Prof. Khaled Shaban | Data Science | Best Researcher Award

Prof. Khaled Shaban | Data Science | Best Researcher Award

Prof. Khaled Shaban, Qatar University, Qatar

Prof. Khaled Shaban is a distinguished researcher and professor in Computer Science and Engineering at Qatar University. With expertise in Computational Intelligence, Machine Learning, and Data Science, he has significantly contributed to advancing pattern recognition, cloud computing, and cybersecurity. A senior member of IEEE and ACM, he has received multiple accolades for his groundbreaking research. He also holds an adjunct professorship at the University of Waterloo, reinforcing his global academic influence. His work focuses on AI-driven disease prediction, smart systems, and optimization techniques, making him a leader in intelligent computing innovations.

🌍 Professional Profile:

Google Scholar

Orcid

Scopus

🏆 Suitability for Best Researcher Award

Prof. Khaled Shaban’s research excellence, innovative contributions, and global recognition make him an ideal candidate for the Best Researcher Award. His pioneering work in Machine Learning, AI, and Computational Intelligence has led to influential publications and prestigious awards, such as the Best Paper Award at IRICT 2021. His ability to merge theory and application in AI, cloud computing, and cybersecurity has significantly impacted academia and industry. His leadership in top-tier conferences and IEEE/ACM communities underscores his commitment to advancing knowledge, making him a highly deserving candidate for this distinguished recognition.

🎓 Education

Prof. Khaled Shaban holds a Ph.D. in Electrical and Computer Engineering from the University of Waterloo, Canada (2006), specializing in Pattern Recognition and Machine Intelligence. His academic journey began with an M.Sc. in Engineering Systems and Computing (2002) from the University of Guelph, Canada, where he developed a strong foundation in computational intelligence and optimization. His interdisciplinary education has enabled him to integrate machine learning, data science, and engineering systems into cutting-edge research. His expertise in algorithms and computing theory has positioned him as a global leader in AI and intelligent systems research.

💼 Experience

Prof. Khaled Shaban has an extensive academic career, currently serving as a Professor at Qatar University’s College of Engineering (since April 2021). He previously held roles as Associate Professor (2016-2021) and Assistant Professor (2008-2016). Additionally, he is an Adjunct Professor at the University of Waterloo (2021-2027), collaborating on AI-driven computing innovations. His professional affiliations with IEEE, ACM, and international research communities enhance his impact on global technological advancements. Over the years, he has mentored numerous students and led transformative research in Artificial Intelligence, Data Science, and Optimization.

🏅 Awards & Honors

  • 🏆 Best Paper AwardIRICT 2021 for “C-SAR: Class-Specific and Adaptive Recognition for Arabic Handwritten Cheques”
  • 🏅 Nomination for Best Paper AwardICVS 2021 for “MARL: Multimodal Attentional Representation Learning for Disease Prediction”
  • 🎖 Promoted to Professor – Qatar University, 2021
  • 🔬 Senior Member, IEEE & ACM – Recognized for contributions to AI and Computational Intelligence
  • 🌍 International Collaborations – Adjunct Professor at the University of Waterloo, fostering global research partnerships

🔬 Research Focus

Prof. Khaled Shaban’s research lies at the intersection of Artificial Intelligence, Computational Intelligence, and Data Science. His work in Machine Learning-driven healthcare analytics, particularly in disease prediction and medical image analysis, is widely recognized. He has also made significant contributions to cybersecurity, cloud computing, and smart grid systems. His studies on optimization and knowledge discovery enhance IoT, AI-based automation, and intelligent computing solutions. Through numerous publications and projects, he has addressed real-world challenges in AI, energy-efficient computing, and adaptive learning systems, making his research impactful across academia and industry.

📖 Publication Top Notes

  • Urban Air Pollution Monitoring System with Forecasting Models

    • Year: 2016
    • Citations: 341
  • Fault Detection, Isolation, and Service Restoration in Distribution Systems: State-of-the-Art and Future Trends

    • Year: 2016
    • Citations: 321
  • Delay-Aware Scheduling and Resource Optimization with Network Function Virtualization

    • Year: 2016
    • Citations: 266
  • A Reliability-Aware Network Service Chain Provisioning with Delay Guarantees in NFV-Enabled Enterprise Datacenter Networks

    • Year: 2017
    • Citations: 224
  • Deep Learning Models for Sentiment Analysis in Arabic

    • Year: 2015
    • Citations: 150

 

 

Muhammad Imran Khan | Machine Learning | Young Scientist Award

Muhammad Imran Khan | Machine Learning | Young Scientist Award

Dr. Muhammad Imran Khan, International Islamic University Islamabad Pakistan, Pakistan.

