Mrs. Guoqin Chang | AI Security | Excellence in Research Award

Posted on by Global Sensor Awards

Mrs. Guoqin Chang | AI Security | Excellence in Research Award 

Mrs. Guoqin Chang | AI Security | Shaanxi Science and Technology Holding Institute | China

Mrs. Guoqin Chang holds a Ph.D. in Cyberspace Security from the School of Cybersecurity at Xidian University and currently leads the Artificial Intelligence Research Group at Shaanxi KeKong Technology Industry Research Institute, while also serving as Technical Advisor to the Shaanxi Printing Science and Technology Research Institute. Over her career, Chang Guoqin has built expertise in natural language processing (NLP), domain-specific large language model deployment, prompt engineering, fine-tuning and adaptation of models, AI model security, detection of text generated by large language models, adversarial text attacks/defenses, and multimodal information fusion. She is skilled in designing and evaluating robust NLP pipelines, adversarial-robust model architectures, secure model deployment, domain adaptation techniques, and text classification systems with security-aware defenses. Her professional experience spans research and development, project leadership, and peer-reviewing for major journals including Information Sciences and Computer Engineering and Design, and she has contributed to both academic publications and applied software/patents through her inventive activity. Through her publication record, patent filings, software copyrights, and R&D project involvement, Chang Guoqin has demonstrated a strong commitment to improving the security, robustness, and domain-adaptability of modern AI. In conclusion, Chang Guoqin brings together advanced academic training, deep technical skills in NLP security and model adaptation, and real-world engineering experience  positioning her as a leading researcher/engineer in secure, domain-specific artificial intelligence and robust natural language processing solutions.

Professional Profiles: ORCID | Google scholar

Selected Publications 

  • Chang, G., Gao, H., Yao, Z., & Xiong, H. (2023). TextGuise: Adaptive adversarial example attacks on text classification model.

  • Chang, G., Gao, H., Pei, G., … & Guo, Q. (2024). The robustness of behavior-verification-based slider CAPTCHAs. Journal of Information Security and Applications.

  • Cheng, L., Zhang, Z., & Chang, G. (2019). Multimedia Social Network Authorization Scheme of Comparison-based Encryption.

  • Cheng, N., Chang, G., & Gao, H. (2020). WordChange: Adversarial Examples Generation Approach for Chinese Text Classification.

  • Chang, G., Gao, H., & Li, B. (2025). TextShelter: Text Adversarial Example Defense Based on Input Reconstruction.

  • Chang, G., & colleagues. (2019). A Survey of Research on CAPTCHA Designing and Breaking Techniques.

  • Cheng, N., Chang, G., Gao, H., … & Zhang, Y. (2020). WordChange: Adversarial Examples Generation Approach for Chinese Text Classification.

Ms. Raghi K R | Federated Learning | Research Excellence Award

Posted on by Global Sensor Awards

Ms. Raghi K R | Federated Learning | Research Excellence Award 

Ms. Raghi K R | Federated Learning | Sathyabama Institute of Science and Technology | India

Mrs. Raghi K.R. is a dedicated computer science educator and researcher with strong experience in both teaching and applied research. She holds a B.E. in Computer Science and Engineering (Anna University), an M.E. in Computer Science and Engineering (PSN Engineering College / Anna University), and has submitted her Ph.D. synopsis in Computer Science and Engineering at College of Engineering, Guindy, Anna University. Her professional journey includes roles as Assistant Professor and Teaching Fellow across several institutions: PSN Engineering College (CSE), College of Engineering Guindy, and currently at St. Joseph’s College of Engineering, Chennai giving her over a decade of teaching experience, spanning undergraduate and postgraduate courses. Her research interests lie in Artificial Intelligence, Deep Learning, Machine Learning, Cloud Security, and Web Mining. She possesses robust technical skills including programming in Python, Java, C, C++, web technologies (HTML), and experience with simulation platforms such as NS2 and MATLAB, as well as familiarity with open-source operating systems. Through her career she has mentored and guided multiple innovative and funded projects — for example leading a seed-research project titled “StepSmart: Design and Development of an Affordable IoT and Deep Learning Based Footwear for Diabetic Health Monitoring,” and supervising award-winning student projects such as “Trash Triage: Trailblazing Waste Management with Real-Time Street Waste Monitoring” and “Identification of Fake Medicinal Raw Materials Using Machine Learning.” These recognitions reflect her commitment to practical, socially relevant research. Her academic repertoire includes teaching diverse subjects like Artificial Intelligence, Mobile Computing, Information Security, Web Programming, Soft Computing, Software Project Management, Cyber Security, Web Technology, and more. She has also supervised substantial academic work: a Ph.D. thesis on “Privacy-Preserving Deep NN Classification over Signature Cryptosystem in Cloud Environments,” a secure payment-scheme design in multihop networks, and semantic similarity computation for natural language. As a scholar and mentor, Mrs. Raghi combines strong theoretical foundations with hands-on applied research, striving to develop secure, intelligent, and socially impactful computing solutions. Her involvement in both education and research along with project funding, awards, and diverse technical competencies — positions her as a proactive and forward-looking professional in the fields of AI, cybersecurity, and data-driven intelligent systems, committed to nurturing student talent and contributing to technological advancement.

