Qiaoning Yang | Signal & Image Processing | Best Researcher Award

Published on by Electrical Engineering

Assoc. Prof. Dr. Qiaoning Yang | Signal & Image Processing | Best Researcher Award

Associate Professor | Beijing University of Chemical Technology | China

Qiaoning Yang is an Associate Professor at the College of Information Science, Beijing University of Chemical Technology, with expertise spanning control science and engineering, signal and information processing, image processing, deep learning, and computer vision. She earned her doctoral degree with a specialization in control science and engineering and has developed a sustained academic career combining teaching, research, and applied innovation within a leading technological institution. Her contributions have advanced the integration of signal processing, image analysis, and computer vision into real-world engineering solutions across industry and applied technology domains. She is a professional member of the China Society of Image and Graphics and is recognized for her sustained research excellence, interdisciplinary innovation, and commitment to advancing intelligent engineering systems, with a scholarly impact reflected by 436 citations, an h-index of 8, and an i10-index of 7.

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Featured Publications

Deep convolution neural network-based transfer learning method for civil infrastructure crack detection
Q. Yang, W. Shi, J. Chen, W. Lin – Automation in Construction (221 citations)
Human posture recognition and fall detection using Kinect V2 camera
Y. Xu, J. Chen, Q. Yang, Q. Guo – Chinese Control Conference (41 citations)
Real-time comprehensive image processing system for detecting concrete bridges crack
W. Lin, Y. Sun, Q. Yang, Y. Lin – Computers and Concrete (15 citations)

Zhenghua Qian | Machine Learning | Research Excellence Award

Published on by Electrical Engineering

Prof. Dr. Zhenghua Qian | Machine Learning | Research Excellence Award

Professor | Nanjing University of Aeronautics and Astronautics | China

Professor Zhenghua Qian is a distinguished scholar in solid mechanics and aerospace engineering, serving as a Professor at the College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, and a core member of the State Key Laboratory of Mechanics and Control of Aerospace Structures, with expertise spanning wave propagation, piezoelectric and smart structures, structural health monitoring, and machine learning–assisted nondestructive evaluation. The candidate’s scholarly impact is evidenced by 1,877 citations across 219 publications, with an h-index of 24.

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Rémi Cogranne | Signal & Image Processing | Research Excellence Award

Published on by Electrical Engineering

Dr. Rémi Cogranne | Signal & Image Processing | Research Excellence Award

Troyes University of Technology | France

Rémi Cogranne is an Associate Professor at Troyes University of Technology (UTT), France, and a leading researcher in signal processing, applied mathematics, computer science, and information forensics. His research focuses on hypothesis testing theory, statistical modeling of digital images, image forensics, steganography and steganalysis, and computer network traffic modeling for attack detection, resulting in a substantial body of high-impact journal articles, conference papers, patents, and book chapters. His scholarly influence is demonstrated by 4,624 citations, an h-index of 32, and an i10-index of 68. He has made significant contributions to the research community through editorial service as Senior Associate Editor and Associate Editor for leading IEEE and international journals, membership in prestigious IEEE technical committees, and leadership roles in major international conferences. His work has been widely recognized through best paper awards, editorial honors, and sustained contributions to advancing theory and practice in signal processing and information forensics.

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Featured Publications

Content-adaptive steganography by minimizing statistical detectability
V. Sedighi, R. Cogranne, J. Fridrich – IEEE Transactions on Information Forensics and Security · Citations: 663

Selection-Channel-Aware Rich Model for Steganalysis of Digital Images
T. Denemark, V. Sedighi, V. Holub, R. Cogranne, J. Fridrich – IEEE WIFS · Citations: 469

Moving steganography and steganalysis from the laboratory into the real world
A. D. Ker et al., including R. Cogranne – ACM Workshop on Information Hiding and Multimedia Security · Citations: 344

Rich Model for Steganalysis of Color Images
M. Goljan, J. Fridrich, R. Cogranne – IEEE WIFS · Citations: 203

The ALASKA Steganalysis Challenge: A First Step Towards Steganalysis “into the wild”
R. Cogranne, É. Giboulot, P. Bas – ACM Workshop on Information Hiding and Multimedia Security · Citations: 186

Arturo Sánchez Pérez | Signal & Image Processing | Best Researcher Award

Published on by Electrical Engineering

Prof. Dr. Arturo Sánchez Pérez | Signal & Image Processing | Best Researcher Award

Porfesor Contratado Doctor | Universidad de Murcia | Spain

Arturo J. Sánchez Pérez, Profesor Contratado Doctor at the Universidad de Murcia, is a specialist in periodontology, implantology, and oral rehabilitation whose academic and clinical career integrates advanced dental practice with sustained scholarly contribution. He holds a Licenciatura en Medicina y Cirugía, a Doctorado in oral health sciences with specialization in morphometric and periodontal research, and a postgraduate Máster in Implantología y Rehabilitación Oral, complemented by extensive professional training in surgical, prosthetic, and aesthetic techniques. His professional experience spans roles as Profesor Asociado, Profesor Ayudante Doctor, Vicedecano de Odontología, and member of key academic leadership committees, including the commission responsible for curriculum development in dentistry. His research focuses on periodontal physiology, mucogingival and regenerative surgery, implant therapy, and diagnostic innovations, supported by numerous book chapters and pedagogical publications in periodontology. He has supervised multiple doctoral theses and master’s research projects, contributing to the advancement of clinical knowledge and evidence-based practice. His academic trajectory is further strengthened by participation in scientific symposia, involvement in university teaching programs across undergraduate and postgraduate levels, and engagement with professional dental societies, reflecting recognition within the broader odontological community.

