Lin Zhu | Image Technology | Best Researcher Award

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Ms. Lin Zhu | Image Technology | Best Researcher Award

Shanghai Chest Hospital, China

Dr. Lin Zhu is a postdoctoral researcher and attending doctor at the Radiology Department of Shanghai Chest Hospital, Shanghai Jiaotong University. Her research is centered on radiology and oncology, with a particular emphasis on tumorigenicity, advanced imaging technologies, AI-assisted diagnostic imaging, quantitative image analysis, and molecular nanoimaging in the context of tumor microenvironments. She has published 9 first-author SCI papers and has served as principal investigator (PI) on four funded research projects.

Publication Profile 

Orcid

Educational Background 🎓

  • Doctor of Medicine (Diagnostic and Interventional Radiology)
    Heidelberg University Clinic, Heidelberg University, Germany
    Sept. 2018 – Feb. 2022
    Supervisors: Prof. Dr. Mark O. Wielpütz MHBA and Prof. Hans-Ulrich Kauczor

  • Master of Medicine (Medical Imaging and Nuclear Medicine)
    Shanghai Changzheng Hospital, The Second Military Medical University, Shanghai, China
    Sept. 2013 – June 2016
    Supervisor: Prof. Dr. Hong Yu

  • Bachelor of Medicine (Medical Imaging)
    School of Medicine, Southeast University, China
    Sept. 2007 – June 2012

Professional Experience 💼

  • Postdoctoral Researcher
    Shanghai Chest Hospital, Shanghai Jiaotong University, China
    Feb. 2022 – Present

  • Training Radiologist
    Shanghai Changzheng Hospital, Second Military Medical University, China
    Aug. 2016 – July 2018

  • Training Radiologist
    The Fifth People’s Hospital of Shanghai, Fudan University, China
    July 2012 – July 2013

  • Clinical Practice (Internship)
    Zhongda Hospital, Southeast University, China
    June 2011 – June 2012

Research Interests 🔬

  • Tumorigenicity and tumor microenvironment

  • Advanced and AI-assisted imaging techniques

  • Quantitative imaging analysis

  • Molecular nanoimaging for cancer diagnostics

  • Multimodal radiological imaging for lung cancer and lymphoma

  • Imaging biomarkers in oncology

Awards and Honors🏆✨

While specific named awards are not listed, notable professional achievements include:

  • Published 9 SCI-indexed papers as first author

  • Principal Investigator of 4 research projects

  • Speaker at major international conferences, including:

    • European Congress of Radiology (ECR) 2018 & 2020

    • Chinese Congress of Radiology (CCR) 2016 & 2022

Conclusion🌟

Dr. Lin Zhu exemplifies a dynamic and highly accomplished medical imaging expert with extensive international research and clinical experience. Her multidisciplinary approach, combining diagnostic radiology with molecular imaging and artificial intelligence, positions her as a leading researcher in the field of thoracic oncology imaging. Her work bridges foundational biological research with state-of-the-art diagnostic practices, contributing meaningfully to advancements in precision medicine and personalized cancer care.

Publications 📚

  1. 🐭 µCT in Muco-obstructive Lung Disease
    Zhu L., et al.
    American Journal of Physiology – Lung Cellular and Molecular Physiology, 2022, 322(3):L401–L411.
    🔬 Topic: Neutrophil elastase in mouse lung disease

  2. 🫁 MRI vs. CT for Lung Nodule Detection in COPD
    Li Q, Zhu L, et al.
    Radiology: Cardiothoracic Imaging, 2023, 5(2):e220176.
    🧠 Topic: MRI as a diagnostic tool compared to CT

  3. 🧬 miR-629 & Lung Cancer Tumorigenesis
    Zhu L, et al.
    Cancer Biomarkers, 2020.
    🧪 Topic: PI3K/AKT pathway & FOXO1 targeting

  4. 🦠 Primary Pulmonary Lymphoepithelial Carcinoma
    Nie K, Zhu L, et al.
    Journal of Surgical Oncology, 2023.
    🩺 Topic: Rare lung cancer subtype prognosis

  5. 🧫 RasGRP4 in Lymphoma Growth
    Zhu L, et al.
    Cell Communication and Signaling, 2019, 17(1), 92.
    🔬 Topic: Oncogene role in B-cell lymphoma

  6. 🧬 miR-101 and Pancreatic Cancer Suppression
    Zhu L, et al.
    Cancer Biomarkers, 2018, 23(2):301–309.
    🧠 Topic: STMN1 targeting to suppress proliferation

  7. 🩻 Ultrasound/CT + CA125 for Ovarian Tumors
    Zhu L, et al.
    Minerva Medica, 2018, 109(6):489–491.
    🧪 Topic: Combined diagnostic method

  8. 📊 TIMP-2 Expression in Lung Cancer
    Zhu L, et al.
    PLOS ONE, 2015, 10(4):e0124230.
    🔍 Topic: Meta-analysis on tumor prognosis

