Omar Soufi | Artificial Intelligence | Best Researcher Award

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Dr. Omar Soufi | Artificial Intelligence | Best Researcher Award

PHD at Ehime University in Japan

Dr. Omar Soufi, né le 13 mai 1993 à Casablanca, est un expert en data science, télédétection spatiale et systèmes d’information géographique. Titulaire d’un doctorat en génie informatique de l’EMI Rabat, ses recherches se concentrent sur l’intelligence artificielle et le deep learning pour améliorer la qualité des images satellites. Avec une expérience notable dans des projets de Business Intelligence et d’applications SIG, il est polyglotte, parlant couramment arabe, français, anglais et espagnol. Dr. Soufi détient plusieurs certifications professionnelles en IA, machine learning et data analytics, renforçant son rôle clé dans l’innovation technologique et la gestion des données.

Professional Profiles

Academic Background

2023 : Doctorat – EMI Rabat Cycle doctoral à l’équipe AMIPS/E3S Option: Génie Informatique : Intelligence Artificielle Sujet: Approche par Deep Learning au profit de la télédétection spatiale : Amélioration de la qualité d’images satellites et du procédé du capteur d’étoile. 2020 : Ingénierie – Polytechnique Grenoble, ENSIMAG 3ème année cycle ingénieur Option: Ingénierie des Systèmes d’Information 2020 : Ingénierie – EMI Rabat Cycle ingénieur en Informatique Option: Ingénierie et Qualité Logicielle 2015 : Diplôme – ARM Merkens Diplôme des Études Universitaires 2014 : Licence Fondamentale – ARM Merkens Option: Sciences et Techniques Filière: Génie Mécanique 2011 : Baccalauréat – 1er LMR Option: Sciences de Vie et de Terre

Expériences Professionnelles

Chargé des missions d’Intelligence Artificielle (2024) Participation à plusieurs projets en intelligence artificielle et data science pour l’analyse et la gestion des données. Chef Pôle Géomatique & Outils décisionnels (2022) Développement d’applications SIG thématiques pour des domaines tels que l’urbanisme, l’environnement, et l’agriculture. Chef département BI & Outils décisionnels (2020) Conception et implémentation de projets de Business Intelligence et de plateformes géospatiales pour la gestion des risques des catastrophes naturelles et d’autres applications stratégiques.

Areas of Research

Data Science Télédétection Spatiale Systèmes d’Information Géographique (SIG)

Research Focus

Dr. Omar Soufi’s research focuses on the application of deep learning techniques to image processing, specifically in the field of Single Image Super-Resolution (SISR). His work involves developing and benchmarking advanced deep learning models to enhance the resolution and quality of satellite imagery. Key publications include studies on novel super-resolution approaches for multispectral satellite images and intelligent methods for spacecraft attitude control. His research contributes to improving the accessibility and analysis of high-resolution satellite imagery, which has significant implications for remote sensing, environmental monitoring, and geospatial applications. Dr. Soufi’s expertise lies at the intersection of artificial intelligence and remote sensing technologies.

Publications

  1. Benchmark of deep learning models for single image super-resolution (SISR), Publication date: 2022.
  2. Study of Deep Learning-based models for Single Image Super-Resolution. Publication date: 2022.
  3. Deep learning technique for image satellite processing, Publication date: 2023.
  4. Enhancing Accessibility to High-Resolution Satellite Imagery: A Novel Deep Learning-Based Super-Resolution Approach, Publication date: 2023.
  5. Analysis and Processing of Spatial Remote Sensing Multispectral Imagery using Deep Learning Techniques, Publication date: 2023.
  6. An intelligent deep learning approach to spacecraft attitude control: the case of satellites, Publication date: 2024.
  7. A Novel Deep Learning-Based Super-Resolution Approach, Publication date: 2023.
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Hafiz Muhammad Naveed | Deep learning | Excellence in Research

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Dr. Hafiz Muhammad Naveed | Deep learning | Excellence in Research

