Deep learning, stochastic gradient descent and diffusion maps

Fjellström, Carmina; Nyström, Kaj

doi:10.1016/j.jcmds.2022.100054

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…In Table (2), we examine the Pearson correlation coefficient between the groups. It can be seen that there is a high significant correlation (marked with **), a significant correlation (marked with *) and a weak correlation between the variables [29,30]. The estimated coefficient for the model was calculated in Table (3), some variables were highly significant since p value was less than 0.02, and some were significant where p value < 0.05.…”

Section: Real Datasetmentioning

confidence: 99%

Enhanced Support Vector Machine Methods Using Stochastic Gradient Descent and Its Application to Heart Disease Dataset

Mahdi,

Seror Faeq Mohammed,

Md Kamrul Hasan Khan

2024

IHJPAS

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Support Vector Machines (SVMs) are supervised learning models used to examine data sets in order to classify or predict dependent variables. SVM is typically used for classification by determining the best hyperplane between two classes. However, working with huge datasets can lead to a number of problems, including time-consuming and inefficient solutions. This research updates the SVM by employing a stochastic gradient descent method. The new approach, the extended stochastic gradient descent SVM (ESGD-SVM), was tested on two simulation datasets. The proposed method was compared with other classification approaches such as logistic regression, naive model, K Nearest Neighbors and Random Forest. The results show that the ESGD-SVM has a very high accuracy and is quite robust. ESGD-SVM is used to analyze the heart disease dataset downloaded from Harvard Dataverse. The entire analysis was performed using the program R version 4.3.

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Section: Real Datasetmentioning

confidence: 99%

Enhanced Support Vector Machine Methods Using Stochastic Gradient Descent and Its Application to Heart Disease Dataset

Mahdi,

Seror Faeq Mohammed,

Md Kamrul Hasan Khan

2024

IHJPAS

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“…Adam differs from classical stochastic gradient descent. Stochastic gradient descent (Fjellström & Nyström, 2022), (Li et al, 2022) maintains a single learning rate (alpha) for all weight updates and the learning rate does not change during training. The learning rate is maintained for each network weight (parameter) and is adapted separately as learning progresses.…”

Section: Literature Reviewmentioning

confidence: 99%

Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201

Saputra

Hindarto

Santoso

2023

SinkrOn

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Rice is a plant that can grow in the tropics. This plant can produce food that can meet the needs of the people of a country. This plant can grow well if it is cared for properly. If the planting has used good care, such as providing adequate water, adding good fertilizer, it can be ascertained that it will produce a lot of rice fruit after harvesting. This often causes concern if rice growers have given good care but often produce less rice fruit because rice plants are attacked by various diseases. This is what makes the problem, that rice plants are attacked by diseases. Before spraying diseases or pests, farmers should have an understanding of diseases in rice. This makes farmers not wrong in choosing drugs for farmers' rice. It is very vulnerable if farmers do not know about the rice disease. Therefore, it is necessary to observe what types of rice diseases attack rice plants. Observations are not enough just to take pictures with a camera. But it is necessary to carry out further analysis of rice diseases. The presence of information technology is now able to recognize any type. One of the machine learning technologies is able to detect rice diseases. One of these branches of machine learning is deep learning. By using a dataset that focuses on rice disease, the model generated from deep learning training is able to detect rice disease. The purpose of this research is to predict disease in rice leaves using deep learning, namely DenseNet. Training using DenseNet, namely DenseNet121, DenseNet169 and DenseNet201. Accuracy using DenseNet121 reached 91.67%, DenseNet169 reached 90%, and DenseNet201 reached 88.33%. The model training time takes 24 seconds.

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“…In the learning process of ANNs the aim is to find weights vector w ∈ ℝ m , what in turn can be considered as an optimization problem as defined in [8]. The optimal weights w * 𝒘 * = arg min 𝒘𝜖ℝ 𝑚 {𝑓(𝒘) =…”

Section: Scenario Faults Sc1mentioning

confidence: 99%

“…where: f : ℝ m → ℝ is the loss function, fi, for i ∈ {1, …N}, denotes the contribution to the loss function from data point i, N denotes the total number of data points [8]. The optimization algorithm implemented for the purpose of ANN training was stochastic gradient descent [42].…”

Section: Scenario Faults Sc1mentioning

confidence: 99%

Feasibility study of a rail vehicle damper fault detection by artificial neural Indexed by: networks

Melnik¹,

Koziak²,

Dižo³

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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The aim of the study was to investigate rail vehicle dynamics under primary suspension dampers faults and explore possibility of its detection by means of artificial neural networks. For these purposes two types of analysis were carried out: preliminary analysis of 1 DOF rail vehicle model and a second one - a passenger coach benchmark model was tested in multibody simulation software - MSC.Adams with use of VI-Rail package. Acceleration signals obtained from the latter analysis served as an input data into the artificial neural network (ANN). ANNs of different number of hidden layers were capable of detecting faults for the trained suspension fault cases, however, achieved accuracy was below 63% at the best. These results can be considered satisfactory considering the complexity of dynamic phenomena occurring in the vibration system of a rail vehicle.

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Deep learning, stochastic gradient descent and diffusion maps

Cited by 10 publications

References 20 publications

Enhanced Support Vector Machine Methods Using Stochastic Gradient Descent and Its Application to Heart Disease Dataset

Enhanced Support Vector Machine Methods Using Stochastic Gradient Descent and Its Application to Heart Disease Dataset

Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201

Feasibility study of a rail vehicle damper fault detection by artificial neural Indexed by: networks

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