  9. 🔬 PET/CT in Small Cell Lung Cancer
    Nie K, Zhu L, et al.
    J Med Imaging Radiat Oncol, 2019, 63(1):84–93

  10. 💉 Serum Angiopoietin-Like Protein 2 as Biomarker
    Chen Y, Zhu L, et al.
    Clin Lab, 2017, 63(1):59–65

  11. 🧠 CT Phenotyping in Pulmonary Lymphoma
    Chen Y, Zhu L, et al.
    J Thorac Dis, 2018, 10(11):6040–6049

  12. 🐭 µCT Monitoring of Neutrophil Elastase Deficiency
    Zhu L, et al.
    ECR 2020 – Book of Abstracts, Insights Imaging, 2020, 11(Suppl 1):34

  13. 🧠 Lung Cancer Imaging & Treatment Methods
    Zhu L, et al.
    Int. J. Med. Radiology, 2018, 41(06):641–645

  14. 🧬 String Beads Sign in Small Cell Lung Cancer
    Xiao YX, Zhu L, et al.
    Journal of Practical Radiology, 2016
    🧠 DOI: 10.3969/j.issn.1002-1671.2016.01.001

  15. 🫁 CT Signs of Peripheral Small Cell Lung Cancer
    Xiao YX, Zhu L, et al.
    Journal of Practical Radiology, 2016
    🩻 DOI: 10.3969/j.issn.1002-1671.2016.01.001

Yousef Daradkeh | Computer Science | Best Academic Researcher Award

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Prof. Dr. Yousef Daradkeh | Computer Science | Best Academic Researcher Award

Professor at Prince Sattam Bin Abdulaziz University, Jordan

Prof. Dr. Yousef Daradkeh is a distinguished professor of Computer Engineering and Information Technology at the Department of Computer Engineering and Information, College of Engineering, Prince Sattam bin Abdulaziz University, Saudi Arabia. He holds a Doctor of Engineering Sciences in Computer Engineering and Information Technology and has over 19 years of extensive academic and administrative experience. Prof. Daradkeh is internationally recognized for his research and teaching in software engineering, computer networks, artificial intelligence, and cybersecurity. As an academic leader, researcher, author, and workshop facilitator, he has significantly contributed to scientific communities through over 128 peer-reviewed publications, multiple books, and active participation in international conferences and editorial boards.

Publication Profile 

Google Scholar

Educational Background 🎓

  • Ph.D. in Computer Engineering and Information Technology
    Belarusian State University of Informatics and Radioelectronics, 2006

    • Dissertation: Interpretation of Polymorphous Networks Models in Real-Time Allocation Systems

    • Accredited by the Higher Education Accreditation Commission (HEAC)

  • Postdoctoral Research Fellowships

    • University of Calgary, Canada (2007–2009) – Systems, Networks, and Devices of Telecommunications

    • University of New South Wales, Australia (2010) – Computer Science and Engineering

    • Massey University, New Zealand (2011) – School of Engineering and Advanced Technology

    • Istanbul Technical University, Turkey (2023–2024) – Electronics and Communication Engineering

  • M.Sc. in Software Engineering
    Belarusian National Technical University, 2003

    • Thesis: Mathematical and Software Optimization of Models of Power Equipment Operations

  • B.Sc. in Computer Engineering
    Technical University of Moldova, 2000

    • Graduation Project: Device for Transcendent Functions Calculation

Professional Experience 💼

  • Current Position:

    • Full Professor & Assistant Dean for Administrative Affairs
      Prince Sattam bin Abdulaziz University, Saudi Arabia

  • Previous Academic Roles:

    • University of Calgary, Canada – Postdoctoral Researcher & Faculty Developer

    • The University of Jordan – Faculty Member, Systems and Information Technology

    • Al-Balqa Applied University, Jordan – Faculty Member, Department of IT

    • Yarmouk University – Instructor, Computer and Information Centre

    • AL-Hussein Bin Talal University – Faculty Member, Engineering & IT

    • Jadara University – Faculty, Department of Software Engineering

    • Kazakh University of Economics, Finance, and International Trade – Online Course Lecturer

  • Other Roles:

    • Designed accredited computer engineering and IT courses

    • Conducted workshops on scientific writing and research methodology

    • Editorial and scientific committee member for numerous international journals and conferences

Research Interests 🔬

  • Wireless Networking and Telecom Systems

  • Modeling of Discrete and Polymorphous Systems

  • Computer Systems and Networks

  • Artificial Intelligence & Data Science

  • Machine Learning & Deep Learning

  • Vision Computing and Digital Image Processing

  • Cybersecurity and Information/Economic Security

  • Agent-Based and Context-Aware Software Engineering

  • E-Government and E-Learning Applications

  • Location-Based Services (LBS) and Geo Services

  • Knowledge Representation and Reasoning

  • Optimization and Real-Time Systems

  • Web and Java Development, Databases

Awards and Honors🏆✨

  • Higher Distinction Scientific Awards – From International Universities

  • Certificate of Appreciation – Ministry of Youth and Sport, Jordan (1999)

