A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning

Dargan, Shaveta; Kumar, Munish; Ayyagari, Maruthi Rohit; Kumar, Gulshan

doi:10.1007/s11831-019-09344-w

Cited by 753 publications

(316 citation statements)

References 65 publications

Supporting

Mentioning

314

Contrasting

Unclassified

Order By: Relevance

“…In the following figure 11, it can be observed that the age group (31)(32)(33)(34)(35)(36)(37)(38)(39)(40) has the highest frequency ratio as compared to other defined age groups. Furthermore, defined age groups are classified based on rental user gender, i.e., male and female.…”

Section: ) Rental Book Analysis Based On Rental User Age Groupmentioning

confidence: 93%

“…The following figure 12 presents the percentage (%) analysis of rental books based on defined user groups. It is evident that the age group (31)(32)(33)(34)(35)(36)(37)(38)(39)(40) has the highest percentage (%) value of 33.71% as compared to all other defined age groups. It can also be observed that the age group (41)(42)(43)(44)(45)(46)(47)(48)(49)(50) has the second-highest rental frequency percentage (%) value of 22.34%.…”

Section: ) Rental Book Analysis Based On Rental User Age Groupmentioning

confidence: 94%

“…There are various machine learning (ML) techniques available to analyze and process discovered patterns from large data automatically in order to build a robust predictive model [15], [37]. Machine learning techniques are commonly used in predicting the future outcome of the resources [38].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The rental frequency percentage of the summer season is 28.85%, which is high percentage value as compared to all other seasons, i.e., Autumn, Spring, and Winter of 23.16%, 23.9%, and 24.09%, respectively. We also investigated the relationship between rental book and the rental user's age and found that the age-group (31)(32)(33)(34)(35)(36)(37)(38)(39)(40) is borrowed the large number of books of 33.71% as compared to other defined user agegroups. The rental book analysis based on the user home address summarizes that the home address ''Donghwa-ro 1-gil'' is a high rental book frequency of 26,457 as compared to other listed home addresses of the rental users.…”

Section: ) Discovered Patters and Featuresmentioning

confidence: 99%

See 3 more Smart Citations

Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries

et al. 2020

View full text Add to dashboard Cite

Large scale data and predictive analytics are the most challenging tasks in the field of academic data mining. Academic libraries are a great source of information and knowledge to provide a wide range of services to meet end-user requirements. Due to the rapid changes in the educational environment and availability of huge library rental book data, it is required to utilize data mining and machine learning techniques in the context of the academic library to extract and analyze underlying knowledge from rental book data, which is important to facilitate library administration to drive better future decisions to improve and manage library resources effectively. These are the following resources, such as managing future demands of the library books, selection and arrangement of the books, operational efficiency, and also improve the quality of interaction between the library and end-users, etc. This work uses and analyzes a real dataset collected from the library of Jeju National University, the Republic of Korea. The dataset contains 2,211,413 rental book records including 173671 unique book records, 57203 unique number of the rental user, and 78 data parameters. In this paper, we propose a novel model to analyze and predict library rental book data to facilitates library administration in order to plan and manage library resources effectively and provide better services to end-users. The proposed model consists of two different modules; library data analysis and prediction modules. Firstly, we use data mining techniques to analyze and extract useful underlying patterns from library rental book data, which can lead to plan and manage library resources effectively. Secondly, a novel prediction model is proposed based on Deep Neural Network (DNN), Support Vector Regressor (SVR), and Random Forest (RF) to predict future usage of the academic libraries rental books. The performance results of the implemented regression models are evaluated in terms of MAE, MSE, and RMSE. In this paper, it is found that the DNN model performs significantly better than SVR and RF. The experimentation results show that the proposed model improves the future usage of library books to facilitate library administration to plan and manage library resources effectively. Based on the proposed model results, the academic library administration can easily plan and manage resources effectively to provide quality services to end-users. INDEX TERMS Data mining, academic libraries, big data, machine learning, predictive analysis.

show abstract

Section: ) Rental Book Analysis Based On Rental User Age Groupmentioning

confidence: 93%

Section: ) Rental Book Analysis Based On Rental User Age Groupmentioning

confidence: 94%

Section: Literature Reviewmentioning

confidence: 99%

Section: ) Discovered Patters and Featuresmentioning

confidence: 99%

See 2 more Smart Citations

Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In recent years, the research of DCNN in the field of radiology shows that the performance of this algorithm is equivalent to that of radiologist. With the continuous development of this field, the possible types and quantities of deep learning are also increasing 25 . Compared with the traditional feature extraction method, DCNN method directly extracts features from the data set without the need for segmentation and complex manual operations 26,27 .…”

mentioning

confidence: 99%

An intelligent platform for ultrasound diagnosis of thyroid nodules

Hang

Chen

et al. 2020

Sci Rep

View full text Add to dashboard Cite

This paper proposed a non-segmentation radiological method for classification of benign and malignant thyroid tumors using B mode ultrasound data. This method aimed to combine the advantages of morphological information provided by ultrasound and convolutional neural networks in automatic feature extraction and accurate classification. Compared with the traditional feature extraction method, this method directly extracted features from the data set without the need for segmentation and manual operations. 861 benign nodule images and 740 malignant nodule images were collected for training data. A deep convolution neural network VGG-16 was constructed to analyze test data including 100 malignant nodule images and 109 benign nodule images. A nine fold cross validation was performed for training and testing of the classifier. The results showed that the method had an accuracy of 86.12%, a sensitivity of 87%, and a specificity of 85.32%. This computeraided method demonstrated comparable diagnostic performance with the result reported by an experienced radiologist based on American college of radiology thyroid imaging reporting and data system (ACR TI-RADS) (accuracy: 87.56%, sensitivity: 92%, and specificity: 83.49%). The automation advantage of this method suggested application potential in computer-aided diagnosis of thyroid cancer.

show abstract