Artificial neural network for classification of depressive and normal in EEG

Mohan, Y. Krishna; Chee, Sia Seng; Xin, Donica Kan Pei; Foong, Lee Poh

doi:10.1109/iecbes.2016.7843459

Cited by 99 publications

(62 citation statements)

References 8 publications

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“…Thus the superiority of the adopted RP-GWO-based Hep-2 cell classification scheme has been confirmed from the simulation outcomes. F1-score Measures GWO [31] WOA [35] LA [30] DSLA […”

Section: Resultsmentioning

confidence: 99%

“…The extracted features I f are further given to NN classifier for classification purpose. NN [30] considers the features as input as given by (11), where nu indicates the count of features.…”

Section: Nn-based Classificationmentioning

confidence: 99%

“…Here if ra insists α as the best position, then the update is as per (29). If ra insists η as the best position, then the update is as per (30) and if ra insists the ζ as the best position, then the update is as per (31). The pseudocode of the proposed RP-GWO by Algorithm 2 (see Fig.…”

Section: Proposed Rp-gwo Modelmentioning

confidence: 99%

“…The extracted features

I_{f}

are further given to NN classifier for classification purpose. NN [30] considers the features as input as given by (11), where nu indicates the count of features.

I_{f} = (}{, I_{f_{1}}, I_{f_{2}}, \dots, I_{f_{n u}})

Since the NN includes an input layer, output layer, and hidden layer, it is a prerequisite to find out the output of the hidden layer before computing the overall network output.…”

Section: Techniques Adopted For Hep‐2 Cell Classification In the Secomentioning

confidence: 99%

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Optimised hybrid classifiers for automatic HEp‐2 cell classification

Chandran

Jose

2020

IET image process

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This paper introduces a new HEp-2 cell classification model under two phases. In the initial phase, the input image is segmented using the morphological operations (opening and closing), and the segmented image is given for Convolutional Neural Network (CNN) classifier that gives the classified output. In the second phase, the given input is processed under (i) segmentation process (ii) Feature Extraction, and (iii) Classification. From the segmented images; the features like Gray level co-occurrence Matrix TGLCM) and Gray level Run Length Matrix (GLRM) are extracted. After extracting the features, they are subjected to a classification process, where Neural Network (NN) is used. Finally, the mean of both classified output (first phase and second phase) is considered to be the final classified output. As the main contribution, to enhance the classification accuracy, the hidden neurons of both classifiers (CNN and NN) are optimally chosen during the classification process. To make this possible, this paper aims to propose a new Randomized Update based Grey Wolf Optimization (RP-GWO) algorithm. Finally, the performance of the implemented approach is compared over other conventional approaches and its superiority is proven with respect to certain measures.

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“…Thus the superiority of the adopted RP-GWO-based Hep-2 cell classification scheme has been confirmed from the simulation outcomes. F1-score Measures GWO [31] WOA [35] LA [30] DSLA […”

Section: Resultsmentioning

confidence: 99%

Section: Nn-based Classificationmentioning

confidence: 99%

Section: Proposed Rp-gwo Modelmentioning

confidence: 99%

“…The extracted features

I_{f}

are further given to NN classifier for classification purpose. NN [30] considers the features as input as given by (11), where nu indicates the count of features.

I_{f} = (}{, I_{f_{1}}, I_{f_{2}}, \dots, I_{f_{n u}})

Since the NN includes an input layer, output layer, and hidden layer, it is a prerequisite to find out the output of the hidden layer before computing the overall network output.…”

Section: Techniques Adopted For Hep‐2 Cell Classification In the Secomentioning

confidence: 99%

See 2 more Smart Citations

Optimised hybrid classifiers for automatic HEp‐2 cell classification

Chandran

Jose

2020

IET image process

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“…The findings provided shreds of evidence that depression is associated with a hyperactive right hemisphere. Mohan et al 44 modeled the raw EEG signals by DFNN to obtain information about the human brain waves. They found that the signals collected from the central (C3 and C4) regions are marginally higher compared with other brain regions, which can be used to distinguish the depressed and normal subjects from the brain wave signals.…”

Section: Electroencephalogram Datamentioning

confidence: 99%

Deep learning in mental health outcome research: a scoping review

et al. 2020

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Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual's physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients' historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.

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Rider exploitation based whale optimization algorithm for rainfall prediction from meteorological data

Das¹,

Mishra²

2022

Concurrency and Computation

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The rainfall prediction is one of the major problems in meteorological department since it is related to the economy and human life. In addition, the heavy rainfall is the major problem of natural disasters like drought and flood. The accuracy of the rainfall forecasting has been spotted for countries like India and economy is greatly dependent by agriculture. This paper introduces a new rainfall prediction model consists of Feature extraction and Prediction. The features are extracted by modified Empirical Mean Curve Decomposition namely Empirical Mean Median Curve Decomposition, and then subjected to statistical analysis. The results of the statistical features are processed by the classification process. Neural network (NN) is used for the rainfall prediction. To improve the accuracy of proposed work, an optimal weight is and predicted result are multiplied, then, the weight in NN is optimally selected by a Rider Exploitation based Whale Optimization Algorithm. Finally, the performance of the proposed work is compared and proved over the state‐of‐the‐art models in terms of prediction rate.

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Artificial neural network for classification of depressive and normal in EEG

Cited by 99 publications

References 8 publications

Optimised hybrid classifiers for automatic HEp‐2 cell classification

Optimised hybrid classifiers for automatic HEp‐2 cell classification

Deep learning in mental health outcome research: a scoping review

Rider exploitation based whale optimization algorithm for rainfall prediction from meteorological data

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