Sadik Kamel Gharghan
@mtu.edu.iq
Department of Medical Instrumentation Techniques Engineering/Electrical Engineering Technical College
Middle Technical University
RESEARCH INTERESTS
His research interests include energy-efficient wireless sensor networks, biomedical sensors, microcontroller applications, WSN Localisation based on artificial intelligence techniques and optimisation algorithms, indoor and outdoor path loss modelling, harvesting technique, wireless power transfer, jamming on direct sequence spread spectrum, drone in medical applications.
116
Scopus Publications
Scopus Publications
- Intelligent air pollution prediction algorithm-based optimized random forest regression for reducing asthmatic attacks
Saif Saad Fakhrulddin, Vaibhav Bhatt, and Sadik Kamel Gharghan
Knowledge E DMCC
Introduction: Air pollution can trigger the attack in asthmatic patients if uncontrolled. Previous works focused on controlling pollution by proposing algorithms to predict air pollution. While these prediction algorithms save patients from attack triggers, they have limitations such as prediction accuracy, mathematical complexity, and lack of adequate patient notification systems.Materials and methods: This study proposed a novel Intelligent Air Pollution Prediction (IAPP) algorithm based on optimizing Random Forest Regression (RFR) to predict air pollution and send an alert message to the patient and hospital in real time. Meanwhile, IAPP utilized reliable data from Internet of Things (IoT)-based air pollution detection nodes. The performance of IAPP was evaluated in a real-world environment during the peak pollutant season to test the prediction accuracy of air pollution.Results: Results showed that the proposed IAPP achieved a high prediction accuracy of 99.98% with an R-squared value of 0.99. This demonstrated that the IAPP algorithm based on the RFR model can effectively protect asthmatic patients from attack triggers.Conclusion: As a result, the IAPP algorithm reduces hospital visits during high pollution and enables patients to complete their daily activities without obstacles or absence. - A novel predictive algorithm for integrating asthma attack monitor device with smartphone
Saif Saad Fakhrulddin, Vaibhav Bhatt, and Sadik Kamel Gharghan
Elsevier BV - Machine learning algorithms for diagnosing of knee osteoarthritis using radiographs
Hussein Najeeb Abdalla, Sadik Kamel Gharghan, and Hayfaa Abdulzahra Atee
AIP Publishing - A new asthma attack prediction based on vital sensors
Saif Saad Fakhrulddin, Vaibhav Bhatt, and Sadik Kamel Gharghan
AIP Publishing - Biomedical signals monitoring system for elderly people using bluetooth technology
Ahmed Bashar Fakhri, Kok Beng Gan, and Sadik Kamel Gharghan
AIP Publishing - Machine learning techniques for cardiovascular disease detection through heart sound analysis: A review
Ali Hussein Shaker, Ibrahim Amer Ibrahim, and Sadik Kamel Gharghan
AIP Publishing - An intelligent system for monitoring and predicting Parkinson's disease: A review
Ahmed Hammed Ayyal, Sadik Kamel Gharghan, and Ammar Hussein Mutlag
AIP Publishing - A survey on the integration of machine learning algorithms with wireless sensor networks for predicting diabetic foot complications
Ahmed Akeel Ali, Sadik Kamel Gharghan, and Adnan Hussein Ali
AIP Publishing - Neural network-based smart stick system for navigation and vital signs monitoring for visually impaired individuals
Sadik Kamel Gharghan, Asaower Ahmad Marir, Lina Akram Saleh, and Jameel Kadhim Abed
AIP Publishing - Eye-to-text communication via advanced optimization techniques and artificial neural networks: Review
Mohammed Riyadh Abbas, Ammar Hussein Mutlag, Sadik Kamel Gharghan, and Rozita Jailani
AIP Publishing - Wireless power transmission for powering environmental sensors
Hussein S. Kamel, Sadik Kamel Gharghan, and Ibrahim Amer Ibrahim
AIP Publishing - Cardiovascular diseases prediction using machine learning algorithms: A comparative study
Ali Hussein Shaker, Ibrahim Amer Ibrahim, and Sadik Kamel Gharghan
AIP Publishing - Artificial neural network for pneumonia disease detection based on chest x-ray photograph
Shahad Ahmed Salih, Sadik Kamel Gharghan, Jinan F. Mahdi, and Ali O. Abid Noor
AIP Publishing - Earthquake detection system based on LoRa communication technology
Siraj Qays Mahdi, Sadik Kamel Gharghan, and Ammar Hussein Mutlag
AIP Publishing - Machine learning algorithms based on voice measurements for detecting Parkinson's disease
