Best Deep Learning Award Archives - Page 2 of 3

Dr. Lei xiang | Deep Learning | Best Researcher Award

Posted on 28/02/202528/02/2025 by ScienceFather

Education

Dr. Lei Xiang holds a Ph.D. in Biomedical Engineering from Shanghai Jiao Tong University (2019). He earned his Master’s degree in System Science from Nanjing University of Information Science & Technology (2015) and a Bachelor’s degree in Systems Engineering from the same university (2012).

Professional Experience

Dr. Xiang is currently the Chief Technology Officer (CTO) at Subtle Medical, Shanghai, a leading company in AI-driven medical imaging solutions. Previously, he worked as a Senior Deep Learning Scientist at Subtle Medical, Menlo Park, USA, where he developed advanced deep learning models for medical image enhancement, focusing on PET, MRI, CT, and SPECT imaging.

He has also gained research experience as an Intern at SenseTime (Shanghai), working on automated pelvis segmentation models, and as a Visiting Scholar at the University of North Carolina at Chapel Hill and Shenzhen Institutes of Advanced Technology.

Research & Publications

Dr. Xiang has an extensive research background in deep learning for medical imaging, with over 13 journal papers and 9 conference papers published in top-tier venues, including Medical Image Analysis, IEEE Transactions on Medical Imaging, Neurocomputing, and Pattern Recognition. His research focuses on multi-modal medical image reconstruction, deep learning-based PET/MRI enhancement, and AI-driven medical diagnostics.

Patents, Honors & Awards

Dr. Xiang holds 10+ international patents, including technologies for SubtlePET, SubtleMR, SubtleSPECT, SubtleCT, and SubtleSpine. He has won numerous prestigious awards, including:

First Place in the Ultra-low Dose PET Imaging Challenge (UDPET, 2022)
1st Rank in the FastMRI Challenge Public Leaderboard (2020)
3rd Prize in Spark: “Digital Body” AI Challenge (2020)
2nd Rank in the Robotic Scene Segmentation Challenge (2018, MICCAI)
1st Prize in the National Postgraduate Mathematic Contest in Modeling (2016)
Outstanding Graduate Award for both Undergraduate and Graduate studies

Professional Services

Dr. Xiang actively contributes to the AI and medical imaging community as a conference reviewer for major AI and medical imaging conferences, including CVPR, ICCV, MICCAI, MIDL, ECCV, and WACV.

Research Interests

AI-powered medical image enhancement
Deep learning for multi-modal fusion in medical imaging
PET/MRI/CT reconstruction and noise-aware image processing
Automated diagnostics and segmentation in medical imaging

Publications

Multimodal MRI reconstruction assisted with spatial alignment network

Authors: Kai Xuan, Lei Xiang, Xiaoqian Huang, Lichi Zhang, Shu Liao, Dinggang Shen, Qian Wang
Journal: IEEE Transactions on Medical Imaging
Year: 2022

Task decomposition and synchronization for semantic biomedical image segmentation

Authors: Xuhua Ren, Sahar Ahmad, Lichi Zhang, Lei Xiang, Dong Nie, Fan Yang, Qian Wang, Dinggang Shen
Journal: IEEE Transactions on Image Processing
Year: 2020

Noise-aware standard-dose PET reconstruction using general and adaptive robust loss

Authors: Lei Xiang, Long Wang, Enhao Gong, Greg Zaharchuk, Tao Zhang
Journal: Machine Learning in Medical Imaging
Year: 2020

Deep learning for fast and spatially constrained tissue quantification from highly accelerated data in magnetic resonance fingerprinting

Authors: Zhenghan Fang, Yong Chen, Mingxia Liu, Lei Xiang, Qian Zhang, Qian Wang, Weili Lin, Dinggang Shen
Journal: IEEE transactions on medical imaging
Year: 2019

Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image

Authors: Lei Xiang, Qian Wang, Dong Nie, Lichi Zhang, Xiyao Jin, Yu Qiao, Dinggang Shen
Journal: Medical image analysis
Year: 2018

Mr. Muhammad Rizwan | Deep Learning | Best Researcher Award

Posted on 24/02/202524/02/2025 by ScienceFather

Summary

Mr. Muhammad Rizwan is an Electronics Postgraduate with a strong background in Machine Learning, Deep Learning, and AI Robotics. He has been working with Python since 2019 and is proficient in TensorFlow, Keras, and PyTorch. Mr. Rizwan seeks opportunities to advance his career in AI and robotics, bringing hands-on experience with innovative research and a passion for solving real-world challenges.

