Meziane Iftene | Artificial Neural Networks | Research Excellence Award

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Dr. Meziane Iftene | Artificial Neural Networks | Research Excellence Award

Agence Spatiale Algérienne | Algeria

Dr. Meziane Iftene is a researcher at the Algerian Space Agency specializing in remote sensing, deep learning, and geospatial data fusion. He has authored and co-authored overpeer-reviewed publications in leading international conferences and journals. His work focuses on hyperspectral unmixing, very high-resolution image classification, and AI-driven environmental monitoring, including forest mapping and precision agriculture. He has led R&D initiatives, supervised nanosatellite projects, and contributed to national space system development such as the Alsat-3 program. Through international collaborations, his research advances GeoAI solutions for sustainable environmental management and intelligent Earth observation systems.

Citation Metrics (Scopus)

40

30

20

10

0

Citations
23

Documents
15

h-index
3

🟦 Citations 🟥 Documents 🟩 h-index

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Featured Publications


Very high resolution images classification by fusing deep convolutional neural networks.

– In Proceedings of the 5th International Conference on Advanced Computer Science and Technology (ACSAT 2017). (2017). Cited By: 6

Transfering super resolution convolutional neural network for remote sensing data sharpening.

– In Proceedings of the 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2018). (2018). Cited By: 5

End-to-end change detection in satellite remote sensing imagery.

– In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2021) (pp. 4356–4359). (2021). Cited By; 3

 

Mokhtar Ferhi | Computer Science and Artificial Intelligence | Research Excellence Award

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Dr. Mokhtar Ferhi | Computer Science and Artificial Intelligence | Research Excellence Award

University of Jendouba | Tunisia

Dr. Mokhtar Ferhi is a researcher at Université de Jendouba, Tunisia, specializing in heat transfer, fluid mechanics, magnetohydrodynamics (MHD), nanofluid convection, and numerical simulation methods, particularly the Lattice Boltzmann Method. He has authored 27 peer-reviewed publications, receiving 140 citations with an h-index of 6 (Scopus). His work focuses on entropy generation, energy optimization, and thermal performance enhancement in cavities and micro-heat exchangers. Ferhi collaborates internationally with experts across North Africa, Europe, and the Middle East, contributing to advances in energy-efficient thermal systems with applications in sustainable engineering and heat exchanger design.

Citation Metrics (Scopus)

400

300

200

100

0

Citations
140

Documents
27

h-index
6

🟦 Citations 🟥 Documents 🟩 h-index

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Featured Publications

 

Aiyshwariya Devi | Artificial Intelligence | Best Researcher Award

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Dr. R. Aiyshwariya Devi | Artificial Intelligence | Best Researcher Award

RMK College Of Engineering and Technology| India

The author demonstrates strong research leadership in IoT security, AI, and data-driven systems, supported by 30+ scholarly documents, 129 citations, and an h-index of 4, reflecting consistent academic impact and relevance. Strengths include interdisciplinary research output, funded project leadership, patent-oriented innovation, editorial and reviewer experience, and sustained contributions to high-quality journals and conferences. Areas for improvement include expanding publications in higher-impact Q1 journals, increasing international co-authorship, and translating research prototypes into large-scale real-world deployments. The author’s future potential is significant, particularly in advancing secure AI-enabled IoT architectures, quantum-enhanced machine learning, and industry–academia collaborative research, positioning them as a strong candidate for research excellence and innovation-focused awards.

Dr. R. Aiyshwariya Devi
Associate Professor, RMK College of Engineering & Technology

Citation Metrics (Google Scholar)

150
120
90
45
0

Citations

129

Since 2020: 112

h-index

4

Since 2020: 4

i10-index

3

Since 2020: 2

Citations

h-index

i10-index



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Featured Publications


Energy-efficient cluster head selection scheme based on FMPDM for MANETs


– International Journal of Innovative Research in Science, Engineering and Technology, 2014 · 13 citations


IoT device security for smart card fraud detection for credit cards


– 2nd International Conference on Advancements in Electrical & Electronics Engineering, 2023 · 5 citations


An IoT Security-Based Electronic Aid for Visually Impaired Detection with Navigation Assistance System


– International Journal of Advanced Science and Technology, 2020 · 4 citations

RaulJavier ChangTam | Data Science and Analytics | Best Researcher Award

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Prof. Dr. RaulJavier ChangTam | Data Science and Analytics | Best Researcher Award

