An artificial neural network approach for detecting skin cancer

Sugiarti, Sugiarti; Yuhandri, Yuhandri; Na’am, Jufriadif; Indra, Dolly; Santony, Julius

doi:10.12928/telkomnika.v17i2.9547

Cited by 24 publications

(17 citation statements)

References 23 publications

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“…Fourth, software design. At this stage, mobile application design is carried out, including implanting the perceptron method into a mobile application, the Perceptron method is used because it produces input -1, 0, 1, and one of the most famous prediction methods in artificial neural networks (ANN) [26], and has a high accuracy above 86% [27]. One method of having a high success rate in the recognition system is that Perceptron is proven by research conducted by Thepade in 2018 in recognizing face gender with successful rate reaches 99.658% [28], also Mishra in 2015 in detecting automatic extraction of water bodies from landsat imagery [29].…”

Section: Methodsmentioning

confidence: 99%

Detection of water quality in crayfish ponds with IoT

et al. 2021

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Data from the Central Bureau of Statistics shows that during the first quarter of 2014 to 2019, the tendency of Indonesian crayfish export increases by an average of 3.54% in 2019 and reach 7.09 million USD. This number still fails to meet the global market demand caused by poor water quality due to cultivators’ lack of experience and education. Two parameters measured in water quality are temperature and pH. Thus, a device was made using IoT to maintain those conditions in order to increase the viability of the crayfish in the pond. The perceptron method is used to classify water quality based on those parameters. To send the data, ESP8266 is used as an intermediary for Arduino and cloud server. The result is that the information about the ponds’ condition can be seen via a smartphone. The method gives a value of 98.06% accurate in determining water quality.

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Section: Methodsmentioning

confidence: 99%

Detection of water quality in crayfish ponds with IoT

et al. 2021

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“…Artificial Neural Network or ANN is a method that adapts the neuron system in the human brain into solving engineering or technology problem such as prediction values, classification, observation etc. ANN is very useful for modelling or predicting even though the input output data relationship is unknown [28][29][30][31][32]. A simple configuration of ANN is built up of three layers.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Application of artificial neural network in sizing a stand-alone photovoltaic system: a review

Nor

Salimin

Abdullah

et al. 2020

IJPEDS

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<span>Artiﬁcial Neural Network (ANN) techniques are becoming useful in the current era due to the vast development of the current computer technologies. ANN has been used in various fields especially in the field of science and technology. One of the advantage that makes ANN so interesting is the ANN’s ability to learn the input and output relationship even though the relationship is non-linear. In addition, ANN is also useful for modelling, optimization, prediction, forecasting, and controlling systems. The main objective of this paper is to present a review of the ANN techniques for sizing a stand-alone photovoltaic (PV) system. The review in this paper shows the potential of ANN as a design tool for a stand-alone PV. In addition, ANN is very useful to improve the sizing process of the stand-alone PV system. The sizing process is of paramount importance to a stand-alone PV system in order to make sure the system can generate ample electrical energy to supply the load demand.</span>

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“…An ANN in its simplest form (Figure 3 (a)) consists of; an input layer, an output layer and a hidden layer, which allows you to modify the weights of the hidden layer according to the input values and the margin of error obtained at the output. Unlike ANN, DLNN (Figure 3 (b)) has more than two hidden layers and many cascaded neurons to perform some transformations of the training data from the input layer to the output layer [16,25,26]. The DLNN can be trained with different algorithms, whose main function is to estimate the weights of the neurons in the different layers of the network.…”

Section: Deep Learning Neuronal Networkmentioning

confidence: 99%

Strategy to determine the foot plantar center of pressure of a person through deep learning neural networks

2020

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Some case studies treated by physiotherapists or orthopedists to measure the alignment of the lower extremities during a gait cycle are based on empirical methods of visual observation. This methodology does not guarantee total success, since it depends on the experience of the specialist, what can cause irreversible damage to patients, such as: hip displacement, wear and overload of the joints of a single lower limb. Although, this problem has been addressed in the investigation by means of devices implementation with sensors or methods of processing sequences of images and videos, this topic is still under investigation because the current methods depend on many external elements and data given by an expert in the area. Therefore, this paper proposes a partial solution to this problem by systematizing the experience of a specialist through a computational learning method.

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An artificial neural network approach for detecting skin cancer

Cited by 24 publications

References 23 publications

Detection of water quality in crayfish ponds with IoT

Detection of water quality in crayfish ponds with IoT

Application of artificial neural network in sizing a stand-alone photovoltaic system: a review

Strategy to determine the foot plantar center of pressure of a person through deep learning neural networks

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