Education

MPhil in Electronics, University of Peshawar (2019-2022)
- Thesis: Appearance-Based Dynamic Hand Gesture Recognition using 3D Separable CNN
BS in Electronics, University of Peshawar (2015-2019)
- Project: Mind-Directed Telepresence Robot
FSC in Pre-Engineering, Agriculture University Public School, Peshawar (2013-2015)
Matric, Kings Hall Public School, Peshawar (2011-2013)

Work Experience

Lecturer, Government College of Technology, Peshawar (2022-2023)
- Supervised final-year projects on Raspberry Pi Pico and ESA 8086/88-3 Trainer Board
- Contact: Engr. Nasrullah Khan (+92 3459419377)

Research Interests

Computer Vision
Deep Learning & Machine Learning
Robotics
Indoor Localization
Plant Disease Detection
Hand Gesture Recognition

Skills

Programming: Python, C, C++
Frameworks: TensorFlow, PyTorch
Hardware: Raspberry Pi, Verilog HDL
Tools: PCB Designing, MS Office
Languages: English (Professional Working), Urdu (Full Professional), Saraiki (Full Professional)

Research and Projects

WIFI RSSI Fingerprint Based Indoor Localization (2024)
- Focus on 3D separable CNN for improved indoor localization.
Plant Disease Detection (2022-2023)
- Developed a model for plant disease detection with high accuracy and efficiency.
Dynamic Hand Gesture Recognition (2020-2022)
- Created a model using 3D Separable CNN for real-time hand gesture recognition.
Virtual Reality-Based Telepresence Robot (2018-2019)
- Designed a robot for remote medical consultations in rural areas of Pakistan.

Publications

Enhancing Indoor Localization with Temporally-Aware Separable Group Shuffled CNNs and Skip Connections

Authors: Muhammad Rizwan, Yin Hoe Ng, Hin-Yong Wong, Chee Keong Tan
Journal: IEEE Access
Year: 2025

Automatic plant disease detection using computationally efficient convolutional neural network

Authors: Muhammad Rizwan, Samina Bibi, Sana Ul Haq, Muhammad Asif, Tariqullah Jan, Mohammad Haseeb Zafar
Journal: Engineering Reports
Year: 2024

Appearance Based Dynamic Hand Gesture Recognition Using 3D Separable Convolutional Neural Network.

Authors: Muhammad Rizwan, Sana Ul Haq, Noor Gul, Muhammad Asif, Syed Muslim Shah, Tariqullah Jan, Naveed Ahmad
Journal: Computers, Materials & Continua
Year: 2023

Assist Prof Dr. Mohammad Javad Parseh | Deep Learning | Editorial Board Member

Posted on 24/02/202524/02/2025 by ScienceFather

Summary

Assist. Prof. Dr. Mohammad Javad Parseh is a dedicated researcher and educator in Computer Science and Artificial Intelligence at Jahrom University, Iran. With expertise in machine learning, deep learning, computer vision, and optimization algorithms, he has developed and taught various courses, supervised student projects, and published numerous research papers. He has also contributed to organizing and chairing AI and ML conferences and workshops.

Education

Ph.D. in Computer Engineering (AI & Robotics), Semnan University, Iran (2022)
- Thesis: Scene Classification using Multi-weighted Scene Graph
M.Sc. in Computer Engineering (AI), University of Tabriz, Iran (2012)
- Thesis: Multiple Maneuvering Aircraft Tracking using Particle Filtering
B.Sc. in Computer Engineering (Software), Shahid Chamran University, Iran (2009)
- Thesis: Anthology Matching in Semantic Web

Professional Experience

Assistant Professor, Jahrom University (2012 – Present)
IT Manager, Jahrom University (2022 – Present)
Head of Computer Engineering Dept., Jahrom University (2014 – 2017)

Research Interests

Computer Vision & Image Processing (Scene Understanding, Image Captioning, VQA, Image Segmentation)
Machine Learning & Deep Learning
Multiple Object Tracking in Video
Optimization Algorithms

Publications

Graph-based image captioning with semantic and spatial features

Authors: Mohammad Javad Parseh, Saeed Ghadiri
Journal: Signal Processing: Image Communication
Year: 2025

Integrated of Hyperspectral Imaging and Machine Learning Algorithms for Nondestructive Detection of Therapeutic Properties of Plants

Authors: Mahmood Mahmoodi‐Eshkaftaki, Saeideh Mohtashami, Mohammad Javad Parseh, Askar Ghani
Journal: Chemistry & Biodiversity
Year: 2025

Scene representation using a new two-branch neural network model

Authors: Mohammad Javad Parseh, Mohammad Rahmanimanesh, Parviz Keshavarzi, Zohreh Azimifar
Journal: The Visual Computer
Year: 2024

Face mask recognition using a custom CNN and data augmentation

Authors: Pooya Fazeli Ardekani, Seyede Zahra Tale, Mohammad Javad Parseh
Journal: Signal, Image and Video Processing
Year: 2024

Semantic embedding: scene image classification using scene-specific objects

Authors: Mohammad Javad Parseh, Mohammad Rahmanimanesh, Parviz Keshavarzi, Zohreh Azimifar
Journal: Multimedia Systems
Year: 2023

Dr. Anita Sebasthiyar | Deep Learning | Best Researcher Award

Posted on 17/02/202517/02/2025 by ScienceFather

Early Academic Pursuits

Dr. Anita Sebasthiyar embarked on her academic journey with a strong foundation in Electronics and Communication Engineering (ECE). Her passion for technology and innovation led her to pursue advanced studies, where she specialized in Digital Image Processing, Machine Learning, Deep Learning Algorithms, and Antenna Design.

