Classification of dyslexic and normal children during resting condition using KDE and MLP

Karim, Izzah; Wahab, Abdul; Kamaruddin, Norhaslinda

doi:10.1109/ict4m.2013.6518886

Cited by 23 publications

(13 citation statements)

References 13 publications

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“…In this work, we focused on the frontal region for eye-open and eyeclose phases. This is because based on our prior knowledge, eye-open and eye-close session can distinguish between dyslexic and non-dyslexic children [17]. Hence, in this paper, such attention is also dedicated to study whether there is distinction between addicted and non-addicted.…”

Section: Resultsmentioning

confidence: 99%

Pornography Addiction Detection based on Neurophysiological Computational Approach

Kamaruddin

Rahman

Handiyani

2018

IJEECS

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<span lang="EN">The rise of Internet access, social media and availability of smart phones intensify the epidemic of pornography addiction especially among younger teenagers. Such scenario may offer many side effects to the individual such as alteration of the behavior, changes in moral value and rejection to normal community convention. Hence, it is imperative to detect pornography addiction as early as possible. In this paper, a method of using brain signal from frontal area captured using EEG is proposed to detect whether the participant may have porn addiction or otherwise. It acts as a complementary approach to common psychological questionnaire. Experimental results show that the addicted participants had low alpha waves activity in the frontal brain region compared to non-addicted participants. It can be observed using power spectra computed using Low Resolution Electromagnetic Tomography (LORETA). The theta band also show there is disparity between addicted and non-addicted. However, the distinction is not as obvious as alpha band. Subsequently, more work need to be conducted to further test the validity of the hypothesis. It is envisaged that with more participants and further investigation, the proposed method will be the initial step to groundbreaking way of understanding the way porn addiction affects the brain.</span>

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Section: Resultsmentioning

confidence: 99%

Pornography Addiction Detection based on Neurophysiological Computational Approach

Kamaruddin

Rahman

Handiyani

2018

IJEECS

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“…The results are analysed and compared to see the performance of each machine learning classifiers. From the previous studies on analysing the EEG data, only the resting state of eye open and eye close data are used because it is sufficient to provide the initial screening of the subjects [25,26].…”

Section: Resultsmentioning

confidence: 99%

Neuro-physiological porn addiction detection using machine learning approach

Kamaruddin

Wahab

Rozaidi

2019

IJEECS

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<span>Pornography is a portrayal of sexual subject contents for the exclusive purpose of sexual arousal that can lead to addiction. The availability and easy accessibility of the Internet connectivity have created unprecedented opportunities for sexual education, learning, and growth for adolescences to be in the rise. Hence, the risk of porn addiction developed by teenagers has also increased due to highly prevalent porn consumption. To date, the only available means of detecting porn addiction is through questionnaire. However, while answering the questions, participants may suppress or exaggerate their answers because porn addiction is considered taboo in the community. Hence, the purpose of this project is to develop an engine with multiple classifiers to recognize porn addiction using electroencephalography (EEG) signals and to compare classifiers performance. In the experimental study, the neuro-physiological signals of EEG data were collected previously in Indonesia among students age 9 to 13 years old by researchers from the International Islamic University Malaysia (IIUM). The EEG data were pre-processed, and relevant features are extracted using Mel-Frequency Cepstral Coefficients (MFCC). Then, the features are classified to produce the outputs of valance and arousal. Subsequently, three different classifiers of Multilayer Perceptron (MLP), Naive Bayesian (NB), and Random Forest (RF) are employed to determine whether the participant is a porn addict or otherwise. The experimental results show that the MLP classifier yields slightly better accuracy compared to Naïve Bayes and Random Forest classifiers making the MLP classifier preferable for porn addiction recognition. Although this work is still at infancy stage, it is envisaged for the work to be expanded for comprehensive porn addiction recognition system so that early intervention and appropriate support can be given for the teenagers with pornography addiction problem.</span>

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“…The proposed system is Automated Prediction of dyslexia, Dyscalculia, Dysgraphia [18] in School going of age group 6 to 10 years using supervised machine learning Algorithm. Diagnosing Learning Difficulties with respect reading, writing, spellings, language processing in School going children [5] using supervised machine learning algorithm.…”

Section: Proposed Modelmentioning

confidence: 99%

“…Early diagnosis reduce stress and anxiety in children and also can help in getting exemption with foreign language etc [1]. In this paper two different papers on early diagnosis of dyslexia are one child to the other, such as problems in differentiating left to right, confusion with homophones and following set of sequential instructions [5], so Electroencephalography EEG is used to monitor the brain activity using metal electrodes and collecting signals that are compared with fMRI Functional Magnetic Resonance Imaging. The EEG signals collected then classified in to delta (δ), theta (θ), alpha (α), beta (β) and gamma (γ) bands the following table lists the frequencies Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…The EEG signals collected then classified in to delta (δ), theta (θ), alpha (α), beta (β) and gamma (γ) bands the following table lists the frequencies Table 1. EEG Bands and Description [5] Gibson's test is used for testing brain skills like cognitive, motor skills, math, memory abilities, And EEG is used in understanding brain process and related functions and "Power spectral density" is used to extract features to recognize differences in brain EEG processing in kids with dyslexia [6] Seven key areas are considered for the matrices they are. 80 children (40 boys and 40 girls)records of 7 to 13 age, were considered, which was analysed and proposed a computerized analytical model which included computing system, with dataset that differentiates into "non dyslexic(Normal )", or "Dyslexic", or ADHD or inattention etc".…”

Section: Introductionmentioning

confidence: 99%

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Review: Mass Screening framework for children with dyslexia using IOT and computing analysis

Mulakaluri¹,

Girisha²

2020

EAI Endorsed Transactions on Internet of Things

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Dyslexia is a medical disorder due to which children have difficulties in learning and reproducing the learnt concepts. In this context children are mostly considered to be not interested or negligent towards their studies. According to dyslexia association of India 10-15 % of children enrolled in schools suffer from some type of Dyslexia. Awareness on Learning difficulties and detection is a complicated. As detection of LD requires diverse features it requires proper guidance and intervention. The issues with explicit learning difficulties in kids have been a reason for worry to parents and educators. Its challenging for the teachers and educationalists to differentiate students with LD and other students. This paper mainly analysis of the paper titled "Diagnosis of Dyslexia using Computing Analysis", Electroencephalogram (EEG) as a tool is used for understanding of brain process and related functions, Number of factors related to Dyslexia and "Power spectral density" is extracted using Gibson test for brain skills. And to identify differences in brain processing using EEG Technology in kids with dyslexia and non-dyslexic. Data sets are generated classifying Dyslexic and non dyslexic and were analysed using the K-means, Fuzzy and ANN classifiers. then the results obtained from these classifiers differentiate between the three different groups (dyslexic, non-dyslexic and disordered). And the next paper "Spatial Blockchain-based Secure Mass Screening Framework for Children with Dyslexia", this paper was mainly focused in detection of symptoms of dyslexia at an age of 8-11 years, so that children can be assisted with various assisting tools and take technologic support so that children can take part in regular schools. This paper is based on Mobile Edge Computing, cloud computing.

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Classification of dyslexic and normal children during resting condition using KDE and MLP

Cited by 23 publications

References 13 publications

Pornography Addiction Detection based on Neurophysiological Computational Approach

Pornography Addiction Detection based on Neurophysiological Computational Approach

Neuro-physiological porn addiction detection using machine learning approach

Review: Mass Screening framework for children with dyslexia using IOT and computing analysis

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