Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells by Tian Xu, Qin Xu, Ran Lu, David N. Oakland, Song Li, Liwu Li, Christopher M. Reilly, Xin M. Luo

“…However, advanced deep learning models such as Single Cell Variational Inference (scVI) have the capability to capture nonlinear gene expression patterns in the sequencing data. …”
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Screening of multi deep learning-based de novo molecular generation models and their application for specific target molecular generation by Yishu Wang, Mengyao Guo, Xiaomin Chen, Dongmei Ai

“…With the development of deep learning techniques for the de novo generation of molecules, also known as inverse molecular design, the increasingly widespread application of various types of deep learning algorithms has led to revolutionary changes in de novo molecular generation research. …”
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Towards Deep Learning in Online Courses: A Case Study in Cross-Pollinating Universal Design for Learning and Dialogic Teaching by Qiongli Zhu, Sarfaroz Niyozov

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Deteksi Kejadian Lalu Lintas pada Teks Twitter dengan Pendekatan Klasidikasi Multi-Label Berbasis Deep Learning by Luthfi Atikah, Diana Purwitasari, Nanik Suciati

“…Klasifikasi dilakukan dengan menggunakan pendekatan deep learning (CNN dan LSTM) dan word embedding (word2vec dan fastText) dengan augmentasi dan non-augmentasi data. …”
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Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities by Munya A. Arasi, Hussah Nasser AlEisa, Amani A. Alneil, Radwa Marzouk

“…Conventional machine learning and deep learning approaches effectively detect human activity. …”
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Deep Learning Image Classification Rontgen Dada pada Kasus Covid-19 Menggunakan Algoritma Convolutional Neural Network by Leni Anggraini Susanti, Agus Mohamad Soleh, Bagus Sartono

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A hybrid approach for intrusion detection in vehicular networks using feature selection and dimensionality reduction with optimized deep learning. by Fayaz Hassan, Zafi Sherhan Syed, Aftab Ahmed Memon, Saad Said Alqahtany, Nadeem Ahmed, Mana Saleh Al Reshan, Yousef Asiri, Asadullah Shaikh

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A deep learning-based method for the intelligent identification of the quantity of coals flushed out during borehole hydraulic flushing by Xiaojun LI, Mingyang ZHAO, Miao LI

“…The results of this study provide a technical and practical foundation for the integration of the YOLO series of algorithms with other deep learning techniques and its wide applications. Besides, these results serve as a valuable reference for achieving intelligent advances in complex underground coal mine scenarios such as drilling sites for gas drainage.…”
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DeepCERES: A deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI by Sergio Morell-Ortega, Marina Ruiz-Perez, Marien Gadea, Roberto Vivo-Hernando, Gregorio Rubio, Fernando Aparici, Maria de la Iglesia-Vaya, Gwenaelle Catheline, Boris Mansencal, Pierrick Coupé, José V. Manjón

“…We have also integrated deep learning with classical machine learning methods incorporating a priori knowledge from multi-atlas segmentation which improved precision and robustness. …”
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Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers by Hui Shang, Tao Feng, Dong Han, Fengying Liang, Bin Zhao, Lihang Xu, Zhendong Cao

Subjects: “…Deep learning…”
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Unfixed Bias Iterator: A New Iterative Format by Zeqing Zhang, Xue Wang, Jiamin Shen, Man Zhang, Sen Yang, Fanchang Yang, Wei Zhao, Jia Wang

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Deep learning radiomics nomogram for preoperatively identifying moderate-to-severe chronic cholangitis in children with pancreaticobiliary maljunction: a multicenter study by Hui-min Mao, Kai-ge Chen, Bin Zhu, Wan-liang Guo, San-li Shi

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A deep learning approach for automatic 3D segmentation of hip cartilage and labrum from direct hip MR arthrography by Malin Kristin Meier, Ramon Andreas Helfenstein, Adam Boschung, Andreas Nanavati, Adrian Ruckli, Till D. Lerch, Nicolas Gerber, Bernd Jung, Onur Afacan, Moritz Tannast, Klaus A. Siebenrock, Simon D Steppacher, Florian Schmaranzer

“…When tested on the external dataset, the DSC was 0.89 ± 0.02 (0.88–0.90) and 0.71 ± 0.04 (0.69–0.73) for cartilage and labrum, respectively.The presented deep learning approach accurately segments hip cartilage and labrum from 3D MRI sequences and can potentially be used in clinical practice to provide rapid and accurate 3D MRI models.…”
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Integration of ground-based and remote sensing data with deep learning algorithms for mapping habitats in Natura 2000 protected oak forests by Lucia Čahojová, Ivan Jarolímek, Barbora Klímová, Michal Kollár, Michaela Michalková, Karol Mikula, Aneta A. Ožvat, Denisa Slabejová, Mária Šibíková

“…This study aims to combine ground-based and remote sensing data utilising deep learning techniques to map protected forest habitats and communities within the Natura 2000 network. …”
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Penerapan Blok SE-NET Pada Deep Learning Inceptionv3 untuk Meningkatkan Deteksi Penyakit Mpox pada Manusia by M. Bakhara Alief Rachman, Aliyah Kurniasih, Andika Sundawijaya, Ahlijati Nuraminah

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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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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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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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An assessment of breast cancer HER2, ER, and PR expressions based on mammography using deep learning with convolutional neural networks by Shun Zeng, Hongyu Chen, Rui Jing, Wenzhuo Yang, Ligong He, Tianle Zou, Peng Liu, Bo Liang, Dan Shi, Wenhao Wu, Qiusheng Lin, Zhenyu Ma, Jinhui Zha, Yonghao Zhong, Xianbin Zhang, Guangrui Shao, Peng Gong

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Quantifying the tumour vasculature environment from CD-31 immunohistochemistry images of breast cancer using deep learning based semantic segmentation by Tristan Whitmarsh, Wei Cope, Julia Carmona-Bozo, Roido Manavaki, Stephen-John Sammut, Ramona Woitek, Elena Provenzano, Emma L. Brown, Sarah E. Bohndiek, Ferdia A. Gallagher, Carlos Caldas, Fiona J. Gilbert, Florian Markowetz

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