DCT-Based White Blood Cell Image Enhancement for Recognition Using Deep Learning by Anh Quynh Vu, Hoan Quoc Bui, Long Tuan Nguyen, Tuyen Ngoc Le

“…Experimental results show that our method dramatically improves deep learning-based WBC recognition accuracy.…”
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Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning by Longfei Zhu, Qun Luo, Qiaochuan Chen, Yu Zhang, Lijun Zhang, Bin Hu, Yuexing Han, Qian Li

“…Based on the image processing and machine learning techniques, this paper proposes a multimodal fusion learning framework that comprehensively considers both composition and microstructure in prediction of the ultimate tensile strength (UTS) of Al‐Si alloys. …”
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Cross-cultural adaptation and validation of the clinical learning evaluation questionnaire with Chinese clinical interns by Luhua Yang, Jiangang Sun, Ruirui Wang, Shaochen Tao, Shanshan Wei, Liang Dong, Yansheng Gu, Jiayue Wang

“…It was translated into Chinese through a six-step forward and backward translation process. Data were analyzed using IBM SPSS Statistics 26 and IBM SPSS AMOS 28 Graphics. …”
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Rapid mapping of landslides using satellite SAR imagery: A progressive learning approach by Nikhil Prakash, Andrea Manconi, Alessandro Cesare Mondini

“…The proposed active learning workflow can start with a small (∼100m2) and incomplete inventory,- and can grow the extent and completeness in iterative steps with manual updates after each step. …”
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Deep learning-based time series prediction in multispectral and hyperspectral imaging for cancer detection by Lijun Hao, Changmin Wang, Jinshan Che, Mingming Sun, Yuhong Wang

“…Deep learning has recently been introduced to address these limitations, yet existing models often lack robust feature extraction, generalization capability, and effective domain adaptation strategies.MethodsIn this study, we propose a novel deep learning-based time series prediction framework for multispectral and hyperspectral medical imaging analysis. …”
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Sampled-data control through model-free reinforcement learning with effective experience replay by Bo Xiao, H.K. Lam, Xiaojie Su, Ziwei Wang, Frank P.-W. Lo, Shihong Chen, Eric Yeatman

“…Given that the continuous states of the plant will be the input of the agent, the state–action value function is approximated by the fully connected feed-forward neural networks (FCFFNN). Instead of learning the controller at every step during the interaction with the environment, the learning and acting stages are decoupled to learn the control strategy more effectively through experience replay. …”
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Predicting executive functioning from walking features in Parkinson’s disease using machine learning by Artur Piet, Johanna Geritz, Pascal Garcia, Mona Irsfeld, Frédéric Li, Xinyu Huang, Muhammad Tausif Irshad, Julius Welzel, Clint Hansen, Walter Maetzler, Marcin Grzegorzek, Nico Bunzeck

“…Specifically, this effect was primarily driven by step time variability, double limb support time variability, and gait speed in the dual task condition with cognitive demands. …”
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Cost-effective approaches for microplastic pellets characterization using a machine learning tool by V.M. Scarrica, P. Cocozza, G. Anfuso, A. Staiano, G. Bonifazi, A. Rizzo, S. Serranti

“…This streamlined methodology can offer a significant step forward in microplastic management and pollution mitigation, contributing to the development of cost-effective, scalable solutions for addressing the environmental impacts of microplastics.…”
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Towards automatic US-MR fetal brain image registration with learning-based methods by Qi Zeng, Weide Liu, Bo Li, Ryne Didier, P. Ellen Grant, Davood Karimi

“…This leads to an end-to-end multi-task learning framework with superior registration performance. …”
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MCOA: A Comprehensive Multimodal Dataset for Advancing Deep Learning in Corneal Opacity Assessment by Xinyu Ma, Jianxia Fang, Yaqi Wang, Zhichao Hu, Zhe Xu, Sha Zhu, Weijia Yan, Mengqi Chu, Jingwei Xu, Siting Sheng, Chujun Liu, Mingxuan Zhang, Ce Shi, Gangyong Jia, Wen Xu

