Application of deep learning models in gastric cancer pathology image analysis: a systematic scoping review by Sijun Xia, Yuanze Xia, Ting Liu, Yiming Luo, Patrick Cheong-Iao Pang

“…The emergence of deep learning (DL) models provides new ways to automate and improve the analysis of GC pathology images. …”
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Cumulative Summation (CUSUM)—as a tool for objective analysis of learning curve for surgeons in acquiring competency in phacoemulsification by Emily Ming Choo Ng, Mohamad Aziz Salowi, Othmaliza Othman, Rosmawati Uthman, Nor Fariza Ngah

“…The study utilised CUSUM analysis to assess the occurrence of PCR for consecutive cases. …”
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A Comprehensive Analysis of Dropout Assisted Regularized Deep Learning Architectures for Dynamic Electricity Price Forecasting by Pooja Joshi, Stale Stordal, Gudbrand Lien, Deepti Mishra, Erik Haugom

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Intention over motivation: A holistic analysis of psychological constructs in Arabic as a foreign language learning by Munassir Alhamami

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Predictors of Purpose Among Young Adults in College: An Exploratory Analysis of the Importance of Relational Supports and Experiential Learning by Terese J. Lund, Grace Fongemy, Brenna Lincoln, Haylee Snow, Annika la Sofia Hakovirta, Belle Liang

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Performance Analysis and Improvement of Machine Learning with Various Feature Selection Methods for EEG-Based Emotion Classification by Sherzod Abdumalikov, Jingeun Kim, Yourim Yoon

“…In this study, the classification of emotional states using electroencephalography (EEG) data were investigated. Specifically, the efficacy of the combination of various feature selection methods and hyperparameter tuning of machine learning algorithms for accurate and robust emotion recognition was studied. …”
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Machine Learning and SHAP-Based Analysis of Deforestation and Forest Degradation Dynamics Along the Iraq–Turkey Border by Milat Hasan Abdullah, Yaseen T. Mustafa

“…This study highlights the effectiveness of an ML-driven RS analysis for geoinformation needs in geopolitically complex and data-scarce regions. …”
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Deep Learning for Video Fluoroscopic Swallowing Study Analysis: A Survey on Classification, Detection, and Segmentation Techniques by Ahmed Fakhry, Sarah Mary Antony, Eunhee Park, Jong Taek Lee

“…Deep learning has significantly advanced the analysis of Video Fluoroscopic Swallowing Study data, an essential diagnostic tool for dysphagia assessment. …”
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Research progress in deep learning-based fundus image analysis for the diagnosis and prediction of hypertension-related diseases by Ping Li, Juan Tao, Quan Yuan, Rongqing Zhang, Peng Gao

“…In recent years, deep learning (DL) has rapidly advanced in the field of color fundus photography (CFP) analysis, demonstrating strong potential in vessel segmentation, artery/vein classification, lesion detection, and systemic disease prediction. …”
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Deciphering Car Crash Dynamics in Greater Melbourne: a Multi-Model Machine Learning and Geospatial Analysis by Christopher JOHNSON, ZHOU Heng, Richard TAY, SUN Qian(Chayn)

“…In the continually evolving landscape of data-driven methodologies addressing car crash patterns, a holistic analysis remains critical to decode the complex nuances of this phenomenon. …”
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Supporting Radiologists with Automated Image Analysis: An Evaluation of Deep Learning Tools for Augmenting Breast Cancer Screening by Imene Bouharati, Khaoula Bouharati

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Retrieval of crop traits using PROSAIL-based hybrid radiative transfer model and EnMAP hyperspectral data by Prachi Singh, Prashant K. Srivastava, Prakash Kumar Jha, Jochem Verrelst, Pashupati Nath Singh, Rajendra Prasad

“…The proposed methodology involves the integration and detailed analysis of Radiative Transfer Modelling (RTM) with an integrated approach of machine learning (ML) and Active Learning (AL) algorithms for the retrieval of the Leaf Chlorophyll Content (LCC), Carotenoids (Car) and Leaf Area index (LAI) of wheat cropland from the continuous three years of the dataset. …”
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Enhancing Pipeline Reliability Analysis through Machine Learning: A Focus on Corrosion and Fluid Hammer Effects by Ajinkya Zalkikar, Bimal Nepal, Mani Venkata Rakesh Mutyala, Anika Varshney, Lianne Dsouza, Hazlina Husin, Om Prakash Yadav

“…The proposed machine learning models based on simulated data aim to predict the safety status of corroded pipelines with an accuracy rate of up to 97% in controlled environments. …”
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Deep learning-based electrical impedance spectroscopy analysis for malignant and potentially malignant oral disorder detection by Zhicheng Lin, Zi-Qiang Lang, Lingzhong Guo, Dawn C Walker, Malwina Matella, Mengxiao Wang, Craig Murdoch

“…We aimed to apply EIS in conjunction with deep learning to assist the clinical diagnosis of OPMD and oral cancer as a non-invasive diagnostic technology. …”
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ENHANCING STUDENTS' FICTION ANALYSIS SKILLS THROUGH QUIZIZZ MEDIA IN A PROBLEM-BASED LEARNING MODEL by Gusti Ketut Alit Suputra, Idris Idris, Moh. Tahir, Syamsuddin Syamsuddin, Gazali Gazali, Riri Apriana N. Nandi

“…Purposes—This study aims to increase students' fiction analysis by integrating Quizizz media into the Project-Based Learning model. …”
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Automatic identification and characteristics analysis of crack tips in rocks with prefabricated defects based on deep learning methods. by Mingtao Gao, Minhui Li, Lu Chen, Zihao Guo, Chengyang Guo, Liping Li, Changsen Bu

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Analysis and validation of hub genes for atherosclerosis and AIDS and immune infiltration characteristics based on bioinformatics and machine learning by Qirui Zheng, Yupeng Wu, Xiaojiao Zhang, Yuzhu Zhang, Zaihan Zhu, Bo Luan, Peizhuo Zang, Dandan Sun

“…The Robust Multichip Average was performed for data preprocessing, and the limma package was used for screening differentially expressed genes. …”
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Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning by Jian Huang, Lu Wang, Jiangfei Zhou, Tianming Dai, Weicong Zhu, Tianrui Wang, Hongde Wang, Yingze Zhang

“…The limma package was used to identify differentially expressed genes (DEGs), and weighted gene coexpression network analysis (WGCNA) screened gene modules, and machine learning algorithms, such as random forest (RF), support vector machine (SVM), generalised linear model (GLM), and extreme gradient boosting (XGB), were employed. …”
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Comparative safety analysis of bevacizumab and alkylating agent in glioblastoma management – What have we learned recently? by Zhizhao Qu, Zhizhao Qu, Jiajia Zhao, Liu Yang, Yuanwei Fu, Rui Bai, Jinchuan Li, Hongqin Wang

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Unraveling global malaria incidence and mortality using machine learning and artificial intelligence–driven spatial analysis by Md. Siddikur Rahman, Md. Abu Bokkor Shiddik

“…Our integrated framework, driven by machine learning and artificial intelligence, offers actionable insights for identifying determinants and hotspots of malaria through spatial analysis. …”
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