Profesor Investigador | Universidad Latina de Costa Rica | Costa Rica

Prof. Dr. RaulJavier ChangTam  is a multidisciplinary researcher whose work connects technology adoption, entrepreneurship, and sustainable innovation in Latin America. His research explores how emerging technologies influence entrepreneurial ecosystems, digital transformation, and user behavior within both business and social contexts. Drawing upon models such as UTAUT2, his studies provide empirical insights into the acceptance and use of new technologies by entrepreneurs, SMEs, and professionals in fields like healthcare and finance. He also contributes to studies on sustainable finance and FinTech, emphasizing the role of digital platforms and neobanks in promoting environmentally responsible entrepreneurship. Beyond business and technology, his work extends into socio-cultural domains examining global consumption patterns such as sneaker culture and analyzing trade dynamics in ornamental species markets. Chang-Tam’s ongoing research reflects a strong orientation toward understanding the intersection of innovation, digitalization, and sustainability, with an applied perspective that links academic insight to real-world economic and cultural transformations. His research has received 3 citations across 9 documents, with an h-index of 1.

Profiles : ORCID | Scopus  | ResearchGate

Featured Publications

1. Taboada Álvarez, J. E., Chang-Tam, R. J., Rueda Varón, M. J., & Hunter Torrealba, R. (2025). Analysis of the entrepreneurial motivational demand in a learning management process in incubators.

2. Araya, J. L. G., Robles Herrera, A. E., & Chang-Tam, R. J. (2025). Analysis of trends in exports and imports of continental and marine ornamental species of aquariums in Costa Rica.

3. Chang-Tam, R. J., Caldera-Gutiérrez, V., & Rivera Shaik, V. (2025). Influence of IT technology on the development of SME entrepreneurs in Costa Rica: Applied study of the adapted model of the Unified Theory of Acceptance and Use of Technology (UTAUT2).

4. Chang-Tam, R. J., Garita Quesada, R., Masís Muñoz, R., & Chang Caldera, A. P. (2025). Influence of new IT technology trends in dental care processes between dental professionals and patients: An analysis of the UTAUT2 theory.

5. Palos-Sanchez, P. R., Chang-Tam, R. J., & Folgado-Fernández, J. A. (2025). The role of neobanks and fintech in sustainable finance and technology: The customer/user perspective for entrepreneurs. Sustainable Technology and Entrepreneurship, 100109.

Deqian Fu | Data Science and Analytics | Best Researcher Award

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Prof. Dr. Deqian Fu | Data Science and Analytics | Best Researcher Award

Professor | Linyi University | China

Prof. Dr. Deqian Fu is a prominent researcher at Linyi University, China, with a strong focus on logistics, data exchange, and trust management in supply chain and intermodal transport systems. His research explores innovative methods for secure and efficient data sharing in the logistics industry, integrating advanced technologies such as blockchain, edge computing, and ontology-based frameworks. Fu has made notable contributions in developing trusted data access control mechanisms and non-intrusive data exchange models that enhance collaboration and operational efficiency across complex logistics networks. He has authored 39 publications, which have collectively garnered 127 citations, reflecting the growing impact of his work in the fields of applied sciences and industrial informatics. His research outputs demonstrate a commitment to advancing the intersection of information technology and logistics, emphasizing both theoretical development and practical applications. With an h-index of 7, Fu’s scholarly contributions have been recognized for their relevance and innovation, particularly in promoting secure and intelligent data-sharing frameworks within the logistics sector. Selected works include “Trusted Data Access Control Based on Logistics Business Collaboration Semantics” in Applied Sciences (2024), alongside conference papers such as “Data Exchange and Sharing Framework for Intermodal Transport Based on Blockchain and Edge Computing” and “Trusted Non-intrusive Data Exchange based on Ontology in Logistics Industry,” underscoring his focus on reliable, technology-driven logistics solutions.

Profiles : ORCID | Scopus 

Featured Publications

1. Wang, W., Li, Q., Jiang, Z., Fu, D., & Camacho, D. (2025). An efficient framework for general long-horizon time series forecasting with Mamba and diffusion probabilistic models. Engineering Applications of Artificial Intelligence.

2.Liu, Z., Shi, Z., Wang, W., Kong, R., Fu, D., & Qiu, J. (2025). Research on data ownership and controllable sharing schemes in the process of logistics data flow.

