Sara A. Shehab | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Sara A. Shehab | Artificial Intelligence | Best Researcher Award

Faculty Of Computer And Artificial Intelligence | Egypt

Assoc. Prof. Dr. Sara A. Shehab  is an Associate Professor in Computer Science at the University of Sadat City, Egypt, with expertise spanning artificial intelligence, bioinformatics, computational biology, quantum computing, and computer security. Her research focuses on developing intelligent algorithms for biological data analysis, optimization, and machine learning applications in medicine and environmental sustainability. She has contributed significantly to the advancement of multiple sequence alignment techniques, parallel and dynamic algorithms, and predictive modeling using machine learning. Her recent work explores deep learning for biomedical image analysis, explainable AI for green energy production, and hybrid optimization approaches for precision classification and prediction tasks. Dr. Shehab has published extensively in peer-reviewed international journals and conferences, collaborating with leading scholars in AI-driven bioinformatics and sustainable computing. She also serves as a reviewer for international journals and conferences, contributing to the academic community through quality evaluation and mentorship. Her professional experience includes leadership in e-learning, digital transformation, and program coordination within higher education, reflecting a strong integration of research, teaching, and institutional development. Through her interdisciplinary approach, she bridges artificial intelligence with biological and environmental sciences, fostering innovation in intelligent systems for healthcare, sustainability, and data-driven decision-making.

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Featured Publications

Shehab, S. A., Keshk, A., & Mahgoub, H. (2012). Fast dynamic algorithm for sequence alignment based on bioinformatics. International Journal of Computer Applications, 37(7), 54–61.

Ahmed, R. A. E. H., Shehab, S. A., Elzeki, O. M., & Darwish, A. (2024). An explainable AI for green hydrogen production: A deep learning regression model. International Journal of Hydrogen Energy, 83, 1226–1242.

Shehab, A. E. H. S., Mohammed, K. K., & Darwish, A. (2024). Deep learning and feature fusion-based lung sound recognition model to diagnose respiratory diseases. Soft Computing.

Shehab, A. E. H. S., & Darwish, A. (2023). Water quality classification model with small features and class imbalance based on fuzzy rough sets. Environment, Development and Sustainability.

Shehab, S., Shohdy, S., & Keshk, A. E. (2017). PoMSA: An efficient and precise position-based multiple sequence alignment technique. arXiv preprint arXiv:1708.01508.

Jun Tang | Computer Science | Best Researcher Award

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Mr. Jun Tang | Computer Science | Best Researcher Award

AI Algorithm Researcher | Chengdu Zhihui Heneng City Technology | China

Mr. Jun Tang is a researcher specializing in intelligent transportation and autonomous driving, with a strong focus on the integration of computer vision and artificial intelligence to enhance vehicular perception and decision making systems. His research primarily explores large vision foundation models and their applications in object detection, scene understanding, and adaptive driving environments. He has contributed to developing advanced detection frameworks that leverage reinforcement learning to improve recognition accuracy, robustness, and real time responsiveness in dynamic traffic conditions. Mr. Tang’s recent interests include prompt-guided object detection methods that utilize natural language and contextual cues to refine visual understanding within autonomous systems. Through his work at Chengdu Zhihui Heneng City Technology, he plays a key role in bridging the gap between theoretical AI models and practical intelligent mobility applications, fostering innovations that advance the safety, efficiency, and scalability of next generation transportation systems. His interdisciplinary approach combines deep learning, machine perception, and cognitive automation, contributing to the development of more adaptive and human like decision making in autonomous vehicles.

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Featured Publications

Tang, J., Li, D., Yang, J., Chen, J., & Yuan, R. (2025). Leveraging large visual models for enhanced object detection: An improved SAM-YOLOv5 model. Knowledge-Based Systems, 114757.

Tang, J. (2025, August 29). RT-DETR-based intelligent transportation object detection optimization method and system with prompt mechanism fusion.

Tang, J. (2025, May 27). Object detection method and system based on prompt engineering and regional text description.

Tang, J. (2025, April 11). Quantitative evaluation method and system for multimodal large models.

Tang, J. (2025, January 17). Evaluation method and system for urban governance multimodal large models based on text labeling.

Juhi Patel | Machine Learning | Best Researcher Award

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Ms. Juhi Patel | Machine Learning | Best Researcher Award

Assistant Professor at GLS University, India

Ms. Juhi Patel is a dedicated early-career researcher and assistant professor at the Faculty of Computer Applications and IT, GLS University, Ahmedabad, since June 2023. She is currently pursuing her Ph.D. in Computer Science at GLS University. Juhi has a strong background in IT, having worked as a Web Developer at ADELSEO (2020–2023) and as a WordPress Developer at Elsner Technologies Pvt. Ltd. (2016–2020).