Professional Endeavors

With an impressive 16 years of teaching experience, Dr. Anita Sebasthiyar has played a pivotal role in engineering education. She is currently a Professor at St. Anne’s College of Engineering & Technology, where she has been a faculty member since 2009. Over the years, she has served in various capacities, including Associate Professor (2017–2023), Assistant Professor (2013–2017), and Lecturer (2009–2013). Before joining St. Anne’s, she worked as a Lecturer at Annai Velankanni Polytechnic College (2007–2009), contributing significantly to shaping young minds.

Contributions and Research Focus

Dr. Anita Sebasthiyar’s research contributions span across Deep Learning, Image Processing, Antenna Design, and AI-driven medical diagnostics. She has been actively involved in filing and securing patents, with notable inventions such as:

“Computer Operator Name with Location, Tracking New System” (Patent No. 202241005961 A, 2022)
“Shoe for Visually Impaired” (Patent No. 424219-001, 2024)
“A System for Analyzing Single Photon Emission Computed Tomography (SPECT) Images” (Patent No. 202241017033 A, 2024)

Her research has paved the way for technological advancements in healthcare, AI applications, and accessibility solutions.

Impact and Influence

As a resource person, Dr. Anita Sebasthiyar has delivered numerous expert talks and workshops, sharing her knowledge with students, researchers, and industry professionals. Some of her notable sessions include:

“Basics to Advanced: Machine Learning” (September 2021)
“Artificial Intelligence in Nursing: Transforming Patient Care” (January 2025)
“Diagnosing Early Stage of Parkinson’s Disease using JAN Net” (London, October 2024)
She has also played an instrumental role in training students in design thinking, research writing, and innovation.

Academic Contributions and Technical Expertise

Dr. Anita Sebasthiyar is actively involved in academic curriculum development, examination panels, and question paper setting. She served as a Question Paper Setter for EC3551/Transmission Lines and RF Systems at Indira Ganesan College of Engineering and Technology. Additionally, she has led several faculty development programs (FDPs) and workshops on Digital Image Processing using MATLAB, AI Tools, and Biomedical Deep Learning Applications.

Teaching Experience

Throughout her career, Dr. Anita Sebasthiyar has been committed to enhancing the learning experience for students. She has taught a wide range of subjects in Electronics and Communication Engineering, integrating the latest advancements in AI, Deep Learning, and Digital Image Processing into her teaching methodologies.

Recognitions and Awards

Dr. Anita Sebasthiyar’s research excellence has been recognized at prestigious international conferences. She received:

Best Paper Award at the Fifth International Conference on Bio-Signals, Images, and Instrumentation (2019)
Best Paper Award at the Ninth International Conference on Mining Intelligence and Knowledge Exploration (MIKE 2023), Norway, for her work on Hybrid Optimal Fine-Tuning in Deep Learning for Parkinson’s Disease Diagnosis

Legacy and Future Contributions

Dr. Anita Sebasthiyar continues to push the boundaries of research and education, mentoring students and researchers in cutting-edge AI-driven medical diagnostics, antenna design, and deep learning applications. As a Coordinator of the MSME Innovative Scheme Incubation Component and UBA, she actively fosters entrepreneurial and research-based learning. Her contributions to engineering education, research, and technological innovations will undoubtedly leave a lasting impact on the academic and industrial landscape, inspiring the next generation of engineers and researchers.

Publications

Modified Exigent Features Block in JAN Net for Analysing SPECT Scan Images to Diagnose Early-Stage Parkinson’s Disease.

Authors: S Jothi, S Anita, S Sivakumar
Journal: Current Medical Imaging
Year: 2023

Improved classification accuracy for diagnosing the early stage of Parkinson’s disease using alpha stable distribution

Authors: S Anita
Journal: IETE Journal of Research
Year: 2023

Mathematical model for early stage identification of Parkinson’s disease using neurotransmitter: GABA

Authors: S Anita, R Arokiadass
Journal: International Journal of Information Technology
Year: 2022

Diagnosis of Parkinson’s disease at an early stage using volume rendering SPECT image slices

Authors: S Anita, P Aruna Priya
Journal: Arabian Journal for Science and Engineering
Year: 2020

Three dimensional analysis of SPECT images for diagnosing early Parkinson’s disease using Radial Basis Function kernel− Extreme learning machine

Authors: Sebasthiyar Anita, Panchnathan A Priya
Journal: Current Medical Imaging
Year: 2019

Assoc Prof Dr. Mohammad Javad Ebadi | Deep Learning | Editorial Board Member

Posted on 29/01/202529/01/2025 by ScienceFather

Early Academic Pursuits

Associate Professor Dr. Mohammad Javad Ebadi embarked on his academic journey with a strong foundation in mathematics, which led him to pursue advanced studies in the field. His dedication and excellence earned him a doctoral scholarship from Iran’s Ministry of Science, Research, and Technology (2013-2017). During this period, he expanded his research expertise as a Visiting Scholar at the Mediterranean University of Reggio Calabria, Italy, under the supervision of Prof. F. Carlo Morabito.

