Nannan Zhang | Artificial Intelligence | Research Excellence Award

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Mrs. Nannan Zhang | Artificial Intelligence | Research Excellence Award

Senior Engineer | Research Institute of Petroleum Exploration & Development | China

Mrs. Nannan Zhang is a senior engineer and researcher specializing in petroleum remote sensing and environmental monitoring using advanced geospatial technologies. Her research focuses on applying high-resolution satellite imagery, deep learning, and data fusion techniques to support oil and gas infrastructure detection, environmental impact assessment, and ecological protection. She has led multiple applied research initiatives that bridge scientific innovation with industrial needs, contributing significantly to the practical deployment of remote sensing in energy and environmental fields. Her scholarly work is published in internationally recognized journals and conferences, complemented by patented technologies and a research monograph. She is also actively engaged in advancing professional collaboration within the remote sensing research community.

Citation Metrics (Scopus)

350

300

200

100

10

0

Citations
304

Documents
12

h-index
5

                    🟦 Citations    🟥 i10-index    🟩 h-index


View Scopus Profile

Featured Publications

Mr. Abir Das | Artificial Intelligence | Research Excellence Award

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Mr. Abir Das | Artificial Intelligence | Research Excellence Award

Siliguri Government Polytechnic College | India

Abir Das is an emerging AI/ML researcher whose work spans deep learning, computer vision, medical imaging, and explainable AI. With a strong foundation in developing end-to-end AI systems, his research focuses on Vision Transformers, self-supervised learning, noisy-label correction, and interpretable models for high-stakes applications such as healthcare, EEG signal analysis, and industrial fault diagnosis. He has contributed as the first author to multiple international journals, working extensively on hybrid deep learning models, CLIP-based zero-shot learning, EEG motor imagery classification, and sensor-driven diagnostic pipelines. His research integrates expertise in PyTorch, TensorFlow, and modern transformer architectures, emphasizing human-centered, reliable, and transparent AI solutions. He has actively explored the intersection of computer vision and embedded systems, enhancing drone autonomy, depth estimation, and real-time object detection, while also contributing to speech technologies through accent-conversion and multimodal learning. His scientific output includes publications in reputable venues such as Scientific Reports, MDPI Sensors, and Computers, Materials & Continua. His growing scholarly impact is reflected in Scopus metrics: 11 citations from 11 documents with an h-index of 1, and Google Scholar metrics: 12 citations, h-index 1, i10-index 1. His work continues to advance practical and theoretically grounded AI methodologies, blending deep learning innovations with real-world applications across biomedical imaging, EEG analysis, and industrial AI systems.

Publication Profile

Scopus | Google Scholar

Featured Publications

Das, A., Singh, S., Kim, J., Ahanger, T. A., & Pisa, A. A. (2025). Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models. Scientific Reports, 15(1), 27161.

Zereen, A. N., Das, A., & Uddin, J. (2024). Machine fault diagnosis using audio sensor data and explainable AI techniques: LIME and SHAP. Computers, Materials & Continua, 80(3).

Das, S. S. A. (2025). Few-shot and zero-shot learning for MRI brain tumor classification using CLIP and Vision Transformers. Sensors, 25(23), 7341.

Mr. Pratik Thantharate | Artificial Intelligence | Editorial Board Member

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Mr. Pratik Thantharate | Artificial Intelligence | Editorial Board Member

Paycor | United States

Pratik Thantharate is a distinguished researcher and Principal Software Engineer whose work spans Agile Software Development, Cybersecurity, DevOps, Cloud Computing, and Code-to-Cloud Security. With dual master’s degrees in Computer Science and Information Systems, he has established a strong research portfolio integrating automation, security, and large-scale distributed architectures. His expertise includes CI/CD pipelines, containerization, microservices, infrastructure as code, observability frameworks, and privacy-preserving systems for modern software ecosystems. Pratik has authored peer-reviewed publications, contributed book chapters, and published impactful research in prominent venues across Elsevier, IEEE, and MDPI. His innovations explore energy-efficient UAV optimization, federated learning for cybersecurity, Zero-Trust blockchain architectures, advanced observability mechanisms for DevOps, and heuristic genetic algorithms for automated vulnerability detection. He has also contributed two patents focused on intelligent monitoring and advanced security analytics for DevSecOps environments. In addition to his research outputs, Pratik has served on numerous technical program committees and peer-reviewed over a hundred scholarly articles across international conferences and journals. His scholarly influence continues to grow, with Scopus indexing showing 128 citations across 119 citing documents, 9 documents, and an h-index of 5. Google Scholar metrics reflect 46 citations, an h-index of 4, and an i10-index of 2. His research aims to advance secure, reliable, and high-performance software delivery by integrating next-generation DevOps automation, AI-driven cybersecurity, and privacy-aware computing frameworks to meet emerging industry and academic challenges.