  • Faculty Teaching Certificate (FTC) – University of Calgary, Canada (2008)

  • Care Services Certificates – University of Calgary (2008)

  • IT Management Professional Award – Microsoft, Excellent Train, Jordan (2011)

  • Staff Development Certificate – University of Jordan (2012)

  • Life Ambassador First Aid Training Certificate – Riyadh

  • Certificate of English Language (CEL) – AMIDEAST, Jordan

  • Certificate of Russian & Romanian Languages – TUM, BNTU, BSUIR

Conclusion🌟

Prof. Dr. Yousef Daradkeh is a visionary academic leader and prolific researcher who has made significant contributions to the fields of computer engineering, software systems, and emerging technologies. His work continues to bridge theoretical advancements with practical applications, particularly in improving security, efficiency, and intelligence in digital systems. Through his extensive teaching, research, international collaborations, and service to the academic community, Prof. Daradkeh remains a highly influential figure in engineering education and innovation.

Publications 📚

  • 📡 6G mobile communication technology: Requirements, targets, applications, challenges, advantages, and opportunities
    🧾 Alexandria Engineering Journal 64, 245-274
    👥 Cited by: 360 | 📅 Year: 2023

  • 🧠 A hybrid deep learning-based approach for brain tumor classification
    🧾 Electronics 11 (7), 1146
    👥 Cited by: 248 | 📅 Year: 2022

  • 🚗 Key challenges, drivers and solutions for mobility management in 5G networks: A survey
    🧾 IEEE Access 8, 172534-172552
    👥 Cited by: 180 | 📅 Year: 2020

  • 🔍 Tools for fast metric data search in structural methods for image classification
    🧾 IEEE Access 10, 124738-124746
    👥 Cited by: 104 | 📅 Year: 2022

  • 🎗️ Intelligent hybrid deep learning model for breast cancer detection
    🧾 Electronics 11 (17), 2767
    👥 Cited by: 101 | 📅 Year: 2022

  • 🤖 Development of effective methods for structural image recognition using fuzzy logic
    🧾 IEEE Access 9, 13417-13428
    👥 Cited by: 98 | 📅 Year: 2021

  • 📷 Methods of classification of images based on statistical distributions
    🧾 IEEE Access 9, 92964-92973
    👥 Cited by: 90 | 📅 Year: 2021

  • ✈️ Handover management of drones in future mobile networks: 6G technologies
    🧾 IEEE Access 9, 12803-12823
    👥 Cited by: 89 | 📅 Year: 2021

  • 🧱 Classification of Images Based on a System of Hierarchical Features
    🧾 Computers, Materials & Continua 72 (1)
    👥 Cited by: 83 | 📅 Year: 2022

  • 🔧 Handover parameters optimisation techniques in 5G networks
    🧾 Sensors 21 (15), 5202
    👥 Cited by: 71 | 📅 Year: 2021

 

Francisco Mena | Machine Learning | Best Researcher Award

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Mr. Francisco Mena | Machine Learning | Best Researcher Award

PhD Candidate at University of Kaiserslautern-Landau, Germany

Francisco Mena is a PhD candidate in Computer Science at the University of Kaiserslautern-Landau (RPTU), Germany, with a strong academic and research background in deep learning, multi-view learning, and unsupervised learning. His work focuses on developing scalable and generalizable machine learning models, particularly in complex real-world domains like Earth observation and astroinformatics, where missing data and multi-source fusion are major challenges. Francisco’s research emphasizes minimizing human intervention and domain dependency, aiming for methods that are more robust, adaptable, and explainable.

Publication Profile 

Orcid

Educational Background 🎓

  • PhD in Computer Science
    University of Kaiserslautern-Landau (RPTU), Germany
    Jan. 2022 – Present
    Thesis: Data Fusion in Multi-view Learning for Earth Observation Applications with Missing Views

  • Magíster en Ciencias de la Ingeniería Informática (Equivalent to M.Sc. in Computer Engineering)
    Federico Santa María Technical University (UTFSM), Valparaíso, Chile
    Mar. 2018 – Sep. 2020
    Thesis: Mixture Models for Learning in Crowdsourcing Scenarios
    GPA: 94%

  • Ingeniería Civil en Informática (Equivalent to Computer Engineering)
    UTFSM, Santiago, Chile
    Mar. 2013 – Sep. 2020
    GPA: 80% | Rank: Top 10% – 4th of 66 students

  • Licenciado en Ciencias de la Ingeniería Informática
    UTFSM, Santiago, Chile
    Mar. 2013 – Nov. 2017

  • High School
    New Little College, Santiago, Chile
    Mar. 2008 – Dec. 2012

Professional Experience 💼

  • Student Research Assistant – German Research Centre for Artificial Intelligence (DFKI), Germany
    Mar. 2022 – Present
    Working on Earth observation data for crop yield prediction using Python, QGIS, and Slurm.

  • Lecturer – University of Kaiserslautern-Landau (RPTU), Germany
    Oct. 2024 – Apr. 2025
    Teaching: Machine Learning for Earth Observation within a broader Data Science course.

