Assoc. Prof. Dr. A’aeshah Alhakamy | Computer Graphics | Best Researcher Award

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Assoc. Prof. Dr. A’aeshah Alhakamy | Computer Graphics | Best Researcher Award

Assoc. Prof. Dr. A’aeshah Alhakamy | Associate Professor | University of Tabuk | Saudi Arabia

Assoc. Prof. Dr. A’aeshah Alhakamy is a distinguished academic and researcher in the field of Computer Science at the University of Tabuk, Saudi Arabia. Her research spans computer graphics, computer vision, visualization, and imaging, with a particular focus on illumination models in mixed and augmented reality (AR/MR), gesture-based interaction, and the convergence of vision and artificial intelligence in immersive environments. Her scholarly contributions bridge the gap between visual perception and computational intelligence, emphasizing human–data interaction, extended reality (XR) technologies, and AI-driven visual analytics. She has successfully supervised graduate research in emerging domains such as extended reality applications for industrial training and biometric authentication systems using AI. Dr. Alhakamy’s research outputs demonstrate a deep commitment to advancing both theoretical and applied dimensions of visual computing and human–computer interaction. Her academic excellence is reflected through strong research metrics, including Scopus with 254 citations from 235 documents and an h-index of 9, and Google Scholar with 417 citations, an h-index of 11, and an i10-index of 13. These indicators highlight her growing influence and recognition in the international research community.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications

  • Alhakamy, A. (2025). Intersecting realms: Examining the convergence of vision and AI in extended reality graphics. IEEE Access, 1–1.

  • Alhakamy, A. (2024). Extended reality (XR) toward building immersive solutions: The key to unlocking Industry 4.0. ACM Computing Surveys, 56(9).

  • Alhakamy, A. (2023). Fathoming the Mandela effect: Deploying reinforcement learning to untangle the multiverse. Symmetry, 15(3).

  • Alatawi, H., Albalawi, N., Shahata, G., Aljohani, K., Alhakamy, A., & Tuceryan, M. (2023). Augmented reality-assisted deep reinforcement learning-based model towards industrial training and maintenance for NanoDrop spectrophotometer. Sensors, 23(13).

  • Albalawi, S., Alshahrani, L., Albalawi, N., Kilabi, R., & Alhakamy, A. (2022). A comprehensive overview on biometric authentication systems using artificial intelligence techniques. International Journal of Advanced Computer Science and Applications, 13(4).

 

GuoXin Chen | Image Processing | Best Researcher Award

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Dr. GuoXin Chen | Image Processing | Best Researcher Award

Dr. GuoXin Chen | Zhejiang University | China

Guoxin Chen is a prominent Chinese geophysicist specializing in marine seismic exploration, currently serving as a researcher at the Ocean College of Zhejiang University. With a strong foundation in both mathematics and geophysics, he has developed innovative techniques in seismic waveform inversion, imaging, and artificial intelligence-driven data processing. He has held various academic roles in China and internationally, including at the University of California, Santa Cruz. In addition to his research, he is an editorial board member of several SCI-indexed journals and contributes as a reviewer for top-tier publications. His scholarly work is widely recognized in the geophysical research community.

Publication Profile

Scopus

ORCID

Google Scholar

Education Background

Guoxin Chen completed his Ph.D. in Geophysics from Zhejiang University, one of China’s leading institutions in scientific research. His academic journey began with a Bachelor’s degree in Mathematics from Shandong University, Jinan, where he built a strong analytical foundation that later enriched his work in geophysics. His interdisciplinary academic background enabled him to approach seismic imaging and inversion with a unique combination of theoretical precision and practical problem-solving skills, contributing to significant advances in marine geophysical research methodologies.

Professional Experience

Guoxin Chen has accumulated extensive experience in geophysics through academic and research roles both in China and the United States. He currently works as a researcher at the Ocean College of Zhejiang University and previously served as an associate researcher and postdoctoral scholar in the same institution. Internationally, he has been affiliated with the Modeling & Imaging Lab at the University of California, Santa Cruz, holding roles from project assistant researcher to junior researcher. His professional appointments include serving as a distinguished expert with BGP, CNPC, and as a guest editor and board member for several prominent journals.

Awards and Honors

Guoxin Chen has been recognized for his contributions to exploration geophysics with multiple prestigious academic appointments and research grants. He serves as a part-time distinguished expert for the Bureau of Geophysical Prospecting under CNPC. He also holds editorial positions in high-impact SCI journals such as Water, Symmetry, Petroleum Science, and Journal of Earth Science. He has served as a principal investigator and core researcher in numerous national-level and provincial research projects, highlighting his leadership in scientific innovation and research development in geophysics and related fields.