Professional Endeavors

Dr. Ebadi has been a faculty member at Chabahar Maritime University since 2006, where he has made significant contributions to the Department of Mathematics. His leadership skills were recognized when he was appointed as the Head of the Mathematics Department in 2023. In addition, he has been serving as a Research Associate at the International Telematic University Uninettuno, Italy, since 2022, further broadening his academic collaborations on a global scale.

Contributions and Research Focus

Dr. Ebadi’s research spans a wide array of mathematical and computational fields, including Deep Learning, Fractional Calculus, Numerical Optimization, (Meta)Heuristic Techniques for Optimization, Image Processing, Data Envelopment Analysis, and Fuzzy Mathematics. His work also extends to Optimal Control, Dynamic Programming, and Fuzzy Stochastic Differential Equations. His ability to integrate these diverse areas into practical problem-solving has led to groundbreaking advancements in mathematical modeling and computational efficiency.

Impact and Influence

Dr. Ebadi’s impact on the academic community is evident through his extensive contributions as an editor and reviewer for numerous high-impact journals. He has served as a Section Editor for the International Journal of Systematic Innovation and a Guest Editor for Mathematics (MDPI), focusing on advanced optimization techniques for machine learning. His outstanding contributions as a reviewer have been recognized with multiple awards, including the Journal of Information Processing & Management Reviewer Award (2021) and Best Reviewer Awards from prestigious journals such as Advances in Science, Technology and Engineering Systems Journal (ASTESJ) and Sustainable Energy Technologies and Assessments.

Academic Citations and Recognitions

Dr. Ebadi has received numerous research awards from Chabahar Maritime University, spanning from 2006 to 2023, reflecting his consistent excellence in academic research. His scholarly influence extends beyond his institution, as he has been acknowledged as an Excellent Reviewer for multiple international journals, further solidifying his reputation as a leading researcher in applied mathematics and optimization.

Technical Skills and Expertise

With a strong command of computational tools, Dr. Ebadi possesses expertise in MATLAB programming, LaTeX, and various mathematical software such as Mathematica, Maple, and Sigma Plot. His proficiency in e-learning platforms, including Blackboard, Microsoft Teams, Zoom, and WebEx, has enabled him to effectively engage with students and researchers worldwide. Additionally, he is well-versed in reference management tools such as EndNote and Mendeley, which facilitate his research documentation and collaboration.

Teaching Experience and Pedagogical Contributions

Dr. Ebadi has been actively involved in teaching for over a decade, delivering both undergraduate and graduate courses at Chabahar Maritime University. His teaching portfolio includes Advanced Operations Research, Operations Research, Ordinary Differential Equations, Calculus, Numerical Calculations, Numerical Analysis, Engineering Mathematics, Biostatistics, Engineering Probability and Statistics, Partial Differential Equations, Linear & Nonlinear Optimization, History of Mathematics, and Mathematical Software. His ability to simplify complex mathematical concepts has made him a respected educator and mentor.

Legacy and Future Contributions

Dr. Ebadi’s commitment to academic excellence, research innovation, and mentorship continues to shape the future of mathematics and computational sciences. His leadership in mathematical research and optimization techniques is paving the way for novel applications in artificial intelligence, data science, and engineering. As a dedicated researcher and educator, he remains committed to advancing knowledge, mentoring young scholars, and fostering international collaborations that contribute to the global scientific community.

Publications

Efficient Deep Neural Networks for Classification of Alzheimer’s Disease and Mild Cognitive Impairment from Scalp EEG Recordings

Authors: S Fouladi, AA Safaei, N Mammone, F Ghaderi, MJ Ebadi
Journal: Cognitive Computation
Year: 2024

Optimal study on fractional fascioliasis disease model based on generalized Fibonacci polynomials

Authors: Zakieh Avazzadeh, Hossein Hassani, Praveen Agarwal, Samrad Mehrabi, MJ Ebadi, M Kazem Hosseini Asl
Journal: Mathematical Methods in the Applied Sciences
Year: 2023

Diagnosis of Alternaria disease and Leafminer pest on tomato leaves using Image processing techniques

Authors: K Nazari, MJ Ebadi, K Berahmand
Journal: Journal of the Science of Food and Agriculture
Year: 2022

An efficient coverage and connectivity algorithm based on mobile robots for wireless sensor networks

