Draft:Dalia Mahmoud

Dalia Mahmoud, born in Sudan, is a university professor and researcher in the field of electronics engineering and industrial control, with extensive experience in artificial intelligence, smart systems, and industrial automation. She currently serves as the Dean of the Faculty of Engineering at Al-Neelain University in Sudan, making her the first woman to hold this position at a Sudanese university.

Dalia Mahmoud
Born	19|11|1981
Citizenship	Sudanese
Education	PhD in Electronic and Electrical Engineering
Alma mater	University of Al-Neelain, Specialization (Electronic Engineering), (Bachelor's/Master's/PhD)
Years active	2006 | Until now
Board member of	Sudanese Engineering Society Institute of Electrical and Electronics Engineers (IEEE)
Children	3

Education and Early Life

Dalia Mahmoud was born on November 19, 1981, in Khartoum, Sudan. She grew up in an academically supportive environment, which played a significant role in shaping her interest in science and engineering from an early age.

She pursued her higher education at Al-Neelain University in Sudan, where she enrolled in the Faculty of Engineering and distinguished herself with outstanding academic performance. Throughout her educational journey, she obtained the following degrees:

• Bachelor’s Degree in Electronic Engineering (2004) – First-Class Honors She specialized in intelligent control systems, with her thesis focusing on the application of neural networks in industrial control systems to enhance performance and reliability. Her passion for research was evident during her studies, as she actively sought innovative technological solutions. She ranked first in her class in recognition of her academic and research excellence.
• Master’s Degree in Electronic Engineering (2008) – Industrial Control Specialization Her thesis focused on developing artificial intelligence systems for voice-controlled industrial processes. She concentrated on designing interactive automation systems based on pattern recognition and signal processing techniques. During her master’s studies, she presented several research papers at international conferences and participated in specialized workshops to expand her expertise in AI applications for control systems.
• Ph.D. in Electronic Engineering (2012) – Control Specialization Her doctoral thesis, titled "Brain Tumor Recognition Using Artificial Neural Networks and Fuzzy Systems," introduced advanced solutions in medical image processing using AI techniques. Her research significantly improved diagnostic accuracy and reduced errors in manual analysis. The findings of her research became a valuable reference for numerous medical and engineering studies, making a significant impact on AI-based medical applications.

Achievements

Dalia Mahmoud has had a distinguished academic and professional career, marked by numerous achievements that have contributed to advancements in electronic engineering, industrial automation, and artificial intelligence. Her accomplishments include:

• Securing research funding from the Ministry of Higher Education and Scientific Research in Sudan in 2017.
• Receiving a research grant from TWAS-IsDB for a project utilizing artificial intelligence to assess drinking water quality.
• Participating in UNESCO’s competition with a project titled "Automatic Water Treatment System for Rural Areas Based on Artificial Intelligence," organized by Sudan’s Water Harvesting Center, where she achieved top rankings.
• Submitting a proposal to the Khartoum State Government to enhance Sudanese agriculture through smart farming technologies.

Academic Achievements

• Dean of the Faculty of Engineering:
  • Al-Neelain University (March 2023 – Present).
  • University of Medical Sciences and Technology (December 2018 – March 2023), where she led the development of innovative academic programs in collaboration with the industry and organized an international conference in partnership with IEEE.

Key Publications

Dalia Mahmoud has an extensive record of research and scientific publications in the fields of artificial intelligence, industrial automation, and medical image processing. Her work has been published in peer-reviewed journals and prestigious international conferences, contributing to innovative solutions for enhancing industrial and medical processes. Below is a selection of her most notable publications:

1. Medical Image Processing Using Artificial Intelligence

• "Brain Tumor Recognition Using Artificial Neural Networks and Fuzzy Systems" Abstract: This study presents an advanced model for brain tumor detection with improved accuracy using a combination of neural networks and fuzzy systems, significantly enhancing medical diagnostics and reducing human errors.
• "Development of an Automated System for Parasite Detection in Blood Samples Using Deep Learning" Abstract: This research introduces a deep learning-based technique utilizing artificial neural networks (ANN) to accelerate and improve the accuracy of diagnosing infectious diseases.

2. Renewable Energy

• "Detection of Dust Accumulation on Solar Panels Using Image Processing and Deep Learning" Abstract: This study presents an innovative and sustainable solution to address environmental challenges in solar power plants, focusing on AI-powered automated monitoring of solar panels.
• "Reduction of Power Fluctuations in a Hybrid Solar-Wind Energy System Using Fractional-Order PID Controller" Abstract: Simulation results demonstrated the efficiency of the Fractional-Order PID (FOPID) controller in minimizing power fluctuations compared to conventional PID controllers, making it an effective solution for enhancing hybrid renewable energy systems.

3. Artificial Intelligence in Water Treatment and Environmental Applications

• "Utilizing Artificial Intelligence to Optimize Water Treatment Processes and Reduce Chemical Usage" Abstract: This research explores machine learning algorithms to determine optimal chemical dosages in water treatment processes, leading to cost reduction and improved water quality.

4. Robotics and Autonomous Systems

• "Development of a Computer Vision System for an Agricultural Harvester" Abstract: This study focuses on developing high-precision computer vision systems for harvesting machines, enabling AI-based crop identification and classification.
• "Design of a Humanoid Robot" Abstract: This research discusses the design and control mechanisms of a humanoid robot, exploring advancements in robotics engineering and AI-driven automation.

Conferences

• (2016)IEEE Life Science Grand Challenges Conference
• ICCCEEE20 (2021)
• Indo-US MSECAI Conference (2022)