Publication profile

Scopus

Education And Experiance

  • 📘 Ph.D. in Applied Mathematics (Expected August 2024): International Islamic University Islamabad, Pakistan.
  • 📗 M.Sc. in Computational Mathematics (2019): COMSATS University Islamabad, Pakistan.
  • 📙 Bachelor’s in Applied Mathematics (2016): University of Sargodha, Pakistan.
  • 📒 FSc (2012): Federal Board of Intermediate and Secondary Education, Islamabad, Pakistan.
  • 📕 Metric (2010): Sargodha Board of Intermediate and Secondary Education.

Suitability For The Award

Dr. Muhammad Imran Khan is an outstanding candidate for the Young Scientist Award, characterized by his profound academic journey, versatile skill set, and commitment to advancing mathematical research. His focus on applied mathematics, specifically in the area of partial differential equations (PDEs) and computational methods, positions him as a promising young researcher. His proficiency in machine learning, deep learning, and advanced scientific software highlights his ability to integrate modern computational tools into mathematical problem-solving, making him an asset to the scientific community.

Professional Development 

Muhammad Imran Khan 🔬 thrives on leveraging mathematics to address real-world challenges. His proficiency spans advanced numerical analysis, machine learning, and deep learning 🧠, alongside extensive experience with scientific software tools such as DUNE PDELab and ANSYS 🔧. Skilled in Python and C++, he applies computational methods to explore innovative solutions for diverse fields. Muhammad actively advocates for mathematical research 📊, engaging with decision-makers and fostering collaboration to enhance knowledge dissemination. He envisions a future where mathematics drives practical advancements, supporting both academic growth and societal progress 🚀.

Research Focus 

Awards and Honors

  • 🏅 Merit-Based Scholarship: For outstanding academic performance during M.Sc. at COMSATS University.
  • 🏆 Best Research Poster Award: Recognized at a national mathematics conference for innovative work on PDE applications.
  • 🎖️ Distinction in FSc: Achieved top honors in Federal Board examinations.
  • 🌟 Programming Excellence Certificate: Awarded for proficiency in Python and C++ during Ph.D. coursework.
  • 📜 Recognition of Contribution: For active participation in research collaboration projects at International Islamic University Islamabad.

Publoication Top Notes

  • Integrated Artificial Intelligence and Non-Similar Analysis for Forced Convection of Radially Magnetized Ternary Hybrid Nanofluid of Carreau-Yasuda Fluid Model Over a Curved Stretching Surface (2024) 🧠
  • Advanced Intelligent Computing ANN for Momentum, Thermal, and Concentration Boundary Layers in Plasma Electro Hydrodynamics Burgers Fluid (2024) – Cited by: 0 🤖
  • Analysis of Nonlinear Complex Heat Transfer MHD Flow of Jeffrey Nanofluid Over an Exponentially Stretching Sheet via Three Phase Artificial Intelligence and Machine Learning Techniques (2024) 🔥
  • Modeling and Predicting Heat Transfer Performance in Bioconvection Flow Around a Circular Cylinder Using an Artificial Neural Network Approach (2024) 🌡️
  • Advanced Computational Framework to Analyze the Stability of Non-Newtonian Fluid Flow Through a Wedge with Non-Linear Thermal Radiation and Chemical Reactions (2024) – Cited by: 1 🧪
  • Computational Intelligence Approach for Optimising MHD Casson Ternary Hybrid Nanofluid Over the Shrinking Sheet with the Effects of Radiation (2023) – Cited by: 17 ⚡
  • Artificial Neural Network Simulation and Sensitivity Analysis for Optimal Thermal Transport of Magnetic Viscous Fluid Over Shrinking Wedge via RSM (2023) – Cited by: 20 🔍

 