Authors: Peyman Tirandazi, Atefeh Rahiminasab
Journal: Journal of Ambient Intelligence and Humanized Computing
Year: 2022

The use of artificial neural networks to diagnose Alzheimer’s disease from brain images

Authors: S Fouladi, AA Safaei, NI Arshad, MJ Ebadi, A Ahmadian
Journal: Multimedia Tools and Applications
Year: 2022

Dr. Naushad Varish | Deep Learning | Best Researcher Award

Posted on 28/01/202528/01/2025 by ScienceFather

Summary

Dr. Naushad Varish holds a Ph.D. in Computer Science and Engineering from IIT (ISM) Dhanbad. With over a decade of academic and professional experience, he specializes in content-based image retrieval, machine learning, deep learning, and cybersecurity. He has published extensively in high-impact journals such as IEEE Access and Applied Intelligence. Dr. Varish has also received prestigious fellowships, including the Junior and Senior Research Fellowships at IIT (ISM). His teaching interests include operating systems, data science, artificial intelligence, and digital image processing.

Education

Ph.D. in Computer Science and Engineering from Indian Institute of Technology (ISM), Dhanbad
MCA from Aligarh Muslim University
B.Sc. (Honors) in Statistics from Aligarh Muslim University

Professional Experience

Dec 2022 – Present: Assistant Professor (Senior Scale), GITAM (Deemed to be University)
Jan 2019 – Dec 2022: Associate Professor, Koneru Lakshmaiah Education Foundation
Feb 2018 – Jan 2019: Assistant Professor, Meerut Institute of Engineering and Technology
2012 – 2013: Assistant Software Engineer at ZMQ Technologies Pvt. Ltd.

Research Interests

Content-Based Image Retrieval (CBIR)
Machine Learning & Deep Learning
Data Science and Artificial Intelligence
Digital Image Processing
Cyber Security

Publications

Diagnostic Accuracy of Artificial Intelligence-Based Algorithms in Automated Detection of Neck of Femur Fracture on a Plain Radiograph: A Systematic Review and Meta-analysis

Authors: Manish Raj, Arshad Ayub, Arup Kumar Pal, Jitesh Pradhan, Naushad Varish, Sumit Kumar, Seshadri Reddy Varikasuvu
Journal: Indian Journal of Orthopaedics
Year: 2024

Improving search result clustering using nature inspired approach

Authors: Shashi Mehrotra, Aditi Sharan, Naushad Varish
Journal: Multimedia Tools and Applications
Year: 2024

A hybrid model: PNM for improving prediction capability of classifier

Authors: Shashi Mehrotra, Vinay Kumar Muttum, Redrouthu Vamsi Krishna, Vinod Kumar, Naushad Varish
Journal: International Journal of Information Technology
Year: 2024

Model-based recognition in robot vision for monitoring built environments

Authors: Asif Khan, Naushad Varish, Dhirendra Pandey, Syed Qasim Afser Rizvi, Shashi Mehrotra, Nikhat Parveen
Journal: Multimedia Tools and Applications
Year: 2024

Deep hierarchical spectral-spatial feature fusion for hyperspectral image classification based on convolutional neural network

Authors: Somenath Bera, Naushad Varish, Mudassir Rafi, Vimal K Shrivastava
Journal: Intelligent Data Analysis
Year: 2024

Dr. Zari Farhadi | Deep Learning | Best Researcher Award

Posted on 06/01/202506/01/2025 by ScienceFather

Summary

Dr. Zari Farhadi is a dedicated lecturer and researcher with a Ph.D. in Data Science, specializing in machine learning, predictive modeling, and deep learning. She has developed innovative hybrid models such as ADeFS, RARTEN, ERDeR, and ECAPRAF, contributing to advanced data analysis. Her work includes impactful publications in esteemed journals and a commitment to interdisciplinary research.

Education

Ph.D. in Data Science
Academic focus on statistical learning and ensemble methods

Professional Experience

Lecturer, University of Tabriz, Iran
Focused on teaching and research in data science and artificial intelligence
Reviewer for leading journals: PLOS ONE, IEEE Access, Frontiers, and Ain Shams Engineering

Research Interests

Machine Learning and Deep Learning
Predictive Modeling and Ensemble Learning
Development of hybrid models for advanced data analysis
Statistical Learning and innovative applications in data science

Publications

Improving random forest algorithm by selecting appropriate penalized method

Authors: Zari Farhadi, Hossein Bevrani, Mohammad-Reza Feizi-Derakhshi
Journal: Communications in Statistics-Simulation and Computation
Year: 2024

ERDeR: The combination of statistical shrinkage methods and ensemble approaches to improve the performance of deep regression

Authors: Zari Farhadi, Mohammad-Reza Feizi-Derakhshi, Hossein Bevrani, Wonjoon Kim, Muhammad Fazal Ijaz
Journal: IEEE Access
Year: 2024

