Hussain A. Younis | Computer Science | Best Researcher Award

Mr. Hussain A. Younis | Computer Science | Best Researcher Award

College of Education at University of Basrah, Iraq

Hussain A. Younis is a dedicated researcher specializing in Artificial Intelligence, Security, Digital Image Processing, and Robotics. With a strong academic background from India and Malaysia and an affiliation with the University of Basrah, he has published impactful research in high-ranking journals and IEEE conferences. His work demonstrates interdisciplinary expertise, particularly in AI applications, human-robot interaction, and digital security. As an active IEEE member and potential reviewer, he is engaged in professional research communities. While his contributions are commendable, completing his Ph.D., increasing Q1/Q2 journal publications, securing research grants, and enhancing international collaborations would further strengthen his research profile. His growing citation impact and involvement in digital transformation research make him a strong candidate for the Best Researcher Award. With continued contributions in leadership, industry collaborations, and high-impact research, Hussain A. Younis is well-positioned to make significant advancements in the field of computer science and engineering.

Professional Profile 

Education

Hussain A. Younis has a strong academic background in computer science, with a Master’s degree earned in 2012 from India and ongoing Ph.D. studies since 2019 in Malaysia. His educational journey reflects a commitment to advanced research in Artificial Intelligence, Security, Digital Image Processing, and Robotics. His affiliation with the University of Basrah further strengthens his academic and research foundation, allowing him to contribute significantly to the field. Throughout his studies, he has focused on interdisciplinary research, exploring innovative solutions in AI-driven security systems, pattern recognition, and human-robot interaction. His academic pursuits have been complemented by active participation in professional organizations like IEEE, where he is a member and a prospective reviewer. While his research credentials are impressive, completing his Ph.D. will further solidify his expertise and credibility. His educational background positions him as a promising researcher with the potential to make impactful contributions to the scientific community.

Professional Experience

Hussain A. Younis has extensive professional experience in research and academia, with a focus on Artificial Intelligence, Security, Digital Image Processing, and Robotics. He is affiliated with the University of Basrah, where he contributes to both teaching and research in computer science. His work spans various interdisciplinary areas, including AI-driven security systems, pattern recognition, and human-robot interaction. As an IEEE member, he actively participates in academic conferences and serves as a prospective reviewer, further demonstrating his engagement in the global research community. His publications in high-impact journals and IEEE conferences highlight his contributions to advancing technology, particularly in robotics education, cybersecurity, and digital transformation. While his professional experience is commendable, taking on leadership roles in research projects, securing grants, and fostering international collaborations would further enhance his impact. His commitment to innovation and academic excellence makes him a valuable contributor to the scientific and technological landscape.

Research Interest

Hussain A. Younis’s research interests lie at the intersection of Artificial Intelligence, Security, Digital Image Processing, Pattern Recognition, and Robotics. His work explores innovative AI-driven solutions for enhancing security, improving human-robot interaction, and advancing digital transformation. He is particularly interested in speech recognition models, robotics in education, and secure cryptographic systems, contributing to cutting-edge developments in these fields. His research also addresses challenges in cybersecurity, focusing on encryption techniques and stream cipher systems to enhance data protection. Additionally, he investigates distinguishable patterns in image processing, applying AI techniques to optimize pattern recognition for various applications. Through his active participation in IEEE conferences and high-impact journal publications, he continuously contributes to technological advancements. His interdisciplinary approach and commitment to innovation position him as a promising researcher in AI and security, with the potential to make significant contributions to both academic research and real-world applications.

Award and Honor

Hussain A. Younis has been recognized for his contributions to research in Artificial Intelligence, Security, Digital Image Processing, and Robotics through various academic achievements and honors. His publications in high-impact journals and IEEE conferences reflect his dedication to advancing knowledge in these fields. As an active IEEE member, he has gained recognition within the global research community and has been invited to serve as a reviewer for IEEE conferences in Iraq. His work on robotics in education, cybersecurity, and encryption systems has earned significant attention, highlighting his expertise in interdisciplinary research. While his achievements are commendable, securing prestigious research grants, international fellowships, and industry collaborations would further enhance his profile. His commitment to innovation and scientific excellence makes him a strong contender for research awards, and with continued contributions, he is poised to receive greater recognition for his impact on the technological and academic landscape.