  • Visiting PhD Researcher – Inria Montpellier, France
    Nov. 2024 – Jan. 2025
    Research in multi-modal co-learning, mutual distillation, and multi-task learning.

  • Academic Roles – Federico Santa María Technical University (UTFSM), Chile
    2014 – 2021
    Lecturer & Assistant roles in:

    • Computational Statistics

    • Artificial Neural Networks

    • Machine Learning

    • Operations Research

    • Mathematics Lab

  • Research Assistant – Chilean Virtual Observatory (ChiVO)
    Jul. 2017 – May 2018
    Astroinformatics projects involving ALMA/ESO datasets and Python-based data reduction.

  • Developer Intern – Farmacia Las Rosas S.A., Chile
    Jan. 2017 – Mar. 2017
    Desktop software automation using Python and QT.

Research Interests 🔬

  • Machine Learning Foundations:
    Deep Learning, Variational Autoencoders, Neural Networks, Representation Learning, Deep Clustering

  • Methodologies:
    Multi-view Learning, Data Fusion, Latent Variable Modeling, Dimensionality Reduction, Unsupervised Learning

  • Applications:
    Earth Observation, Remote Sensing, Vegetation Monitoring, Crowdsourcing, Neural Information Retrieval, Astroinformatics

Awards and Honors🏆✨

  • PhD Scholarship – RPTU, Germany (2022–present)

  • Scientific Initiation Award (PIIC) – UTFSM, Chile (2019–2020)

  • Master Program Scholarship – UTFSM, Chile (2018–2020)

  • Honor Roll – UTFSM, Chile (2013)

Conclusion🌟

Francisco Mena is a dedicated machine learning researcher whose work blends theoretical rigor with impactful real-world applications. His interdisciplinary approach spans remote sensing, astroinformatics, and crowdsourcing, focusing on creating models that are resilient to missing data, efficient at scale, and minimally reliant on labeled supervision. With a growing publication record, international experience, and teaching background, he is well-positioned to make significant contributions to both academia and applied AI research.

Publications 📚

  1. 📄 Missing data as augmentation in the Earth Observation domain: A multi-view learning approach
    Neurocomputing, 2025-07
    DOI: 10.1016/j.neucom.2025.130175
    👥 Francisco Mena, Diego Arenas, Andreas Dengel

  2. 🌾 Adaptive fusion of multi-modal remote sensing data for optimal sub-field crop yield prediction
    Remote Sensing of Environment, 2025-03
    DOI: 10.1016/j.rse.2024.114547
    👥 Francisco Mena et al.

  3. 🛰️ Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications
    IEEE JSTARS, 2024
    DOI: 10.1109/JSTARS.2024.3361556
    👥 Francisco Mena, Diego Arenas, Marlon Nuske, Andreas Dengel

  4. 📉 Impact Assessment of Missing Data in Model Predictions for Earth Observation Applications
    IGARSS Proceedings, 2024
    DOI: 10.1109/IGARSS53475.2024.10640375
    👥 Francisco Mena et al.

  5. 🛰️ Assessment of Sentinel-2 Spatial and Temporal Coverage Based on the Scene Classification Layer
    IGARSS 2024, 2024-07-07
    DOI: 10.1109/igarss53475.2024.10642213
    👥 Cristhian Sanchez, Francisco Mena et al.

  6. 🌽 Crop Yield Prediction: An Operational Approach to Crop Yield Modeling on Field and Subfield Level with ML Models
    IGARSS 2023
    DOI: 10.1109/IGARSS52108.2023.10283302
    👥 Francisco Mena et al.

  7. 🧩 Feature Attribution Methods for Multivariate Time-Series Explainability in Remote Sensing
    IGARSS 2023
    DOI: 10.1109/IGARSS52108.2023.10282120
    👥 Francisco Mena et al.

  8. 🧹 Influence of Data Cleaning Techniques on Sub-Field Yield Predictions
    IGARSS 2023
    DOI: 10.1109/IGARSS52108.2023.10282955
    👥 Francisco Mena et al.

  9. 🗂️ A Comparative Assessment of Multi-View Fusion Learning For Crop Classification
    IGARSS 2023, 2023-07-16
    DOI: 10.1109/igarss52108.2023.10282138
    👥 Francisco Mena, Diego Arenas, Marlon Nuske, Andreas Dengel

  10. 📊 Predicting Crop Yield with Machine Learning: Input Modalities and Models on Field and Sub-Field Level
    IGARSS 2023, 2023-07-16
    DOI: 10.1109/igarss52108.2023.10282318
    👥 Francisco Mena et al.