PHD at Jiangsu University, China

Dr. Hafiz Muhammad Naveed is a postdoctoral researcher at the College of Management, Shenzhen University, China. He earned his Ph.D. in Management Sciences and Engineering with a specialization in Finance from Jiangsu University. His research focuses on applying advanced machine learning and deep learning techniques to financial risk management, green finance, and environmental risk assessment. Dr. Naveed’s work has been published in prestigious journals indexed in SSCI, SCIE, and ESCI. He has a strong background in financial predictions and economic modeling, contributing significantly to understanding currency exchange rate dynamics, commodity markets, and the impacts of global events on financial systems.

Professional Profiles

Academic Background

2019-2023 Postdoctoral Researcher Focus: Machine learning, Deep learning, Financial Risk Management, Green finance, renewable energy, environmental risk assessment College of Management, Shenzhen University, Guangdong, China 2019-2023 PhD in Management Sciences and Engineering (specialization in Finance) CGPA: 3.6/4.0 School of Finance and Economics, Jiangsu University, China Dissertation: Financial System Modeling Using Deep Intelligent Networks for Financial Predictions and Assessments Major Courses: Management research methodology Multivariate statistical analysis Econometric model of economics and management Frontier of management theory Financial preface Advanced industrial economics Overview of China & Integrated Chinese level-3 2015-2017 Master of Philosophy (M. Phil) in Business Administration (Specialization in Finance) CGPA: 3.5/4.00 School of Management Sciences, National College of Business Administration & Economics (NCBA&E), Lahore, Pakistan Dissertation: The Impact of Power Shortages on Capital Flight of Pakistan Major Courses:

Professional Experience

2017-2019 Lecturer in Economics Punjab Group of Colleges (Fort Abbas Campus), Pakistan 2019-2023 English Teacher Assistant (ETA) Graduate School, Jiangsu University, China Courses Taught: Writing academic papers for international journals: Strategy and skills Achievement: Excellent Teacher Award 2019-2023 Teaching Management Assistant (TMA) Overseas Education College (OEC), Jiangsu University, China

Awards and Honors

Received 1st class presidential scholarship every year during Ph.D. duration based on academic performances and social activities

Research Focus

Dr. Hafiz Muhammad Naveed’s research primarily focuses on the application of advanced machine learning and deep learning techniques in financial risk management and economic modeling. His work encompasses various domains such as financial predictions, environmental risk assessment, and the impact of global events like the COVID-19 pandemic and geopolitical conflicts on financial markets. He has made significant contributions to understanding currency exchange rate dynamics, commodity market behavior, and the efficiency of stock markets. His research is well-recognized, with publications in high-impact journals indexed in SSCI, SCIE, and ESCI, showcasing his expertise in integrating intelligent networks and neural network-based analyses into financial and economic studies.

Publications

  1. Assessing the nexus between currency exchange rate returns, currency risk hedging and international investments: Intelligent network-based analysis, Publication date: 2024.
  2. Influence of the Russia–Ukraine War and COVID-19 Pandemic on the Efficiency and Herding Behavior of Stock Markets: Evidence from G20 Nations, Publication date: 2024.
  3. Electricity shortfalls and financial leverage of listed firms in Pakistan, Publication date: 2024.
  4. Recovery time of the hotel and restaurant sector in Indonesia after COVID-19 crisis: a survival analysis,  Publication date: 2024.
  5. Influence of the Russia–Ukraine War and COVID-19 Pandemic on the Efficiency and Herding Behavior of Stock Markets: Evidence from G20 Nations, Publication date: 2024.
  6. The role of environmental knowledge, policies and regulations toward water resource management: A mediated‐moderation of attitudes, perception, and sustainable consumption patterns, Publication date: 2024.
  7. Artificial neural network (ANN)-based estimation of the influence of COVID-19 pandemic on dynamic and emerging financial markets, Publication date: 2023.
  8. Connectedness between Currency Risk Hedging and Firm Value: A Deep Neural Network‑based Evaluation, Publication date: 2023.
  9. Examining the efficiency and herding behavior of commodity markets using multifractal detrended fluctuation analysis. Empirical evidence from energy, agriculture, and metal markets, Publication date: 2022.
  10. Evaluation Optimal Prediction Performance of MLMs on High-volatile Financial Market Data, Publication date: 2022.
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Monika Nagy-Huber | Machine learning | Best Researcher Award