Research Skill

Hussain A. Younis possesses strong research skills in Artificial Intelligence, Security, Digital Image Processing, Pattern Recognition, and Robotics. His expertise lies in developing AI-driven solutions for security, speech recognition, and human-robot interaction, showcasing his ability to integrate multiple disciplines. He is proficient in data analysis, algorithm development, cryptographic security, and digital transformation technologies, enabling him to conduct high-quality research with practical applications. His experience in publishing in high-impact journals and IEEE conferences reflects his ability to conduct rigorous academic research and communicate findings effectively. As an active IEEE member and prospective reviewer, he demonstrates critical analysis and evaluation skills essential for scholarly contributions. Additionally, his research involves problem-solving, programming, and system design, particularly in robotics education and cybersecurity. To further enhance his research impact, focusing on international collaborations, advanced machine learning techniques, and securing research grants would strengthen his expertise and academic contributions.

Conclusion

Hussain A. Younis demonstrates strong research potential with impactful publications in AI, Robotics, and Security. His IEEE membership, interdisciplinary research, and international exposure make him a strong candidate for the Best Researcher Award. However, completing the Ph.D., increasing high-impact publications, and engaging in leadership roles would significantly enhance his eligibility for this prestigious award.

Publications Top Noted

  1. Hussain A. Younis, TAE Eisa, M Nasser, TM Sahib, AA Noor, OM Alyasiri, … (2024)

    • A systematic review and meta-analysis of artificial intelligence tools in medicine and healthcare: applications, considerations, limitations, motivation and challenges
    • Citations: 114
  2. Hussain A. Younis, NIR Ruhaiyem, W Ghaban, NA Gazem, M Nasser (2023)

    • A systematic literature review on the applications of robots and natural language processing in education
    • Citations: 48
  3. IM Hayder, TA Al-Amiedy, W Ghaban, F Saeed, M Nasser, GA Al-Ali, HA Younis, … (2023)

    • An intelligent early flood forecasting and prediction leveraging machine and deep learning algorithms with advanced alert system
    • Citations: 40
  4. OM Alyasiri, K Selvaraj, Hussain A. Younis, TM Sahib, MF Almasoodi, IM Hayder (2024)

    • A survey on the potential of artificial intelligence tools in tourism information services
    • Citations: 38
  5. S Salisu, NIR Ruhaiyem, TAE Eisa, M Nasser, F Saeed, HA Younis (2023)

    • Motion capture technologies for ergonomics: A systematic literature review
    • Citations: 25
  6. IM Hayder, GANA Ali, Hussain A. Younis (2023)

    • Predicting reaction based on customer’s transaction using machine learning approaches
    • Citations: 20
  7. Hussain A. Younis, ASA Mohamed, R Jamaludin, MNA Wahab (2021)

    • Survey of robotics in education, taxonomy, applications, and platforms during COVID-19
    • Citations: 20
  8. OM Alyasiri, AM Salman, S Salisu (2024)

    • ChatGPT revisited: Using ChatGPT-4 for finding references and editing language in medical scientific articles
    • Citations: 18
  9. Hussain A. Younis, OM Alyasiri, Muthmainnah, TM Sahib, IM Hayder, S Salisu, … (2023)

    • ChatGPT Evaluation: Can It Replace Grammarly and Quillbot Tools
    • Citations: 16
  10. MA Hussain, Hussain A. Younis, Iznan H. Hasbullah, Ghofran Kh. Shraida, Hameed A … (2023)

  • An Efficient Color-Image Encryption Method Using DNA Sequence and Chaos Cipher
  • Citations: 14
  1. Hussain A. Younis, ASA Mohamed, MN Ab Wahab, R Jamaludin, S Salisu (2021)
  • A new speech recognition model in a human-robot interaction scenario using NAO robot: Proposal and preliminary model
  • Citations: 11
  1. Hussain A. Younis, TY Abdalla, AY Abdalla (2009)
  • Vector quantization techniques for partial encryption of wavelet-based compressed digital images
  • Citations: 11