Profile

Scopus | ORCID

Featured Publications

Thantharate, P., Thantharate, A., & Kulkarni, A. (2024). GREENSKY: A fair energy-aware optimization model for UAVs in next-generation wireless networks. Green Energy and Intelligent Transportation, 3(1), 100130.

Thantharate, P., & Thantharate, A. (2023). ZeroTrustBlock: Enhancing security, privacy, and interoperability of sensitive data through ZeroTrust permissioned blockchain. Big Data and Cognitive Computing, 7(4), 165.

Thantharate, P., & Anurag, T. (2023). CYBRIA: Pioneering federated learning for privacy-aware cybersecurity with brilliance. Proceedings of the IEEE International Conference on Smart Communities.

Thantharate, P. (2023). IntelligentMonitor: Empowering DevOps environments with advanced monitoring and observability. Proceedings of the International Conference on Information Technology, 800–805.

Thantharate, P. (2023). GeneticSecOps: Harnessing heuristic genetic algorithms for automated security testing and vulnerability detection in DevSecOps. Proceedings of the International Conference on Contemporary Computing and Informatics.

Mr. Sachin Sravan Kumar Komati | Deep Learning | Best Researcher Award

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Mr. Sachin Sravan Kumar Komati | Deep Learning | Best Researcher Award

AI Engineer | Florida International University | United States

Sachin Sravan Kumar Komati is an accomplished researcher in Artificial Intelligence and Machine Learning, specializing in biomedical applications, particularly in gastrointestinal disease diagnosis, cancer prognosis, and postoperative complication prediction. His research integrates deep learning, computer vision, and multimodal AI frameworks to develop intelligent healthcare solutions. He has contributed significantly to the fields of predictive analytics, medical imaging, and surgical AI, creating advanced models using LSTM, Vision Transformers, and Autoencoders for enhanced diagnostic precision. His works explore AI-driven insights in clinical and imaging datasets, focusing on improving real-time disease detection and patient-specific treatment strategies. Sachin’s scholarly contributions include numerous peer-reviewed publications in reputed international journals such as PLOS One, Gastroenterology, Gastrointestinal Endoscopy, Critical Care Medicine, and the Journal of Clinical Oncology. His research has earned global recognition through multiple conference acceptances, including at ACG, AASLD, and UEG Week. According to Google Scholar, he has received 2 citations, with an h-index of 1 and an i10-index of 0, reflecting his emerging influence in AI-driven healthcare research. His Scopus metrics also indicate growing visibility and scholarly impact. Sachin’s research continues to advance the integration of artificial intelligence into clinical decision-making and medical imaging, aiming to bridge the gap between AI innovation and patient-centered healthcare.

Profile

Google Scholar | ORCID

Featured Publications

Boppana, S. H., Tyagi, D., Komati, S. S. K., Boppana, S. L., Raj, R., & Mintz, C. D. (2025). AI-delirium guard: Predictive modeling of postoperative delirium in elderly surgical patients. PLOS One, 20(6), e0322032.

Boppana, S. H., Thota, M., Maddineni, G., Komati, S. S. K., Aakash, F., & Dang, A. K. (2025). Enhancing gastrointestinal bleeding detection in wireless capsule endoscopy using convolutional autoencoders. American College of Gastroenterology, 120(10S2).

Boppana, S. H., Chitturi, R. H., Komati, S. S. K., Raj, R., & Mintz, C. D. (2025). DiabCompSepsAI: Integrated AI model for early detection and prediction of postoperative complications in diabetic patients using a Random Forest Classifier. Journal of Clinical Medicine, 14(20), 7173.

Boppana, S. H., Thota, M., Maddineni, G., Komati, S. S. K., & Mintz, C. D. (2025). Predictive modeling of GI disease: GastroEndo-Seq for progression and outcome forecasting. Gastroenterology, 120(10S2).

Boppana, S. H., Thota, M., Maddineni, G., Komati, S. S. K., & Mintz, C. D. (2025). Vision Transformer-based framework for risk stratification and prognostic assessment in gastrointestinal lesion management. Gastrointestinal Endoscopy, 120(10S2).