ADeFS: A Deep Forest Regression-Based Model to Enhance the Performance Based on LASSO and Elastic Net

Authors: Zari Farhadi, Mohammad-Reza Feizi-Derakhshi, Israa Khalaf Salman Al-Tameemi, Wonjoon Kim
Journal: Mathematics
Year: 2024

Corrections to “ERDeR: The Combination of Statistical Shrinkage Methods and Ensemble Approaches to Improve the Performance of Deep Regression”

Authors: Zari Farhadi, Mohammad-Reza Feizi-Derakhshi, Hossein Bevrani, Wonjoon Kim, Muhammad Fazal Ijaz
Journal: IEEE Access
Year: 2024

An Ensemble-based Model for Sentiment Analysis of Persian Comments on Instagram Using Deep Learning algorithms

Authors: Soheyla Eyvazi-Abdoljabbar, SeongKi Kim, Mohammad-Reza Feizi-Derakhshi, Zari Farhadi, Dheyaa Abdulameer Mohammed
Journal: IEEE Access
Year: 2024

Dr. Youjie Li | Deep Learning | Best Researcher Award

Posted on 30/12/202430/12/2024 by ScienceFather

Summary

Dr. Youjie Li is a distinguished academic at Kunming University of Science and Technology, renowned for his contributions to finance and science. With decades of dedication to teaching, research, and editorial excellence, he has significantly influenced his field.

Education

Dr. Li earned his doctorate from the prestigious Southwestern University of Finance and Economics, laying a strong foundation for his illustrious academic journey.

Professional Experience

Dr. Li has devoted his career to Kunming University of Science and Technology, where he has excelled as a professor and mentor. He has also served as an editor for various SCI-indexed journals, showcasing his expertise in research dissemination.

Research Interests

His research focuses on finance, economics, and interdisciplinary studies, reflected in numerous SCI-indexed publications that advance knowledge and innovation.

Publications

A novel nonlinear direct-mapping approach for multiple time scale driving force analysis of surface water quality variations under intense human interference

Authors: Wang, Y., Cai, Y., Li, B., Li, Y., Zhao, S.
Journal: Journal of Environmental Management
Year: 2024

An intelligently adjusted carbon price forecasting approach based on breakpoints segmentation, feature selection and adaptive machine learning

Authors: Zhao, S., Wang, Y., Deng, G., Chen, Z., Li, Y.
Journal: Applied Soft Computing
Year: 2023

A carbon price hybrid forecasting model based on data multi-scale decomposition and machine learning

Authors: Yang, P., Wang, Y., Zhao, S., Chen, Z., Li, Y.
Journal: Environmental Science and Pollution Research
Year: 2023

Ms. Hyun Ju Kim | Deep Learning | Best Researcher Award

Posted on 27/12/202427/12/2024 by ScienceFather

Early Academic Pursuits

Ms. Hyun Ju Kim’s academic journey began with a strong foundation in marine engineering, earning her Bachelor’s degree from the Korea Maritime and Ocean University in 2017. She further honed her expertise by pursuing a Master’s in Industrial and Data Engineering at Pukyong National University, completing it in 2023. Now a Ph.D. student in the same department, Ms. Kim continues to explore the intersections of industrial engineering and data science, applying her research to the maritime sector.

Professional Endeavors

Ms. Kim has made significant strides in both academia and industry. Starting her professional career at the Korea Marine Equipment Research Institute (KOMERI) as a Senior Researcher, she contributed to numerous projects, including the development of integrated power management systems for hybrid vessels. In December 2024, she transitioned to the Busan Institute of Science and Technology Promotion, where she continues her research. These roles have shaped her into a leader in ship engine and navigation data analysis.

Contributions and Research Focus

Ms. Kim’s research is rooted in data-driven approaches to improving maritime energy efficiency and reducing carbon emissions. As a principal investigator, she developed an integrated power management system for hybrid vessels. Her work extends into predictive modeling, fuel consumption analysis, and environmental impact reduction in the maritime industry. Notably, her journal publications cover topics like fuel-switching behavior in dual-fuel vessels, the prediction of bulk carrier fuel consumption, and the use of explainable AI in energy efficiency optimization.

Impact and Influence

Through her innovative use of AI and machine learning, Ms. Kim has had a significant impact on both the academic and industrial sectors. Her research on fuel consumption prediction models and carbon emission reduction has influenced the maritime industry’s approach to sustainability. Furthermore, her work has been recognized with a Best Paper Award at the ICIoTC 2024 conference, solidifying her reputation as an emerging expert in the field.

Academic Citations

Ms. Kim’s scholarly contributions are steadily gaining recognition in the academic community, particularly for her first-author journal articles. Her work, such as “A Data-Driven Approach to Analyzing Fuel-Switching Behavior” and “Development of a Carbon Emission Prediction Model for Bulk Carrier,” has been published in prominent journals like the Journal of Marine Science and Engineering and the Journal of the Korean Institute of Industrial Engineers. These publications highlight her leadership in integrating AI with maritime technologies to solve pressing environmental challenges.

