Abdullah Ammar Karcioglu| Computer Science | Best Review Paper Award

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Assist. Prof. Dr. Abdullah Ammar Karcioglu | Computer Science | Best Review Paper Award

Ataturk University | Turkey

Asst. Prof. Abdullah Ammar Karcıoğlu is a faculty member at Atatürk University, Erzurum, Turkey, specializing in software engineering, algorithms, bioinformatics, artificial intelligence, and natural language processing. He earned his Ph.D. in Computer Engineering from Ege University , with a dissertation focused on string matching algorithms for bioinformatics sequences. With academic experience spanning Atatürk, Ege, and Recep Tayyip Erdoğan Universities, he has contributed to research in machine learning, pattern recognition, and image processing. His publications in high-impact journals, including Computers in Biology and Medicine, Concurrency and Computation: Practice & Experience, and Lasers in Medical Science, reflect his commitment to advancing computational methods for biological data and medical applications. With over 50 citations and an h-index of 4, he has also supervised graduate theses on AI-driven sentiment analysis, disease outbreak mapping, and biomedical imaging. He actively serves as a peer reviewer for leading SCI journals and TÜBİTAK projects, further strengthening his academic impact.

Profiles: Scopus | Google Scholar

Featured Publications

Sentiment analysis of Turkish and english twitter feeds using Word2Vec model

ML-based tooth shade assessment to prevent metamerism in different clinic lights

Machine Learning-Based Tooth Color Assessment Using Color Moments to Prevent Metamerism in Different Clinical Lights

Advancing forensic dentistry: a comprehensive review of machine learning and deep learning applications in dental image analysis

Performance evaluation of classification algorithms using hyperparameter optimization

 

Yabo Wu | Computer Science | Best Researcher Award

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Mr. Yabo Wu | Computer Science | Best Researcher Award

Mr. Yabo Wu, Guizhou University, China.

🎓 Mr. YaBo Wu, a Ph.D. candidate in Software Engineering at Guizhou University, focuses on cutting-edge computer vision research, especially in image enhancement and depth estimation using deep learning. 🧠 He has published in SCI Q1 and Q3 journals, showcasing his ability to blend theory with practical innovation. 📚 His work on image dehazing algorithms pushes the boundaries of AI-driven multimedia systems. 🔍 A tech-savvy and passionate researcher, he thrives in collaborative R&D environments. 💡

Profile

Orcid Profile

🎓 Early Academic Pursuits

Mr. YaBo Wu embarked on his academic journey at Guizhou University, earning a Bachelor’s degree in Computer Science and Technology. He demonstrated a strong foundation in computational theory and programming early in his career. Building on this solid base, he advanced into doctoral studies in Software Engineering, deepening his focus on artificial intelligence and its applications in computer vision.

💼 Professional Endeavors

Currently pursuing his Ph.D., Mr. Wu has demonstrated a keen ability to integrate theoretical insights with practical innovation. His work in image enhancement and depth estimation reflects his dedication to solving complex visual computing challenges. Through a combination of deep learning and multimedia processing, he seeks to improve the performance of intelligent systems.

🔬 Contributions and Research Focus On Computer Science

Dr. YaBo Wu, a Ph.D. candidate in Software Engineering at Guizhou University, specializes in computer vision with a strong focus on image enhancement and depth estimation using deep learning. His work emphasizes developing robust, AI-driven solutions for multimedia and autonomous systems. Notable contributions include two SCI-indexed papers—one Q1 and one Q3—on innovative single-image dehazing methods. His research introduces novel architectures like DAF-Net and DDLNet, pushing the boundaries of visual clarity and semantic preservation in real-world environment

🌍 Impact and Influence

Mr. Wu’s work is not only technically sound but socially relevant, as his contributions play a key role in enhancing the performance of autonomous systems, smart surveillance, and AI-powered multimedia applications. His proposed methods outperform state-of-the-art models and bring clarity to otherwise degraded visual data, thus influencing both academic discourse and industrial application.

🧠 Research Skills

  • Deep Learning Algorithms for Image Enhancement

  • Frequency-Domain Filtering and Amplitude Modulation

  • Two-stream Spatial Modulation Networks

  • Robust semantic detail recovery from degraded images

  • Efficient use of data calibration techniques for real-world deployment

  • Rapid adaptation to emerging computational paradigms and tools

🏅 Awards and Honors

At just 26 years old, Mr. Wu has already made a name for himself by publishing in high-impact SCI journals, demonstrating excellence in both research quality and originality. While formal awards may still be on the horizon, his publication record and ongoing contributions mark him as a rising star in the field of AI and computer vision.

🏛️ Legacy and Future Contributions

Looking ahead, YaBo Wu is committed to pushing the boundaries of AI and computer vision. He envisions building intelligent visual systems capable of functioning seamlessly in dynamic and challenging environments. His work aims to empower future autonomous vehicles, smart cities, and human-computer interaction platforms with refined, real-time vision technologies. Through continued innovation, collaboration, and academic leadership, YaBo aspires to leave a lasting legacy in the AI research community. 🌟

Publications Top Notes

A frequency-domain dynamic amplitude filtering method for single-image dehazing with harmony enhancement

Distribution-decouple learning network: an innovative approach for single image dehazing with spatial and frequency decoupling