Dr. Ananthoju Vijay Kumar | Artificial Intelligence | Best Researcher Award

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Dr. Ananthoju Vijay Kumar | Artificial Intelligence | Best Researcher Award

Associate Professor | Jain Deemed to be University | India

Dr. Ananthoju Vijay Kumar is an accomplished academician and researcher currently serving as an Associate Professor in the Department of Computer Science and Engineering at Jain University, Bangalore. With nearly two decades of dedicated teaching and research experience, he has established himself as a recognized guide and mentor, supervising multiple doctoral candidates. His expertise spans across Cyber Security, Data Mining, Data Warehousing, Data Science, and Natural Language Processing. Dr. Kumar has made significant contributions to his field through impactful research collaborations, scholarly publications, and active participation in professional academic communities.

Publication Profile

Scopus

ORCID

Google Scholar

Education Background

Dr. Ananthoju Vijay Kumar pursued his doctoral studies in Computer Science and Engineering at Acharya Nagarjuna University, where he developed a strong foundation in computational theories and advanced research methodologies. His education provided him with specialized knowledge in core computer science disciplines and helped him build a research-oriented outlook. This academic journey laid the groundwork for his professional career in both teaching and research, equipping him to mentor students and lead projects across multiple domains. His academic credentials reflect his deep engagement in the field and his commitment to advancing the boundaries of computer science education and innovation.

Professional Experience

Dr. Ananthoju Vijay Kumar has held several important academic positions during his career, shaping his path as a teacher, researcher, and guide. Prior to joining Jain University as an Associate Professor, he served in Sree Chaitanya College of Engineering in Telangana, where he contributed to academic growth and program development in computer applications. At Jain University, he continues to lead both undergraduate and postgraduate courses, while simultaneously mentoring doctoral candidates. His ongoing research includes international collaborations, such as a major project with Melbourne University, Australia, further reflecting his active contribution to the global research community.

Awards and Honors

Throughout his career, Dr. Ananthoju Vijay Kumar has been recognized for his excellence in teaching, research, and academic leadership. Notably, he was honored with the APJ Abdul Kalam Lifetime Achievement National Award, presented by the International Institute of Socio Economic Reforms in Bangalore. This recognition underscores his significant contributions to the academic and research ecosystem. His role as a recognized doctoral guide at Jain University further highlights his influence and dedication to nurturing future researchers. His academic and professional achievements stand as a testament to his dedication to advancing knowledge and societal progress through impactful research and mentorship.

Research Focus

Dr. Ananthoju Vijay Kumar’s primary research interests encompass a wide range of areas within computer science. His focus extends across Cyber Security, Data Mining, Data Warehousing, Data Science, and Natural Language Processing. He has successfully guided research scholars in emerging domains such as agricultural data mining and advanced applications of security systems. His collaboration with international institutions has allowed him to address interdisciplinary challenges and deliver innovative solutions. With more than forty publications in reputed national and international journals, he continues to explore cutting-edge topics while contributing to both academic literature and practical applications of technology.

Publication Notes

  • Enhancing drug discovery and patient care through advanced analytics with the power of NLP and machine learning in pharmaceutical data interpretation
    Published Year: 2025
    Citation: 5

  • Investigating the Determinants of Indian Rupee Exchange Rate: An Empirical Analysis of Influential Factors and Their Impact Level: Part 1
    Published Year: 2024
    Citation: 1

  • A Personalized System to Recommend a Healthy Diet Based on an Individual’s Unique Dietary Needs and Goals
    Published Year: 2023
    Citation: 2

  • Penetration Testing to Investigate Security Vulnerabilities, Bugs and Potential Threats in Flip Kart, JioMart, and Amazon Mobile Application
    Published Year: 2023
    Citation: 1

  • Hybrid Algorithm for Real-Time Sign Language Detection System
    Published Year: 2023
    Citation: 5

Conclusion

In summary, Dr. Ananthoju Vijay Kumar stands out as a distinguished academician with a strong record of teaching, mentoring, and impactful research. His academic background, professional experience, and recognized contributions to the field of computer science demonstrate his commitment to innovation and academic growth. His awards and ongoing projects highlight his active role in both national and international research communities. Through his expertise and dedication, Dr. Kumar continues to inspire students and researchers while making meaningful contributions to technology and society.

Hisham AbouGrad | Artificial intelligence | Best Academic Researcher Award

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Dr. Hisham AbouGrad | Artificial intelligence | Best Academic Researcher Award

Dr. Hisham AbouGrad , Senior Lecturer , University of East London – UEL , United Kingdom.

Dr. Hisham AbouGrad is a dynamic academic and industry expert in computer science and digital technologies. Currently a Senior Lecturer at the University of East London, he brings over two decades of experience in higher education and IT. Known for bridging theory with practice, he has supervised innovative projects in AI, FinTech, and mobile app development. Dr. AbouGrad also leads international academic collaborations and contributes to top-tier journals. He is a Fellow of the Higher Education Academy and an active member of the British Computer Society, with a passion for enhancing digital learning, scientific problem-solving, and sustainable technology.