Research Focus

Guoxin Chen’s research primarily focuses on marine seismic exploration, especially seismic wave full waveform inversion, reverse time migration imaging, and the application of AI in geophysical data processing. He aims to improve imaging accuracy and efficiency for complex geological structures, such as salt bodies and sub-seafloor sediments. His recent work integrates deep learning algorithms into conventional geophysical workflows, offering enhanced solutions for noise reduction, model building, and data interpretation. His work has significantly contributed to advancements in both academic geophysics and practical seismic exploration technologies.

Top Publications

Efficient Seismic Data Denoising via Deep Learning with Improved MCA-SCUNet
Published Year: 2024
Cited by: 14

Joint Model and Data-Driven Simultaneous Inversion of Velocity and Density
Published Year: 2024
Cited by: 12

Salt Structure Elastic Full Waveform Inversion Based on the Multi-scale Signed Envelope
Published Year: 2022
Cited by: 38

Application of Envelope in Salt Structure Velocity Building
Published Year: 2020
Cited by: 55

Multi-scale Direct Envelope Inversion: Algorithm and Methodology for Application to the Salt Structure Inversion
Published Year: 2019
Cited by: 42

Conclusion

Guoxin Chen stands as a distinguished figure in the field of exploration geophysics with a career marked by academic excellence, groundbreaking research, and international collaboration. His fusion of mathematical insight, geophysical expertise, and cutting-edge artificial intelligence places him at the forefront of seismic imaging research. Through numerous publications, editorial contributions, and funded projects, he continues to influence the direction of marine seismic data processing and inversion technologies. His work not only contributes to academic knowledge but also addresses real-world challenges in geophysical exploration and energy resource discovery.

 

Awais Khan Jumani | Image processing | Best Researcher Award

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Mr. Awais Khan Jumani | Image processing | Best Researcher Award

PhD Scholar, South China University of Technology, Guangzhou, Guangdong, China

Dr. Awais Khan is a dedicated researcher specializing in deep learning, multimedia cloud computing, and artificial intelligence 🌐. Currently pursuing a Ph.D. in Information & Communication Engineering at South China University of Technology 🎓, his research focuses on deep learning models for Quality of Experience (QoE) in cloud environments. With a strong academic and professional background, Dr. Khan has contributed significantly to the fields of machine learning, computer vision, and multimedia processing. His work integrates innovative AI techniques for real-world applications, making him a prominent figure in computational research 🤖.

Publication Profile

📚 Education

Dr. Khan is on track to complete his Ph.D. (2021-2025) at South China University of Technology 🇨🇳, where he explores deep learning techniques for emotion-based QoE in cloud computing. He previously earned his M.S. in Computer Science (2016-2018) from Shah Abdul Latif University, Pakistan 🇵🇰, focusing on Sindhi text categorization using Support Vector Machines. His academic journey began with a B.S. in Computer Science (2011-2014) from the same institution, achieving commendable academic performance 📊.

👨‍🏫 Experience

Dr. Khan served as an Assistant Professor at ILMA University (2019-2022) in Pakistan, where he developed curriculum content, mentored students, and engaged in academic research. Prior to this, he was an Instructor at APTECH Computer Center (2014-2018), guiding students through machine learning projects and real-world applications 🎓. His early experience includes a Teaching Assistant role at Shah Abdul Latif University, where he supported research initiatives and practical learning in AI-related subjects 🔍.

🏆 Awards and Honors

Dr. Khan has received recognition for his contributions to AI, deep learning, and multimedia computing. His work has been featured in top-tier journals, and he has actively participated in research-driven initiatives. His academic excellence is reflected in his high GPA scores, international collaborations, and impactful research publications 📜.

🔬 Research Focus

Dr. Khan’s research spans deep learning, machine learning, and multimedia cloud computing. His core areas include domain generalization, multimodal learning, and fairness in AI models. He actively explores AI-driven QoE assessment for cloud gaming, representation learning for multimedia data, and security models for cloud environments. His interdisciplinary approach bridges AI, image/audio processing, and user experience enhancement 🌍.

📝 Conclusion

Dr. Awais Khan stands out as a researcher and educator dedicated to advancing AI applications in multimedia and cloud computing. With a solid academic foundation, extensive teaching experience, and an impressive publication record, he continues to push the boundaries of deep learning and machine learning research. His work significantly impacts QoE evaluation, multimedia security, and AI-driven automation, positioning him as a key contributor to the AI research community 🚀.