3.Wang, L., Zhang, X., Xu, L., Fu, D., & Qiu, J. (2024). Data exchange and sharing framework for intermodal transport based on blockchain and edge computing. In Communications in Computer and Information Science. Springer.

4.Zhang, X., Jing, C., Chen, Y.-C., Wang, L., Xu, L., & Fu, D. (2024). Trusted data access control based on logistics business collaboration semantics.

5.Zhang, X., Wang, L., Xu, L., & Fu, D. (2023). A distributed logistics data security sharing model based on semantics and CP-ABE. In Proceedings of the ACM International Conference (pp. 1–8).

Alexios Kaponis | Computer Science | Excellence in Research

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Mr. Alexios Kaponis | Computer Science | Excellence in Research

PhD Candidate,Ionian University , Greece

Alexios Kaponis is a promising researcher with a robust portfolio of work in AI and digital marketing, focused on both technical innovation and ethical implications. His research output, coupled with hands-on project experience and a solid educational foundation, positions him as a dedicated and impactful researcher. He continues to develop expertise that addresses both theoretical and applied challenges in computer science.

Professional Profile

🎓 Educational Background

Alexios Kaponis was born in Patras on August 6, 1987. He earned his diploma in Cultural Management from the Department of Management of Cultural Environment and New Technologies at the University of Ioannina in 2009. Later, he obtained a master’s degree in Technologies and Management from the Department of Information and Communication Systems Engineering at the University of the Aegean in 2017. Currently, Alexios is pursuing a doctoral degree in Computer Science at the University of the Ionian Islands. His PhD research focuses on “Data analysis in digital marketing using machine learning and artificial intelligence techniques, business analysis, practices, and ethical dimensions in e-commerce.”

🧑‍🏫 Professional Experience

Alexios currently works as an Intelligent Software Solutions expert at the National Research Centre for Physical Sciences (NCRS) “Demokritos.” Since April 2024, he has been involved in the WP2 Data Inspection and Generation and WP5 Trustworthy Efficiency & Performance Assessment Framework projects, focusing on advanced machine learning and AI tools to improve risk prediction and fraud detection. His responsibilities include proposing new intelligence tool developments, conducting data analysis, and leveraging big data and cloud-based technologies.

🔬 Research Focus

Alexios’s research primarily centers on the application of machine learning and AI techniques in digital marketing, with a strong emphasis on ethical and legal dimensions in e-commerce. He investigates the use of natural language processing and large-scale data mining for business intelligence and enhanced customer engagement. His ongoing doctoral work explores innovative data analysis methodologies to support decision-making in marketing strategies. Furthermore, he contributes to projects aiming to improve AI reliability and trustworthiness in practical applications, such as fraud detection and chatbot development.

🛠️ Skills and Expertise

Alexios possesses strong expertise in big data, data analytics, artificial intelligence, data management, and cloud computing technologies. He has hands-on experience with machine learning, natural language processing, semantic web technologies, and digital marketing analytics. Additionally, Alexios is proficient in web development tools such as Joomla and WordPress and skilled in Google Analytics. He is fluent in Greek and highly proficient in English, complemented by a computer diploma certified by the University of Ioannina.

🏅 Awards & Honours

Alexios was distinguished by the General Secretariat for Lifelong Learning for his successful completion of a 25-hour seminar dedicated to training teachers in vocational adult education. His active participation as an examiner in national qualification certification examinations highlights his commitment to professional excellence in IT education. He has also presented and published multiple papers at prestigious international conferences, reflecting recognition of his research contributions in artificial intelligence, digital marketing, and assistive technologies.

Publication Top Notes

  1. Assist of AI in a Smart Learning Environment

    • Authors: K.C. Sofianos, Michalis Stefanidakis, Alexios Kaponis, Linas Bukauskas

    • Year: 2024

    • Citation count: 1

  2. Data Analysis in Digital Marketing using Machine Learning and Artificial Intelligence Techniques, Ethical and Legal Dimensions, State of the Art

    • Author: Alexios Kaponis, M. Maragoudakis

    • Year: 2022

    • Citation count: (Not provided, please add if known)

  3. Case Study Analysis of Medical and Pharmaceutical Chatbots in Digital Marketing and Proposal to Create a Reliable Chatbot with Summary Extraction Based on Users’ Keywords

