Zubaida Rehman- IoT security – Best Researcher Award

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Zubaida Rehman- IoT security

Dr.   Zubaida  Rehman distinguished academic and researcher in the field IoT security. The focal point of her research was to enhance energy production while minimizing wake effects, a challenge that she addressed effectively through the application of Genetic Algorithms. This comprehensive study not only deepened her understanding of cutting-edge technologies in the field but also contributed to the ongoing discourse surrounding sustainable energy solutions.

Education

From 2012 to 2015, she pursued her Master of Computer Science at the National University of Computer and Emerging Sciences in Islamabad, Pakistan. Throughout this academic journey, she specialized in various domains such as Computational Intelligence, Advanced Databases, Cloud Computing, and Evolutionary Computation. The culmination of her academic endeavors was manifested in her research thesis, where she delved into the intricate realm of the Wind Turbine Layout Optimization Problem. Employing the innovative approach of Genetic Algorithms, she endeavored to optimize the layout design by incorporating diverse turbine sizes.

Professional Profiles:

Research Interest
With a diverse set of technical skills and a keen interest in cutting-edge research, she has a strong foundation in data science, encompassing data analysis and business analytics. Her expertise extends to areas such as cybersecurity, Internet of Things (IoT), and machine learning. Adept in computational intelligence, she excels in addressing real-world optimization problems with a focus on cost-effective and efficient analyses. Her proficiency in big data management within cloud environments is demonstrated through her work on effective scheduling for incoming data stream processing.
Work Experience
Her professional journey includes roles as a Software Engineer at KN Technologies Islamabad (Part Time) from January 2016 to December 2016, where she contributed to projects developed in C, C#, and PHP, demonstrating her skills in project planning and execution. As a Software Developer at Technology Architect Islamabad from January 2014 to December 2015, she engaged in the design and testing of ongoing projects, particularly focusing on Infinite-Space Shortest Path Problems and Semicontractive Dynamic Programming.
In the academic realm, she has made notable contributions with research papers published in renowned conferences such as IEEE Conference on Cloud Computing and Big Data Analysis 2016. Her survey paper on “Survey on Branch Prediction Techniques” was published in RJIIT 2017. She has further showcased her research acumen through term papers and reviews, including topics like the automatic design of multi-objective ant colony optimization algorithms, emerging trends in cloud computing, imperfect distributed learning in traffic light control, and a survey paper on the Rapid Multi-Agent Development Toolkit. Through her extensive academic and professional background, she continues to be a valuable asset in the intersection of technology, research, and software development.
Publication

Tumlumbe Juliana Chengula – Computer Vision -Best Researcher Award

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Tumlumbe Juliana Chengula  – Computer Vision

Tumlumbe Juliana Chengula  a distinguished academic and researcher in the field of Computer Vision. He possesses proficiency in several programming languages, with a focus on Python. His expertise extends to utilizing various tools such as Tableau, QGIS, PyTorch, and Tensorflow, showcasing a well-rounded skill set in data science and machine learning. Additionally, he has earned certifications in Data Science Tools, SQL for Data Science, and Machine Learning with Python, all from IBM. Furthermore, he has completed the “Using Python for Research” certification from Harvard University, underscoring his commitment to continuous learning and staying at the forefront of relevant technologies in the field. These skills and honors collectively highlight his comprehensive knowledge and dedication to the dynamic and evolving realm of data science.

Eduvation

His master’s studies at Amirkabir University of Technology (AUT) in Tehran, Iran, from September 2018 to October 2021, he specialized in Electrical Engineering with a focus on Control. During this period, he maintained a GPA of 3.5/4, and his final project earned a perfect score of 4/4. Prior to his master’s degree, he completed his Bachelor’s in Power Electrical Engineering at Yazd University, Iran, from September 2014 to August 2018, achieving a GPA of 3.1/4.

Professional Profiles:

Employment Experience
As a Graduate Research Assistant at South Carolina State University since August 2022, she has been actively engaged in the collection, recording, and analysis of transportation data, utilizing proficient tools such as Python, Tableau, PowerBI, and QGIS. Her research focus involves the application of cutting-edge technologies, including Machine Learning, Deep Learning, and Artificial Intelligence, to address challenges within the transportation industry.
Over the course of her tenure, she has showcased her contributions by delivering six impactful presentations on her research in Machine Learning and Artificial Intelligence at seven distinguished transportation conferences. Furthermore, her commitment to scholarly dissemination is evident through the submission and acceptance of two peer-reviewed articles, which are slated for presentation at the prestigious 2024 Annual Transportation Research Board conference. These accomplishments underscore her dedication to advancing knowledge and providing innovative solutions to enhance the efficiency and effectiveness of the transportation sector.
Research Project Highlights
She has made notable contributions to the field of transportation through her research endeavors, addressing critical issues with cutting-edge technologies. One of her significant projects involves enhancing road safety through Ensemble Learning, specifically in detecting driver anomalies using vehicle inbuilt cameras. In another study, she employed Topic Modeling and Categorical Correlations to unveil patterns associated with autonomous vehicle disengagements, shedding light on crucial aspects of autonomous driving systems.
Furthermore, she delved into the realm of quantum computing to improve classification performance in traffic sign recognition, utilizing an optimized hybrid classical-quantum approach. Additionally, her research extends to the realm of sustainable urban mobility, where she has applied Explainable Artificial Intelligence to predict bike-sharing station capacity. These diverse projects showcase her proficiency in utilizing advanced technologies and methodologies to address multifaceted challenges within the transportation sector.
Publication

Improving road safety with ensemble learning: Detecting driver anomalies using vehicle inbuilt cameras

Machine Learning with Applications
2023-12 | Journal article
DOI: 10.1016/j.mlwa.2023.100510
CONTRIBUTORS: Tumlumbe Juliana Chengula; Judith Mwakalonge; Gurcan Comert; Saidi Siuhi

Mahmoud Owais |Transportation

Published on by Network Science

Mahmoud Owais |Transportation

Academician/Research Scholar

Mahmoud Owais is an Associate Professor in Transportation Planning and traffic engineering in the
Civil Engineering Department, Faculty of Engineering, Assiut University. He was born on 1/10/1987,
El-Mansoura, Egypt. He graduated from Assiut University – Faculty of Engineering in 2009.

 

Professional Profiles:

Education:

  • He obtained his M.S. in Transportation Planning from the University of Assiut in 2011.
  • He undertook his Ph.D. in Transit Planning from the same University in 2014

Academic Posts

  • > Faculty of Engineering, Assiut Univ., Egypt Associate Professor 2020 – until now FT.
    > College of Engineering, Sphinx Univ., New Assiut, Egypt Associate Professor 2022 – until now PT/on leave.

Main Research Interest areas

Public Transportation – Transportation data modeling and simulation – Traffic Accidents
analysis – Statistics and theory of probability – Combinatorial Optimization – Heuristics
and Meta-heuristics – Artificial Intelligence& Machine Learning – Neural Networks and
Deep Learning – Traffic Engineering Operation Management & Control – Pavement Design and Traffic Micro-Simulation.

Research Significance
Dr. Owais’s citation record, “more than 500 citations to date; hindex=14 & iindex=18,” indicates that he has had a major influence on other scholars’ understanding of topics such as
solving transit network design problems, optimizing traffic estimation results, and achieving
dynamic traffic assignment.

publications