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💼 Professional Experience:

  • Assistant Professor at Faculty of Computer Applications and IT, GLS University since June 2023.

  • Former Web Developer at ADELSEO (2020–2023).

  • WordPress Developer at Elsner Technologies Pvt. Ltd. (2016–2020).

🧠 Research & Innovation:

  • Holds a design patent for a “Safety Helmet Detection Device” (issued July 2024).

  • Published papers on Machine Learning, IoT, and Sustainable Agriculture in international conferences and journals.

  • Active participant and presenter at prestigious events such as ICTIS 2025 and ColCI 2024.

📚 Professional Development:

  • Completed numerous Faculty Development Programs (FDPs) on AI, Machine Learning, Blockchain, NEP 2020, and ICT tools.

  • Moderator of the ‘International Conference on Research and Innovations’.

🎓 Educational Qualifications:

  • Pursuing Ph.D. in Computer Science, GLS University

  • MCA (8.96 CGPA), Gujarat Technical University, 2017

  • BCA (7.65 CGPA), Gujarat University, 2015

Publication:

Navigating GMO Adoption in Agriculture: Balancing Controversies and Benefits
Current Agriculture Research Journal, 2025-01-15
DOI: 10.12944/CARJ.12.3.32
Contributors: Juhi Patel, Tejaskumar Bhatt, Aditi Joshi

Wen-Chung Tsai | Computer Science and Artificial Intelligence | Best Researcher Award

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Prof. Dr. Wen-Chung Tsai | Computer Science and Artificial Intelligence | Best Researcher Award

Associate Professor National Taichung University of Science and Technology Taiwan

Dr. Wen-Chung Tsai is an esteemed academic and researcher specializing in electronics engineering and computer science. He obtained his Ph.D. from National Taiwan University and has extensive experience in both academia and industry. Currently, he serves as an Associate Professor at the National Taichung University of Science and Technology, focusing on embedded systems, AI, and information security.

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🎓 Education

  • Ph.D. in Electronics Engineering – National Taiwan University (2006–2011)

  • M.S. in Electrical Engineering – National Cheng Kung University (1996–1998)

  • B.S. in Computer Science & Information Engineering – Tamkang University (1992–1996)

💼 Experience

  • Associate Professor – National Taichung University of Science and Technology (2022–present)

  • Associate Professor – Chaoyang University of Technology (2020–2022)

  • Assistant Professor – Chaoyang University of Technology (2013–2020)

  • Engineer – Industrial Technology Research Institute (2011–2013)

  • Visiting Scholar – University of Wisconsin-Madison (2010)

  • Deputy Manager – VIA Technologies (2000–2009)

🔬 Research Interests

  • Embedded Systems & Internet of Things

  • Software & Hardware Design Integration

  • Artificial Intelligence & Information Security

  • Wireless Networks & Communication Protocols

📚 Publications Top Notes:

Field-Programmable Gate Array-Based Implementation of Zero-Trust Stream Data Encryption for Enabling 6G-Narrowband Internet of Things Massive Device Access

Anticipative QoS Control: A Self-Reconfigurable On-Chip Communication

Automatic Key Update Mechanism for Lightweight M2M Communication and Enhancement of IoT Security: A Case Study of CoAP Using Libcoap Library

Network-Cognitive Traffic Control: A Fluidity-Aware On-Chip Communication

Implementatons of Health-Promotion IoT Devices for Secure Physiological Information Protection

Anticipative QoS Control: A Self-Reconfigurable On-Chip Communication

3D Bidirectional-Channel Routing Algorithm for Network-Based Many-Core Embedded Systems

Bi-routing: a 3D bidirectional-channel routing algorithm for network-based many-core embedded systems

A Configurable Networks-on-Chip Router Using Altera FPGA and NIOS2 Embedded Processor

Analysis of the relationship between the radial pulse and photoplethysmography based on the spring constant method

Ali Raza | Computer Science and Artificial Intelligence | Young Scientist Award

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Mr. Ali Raza | Computer Science and Artificial Intelligence | Young Scientist Award

lecturer The University Of Lahore Pakistan

Ali Raza is a passionate researcher, educator, and developer specializing in computer science. With a strong academic background and extensive experience in machine learning, deep learning, and computer vision, he has contributed significantly to cutting-edge research. Currently serving as a Lecturer at the University of Lahore, Ali has also worked as a Visiting Lecturer at KFUEIT and a Full Stack Python Developer in the software industry. His expertise lies in AI-driven solutions, research writing, and technological advancements in artificial intelligence.