    • Authors: Alexios S. Kaponis, Alexios A. Kaponis, Manolis Maragoudakis

    • Year: 2023

    • Citation count: (Not provided)

  4. Enhancing Disease Diagnosis: A CNN-Based Approach for Automated White Blood Cell Classification

    • Authors: Athanasios Kanavos, Orestis Papadimitriou, Alexios Kaponis, Manolis Maragoudakis

    • Year: 2023

    • Citation count: (Not provided)

Conclusion

Given his achievements and ongoing contributions, Alexios Kaponis is a fitting candidate for the Excellence in Research Award. Recognizing his work would not only honor his past accomplishments but also encourage further advancements in AI-driven research that balances innovation with ethical responsibility. With continued focus on increasing research impact and leadership, Alexios is well poised for future excellence in his field.

Prof . Len Gelman | Artificial Intelligence | Best Researcher Award

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Prof . Len Gelman | Artificial Intelligence | Best Researcher Award

Prof. Len Gelman , University of Huddersfield , United Kingdom

Professor Len Gelman 🇬🇧 is a globally recognized expert in signal processing and condition monitoring 🔍. He currently serves as Chair Professor and Director at the University of Huddersfield 🏫. With over two decades of academic leadership, he has significantly contributed to vibro-acoustics and non-destructive testing 🔧. A Fellow of multiple prestigious organizations 🌐, Prof. Gelman’s international collaborations span across Europe, Asia, and the USA 🌏. His innovations have advanced aerospace and medical diagnostics ✈️🧬. He continues to lead global initiatives and research committees, shaping the future of engineering diagnostics and reliability technologies 🔬🛠️.

Professional Profile

SCOPUS

Education and Experience 

Prof. Len Gelman holds a PhD and Doctor of Science (Habilitation) 🎓, with BSc (Hons) and MSc (Hons) degrees in engineering 📘. He is a British citizen 🇬🇧. Since 2017, he has been a Professor and Chair at the University of Huddersfield 🏛️. Prior to that, he served at Cranfield University (2002–2017) as Chair in Vibro-Acoustical Monitoring 🔊. His distinguished academic journey includes visiting professorships in China 🇨🇳, Denmark 🇩🇰, Poland 🇵🇱, Spain 🇪🇸, Italy 🇮🇹, and the USA 🇺🇸. Prof. Gelman combines deep technical expertise with global educational outreach 🌍👨‍🏫.

Professional Development 

Prof. Gelman has held key international leadership roles including Chair of the International Scientific Committee of the Condition Monitoring Society 🌐. He is a Fellow of BINDT, IAENG, IDE, and HEA 🎖️, and an Academician of the Academy of Sciences of Applied Radio Electronics 🧠. He chairs award and honors committees for top acoustics and vibration institutions 🏅. As Visiting Professor at Tsinghua, Jiao Tong, and Aalborg Universities, among others 🎓, he mentors emerging researchers globally 🌎. Prof. Gelman’s commitment to professional excellence shapes the advancement of diagnostic technologies and engineering education 📈🔧.

Research Focus 

Prof. Gelman’s research focuses on signal processing, vibro-acoustics, and condition monitoring of engineering systems 🔍🔊. His work spans non-destructive testing (NDT), fault diagnostics, and performance optimization in sectors such as aerospace, healthcare, and manufacturing ✈️🏥🏭. He develops advanced algorithms for fault detection and predictive maintenance using machine learning and big data 🧠📊. His interdisciplinary approach benefits both industry and academia 🌐🔬. Prof. Gelman also pioneers applications in medical diagnostics and intelligent systems for real-time monitoring 🧬⚙️. His innovations contribute to safer, more efficient engineering systems across global platforms 🌍🚀.

Awards and Honors 

Prof. Gelman has received numerous prestigious awards for innovation and research excellence 🏅. These include the Rolls-Royce Innovation Award (2012, 2019) ✈️, William Sweet Smith Prize by IMechE 🛠️, and COMADIT Prize by BINDT for impactful contributions to condition monitoring 🧲. He also received Best Paper Awards at CM/MFPT conferences 📄 and recognition from the USA Navy and Acoustical Society of America 🇺🇸🔊. His European and UK fellowships support cutting-edge human capital projects 🧠🇪🇺. He has chaired international committees in NDT and acoustics, continuing to shape future technologies through global leadership and innovation 🌐👨‍🔬.