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Ms. Monika Nagy-Huber | Machine learning | Best Researcher Award

PHD at the University of Basel, Switzerland

Monika Timea Nagy-Huber, a Swiss national, is a PhD candidate in Computer Science at the University of Basel, specializing in Physics-informed Machine Learning Algorithms under the guidance of Prof. Dr. Volker Roth. She is part of the Biomedical Data Analysis research group. Monika holds a Master of Science in Mathematics from the University of Basel, where she focused on Numerics and Algebra-Geometry-Number Theory. Her master’s thesis explored the Local Discontinuous Galerkin Method for solving the Wave Equation. She also earned her Bachelor of Science in Mathematics from the University of Basel. Monika is passionate about integrating advanced mathematical techniques with cutting-edge computer science applications.

Professional Profiles

Education

09/2019 – Present: PhD in Computer Science, University of Basel ‣ Supervisor: Prof. Dr. Volker Roth ‣ Research group: Biomedical Data Analysis ‣ Specialisation: Physics-informed Machine Learning Algorithms 02/2016 – 02/2019: Master of Science in Mathematics, University of Basel ‣ Areas of specialisation: Numerics (Partial Differential Equations for Wave Equations), Algebra-Geometry-Number Theory (Elliptic Curves) ‣ Master’s thesis: “Das lokale diskontinuierliche Galerkin-Verfahren mit lokalem Zeitschrittverfahren zur Lösung der Wellengleichung” (translated: The Local Discontinuous Galerkin Method with Local Time Stepping Method for solving the Wave Equation), Grade 5.5 ‣ Supervisor: Prof. Dr. Marcus J. Grote 09/2011 – 02/2016: Bachelor of Science in Mathematics, University of Basel

Research Focus

Monika Timea Nagy-Huber’s research primarily focuses on the intersection of advanced computational methods and biomedical applications. Her work involves developing and applying physics-informed machine learning algorithms to solve complex problems, such as partial differential equations, relevant to biomedical data analysis. She has contributed to various projects, including studying the effects of LSD on brain connectivity, learning invariances with input-convex neural networks, and creating mesh-free Eulerian physics-informed neural networks. Her interdisciplinary approach leverages deep learning and computational science to address challenges in neuroscience, exercise science, and environmental monitoring, demonstrating a robust expertise in integrating theoretical mathematics with practical applications.

Publications

  1. Physics-informed boundary integral networks (PIBI-Nets): A data-driven approach for solving partial differential equations, Publication date: 2024.
  2. Using Machine Learning–Based Algorithms to Identify and Quantify Exercise Limitations in Clinical Practice: Are We There Yet?, Publication date: 2023.
  3. The effect of lysergic acid diethylamide (LSD) on whole-brain functional and effective connectivity, Publication date: 2023.
  4. Learning invariances with generalised input-convex neural networks, Publication date: 2022.
  5. Mesh-free eulerian physics-informed neural networks, Publication date: 2022.
  6. Mesh-free Eulerian Physics-Informed Neural Networks, Publication date: 2022.
  7. Visual Understanding in Semantic Segmentation of Soil Erosion Sites in Swiss Alpine Grasslands, Publication date: 2022.
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Saad Shauket Sammen | Machine Learning | Best Researcher Award

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Assist Prof Dr. Saad Shauket Sammen | Machine Learning | Best Researcher Award

