Sadik Kamel Gharghan

Scopus Publications

Diabetic Foot Ulcer Prediction Using Machine Learning Algorithms-Based on Multimodal Sensors Measurements
International Journal of Integrated Engineering, 2026
Machine Learning Algorithms and Their Effects on Crop Production in Agriculture: A Review
Nada M. Khalil Al-Ani, Sadik Kamel Gharghan, Ziad Qais Al-Abbasi, Razan Alenezi, Muhammed Abdelhameed
Journal of Techniques, 2026
Agricultural production faces challenges, such as water scarcity, climate change, and the rising demand for food to feed the growing global population. Incorporating wireless sensor networks (WSNs), Unmanned Aerial Vehicles (UAVs), and Machine Learning (ML) enables an efficient Synergistic approach for crop monitoring, predicting, and managing. WSNs prefer environmental data instantaneously, UAVs provide data collection for large-scale aerial, and ML processes this data to make actionable decisions. The primary objective of this review is to explore the comprehensive effect of WSNs, UAVs, and ML in the agriculture sector, emphasizing crop yield, environmental sustainability, and optimizing resources. In addition, this review aims to detail the benefits and limitations of ML technology and its impact on farming. Integrating state-of-the-art technologies has expressed several key areas of significant potential, such as crop health monitoring, precision irrigation, yield prediction, disease and pest detection, and resource efficiency. Hence, the collaboration of recent technologies in modern farming significantly enhances monitoring and management in real-time, improves productivity and sustainability, and addresses global food security challenges. At the same time, this would shape agriculture's future through innovative promotion and more sustainable farming practices.
A novel anomaly detection approach for structural health monitoring using multiple linear regression technique
Siraj Qays Mahdi, Sadik Kamel Gharghan, Ammar Hussein Mutlag
Journal of Building Pathology and Rehabilitation, 2026
A Hybrid Algorithm for Optimising Power Consumption of Wireless Sensor Networks in Precision Agriculture
Nada M. Khalil Al‐Ani, Sadik Kamel Gharghan, Ziad Qais Al‐Abbasi, Ali Al‐Naji, Javaan Chahl
Iet Networks, 2026
Recently, precision agriculture has used wireless sensor networks (WSNs) to gain valuable insights and improve crop yields, promoting efficient resource use and data‐driven decisions. However, WSNs face challenges, such as high power consumption from continuous sensing, data processing and communication, especially in large‐scale setups, which limits their lifespan. This paper focuses on reducing power use in agricultural WSN sensor nodes during data transmission of soil moisture, rainfall, light intensity, air temperature and humidity from the transmitting sensor node to the base station. Four algorithms are proposed to cut power consumption. First, a sleep/wake (S/W) scheme using a simple duty cycle called S/W‐DC. Second, the S/W scheme combined with adaptive data sampling (ADS) based on redundant data (RD), called S/W‐ADS‐RD. Third, the S/W scheme integrated with dynamic voltage scaling (DVS), named S/W‐DVS. Fourth, a hybrid of all three, called S/W‐ADS‐RD‐DVS. The sensor uses a 12 V/5 W solar panel for energy harvesting to maintain operation. The hybrid algorithm achieved 99.232% power savings and extended battery life to approximately 1.83 years. During a 6‐h session, data transmission was reduced by 99.93%. This research could significantly improve WSN efficiency in precision agriculture and can be applied to energy‐efficient WSN deployment across various fields, supporting Internet of Things (IoT) applications.
High-Accuracy Diabetic Foot Prediction Using Thermal Imaging and KNN-Based Machine Learning
Ahmed Akeel Ali, Sadik Kamel Gharghan, Adnan Hussein Ali
Sensing and Imaging, 2025
Intelligent air pollution prediction algorithm-based optimized random forest regression for reducing asthmatic attacks
Saif Saad Fakhrulddin, Vaibhav Bhatt, Sadik Kamel Gharghan
Journal of Air Pollution and Health, 2025
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.
Integrating WSN and IoT for enhanced structural health monitoring in real-time using neural networks: a novel approach
Siraj Qays Mahdi, Sadik Kamel Gharghan, Hayder Amer Al-Baghdadi, Ammar Hussein Mutlag
Asian Journal of Civil Engineering, 2025
Review of Biomedical Signal-Based Control Systems for Electric Wheelchairs
Hanan Jabbar Abdulkareem, Sadik Kamel Gharghan, Saad Mutashar