Technical Skills

Ms. Kim’s technical skills span across several domains, including data analysis, machine learning, and AI. She is proficient in developing predictive models using machine learning algorithms, particularly in the context of maritime fuel consumption and carbon emissions. Her expertise also extends to using Explainable AI (XAI) methodologies, which ensure transparency and interpretability in the results of her research, making her contributions highly applicable to real-world maritime applications.

Teaching Experience

Although Ms. Kim’s primary focus has been on research, her academic background and professional experience have positioned her to share her knowledge with the next generation of engineers. Through her roles at Pukyong National University and as a mentor to fellow researchers, she has contributed to the academic development of students in industrial and data engineering, especially those focusing on maritime applications of AI and data science.

Legacy and Future Contributions

As Ms. Kim continues her Ph.D. studies, her legacy is poised to shape the future of sustainable maritime practices. Her work in developing carbon emission prediction models and optimizing fuel consumption in vessels is crucial for the maritime industry’s transition to more environmentally friendly technologies. With the integration of AI, machine learning, and data analytics into her projects, she is positioned to make groundbreaking contributions to both academia and industry in the coming years.

Publications

A Data-Driven Approach to Analyzing Fuel-Switching Behavior and Predictive Modeling of Liquefied Natural Gas and Low Sulfur Fuel Oil Consumption in Dual-Fuel Vessels

Author: Hyunju Kim, Sangbong Lee, Jihwan Lee, Donghyun Kim
Journal: Journal of Marine Science and Engineering
Year: 2024

Development of a Carbon Emission Prediction Model for Bulk Carrier Based on EEDI Guidelines and Factor Interpretation Using SHAP

Authors: Hyunju Kim, Byeongseok Yu, Donghyun Kim
Journal: International Journal of Advanced Smart Convergence
Year: 2024

Navigating Energy Efficiency: A Multifaceted Interpretability of Fuel Oil Consumption Prediction in Cargo Container Vessel Considering the Operational and Environmental Factors

Authors: Melia Putri Handayani, Hyunju Kim, Sangbong Lee, Jihwan Lee
Journal: Journal of Marine Science and Engineering
Year: 2023

Anomaly Detection and Root Cause Analysis of Ship Main Engines: Explainable Artificial Intelligence-Based Methodology Considering Internal Sensors and External Environmental Factors

Authors: Mingyu Park, Hyunjoo Kim, Sangbong Lee, Jihwan Lee
Journal: Journal of the Korean Institute of Industrial Engineers
Year: 2023

A Study on the Prediction of Fuel Consumption of Bulk Ship Main Engine Using Explainable Artificial Intelligence

Authors: Hyun-Ju Kim, Min-Gyu Park, Ji-Hwan Lee
Journal: Journal of Navigation and Port Research
Year: 2023

Dr. Bader Alsharif | Deep Learning | Best Researcher Award

Posted on 27/12/202427/12/2024 by ScienceFather

Early Academic Pursuits

Dr. Bader Alsharif began his academic journey with a Bachelor of Science in Computer Engineering from the College of Technology in Riyadh, Saudi Arabia, where he graduated in 2008. He further advanced his knowledge with a Master of Science in Computer Engineering from the Florida Institute of Technology in 2017. Currently, he is pursuing a Doctorate in Computer Engineering at Florida Atlantic University, with an expected graduation in May 2025. This strong academic foundation has equipped him with a profound understanding of advanced computing principles and methodologies.

Professional Endeavors

Dr. Alsharif has contributed significantly to academia and technology for over a decade. As a lecturer at the Communications and Information College in Riyadh, he led pioneering projects, including establishing Saudi Arabia’s first CISCO simulation lab. At Florida Atlantic University, he currently serves as a Teaching Assistant, mentoring students and fostering innovation through hands-on learning experiences.

Contributions and Research Focus

Dr. Alsharif’s research portfolio spans Artificial Intelligence (AI), Cybersecurity, and the Internet of Things (IoT). His work emphasizes integrating these technologies to enhance healthcare systems, focusing on improving patient outcomes and addressing critical challenges in digital health. With over seven peer-reviewed publications, his research is recognized for its innovation and real-world applications.

Impact and Influence

Dr. Alsharif has demonstrated an unwavering commitment to empowering individuals with special needs. He has designed and implemented curricula tailored to their unique requirements, directly impacting the academic success of over 3,000 students. His efforts to showcase their talents during Arab Deaf Week have brought international attention to inclusivity and accessibility in education.

Academic Citations and Legacy

Dr. Alsharif’s scholarly contributions have been widely cited in fields intersecting AI and Cybersecurity. His dedication to bridging the gap between research and practical applications ensures his legacy as a thought leader in technology-driven healthcare innovation.