Professional Profiles: ORCID | Google Scholar 

Selected Publications

  1. Thomas, R. K. L., Sanjay, G. J., Pandeeswaran, C., & Raghi, K. R. (2024). Advanced CCTV Surveillance Anomaly Detection, Alert Generation and Crowd Management using Deep Learning Algorithm.

  2. Vethavikashini, A. M., Jamal, S. M., & Raghi, K. R. (2024). Huntington’s Disease Prediction Using Xception CNN.

  3. Devi, S. R., Geetha Priya, S., Sathi, G., Naveen Kumar, S., Dinesh, M., & Raghi, K. R. (2024). Design and Development of a Touch Free Smart Home Controlling System Based on Virtual Reality (VR) Technology.

  4. Raghi, K. R., Sudha, K., Sreeram, A. M., Steve Joshua, S. (2024). Software Development Automation Using Generative AI.

  5. Raghi, K. R. (2023). Privacy-Preserving Deep NN Classification over Signature Cryptosystem in Cloud Environments.

  6. Anitha, T., Sai Srihitha, G. R. P. Lakshmi Aiswarya, & Raghi, K. R. (2025). Predictive Modeling of Social Media Data Using Machine Learning Techniques.

  7. (As mentor) StepSmart: Design and Development of an Affordable IoT and Deep Learning Based Footwear for Diabetic Health Monitoring.

Prof. Din-Yuen Chan | Deep Learning | Best Scholar Award

Posted on by Global Sensor Awards

Prof. Din-Yuen Chan | Deep Learning | Best Scholar Award 

Prof. Din-Yuen Chan, National Chiayi University, Taiwan

Din-Yuen Chan is a prominent scholar in electrical engineering with extensive experience in visual signal processing and computer vision. He earned his Ph.D. in Electrical Engineering from National Cheng Kung University, Taiwan, in 1996. A member of the Visual Signal Processing and Communication Technical Committee (VSPC TC) since 2010, he served as the founding director of the Department of Electrical Engineering (2007–2011) and as Dean of the College of Science and Engineering at National Chiayi University (2017–2020). His research spans semantic object detection, video/audio coding, stereoscopic 3D, AI-based pattern recognition, and deep learning neural networks. In the past five years, he has published multiple SCI-indexed journal papers on topics such as stereo matching, instance segmentation, speaker diarization, depth estimation, and autonomous robotics. As a frequent corresponding author, he continues to lead innovations in applied AI and multimedia processing.

Professional Profile:

SCOPUS

Summary of Suitability for the Best Scholar Award

Dr. Din-Yuen Chan has maintained an outstanding academic career for over two decades, contributing significantly to the fields of electrical engineering and computer vision. His long-standing commitment to advancing knowledge is reflected in his leadership roles and consistent research output in areas such as semantic object detection, AI-based pattern recognition, video/audio coding, and stereoscopic 3D.

🎓 Education

  • Ph.D. in Electrical Engineering
    National Cheng Kung University, Taiwan 🇹🇼
    Completed in 1996

💼 Work Experience

  • 🧠 Member, Visual Signal Processing and Communication Technical Committee (VSPC TC)
    Since 2010

  • 🏛️ Founding Director, Department of Electrical Engineering, National Chiayi University
    2007 – 2011

  • 🎓 Dean, College of Science and Engineering, National Chiayi University
    2017 – 2020

🧪 Research Interests

  • 🔍 Computer Vision

  • 🎯 Semantic Object Detection

  • 🎞️ Video/Audio Coding

  • 🤖 AI-based Pattern Recognition

  • 🥽 Stereoscopic 3D

  • 🧠 Deep Learning Neural Networks

🏅 Achievements & Honors

  • ✍️ Published multiple SCI-indexed journal papers in high-impact venues, including:

    • EURASIP Journal on Image and Video Processing

    • IET Computer Vision

    • Multimedia Tools and Applications

    • Applied Sciences

  • ⭐ First or corresponding author in many significant papers on stereo matching, depth estimation, 3D object placement, and speaker diarization.

