An Efficient Deep Neural Network Multilayer Perceptron Based Classifier in Healthcare System

Krishna, Chittem Leela; Reddy, Poli Venkata Subba

doi:10.1109/iccct2.2019.8824913

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…The ML process of a perceptron starts with random weights assigned to each input, which are summed and passed through an activation function that produces an output. The model training process continues with multiple iterations, adjusting the weights, where the ultimate goal is to minimize the total error in the output, i.e., the difference between the output of the model and the actual outputs that should be achieved with the given data instances [41,42].…”

Section: Deep Learningmentioning

confidence: 99%

“…Moreover, when a line is fitted on the data, the output of the function (i.e., predictions) can range from negative infinity to positive infinity (not limited between any ranges). In these cases, non-linear activation functions are a useful tool to remap available data points to a specific range between the output of the model and the actual outputs that should be achieved with the given data instances [41,42].…”

Section: Deep Learningmentioning

confidence: 99%

“…Micromachines 2022, 13, 260 3 between the output of the model and the actual outputs that should be achieved with given data instances [41,42].…”

Section: And Y(v I ) = Tanhmentioning

confidence: 99%

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Deep Learning-Enabled Technologies for Bioimage Analysis

et al. 2022

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Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.

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Section: Deep Learningmentioning

confidence: 99%

Section: Deep Learningmentioning

confidence: 99%

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Deep Learning-Enabled Technologies for Bioimage Analysis

et al. 2022

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“…Artificial neural network (ANN) is biologically inspired by the human brain, consisting of a set of interconnected processing nodes distributed across several layers [5,22,23]. The processing nodes in an ANN are termed as neurons, spread across different layers viz., input, hidden and output layers.…”

Section: Backpropagation Neural Networkmentioning

confidence: 99%

“…Deep Neural Network is an Artificial Neural Network with more number of hidden layers between input and output layers[23]. The structure of the proposed Deep neural network (DNN) consists of twenty hidden layers interconnecting input and output layers.…”

mentioning

confidence: 99%

Deep Neural Networks for the Classification of Bank Marketing Data using Data Reduction Techniques

Krishna¹,

Reddy²

2019

IJRTE

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The amount of data belonging to different domains are being stored rapidly in various repositories across the globe. Extracting useful information from the huge volumes of data is always difficult due to the dynamic nature of data being stored. Data Mining is a knowledge discovery process used to extract the hidden information from the data stored in various repositories, termed as warehouses in the form of patterns. One of the popular tasks of data mining is Classification, which deals with the process of distinguishing every instance of a data set into one of the predefined class labels. Banking system is one of the realworld domains, which collects huge number of client data on a daily basis. In this work, we have collected two variants of the bank marketing data set pertaining to a Portuguese financial institution consisting of 41188 and 45211 instances and performed classification on them using two data reduction techniques. Attribute subset selection has been performed on the first data set and the training data with the selected features are used in classification. Principal Component Analysis has been performed on the second data set and the training data with the extracted features are used in classification. A deep neural network classification algorithm based on Backpropagation has been developed to perform classification on both the data sets. Finally, comparisons are made on the performance of each deep neural network classifier with the four standard classifiers, namely Decision trees, Naïve Bayes, Support vector machines, and k-nearest neighbors. It has been found that the deep neural network classifier outperforms the existing classifiers in terms of accuracy

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