Leila Malihi | Knowledge Distillation | Research Excellence Award

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Dr. Leila Malihi | Knowledge Distillation | Research Excellence Award

Research Assistant | Osnabrück University | Germany

Dr. Leila Malihi is an emerging scholar whose work advances the intersection of medical image analysis, digital health technologies, and clinical decision-support systems. With a developing portfolio of 10 scholarly publications, 88 citations and 5 h-index her research demonstrates both growing influence and clear relevance to contemporary healthcare challenges. Her primary focus lies in applying machine learning and computer-vision techniques to improve diagnostic accuracy, particularly in the context of wound analysis and healing-complication detection, including notable contributions to the automatic classification of wound images and the optimisation of algorithms to detect maceration—an area critical for improving patient care, reducing clinical workload, and supporting early intervention. Disseminated through open-access venues, this work reflects a strong commitment to practical, clinically meaningful impact. Malihi’s collaborative record, involving more than 20 co-authors across computer science, biomedical engineering, and clinical research, highlights her active engagement in interdisciplinary teams that blend methodological rigour with clinical insight, enhancing the translational quality of her contributions. Despite being in an early career stage, she has already established measurable academic impact through consistent citation uptake and growing recognition within the health-technology community. Her research carries significant societal value by enabling more accurate and automated assessment of wound healing, supporting the development of scalable healthcare solutions, strengthening telemedicine workflows, and ultimately contributing to improved patient outcomes, particularly in resource-limited environments.

Profile: Scopus

Featured Publication

1. Dührkoop, E., Malihi, L., Erfurt-Berge, C., Heidemann, G., Przysucha, M., Busch, D., & Hübner, U. H. (2024). Automatic Classification of Wound Images Showing Healing Complications: Towards an Optimised Approach for Detecting Maceration. In R. Rohrig et al. (Eds.), German Medical Data Sciences 2024.
Cited by: 2

Dr. Malihi’s research advances intelligent medical-image analysis tools that strengthen diagnostic precision and support clinicians in delivering timely, data-driven care. Her vision is to develop globally accessible digital-health solutions that reduce healthcare disparities and promote more efficient, technology-enhanced clinical workflows.

Hao-Ting Lin | Feature-Driven Optimization | Best Researcher Award

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Assoc Prof. Dr. Hao-Ting Lin | Feature-Driven Optimization | Best Researcher Award

Associate Professor | National Chung Hsing University | Taiwan

Dr. Hao-Ting Lin is an Associate Professor at National Chung Hsing University specializing in robotics, control systems, and bio-industrial mechatronics engineering, with a strong focus on automation, intelligent systems, and sustainable agricultural technologies. He holds advanced degrees in mechanical and control engineering and has developed expertise in the design and integration of robotic mechanisms, sensor-based control, and pneumatic power systems for poultry and agricultural applications. His professional experience includes leading over 30 completed and several ongoing research and consultancy projects, authoring 20 peer-reviewed journal papers, and publishing a book chapter on smart agricultural technologies. Dr. Lin’s research focuses on IoT-enabled humane poultry slaughtering systems, deep learning-based chicken image recognition, precision pneumatic seeders, energy-efficient control strategies for wastewater and agricultural machinery, and robotic sorting systems. He has been granted 12 patents covering innovations in pneumatic automation, exoskeleton systems, aquaculture water quality management, and intelligent sewing and seeding devices. Dr. Lin’s contributions integrate control theory, mechatronics, artificial intelligence, and real-time systems to advance productivity, animal welfare, and sustainability in agriculture and industry. His scholarly impact is reflected in 170 citations by 161 documents, 20 published documents, and an h-index of 6.

Profile: Scopus | ORCID

Featured Publications

1. Lin H.T.*, Suhendra, Development and Implementation of an IoT-Enabled Smart Poultry Slaughtering System Using Dynamic Object Tracking and Recognition. Sensors, 2025, 25(16), 5028.

2. Chen H.C., Rohman Y.F., Ashlah M.B., Lin H.T., Sean W.Y.*, Electrification of Agricultural Machinery: One Design Case of a 4 kW Air Compressor. Energies, 2024, 17(15), 3647.

3. Lin H.T.*, Lee Y.H., Implementing a Precision Pneumatic Plug Tray Seeder with High Seeding Rates for Brassicaceae Seeds via Real-Time Trajectory Tracking Control. Actuators, 2023, 12(9), 340.

4. Liu H.W., Chen C.H., Tsai Y.C., Hsieh K.W., Lin H.T.*, Identifying Images of Dead Chickens with a Chicken Removal System Integrated with a Deep Learning Algorithm. Sensors, 2021, 21(11), 3579.

5. Lin H.T.*, A Novel Real-Time Path Servo Control of a Hardware-in-the-Loop for a Large-Stroke Asymmetric Rod-Less Pneumatic System under Variable Loads. Sensors, 2017, 17(6), 1283.

 

Mohammad Amin | Gradient boosting | Young Scientist Award

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Mr. Mohammad Amin | Gradient boosting | Young Scientist Award

Mohammad Amin | RWTH Aachen University | Germany

Mr. Mohammad Yazdi is a distinguished researcher and academic with expertise in information technology, e-learning systems, and research data management. He holds advanced degrees in information systems and has built a robust academic and professional profile through his work in integrating IT solutions for collaborative research, developing interactive e-learning media, and enabling process mining for research data life cycles. Professionally, he has contributed to several high-impact projects, including the implementation of web services for integrated government systems, evaluation of FAIR data management architectures, and the orchestration of research cluster collaborations, demonstrating strong technical and leadership skills. His research focuses on e-learning as an interactive IT-based learning medium, process mining, IT resource management in research projects, and operational support systems, with a publication record spanning reputable conferences and journals. His works, including “E-learning sebagai media pembelajaran interaktif berbasis teknologi informasi,” “How to Manage IT Resources in Research Projects? Towards a Collaborative Scientific Integration Environment,” and “Event Log Abstraction in Client-Server Applications,” have collectively garnered significant citations, underscoring their academic impact. He has been recognized for his scholarly contributions and actively participates in academic dissemination through editorial roles and conference presentations. With a commitment to advancing digital transformation in education and research, Mohammad Yazdi stands out as a thought leader and innovator in his field, with 29 citations across 23 documents, 10 publications, and an h-index of 4

Profile: Google Scholar | Scopus | ORCID

Featured Publications

1. M. Yazdi*, E-learning sebagai media pembelajaran interaktif berbasis teknologi informasi. Foristek, 2012, 2(1), 680.

2. M. Yazdi*, Implementasi Web-Service pada Sistem Pelayanan Perijinan Terpadu Satu Atap di Pemerintah Kota Palu. Seminar Nasional Teknologi Informasi & Komunikasi Terapan, 2012, 450–457, 24.

3. M. Politze, F. Claus, B. Brenger, M.A. Yazdi*, B. Heinrichs, A. Schwarz, How to Manage IT Resources in Research Projects? Towards a Collaborative Scientific Integration Environment. 2020, 22.

4. M.A. Yazdi*, P. Farhadi Ghalati, B. Heinrichs, Event Log Abstraction in Client-Server Applications. 13th Int. Conf. Knowledge Discovery and Information Systems, 2021, 13.

5. M.A. Yazdi*, Enabling Operational Support in the Research Data Life Cycle. Proc. 1st Int. Conf. Process Mining – Doctoral Consortium, 2019, 13.