Md. Humaun Kabir | Sign Language Recognition | Best Researcher Award

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Mr. Md. Humaun Kabir | Sign Language Recognition | Best Researcher Award

Assistant Professor at Jamalpur Science & Technology University, Bangladesh

Md. Humaun Kabir is a dynamic academic and researcher in the field of Computer Science and Engineering, currently serving as an Assistant Professor and Chairman of the Department of Computer Science and Engineering at Jamalpur Science and Technology University (JSTU), Bangladesh. He also holds an administrative position as Additional Director of the ICT Cell at the same university. With over seven years of professional experience, he has contributed significantly to both academic instruction and applied research, focusing on emerging areas in computer science such as brain–computer interfaces, biomedical engineering, signal and image processing, and artificial intelligence. His passion for innovation and leadership in academia has established him as a key figure in his field.

Publication Profile 

Orcid

Google Scholar

Educational Background 🎓

Md. Kabir completed his M.Sc. Engineering in Applied Physics and Electronic Engineering from the University of Rajshahi in 2015, securing a CGPA of 3.88 out of 4.00 (exam held in 2017). He earned his B.Sc. Engineering from the same department and university in 2014 with a CGPA of 3.82 (exam held in 2015). Prior to university, he achieved excellent academic results in his secondary and higher secondary education, scoring a GPA of 5.00 in HSC from Police Line’s School and College, Kushtia, and a GPA of 4.94 in SSC from Patachora Secondary School, Chuadanga, under the Jashore Education Board. Notably, his department was renamed to Electrical and Electronic Engineering in 2018.

Professional Experience 💼

Kabir began his academic career as a Lecturer in the Department of Computer Science and Engineering at Pundra University of Science and Technology in Bogura, where he served from May 2017 to February 2019. In February 2019, he joined Jamalpur Science and Technology University as a Lecturer and was later promoted to Assistant Professor in February 2022. In April 2022, he took on the additional responsibilities of Chairman of the CSE department and Additional Director of the university’s ICT Cell. Throughout his academic career, he has maintained an active role in teaching, research, and university administration.

Research Interests 🔬

Md. Humaun Kabir’s research spans a diverse range of topics within computer science and engineering. His primary areas of interest include brain–computer interface (BCI), bioinformatics and biomedical engineering, signal and image processing, and machine learning. His work often explores the intersection of human cognition and machine intelligence, particularly through EEG-based applications and gesture recognition systems.

Awards and Honors🏆✨

Kabir has published a total of 25 scholarly articles, including 23 peer-reviewed journal papers and 2 conference proceedings. Many of his works have appeared in prestigious Q1 and Q2 journals such as Sensors, Mathematics, Applied Sciences, and IEEE Access. He has also demonstrated competence in English proficiency by achieving an IELTS score of Band 6.0 in October 2023. His contributions have been widely recognized within the academic community, both nationally and internationally.

Conclusion🌟

Md. Humaun Kabir stands out as an influential and emerging scholar in Bangladesh’s academic and research landscape. His dedication to teaching, commitment to interdisciplinary research, and leadership within his institution reflect a well-rounded professional with a promising future. His work not only contributes to theoretical advancements but also addresses practical challenges in technology and healthcare, positioning him as a researcher with both depth and societal relevance.

Publications 📚

🧠 Combining Deep Learning for Sign Language Recognition

📅 Date: April 2025
📘 Title: Combining state-of-the-art pre-trained deep learning models: A noble approach for Bangla sign language recognition using Max Voting Ensemble
📰 Journal: Systems and Soft Computing
🔗 DOI: 10.1016/j.sasc.2025.200230
👥 Authors: Md. Humaun Kabir, Abu Saleh Musa Miah, Md. Hadiuzzaman, Jungpil Shin

✋ Hand Gesture Recognition Review

📅 Date: 2024
📘 Title: A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities
📰 Journal: IEEE Access
🔗 DOI: 10.1109/ACCESS.2024.3456436
👥 Authors: Jungpil Shin, Abu Saleh Musa Miah, Md. Humaun Kabir, Md. Abdur Rahim, Abdullah Al Shiam

🌱 Trie-PMS8 for Motif Search

📅 Date: 2024
📘 Title: Trie-PMS8: A trie-tree based robust solution for planted motif search problem
📰 Journal: International Journal of Cognitive Computing in Engineering
🔗 DOI: 10.1016/j.ijcce.2024.07.004
👥 Authors: Mohammad Hasan, Abu Saleh Musa Miah, Md. Humaun Kabir, Mahmudul Alam

🧠⚡ EEG-Based BCI System with Feature Selection

📅 Date: August 2024
📘 Title: Exploring Feature Selection and Classification Techniques to Improve the Performance of an Electroencephalography-Based Motor Imagery Brain–Computer Interface System
📰 Journal: Sensors
🔗 DOI: 10.3390/s24154989
👥 Authors: Md. Humaun Kabir, Nadim Ibne Akhtar, Nishat Tasnim, Abu Saleh Musa Miah, Hyoun-Sup Lee, Si-Woong Jang, Jungpil Shin

🌐 Neural Attention for English-Bangla Translation

📅 Date: April 2023
📘 Title: A Neural Attention-Based Encoder-Decoder Approach for English to Bangla Translation
📰 Journal: Computer Science Journal of Moldova
🔗 DOI: 10.56415/csjm.v31.04
👥 Authors: Abdullah Al Shiam, Sadi Md. Redwan, Md. Humaun Kabir, Jungpil Shin

