Mr. Mohammed Aljamal | Artificial Intelligence | Best Researcher Award

Posted on 24/02/2025 by ScienceFather

Mr. Mohammed Aljamal | Artificial Intelligence | Best Researcher Award

Mr. Mohammed Aljamal, University of Bridgeport, United States

Mohammed Aljamal is a Laboratory Engineer and Ph.D. candidate in Computer Science & Engineering, based in the New York City Metropolitan Area. He holds a Master’s degree in Artificial Intelligence from the University of Bridgeport and is actively engaged in academic and professional communities as the President of the UB Robotics Club and a member of AIAA, UPE, and the Honor Society. With over four years of experience at the University of Bridgeport, he has contributed as a Laboratory Engineer, Graduate Research Assistant, and Teaching Assistant, specializing in laboratory management, hardware and software solutions, and IT infrastructure. His expertise spans project leadership, problem-solving, cross-functional team management, and innovative solution design. Beyond academia, Mohammed has a strong background in consulting, resource allocation, and international collaboration, having successfully led and completed critical projects. Passionate about technology and innovation, he continuously seeks opportunities to develop solutions that enhance user experiences and drive technological advancement.

Professional Profile:

GOOGLE SCHOLAR

Suitability of Mohammed Aljamal for the Best Researcher Award

Mohammed Aljamal is a highly skilled and innovative researcher with a strong background in Artificial Intelligence, Computer Science, and Engineering. His Ph.D. candidacy, extensive teaching experience, and leadership roles at the University of Bridgeport demonstrate his dedication to academic excellence and technological advancements.

Education 🎓

Ph.D. Candidate in Computer Science & Engineering – University of Bridgeport (Ongoing)
Master’s Degree in Artificial Intelligence – University of Bridgeport
Bachelor’s Degree in [Field Not Specified] – [University Not Specified]

Work Experience 💼

University of Bridgeport (4 years 1 month)

Labs Engineer (Feb 2022 – Present) ⚙️
- Improved and maintained laboratory equipment.
- Developed detailed hardware and software data for lab management.
- Conducted inspections and routine maintenance on lab equipment.
- Implemented new technology solutions and disaster recovery plans.
- Coordinated IT services to ensure data availability and security.
Graduate Research & Teaching Assistant (Jan 2022 – Feb 2022) 📚
- Assisted in research projects and student instruction.
Teaching and Laboratory Assistant (Feb 2021 – Dec 2021) 🏫
- Assisted undergraduate and graduate students in Intro to Robotics.
- Managed lab hours, discussions, assignments, and exams.

Achievements & Leadership 🌟

President of UB Robotics Club 🤖 – Leading robotics initiatives and student projects.
Successfully completed two delayed projects 🎯 – Resolved critical issues and met client satisfaction.
Consulted and collaborated with international vendors 🌍 – Gained experience in global tech solutions.
Designed and implemented innovative lab solutions 🔧 – Optimized university lab resources.

Awards & Honors 🏆

Member of AIAA (American Institute of Aeronautics and Astronautics) 🚀
Member of UPE (Upsilon Pi Epsilon – International Honor Society for Computing) 🖥️
Honor Society Member 🎖️

Publication Top Notes:

Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics

Enhancement of CNNHQ with an Activation Function

Mr. Fangzhou Lin | Deep Learning | Best Scholar Award

Posted on 10/02/2025 by ScienceFather

Mr. Fangzhou Lin | Deep Learning | Best Scholar Award

Mr. Fangzhou Lin, Hong Kong University of Science and Technology, Hong Kong

Fangzhou Lin is a Ph.D. researcher in Civil Engineering at the Hong Kong University of Science and Technology (HKUST), specializing in deep learning, machine vision, construction robots, and multimodal data fusion. He holds a Bachelor’s degree in Civil Engineering from Fuzhou University (2015-2019) and a Master’s degree in Structural Engineering from Southeast University (2019-2022). Fangzhou Lin’s research focuses on the integration of artificial intelligence and robotics in construction automation, with applications in fire safety inspection, resource management, visual measurement, and quality assessment. His work has been published in leading journals such as Automation in Construction, Computer-Aided Civil and Infrastructure Engineering, and Advanced Engineering Informatics. He has contributed to multiple cutting-edge studies on robotic systems for construction site management, vision-based measurement techniques, and reinforcement learning-based scheduling for electric concrete vehicles. As an emerging scholar in construction automation and AI-driven inspection technologies, Fangzhou Lin actively collaborates on multi-disciplinary research projects to enhance efficiency, safety, and sustainability in the built environment. His contributions to automated reality capture, rebar positioning, and construction robotics are shaping the future of intelligent construction and infrastructure development.

