Asthma, Alzheimer's and Dementia Disease Detection based on Voice Recognition using Multi-Layer Perceptron Algorithm

2018 ◽

Vol 10 (2) ◽

pp. 81-86

Author(s):

Marlinda Vasty Overbeek

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Facial Expressions ◽

Moving Objects ◽

Image Data ◽

Gradient Algorithm ◽

Multi Layer Perceptron ◽

Histogram Of Oriented Gradient ◽

Perceptron Algorithm ◽

Feature Extracting

This research focuses on the detection of human facial expressions using the Histogram of Oriented Gradient algorithm. Whereas for the classification algorithm, Convolutional Neural Network is used. Image data used in the form of seven different expressions of humans with the extraction of 48x48 pixels. The use of Histogram of Oriented Gradient as a feature extracting algorithm, because Histogram of Oriented Gradient is good to be used in detecting moving objects. Whereas Convolutional Neural Network is used because it is an improvement of the Multi Layer Perceptron algorithm. Of the three epoches done, it produced the best accuracy of 77% re-introduction of human facial expressions. These results are quite convincing because it only uses three epochs.

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A Novel PCA based Multi-layer perceptron algorithm for Maintainability Prediction

International Journal of Engineering Trends and Technology ◽

10.14445/22315381/ijett-v37p215 ◽

2016 ◽

Vol 37 (2) ◽

pp. 90-96

Author(s):

Deeksha Datyal ◽

◽

Aman Kaushik ◽

Abhishek Tomar

Keyword(s):

Multi Layer Perceptron ◽

Perceptron Algorithm

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POTENTIAL AREA MAPPING FOR SEAWEED AQUACULTURE BASED ON INTERVAL TYPE-2 FUZZY SETS AND MULTI-LAYER PERCEPTRON ALGORITHM.

10.21474/ijar01/8584 ◽

2019 ◽

Vol 7 (2) ◽

pp. 1079-1089

Author(s):

Sarinah a ◽

Keyword(s):

Fuzzy Sets ◽

Multi Layer Perceptron ◽

Seaweed Aquaculture ◽

Perceptron Algorithm ◽

Interval Type ◽

Potential Area

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Smart Plant Care Voice Recognition and Plant Disease Detection Using IOT

International Journal for Research in Engineering Application & Management ◽

10.35291/2454-9150.2020.0384 ◽

2020 ◽

pp. 166-170

Keyword(s):

Plant Disease ◽

Voice Recognition ◽

Analysis Data ◽

Plant Diseases ◽

Quality Data ◽

Agricultural Product ◽

Disease Detection ◽

Water Quality Data ◽

Analysis Process ◽

Tremendous Amount

Nowadays Internet of Things (IoT) and Remote Sensing (RS) techniques are used in different area of research for monitoring, collecting and analysis data from remote locations. Identification of the plant diseases is the key to preventing the losses in the yield and quantity of the agricultural product. The studies of the plant diseases mean the studies of visually observable patterns seen on the plant. Health monitoring and disease detection on plant is very critical for sustainable agriculture. It is very difficult to monitor the plant diseases manually. It requires tremendous amount of work, expertise in the plant diseases, and also require the excessive processing time. Hence, image processing is used for the detection of plant diseases and also care of plant using voice recognition. Through the Internet, Remote Data Center gets the real-time water quality data, and then analysis, process and record the data.

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Role Comparison between Deep Belief Neural Network and NeuroEvolution of Augmenting Topologies to Detect Diabetes

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.5.2.448 ◽

2021 ◽

Vol 5 (2) ◽

Author(s):

A.B.M. Wijaya ◽

D.S. Ikawahyuni ◽

Rospita Gea ◽

Febe Maedjaja

Keyword(s):

Neural Network ◽

Network Architecture ◽

The Other ◽

Multi Layer Perceptron ◽

Initial Weight ◽

Restricted Boltzmann Machines ◽

Regression Problem ◽

Perceptron Algorithm ◽

Other Hand ◽

The Neural Network

Diabetes in Indonesia has been perceived as a grave health problem and has been a concern since the early 1980’s [2]. The prevalence of diabetes in adults in Indonesia, as stated by IDF, was 6.2% with the total case amounting to 10.681.400. Moreover, Indonesia is also in the top ten global countries with the highest diabetes case in 2013. This research will investigate the role of Deep Belief Network (DBN) and NeuroEvolution of Augmenting Topology (NEAT) in solving regression problems in detecting diabetes. DBN works by processing the data in unsupervised network architectures. The algorithm puts Restricted Boltzmann Machines (RBM) into a stacked process. The output of the first RBM will be the input for the next RBM. On the other hand, the NEAT algorithm works by investigating the neural network architecture and evaluating the architecture using a multi-layer perceptron algorithm. Collaboration with a Genetic Algorithm in NEAT is the key process in architecture development. The research results showed that DBN could be utilized as the initial weight for Backpropagation Neural Network at 22.61% on average. On the other hand, the NEAT algorithm could be used by collaborating with a multi-layer perceptron to solve this regression problem by providing 74.5% confidence. This work also reveals potential works in the future by combining the Backpropagation algorithm with NEAT as an evaluation function and by combining it with DBN algorithms to process the produced initial weight.

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A hybrid particle swarm optimization and multi-layer perceptron algorithm for bivariate fractal analysis of rock fractures roughness

International Journal of Rock Mechanics and Mining Sciences ◽

10.1016/j.ijrmms.2012.12.028 ◽

2013 ◽

Vol 60 ◽

pp. 66-74 ◽

Cited By ~ 24

Author(s):

Nima Babanouri ◽

Saeed Karimi Nasab ◽

Soroor Sarafrazi

Keyword(s):

Particle Swarm Optimization ◽

Fractal Analysis ◽

Particle Swarm ◽

Rock Fractures ◽

Multi Layer Perceptron ◽

Swarm Optimization ◽

Hybrid Particle ◽

Perceptron Algorithm ◽

Hybrid Particle Swarm Optimization

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Distance transform-watershed segmentation and multi-layer perceptron algorithm to separate touching orange fruit in digital images

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/922/1/012047 ◽

2021 ◽

Vol 922 (1) ◽

pp. 012047

Author(s):

I S Nasution ◽

C Keke

Keyword(s):

National Standard ◽

Distance Transform ◽

Multi Layer Perceptron ◽

Citrus Fruits ◽

Watershed Segmentation ◽

Corn Kernel ◽

Perceptron Algorithm ◽

Class A ◽

Orange Fruit ◽

Accuracy Rates

Abstract An algorithm to separate touching oranges using a distance transform-watershed segmentation is presented. In this study, there are four classes of oranges, such as class A, B, C, and D, respectively. The size of each class is based on the Indonesian National Standard (SNI), the sample used is 168 oranges of which 140 are for training and 28 oranges are for testing. The image of citrus fruits was captured using Kinect v2 camera with a camera resolution of 1920 × 1080 pixels, the distance from the camera to the background is 23 cm. The images were captured in PNG format. The watersheds were computed based on the distance transformed by orange regions. The corresponding basins were finally used to split the falsely connected corn kernel by intersecting the basins with the corn kernel regions. Experimental results show that the multi-layer perceptrons have classification accuracy rates of 92.85%. The algorithm appears to be robust enough to separate most of the multiple touching scenarios.

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