Publication Profile

Scopus

ORCID

Google scholar

🎓 Education Background

Dr. Hisham AbouGrad earned his Doctorate in Professional Studies (DProf) from London South Bank University, focusing on Workflow Information Systems Performance using BPM methodologies. He also holds a Master of Science (MSc) in Software Engineering from the University of Bradford and a Master of Business Administration (MBA) in Management from the University of Lincoln. Additionally, he completed a Postgraduate Certificate in Higher Education Practice (PGCHEP) from the University of Plymouth. His academic credentials are enhanced by certifications in project management and IT, including CITP from BCS and PMP qualifications, reinforcing his foundation in both pedagogy and technical leadership.

💼 Professional Experience

Dr. AbouGrad’s career spans prestigious academic institutions and industry roles. Since 2021, he has served as a Senior Lecturer at the University of East London, where he also fosters international collaborations. Previously, he held teaching and leadership roles at ICON College, QA Higher Education, GSM London, and the University of Plymouth. From 2011 to 2019, he was a doctoral researcher at London South Bank University. With vast teaching experience in computing, business management, and information systems, Dr. AbouGrad has mentored numerous PhD and DProf students while shaping curricula aligned with technological advancements and practical industry applications.

🏆 Awards and Honors

Dr. Hisham AbouGrad has been recognized for his commitment to academic excellence and professional contribution. He is a Fellow of the UK Higher Education Academy (FHEA), a Certified IT Professional (CITP) with the British Computer Society (BCS), and has received qualifications in IT Quality Management (ITQM). He is a founding member of UEL’s FinTech Centre and contributes actively to academic committees and journal editorial boards. As a reviewer for reputed journals like IEEE TCE, SAGE, Elsevier, and Emerald, he consistently upholds research quality, earning professional credibility and trust in the global academic and scientific communities.

🔬 Research Focus

Dr. AbouGrad’s research integrates Artificial Intelligence, FinTech, Machine Learning, Information Security, and Multi-Criteria Decision Making (MCDM) with Business Process Management (BPM) and Workflow Systems. His work aims to create scalable, secure, and intelligent digital solutions. Projects under his supervision include AI-based financial prediction systems, eCommerce fraud detection using neural networks, and mobile payment technologies. His recent studies explore AI-driven stock prediction, sentiment analysis, and fake review detection—highlighting his goal to solve real-world problems through data science, machine learning, and performance analysis. He also researches Decision Support Systems (DSS), ECM, GIS, and user-centered eCommerce design.

🔚 Conclusion

Dr. Hisham AbouGrad is a passionate educator, strategic researcher, and technology advocate whose career is marked by innovation, collaboration, and impact. His multifaceted expertise across academia and industry supports students, institutions, and global communities in adapting to digital transformation. Through research, mentorship, and leadership, he contributes to solving complex challenges in AI, FinTech, and Information Systems. With a forward-thinking mindset, he continues to influence academic practices, elevate IT performance, and foster global academic relationships. His legacy reflects both the rigor of scholarly inquiry and the relevance of applied science in the 21st century.

📚 Top Publications with Details

  1. AI-Framework to Detect eCommerce Fake Reviews: A Hybrid Neural Network Machine Learning Model
    Published: 2024, Book: Artificial Intelligence and Computational Technologies
    Cited by: 1

  2. Financial Decision-Making AI-Framework to Predict Stock Price Using LSTM Algorithm and NLP-Driven Sentiment Analysis Model
    Published: 2025, Conference: Annual International Congress on Computer Science
    Cited by: 1

  3. Decision Making by Applying Machine Learning Techniques to Mitigate Spam SMS Attacks
    Published: 2023, Conference: International Conference on Deep Learning, Artificial Intelligence and Robotics
    Cited by: 5

  4. Developing the Business Process Management Performance of an Information System Using the Delphi Study Technique
    Published: 2019, Conference: EAI International Conference on Technology, Innovation, Entrepreneurship and Education
    Cited by: 5

  5. Applying the Delphi Method to Measure Enterprise Content Management Workflow System Performance
    Published: 2022, Journal: Lecture Notes in Networks and Systems (Springer)
    Cited by: 1

  6. The Impact of Business Process Management Values on Enterprise Content Management Workflow Systems Performance
    Published: 2020, Thesis: London South Bank University
    Cited by: 1

  7. Intelligent Computing, Proceedings of the 2022 Computing Conference
    Published: 2022, Publisher: Springer International Publishing
    Cited by: 23

  8. Key Digital Trends in Artificial Intelligence and Robotics: Proceedings of ICDLAIR 2022
    Published: 2023, Publisher: Springer International Publishing
    Cited by: 1

 

Ms. Yin ZiJuan | artificial intelligence | Best Researcher Award

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Ms. Yin ZiJuan | artificial intelligence | Best Researcher Award

Ms. Yin ZiJuan, graduate student, Shanghai University of Engineering Science, China.