🧬 EEG Motor Imagery Tasks & Feature Selection

📅 Date: April 2023
📘 Title: Investigating Feature Selection Techniques to Enhance the Performance of EEG-Based Motor Imagery Tasks Classification
📰 Journal: Mathematics
🔗 DOI: 10.3390/math11081921
👥 Authors: Md. Humaun Kabir, Shabbir Mahmood, Abdullah Al Shiam, Abu Saleh Musa Miah, Jungpil Shin, Md. Khademul Islam Molla

👤 Gender Recognition from Bangla Names

📅 Date: December 2022
📘 Title: Gender Recognition of Bangla Names Using Deep Learning Approaches
📰 Journal: Applied Sciences
🔗 DOI: 10.3390/app13010522
👥 Authors: Md. Humaun Kabir, Faruk Ahmad, Md. Al Mehedi Hasan, Jungpil Shin

📡 Patch Antenna Design at 3.3 GHz

📅 Date: August 2022
📘 Title: Design of Rectangular Microstrip Patch Antenna at 3.3 GHz Frequency for S-band Applications
📰 Journal: International Journal of Engineering and Manufacturing (IJEM)
👥 Author: Md. Humaun Kabir

🧾 Web-Based Student Registration System

📅 Date: April 2022
📘 Title: Web Based Student Registration and Exam Form Fill-up Management System for Educational Institute
📰 Journal: IJIEEB
👥 Author: Md. Humaun Kabir

🗓️ Smart Routine Management System

📅 Date: April 2022
📘 Title: Design and Implementation of Web-based Smart Class Routine Management System for Educational Institutes
📰 Journal: I. J. Education and Management Engineering
👥 Author: Md. Humaun Kabir

Chitra P | Image Processing | Best Researcher Award

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Dr. Chitra P | Image Processing | Best Researcher Award

Professor at Sathyabama Institute of Science and Technology, India

Dr. P. Chitra is an accomplished academic and researcher with a Ph.D. in Applied Electronics from Sathyabama University (2014) and a Master’s in Applied Electronics (M.E.) from Coimbatore Institute of Technology (2004). She has been serving as an Assistant Professor in Sathyabama University, Chennai, since 2004. Her research interests span across image processing, signal processing, communication systems, and electronic circuit analysis. She has secured multiple sponsored projects, including collaborations with the Indira Gandhi Centre for Atomic Research and the Department of Biotechnology. Dr. Chitra has contributed significantly to the field with numerous publications in both international journals and conferences, with notable works in deep learning for medical diagnostics, radiographic image processing, and PCOS detection using AI. Apart from her academic duties, she is a life member of the Indian Society for Technical Education (MISTE) and Institution of Engineers (IEI), and actively participates as a reviewer for various journals. Her dedication to advancing technology is reflected in her extensive training, certifications, and contributions to AI and machine learning. 🌐📚🔬

Publication Profile : 

Scopus

Google Scholar

Educational Background 🎓

  • Ph.D. in Electronics and Communication Engineering (2014), Sathyabama University, Chennai
  • M.E. in Applied Electronics (2004), Coimbatore Institute of Technology, Coimbatore (CGPA: 8.16)
  • B.E. in Electronics and Communication Engineering (2002), Nooral Islam College of Engineering, Kumaracoil, Nagercoil (73.55%)
  • H.S.C. (1998), Duthie Girls Higher Secondary School, Nagercoil (85.58%)
  • S.S.L.C. (1996), Duthie Girls Higher Secondary School, Nagercoil (87.00%)

Professional Experience 💼

Dr. P. Chitra has been an Assistant Professor at Sathyabama University since June 2004. With a career spanning over two decades, she has a rich academic experience in teaching and research, specializing in image processing, signal processing, and communication systems. She has been actively involved in various sponsored projects, including collaborations with Indira Gandhi Centre for Atomic Research and the Department of Biotechnology. She has led and contributed to multiple funded projects, including the development of digitization protocols for weld images and the application of AI for detecting PCOS (Polycystic Ovary Syndrome). Her expertise also extends to deep learning and AI-based algorithms in medical imaging and diagnostics.

Research Interests 🔬

Dr. Chitra’s research primarily focuses on image processing, signal processing, communication systems, and electronic circuit analysis. She is particularly passionate about applying AI and deep learning techniques for medical image analysis and diagnostic applications. Her ongoing research explores areas such as PCOS detection, brain tumor detection, and lung cancer classification, leveraging AI for better healthcare solutions.

Certifications & Achievements🎓

Dr. Chitra is a proud recipient of numerous NPTEL certifications, including courses in Deep Learning, Data Science for Engineers, and Introduction to AI. She is also a Life Member of Indian Society for Technical Education (ISTE) and the Institution of Engineers (IEI).

