Shaogang Hu | Inspired Computing | Best Researcher Award

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Prof. Shaogang Hu | Inspired Computing | Best Researcher Award

Prof. Shaogang Hu | Inspired Computing | University of Electronic Science and Technology | China

Prof. Shaogang Hu is a distinguished academic and researcher affiliated with the University of Electronic Science and Technology of China. Renowned for his work in neuromorphic computing, edge artificial intelligence, and spiking neural networks, he has established himself as a thought leader in energy-efficient computing systems. With a robust academic presence and strong publication record, Prof. Hu contributes significantly to the evolution of intelligent sensing technologies, particularly in the domains of hardware-software co-design, sensor fusion, and low-power AI processing. His interdisciplinary approach and collaboration with both academic and industrial partners position him as a leading figure in next-generation AI systems.

Academic Profile:

Scopus

Education:

Prof. Shaogang Hu holds a Ph.D. in Electronic Engineering, where he specialized in advanced chip architecture and intelligent signal processing. His academic training emphasized the development of computational models that bridge hardware limitations with evolving AI algorithms. Throughout his doctoral studies, Prof. Hu demonstrated a strong aptitude for interdisciplinary research, integrating concepts from neuroscience, electrical engineering, and computational theory. His academic background provided a solid platform for his current research into neuromorphic computing and low-energy embedded systems.

Experience:

Prof. Hu has gained significant experience in both academic and research environments. At the University of Electronic Science and Technology of China, he leads research teams focusing on neuromorphic circuits and edge AI applications. His academic role involves supervising graduate students, managing collaborative research projects, and developing experimental platforms for energy-efficient intelligent systems. He has worked closely with international research teams to push the boundaries of real-time computing, particularly in sensor-based systems, biomedical devices, and real-time video analytics. His active involvement in the broader academic community includes peer reviewing for indexed journals, technical committee memberships, and panel participation in various research forums.

Research Interest:

Prof. Shaogang Hu’s primary research interests include neuromorphic computing, spiking neural networks, energy-efficient AI chips, event-based sensors, and intelligent edge systems. He is particularly focused on optimizing hardware architectures to support real-time data processing with minimal energy consumption. His work in developing algorithms and chip systems that mimic neural behavior offers promising solutions for low-latency, low-power intelligent devices. Prof. Hu also explores hybrid models that combine frame-based and event-based sensor technologies to enhance system responsiveness in dynamic environments, such as robotics and smart surveillance systems.

Award:

Prof. Hu has been recognized for his contributions through various academic accolades, invitations to international conferences, and peer-reviewed editorial roles. His work has been consistently acknowledged for its originality and practical value in applied sciences. As a senior member of professional organizations such as IEEE and ACM, Prof. Hu continues to lead and contribute to the development of high-impact research. His efforts in mentoring early-career researchers and promoting scientific exchange further reflect his leadership in the academic and research landscape.

Selected Publications:

  • “YOLO-fall: a YOLO-based fall detection model with high precision, shrunk size, and low latency” (2025)

  • “An Image Encryption Algorithm Based on HNN with Memristor” (2025) – 1 Citation

  • “Spatio-Temporal Fusion Spiking Neural Network for Frame-Based and Event-Based Camera Sensor Fusion” (2024) – 4 Citations

  • “Floating-Point Approximation Enabling Cost-Effective and High-Precision Digital Implementation of FitzHugh-Nagumo Neural Networks” (2024) – 3 Citations

Conclusion:

Prof. Shaogang Hu is a highly accomplished researcher whose innovative contributions to neuromorphic systems and energy-efficient AI make him an outstanding candidate for this award. His scholarly output, leadership in collaborative research, and continued pursuit of intelligent sensing technologies have made a measurable impact in the field. With a focus on real-world application, Prof. Hu’s research advances the capabilities of AI in hardware-constrained environments. His academic integrity, technical leadership, and forward-looking vision make him not only a deserving recipient of this recognition but also a role model in shaping the future of intelligent systems research.

 

 

 

 

 

Prof. Dr. Osman Erogul | Artificial | Best Researcher Award

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Prof. Dr. Osman Erogul | Artificial | Best Researcher Award 

Prof. Dr. Osman Erogul | TOBB University of Economics and Technology | Turkey

Prof. Dr. Osman Erogul is a distinguished academic and researcher in the field of biomedical engineering, medical device design, and artificial intelligence applications in healthcare. With a strong foundation in electrical and electronics engineering, his career has spanned academia, medical research, and international collaboration, earning him recognition as one of the leading figures in integrating engineering innovation with medical sciences. He has held various leadership positions, contributed to high-impact research, authored numerous scientific publications, and secured patents in the field of medical devices. Currently, he serves as the Dean of the Faculty of Engineering and Director of the Graduate School of Natural and Applied Sciences at TOBB University of Economics and Technology, where he continues to guide research and development in cutting-edge medical technologies.