Amr Shafik | Engineering | Best Researcher Award

Mr. Amr Shafik | Engineering | Best Researcher Award

Civil Engineering Department at Virginia Tech, United States

Amr Shafik is a dedicated researcher specializing in transportation systems engineering, with over seven years of academic and industry experience in transportation planning, traffic engineering, and intelligent mobility solutions. Currently a Ph.D. candidate in Civil and Environmental Engineering at Virginia Tech, his research focuses on optimizing eco-driving systems for connected and automated vehicles, stochastic traffic signal control, and predictive modeling. He has published extensively in IEEE Transactions on Intelligent Transportation Systems and presented at prestigious conferences such as the IEEE Smart Mobility Conference and the Transportation Research Board Annual Meetings. Amr has collaborated with global organizations like the World Bank and EBRD on large-scale mobility projects. With expertise in simulation modeling, data science, and machine learning, he contributes to sustainable transportation innovations. Additionally, he has extensive teaching experience, mentoring students in traffic engineering and transportation planning. His technical skills include Python, R, AutoCAD, GIS, and advanced traffic simulation tools.

Professional Profile

Education

Amr Shafik holds a strong academic background in transportation engineering and data-driven mobility solutions. He is currently pursuing a Ph.D. in Civil and Environmental Engineering at Virginia Tech, where his research focuses on eco-driving optimization for connected and automated vehicles, stochastic traffic signal control, and predictive modeling. He earned his Master’s degree in Transportation Engineering from Cairo University, where he specialized in traffic flow theory, simulation modeling, and intelligent transportation systems. His thesis explored data-driven approaches to optimizing urban traffic networks. Prior to that, he completed his Bachelor’s degree in Civil Engineering from Cairo University with distinction, laying the foundation for his expertise in infrastructure design, traffic analysis, and sustainable mobility. Throughout his academic journey, he has engaged in interdisciplinary research, collaborated with global institutions, and honed advanced technical skills in Python, GIS, and transportation simulation tools. His education equips him to tackle real-world transportation challenges with innovative solutions.

Professional Experience

Amr Shafik has extensive professional experience in transportation engineering, data-driven mobility solutions, and intelligent transportation systems. He has worked as a Research Assistant at Virginia Tech, contributing to projects on eco-driving optimization, stochastic traffic signal control, and predictive modeling for connected and automated vehicles. Prior to this, he served as a Transportation Engineer at a leading consultancy, where he specialized in traffic flow analysis, microsimulation modeling, and urban mobility planning. His expertise extends to working with big data analytics, GIS applications, and machine learning for transportation systems. He has collaborated with government agencies and research institutions to develop sustainable and efficient mobility solutions. Additionally, he has experience in teaching and mentoring students in transportation engineering concepts. His strong analytical skills, combined with his hands-on experience in software tools like Python, MATLAB, and traffic simulation platforms, position him as a key contributor to the advancement of smart and sustainable transportation networks.

Research Interest

Amr Shafik’s research interests lie at the intersection of transportation engineering, intelligent mobility, and data-driven traffic management. He focuses on optimizing traffic flow and enhancing transportation efficiency through connected and automated vehicle technologies, eco-driving strategies, and stochastic traffic signal control. His work integrates machine learning, big data analytics, and artificial intelligence to develop predictive models for traffic behavior and mobility patterns. He is particularly interested in sustainable urban transportation, leveraging smart mobility solutions to reduce congestion, emissions, and energy consumption. His research also explores the application of Geographic Information Systems (GIS) and simulation modeling in transportation planning. By collaborating with industry partners and academic institutions, he aims to contribute to the development of next-generation intelligent transportation systems that improve safety, efficiency, and environmental sustainability. His passion for innovation and interdisciplinary research drives him to address real-world transportation challenges through advanced computational and analytical techniques.

Awards and honor

Amr Shafik has received numerous awards and honors in recognition of his contributions to transportation engineering and intelligent mobility research. He has been honored with prestigious research grants and fellowships for his work on data-driven traffic management and sustainable transportation solutions. His innovative research has earned him accolades at international conferences, where he has received Best Paper and Outstanding Research awards. He has also been recognized by professional engineering societies for his significant advancements in traffic optimization and eco-driving strategies. Additionally, he has been awarded competitive scholarships for academic excellence and leadership in the field of intelligent transportation systems. His contributions to collaborative projects with industry and government agencies have further solidified his reputation as a leading researcher in the field. Through his dedication to advancing transportation science, Amr Shafik continues to receive recognition for his impactful work in shaping the future of smart and sustainable mobility solutions.

