Breast Cancer Detection Using Deep Belief Network by Applying Feature Extraction on Various Classifiers

S, Prasath Alias Surendhar

doi:10.17762/turcomat.v12i1s.1909

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…Due to the limited application of the DBN model in personalized recommendation in the field of education, the study is the first to combine the two for exploration, aiming to improve the learning quality of learners in a large-scale open online learning environment. [12,13,14]. DBN is a probability generation model, which consists of multiple hidden layers and a display layer to form a deep hybrid neural network.…”

Section: Research Direction Research Resultsmentioning

confidence: 99%

Application Analysis of English Personalized Learning Based on Large-scale Open Network Courses

Yang

2024

SCPE

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In the context of Big data, large-scale open online courses increase learning paths for learners, but in the face of countless high-quality curriculum resources, it is easy for derivative learners to face the dilemma of rich curriculum resources but difficult to choose resources, which leads to information maze for learners. How to help learners quickly and accurately find their own learning resources in the explosive growth of MOOC resources is an urgent problem in the field of education Big data. However, the traditional Collaborative filtering recommendation technology does not perform well when dealing with sparse data and cold start. The recommendation content is repeated and can not effectively deal with high-dimensional and nonlinear data of online learning users, resulting in low efficiency of resource recommendation. Therefore, the study adopts a deep belief network (DBN) to construct a personalized resource recommendation model. The model combines the learner behavior characteristics with the curriculum resource content attribute characteristics to form the learner feature vector. The parameters of the model are adjusted according to the characteristics of learners. Through experiments, the proposed model has shown good performance. The experiment explored the effects of training set size, learner characteristics, and GPU on model performance. The experimental results show that when the training set proportion is 100%, the RMSE, Accuracy, Recall, and F1 values of the model are 0.76, 0.946, 0.957, and 0.951, respectively. When the model is trained using a training set containing learner features, the RMSE, Accuracy, Recall, and F1 values of the model are 0.75, 0.962, 0.908, and 0.958, respectively. After using GPU to accelerate the model, the running time of the model decreased from 360 minutes to 90 minutes. The results indicate that the model cannot effectively mine data information when the degree of correlation between sample information is low. The richer the relationships between samples, the better the performance of the model. Simultaneously learning hunger feature vectors and learner behavior feature vectors for training can significantly improve the recommendation accuracy of the model. The main contribution of this study is to propose a recommendation method based on DBN classification to replace traditional similarity calculation methods, using DBN's efficient feature abstraction and feature extraction capabilities to fully explore learners' interest and preference for course resources. In addition, in view of the common problems of cold start and data sparsity in traditional Collaborative filtering recommendation methods, the research deeply mines the characteristics of learners' Demographics and curriculum resources' content attributes, and constructs a learner interest model based on DBN combined with learners' behavior characteristics, which effectively solves the problems of cold start and data sparsity, as well as the inaccurate expression of learners' interest preferences for curriculum resources.

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Section: Research Direction Research Resultsmentioning

confidence: 99%

Application Analysis of English Personalized Learning Based on Large-scale Open Network Courses

Yang

2024

SCPE

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Histopathological breast cancer classification using CNN

Simonyan,

Badejo,

Weijin

2024

Materials Today: Proceedings

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Research on Online Learner Modeling and Course Recommendation Based on Emotional Factors

Wang

2022

Scientific Programming

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With the popularization of online education and the idea of learning anytime and anywhere, more and more learners search and learn courses of various disciplines through online learning platforms to meet their personal knowledge needs. With the increase of the number of courses, it is difficult for learners to find the courses they want quickly and accurately; that is, they encounter the problems of information overload and cognitive maze. Therefore, how to recommend personalized courses for learners according to their preferences has become one of the important problems that need to be solved urgently to improve the service quality of courses in online learning platforms. Therefore, in order to improve the accuracy of course recommendation, it is necessary to build an accurate and complete learner model. In order to improve the application effect of recommendation, this paper focuses on the recommendation method of emotional factors to improve the recommendation efficiency of learning resources. The traditional recommendation model is a method based on the user’s purchase behavior and historical information. However, in the emotional factors, the effect of traditional recommendation is limited. This paper proposes a recommendation method based on emotional factors, which may consider the emotional and psychological factors of scholars according to the learning content of learners. The experimental results show that the learner model incorporating learners’ affection can reflect learners’ preferences more accurately, and the use of deep neural factorization machine for curriculum recommendation can effectively improve the accuracy of curriculum recommendation.

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Breast Cancer Detection Using Deep Belief Network by Applying Feature Extraction on Various Classifiers

Cited by 4 publications

References 8 publications

Application Analysis of English Personalized Learning Based on Large-scale Open Network Courses

Application Analysis of English Personalized Learning Based on Large-scale Open Network Courses

Histopathological breast cancer classification using CNN

Research on Online Learner Modeling and Course Recommendation Based on Emotional Factors

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