📄 Publications

Fog computing security: A review – Security and Privacy (2025) 🔗 [Cited By: TBD]

Deep learning-based QoE assessment of cloud gaming via emotions in a virtual reality environment – Journal of Cloud Computing (2025) 🔗 [Cited By: TBD]

Quality of experience (QoE) in cloud gaming: A comparative analysis of deep learning techniques via facial emotions in virtual reality environment – Sensors (2025) 🔗 [Cited By: TBD]

A proposed model for security of QoE data in cloud gaming environment – International Journal of Electronic Security and Digital Forensics (2025) 🔗 [Cited By: TBD]

Quality of experience that matters in gaming graphics: How to blend image processing and virtual reality – Electronics, vol. 13, no. 15 (2024) 🔗 [DOI: 10.3390/electronics13152998] [Cited By: TBD]

Unintended data behavior analysis using cryptography stealth approach against security and communication network– Mobile Networks and Applications (2023) 🔗 [Cited By: TBD]

Prediction of diabetic patients in Iraq using binary dragonfly algorithm with LSTM neural network – AIMS Electronics & Electrical Engineering, vol. 7, no. 3 (2023) 🔗 [Cited By: TBD]

Unmanned aerial vehicles: A review– Cognitive Robotics (2022) 🔗 [Cited By: TBD]

Analysis of the teaching quality on deep learning-based innovative ideological political education platform – Progress in Artificial Intelligence (2022) 🔗 [DOI: 10.1007/s13748-021-00272-0] [Cited By: TBD]

Examining the present and future integrated role of artificial intelligence in business: A survey study on the corporate sector – Journal of Computer and Communications, vol. 9, no. 1 (2021) 🔗 [Cited By: TBD]

 

CHANDRASEKHAR C | Image Processing | Lifetime achievement Award

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Dr. CHANDRASEKHAR C | Image Processing | Lifetime achievement Award

PRINCIPAL, PARVETE, India

🎓 Dr. C. Chandrasekhar is an esteemed academician and researcher specializing in Electronics and Communication Engineering. With over two decades of rich experience in teaching, research, and industrial roles, he has significantly contributed to advancing low-power VLSI designs, MEMS technology, and image processing techniques. Currently, he serves as the In-charge Principal at SVEC and Professor in the Department of ECE. He is a Fellow of the Institution of Engineers (India) and has led several impactful research projects funded by prestigious organizations.

Publication Profile

Education

📘 Dr. Chandrasekhar holds a Ph.D. in Electronics and Communication Engineering from Sri Venkateswara University, Tirupati (2014), where his thesis focused on Discrete Wavelet Transform-based image fusion and compression for micro air vehicle applications. He earned an M.E. in Digital Electronics from BVB College of Engineering, Karnataka (2003), and a B.E. in Instrumentation Technology from Govt. BDT College of Engineering, Karnataka (1994), showcasing his strong academic foundation in electronics and instrumentation.

Experience

💼 Dr. Chandrasekhar has an impressive career trajectory, ranging from industrial roles to academic leadership positions. He served as an Engineer at Hindustan Aeronautics Limited, Bangalore, and has held several academic roles, including Associate Professor, Head of Department, and Professor at institutions like NBKR Institute of Science & Technology and SVCET, Chittoor. His teaching portfolio includes courses such as Microprocessors, Digital Signal Processing, MEMS, and Embedded Systems.

Awards and Honors

🏆 Dr. Chandrasekhar is the recipient of numerous grants, including the DST-SEED/TIDE grant for profiling skill development activities and a DSIR-PRISM-funded project for developing security gadgets for pilgrims. His achievements underline his dedication to addressing societal and technological challenges through research.

Research Focus

🔬 Dr. Chandrasekhar’s research interests encompass low-power VLSI architectures for 2D/3D DWT-IDWT, MEMS technology, and advanced image compression, registration, and fusion techniques. His work is characterized by its emphasis on practical applications, particularly in vehicular communications, cognitive radio, and micro air vehicle systems.

Conclusion

🌟 Dr. C. Chandrasekhar stands out as a visionary educator and researcher committed to the growth of electronics and communication engineering. With a legacy of impactful projects, scholarly contributions, and innovative designs, he continues to inspire the next generation of engineers and researchers.

Publications

Pass-Transistor-Enabled Split Input Voltage Level Shifter for Ultra-Low-Power Applications, Micromachines, 2025.

Varactor Tunable Compact MIMO Antenna with Reconfigurable Multi-Band Operating and Notching for Cognitive Radio Applications, Wireless, Antenna and Microwave Symposium WAMS, 2024.

A Miniaturized-Slotted Planar MIMO Antenna with Switchable Configuration for Dual-/Triple-Band Notches, AIP Advances, 2024.

A Triple Band Pattern Reconfigurable Planar Antenna for 5G Applications, Frequenz, 2022.

A Study and Review on Frequency Band Notch Characteristics in Reconfigurable MIMO-UWB Antennas, Wireless Personal Communications, 2021.

Compact Quad Band Radiator for Wireless Applications, Lecture Notes in Electrical Engineering, 2021.

Micro Organic Photo Detector Characterization using Data Acquisition, TEST Engineering Management, 2020.

Review of 2D/3D DWT-IDWT VLSI Architectures for Image Compression, International Journal of Signal and Imaging Systems Engineering, 2014.

Analysis of Fractional Frequency Reuse (FFR) over Classical Reuse Scheme in 4G (LTE) Cellular Network, Advances in Intelligent Systems and Computing, 2012.

A Conformal Multi-Band MIMO Antenna for Vehicular Communications, Journal Article, year not specified.