Professional Profile:

SCOPUS

Suitability of Fangzhou Lin for the Best Scholar Award

Fangzhou Lin is an outstanding early-career scholar with a strong background in deep learning, machine vision, construction robotics, and multimodal data fusion within the field of civil engineering. His academic trajectory, research productivity, and innovative contributions make him a compelling candidate for the Best Scholar Award. Below is a detailed assessment of his suitability based on key criteria.

🎓 Education

2015.09 – 2019.06 | Fuzhou University – Bachelor’s Degree in Civil Engineering
2019.09 – 2022.06 | Southeast University – Master’s Degree in Structural Engineering
2022.09 – Present | Hong Kong University of Science and Technology – Ph.D. in Civil Engineering

🏗️ Work & Research Experience

Expertise in: Deep learning, machine vision, construction robots, multimodal data fusion
Published in top journals such as Automation in Construction and Computer-Aided Civil and Infrastructure Engineering
Conducting research on:
- 🔥 Fire Safety Inspection using AI-driven visual inspection
- 🤖 Robotics for Construction Management with multi-task planning and automatic grasping
- 🏗️ BIM-integrated Reality Capture for indoor inspection using multi-sensor quadruped robots
- 🎯 Vision-based Monitoring for assembly alignment of precast concrete bridge members

🏆 Achievements & Awards

Published multiple high-impact journal papers 📚
Lead researcher on innovative construction technology projects 🔍
Contributed to advanced AI-driven automation for civil engineering 🤖
Research works under review in prestigious engineering journals 🏅
Collaborated with leading experts in civil engineering and robotics 🤝

Publication Top Notes:

Efficient visual inspection of fire safety equipment in buildings

Dr. Jany Shabu | Artificial Intelligence Awards | Best Researcher Award

Posted on 09/01/2025 by ScienceFather

Dr. Jany Shabu | Artificial Intelligence Awards | Best Researcher Award

Dr. Jany Shabu, Sathyabama Institute of Science & Technology, India

Dr. S.L. Jany Shabu is an accomplished Associate Professor in the Department of Computer Science Engineering at Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India. With a Ph.D. in Image Fusion, her research focuses on multimodal image fusion using intelligent optimization techniques, particularly in the context of brain tumor detection. Dr. Shabu has a strong academic background, holding both M.Tech and MS degrees in Information Technology, and has published extensively, with 58 papers indexed in Scopus and four in WoS. She has received multiple accolades for her contributions to research and education, including cash awards for publishing in high-impact journals and the prestigious NPTEL Discipline Star Certificate. As an active member of the National Institute for Technical Training and Skill Development, Dr. Shabu is dedicated to advancing the field of computer science through her research, teaching, and professional engagement. Her innovative projects, including a Safety Stick for Elders, and her patents in smart traffic control and gesture-based systems, exemplify her commitment to leveraging technology for societal benefit. She has also authored several books on machine learning, cloud computing, and data analytics, further solidifying her reputation as a thought leader in her field. With a robust online presence, including profiles on ORCID and Scopus, Dr. Shabu continues to contribute to academic excellence and innovation in computer science.

Professional Profile:

SCOPUS

Summary of Suitability for Best Researcher Award:

Dr. S.L. Jany Shabu is a commendable candidate for the Best Researcher Award, recognized for her significant contributions to computer science engineering and her innovative research in image fusion and optimization techniques.