Publication Top Notes

1. Vibration Analysis of Rotating Porous Functionally Graded Material Beams Using Exact Formulation

  • Citation: Amoozgar, M.R., & Gelman, L.M. (2022). Vibration analysis of rotating porous functionally graded material beams using exact formulation. Journal of Vibration and Control, 28(21–22), 3195–3206. https://doi.org/10.1177/10775463211027883Nottingham Repository+1SAGE Journals+1

  • Summary: This study investigates the free vibration behavior of rotating functionally graded material (FGM) beams with porosity, employing geometrically exact fully intrinsic beam equations. The research considers both even and uneven porosity distributions to simulate manufacturing imperfections. Findings reveal that material gradation and porosity significantly influence natural frequencies and mode shapes, emphasizing the necessity of accounting for these factors in the design and analysis of rotating FGM structures. Huddersfield Research Portal+2SAGE Journals+2Nottingham Repository+2

2. Vibration Health Monitoring of Rolling Bearings Under Variable Speed Conditions by Novel Demodulation Technique

  • Citation: Zhao, D., Gelman, L.M., Chu, F., & Ball, A.D. (2021). Vibration health monitoring of rolling bearings under variable speed conditions by novel demodulation technique. Structural Control and Health Monitoring, 28(2), e2672. https://doi.org/10.1002/stc.2672Wiley Online Library

  • Summary: Addressing the challenges of diagnosing rolling bearing faults under variable speed conditions, this paper introduces an optimization-based demodulation transform method. The technique effectively estimates fault characteristic frequencies with weak amplitudes and adapts to time-varying rotational speeds. Validation through simulations and experimental data demonstrates the method’s superior diagnostic capabilities compared to existing approaches. Huddersfield Research Portal+1Wiley Online Library+1

3. Novel Method for Vibration Sensor-Based Instantaneous Defect Frequency Estimation for Rolling Bearings Under Non-Stationary Conditions

  • Citation: Zhao, D., Gelman, L.M., Chu, F., & Ball, A.D. (2020). Novel method for vibration sensor-based instantaneous defect frequency estimation for rolling bearings under non-stationary conditions. Sensors, 20(18), 5201. https://doi.org/10.3390/s20185201MDPI

  • Summary: This research presents a novel approach for estimating instantaneous defect frequencies in rolling bearings operating under non-stationary conditions. Utilizing vibration sensor data, the method enhances the accuracy of defect frequency estimation, facilitating improved fault diagnosis in dynamic operational environments. MDPI

4. Novel Fault Identification for Electromechanical Systems via Spectral Technique and Electrical Data Processing

  • Citation: Ciszewski, T., Gelman, L.M., & Ball, A.D. (2020). Novel fault identification for electromechanical systems via spectral technique and electrical data processing. Electronics, 9(10), 1560. https://doi.org/10.3390/electronics9101560MDPI

  • Summary: This paper introduces an innovative method for fault identification in electromechanical systems by integrating spectral analysis with electrical data processing. The approach enhances the detection and diagnosis of faults, contributing to the reliability and efficiency of electromechanical system operations. MDPI

5. Novel Prediction of Diagnosis Effectiveness for Adaptation of the Spectral Kurtosis Technology to Varying Operating Conditions

  • Citation: Kolbe, S., Gelman, L.M., & Ball, A.D. (2021). Novel prediction of diagnosis effectiveness for adaptation of the spectral kurtosis technology to varying operating conditions. Sensors, 21(20), 6913. https://doi.org/10.3390/s21206913PMC

  • Summary: This study proposes two novel consistency vectors combined with machine learning algorithms to adapt spectral kurtosis technology for optimal gearbox damage diagnosis under varying operating conditions. The approach enables computationally efficient online condition monitoring by predicting diagnosis effectiveness, thereby improving maintenance strategies.

Conclusion

Professor Len Gelman exemplifies the ideal candidate for the Best Researcher Award due to his groundbreaking contributions to condition monitoring, signal processing, and diagnostic technologies. His work not only advances academic knowledge but also addresses critical industry challenges in aerospace, healthcare, and manufacturing. With a sustained record of high-impact research, international leadership, and technological innovation, he stands out as a world-class researcher whose work continues to benefit both academia and society.