Yin Zijuan is a dedicated graduate researcher at the School of Materials Science and Engineering, Shanghai University of Engineering Science. She has cultivated a unique interdisciplinary expertise that bridges materials science with artificial intelligence. Her notable work centers around intelligent surface defect detection using deep learning models. Yin gained international recognition for developing the BBW YOLO algorithm, which improves defect detection accuracy in aluminum profile manufacturing. With a passion for integrating AI into industrial applications, Yin exemplifies the new generation of scholars who are redefining engineering research through innovation, precision, and automation.

Publication Profile

Scopus

🎓 Education Background

Yin Zijuan is currently pursuing her graduate studies at the Shanghai University of Engineering Science, within the School of Materials Science and Engineering. Her academic focus lies in fusing materials engineering with advanced computational methods. During her studies, she developed specialized knowledge in deep learning, computer vision, and image processing as they relate to quality control in industrial materials. Her academic journey is marked by excellence, with her research earning publication in reputable international journals. Yin’s education reflects a strong foundation in both traditional materials science and cutting-edge AI methodologies.

🧪 Professional Experience

As a graduate researcher, Yin Zijuan has contributed to high-impact research projects focused on AI-driven defect detection in industrial materials. Her most distinguished project involved the development and implementation of the BBW YOLO algorithm, which blends Bidirectional Feature Pyramid Networks and attention mechanisms for enhanced image recognition. She has collaborated with institutions like Harbin Institute of Technology and participated in interdisciplinary studies that bridge academia and industry. Through her ongoing work, she aims to revolutionize quality assurance processes in manufacturing by deploying real-time and lightweight neural network systems.

🏆 Awards and Honors

Yin Zijuan has earned increasing recognition in the field of intelligent detection systems. Her research achievements culminated in a significant journal publication in Coatings, a Scopus and SCI-indexed journal, in 2025. This milestone established her as a rising scholar with contributions relevant to both academic and industrial domains. Her work on BBW YOLO has been lauded for its innovation, performance efficiency, and potential impact on industrial automation. Yin is also a nominee for prestigious awards including the Best Scholar Award, Outstanding Innovation Award, and Best Paper Award, all reflecting the excellence of her work.

🔬 Research Focus

Yin Zijuan’s research encompasses a wide spectrum of interdisciplinary themes including materials science, deep learning, and computer vision. Her primary focus is on developing intelligent detection algorithms for identifying surface defects in aluminum profiles. She has pioneered the BBW YOLO model, which integrates BiFPN and BiFormer attention mechanisms with a Wise-IoU v3 loss function. Her innovations improve defect detection accuracy while maintaining high processing speeds and model efficiency. Yin’s work supports the evolution of smart manufacturing and industrial automation, positioning her as a key contributor to the fusion of AI and engineering.

📌 Conclusion

Yin Zijuan exemplifies the future of smart materials research through her fusion of artificial intelligence and industrial materials science. Her work is not only academically rigorous but also practically relevant, addressing real-world problems in manufacturing. From algorithmic innovation to high-impact publication and inter-institutional collaboration, she has demonstrated exceptional promise as a research scholar. With her continued contributions, Yin is poised to lead transformative advancements in intelligent quality control systems. She stands as a worthy nominee for multiple academic honors and awards recognizing innovation, research excellence, and scholarly distinction.

📄 Top Publications Notes

  1. BBW YOLO: Intelligent Detection Algorithms for Aluminium Profile Material Surface Defects

  2. Thermal deformation behavior and microstructural evolution of the rapidly-solidified Al–Zn–Mg–Cu alloy in hot isostatic pressing state

 

 

 

 

 

Lirong Wang | Artifical Intelligence | Best Researcher Award

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Ms. Lirong Wang | Artifical Intelligence | Best Researcher Award

professor at Suzhou University, China

Professor Lirong Wang is a distinguished researcher at Soochow University, specializing in intelligent wearable devices and information processing. She earned her B.S. and Ph.D. from Jilin University and has been serving as a professor since 2014. Her research integrates microelectronics, machine learning, and biomedical engineering, with a strong focus on signal acquisition and analysis. Professor Wang leads several interdisciplinary projects and supervises graduate students, fostering innovation and academic growth. As the Principal Investigator of a National Key R&D Program, she demonstrates outstanding leadership in advancing cutting-edge technologies. She has authored over 40 peer-reviewed publications in prestigious journals such as IEEE Transactions on Biomedical Engineering and holds more than 20 invention patents, highlighting her contributions to both academic research and practical innovation. In addition to her research work, she actively participates in the global scientific community as a journal reviewer and organizer of international conference sessions in wearable technology and computer science.