International Journal of Engineering and Technology Innovation, 2025
Mobility impairments significantly challenge independence and quality of life, especially for individuals who rely on wheelchairs. Recent advances in intelligent control systems for electric wheelchairs aim to address these challenges by enabling hands-free operation using biomedical signals. This review aims to provide a comprehensive overview of control strategies that utilize physiological and biological signals—such as head movements, voice commands, electroencephalogram, electrooculography, and electromyography—for wheelchair navigation. The study categorizes and compares these systems based on input modality, signal type, adaptability, and integration with soft computing techniques. Key findings highlight the strengths of multimodal approaches, the challenges posed by signal noise and user fatigue, and the need for improved real-world validation. By synthesizing the current research landscape, this review identifies future research directions focused on enhancing usability, safety, and accessibility in smart wheelchair technologies.
A novel predictive algorithm for integrating asthma attack monitor device with smartphone
Saif Saad Fakhrulddin, Vaibhav Bhatt, Sadik Kamel Gharghan
Measurement Journal of the International Measurement Confederation, 2025
Drug Abuse Detection Systems Based on Machine Learning Using Data Fusion of Biomedical Sensor Network: A Comparative Study
Marwa Adel Abdul Reda, Sadik Kamel Gharghan, Mohammed Saeed Mohammed
Icatei 2025 International Conference on Advanced Technologies in Energy and Informatic, 2025
Hypertension Prediction Using Machine Learning Algorithms
Alhasan Nabeh Fadhil, Sadik Kamel Gharghan, Hayfaa Abdulzahra Atee
Icatei 2025 International Conference on Advanced Technologies in Energy and Informatic, 2025
Intelligent Wheelchair Control System Using Real-Time Head Movement Detection
Hanan Jabbar Abdulkareem, Sadik Kamel Gharghan, Saad Mutashar
Mathematical Modelling of Engineering Problems, 2025
Multi-Layer Square Coil-Based Wireless Power Transfer for Biomedical Implants
Hala Kamal Abduljaleel, Sadik Kamel Gharghan, Ahmed Jamal Abdullah Al-Gburi
Progress in Electromagnetics Research B, 2025
A Comprehensive Review of Using WSNs and Drones for Improving Crop Production in Precision Agriculture
Nada M. Khalil Al‐Ani, Sadik Kamel Gharghan, Ziad Qais Al‐Abbasi, Hasan Kahtan
Iet Wireless Sensor Systems, 2025
Wireless power transfer-based single layer inductive coupling for biomedical implantable devices
Hala K. Abduljaleel, Sadik Kamel Gharghan
Aip Conference Proceedings, 2024
A new asthma attack prediction based on vital sensors
Saif Saad Fakhrulddin, Vaibhav Bhatt, Sadik Kamel Gharghan
Aip Conference Proceedings, 2024
Machine learning algorithms based on voice measurements for detecting Parkinson's disease
Ahmed Hammed Ayyal, Sadik Kamel Gharghan, Ammar Hussein Mutlag
Aip Conference Proceedings, 2024
Earthquake detection system based on LoRa communication technology
Siraj Qays Mahdi, Sadik Kamel Gharghan, Ammar Hussein Mutlag
Aip Conference Proceedings, 2024
A survey on the integration of machine learning algorithms with wireless sensor networks for predicting diabetic foot complications
Ahmed Akeel Ali, Sadik Kamel Gharghan, Adnan Hussein Ali
Aip Conference Proceedings, 2024
Cardiovascular diseases prediction using machine learning algorithms: A comparative study
Ali Hussein Shaker, Ibrahim Amer Ibrahim, Sadik Kamel Gharghan
Aip Conference Proceedings, 2024
An intelligent system for monitoring and predicting Parkinson's disease: A review
Ahmed Hammed Ayyal, Sadik Kamel Gharghan, Ammar Hussein Mutlag
Aip Conference Proceedings, 2024
Diagnosis diabetic foot-based machine learning algorithms
Ahmed Akeel Ali, Sadik Kamel Gharghan, Adnan Hussein Ali
Aip Conference Proceedings, 2024
Wireless power transmission for powering environmental sensors
Hussein S. Kamel, Sadik Kamel Gharghan, Ibrahim Amer Ibrahim
Aip Conference Proceedings, 2024
Machine learning algorithms for diagnosing of knee osteoarthritis using radiographs
Hussein Najeeb Abdalla, Sadik Kamel Gharghan, Hayfaa Abdulzahra Atee
Aip Conference Proceedings, 2024
Deep learning approaches for osteoarthritis diagnosis via patient activity data and medical imaging: A review
Hussein Najeeb Abdalla, Sadik Kamel Gharghan, Hayfaa Abdulzahra Atee
Aip Conference Proceedings, 2024

Sadik Kamel Gharghan

RESEARCH INTERESTS

Scopus Publications