Education 🎓

Ph.D. in Image Fusion
Sathyabama Institute of Science and Technology
Thesis Title: Multimodal Image Fusion using Intelligent Optimization Techniques with Brain Tumor Detection
M.Tech (IT) in Information Technology
Sathyabama Institute of Science and Technology
Graduated with First Class
M.S. (IT) in Information Technology
Manonmaniam Sundaranar University
Graduated with First Class

Work Experience 💼

Current Position: Associate Professor, Computer Science Engineering
Sathyabama Institute of Science and Technology

Achievements 🌟

Seed Funding:
Project Title: Safety Stick for Elders
Amount: ₹300,000
Period: Oct 2021 – June 2022
Role: Co Principal Investigator
Patent Holder:
1. SMART TRAFFIC CONTROL SYSTEM USING IOT BASED MONITORING SYSTEM
  Application No: 201741038384 – Published
2. GARMENT STEAMER MANAGEMENT SYSTEM
  Application No: 367890-001 – Published
3. GESTURE BASED ELECTRONIC GADGET OPERATING SYSTEM
  Application No: 202341088351 A – Published
Reviewer:
- Journal of Scientific Research and Reports
- Journal of Pharmaceutical Research International
- International Conference on Computational Intelligence, Networks & Security
- Book Chapter for CRC PRESS Taylor & Francis Group

Awards and Honors 🏆

Cash Award for Publishing Paper in High Impact WOS Journal
Sathyabama Institute of Science and Technology (Teachers Day 2022 & 2024)
NPTEL Discipline Star Certificate
Disciplinarian Award
Sathyabama Institute of Science & Technology, Chennai

Publication Top Notes:

DeepExuDetectNet: Diabetic retinopathy diagnosis: Blood vessel segmentation and exudates disease detection in fundus images

A swarm intelligence optimization for lung cancer detection from RNA-seq gene expression data using convolutional neural networks

A novel framework for entertainment robots in personalized elderly care using adaptive emotional resonance technologies

An Improved Adaptive Neuro-fuzzy Inference Framework for Lung Cancer Detection and Prediction on Internet of Medical Things Platform

Rainfall prediction using machine learning techniques

Online product review using sentiment analysis

Akmal Jahan Mohamed Abdul Cader | Artificial Intelligence | Best Researcher Award

Posted on 28/12/202428/12/2024 by ScienceFather

Akmal Jahan Mohamed Abdul Cader | Artificial Intelligence | Best Researcher Award

Dr. Akmal Jahan Mohamed Abdul Cader, South Eastern University, Sri Lanka.

Dr. Akmal Jahan Mohamed Abdul Cader is a distinguished academic and researcher currently serving as a Senior Lecturer in Computer Science at the South Eastern University of Sri Lanka. With extensive experience in higher education, he is a Visiting Research Fellow at QUT, Australia. His research interests include artificial intelligence, data science, and document image analysis. Dr. Cader has published numerous high-impact articles and is actively involved in academic development and curriculum design. He is committed to advancing education and research in the field of computer science.

Publication Profiles

Googlescholar

Education and Experience

Visiting Research Fellow – QUT Momentum Visiting Fellow, QUT, Australia (2021 – Present)
Senior Lecturer (Computer Science) – South Eastern University of Sri Lanka (2020 – Present)
Sessional Academic – School of Electrical Engineering & Computer Science, QUT (2016 – 2019)
Lecturer (Computer Science) – South Eastern University of Sri Lanka (2012 – 2015)
Assistant Lecturer – South Eastern University of Sri Lanka (2010 – 2012)
Demonstrator in Computer Science – South Eastern University of Sri Lanka (2009 – 2010)

Suitability For The Award

Dr. Mac Akmal Jahan Mohamed Abdul Cader, Senior Lecturer in Computer Science at the South Eastern University of Sri Lanka, is a highly accomplished academic and researcher, making him an exemplary candidate for the Best Researcher Award. With a career spanning over a decade, Dr. Cader has consistently demonstrated leadership in research, teaching, and academic development, particularly in the fields of artificial intelligence, computer science, and digital technologies. His research contributions, coupled with his active involvement in academic service, professional organizations, and international collaborations, solidify his standing as a leading figure in his domain.

Professional Development

Dr. Cader has participated in several professional development programs focused on effective communication, teaching and learning, and project-based learning. He has completed various certifications at QUT, enhancing his skills in pedagogy and curriculum development. His commitment to continuous improvement in education is evident in his active engagement in workshops and training sessions aimed at promoting best practices in teaching. As a Fellow of the Higher Education Academy, he champions high standards in academic instruction and student engagement.