Publication Profile

Education🎓

Professor Lirong Wang received her formal education at Jilin University, one of China’s premier institutions, where she earned both her Bachelor of Science (B.S.) and Doctor of Philosophy (Ph.D.) degrees. Her academic training focused on electronic engineering and information processing, laying a strong foundation for her specialization in intelligent wearable devices. Throughout her educational journey, she developed expertise in signal acquisition technologies, microelectronics, and data analysis, which later became the core pillars of her research. During her Ph.D. studies, Professor Wang engaged in interdisciplinary work that bridged engineering, computer science, and biomedical applications, positioning her at the forefront of next-generation health monitoring technologies. Her rigorous academic background and commitment to research excellence have equipped her with the analytical skills and innovative mindset needed to lead complex scientific projects. This strong educational grounding has played a pivotal role in shaping her successful academic and research career at Soochow University.

Professional Experience 💼

Professor Lirong Wang has built a robust professional career centered on interdisciplinary research and academic leadership. Since 2014, she has served as a professor at Soochow University, where she specializes in intelligent wearable devices, signal acquisition, and biomedical information processing. Her professional experience spans leading national-level R&D programs and supervising numerous graduate students, fostering innovation in both academia and applied technology. As the Principal Investigator of a National Key Research and Development Program, she has demonstrated exceptional capability in managing large-scale, collaborative research projects. Professor Wang has authored over 40 peer-reviewed publications and holds more than 20 invention patents, reflecting a strong commitment to both theoretical advancement and technological innovation. Beyond her university role, she contributes to the global research community as a reviewer for prestigious journals and an organizer of international conference sessions, particularly in wearable technology and computer science. Her experience reflects a deep integration of research, mentorship, and scientific engagement.

Research Interest 🔬

Professor Lirong Wang has a diverse and forward-thinking research portfolio centered on the development and application of intelligent wearable devices and biomedical information processing. Her primary interests lie in signal acquisition technology, physiological data analysis, and the integration of machine learning with microelectronic systems for real-time health monitoring and diagnostics. She is particularly focused on designing wearable platforms capable of accurately capturing and interpreting complex biological signals, such as ECG and EMG, to support early disease detection and personalized healthcare. Her interdisciplinary approach merges principles from biomedical engineering, computer science, and electrical engineering, creating practical solutions for next-generation health technologies. Additionally, she explores low-power sensor systems, data fusion algorithms, and human-computer interaction interfaces within wearable technologies. Professor Wang’s research aims to bridge the gap between theoretical modeling and real-world applications, ultimately enhancing the reliability and usability of wearable systems in clinical, athletic, and daily life settings.

Research Skill🔎

Professor Lirong Wang possesses a comprehensive set of research skills that reflect her expertise in intelligent wearable technology, biomedical engineering, and data-driven signal processing. She is highly skilled in designing and developing advanced wearable systems, with a strong command of microelectronic circuit design, sensor integration, and embedded system programming. Her proficiency in signal acquisition and processing allows her to extract meaningful insights from complex physiological data such as ECG, EMG, and PPG. She is also adept at applying machine learning algorithms for pattern recognition, anomaly detection, and predictive modeling in healthcare applications. In addition, she demonstrates expertise in managing interdisciplinary research teams, coordinating large-scale projects, and supervising graduate-level research. Professor Wang is experienced in securing research funding, particularly as a Principal Investigator on national R&D initiatives. Her ability to bridge theoretical knowledge with practical innovation highlights her strong analytical, experimental, and collaborative research capabilities across multiple scientific domains.

Award and Honor🏆

Professor Lirong Wang has received several prestigious awards and honors in recognition of her outstanding contributions to research and innovation in the fields of intelligent wearable devices and biomedical engineering. As the Principal Investigator of a National Key R&D Program, she has been recognized at the national level for her leadership and scientific excellence. Her pioneering work has earned accolades from academic institutions and government agencies, including awards for Technological Innovation and Excellence in Research. She has also been honored for her contributions to patent development, with over 20 invention patents credited to her name, many of which have led to real-world applications. Professor Wang’s high-impact publications in leading journals such as IEEE Transactions on Biomedical Engineering have further contributed to her reputation as a top researcher. Additionally, she has received invitations to serve as a reviewer and session chair at international conferences, reflecting her respected status in the global scientific community.

