An explainable deep learning model for diabetic foot ulcer classification using swin transformer and efficient multi-scale attention-driven network by R. Karthik, Armaano Ajay, Anshika Jhalani, Kruthik Ballari, Suganthi K

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Assessing the impact of climate and land use change on flood vulnerability: a machine learning approach in coastal region of Tamil Nadu, India by Devanantham Abijith, Subbarayan Saravanan, K S S Parthasarathy, Nagireddy Masthan Reddy, Janardhanam Niraimathi, Ahmed Ali Bindajam, Javed Mallick, Maged Muteb Alharbi, Hazem Ghassan Abdo

“…This research applies geospatial tools and machine learning to improve flood susceptibility mapping across the Tamil Nadu coast in India, using projections of Land Use and Land Cover (LULC) changes under current and future climate change scenarios. …”
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Mixed reality infrastructure based on deep learning medical image segmentation and 3D visualization for bone tumors using DCU-Net by Kun Wang, Yong Han, Yuguang Ye, Yusi Chen, Daxin Zhu, Yifeng Huang, Ying Huang, Yijie Chen, Jianshe Shi, Bijiao Ding, Jianlong Huang

“…The MR system based on deep learning and three-dimensional visualization technology has great potential in the diagnosis and treatment of bone tumors, and is expected to promote clinical practice and improve efficacy.…”
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Method for Evaluating Urban Building Renewal Potential Based on Multimachine Learning Integration: A Case Study of Longgang and Longhua Districts in Shenzhen by Dengkuo Sun, Yuefeng Lu, Yong Qin, Miao Lu, Zhenqi Song, Ziqi Ding

“…A regression analysis based on building characteristics and locational factors was conducted using a stacking ensemble machine learning model. In addition, buildings were categorized into residential, industrial, and commercial types based on their usage, enabling both overall- and category-specific predictions of building renewal. …”
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Examining the role of ClassPoint tool in shaping EFL students' perceived E-learning experiences: A social cognitive theory perspective by Huma Akram, Abbas Hussein Abdelrady

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Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal by Supath Dhital, Kapil Lamsal, Sulav Shrestha, Umesh Bhurtyal

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Impact assessment of planned and unplanned urbanization on land surface temperature in Afghanistan using machine learning algorithms: a path toward sustainability by Sajid Ullah, Xiuchen Qiao, Aqil Tariq

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User Experiences from L2 Children Using a Speech Learning Application: Implications for Developing Speech Training Applications for Children by Maria Uther, Anna-Riikka Smolander, Katja Junttila, Mikko Kurimo, Reima Karhila, Seppo Enarvi, Sari Ylinen

“…We investigated user experiences from 117 Finnish children aged between 8 and 12 years in a trial of an English language learning programme that used automatic speech recognition (ASR). …”
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Study of a Deep Learning Reconstruction Algorithm for Displaying Small- and Medium-sized Blood Vessels in Upper Abdominal Energy Spectrum CT by Qin WANG, Weijie YAN, Yuan YUAN, Hehan TANG, Liping DENG

Subjects: “…deep learning…”
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Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data by Johnny Downs, Robert Stewart, Alice Wickersham, Sumithra Velupillai, Lucile Ter-Minassian, Natalia Viani, Lauren Cross

“…Using a unique linked health and education data resource, we examined how machine learning (ML) approaches can predict risk of ADHD.Design Retrospective population cohort study.Setting South London (2007–2013).Participants n=56 258 pupils with linked education and health data.Primary outcome measures Using area under the curve (AUC), we compared the predictive accuracy of four ML models and one neural network for ADHD diagnosis. …”
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Machine learning-assisted design of Ti–V–Nb–Mo refractory high-entropy alloys with higher ductility and specific yield strength by Yan Li, Junjie Gong, Shilong Liang, Wei Wu, Yongxin Wang, Zheng Chen

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Comparison of Different Machine Learning Methodologies for Predicting the Non‐Specific Treatment Response in Placebo Controlled Major Depressive Disorder Clinical Trials by Roberto Gomeni, Françoise Bressolle‐Gomeni

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Enhancing Transient Stability of DFIG-Based Wind Turbine Systems Using Deep Learning-Controlled Resistance-Based Fault Current Limiters by Yaoying Wang

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A Novel Rubber Composite Sleeper-Deformation-Prediction Model Based on Response Surface Method (RSM) and Machine Learning (ML) Techniques by Abdulmumin Ahmed Shuaibu, Zhiping Zeng, Ibrahim Hayatu Hassan, Wang Weidong, Hassan Suleiman Otuoze, Suleiman Abdulhakeem, Bushrah Baba Abdulrahman

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Insights into the contribution of multiple factors on Ixodes ricinus abundance across Europe spanning 20 years using different machine learning algorithms by Samantha Lansdell, Abin Zorto, Misaki Seto, Edessa Negera, Saeed Sharif, Sally Cutler

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Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models by RN Singh, P. Krishnan, C. Bharadwaj, Sonam Sah, B. Das

“…Yield reductions of 44-69% were observed in susceptible genotypes. Machine learning models were able to give accurate early yield predictions. …”
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Over-Dimension and Over-Load (ODOL) truck in highways: Prevalence and modeling intention to operate ODOL truck, lesson learned from Indonesia by Ari Widyanti, Justin Mulya Gananda, Titah Yudhistira, Widyarini Weningtyas, Ludfi Pratiwi Bowo, Sinung Nugoroho

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Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax by Diego A. Ramos-Briceño, Alessandro Flammia-D’Aleo, Gerardo Fernández-López, Fhabián S. Carrión-Nessi, David A. Forero-Peña

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Flashcard Berbasis Augmented Reality Materi Pecahan Melalui Team Games Tournament Meningkatkan Self Regulated Learning Siswa Sekolah Dasar by Siti Fadila Rahma Putri, Mohamad Fatih, Cindya Alfi

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Plant Detection in RGB Images from Unmanned Aerial Vehicles Using Segmentation by Deep Learning and an Impact of Model Accuracy on Downstream Analysis by Mikhail V. Kozhekin, Mikhail A. Genaev, Evgenii G. Komyshev, Zakhar A. Zavyalov, Dmitry A. Afonnikov

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