Publications 📚

  • Chitra P., Beryl Vedha Y. Johnson, Retnaraj Samuel S., et al. (2024), “Classification of Microglial Cells using Deep Learning Techniques”, Proceedings – 2nd International Conference on Advancement in Computation and Computer Technologies, InCACCT 2024
  • Sheeba I.R., Jegan G., Jayasudha F.V., Chitra P., et al. (2024), “Brain Tumor Detection- ISM Band SAR Reduction Analysis Using Microstrip Patch Antenna”, Proceedings of the 2024 10th International Conference on Communication and Signal Processing, ICCSP 2024
  • Chitra P., Srilatha K., Sumathi M., et al. (2023), “Automated Detection of Polycystic Ovaries using Pretrained Deep Learning Models”, AICERA/ICIS 2023
  • Chitra P., Srilatha K., Jayasudha F.V., et al. (2023), “Lung Cancer Detection Using Classification Algorithm”, RAEEUCCI 2023
  • Amudha S., Shobana J., Satheesh Kumar M., Chitra P. (2022), “Modelling Air Pollution and Traffic Congestion Problem Through Mobile Application”, IconDeepCom 2022
  • Chitra P., Sheela Rani B., Venkataraman B., et al. (2011), “Comparison of Image Enhancement Techniques for Radiographic Weld Images”, Instrumentation Society Of India
  • Chitra P., Sheela Rani B., Venkataraman B., et al. (2011), “Evaluation of Signal To Noise in Different Radiographic Methods and Standard Digitizer”, Indian Journal of Computer Science and Engineering
  • Chitra P., Sheela Rani B., Manoharan N., et al. (2007), “A Comparative Study on the Digitization Parameters of Radiographic Weld Image Digitizers for Weld Defect Detection”, ECHDEM 2007, Chennai
  • Chitra P., Arulmozhi N., Sheela Rani B., et al. (2008), “Evaluation of Radiographic Image Quality through Standard Weld Image Digitizers”, ESSTA 2008
  • Chitra P., Sheela Rani B. (2012), “Study and Analysis on the Effect of Source to Film Distance on the Radiographic Image”, ICCCT 2012
  • Chitra P., Arulmozhi N., et al. (2009), “SNR Based Evaluation of Radiographic Weld Image Using Selenium 75, Ir-192, and X-rays”, National Seminar and Exhibition on NDE
  • Chitra P., Sheela Rani B., et al. (2011), “Extraction of Radiographic Weld Defects Using Pixel Based Segmentation”, NCICM 2011

 

 

 

Kothai Ganesan | Computer Vision | Best Researcher Award

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Assist. Prof. Dr. Kothai Ganesan | Computer Vision | Best Researcher Award

Assistant Professor at KPR Institute of Engineering and Technology, India

Dr. Kothai Ganesan is an Assistant Professor in Computer Science and Engineering (Artificial Intelligence and Machine Learning) at KPR Institute of Engineering and Technology. She holds a Bachelor’s degree and Master’s degree in Engineering from Anna University and a Ph.D. from SRM Institute of Science and Technology, with research focusing on Intelligent Transport Systems, traffic prediction, and secure data transmission. Her published work includes studies in vehicular networks, traffic congestion, and security protocols, alongside ongoing research in medical imaging, AI-enhanced diagnostic tools, and image processing. A recognized mentor and department coordinator, Dr. Kothai integrates modern tools like Docker into her curriculum, advancing industry-aligned education. Her contributions have been acknowledged in journals, conferences, and professional memberships, including IEEE Computational Intelligence Society.

Publication Profile : 

Scopus

 

🎓 Educational Background :

Dr. Kothai Ganesan is an accomplished Assistant Professor in the Computer Science and Engineering (Artificial Intelligence and Machine Learning) department at KPR Institute of Engineering and Technology. Her academic journey began with a Bachelor’s degree in Computer Science and Engineering from Anna University (2012–2016), followed by a Master’s in the same field, earned with first-class distinction at Anna University (2016–2018). Pursuing her passion for advanced research, Dr. Ganesan completed her Ph.D. in November 2023 at SRM Institute of Science and Technology, where her research centered on Intelligent Transport Systems, with a particular focus on traffic prediction, congestion avoidance, and secure data transmission, all aimed at improving urban mobility.

💼 Professional Experience :

Professionally, Dr. Ganesan has contributed significantly to the field of AI-driven transport systems, publishing extensively on vehicular ad hoc networks (VANETs) and innovative machine learning techniques. Her research outputs include collision prediction and secure communication protocols, enhancing safety in smart cities. She has also expanded her focus into medical AI, exploring Alzheimer’s diagnosis, pediatric epilepsy recognition, and cancer detection using optimized learning models. Alongside her research, Dr. Ganesan is an active mentor, guiding student-led projects in machine learning and artificial intelligence. As a dedicated faculty member, she serves as the IQAC Coordinator, Exam Cell Coordinator, Autonomous Coordinator, and R&D Coordinator, furthering the program’s reputation through academic rigor and practical industry integration. 📚💡

📚 Research Interests : 