Research Focus

Dr. Cader’s research primarily focuses on artificial intelligence, data science, and document image analysis. He explores the synthesis and application of synthetic metals, aiming to develop innovative solutions in electronics and energy storage. His work on TCNQ chemistry has significant implications for biotechnology and medicine, including the construction of electrochemical sensors and drug delivery systems. By synthesizing novel compounds, he contributes to advancements in both theoretical and practical aspects of computer science and materials research.

Awards and Honors

Senate Honours Award for High Impact Publications – SEUSL (2022 & 2023)
Queensland University of Technology Postgraduate Award (QUTPRA) (2015)
Faculty Write Up (FWU) Scholarship – QUT, Australia (2019)
Effective Communication in Teaching and Learning – QUT, Australia (2019)
Foundation of Teaching and Learning – QUT (2018)

Publication Top Notes

Locating tables in scanned documents for reconstructing and republishing | Cited by: 46 | Year: 2014
Plagiarism Detection on Electronic Text based Assignments using Vector Space Model (ICIAfS14) | Cited by: 37 | Year: 2014
AntiPlag: Plagiarism Detection on Electronic Submissions of Text Based Assignments | Cited by: 34 | Year: 2014
Plagiarism detection tools and techniques: A comprehensive survey | Cited by: 23 | Year: 2021
Fingerprint Systems: Sensors, Image Acquisition, Interoperability and Challenges | Cited by: 11 | Year: 2023
Contactless finger recognition using invariants from higher order spectra of ridge orientation profiles | Cited by: 10 | Year: 2018
Accelerating text-based plagiarism detection using GPUs | Cited by: 10 | Year: 2015
Contactless multiple finger segments based identity verification using information fusion from higher order spectral invariants | Cited by: 9 | Year: 2018

Assoc. Prof. Dr. Mahmoud Bayat | Artificial intelligence Award | Best Researcher Award

Posted on 20/12/2024 by ScienceFather

Assoc. Prof. Dr. Mahmoud Bayat | Artificial intelligence Award | Best Researcher Award

Assoc. Prof. Dr. Mahmoud Bayat, Research Institute of Forests and Rangelands, Iran

Mahmoud Bayat is an Assistant Professor at the Research Institute of Forests and Rangelands, part of the Agricultural Research, Education, and Extension Organization (AREEO) in Tehran, Iran. He earned his B.A., M.Sc., and Ph.D. degrees from the University of Tehran, specializing in forestry science. Mahmoud has collaborated with renowned researchers, including Dr. Charles P.-A. Bourque, Dr. Pete Bettinger, Dr. Eric Zenner, Dr. Aaron Weiskittel, Dr. Harold Burkhart, and Dr. Timo Pukkala. His research focuses on forest modeling and inventory, with particular interest in applying artificial intelligence and machine learning techniques in forestry. Currently, he is working on projects related to growth and yield models for uneven-aged and mixed broadleaf forests using neural networks and the monitoring and mapping of tree species richness in northern Iran’s forests through symbolic regression and artificial neural networks. Mahmoud is proficient in statistical tools such as SPSS and MATLAB, and he is eager to share his expertise and discuss potential collaborations. For more information, his profiles can be found on ResearchGate, Google Scholar, and Scopus.

Professional Profile:

SCOPUS

Mahmoud Bayat’s Suitability for the Research for Best Researcher Award

Based on the provided details, Mahmoud Bayat demonstrates a strong candidacy for the Research for Best Researcher Award due to his extensive academic and professional contributions. Below is a summary supporting his suitability

Education 🎓

Ph.D. in Forestry Science
University of Tehran, Iran
M.Sc. in Forestry Science
University of Tehran, Iran
B.A. in Forestry Science
University of Tehran, Iran

Work Experience 🏢

Assistant Professor
Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO)
Tehran, Iran
Year: [Specify Year] – Present
Research Collaborator
Worked with:
- Dr. Charles P.-A. Bourque
- Dr. Pete Bettinger
- Dr. Eric Zenner
- Dr. Aaron Weiskittel
- Dr. Harold Burkhart
- Dr. Timo Pukkala

Research Interests 🔍

Forest modeling and inventory
Application of artificial intelligence and machine learning in forestry