Professional Profile

Orcid

Google Scholar

Suitability Summary 

Prof. Dr. Osman Erogul is highly suitable for the Best Researcher Award due to his outstanding academic, research, and leadership contributions in biomedical engineering and medical technology. With a solid educational background and advanced training across the USA, Germany, Holland, and Japan, he has established himself as an international authority in medical device innovation and healthcare technologies.

Education

Prof. Dr. Osman Erogul began his academic journey with a Bachelor of Science degree in Electrical and Electronics Engineering from the Military Academy, building a strong technical base. He pursued his Master of Science degree at Middle East Technical University, where he specialized further in electrical and electronics engineering with a focus on medical applications. His academic training culminated with a Ph.D. in Electronics Engineering from Ankara University, where he deepened his expertise in biomedical instrumentation and signal processing. Alongside these formal qualifications, he undertook specialized professional training in the United States, Germany, Holland, and Japan, focusing on advanced imaging technologies such as Computed Tomography, Digital Angiography, and Magnetic Resonance Imaging.

Experience

His professional experience is marked by leadership and innovation. He served as the Head of the Biomedical Engineering Centre and the Medical Design and Manufacturing Centre at Gulhane Military Medical Academy (GATA), where he directed groundbreaking projects in medical device development and healthcare technology. Additionally, he gained international exposure as a research scientist at the Communications Research Centre in Ottawa, Canada, enhancing his expertise in medical imaging and communication technologies. His experience in these roles not only bridged medical sciences and engineering but also positioned him as a key contributor to Turkey’s advancement in biomedical technologies. As a representative of Turkey, he was also designated to the Medical Applications of Knowledge Transfer Forum at CERN in Switzerland, underscoring his global standing in scientific collaboration. Currently, as Dean and Director at TOBB University of Economics and Technology, he leads engineering education and graduate research programs, preparing the next generation of researchers and innovators.

Research Interests

Prof. Dr. Osman Erogul research encompasses a wide array of domains in biomedical engineering and applied sciences. His key focus areas include physiological signals and image processing, sleep signals analysis, artificial intelligence for medical diagnostics, and the design and manufacturing of custom-made implants. He has a strong interest in additive manufacturing techniques for medical applications, ensuring that patient-specific solutions are developed with high precision. His research also spans medical technology management, quality assurance systems, and radiation physics, contributing to both the academic community and the healthcare industry. His interdisciplinary approach demonstrates a unique integration of engineering principles with real-world medical applications.

Awards

Throughout his career, Prof. Dr. Osman Erogul has been recognized for his leadership in biomedical engineering research and academic excellence. His contributions to medical device innovation, training of young researchers, and international collaboration have positioned him as a valuable asset to both the scientific and healthcare communities. His designation as the national representative in international knowledge transfer forums such as CERN highlights the trust placed in his expertise and his role in advancing Turkey’s global scientific presence. He has also been honored through various institutional and professional recognitions for his contributions to medical device design and applied research.

Publication Top Notes

  • Effects of electromagnetic radiation from a cellular phone on human sperm motility: an in vitro study
    Year: 2006
    Citation: 410

  • Epileptic EEG detection using the linear prediction error energy
    Year: 2010
    Citation: 228

  • An efficient method for snore/nonsnore classification of sleep sounds
    Year: 2007
    Citation: 167

  • Efficient sleep spindle detection algorithm with decision tree
    Year: 2009
    Citation: 119

  • Selective brain cooling seems to be a mechanism leading to human craniofacial diversity observed in different geographical regions
    Year: 2004
    Citation: 97

  • Investigation of sequential properties of snoring episodes for obstructive sleep apnoea identification
    Year: 2008
    Citation: 71

  • Automatic recognition of vigilance state by using a wavelet-based artificial neural network
    Year: 2005
    Citation: 60

  • Obstructive sleep apnea prediction from electrocardiogram scalograms and spectrograms using convolutional neural networks
    Year: 2021
    Citation: 38

Conclusion

Prof. Dr. Osman Erogul professional journey reflects a blend of technical mastery, innovative research, and academic leadership. His contributions to the fields of biomedical engineering, medical imaging, and artificial intelligence highlight his dedication to advancing healthcare through engineering. By combining extensive educational training, practical experience, and global collaboration, he has shaped impactful research that benefits both academia and industry. His publications, patents, and leadership roles reinforce his reputation as a pioneer in medical technology innovation. Prof. Dr. Osman Erogul continues to inspire the academic community while driving forward new discoveries and applications that integrate engineering with medicine.

