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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Marwa Mawfaq Mohamedsheet Al-Hatab
Northern Technical University
Computer Science & IT Engineering

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Using IoT technology for monitoring Alzheimer’s and elderly patients Aseel Thamer Ebrahem; Marwa Mawfaq Mohamedsheet Al-Hatab; Entisar Younis Abd Al-Jabbar; Warqaa H. Alkhaled; Zaid Husham Al-Sawaff
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp986-994

Abstract

Alzheimer’s disease (AD) is a neurological disorder that results in the death of brain cells, causing memory loss, behavioral changes, and cognitive impairment. It drastically affects the individual’s work and social life, often leading to death, and is now the sixth leading cause of mortality worldwide. AD patients have limited mobility, which restricts their movement outside their homes. Thankfully, new internet of things (IoT) applications have made it possible to monitor people with various illnesses in their everyday lives, providing valuable assistance to caregivers. This study aims to create an IoT prototype that can locate an AD patient in real time and remind them to take their medication on schedule via an alarm. The small, lightweight, portable patient carrier has a NodeMCU-23DSP board, a Neo-06 global positioning system (GPS) module, and a wireless modem/Wi-Fi router. Remote patient follow-up through the Blynk 2.0 application on computers and Android devices allows for monitoring of the patient’s medication regimen and daily activities. As AD patients struggle with memory and organization, the prototype’s design enables monitoring of a patient’s course of medication, making it easier for caregivers to provide the necessary assistance. The prototype was tested to demonstrate its efficiency and performance.
Stacking ensemble techniques for automated peripheral blood cell classification using Inception v3 features Marwa Mawfaq Mohamedsheet Al-Hatab; Maysaloon Abed Qasim; Nawar A. Sultan
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.pp2247-2259

Abstract

Robust distinction of blood cells is crucial in clinical evaluation. Manual examination is slow and exposed to errors. This work investigates using machine learning (ML) techniques for automated classification of eight categories of peripheral blood cell types from multi-color images. The Inception v3 network was used to extract features, a split of 66%/34% were used to evaluate the model along with 20-fold cross-validation. To reduce computational complexity, principal component analysis (PCA) was used to reduce the 2048-dimensional feature vectors to 100 components. Among all classifiers used, the highest performance without using PCA was achieved using the support vector machine (SVM) with an accuracy equal to 93.4% and an area under the curve (AUC) of 0.996. Using PCA, affected monocytes and immature granulocytes most due to the slight reduction in the accuracy and AUC which became 90.1% and to 0.991 respectively. Results were further enhanced when a stacked ensemble of neural network (NN), logistic regression (LR), and SVM were used, achieving an accuracy of 95.2% and an AUC of 0.998. The obtained findings confirmed the effectiveness of using stacked ensembles in providing a robust, high accuracy framework for automated blood cell classification, while PCA efficiently reduced dimensions with minimal performance loss.
Optimizing blood cell classification: evaluating feature dimensionality and validation strategies Ruaa H. Ali Al-Mallah; Marwa Mawfaq Mohamedsheet Al-Hatab; Maysaloon Abed Qasim
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 1: February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i1.27269

Abstract

Manual blood cell classification is time consuming and may lead to inconsistent results. This study aims to assist pathologists in diagnosing hematological disorders using machine learning (ML) techniques for automated classification of blood cells in multi-color test images, distinguishing red blood cells (RBCs) and white blood cells (WBCs). Features were extracted using the InceptionV3 network, and several ML models were evaluated for classifying blood cells into eight categories. Two validation strategies: a 66%–34% train–test split and 20-fold cross-validation were applied. The effect of dimensionality reduction through principal component analysis (PCA) was also examined, reducing the feature space from 2,048 to 100 components. Among all models, support vector machine (SVM) achieved highest performance, with 93.4% accuracy and an area under the curve (AUC) of 0.996 without PCA, and 90.1% accuracy with an AUC of 0.991 after PCA. Although PCA slightly reduced accuracy, it improved computational efficiency. Overall, SVM provided the most accurate, stable, and generalizable classification results for automated blood cell analysis.
Co-Authors Aseel Thamer Ebrahem Entisar Younis Abd Al-Jabbar Maysaloon Abed Qasim Nawar A. Sultan Ruaa H. Ali Al-Mallah Warqaa H. Alkhaled Zaid Husham Al-Sawaff