  • 🤖 Developed a low-cost autonomous outdoor robot with end-to-end deep learning navigation.

  • 🧏 Invented a new speaker-diarization technology using spectral-LSTM.

  • 🎓 Recognized leader in academia for establishing and leading research and administrative departments.

Publication Top Notes:

A new speaker-diarization technology with denoising spectral-LSTM for online automatic multi-dialogue recording

Natural-Prosodic Cross-Lingual Personalized TTS

New Efficient Depth Estimation and Real-Time Object 3D Recognition Models for Humanoid Robotic Environment Understanding

Rational 3D object placement based on deep learning based plane detection

INTEGRATED LIGHT-RESNET AND POOLFORMER NETWORKS FOR SHAPE-PRESERVING LANE DETECTION

Assoc. Prof. Dr. Waleed Mahmoud Elsayed | Machine learning Awards | Best Researcher Award

Posted on by Global Sensor Awards

Assoc. Prof. Dr. Waleed Mahmoud Elsayed | Machine learning Awards | Best Researcher Award 

Assoc. Prof. Dr. Waleed Mahmoud Elsayed, Beni-suef university, Saudi Arabia

Dr. Waleed Mahmoud Ead is an accomplished Assistant Professor in the Faculty of Computing and Information at Al-Baha University, Saudi Arabia, with over 15 years of experience in digital business transformation, data science, and applied research. He holds a Ph.D. in Computers and Informatics from Menoufia University, Egypt, where he focused on privacy-preserving techniques in social networks. Throughout his career, Dr. Ead has developed expertise in business intelligence, data mining, machine learning, cloud computing, and big data analytics, and he is SAS-certified in multiple disciplines, including machine learning and visual analytics. His research interests span social network analysis, distributed databases, precision medicine, and cybersecurity. He has served in various academic roles across prominent Egyptian institutions and has co-supervised doctoral and master’s research in genetics, AI, and privacy in healthcare. A dedicated peer reviewer for renowned journals such as Springer Nature and Inderscience, Dr. Ead is also an active contributor to academic conferences and international workshops. Beyond academia, he is a technology enabler, STEM judge, and entrepreneur, with projects focused on sustainable agriculture and digital innovation.

Professional Profile:

GOOGLE SCHOLAR

ORCID

SCOPUS

✅ Summary of Suitability for Best Researcher Award: Dr. Waleed Mahmoud Ead

Dr. Waleed Mahmoud Ead is highly suitable for the Best Researcher Award, given his exceptional combination of research depth, academic leadership, interdisciplinary engagement, and societal impact. His qualifications are supported by the following key strengths

🎓 Education

  • 🥇 2004: B.Sc. (Honor) in Information and Technology Systems – Zagazig University, Egypt

  • 📚 2012: M.Sc. in Computers and Informatics – Menoufia University, Egypt
      📘 Thesis: “Developing an Intelligent Technique in Web Mining”

  • 🎓 2018: Ph.D. in Computers and Informatics – Menoufia University, Egypt
      📗 Thesis: “Privacy Preserving in Social Networks”

👨‍🏫 Academic Work Experience

  • 🇸🇦 2024–Present: Assistant Professor, Faculty of Computing and Information – Al-Baha University, Saudi Arabia

  • 🇪🇬 2022–2023: Assistant Professor, CSIT – Egypt-Japan University of Science and Technology

  • 🇪🇬 2018–2022: Assistant Professor, Faculty of Computers & AI – Beni-Suef University

  • 🇪🇬 2015–2018: Lecturer Associate, Faculty of Information Technology – MUST University

  • 🇪🇬 2014: Lecturer Associate, Faculty of Computers & Information – Beni-Suef University

  • 🇪🇬 2012: Lecturer Associate, CSC – October 6 University

  • 🇪🇬 2006–2012: Teaching Assistant, CSC – October 6 University

🏆 Achievements & Honors

  • 🧠 SAS Certified: Machine Learning, Visual Analytics, Business Planning

  • 💡 Developed systems for international conferences

  • 🌍 Peer Reviewer for top journals & publishers (Inderscience, Springer, EAI, etc.)

  • 🧬 Co-supervisor for Ph.D. and Master’s students in AI, bioinformatics, and precision medicine

  • 🥇 Honor degree in B.Sc.

