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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Fahad Taha Al-Dhief
Universiti Kebangsaan Malaysia
Computer Science & IT Engineering

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Detection of autism spectrum disorder using multilayer perceptron classifier Ahmed Q. Hadi; Saif H. Alrubaee; Fahad Taha Al-Dhief; Ammar AbdRaba Sakran
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2103-2112

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by distinctive challenges in both verbal and nonverbal communication, social interaction, and repetitive behaviors. However, the diagnosis of ASD usually occurs within a clinical setting, conducted by licensed professionals, and often involves lengthy and costly procedures. On the other hand, machine learning holds significant promise for improving diagnostic and intervention research within the behavioral sciences, particularly in research concerning ASD disease. Hence, a deep investigation of a machine learning algorithm for ASD detection is crucial. Therefore, this paper presented a new system for differentiating the ASD samples from non-ASD (i.e., healthy) samples. The samples of ASD have been compiled from toddlers. The multilayer perceptron (MLP) algorithm is used to classify ASD samples from non-ASD samples. The proposed MLP classifier is implemented based on different numbers of neurons (i.e., nodes). In other words, the proposed MLP classifier started with 10 neurons and finished with 50 neurons with an increment step of 5 neurons. The outcomes demonstrate that the MLP classifier acquired different results concerning the number of neurons. The MLP obtained the best performance, reaching an accuracy rate of 100% in identifying ASD cases.
Text emotion recognition based on deep learning and attention with whale optimization algorithm Tabarek Alwan Tuib; Mohammad-Reza Feizi-Derakhshi; Yaqdhan Mahmood Hussein; Fahad Taha Al-Dhief
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3692-3702

Abstract

Text emotion recognition (TER) represents one of the crucial tasks in natural language processing (NLP), and it is highly important for many applications. To improve the performance of TER, advanced deep learning (DL) model GRU-BiGRU-CNN-Att is presented in this research. It is a combination of gated recurrent units (GRU), bidirectional gated recurrent units (BiGRU) with a convolutional neural network (CNN), and an attention mechanism. Using a GRU, BiGRU, and CNN as part of a deep feature extraction model, combined with an attention mechanism that gives important information different weights, the method that is proposed in the present work enhances the quality of the word vectors and leads to increasing sentiment analysis judgment accuracy. The whale optimization algorithm (WOA), which selects the most informative features for further enhancing the model, is used lastly for the optimization of the feature selection process. After such optimization, those chosen features are trained on a multi-layer perceptron (MLP), successfully combining machine learning (ML) and DL approaches for improving TER. A varied corpus of tweets, sentences, and dialogues had been used in the present work for the assessment of the performance of the suggested model. The proposed method achieves 83.76% accuracy in emotion recognition.
Co-Authors Ahmed Q. Hadi Ammar AbdRaba Sakran Mohammad-Reza Feizi-Derakhshi Saif H. Alrubaee Tabarek Alwan Tuib Yaqdhan Mahmood Hussein