Ahmed Hammed Ayyal, Sadik Kamel Gharghan, and Ammar Hussein Mutlag
AIP Publishing - Deep learning approaches for osteoarthritis diagnosis via patient activity data and medical imaging: A review
Hussein Najeeb Abdalla, Sadik Kamel Gharghan, and Hayfaa Abdulzahra Atee
AIP Publishing - Wireless power transfer-based single layer inductive coupling for biomedical implantable devices
Hala K. Abduljaleel and Sadik Kamel Gharghan
AIP Publishing - Diagnosis diabetic foot-based machine learning algorithms
Ahmed Akeel Ali, Sadik Kamel Gharghan, and Adnan Hussein Ali
AIP Publishing - Heart disease classification based on combination of PCA /ANFIS model
Ali Ghazi Shabeeb, Huda Ali Hashim, and Sadik Kamel Gharghan
Springer Science and Business Media LLC - Smart Stick Navigation System for Visually Impaired Based on Machine Learning Algorithms Using Sensors Data
Sadik Kamel Gharghan, Hussein S. Kamel, Asaower Ahmad Marir, and Lina Akram Saleh
MDPI AG
Visually Impaired People (VIP) face significant challenges in their daily lives, relying on others or trained dogs for assistance when navigating outdoors. Researchers have developed the Smart Stick (SS) system as a more effective aid than traditional ones to address these challenges. Developing and utilizing the SS systems for VIP improves mobility, reliability, safety, and accessibility. These systems help users by identifying obstacles and hazards, keeping VIP safe and efficient. This paper presents the design and real-world implementation of an SS using an Arduino Nano microcontroller, GPS, GSM module, heart rate sensor, ultrasonic sensor, moisture sensor, vibration motor, and Buzzer. Based on sensor data, the SS can provide warning signals to VIP about the presence of obstacles and hazards around them. Several Machine Learning (ML) algorithms were used to improve the SS alert decision accuracy. Therefore, this paper used sensor data to train and test ten ML algorithms to find the most effective alert decision accuracy. Based on the ML algorithms, the alert decision, including the presence of obstacles, environmental conditions, and user health conditions, was examined using several performance metrics. Results showed that the AdaBoost, Gradient boosting, and Random Forest ML algorithms outperformed others and achieved an AUC and specificity of 100%, with 99.9% accuracy, F1-score, precision, recall, and MCC in the cross-validation phase. Integrating sensor data with ML algorithms revealed that the SS enables VIP to live independently and move safely without assistance. - Path-Loss Model for Wireless Sensor Networks in Air Pollution Environments Leveraging of Drones
Muthna J. Fadhil, Sadik Kamel Gharghan, and Thamir R. Saeed
Springer Science and Business Media LLC - A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques
Sadik Kamel Gharghan and Huda Ali Hashim
Elsevier BV - Air pollution forecasting based on wireless communications: review
Muthna J. Fadhil, Sadik Kamel Gharghan, and Thamir R. Saeed
Springer Science and Business Media LLC - Deep learning-based COVID-19 diagnosis using CT scans with laboratory and physiological parameters
Humam Adnan Sameer, Ammar Hussein Mutlag, and Sadik Kamel Gharghan
Institution of Engineering and Technology (IET)
The global economy has been dramatically impacted by COVID-19, which has spread to be a pandemic. COVID-19 virus affects the respiratory system, causing difficulty breathing in the patient. It is crucial to identify and treat infections as soon as possible. Traditional diagnostic reverse transcription-polymerase chain reaction (RT-PCR) methods require more time to find the infection. A high infection rate, slow laboratory analysis, and delayed test results caused the widespread and uncontrolled spread of the disease. This study aims to diagnose the COVID-19 epidemic by leveraging a modified convolutional neural network (CNN) to quickly and safely predict the disease's appearance from computed tomography (CT) scan images and a laboratory and physiological parameters dataset. A dataset representing 500 patients was used to train, test, and validate the CNN model with results in detecting COVID-19 having an accuracy, sensitivity, specificity, and F1-score of 99.33%, 99.09%, 99.52%, and 99.24%, respectively. These experimental results suggest that our strategy performs better than previously published approaches. - Human brain hypothermia monitoring and treatment systems
Rabab Talib Abdullah, Sadik Kamel Gharghan, and Ahmed J. Abid
AIP Publishing