Profile:  ORCID

Featured Publications

Arturo Sánchez-Pérez*, Masticatory efficacy following implant rehabilitation: objective assessment and patient perception through two-color mixing test and Viewgum software. Prosthesis, Accepted.

Arturo Sánchez-Pérez*, Efficacy of a deproteinized bovine bone mineral graft for alveolar ridge preservation: a histologic study in humans. Biomedicines, Accepted.

Arturo Sánchez-Pérez, Objective and subjective evaluation of masticatory efficiency in periodontal patients before and after basic periodontal therapy. Applied Sciences, Accepted.

Arturo Sánchez-Pérez, Evaluation of the Wachtel Healing Index and its correlation with early implantation success or failure. Applied Sciences, Accepted.

Kia Jahanbin | Deep Transfer Learning | Best Researcher Award

Published on by Electrical Engineering

Dr. Kia Jahanbin | Deep Transfer Learning | Best Researcher Award

Data Analyst | Ministry of Economic Affairs and Finance | Iran

Dr. Kia Jahanbin is a highly accomplished data analyst, software engineer, and academic associated with the Ministry of Economic Affairs and Finance and Islamic Azad University (Firuzkoh Branch). He earned his Ph.D. in Software Engineering from Yazd University, focusing on sentiment analysis using transfer learning for cryptocurrency market forecasting. With over a decade of experience, he has contributed to more than 25 research projects and four major national-level initiatives in financial intelligence and data analytics. His expertise covers deep learning, transfer learning, data and text mining, web mining, and public health data analytics, with his works published in reputed journals such as Knowledge-Based Systems, IEEE Access, International Journal of Intelligent Systems, and Financial Innovation. He has authored two academic books, holds a patent on a Wireless Sensor Network Training Simulator, and actively serves as a reviewer for IEEE Access, Ad Hoc & Sensor Wireless Networks, and Financial Innovation, besides being on the editorial board of Journal La Multiapp (Indonesia). His collaborations with institutions like Yazd University and the University of Windsor (Canada) emphasize his international engagement in AI research. Through his innovative contributions, Dr. Jahanbin has played a crucial role in enhancing data-driven decision-making and digital transformation within Iran’s financial sector, while advancing global knowledge in artificial intelligence and predictive analytics. He has a total of 367 citations, with an h-index of 6 and an i10-index of 5.

Profile: Google Scholar

Featured Publications

Kia Jahanbin*, Sentiment analysis using transfer learning for cryptocurrency market forecasting. Ph.D. Thesis, Yazd University.

Kia Jahanbin*, Deep learning-based hybrid framework for cryptocurrency prediction using social media sentiment. Knowledge-Based Systems, 2024, 302, 112345.

Kia Jahanbin, Predictive modeling of epidemic outbreaks using AI-driven web mining and sentiment analysis. IEEE Access, 2023, 11, 65789–65798.

Kia Jahanbin, Financial data analytics and intelligent forecasting through transfer learning techniques. International Journal of Intelligent Systems, 2023, 38(7), 14562–14579.

Kia Jahanbin*, A deep transfer learning model for cryptocurrency market behavior forecasting. Financial Innovation, Accepted.

Assoc. Prof. Dr. Krzysztof Stepien | Signal & Image Processing | Best Researcher Award

Published on by Electrical Engineering

Assoc. Prof. Dr. Krzysztof Stepien | Signal & Image Processing | Best Researcher Award

Head of Department of Metrology and Modern Manufacturing | Kielce University of Technology | Poland

Assoc. Prof. Krzysztof Stępień is a distinguished researcher and academic leader at the Department of Metrology and Modern Manufacturing, Kielce University of Technology, specializing in precision engineering, geometrical metrology, and surface texture analysis. He earned his Master of Science and Doctor of Science degrees in Mechatronics and Mechanical Engineering from Kielce University of Technology, where his doctoral research focused on cylindricity measurement errors using the V-block method. He later obtained his habilitation from the Warsaw University of Technology for pioneering work on new methods for measuring and evaluating form deviations of rotating elements. Throughout his academic career, he has held multiple leadership roles, including Head of the Department of Metrology and Modern Manufacturing, Head of the Institute of Technological Measuring Systems, and Head of the Laboratory of Computer-Aided Measurements of Geometrical Quantities, contributing significantly to advancing metrological research and education. His research focuses on form and surface metrology, signal processing in measurement systems, and adaptive measurement methods, with publications in top journals such as Precision Engineering, Measurement Science and Technology, and the International Journal of Advanced Manufacturing Technology. Prof. Stępień’s contributions have been widely recognized through professional honors, research collaborations, and editorial and scientific committee memberships, reflecting his commitment to innovation and excellence in modern manufacturing metrology.

Profile: ORCID

Featured Publications

Stępień, K.*, Algorithm for sensor nonlinearity compensation in measurements of geometric deviations of rotating elements with variable diameter. Precision Engineering, Accepted.

Janecki, D., Stępień, K.*, & Adamczak, S., Adaptive cylindricity measurements with the use of circumferential section strategy. Int. J. Adv. Manuf. Technol., 2024, 132, 585–600.

Stępień, K., In situ measurement of cylindricity—Problems and solutions. Precision Engineering, 2014, 38(3), 697–701.

Janecki, D., Stępień, K., & Adamczak, S., Sphericity measurements by the radial method: I. Mathematical fundamentals. Meas. Sci. Technol., 2016, 27(1), 015005.