Research skill

Amr Shafik possesses a diverse set of research skills that contribute to his expertise in transportation engineering and intelligent mobility solutions. He excels in data analysis, statistical modeling, and machine learning applications for traffic flow optimization and predictive analytics. His proficiency in programming languages such as Python, MATLAB, and R enables him to develop advanced algorithms for real-time traffic monitoring and control. He is skilled in using Geographic Information Systems (GIS) and simulation software like VISSIM and SUMO to model transportation networks and assess the impact of smart mobility solutions. Additionally, he has a strong background in sensor data processing and Internet of Things (IoT) applications for connected and autonomous vehicles. His ability to conduct interdisciplinary research, collaborate with industry stakeholders, and publish high-impact studies demonstrates his analytical thinking, problem-solving abilities, and dedication to innovation in the field of intelligent transportation systems and sustainable urban mobility.

Conclusion

Amr Shafik is a strong candidate for the Best Researcher Award due to his extensive contributions to transportation engineering, expertise in traffic optimization, and impactful research in connected and automated vehicles. His impressive academic and industry experience, along with publications in top-tier conferences and journals, showcases his research excellence. To further strengthen his profile, expanding interdisciplinary collaborations, securing independent research funding, and pursuing patents or industry partnerships would be beneficial.

Publications Top Noted

  • Optimization of vehicle trajectories considering uncertainty in actuated traffic signal timings

    • Authors: AK Shafik, S Eteifa, HA Rakha
    • Year: 2023
    • Citations: 19
  • Queue Length Estimation and Optimal Vehicle Trajectory Planning Considering Queue Effects at Actuated Traffic Signal Controlled Intersections

    • Authors: A Shafik, H Rakha
    • Year: 2024
    • Citations: 5
  • Environmental Impacts of MSW Collection Route Optimization Using GIS: A Case Study of 10th of Ramadan City, Egypt

    • Authors: A Shafik, M Elkhedr, D Said, A Hassan
    • Year: 2022
    • Citations: 4
  • Integrated Back of Queue Estimation and Vehicle Trajectory Optimization Considering Uncertainty in Traffic Signal Timings

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: 3
  • Optimal Trajectory Planning Algorithm for Connected and Autonomous Vehicles Towards Uncertainty of Actuated Traffic Signals

    • Authors: A Shafik, S Eteifa, HA Rakha, E Center
    • Year: 2023
    • Citations: 3
  • Development of Online VISSIM Traffic Microscopic Calibration Framework Using Artificial Intelligence for Cairo CBD Area

    • Authors: AK Shafik, A Hassan, AM Saied, AE & Abdelmegeed
    • Year: 2022
    • Citations: 2
  • Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Traffic Signal Switch Times

    • Authors: S Eteifa, AK Shafik, H Eldardiry, HA Rakha
    • Year: 2024
    • Citations: 1
  • Kalman Filter-based Real-Time Traffic State Estimation and Prediction using Vehicle Probe Data

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: 1
  • Enhancing and Evaluating a Decentralized Cycle-Free Game-Theoretic Adaptive Traffic Signal Controller on an Isolated Signalized Intersection

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: 1
  • Real-Time Turning Movement, Queue Length, and Traffic Density Estimation and Prediction Using Vehicle Trajectory and Stationary Sensor Data

    • Authors: AK Shafik, HA Rakha
    • Year: 2025
    • Citations: N/A
  • Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Signal Phase and Timing Information at Actuated Traffic Signals

    • Authors: S Eteifa, A Shafik, H Eldardiry, HA Rakha
    • Year: 2025
    • Citations: N/A
  • Real-Time Turning Movement, Queue Length and Traffic Density Estimation and Prediction from Probe Vehicle Data: A Kalman Filter Approach

    • Authors: A Shafik, HA Rakha
    • Year: 2025
    • Citations: N/A
  • Decentralized Cycle-Free Game-Theoretic Adaptive Traffic Signal Control: Model Enhancement and Testing on Isolated Signalized Intersections

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: N/A
  • Real-Time Traffic State Estimation and Short-Term Prediction Using Probe Vehicle Data: A Kalman Filter Approach

    • Authors: A Shafik, H Rakha
    • Year: 2024
    • Citations: N/A
  • Queue Estimation and Consideration in Vehicle Trajectory Optimization at Actuated Signalized Intersections

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: N/A