Conclusion📝

Professor Lirong Wang is highly suitable for the Best Researcher Award. His sustained contributions to interdisciplinary research, innovation through patents, and leadership in national research programs mark him as a leading figure in the field of intelligent wearable devices and biomedical engineering. With some enhancement in international collaboration and outreach, his profile stands as exemplary in both academic and practical domains.

Publications Top Noted📚

  • End-to-End ECG Signal Compression Based on Temporal Information and Residual Compensation

    • Year: 2025

    • Journal: Circuits, Systems, and Signal Processing

  • QRS Wave Detection Algorithm of Dynamic ECG Signal Based on Improved U-Net Network

    • Year: 2025

    • Journal: ICIC Express Letters, Part B: Applications

  • TrCL-AGS: A Universal Sequential Triple-Stage Contrastive Learning Framework for Bacterial Detection With Across-Growth-Stage Information

    • Year: 2025

    • Journal: IEEE Internet of Things Journal

  • Multi-label Few-Shot Classification of Abnormal ECG Signals Using Metric Learning

    • Year: 2025

    • Journal: Circuits, Systems, and Signal Processing

  • Automated Deep Learning Model for Sperm Head Segmentation, Pose Correction, and Classification (Open Access)

    • Year: 2024

    • Journal: Applied Sciences (Switzerland)

  • Instance Segmentation of Mouse Brain Scanning Electron Microscopy Images Based on Fine-Tuning Nature Image Model

    • Year: 2024

    • Journal: Guangxue Jingmi Gongcheng / Optics and Precision Engineering

    • Citations: 1

  • Multi-label Classification of Arrhythmia Using Dynamic Graph Convolutional Network Based on Encoder-Decoder Framework

    • Year: 2024

    • Journal: Biomedical Signal Processing and Control

    • Citations: 4

  • Two-Stage Error Detection to Improve Electron Microscopy Image Mosaicking

    • Year: 2024

    • Journal: Computers in Biology and Medicine

    • Citations: 2

Dr. Keyong Hu | artificial intelligence | Best Researcher Award

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Dr. Keyong Hu | artificial intelligence | Best Researcher Award

Teacher, Hangzhou Normal University, China

Dr. KeYong Hu is an accomplished academic and researcher specializing in artificial intelligence and new energy technology. He earned his Ph.D. from the Zhejiang University of Technology in 2016 and is currently serving as an Associate Professor at Hangzhou Normal University, within the School of Information Science and Technology. Dr. Hu has contributed significantly to the intersection of AI and energy systems, with numerous publications in international journals, showcasing his expertise in predictive modeling and intelligent optimization.

Publication Profile

ORCID

🎓 Education Background

Dr. KeYong Hu completed his doctoral studies at the Zhejiang University of Technology, Hangzhou, China, where he received his Ph.D. in 2016. His academic training laid a strong foundation in computational intelligence and energy-related engineering applications.

💼 Professional Experience

Dr. Hu holds the position of Associate Professor at Hangzhou Normal University, Hangzhou, Zhejiang, China, affiliated with the School of Information Science and Technology. He has been actively involved in teaching, mentoring, and high-impact research since earning his doctorate.

🏆 Awards and Honors

While specific awards are not listed, Dr. Hu’s prolific publishing record in top-tier peer-reviewed journals like Mathematics, Heliyon, Sustainability, and Computers and Electrical Engineering underscores his recognition and influence in the fields of AI and energy optimization.

🔬 Research Focus

Dr. Hu’s research centers on the integration of artificial intelligence with new energy technologies, particularly photovoltaic power forecasting, energy system optimization, and cross-modal data analysis. His innovative use of algorithms such as Copula functions, Transformers, and Dung Beetle Optimization showcases his depth in AI-driven energy analytics.