Dr. Julius Olaniyan | Machine Learning Award |Best Researcher Award

Dr. Julius Olaniyan | Machine Learning Award |Best Researcher Award

Dr. Julius Olaniyan, Bowen University, Nigeria 

Olaniyan Julius in Odo-Owa, Kwara State, Nigeria. He is a Lecturer II in the Computer Science Department at Bowen University, Iwo, Osun State, Nigeria. Julius holds a Ph.D. in Computer Science (2023) and has extensive experience in software development, data analysis, and teaching. He has worked in several institutions, including Landmark University, Federal Polytechnic Auchi, and Feghas Solutions Ltd. Over his career, he has developed various applications using programming languages such as C, C++, Java, Python, and PHP. Julius specializes in Artificial Intelligence, Computer Vision, Natural Language Processing, and Machine Translation. A devoted husband and father of three, Julius is passionate about advancing AI and its application in healthcare and education. He has contributed to several innovative research papers in the field of computer science and AI.

Professional Profile:

Google Scholar

Summary of Suitability for Award:

Dr. Olaniyan has demonstrated outstanding proficiency and expertise in the fields of Artificial Intelligence, Computer Vision, Natural Language Processing, and Machine Translation, with a solid academic background in Computer Science. He holds a Ph.D. in Computer Science from Landmark University, and has published extensively in high-impact journals and conferences. His work on cataract detection using deep learning, as well as his innovative contributions in areas like speech refinement and emotion recognition, highlights his commitment to advancing technology for real-world applications. Furthermore, his ability to collaborate across interdisciplinary research teams and contribute to several peer-reviewed articles reflects his academic rigor and leadership.

🎓Education: 

Olaniyan Julius completed his Ph.D. in Computer Science at Landmark University (2023). He also holds a Master’s in Computer Science (M.Tech) from the Federal University of Technology, Akure (2019), where he also earned a Postgraduate Diploma (PGD) in 2012. Julius started his academic journey with a Bachelor’s in Computer Science from the Federal University of Oye Ekiti (2022). His earlier qualifications include a Higher National Diploma (HND) in Computer Science from Auchi Polytechnic (2006), and a National Diploma (ND) in the same field (2000). Julius completed his Secondary Education at Orota Community High School, Odo-Owa (1994) and his Primary Education at St. Thomas Catholic School (1988). His strong educational foundation in Computer Science has shaped his successful academic and professional career.

🏢Work Experience:

Olaniyan Julius has a diverse career in academia and industry. He is currently a Lecturer II at Bowen University, Nigeria. Previously, he served as a Lecturer II at Landmark University (2023-2024) and as a Data Analyst at Federal Polytechnic Auchi (2013-2022). His industry experience includes working as a Software Developer/Business Developer at Feghas Solutions Ltd. (2009-2012) and a Tutor/Application Developer at Pesoka Systems Ltd. (2008). Julius also has teaching experience from his time as a Lecturer during his NYSC service at Maritime Academy of Nigeria (2007-2008). His early career included roles like Data Processing Officer at Ajaokuta Steel Company (2002-2004) and School Database Admin at Sani Bello Secondary School (2001). Julius’s experience spans academic teaching, research, software development, data analysis, and project management.

🏅Awards:

Olaniyan Julius has received numerous accolades throughout his academic and professional journey. His Ph.D. dissertation was highly recognized, contributing to his recognition as an emerging scholar in Computer Science. He was awarded a best student award during his time at Landmark University and has been recognized by the Federal Polytechnic Auchi for his outstanding performance as a Data Analyst. Julius’s commitment to education and research has earned him several institutional commendations for his efforts in developing AI-driven solutions in healthcare and education. His research in Artificial Intelligence and Machine Translation has garnered him recognition at international conferences. He is also an active member of several professional organizations in computer science and artificial intelligence. Julius’s leadership and contributions to academic and professional initiatives have cemented his reputation as a passionate educator and researcher.

🔬Research Focus:

Olaniyan Julius specializes in Artificial Intelligence (AI), with a focus on Computer Vision, Natural Language Processing (NLP), and Machine Translation. His work primarily involves using deep learning techniques to create solutions for healthcare (e.g., cataract detection) and education (e.g., student performance evaluation). Julius is dedicated to developing hybrid AI models that combine traditional methods with transformative learning approaches. His research in audio signal denoising and speech-to-speech translation aims to enhance communication and multilingual interaction. He is passionate about designing AI-powered systems that can automate and optimize processes, improving outcomes in health diagnostics and online learning environments. Julius’s work on emotion detection in virtual classrooms and the integration of CNN models for speech emotion recognition represents a significant contribution to the AI field. His interdisciplinary research approach holds promise for real-world AI applications in various domains.

