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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Aisyah Hartini Jahidin
University of Malaya
Computer Science & IT Engineering

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IQ level prediction and cross-relational analysis with perceptual ability using EEG-based SVM classification model Noor Hidayah Ros Azamin; Mohd Nasir Taib; Aisyah Hartini Jahidin; Dyg Suzana Awang; Megat Syahirul Amin Megat Ali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (518.92 KB) | DOI: 10.11591/ijai.v8.i4.pp436-442

Abstract

This paper presents IQ level prediction and cross-relational analysis with perceptual ability using EEG-based SVM classification model. The study hypothesized that measure of perceptual ability and intelligence is strongly connected through the brain’s attention regulatory mechanism. Therefore, an intelligent classification model should be able to predict and map IQ levels from a dataset associated with varying levels of perception. 115 samples of resting EEG is acquired from the left prefrontal cortex. Sixty-five is used for perceptual ability analysis via CTMT, while another fifty is used in the development of IQ level classification model using SVM. The mean pattern of theta, alpha and beta bands show positive correlation between perceptual ability and IQ level datasets. Meanwhile, the developed SVM model outperforms the previous ANN method; yielding 100% accuracy for training and testing. Subsequently, the classification model successfully predicts and mapped samples from the perceptual ability dataset to its corresponding IQ levels with 98.5% accuracy. Therefore, validity of the study is confirmed through positive correlation demonstrated by both traits of cognition using the pattern of mean power ratio features, and successful prediction of IQ level for perceptual ability dataset via SVM classification model.
Classification of nutrient deficiency in oil palms from leaf images using convolutional neural network Muhammad Ikmal Hafiz Razali; Muhammad Asraf Hairuddin; Aisyah Hartini Jahidin; Mohd Hanafi Mat Som; Megat Syahirul Amin Megat Ali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Oil palm is a perennial plant that thrives well in tropical climate. Apart from humid environment, the plant also requires a wide variety of nutrients. Any deficiencies will directly affect its growth and palm oil production. These can often be detected from the change of leaf colour and texture. Deviations from the standard dark green colour indicates lack of certain nutrients. Therefore, this study proposes convolutional neural network (CNN) to classify nutrient deficiency in oil palms using leaf images. A total of 180 leaf images are acquired using standardized protocol. The samples are evenly distributed into healthy, nitrogen-deficient, and potassium-deficient groups. Residual network (ResNet)-50, visual geometry group-16 (VGG16), Densely connected network (DenseNet)-201, and AlexNet are trained and tested using the randomized samples. Each attained classification accuracies of 96.7%, 100%, 98.3%, and 100% respectively. Despite yielding similar performance, AlexNet is the more computational efficient architecture with less convolutional layers compared to VGG-16.
Co-Authors Dyg Suzana Awang Megat Syahirul Amin Megat Ali Mohd Hanafi Mat Som Mohd Nasir Taib Muhammad Asraf Hairuddin Muhammad Ikmal Hafiz Razali Noor Hidayah Ros Azamin