Mr. Koagne Silas | Neural Networks | Pioneer Researcher Award

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Mr. Koagne Silas | Neural Networks | Pioneer Researcher Award 

Mr. Koagne Silas, University of Dschang, Cameroon

KOAGNE LONGPA TAMO Silas is a Cameroonian researcher and Ph.D. student in Physics at Dschang State University, specializing in medical physics with a strong focus on automation and applied computer science. His academic background spans both physics and electrical engineering, with degrees from the University of Dschang and the University of Bamenda, where he developed expertise in embedded systems, analog artificial neural networks, and electronics. Silas has extensive experience in microcontroller programming, analog and digital circuit simulation, and tools such as MATLAB, Arduino, Proteus, and Cadence Virtuoso. In addition to his research, he has served as an electronics teacher at various technical colleges and as a junior lecturer in computer science. His hands-on experience includes internships in electronics maintenance and electrical network installation. A bilingual communicator in English and French, Silas is known for his leadership, creativity, and commitment to advancing applied technologies in medical physics.

Professional Profile:

SCOPUS

🏅 Summary of Suitability Pioneer Researcher Award 

KOAGNE LONGPA TAMO Silas is an emerging research talent in the field of medical physics and electronics, demonstrating a rare combination of early innovation, technical depth, and applied problem-solving across interdisciplinary domains. As a Ph.D. candidate with an M.Sc. specialization in analog artificial neural networks for medical applications, Silas is pioneering research at the intersection of electronics, embedded systems, and health technologies, aligning closely with the spirit of the Pioneer Researcher Award.

🎓 Education Background

  • Ph.D. in Physics (Medical Physics) – Dschang State University, Cameroon (📅 Dec 2022 – Present)

    • 🧠 Research Focus: Analog Artificial Neural Networks

    • 👨‍🏫 Supervisor: Prof. Geh Wilson Ejuh

  • M.Sc. in Physics, Electronics Speciality – Dschang State University, Cameroon (📅 July 2022)

    • 📘 Thesis: Specification and implementation of multilayer perceptron analog artificial neural networks

    • 👨‍🏫 Supervisor: Dr. Djimeli Tsajio Alain B.

  • B.Sc. in Physics – Dschang State University, Cameroon (📅 Aug 2021)

  • DIPET 2 in Electronics – University of Bamenda (📅 July 2020)

    • 🛰 Dissertation: Design and implementation of a digital breath alcohol detection system with SMS alert and vehicle tracking

  • DIPET 1 in Electronics – University of Bamenda (📅 Aug 2018)

    • 🚪 Project: RFID-based electronic attendance system with automatic door unit

  • GCE A/L – Government Bilingual High School, Mbouda (📅 July 2015)

  • GCE O/L – Government Bilingual High School, Mbouda (📅 June 2013)

  • FSLC – Ecole Primaire Bilingue de la Promotion, Mbouda (📅 June 2008)

💼 Work Experience

  • Electronics Teacher – Government Technical College Ngombo-ku, Cameroon (📅 Jan 2021 – Present)

  • Junior Lecturer in Computer Science – Higher Technical Teacher Training College Bambili (📅 2019–2020)

  • Electronics Teacher – Government Technical High School Bambui (📅 2017–2018)

  • Internship – Electronics & Maintenance

    • 📍 HYTECHS, Yaoundé (📅 2019)

    • 🔧 Worked on printer maintenance & installation

  • Internship – Electrical Network Installation

    • 📍 MEECH CAM Sarl, Yaoundé (📅 2016)

    • ⚡ Focus on underground cable installation and high voltage network

🏆 Achievements & Awards

  • ✅ Successfully designed and implemented:

    • 🤖 An analog artificial neural network (M.Sc. Thesis)

    • 🚘 A breath alcohol detection system with GPS and SMS alerts

    • 🛂 An RFID-based attendance system with automated doors

  • 📚 Published and presented academic work in medical physics and embedded systems

  • 👨‍🏫 Contributed to higher education through teaching and mentoring roles across several institutions

  • 🎓 Admitted to Ph.D. program based on excellent academic performance

  • 💻 Advanced skills in MATLAB, Arduino, MikroC, Cadence Virtuoso, PSPICE & Proteus

  • 🗣️ Bilingual in English and French – great asset for teaching and collaboration

Publication Top Notes:

Breast cancer detection and classification: A study on the specification and implementation of multilayer perceptron analog artificial neural networks

Mr. Mohammed Aljamal | Artificial Intelligence | Best Researcher Award

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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:

 

 

Mr. Fangzhou Lin | Deep Learning | Best Scholar Award

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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

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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

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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

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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 📊

  1. Growth and Yield Models for Uneven-Aged and Mixed Broadleaf Forest
    • Method: Neural Network
  2. 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

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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 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