Publication Top Notes: 

  • “Utilizing an Attention-Based LSTM Model for Detecting Sarcasm and Irony in Social Media”
  • “Implementation of Audio Signals Denoising for Perfect Speech-to-Speech Translation Using Principal Component Analysis”
  • “Advancements in Accurate Speech Emotion Recognition Through the Integration of CNN-AM Model”
  • “Transformative Transparent Hybrid Deep Learning Framework for Accurate Cataract Detection”
  • “Parallel Attention Driven Model for Student Performance Evaluation”

 

 

 

 

Mr. Tohid Sharifi | Machine Learning Award | Best Researcher Award

Mr. Tohid Sharifi | Machine Learning Award | Best Researcher Award

Mr. Tohid Sharifi, Niroo Research Institute, Iran

Mr. Tohid Sharifi is a proficient electrical engineer with an M.Sc. in Electrical Machines and Power Electronics from Amirkabir University of Technology and a B.Sc. in Electrical Power Engineering from Urmia University. His research encompasses notable projects such as a hybrid estimation model for real-time temperature monitoring in electric motors, published in Case Studies in Thermal Engineering, and he is actively working on heat transfer investigations for advanced motor designs. With industrial experience as a CFD Specialist and Cooling System Design Engineer, he has contributed to thermal analysis for a 100kW flywheel energy storage system and optimized heat transfer for a 200kW water-cooled motor using artificial neural networks. His research interests include power electronics, electrical machines, electric vehicles, and metaheuristics, and he holds a patent for a hybrid excited flux switching permanent magnet motor for electric vehicle applications.

Professional Profile:

Orcid
Google Scholar

Suitability for the Best Researcher Award:

Mr. Tohid Sharifi’s extensive research and industrial contributions make him an ideal candidate for the Best Researcher Award. His focus on heat transfer and cooling systems for electric motors, coupled with his work in metaheuristic optimization for motor efficiency, reflects his forward-thinking approach to solving key challenges in power electronics and energy systems. His innovative contributions to electric vehicle motor design and the optimization of thermal systems using advanced algorithms showcase his potential for significant future impact in the field.

🎓 Education:

Mr. Tohid Sharifi holds an M.Sc. in Electrical Machines and Power Electronics from Amirkabir University of Technology (Tehran Polytechnic) and a B.Sc. in Electrical Power Engineering from Urmia University.

🛠️ Academic Projects:

His research includes significant projects such as a hybrid estimation model for real-time temperature monitoring in electric motors, published in Case Studies in Thermal Engineering. He has also worked on heat transfer investigations for advanced motor designs, with papers under revision in prominent journals.

🏭 Industrial Experience:

In the industrial sector, Mr. Sharifi has contributed as a CFD Specialist and Cooling System Design Engineer for electric motors. He played a crucial role in thermal analysis for a 100kW flywheel energy storage system at Niroo Research Institute and optimized heat transfer for a 200kW water-cooled motor using artificial neural networks.

🔍 Research Focus:

His research interests lie in power electronics, electrical machines, electric vehicles, metaheuristics, and heat transfer. He is also an inventor, with a patented hybrid excited flux switching permanent magnet motor for electric vehicle applications.

Publication Top Notes:

  • “An asymmetrical cascaded single-phase quasi Z-source multilevel inverter with reduced number of switches and lower THD”
    • Citations: 9
    • Published: 2020
  • “Optimal design of a synchronous reluctance motor using biogeography-based optimization”
    • Citations: 5
    • Published: 2021
  • “Optimal Design of a Permanent Magnet Synchronous Motor Using the Cultural Algorithm”
    • Citations: 4
    • Published: 2021
  • “Analytical Modeling and Electrical Equivalent Circuit Extraction for a Flux Switching PM Motor for EVs”
    • Citations: 3
    • Published: 2022
  • “Torque Ripple Minimization for a Switch Reluctance Motor Using the Ant Lion Optimization Algorithm”
    • Citations: 2
    • Published: 2022