Current Projects 📊

Growth and Yield Models for Uneven-Aged and Mixed Broadleaf Forest
- Method: Neural Network
Monitoring, Mapping, and Modeling Variation in Tree Species Richness
- Method: Symbolic Regression and Artificial Neural Networks
- Location: Northern Iran Forests

Publication Top Notes:

Comparison of Random Forest Models, Support Vector Machine and Multivariate Linear Regression for Biodiversity Assessment in the Hyrcanian Forests

Projected biodiversity in the Hyrcanian Mountain Forest of Iran: an investigation based on two climate scenarios

Recreation Potential Assessment at Tamarix Forest Reserves: A Method Based on Multicriteria Evaluation Approach and Landscape Metrics

Comparison between graph theory connectivity indices and landscape connectivity metrics for modeling river water quality in the southern Caspian sea basin

Development of multiclass alternating decision trees based models for landslide susceptibility mapping

Modeling Tree Growth Responses to Climate Change: A Case Study in Natural Deciduous Mountain Forests

Ruochen Li | Artificial Intelligence | Best Researcher Award

Posted on 13/12/202413/12/2024 by ScienceFather

Ruochen Li | Artificial Intelligence | Best Researcher Award

Dr. Ruochen Li, BOHUA UHD Co., Ltd. , China.

Ruochen Li, PhD candidate at Macau University of Science and Technology, specializes in Artificial Intelligence with a focus on no-reference video quality assessment, cross-modal audio-visual retrieval, and image-based sound source localization. With expertise in cutting-edge AI technologies like PyTorch, TensorFlow, and MindSpore, Li has achieved groundbreaking research in video quality evaluation and audio-visual content correlation, earning recognition in top-tier journals. He has also received a prize in the National Artificial Intelligence Competition for his contributions to ultra-high-definition video processing.

Publication Profile

Scopus

Education and Experience

PhD in Artificial Intelligence (2021-2024), Macau University of Science and Technology.
Master’s in Control Engineering (2016-2019), Jiangsu University of Science and Technology.
- Supervisor: Associate Prof. Shuxia Ye.
Bachelor’s in Control Engineering (2012-2016), Jiangsu University of Science and Technology.
Research Participant: National Ultra-High Definition Video Innovation Center.
Research Contributor: China Science and Technology Information Research Institute.

Suitability For The Award

Dr. Ruochen Li is an accomplished researcher specializing in artificial intelligence, video quality assessment, and audio-visual event retrieval. With a Ph.D. in Artificial Intelligence from Mauca University of Science and Technology and extensive expertise in PyTorch, TensorFlow, and MindSpore, Li has contributed significantly to advancing multimedia technologies. Their innovations include state-of-the-art datasets, algorithms like Reformer, and multimodal fusion techniques with applications in accessibility, entertainment, and surveillance. Recognized through high-impact publications and awards, including third prize in the National Artificial Intelligence Competition, Ruochen Li exemplifies excellence in research and innovation, making them a strong candidate for prestigious honors such as the Best Researcher Award.

Professional Development

Ruochen Li’s professional journey is defined by innovations in AI and deep learning. He developed the UHD-VQ5k dataset and proposed novel algorithms for ultra-high-definition video quality assessment, utilizing advanced models like Resformer. His work in audio-visual content analysis, featured in his doctoral dissertation, emphasizes the integration of audio-visual features using deep neural networks. As a key participant in national projects, he has contributed to cloud-based UHD video platforms and AI policy analysis. His collaborations and publications underscore his commitment to advancing AI research and applications.

Research Focus

Ruochen Li’s research revolves around Artificial Intelligence applications in multimedia. His expertise spans no-reference video quality assessment, where he develops datasets and benchmarks for UHD video, to cross-modal audio-visual retrieval, enhancing machine understanding of multimodal content. His work also extends to image-based sound source localization, integrating audio-visual data for precise event detection. Through pioneering algorithms, Li bridges gaps between modalities, advancing the interplay of audio and video content in deep learning applications. His contributions drive progress in multimedia AI.

Awards and Honors

Prize Winner: National Artificial Intelligence Competition.
CET-6 Certificate: Scored 490.
CET-4 Certificate: Scored 552.

