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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Ammar AbdRaba Sakran
University of Information Technology and Communications
Computer Science & IT Engineering

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Towards secure smart cities: design and implementation of smart home digital communication system Nael Al-Shareefi; Sura Adil Abbas; Mohanad S. Alkhazraji; Ammar AbdRaba Sakran
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i1.pp271-277

Abstract

Home and building security are major concern in our daily life and digital smart door lock (DSDL) have become an essential part of these systems. In this paper, a secure DSDL which can grant access to home with a fingerprint is designed and implemented. An Arduino Nano microcontroller board, finger print sensor and servo motor have been utilized for lock/unlock door based on finger print. The DSDL is an automatic authenticate and validate the user for secure access. The implemented system aims to develop a cost−effective DSDL based on low−cost components compared to the systems already on the domestic market. The−ease of−use and cost−effectiveness makes the DSDL a strong competitor to the digital security system on the domestic market and outperforms it and suitable for security–based home automation systems.
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.
Co-Authors Ahmed Q. Hadi Fahad Taha Al-Dhief Mohanad S. Alkhazraji Nael Al-Shareefi Saif H. Alrubaee Sura Adil Abbas