Her current research interests are broad, covering deep learning, machine learning, computer vision, and natural language processing. Dr. Ganesan’s work has gained her recognition in prestigious journals and conferences, and she actively participates in the IEEE Computational Intelligence Society as a Faculty Advisor. As a reviewer for leading journals, she contributes her expertise to the scholarly community. Dr. Ganesan’s unique blend of academic insight, mentorship, and professional innovation showcases her commitment to advancing AI and machine learning for impactful, real-world applications. 🌟

📝 Publication Top Notes :

  1. Ganesan, K., & [Co-authors, if applicable]. (2021). A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities. Wireless Communications and Mobile Computing, 2021, Article ID 5583874, 13 pages. https://doi.org/10.1155/2021/5583874.
  2. Ganesan, K., & [Co-authors, if applicable]. (2020). Performance Analysis of Stationary and Deterministic AODV Model. International Journal of Interactive Mobile Technologies (IJIM), 14(17), 33–44.
  3. Ganesan, K., & [Co-authors, if applicable]. (2022). IoT-Based Automatic SOP Adoption in Pandemic Scenario. International Journal of High Technology Letters, June 2022.
  4. Ganesan, K., & [Co-authors, if applicable]. (2024). A Hybrid CNN-GRU based Intrusion Detection System for Secure Communication in Vehicular Adhoc Network. Information Security Journal, Taylor & Francis, June 2024.

 

 

 

Hayelom Gebrye | Computer Vision | Best Researcher Award

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Mr. Hayelom Gebrye | Computer Vision | Best Researcher Award

Ph.D. student of UESTC, China, Ethiopia

Hayelom Muleta Gebrye is a dedicated researcher and educator specializing in computer science and information technology. He is currently pursuing his Ph.D. in Computer Science and Technology at the University of Electronic Science and Technology of China (UESTC), where his research focuses on machine learning, deep learning, computer vision, and network security, particularly in the context of IoT and cybersecurity. He holds an MSc in Information Technology from Aksum University and a BSc in Information Technology from Hawassa University.

Publication Profile : 

Scopus

 

🎓 Educational Background :

Hayelom Muleta Gebrye is currently pursuing a Ph.D. in Computer Science and Technology at the University of Electronic Science and Technology of China (UESTC), which he commenced in September 2019. He holds a Master’s degree in Information Technology from Aksum University (2015-2017) and a Bachelor’s degree in Information Technology from Hawassa University (2008-2012). Additionally, he completed his Secondary School Certificate at Tadagiwa Ethiopia in 2008.

💼 Professional Experience :

Hayelom has a robust teaching and training background in computer science and technology. He served as a lecturer at several institutions, including Harambee University, where he taught courses such as Fundamentals of Programming I, Web Programming, and Research Methods in Computer Science (Sep 2022 – Apr 2023), and Adama Science and Technology University, focusing on Data Structures and Algorithms (Jul 2022 – Jun 2023). He has also provided training on digital technology and effective social media management through the local NGO LIVE ADDIS. In addition to his academic roles, he has practical experience as a Data Manager for TZG General Development Research, leading data collection efforts and ensuring data quality standards. Previously, he worked in various capacities at Raya University, including Quality Assurance Coordinator and Assistant Registrar, where he was instrumental in enhancing educational quality and managing student records. His earlier role as the Head of IT at Jigjiga University involved overseeing the IT department, lecturing on advanced programming and system simulation, and mentoring students.

📚 Research Interests : 

Hayelom’s research interests encompass machine learning, deep learning, computer vision, network security, and intrusion detection systems. He has contributed to several research projects, including studies on traffic data extraction for attack detection in IoT networks and deep reinforcement learning for resource allocation in blockchain-based computing. His published work includes articles in prestigious journals, demonstrating his commitment to advancing knowledge in the fields of cybersecurity and machine learning.

📝 Publication Top Notes :

  1. Gebrye, H., Wang, Y., & Li, F. (2023). Traffic data extraction and labeling for machine learning-based attack detection in IoT networks. International Journal of Machine Learning & Cybernetics, 14, 2317–2332. https://doi.org/10.1007/s13042-022-01765-7. (Impact Factor: 5.6)
  2. Mohammed, A., Nahom, H., Tewodros, A., Habtamu, Y., & Gebrye, H. (2020). Deep Reinforcement Learning for Computation Offloading and Resource Allocation in Blockchain-Based Multi-UAV-Enabled Mobile Edge Computing. In 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) (pp. 295-299). Chengdu, China. doi: 10.1109/ICCWAMTIP51612.2020.9317445.
  3. Gebrye, H., Wang, Y., & Li, F. (In Progress). Computer Vision based DDoS Attack Detection for resource-limited devices. Computers and Electrical Engineering. (Status: 1st Revision)
  4. Gebrye, H., Wang, Y., & Li, F. (Under Review). Undersampling and IGFS-GIWRF Boosting Machine Learning Models for IoT Botnet Detection using Unbalanced Dataset. International Journal of Machine Learning and Cybernetics. (Status: Under Review)