A survey on the applications of Deep Neural Networks

Latha, R.; Sreekanth, G. R.; Suganthe, R.C.; Selvaraj, Ranjithkumar

doi:10.1109/iccci50826.2021.9457016

Cited by 17 publications

(5 citation statements)

References 12 publications

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“…With the continuous development of deep learning, its research has been extended to the recommender system field, which effectively improves the recommendation performance. The common network models of deep learning include attention mechanism [16,17], convolutional neural network (CNN) [18,19] and deep neural network (DNN) [20] and so on. Yang et al [21] proposed a neural network model named preference and context embedding (PACE) that combined CF and semi-supervised learning to predict user preferences by learning the embeddings of users and POIs jointly.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Point of Interest Recommendation Method Based on Bidirectional Matrix and Deep Belief Network

Song

et al. 2023

IEEJ Transactions Elec Engng

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Point‐of‐interest (POI) recommendation has always faced serious data sparsity problem. Most of the existing POI recommendation methods do not consider the bidirectional relationship between the user‐preference for locations and the location‐ attractiveness to users in the original dataset when solving the sparsity. For this problem, a point‐of‐interest recommendation method based on bidirectional matrix and deep belief network (POI_DBNBM) is proposed in this paper. Specially, from the perspectives of users and locations, we first construct two different matrices with bidirectional relationship by mapping the original dataset, and we use deep belief network (DBN) with strong learning ability to predict the two matrices. Then, a local–global weight optimization method is developed to weight and fuse the two result matrices obtained by DBN, so as to achieve the more precise user's rating prediction matrix for locations. The abundant experimental studies and evaluations are conducted on FourSquare dataset. Results are encouraging and better than those previously reported on the dataset. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Point of Interest Recommendation Method Based on Bidirectional Matrix and Deep Belief Network

Song

et al. 2023

IEEJ Transactions Elec Engng

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“…The solution is based on supervised deep learning algorithms which are widely and successfully used as solutions for problems requiring analysis of complex data sets [ 82 ]. As deep learning brings beneficial results in a number of applications [ 83 ], especially when analytical models (most often do to high number of parameters) are barely computable and hard to implement, it seems logical to apply it in our case.…”

Section: Proposed Wireless Link Selection Based On Deep Learningmentioning

confidence: 99%

Wireless Link Selection Methods for Maritime Communication Access Networks—A Deep Learning Approach

Hoeft

Gierłowski

Woźniak

2022

Sensors

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In recent years, we have been witnessing a growing interest in the subject of communication at sea. One of the promising solutions to enable widespread access to data transmission capabilities in coastal waters is the possibility of employing an on-shore wireless access infrastructure. However, such an infrastructure is a heterogeneous one, managed by many independent operators and utilizing a number of different communication technologies. If a moving sea vessel is to maintain a reliable communication within such a system, it needs to employ a set of network mechanisms dedicated for this purpose. In this paper, we provide a short overview of such requirements and overall characteristics of maritime communication, but our main focus is on the link selection procedure—an element of critical importance for the process of changing the device/system which the mobile vessel uses to retain communication with on-shore networks. The paper presents the concept of employing deep neural networks for the purpose of link selection. The proposed methods have been verified using propagation models dedicated to realistically represent the environment of maritime communications and compared to a number of currently popular solutions. The results of evaluation indicate a significant gain in both accuracy of predictions and reduction of the amount of test traffic which needs to be generated for measurements.

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“…The spectrum of applications of an artificial neural network is very wide. ANNs is used for the diagnosis of different diseases in both plant and animal caused due to different factors 19 . The capacity of ANNs to analyze large amounts of data and detect patterns warrants application in the analysis of medical images, classification of tumors, and prediction of survival 20 have somehow made easy research in medical biology.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Classification Of Mushrooms Using Artificial Neural Network

Gupta¹

2022

Preprint

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The artificial neural network (ANN) has had remarkable success in pattern recognition in recent years. It stands for a new learning paradigm in artificial intelligence (AI) and machine learning and has been applied to problems ranging from speech recognition to the prediction of protein secondary structure, cancers, and gene prediction. Recent breakthrough results in image analysis and speech recognition have generated massive interest in this field. However, the mathematical and computational methodology underlying deep learning models is very challenging, especially for interdisciplinary scientists. In this manuscript, a Neural Network model is used to classify whether a given mushroom is edible or poisonous using Tensorflow in Python based on the attributes present in the dataset. The dataset includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family Mushroom, drawn from The Audubon Society Field Guide to North American Mushrooms (1981).

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A survey on the applications of Deep Neural Networks

Cited by 17 publications

References 12 publications

Point of Interest Recommendation Method Based on Bidirectional Matrix and Deep Belief Network

Point of Interest Recommendation Method Based on Bidirectional Matrix and Deep Belief Network

Wireless Link Selection Methods for Maritime Communication Access Networks—A Deep Learning Approach

Classification Of Mushrooms Using Artificial Neural Network

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