PHD at University Putra Malaysia, Malaysia

Dr. Saad Shauket Sammen, an Assistant Professor at the Department of Civil Engineering, University Putra Malaysia, specializes in water resources engineering. Born in Diyala, Iraq, in 1978, he holds a Ph.D. from University Putra Malaysia and a Master’s from the University of Technology, Iraq. His research interests include water resources management, hydraulic and hydrology modeling, groundwater modeling, and climate change. Dr. Sammen has extensive teaching experience and has supervised several postgraduate students. He has also served as a consultant on numerous hydrological and construction projects in Iraq and Malaysia, contributing significantly to the field of water resources engineering. Machine Learning

Professional Profiles

Education

Ph.D. in Water Resources Engineering Department of Civil Engineering, Faculty of Engineering, University Putra Malaysia, Malaysia (2017) Master of Water Resources Engineering Department of Building and Construction, University of Technology, Iraq (2009) Bachelor of Civil Engineering Department of Civil Engineering, College of Engineering, University of Anbar, Iraq (2001) Machine Learning

Work Experience

Assistant Professor Department of Civil Engineering, University Putra Malaysia (2021-present) Lecturer Civil Engineering Department, University of Diyala (2017-2021) Ph.D. Student Department of Civil Engineering, University Putra Malaysia (2014-2017) Lecturer Civil Engineering Department, University of Diyala (2011-2013) Assistant Lecturer Civil Engineering Department, University of Diyala (2009-2011) Civil Engineer Ministry of Higher Education and Scientific Research, Iraq (2006-2009) Civil Engineer Ministry of Water Resources, Iraq (2004-2006)

Teaching Experience

Lecturer Civil Engineering Department, University of Diyala, teaching: Fluid Mechanics for 2nd-year undergraduates Mathematics for 2nd-year undergraduates Hydraulic Structures for 3rd-year undergraduates Irrigation and Drainage Engineering for 4th-year undergraduates Machine Learning

Research Area Interest

Water Resources Management Hydraulic Modeling Hydrology Modeling Ground Water Modeling Climate Change Water Quality Modeling GIS and RS in Water Resources Application of Artificial Intelligence and Optimization Algorithms in Water Resources Engineering

Research Focus

Dr. Saad Shauket Sammen is an expert in water resources engineering, focusing on rainfall-runoff modeling, water quality assessment, and drought prediction using advanced machine learning techniques. His research, widely published in high-impact journals, explores the application of evolutionary algorithms like Harris Hawks Optimizer and Particle Swarm Optimization for hydrological modeling. Dr. Sammen has made significant contributions to understanding and predicting water-related phenomena, utilizing hybrid machine learning models to improve the accuracy and efficiency of environmental and hydrological predictions. His work is instrumental in developing sustainable water management practices, particularly in the face of climate change and increasing water scarcity. Machine Learning

Publications

  1. Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India, Publication date: 2024.
  2. Predictive modelling of nitrogen dioxide using soft computing techniques in the Agra, Uttar Pradesh, India,  Publication date: 2024.
  3. Toward Decontamination in Coastal Regions: Groundwater Quality, Fluoride, Nitrate, and Human Health Risk Assessments within Multi-Aquifer Al-Hassa, Saudi Arabia, Publication date: 2024.
  4. Developing an ensembled machine learning model for predicting water quality index in Johor River Basin, Publication date: 2024.
  5. Isotherms, kinetics and thermodynamic mechanism of methylene blue dye adsorption on synthesized activated carbon, Publication date: 2024.
  6. Three novel cost-sensitive machine learning models for urban growth modelling, Publication date: 2024.
  7. Strength assessment of structural masonry walls: analysis based on machine learning approaches, Publication date: 2024.
  8. Prediction of daily leaf wetness duration using multi-step machine learning, Publication date: 2024.
  9. New strategy based on Hammerstein–Wiener and supervised machine learning for identification of treated wastewater salinization in Al-Hassa region, Saudi Arabia, Publication date: 2024.
  10. Comparison Analysis of Seepage Through Homogenous Embankment Dams Using Physical, Mathematical and Numerical Models,  Publication date: 2024.
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