Publication Top Notes

SgLFT: Semantic-guided Late Fusion Transformer for Video Corpus Moment Retrieval – Neurocomputing, 2024.
Ultrahigh-definition Video Quality Assessment: A New Dataset and Benchmark – Neurocomputing, 2024,
TA2V: Text-Audio Guided Video Generation – IEEE Transactions on Multimedia, 2024,
Cross-Modality Knowledge Calibration Network for Video Corpus Moment Retrieval – IEEE Transactions on Multimedia, 2024,
Maximizing Mutual Information Inside Intra- and Inter-Modality for Audio-Visual Event Retrieval – International Journal of Multimedia Information Retrieval, 2023,

Mr. Seyed matin malakouti | Deep learning Awards | Best Researcher Award

Posted on 18/11/2024 by ScienceFather

Mr. Seyed matin malakouti | Deep learning Awards | Best Researcher Award

Mr. Seyed matin malakouti, University of Rijeka, Croatia

Seyed Matin Malakouti is an accomplished electrical engineer and researcher specializing in control systems engineering and machine learning. He completed his Master of Science in Electrical Engineering from the University of Tabriz, Iran, after earning his Bachelor’s degree from Isfahan University of Technology. His research spans various applications of machine learning, including wind power generation prediction, heart disease classification using ECG data, and solar farm power generation forecasting. Seyed’s work has resulted in several high-impact publications in prestigious journals, with his research on wind energy and machine learning techniques receiving significant citations. He has also been involved in cutting-edge projects such as predicting global temperature change and advancing renewable energy solutions. In recognition of his contributions, Seyed has received multiple awards, including the Best Researcher Award at the International Conference on Cardiology and Cardiovascular Medicine in 2023, and nominations for Best Paper and Best Researcher Awards in other international conferences. Additionally, he actively contributes to the scientific community as a peer reviewer for numerous journals in the fields of artificial intelligence, environmental sciences, and electrical engineering.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award

Seyed Matin Malakouti is a highly qualified and accomplished researcher in the field of Electrical Engineering, specializing in Control Systems, Machine Learning, and Data Science. His impressive academic background includes a Master’s degree in Electrical Engineering from the University of Tabriz and a Bachelor’s degree from Isfahan University of Technology.

Education & Training 🎓

2020 – 2022: M.Sc. in Electrical Engineering – Control System Engineering, University of Tabriz, Iran
2014 – 2019: B.Sc. in Electrical Engineering, Isfahan University of Technology, Iran

Awards & Honors 🏆

2023: Best Researcher, International Conference on Cardiology and Cardiovascular Medicine
2023: Nominated for Best Paper Award, International Research Awards on Mathematics and Optimization Methods
2024: International Young Scientist Awards, Best Researcher Category

Technical Skills 🛠️

Machine Learning 🤖
Data Science 📊
Programming Languages: MATLAB, Python 💻

Peer Review Activities 🧐

Seyed has reviewed articles for prestigious journals, such as:

IEEE Access
Artificial Intelligence Review
BMC Public Health
Environmental Monitoring and Assessment 🌱

Publication top Notes:

Machine learning and transfer learning techniques for accurate brain tumor classification

ML: Early Breast Cancer Diagnosis

Advanced techniques for wind energy production forecasting: Leveraging multi-layer Perceptron + Bayesian optimization, ensemble learning, and CNN-LSTM models

Babysitting hyperparameter optimization and 10-fold-cross-validation to enhance the performance of ML methods in predicting wind speed and energy generation

Discriminate primary gammas (signal) from the images of hadronic showers by cosmic rays in the upper atmosphere (background) with machine learning

Estimating the output power and wind speed with ML methods: A case study in Texas

Assoc Prof Dr. Wenlong Hang | Artificial Intelligence Award | Best Researcher Award

Posted on 27/09/2024 by ScienceFather

Assoc Prof Dr. Wenlong Hang | Artificial Intelligence Award | Best Researcher Award

Assoc Prof Dr. Wenlong Hang, Nanjing Tech University, China

Wenlong Hang holds a Doctor of Engineering degree from Jiangnan University, where he graduated in June 2017, specializing in Light Industry Information Technology. During his doctoral studies, he visited both Hong Kong Polytechnic University and the Shenzhen Institutes of Advanced Technology. Since September 2017, Dr. Hang has been a faculty member at the School of Computer Science and Technology at Nanjing Tech University. His research interests primarily focus on artificial intelligence and machine learning, with a particular emphasis on medical image analysis and EEG signal processing. He has published more than 30 papers in reputable journals and conferences, contributing significantly to semi-supervised learning, federated learning, and EEG classification techniques. His representative works include research on medical image segmentation, reliability-aware semi-supervised frameworks, and domain-generalized EEG classification.

