scholarly journals Auto-Ensemble: An Adaptive Learning Rate Scheduling Based Deep Learning Model Ensembling

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 217499-217509
Author(s):  
Jun Yang ◽  
Fei Wang
Keyword(s):  
Deep Learning ◽  
Adaptive Learning ◽  
Learning Model ◽  
Learning Rate ◽  
Adaptive Learning Rate ◽  
Rate Scheduling ◽  
Deep Learning Model

scholarly journals Plant Diseases Identification through a Discount Momentum Optimizer in Deep Learning

2021 ◽  
Vol 11 (20) ◽  
pp. 9468
Author(s):  
Yunyun Sun ◽  
Yutong Liu ◽  
Haocheng Zhou ◽  
Huijuan Hu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Adaptive Learning ◽  
Learning Rate ◽  
Plant Diseases ◽  
Stochastic Gradient Descent ◽  
Automatic Identification ◽  
Deep Convolutional Neural Networks ◽  
Adaptive Learning Rate

Deep learning proves its promising results in various domains. The automatic identification of plant diseases with deep convolutional neural networks attracts a lot of attention at present. This article extends stochastic gradient descent momentum optimizer and presents a discount momentum (DM) deep learning optimizer for plant diseases identification. To examine the recognition and generalization capability of the DM optimizer, we discuss the hyper-parameter tuning and convolutional neural networks models across the plantvillage dataset. We further conduct comparison experiments on popular non-adaptive learning rate methods. The proposed approach achieves an average validation accuracy of no less than 97% for plant diseases prediction on several state-of-the-art deep learning models and holds a low sensitivity to hyper-parameter settings. Experimental results demonstrate that the DM method can bring a higher identification performance, while still maintaining a competitive performance over other non-adaptive learning rate methods in terms of both training speed and generalization.

scholarly journals Research on Students’ Adaptive Learning System Based on Deep Learning Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xi Yang ◽  
Zhihan Zhou ◽  
Yu Xiao
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Adaptive Learning ◽  
Rapid Development ◽  
Learning Model ◽  
Learning System ◽  
Learning Resources ◽  
Recommendation Algorithm ◽  
Adaptive Learning System ◽  
Deep Learning Model

With the rapid development of deep learning in recent years, recommendation algorithm combined with deep learning model has become an important direction in the field of recommendation in the future. Personalized learning resource recommendation is the main way to realize students’ adaptation to the learning system. Based on the in-depth learning mode, students’ online learning action data are obtained, and further learning analysis technology is used to construct students’ special learning mode and provide suitable learning resources. The traditional method of introducing learning resources mainly stays at the level of examination questions. What ignores the essence of students’ learning is the learning of knowledge points. Students’ learning process is affected by “before” and “after” learning behavior, which has the characteristics of time. Among them, bidirectional length cyclic neural network is good at considering the “front” and “back” states of recommended nodes when recommending prediction results. For the above situation, this paper proposes a recommendation method of students’ learning resources based on bidirectional long-term and short-term memory cyclic neural network. Firstly, recommend the second examination according to the knowledge points, predict the scores of important steps including the accuracy of the recommended examination of the target students and the knowledge points of the recommended examination, and finally cooperate with the above two prediction results to judge whether the examination questions are finally recommended. Through the comparative experiment with the traditional recommendation algorithm, it is found that the student adaptive learning system based on the deep learning model proposed in this paper has better stability and interpretability in the recommendation results.

scholarly journals Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN)

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256500
Author(s):  
Maleika Heenaye-Mamode Khan ◽  
Nazmeen Boodoo-Jahangeer ◽  
Wasiimah Dullull ◽  
Shaista Nathire ◽  
Xiaohong Gao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Learning Model ◽  
Learning Rate ◽  
Deep Convolutional Neural Network ◽  
Deep Learning Model

The real cause of breast cancer is very challenging to determine and therefore early detection of the disease is necessary for reducing the death rate due to risks of breast cancer. Early detection of cancer boosts increasing the survival chance up to 8%. Primarily, breast images emanating from mammograms, X-Rays or MRI are analyzed by radiologists to detect abnormalities. However, even experienced radiologists face problems in identifying features like micro-calcifications, lumps and masses, leading to high false positive and high false negative. Recent advancement in image processing and deep learning create some hopes in devising more enhanced applications that can be used for the early detection of breast cancer. In this work, we have developed a Deep Convolutional Neural Network (CNN) to segment and classify the various types of breast abnormalities, such as calcifications, masses, asymmetry and carcinomas, unlike existing research work, which mainly classified the cancer into benign and malignant, leading to improved disease management. Firstly, a transfer learning was carried out on our dataset using the pre-trained model ResNet50. Along similar lines, we have developed an enhanced deep learning model, in which learning rate is considered as one of the most important attributes while training the neural network. The learning rate is set adaptively in our proposed model based on changes in error curves during the learning process involved. The proposed deep learning model has achieved a performance of 88% in the classification of these four types of breast cancer abnormalities such as, masses, calcifications, carcinomas and asymmetry mammograms.

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