  • 👩‍⚖ STEM Judge: INTEL ISEF & Graduation Projects

  • 💼 Speaker and participant in events by DAAD, UNESCO, Microsoft, SAS, Oracle

  • 🌱 Founder of IGreen (Intelligent Adaptive Environmental Farm)

  • 🚀 Participated in entrepreneurship programs (Start Egypt, Flat6Labs)

  • 🧭 Bridging analytics and IT knowledge for social development

Publication Top Notes:

An Optimized Hierarchal Cluster Formation Approach for Management of Smart Cities

ODCS: On-Demand Hierarchical Consistent Synchronization Approach for the IoT

A General Cyber Hygiene Approach for Financial Analytical Environment

Feedforward Deep Learning Optimizer-based RNA-Seq Women’s cancers Detection with a hybrid Classification Models for Biomarker Discovery

Semantic Sentiment Classification for COVID-19 Tweets Using Universal Sentence Encoder

Automated Prediction of Employee Attrition Using Ensemble Model Based on Machine Learning Algorithms

Mr. Mohammed Aljamal | Artificial Intelligence | Best Researcher Award

Posted on by Global Sensor Awards

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:

 

 

Masoud DANESHTALAB | deep learning | Best Researcher Award

Posted on by Global Sensor Awards

Prof. Masoud DANESHTALAB | deep learning | Best Researcher Award 

Prof. Masoud DANESHTALAB, Mälardalen University, Sweden.

Masoud Daneshtalab, Ph.D., Docent, Full Professor
Masoud Daneshtalab is a globally recognized scholar and Full Professor at Mälardalen University (MDU), Sweden. With over two decades of academic and professional excellence, he has made significant contributions to computer science and engineering, specializing in dependable systems, AI, and hardware/software co-design. A prolific researcher with an H-index of 35 and over 5,100 citations, Dr. Daneshtalab is included in the prestigious World’s Top 2% Scientists Ranking. He serves as the Scientific Director of Fundamental AI at MDU and collaborates internationally, holding adjunct professorships and contributing to cutting-edge research initiatives.

Professional Profile:

Google Scholar

Suitability of Masoud Daneshtalab for the Best Researcher Award

Dr. Masoud Daneshtalab is a highly suitable candidate for the “Research for Best Researcher Award,” based on his exceptional academic achievements and professional contributions. Here are the key reasons

Education

🎓 Academic Journey

  • Docent (2018): Qualified in Computer Science and Electronics, Mälardalen University, Sweden.
  • Ph.D. (2008–2011): Information and Communication Technology, University of Turku, Finland. Dissertation: Adaptive Implementation of On-Chip Networks under Prof. Hannu Tenhunen.
  • M.Sc. (2004–2006): Computer Engineering, University of Tehran, Iran. Thesis: Low Power Methods in Network-on-Chips under Prof. Ali Afzali-Kusha.
  • B.Sc. (1998–2002): Computer Engineering, Shahid Bahonar University of Kerman, Iran.

Experience

💼 Professional Contributions

  • Scientific Director (2024–Present): Fundamental AI, Mälardalen University, Sweden.
  • Full Professor (2020–Present): Innovation, Design & Engineering, MDU.
  • Adjunct Professor (2019–Present): Computer Systems, Tallinn University of Technology, Estonia.
  • Previous Roles: Associate Professor at MDU (2016–2020), EU Marie Curie Fellow at KTH Royal Institute of Technology (2014–2016), Lecturer at the University of Turku (2011–2014), and Researcher at the University of Tehran (2006–2008).

Research Interests

🔬 Key Areas

  • Optimization and robustness in deep learning models.
  • HW/SW co-design and heterogeneous computing.
  • Dependable systems, memory architectures, and interconnection networks.
  • Cutting-edge projects include sustainable AI, federated learning, and reliable autonomous systems.

Awards

🏆 Recognitions

  • Best Paper Awards: IEEE ECBS (2019), IEEE MCSoC (2018), and multiple HiPEAC Paper Awards (2013–2017).
  • Research Grants: Marie Skłodowska-Curie Fellowship (2014), Nokia Foundation (2009), and others.
  • Top Reviewer: IEEE Transactions on Computers (2013).
  • Fellowships: GETA, Helsinki University of Technology (2008–2011).

Publications

A review on deep learning methods for ECG arrhythmia classification

CITIED: 490

Time-Sensitive Networking in automotive embedded systems: State of the art and research opportunities

CITIED: 147

Routing algorithms in networks-on-chip

CITIED: 136

Smart hill climbing for agile dynamic mapping in many-core systems

CITIED: 125

EDXY–A low cost congestion-aware routing algorithm for network-on-chips

CITIED: 124

Deep Maker: A multi-objective optimization framework for deep neural networks in embedded systems

CITIED: 122