Professional Profile:

GOOGLE SCHOLAR

Summary of Suitability for Best Researcher Award :

Wenlong Hang is highly suitable for the Best Researcher Award based on his extensive research and contributions in the fields of artificial intelligence, machine learning, and medical image processing. His academic background, with a Doctor of Engineering degree from Jiangnan University, and professional experiences at institutions like Hong Kong Polytechnic University and Shenzhen Institutes of Advanced Technology, demonstrates his deep involvement in advanced technological research.

Education:

Doctor of Engineering (Graduated in June 2017)
- Major: Light Industry Information Technology
- Institution: Jiangnan University
- Doctoral Visits: Hong Kong Polytechnic University, Shenzhen Institutes of Advanced Technology

Work Experience:

Since September 2017: Faculty Member
- Position: Professor at the School of Computer Science and Technology
- Institution: Nanjing Tech University

Research Areas:

Artificial Intelligence (AI)
Machine Learning (ML)
Medical Image Segmentation
EEG Classification

Publication top Notes:

A novel negative-transfer-resistant fuzzy clustering model with a shared cross-domain transfer latent space and its application to brain CT image segmentation

CITED: 109

Local and global structure-aware entropy regularized mean teacher model for 3d left atrium segmentation

CITED: 109

Cross-subject EEG signal recognition using deep domain adaptation network

CITED: 73

Exploring a rich spatial–temporal dependent relational model for skeleton-based action recognition by bidirectional LSTM-CNN

CITED: 67

Walking imagery evaluation in brain computer interfaces via a multi-view multi-level deep polynomial network

CITED: 34

Transfer affinity propagation-based clustering

CITED: 33

Mr. Zhongwen Hao | Deep learning Award | Best Researcher Award

Posted on 09/09/2024 by ScienceFather

Mr. Zhongwen Hao | Deep learning Award | Best Researcher Award

Mr. Zhongwen Hao, Cranfield University, China

Zhongwen Hao is a Master’s candidate in Aerospace Manufacturing at Cranfield University, UK, and concurrently pursuing a Master of Mechanical Engineering at Nanjing University of Aeronautics and Astronautics, China. He completed his Bachelor’s degree in Electronic Information with a focus on Image Processing from China University of Mining and Technology. His research interests include robot control, visual servoing, image processing, and deep learning. Zhongwen has led notable projects such as visual servoing of robotic arms using deep learning techniques and galaxy image classification. His proficiency in programming with C++, Python, and MATLAB, coupled with his skills in deep learning and image processing, underscores his technical expertise. He has published research on motion prediction and object detection in visual servoing systems. Zhongwen is known for his strong project execution abilities, team spirit, and resilience.

Professional Profile:

ORCID

Summary of Suitability:

Hao’s research direction aligns well with cutting-edge fields such as robot control, visual servoing, image processing, and deep learning. These areas are highly relevant and significant in contemporary technological advancements. Hao has a solid educational foundation with advanced studies in Aerospace Manufacturing and Mechanical Engineering, complemented by a bachelor’s degree in Electronic Information with a focus on Image Processing. This diverse yet interconnected educational background enhances his research capabilities.

