investigative outcomes, the best-suited process variable combinations will be used to provide improved and enhanced multiperformance characteristics.…”
Get full text