A Novel Model to Detect and Classify Fresh and Damaged Fruits to Reduce Food Waste Using a Deep Learning Technique by T. Bharath Kumar, Deepak Prashar, Gayatri Vaidya, Vipin Kumar, S. Deva Kumar, F. Sammy

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Deep Learning Approaches for 3D Model Generation from 2D Artworks to Aid Blind People with Tactile Exploration by Rocco Furferi

“…The findings suggest that, while deep learning models demonstrate that they are effective for 3D retrieval from paintings, they also call for post-processing and user interaction to improve the accuracy of the 3D models.…”
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Colorimetric aptasensor coupled with a deep-learning-powered smartphone app for programmed death ligand-1 expressing extracellular vesicles by Adeel Khan, Adeel Khan, Haroon Khan, Nongyue He, Zhiyang Li, Heba Khalil Alyahya, Yousef A. Bin Jardan

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Clinical utility of receptor status prediction in breast cancer and misdiagnosis identification using deep learning on hematoxylin and eosin-stained slides by Gil Shamai, Ran Schley, Alexandra Cretu, Tal Neoran, Edmond Sabo, Yoav Binenbaum, Shachar Cohen, Tal Goldman, António Polónia, Keren Drumea, Karin Stoliar, Ron Kimmel

“…Methods We developed a deep learning system to predict ER, PR, and ERBB2 statuses from digitized H&E slides and evaluated its utility in three clinical applications: identifying hormone receptor-positive patients, serving as a second-read tool for quality assurance, and addressing intratumor heterogeneity. …”
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Digital twin-centered hybrid data-driven multi-stage deep learning framework for enhanced nuclear reactor power prediction by James Daniell, Kazuma Kobayashi, Ayodeji Alajo, Syed Bahauddin Alam

“…This paper introduces a novel hybrid digital twin-focused multi-stage deep learning framework that addresses these limitations, offering a faster and more robust solution for predicting the final steady-state power of reactor transients. …”
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Identification of standing dead trees in Robinia pseudoacacia plantations across China’s Loess Plateau using multiple deep learning models by Li Zhang, Xiaodong Gao, Shuyi Zhou, Zhibo Zhang, Tianjie Zhao, Yaohui Cai, Xining Zhao

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SFSCDNet: A Deep Learning Model With Spatial Flow-Based Semantic Change Detection From Bi-Temporal Satellite Images by K. S. Basavaraju, N. Sravya, Vibha Damodara Kevala, Shilpa Suresh, Shyam Lal

Subjects: “…Deep learning…”
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Automatic Detection and Classification of Eye Diseases from Retinal Images Using Deep Learning: A Comprehensive Research on the ODIR Dataset by Asadi Srinivasulu, Clement Varaprasad Karu, G Sreenivasulu, Gayathri R

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Prediction of post-treatment recurrence in early-stage breast cancer using deep-learning with mid-infrared chemical histopathological imaging by Abigail Keogan, Thi Nguyet Que Nguyen, Pascaline Bouzy, Nicholas Stone, Karin Jirstrom, Arman Rahman, William M. Gallagher, Aidan D. Meade

“…In this study, Fourier-transform infrared (FTIR) chemical images of BC tissue were used to train deep learning models to predict future disease recurrence. …”
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Analisis Perbandingan Algoritma Machine Learning dan Deep Learning untuk Klasifikasi Citra Sistem Isyarat Bahasa Indonesia (SIBI) by Mohammad Farid Naufal, Selvia Ferdiana Kusuma

“…Algoritma klasifikasi machine learning memiliki waktu komputasi lebih rendah sedangkan Deep learning memiliki performa klasifikasi lebih tinggi. …”
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Hybrid deep learning approach for brain tumor classification using EfficientNetB0 and novel quantum genetic algorithm by Kerem Gencer, Gülcan Gencer

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Risk Prediction for Ship Encounter Situation Awareness Using Long Short-Term Memory Based Deep Learning on Intership Behaviors by Jie Ma, Wenkai Li, Chengfeng Jia, Chunwei Zhang, Yu Zhang

“…To address this issue, we propose a deep learning framework. To represent the temporal dynamics of the encounter process, we use the sliding window technique to generate the sequences of behavioral features. …”
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A joint three-plane physics-constrained deep learning based polynomial fitting approach for MR electrical properties tomography by Kyu-Jin Jung, Thierry G. Meerbothe, Chuanjiang Cui, Mina Park, Cornelis A.T. van den Berg, Stefano Mandija, Dong-Hyun Kim

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Blinking characteristics analyzed by a deep learning model and the relationship with tear film stability in children with long-term use of orthokeratology by Yue Wu, Siyuan Wu, Yinghai Yu, Yinghai Yu, Xiaojun Hu, Ting Zhao, Yan Jiang, Bilian Ke

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Genome data based deep learning identified new genes predicting pharmacological treatment response of attention deficit hyperactivity disorder by Yilu Zhao, Zhao Fu, Eric J. Barnett, Ning Wang, Kangfuxi Zhang, Xuping Gao, Xiangyu Zheng, Junbin Tian, Hui Zhang, XueTong Ding, Shaoxian Li, Shuyu Li, Qingjiu Cao, Suhua Chang, Yufeng Wang, Stephen V. Faraone, Li Yang

“…The current study employed a comprehensive approach, combining genome-wide association analyses (GWAS) and deep learning (DL) methods, to elucidate the genetic underpinnings of pharmacological treatment response in ADHD. …”
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Identifying the Factors Affecting Student Academic Performance and Engagement Prediction in MOOC Using Deep Learning: A Systematic Literature Review by Shahzad Rizwan, Chee Ken Nee, Salem Garfan

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Deep Learning for Predicting Spheroid Viability: Novel Convolutional Neural Network Model for Automating Quality Control for Three-Dimensional Bioprinting by Zyva A. Sheikh, Oliver Clarke, Amatullah Mir, Narutoshi Hibino

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Fluo-Cast-Bright: a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live oocytes by Xiangyu Zhang, Claudia Baumann, Rabindranath De La Fuente

“…Here, we developed a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live mouse oocytes. …”
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High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps by Carlos Fernández, Jesús Muñoz-Bulnes, David Fernández-Llorca, Ignacio Parra, Iván García-Daza, Rubén Izquierdo, Miguel Á. Sotelo

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Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections by Asadulla Y. Ashurov, Mehdhar S. A. M. Al-Gaashani, Nagwan A. Samee, Reem Alkanhel, Ghada Atteia, Hanaa A. Abdallah, Mohammed Saleh Ali Muthanna

Subjects: “…deep learning…”
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