Optimising deep learning models for ophthalmological disorder classification by S. Vidivelli, P. Padmakumari, C. Parthiban, A. DharunBalaji, R. Manikandan, Amir H. Gandomi

“…In this article, we have used different deep learning models for the categorisation of ophthalmological disorders into multiple classes and multiple labels utilising transfer learning-based convolutional neural network (CNN) methods. …”
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Research on test strategy for randomness based on deep learning by Dongyu CHEN, Hua CHEN, Limin FAN, Yifang FU, Jian WANG

Subjects: “…deep learning…”
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Fast QTMT partition decision based on deep learning by Shuang PENG, Xiaodong WANG, Zongju PENG, Fen CHEN

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Diagnosis of oral cancer using deep learning algorithms by Mayra Alejandra Dávila Olivos, Henry Miguel Herrera Del Águila, Félix Melchor Santos López

“… The aim of this study was to use deep learning for the automatic diagnosis of oral cancer, employing images of the lips, mucosa, and oral cavity. …”
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Deep learning captures the effect of epistasis in multifactorial diseases by Vladislav Perelygin, Alexey Kamelin, Alexey Kamelin, Nikita Syzrantsev, Layal Shaheen, Layal Shaheen, Anna Kim, Nikolay Plotnikov, Anna Ilinskaya, Valery Ilinsky, Alexander Rakitko, Alexander Rakitko, Maria Poptsova

“…From non-linear models, gradient boosting appeared to be the best model in obesity and psoriasis while deep learning methods significantly outperform linear approaches in type 1 diabetes.ConclusionOverall, our study underscores the efficacy of non-linear models and deep learning approaches in more accurately accounting for the effects of epistasis in simulations with specific configurations and in the context of certain diseases.…”
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Retracted: Deep Learning-Based Music Quality Analysis Model by Applied Bionics and Biomechanics

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Classification of Dyslexia Among School Students Using Deep Learning by Alia Hussein, Ahmed Talib Abdulameer, Ali Abdulkarim, Husniza Husni, Dalia Al-Ubaidi

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A Mobile Deep Learning Classification Model for Diabetic Retinopathy by Daniel Rimaru, Antonio Nehme, Musaed Alhussein, Khaled Mahbub, Khusheed Aurangzeb, Anas Khan

“…The early diagnosis of DR using computer-aided automated tools is possible due to tremendous advancements in machine and deep learning models in the last decade. Devising and implementing innovative deep learning models for retinal structural analysis is crucial to the early diagnosis of DR and other eye diseases. …”
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Oral English Auxiliary Teaching System Based on Deep Learning by Chenhui Qu, Yuanbo Li

“…In order to solve the problem of the oral English auxiliary teaching system, a research based on Deep Learning was proposed. Based on the theory of Deep Learning, the teaching mode of Deep Learning for college students built on information technology was investigated in the research. …”
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Deep learning enhanced transmembranous electromyography in the diagnosis of sleep apnea by Ross Mandeville, Hooman Sedghamiz, Perry Mansfield, Geoffrey Sheean, Chris Studer, Derrick Cordice, Ghodsieh Ghanbari, Atul Malhotra, Shamim Nemati, Jejo Koola

Subjects: “…Deep learning in medicine…”
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Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification by Narut Butploy, Wanida Kanarkard, Pewpan Maleewong Intapan

“…This research proposes deep learning for A. lumbricoides’s egg image recognition to be used as a prototype tool for parasite egg detection in medical diagnosis. …”
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Predictive Control for Steel Rib Bending Based on Deep Learning by Yijiang Xia, Jinhui Luo, Zhuolin Ou, Xin Han, Junlin Deng, Ning Wu

“…This study proposes control methods for cold bending machines based on deep learning models to address this challenge, including CNN and Transformer-CNN (T-CNN), to predict the elastic spring-back rate of cold-processed metal profiles and generate precise control pulses for achieving target bending angles. …”
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Attention-based deep learning for accurate cell image analysis by Xiangrui Gao, Fan Zhang, Xueyu Guo, Mengcheng Yao, Xiaoxiao Wang, Dong Chen, Genwei Zhang, Xiaodong Wang, Lipeng Lai

“…Noisy and redundant signals in cell images impede accurate deep learning-based image analysis. To address these issues, we introduce X-Profiler, a novel HCA method that combines cellular experiments, image processing, and deep learning modeling. …”
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Application of Improved Deep Learning Method in Intelligent Power System by HuiJie Liu, Yang Liu, ChengWen Xu

“…In view of the inaccurate short-term power load prediction in the power system, where the smart grid cannot effectively coordinate the production, transportation, and distribution of electric energy, the authors propose the application of improved deep learning methods in intelligent power systems. The method uses the convolutional neural network to establish the energy prediction calculation model, uses CNN adaptive data features to mine characteristics, quantifies power uncertainty, uses drop regularization to optimize the deep network structure, uses the deep forest to learn the extracted data features, and builds a prediction model, in order to achieve accurate prediction of power load and solve the problem that the accuracy of existing forecasting methods decreases due to random fluctuations of power. …”
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Face Recognition using Deep Learning and TensorFlow framework by Makrem Beldi

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Predicting carbon peak at the provincial level using deep learning by Xiaoyan Tang, Kunsheng Fang

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Research and application of traffic engineering algorithm based on deep learning by Daoyun HU, Jin QI, Qianchun LU, Feng LI, Hongqiang FANG

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Translation Invariance-Based Deep Learning for Rotating Machinery Diagnosis by Wenliao Du, Shuangyuan Wang, Xiaoyun Gong, Hongchao Wang, Xingyan Yao, Michael Pecht

“…Discriminative feature extraction is a challenge for data-driven fault diagnosis. Although deep learning algorithms can automatically learn a good set of features without manual intervention, the lack of domain knowledge greatly limits the performance improvement, especially for nonstationary and nonlinear signals. …”
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Knowledge-Based Deep Learning for Time-Efficient Inverse Dynamics by Shuhao Ma, Yu Cao, Ian D. Robertson, Chaoyang Shi, Jindong Liu, Zhi-Qiang Zhang

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Deep learning Chinese input method with incremental vocabulary selection by Huajian REN, Xiulan HAO, Wenjing XU

“…The core task of an input method is to convert the keystroke sequences typed by users into Chinese character sequences.Input methods applying deep learning methods have advantages in learning long-range dependencies and solving data sparsity problems.However, the existing methods still have two shortcomings: the separation structure of pinyin slicing in conversion leads to error propagation, and the model is complicated to meet the demand for real-time performance of the input method.A deep-learning input method model incorporating incremental word selection methods was proposed to address these shortcomings.Various softmax optimization methods were compared.Experiments on People’s Daily data and Chinese Wikipedia data show that the model improves the conversion accuracy by 15% compared with the current state-of-the-art model, and the incremental vocabulary selection method makes the model 130 times faster without losing conversion accuracy.…”
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