Performance Comparison of Neural Network Backpropagation Algorithms in Detecting P300 Signals from Mind-Speller Data

Artificial intelligence is the leading branch of technology and innovation. The utility of artificial intelligence in the field of medicine is also remarkable. From drug discovery and development till the introduction of products in the market, artificial intelligence can play its role. As people age, they are more prone to be affected by eye diseases around the globe. Early diagnosis and detection help in minimizing the risk of vision loss and providing a quality life. With the help of artificial intelligence, the workload of humans and manmade errors can be reduced to an extent. The need for artificial intelligence in the area of ophthalmic is also found to be significant. As people age, they are more prone to be affected by eye diseases around the globe. Early diagnosis and detection help in minimizing the risk of vision loss and providing a quality life. In this review, we elaborated on the use of artificial intelligence in the field of pharmaceutical product development mainly with its application in ophthalmic care. AI in the future has a high potential to increase the success rate in the drug discovery phase has already been established. The application of artificial intelligence for drug development, diagnosis, and treatment is also reported with the scientific evidence in this paper.

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Implementation of Modified Backpropagation with Conjugate Gradient as Microarray Data Classifier with Binary Particle Swarm Optimization as Feature Selection for Cancer Detection

Amrullah

Adiwijaya

Astuti

2020

SISFOKOM

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Cancer is one of the deadliest diseases in the world that needs to be handled as early as possible. One of the methods to detect the presence of cancer cells early on is by using microarray data. Microarray data can store human gene expression and use it to classify cancer cells. But one of the challenges of using microarray is its vast number of features, not proportional to its small number of samples. To resolve that problem, dimensionality reduction is needed to reduce the number of features stored in microarray data. Binary Particle Swarm Optimization (BPSO) is one of the methods to reduce dimensionality of microarray data that can increase classification performance. Although when combined with Backpropagation, BPSO still shows a relatively low performance. In this research, Modified Backpropagation with Conjugate Gradient is used to classify data that has been reduced with BPSO. The average accuracy result of BPSO+CGBP is 86.1%, giving it an improvement compared to BPSO+BP which averaged to 80.8%.

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Performance Comparison of Neural Network Backpropagation Algorithms in Detecting P300 Signals from Mind-Speller Data

Cited by 3 publications

References 19 publications

Letter Identification in EEG Signals Using Scalograms

Letter Identification in EEG Signals Using Scalograms

A Review on an Artificial Intelligence Based Ophthalmic Application

Implementation of Modified Backpropagation with Conjugate Gradient as Microarray Data Classifier with Binary Particle Swarm Optimization as Feature Selection for Cancer Detection

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