✅ Conclusion

Dr. KeYong Hu stands out as a forward-thinking researcher contributing impactful work at the intersection of artificial intelligence and sustainable energy. Through his academic leadership and research contributions, he continues to shape the future of intelligent energy systems in China and beyond. 🌍📈

📚 Top Publications 

🔗 Two-Stage Distributionally Robust Optimal Scheduling for Integrated Energy Systems Considering Uncertainties in Renewable Generation and Loads
Journal: Mathematics | Year: 2025
Cited by: Check on Google Scholar

🔗 Short-term Photovoltaic Forecasting Model with Parallel Multi-Channel Optimization Based on Improved Dung Beetle Algorithm
Journal: Heliyon | Year: 2024
Cited by: Check on Google Scholar

🔗 Distributed Regional Photovoltaic Power Prediction Based on Stack Integration Algorithm
Journal: Mathematics | Year: 2024
Cited by: Check on Google Scholar

🔗 Automatic Depression Prediction via Cross-Modal Attention-Based Multi-Modal Fusion in Social Networks
Journal: Computers and Electrical Engineering | Year: 2024
Cited by: Check on Google Scholar

🔗 Short-Term Photovoltaic Power Generation Prediction Based on Copula Function and CNN-CosAttention-Transformer
Journal: Sustainability | Year: 2024
Cited by: Check on Google Scholar

Seyed Abolfazl Aghili | Deep Learning | Best Review Paper Award

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Dr. Seyed Abolfazl Aghili | Deep Learning | Best Review Paper Award

lecturer, iran university of science and technology, Iran

Seyed Abolfazl Aghili is a passionate civil engineer with a strong focus on construction engineering and management. With a Ph.D. in Civil Engineering from the prestigious Iran University of Science and Technology (IUST), he specializes in using artificial intelligence for enhancing the resilience of HVAC systems in hospitals. His research integrates cutting-edge technologies such as machine learning and deep learning to optimize building systems and improve decision-making in construction projects. Seyed’s dedication to his field has earned him a reputation as a driven academic and professional in the civil engineering community. 🏗️🤖

Publication Profile

ORCID

Education Background

Seyed Abolfazl Aghili completed his Ph.D. in Civil Engineering with a specialization in Construction Engineering and Management from Iran University of Science and Technology (IUST) between 2019 and 2024. His doctoral thesis focused on developing a framework to assess the long-term resilience of hospital air conditioning systems using artificial intelligence. Prior to that, he earned his M.Sc. in Civil Engineering with a focus on Construction Engineering and Management at IUST, where he investigated employee selection methods in construction firms. He also holds a B.Sc. in Civil Engineering from Isfahan University of Technology (IUT). 🎓📚

Professional Experience

Seyed Abolfazl Aghili has extensive experience in both academic research and practical applications of civil engineering, particularly in construction management. He has worked on various projects involving energy management, risk management, and resilience within the construction industry. His academic journey has seen him contribute significantly to the research community, particularly in the areas of AI in construction systems and HVAC performance. Furthermore, he has been an integral part of various conferences and publications, sharing his insights on improving construction management processes through technology. 💼🏢

Awards and Honors

Seyed Abolfazl Aghili has earned several prestigious awards throughout his academic journey. He was ranked 5th among 2200 participants in the Nationwide University Entrance Exam for the Ph.D. program in Iran in 2019. Additionally, he ranked 2nd among all construction management students at Iran University of Science and Technology during his M.Sc. studies. He was also ranked in the top 1% (220th out of 32,663) in the Nationwide University Entrance Exam for the M.Sc. program in Iran in 2013. 🏆🥇

Research Focus

Seyed’s primary research interests lie in the application of machine learning and deep learning techniques in construction engineering. His work focuses on enhancing the resilience of building systems, especially HVAC systems in healthcare settings. He also explores risk management, sustainability, lean construction, and decision-making systems for project managers. His interdisciplinary research combines civil engineering with advanced AI methodologies, driving innovations in construction management and systems optimization. 🔍💡

Conclusion

Seyed Abolfazl Aghili’s academic and professional journey reflects his commitment to advancing civil engineering through innovative solutions. His focus on integrating artificial intelligence into construction systems is helping to create smarter, more sustainable, and resilient built environments. Through his work, he continues to contribute valuable insights to both the academic world and the practical sector of construction engineering. 🌍🔧

Publications Top Notes

Artificial Intelligence Approaches to Energy Management in HVAC Systems: A Systematic Review. Journal of Buildings, 15.7 (2025).

Data-driven approach to fault detection for hospital HVAC system. Journals of Smart and Sustainable Built Environment, ahead-of-print (2024).

Feasibility Study of Using BIM in Construction Site Decision Making in Iran. International Conference on Civil Engineering, Architecture and Urban Infrastructure, July 2015.

Review of digital imaging technology in safety management in the construction industry. 1st National Conference on Development of Civil Engineering, Architecture, Electricity and Mechanical in Iran (December, 2014).

The role of insurance companies in managing the crisis after earthquake. 1st National Congress of Engineering, Construction, and Evaluation of Development Projects, May 2013.

The need for a new approach to pre-crisis and post-crisis management of earthquake. 1st National Conference on Seismology and Earthquake, February 2013.