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Education 🎓

  • MS Computer Science (2021-2023) | Khwaja Fareed University of Engineering and Information Technology (KFUEIT), CGPA: 3.93
  • BS Computer Science (2017-2021) | KFUEIT, CGPA: 3.47

Professional Experience 💼

  • Lecturer | University of Lahore (2024 – Present)
  • Visiting Lecturer | KFUEIT (2022 – 2023)
  • Full Stack Python Developer | BuiltinSoft Software Industry (2020 – 2021)

Research Interests 📈

Ali Raza’s research focuses on artificial intelligence, machine learning, deep learning, and computer vision. He is particularly interested in developing AI-driven solutions for medical imaging, agricultural applications, and energy consumption prediction. His contributions span multiple domains, showcasing his ability to integrate AI with real-world challenges.

Awards & Certifications 🏆

  • Best Researcher Award | ScienceFather (26/06/2024)
  • Use of Generative AI in Higher Education | Punjab Higher Education Commission
  • Machine Learning with Python (ML0101EN) | IBM Developer Skills Network

Publications Top Notes: 📚

Ali Raza has authored 61 research publications in reputed journals with high impact factors. Below are some of his recent publications:

“Novel Transfer Learning Approach for Hand Drawn Mathematical Geometric Shapes Classification” (2025) PeerJ Computer Science (IF: 3.8)

“Citrus Diseases Detection Using Innovative Deep Learning Approach and Hybrid Meta-Heuristic” (2025) PLOS ONE (IF: 2.9)

“Novel Deep Neural Network Architecture Fusion for Energy Consumption Prediction” (2025) PLOS ONE (IF: 2.9)

“Novel Transfer Learning Based Bone Fracture Detection Using Radiographic Images” (2025) BMC Medical Imaging (IF: 2.9)

“Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crops” (2025) Food Science & Nutrition (IF: 3.5)

“BdSentiLLM: A Novel LLM Approach to Sentiment Analysis of Product Reviews” (2024) IEEE Access (IF: 3.4)

“An Innovative Artificial Neural Network Model for Smart Crop Prediction” (2024) PeerJ Computer Science (IF: 3.8)

“Enhanced Interpretable Thyroid Disease Diagnosis Using Synthetic Oversampling and Machine Learning” (2024) BMC Medical Informatics (IF: 3.3)

“Diagnosing Epileptic Seizures Using EEG Data and Independent Components” (2024) Digital Health (IF: 3.7)

“A Novel Meta Learning Based Approach for Thyroid Syndrome Diagnosis” (2024) PLOS ONE (IF: 2.9)

 

Ke-Lin Du | Artificial Neural Networks | Best Researcher Award

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Prof. Dr. Ke-Lin Du | Artificial Neural Networks | Best Researcher Award

Professor Guangdong University of Science and Technology China

Dr. Ke-Lin Du is a distinguished professor with a career spanning academia and industry. He received his PhD in Electrical Engineering from the Huazhong University of Science and Technology, China, in 1998. Currently, he serves as a professor at the Faculty of Mechanical and Electronic Engineering at Guangdong University of Science and Technology, Dongguan, China, since 2024. His journey includes impactful roles at leading institutions such as Concordia University, Huawei Technologies, and top research centers in Hong Kong. With a commitment to advancing technology, he has contributed significantly to signal processing, wireless communications, and machine learning.

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Education 🎓

  • PhD in Electrical Engineering – Huazhong University of Science and Technology, China (1998)

Experience 💼

  • Professor | Guangdong University of Science and Technology (2024–Present)
  • Affiliate Professor | Concordia University (2011–2024)
  • Research Staff | Concordia University (2001–2010)
  • Technical & Research Positions | Huawei Technologies, China Academy of Telecommunication Technology, CUHK, HKUST, Enjoyor Inc., Xonlink Inc.

With 20+ years of experience, Dr. Du has significantly contributed to academic teaching, cutting-edge research, and technological innovations in various global institutions.

Research Interests 🔬

Dr. Ke-Lin Du’s research focuses on:

  • Signal Processing
  • Wireless Communications
  • Machine Learning

His interdisciplinary expertise bridges theoretical concepts and practical applications, driving advancements in artificial intelligence and communication systems.

Awards and Honors 🏆

  • Stanford’s Top 2% Most Cited Scientists (2019–2023, 5 consecutive years)
  • Senior Member of IEEE (Since 2009)
  • Recognized for outstanding contributions to academia, research, and leadership in over 115 international conferences.

Publications Top Notes: 📚

Dr. Du is an accomplished author with 5 coauthored books, 13 co-edited books, 60 research papers, and 6 U.S. patents. Some notable publications include:

Neural Networks and Statistical Learning

Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature

Neural Networks in a Softcomputing framework

Clustering: A neural network approach

Wireless communication systems: From RF subsystems to 4G enabling technologies

Using radial basis function networks for function approximation and classification

Particle swarm optimization

Exploiting multiple antennas for spectrum sensing in cognitive radio networks