“…This robust dataset represented a significant step forward in leveraging deep learning for corneal opacity recognition, empowering AI-driven clinical decision-making and facilitating the creation of personalized treatment plans for affected patients.…”
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Deep learning-based automatic image quality assessment in ultra-widefield fundus photographs by Sang Jun Park, Kyu Hyung Park, Chang Ki Yoon, Richul Oh, Un Chul Park

“…Objective With a growing need for ultra-widefield fundus (UWF) fundus photographs in clinics and AI development, image quality assessment (IQA) of UWF fundus photographs is an important preceding step for accurate diagnosis and clinical interpretation. …”
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Advancing Enzyme-Based Detoxification Prediction with ToxZyme: An Ensemble Machine Learning Approach by Kashif Iqbal Sahibzada, Shumaila Shahid, Mohsina Akhter, Muhammad Faisal, Reham A. Abd El Rahman, Muhammad Imran, Yangyong Lv, Dongqing Wei, Yuansen Hu

“…This study highlights the power of combining classical machine learning with deep learning to advance prediction. …”
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Reactor physics fast calculation method based on model order reduction and machine learning by Chen Zhao, Qinyi Zhang, Bin Zhang, Jiangyu Wang, Jiayi Liu, Lianjie Wang, Bangyang Xia, Xiaoming Chai, Xingjie Peng

“…During the training process, the full-order model is established using the two-step core nuclear design software package TORCH, and the model order reduction theory is applied, which are then trained using the random forest machine learning method. …”
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Scalable and robust machine learning framework for HIV classification using clinical and laboratory data by Qian Sui, Gaoxu Li, Yaqi Peng, Jiasheng Zhang, Yibo Zhang, Riyang Zhao

“…Additionally, we enhance dataset quality by removing outliers using the interquartile range (IQR) method. A comprehensive two-step feature selection process is employed, resulting in a reduction from 22 original features to 12 critical variables. …”
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From Stationary to Nonstationary UAVs: Deep-Learning-Based Method for Vehicle Speed Estimation by Muhammad Waqas Ahmed, Muhammad Adnan, Muhammad Ahmed, Davy Janssens, Geert Wets, Afzal Ahmed, Wim Ectors

“…Unmanned aerial vehicles (UAVs) and deep learning (DL) models are examples of such disruptive technologies with diverse industrial applications that are gaining traction. …”
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Development of Digital Research-Based Learning (D-RBL) strategy in instructional media course by Dian Olifia Talaksoru, Dedi Kuswandi, Saida Ulfa

“…D-RBL is considered an innovative step in improving the quality of learning in learning media courses.   …”
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PyCSBtool: Python-Based GUI Learning Tool for Basics Control System Education by C. H. Manathunga, R. A. D. D. Dharmasiri, W. K. Indika L. Wanniarachchi, M. K. Jayananda, Tadachika Nakayama

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Deep Learning-Enabled Dynamic Model for Nutrient Status Detection of Aquaponically Grown Plants by Mohamed Farag Taha, Hanping Mao, Samar Mousa, Lei Zhou, Yafei Wang, Gamal Elmasry, Salim Al-Rejaie, Abdallah Elshawadfy Elwakeel, Yazhou Wei, Zhengjun Qiu

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From Voxels to Viruses: Using Deep Learning and Crowdsourcing to Understand a Virus Factory by Avery Pennington, Oliver N. F. King, Win Min Tun, Mark Boyce, Geoff Sutton, David I. Stuart, Mark Basham, Michele C. Darrow

“…The application of citizen science–driven crowdsourcing to the generation of instance segmentations of volumetric bioimages is a step towards developing annotation-efficient segmentation workflows for bioimaging data. …”
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Time Series Analysis of Solar Power Generation Based on Machine Learning for Efficient Monitoring by Umer Farooq, Muhammad Faheem Mushtaq, Zahid Ullah, Muhammad Talha Ejaz, Urooj Akram, Sheraz Aslam

“…The study focuses on utilizing machine learning (ML) methodologies for accurate forecasting of solar power generation, addressing challenges related to integrating renewable energy into the power grid. …”
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