Technical Expertise

Proficient in programming languages like C, Python, and MATLAB, Dr. Alsharif possesses advanced skills in GitHub, MS Office, and CISCO technologies. As a Certified Trainer of Trainers and CCNA1 expert, he excels in both technical implementation and knowledge transfer.

Teaching Excellence

With extensive teaching experience, Dr. Alsharif has guided over 3,000 students, including those with special needs, achieving exemplary performance ratings. His role at Florida Atlantic University involves mentoring engineering design projects and addressing technical queries, fostering a supportive learning environment.

Legacy and Future Contributions

Dr. Alsharif envisions leveraging his expertise in AI, IoT, and Cybersecurity to drive impactful innovations in healthcare. His ongoing doctoral research aims to create scalable, technology-driven solutions that revolutionize patient care and accessibility. By integrating technical excellence with a commitment to inclusivity, Dr. Alsharif is poised to make transformative contributions to the fields of education, research, and healthcare technology.

Publications

Enhancing cybersecurity in healthcare: Evaluating ensemble learning models for intrusion detection in the internet of medical things

Authors: Theyab Alsolami, Bader Alsharif, Mohammad Ilyas
Journal: Sensors
Year: 2024

Transfer learning with YOLOV8 for real-time recognition system of American Sign Language Alphabet

Authors: Bader Alsharif, Easa Alalwany, Mohammad Ilyas
Journal: Franklin Open
Year: 2024

Deep learning technology to recognize american sign language alphabet

Authors: Bader Alsharif, Ali Salem Altaher, Ahmed Altaher, Mohammad Ilyas, Easa Alalwany
Journal: Sensors
Year: 2023

Deep Learning Technology to Recognize American Sign Language Alphabet Using Mulit-Focus Image Fusion Technique

Authors: Bader Alsharif, Munid Alanazi, Ali Salem Altaher, Ahmed Altaher, Mohammad Ilyas
Year: 2023

Machine Learning Technology to Recognize American Sign Language Alphabet

Authors: Bader Alsharif, Munid Alanazi, Mohammad Ilyas
Year: 2023

Doctorate at Subtle Medical, Inc, United States

Profiles

Education

Professional Experience

Research & Publications

Patents, Honors & Awards

Professional Services

Research Interests

Multimodal MRI reconstruction assisted with spatial alignment network

Task decomposition and synchronization for semantic biomedical image segmentation

Noise-aware standard-dose PET reconstruction using general and adaptive robust loss

Deep learning for fast and spatially constrained tissue quantification from highly accelerated data in magnetic resonance fingerprinting

Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image

Multimedia University, Malaysia

Profiles

Summary

Education

Work Experience

Research Interests

Skills

Research and Projects

Enhancing Indoor Localization with Temporally-Aware Separable Group Shuffled CNNs and Skip Connections

Automatic plant disease detection using computationally efficient convolutional neural network

Appearance Based Dynamic Hand Gesture Recognition Using 3D Separable Convolutional Neural Network.

Jahrom University, Iran

Profiles

Summary

Education

Professional Experience

Research Interests

Graph-based image captioning with semantic and spatial features

Integrated of Hyperspectral Imaging and Machine Learning Algorithms for Nondestructive Detection of Therapeutic Properties of Plants

Scene representation using a new two-branch neural network model

Face mask recognition using a custom CNN and data augmentation

Semantic embedding: scene image classification using scene-specific objects

Doctorate at ST. Anne’s College of Engineering and Technology, India

Profiles

Early Academic Pursuits

Professional Endeavors

Contributions and Research Focus

Impact and Influence

Academic Contributions and Technical Expertise

Teaching Experience

Recognitions and Awards

Legacy and Future Contributions

Modified Exigent Features Block in JAN Net for Analysing SPECT Scan Images to Diagnose Early-Stage Parkinson’s Disease.

Improved classification accuracy for diagnosing the early stage of Parkinson’s disease using alpha stable distribution

Mathematical model for early stage identification of Parkinson’s disease using neurotransmitter: GABA

Diagnosis of Parkinson’s disease at an early stage using volume rendering SPECT image slices

Three dimensional analysis of SPECT images for diagnosing early Parkinson’s disease using Radial Basis Function kernel− Extreme learning machine

Mohammad Javad Ebadi at Chabahar Maritime University, Iran

Profiles

Early Academic Pursuits

Professional Endeavors

Contributions and Research Focus

Impact and Influence

Academic Citations and Recognitions

Technical Skills and Expertise

Teaching Experience and Pedagogical Contributions

Legacy and Future Contributions

Doctorate at GITAM Deemed to be University Hyderabad, India

Profiles

Summary

Education

Professional Experience

Research Interests

Doctorate at University of Tabriz, Iran

Profiles

Summary

Education

Professional Experience

Research Interests

Doctorate at Kunming University of Science and Technology, China

Profiles

Summary

Education

Professional Experience

Research Interests

Hyun Ju Kim at Pukyong National University, South Korea

Profiles