Education

Cranfield University, Bedford, UK
Master’s Candidate of Aerospace Manufacturing
Major: Deep Learning and Image Processing
September 2023 – September 2024
Nanjing University of Aeronautics and Astronautics, Nanjing, China
Master of Mechanical Engineering
Major: Mechanical
September 2022 – June 2025 (Expected)
China University of Mining and Technology, Xuzhou, China
Bachelor of Electronic Information
Major: Image Processing
September 2017 – June 2021

Work Experience

Project Leader
Research on Visual Servoing of Robotic Arms Based on Deep Learning
June 2024 – September 2024
- Led research on target detection using the DETR model, trajectory planning with the PSO algorithm, and motion prediction using BiLSTM and KAN neural networks.
- Integrated and simulated algorithms in ROS using Gazebo to validate their effectiveness.
Participator
Galaxy Image Classification Based on Deep Learning
February 2024 – March 2024
- Handled image preprocessing and reconstruction, and implemented galaxy image classification using the VIT model, achieving a classification accuracy of 90%.

Publication top Notes:

Motion Prediction and Object Detection for Image-Based Visual Servoing Systems Using Deep Learning

Mr. Omer Tariq | Artificial Intelligence Award | Best Researcher Award

Posted on 14/08/2024 by ScienceFather

Mr. Omer Tariq | Artificial Intelligence Award | Best Researcher Award

Mr. Omer Tariq, Korea Advanced Institute of Science and Technology, KAIST, South Korea

Omer Tariq is a Ph.D. candidate at the Korea Advanced Institute of Science and Technology (KAIST), specializing in efficient and privacy-preserving deep learning for AIoT and autonomous systems. With a strong foundation in digital ASIC design, embedded systems, and hardware design, Omer has over seven years of experience in developing and deploying innovative machine learning solutions using TensorFlow, TensorRT, and PyTorch. His research includes advanced robotics software systems, autonomous navigation, and state-of-the-art motion planning algorithms. He has led teams in high-performance SoC/RTL design and verification at the National Electronics Complex, Pakistan, and contributed to satellite imaging systems at SUPARCO. Omer holds a BSc in Electrical Engineering from the University of Engineering and Technology, Taxila, and has published several papers in prominent journals. His technical skills are complemented by a range of certifications in machine learning, data science, and digital signal processing.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award

Omer Tariq is a Ph.D. candidate specializing in efficient and privacy-preserving deep learning for AIoT and Autonomous Systems. His work is highly relevant to current technological advancements and addresses significant challenges in machine learning, robotics, and autonomous systems. His research includes:

Education

Korea Advanced Institute of Science and Technology (KAIST)
Doctor of Philosophy (Ph.D.) in Computer Science
May 2021 – July 2025

Majors: Machine Learning & AI
CGPA: 3.74/4.3
Coursework: Programming for AI, Introduction to Artificial Intelligence, Design and Analysis of Algorithms, Intelligent Robotics, Human-Computer Interaction, Artificial Intelligence and Machine Learning, Technical Writing for Computer Science, Advanced Machine Learning, IoT Datascience

University of Engineering and Technology (UET), Taxila
Bachelor of Science in Electrical Engineering
Nov 2010 – July 2014

CGPA: 3.25/4.0
Thesis: Computer Vision-Assisted Object Detection and Control Framework for 3-DoF Robotic Arm
Area: Microelectronics, Control Systems, and Advanced Computer Architecture

Work Experience

Department of Industrial & Systems Engineering (ISysE), KAIST
Research Assistant
Nov. 2023 – March 2024

Designed and developed the electronics and power management module for the DAIM-Autonomous Mobile Robot, enhancing operational efficiency and reliability.
Engineered advanced robotics software systems for autonomous navigation and task execution.
Implemented state-of-the-art robot motion planning, mapping, and localization (SLAM) algorithms to improve real-time navigation accuracy.

National Electronics Complex, Pakistan (NECOP)
Engineering Manager & Team Lead
Apr. 2019 – Sep. 2022

Led verification and validation of high-performance SoC/RTL designs, ensuring system performance and reliability.
Spearheaded RTL development and optimization for high-performance IC designs, including logic synthesis, DFT, scan chain insertion, formal verification, and static timing analysis.
Managed the use of Synopsys and Cadence EDA tools for front-end and back-end digital IC design processes.

National Space Agency, Pakistan (SUPARCO)
Assistant Manager
Oct. 2014 – Apr. 2019

Designed and developed satellite imaging payload systems for national satellite missions.
Engineered high-speed, multi-layer PCB designs and conducted signal/power integrity simulations for satellite systems.
Developed embedded systems for the Satellite Ku-Band Positioning Unit, enhancing communication and positioning capabilities.