Necla Koralay | Environmental Data Analysis | Best Researcher Award

Published on by Research Data Analysis

Assist. Prof. Dr. Necla Koralay | Environmental Data Analysis | Best Researcher Award

Karadeni̇z Technical University | Turkey

Assist. Prof. Dr. Necla Koralay is an environmental scientist and forest engineer whose academic work focuses on watershed management, soil erosion, hydrology, and water quality. She completed her undergraduate studies in Forest Engineering at Ankara University and later earned her postgraduate and doctoral degrees from Karadeniz Technical University, where she specialized in watershed management, erosion modeling, and hydrological assessment. She also continues undergraduate education in geography to broaden her interdisciplinary expertise. Over her academic career, she has served as a research assistant, research assistant (PhD), and currently as an assistant professor in the Faculty of Forestry. Dr. Koralay has contributed extensively to the scientific literature through articles published in SCI, SCI-Expanded, ESCI, and peer-reviewed journals, focusing on RUSLE-based erosion modeling, morphometric analysis, and the effects of hydropower plants and forestry activities on water quality. She has presented numerous papers at national and international conferences and has been involved in funded research projects related to erosion–flood risk mapping and sediment dynamics. In addition to her research, she serves as an editor for the Ormancılık Araştırma Dergisi. Her academic achievements have been recognized with notable awards for outstanding performance during her undergraduate education, reflecting her long-standing dedication to environmental and forestry sciences.

Profile : Orcid

Featured Publications

Koralay, N., & Kara, Ö. (2025). “Risk assessment of soil erosion with RUSLE using geographic information system and organic carbon and total nitrogen loadings of suspended sediment in Sogutlu Stream Watershed of Trabzon, Turkey” in Environmental Earth Sciences.

Koralay, N., & Kara, Ö. (2024). “Assessment of flood risk in Söğütlü Stream Watershed of Trabzon Province in Turkey using geographic information systems and analytic hierarchy process approach” in Natural Hazards.

Koralay, N. (2024). “An Investigation into the Carbon and Total Nitrogen Content of Suspended Sediments in Değirmendere Watershed and Its Implications for Erosion Risk” in Forestist.

Koralay, N., & Kara, Ö. (2023). “Effects of morphometric characteristics on flood in Degirmendere sub-watersheds, Northeastern Turkey” in International Journal of River Basin Management.

Koralay, N., & Kara, Ö. (2022). “Trabzon Değirmendere Çatak alt havzasının erozyon risk haritasının oluşturulması ve sediment iletim oranının belirlenmesi” in Ormancılık Araştırma Dergisi.

Piotr Porwik | Machine Learning and AI Applications | Best Researcher Award

Published on by Research Data Analysis

Prof. Dr. Piotr Porwik | Machine Learning and AI Applications | Best Researcher Award

University of Silesia | Poland

Prof. Dr. Piotr Porwik is a full professor in the Institute of Computer Science at the University of Silesia in Katowice, specializing in engineering and technical sciences. He earned his M.Sc. in computer science from the University of Silesia in 1978, his Ph.D. in 1985, and completed his habilitation in 2006 at AGH University of Science and Technology in Krakow. His research spans machine learning, biometrics, image processing, biomedical imaging, and spectral methods for Boolean functions. Across his career, he has authored 81 scientific documents that have attracted 1,003 citations from 783 citing documents, and he holds an h-index of 17. He has published extensively in journals, international conferences, books, and book chapters, while also supervising Master’s and Ph.D. students. Prof. Porwik has served in major academic leadership roles, including Deputy Dean and Director of the Institute, and played a key role in shaping academic publishing as Editor-in-Chief of the Journal of Medical Informatics and Technologies. His achievements have been recognized through numerous distinctions, including the Bronze Cross of Merit and multiple awards from the President of the University of Silesia for scientific accomplishments. His work continues to advance biometric security, classifier development, and biomedical informatics, underscoring his long-standing academic influence and research innovation.

Profiles : Scopus | Orcid

Featured Publications

Wrobel, K., Porwik, P., & Orczyk, T. (2025). “Evaluation of the Effectiveness of Ranking Methods in Detecting Feature Drift in Artificial and Real Data” in (Book) / Lecture Notes in?

Mensah Dadzie, B., & Porwik, P. (2025). “Feature-Based Drift Detection in Non-stationary Data Streams Using Multiple Classifiers: A Comprehensive Analysis” in (Book)

Porwik, P., Orczyk, T., Wrobel, K., & Mensah Dadzie, B. (2025). “A Novel Method for Drift Detection in Streaming Data Based on Measurement of Changes in Feature Ranks” in Journal of Artificial Intelligence and Soft Computing Research, (DOI: 10.2478/jaiscr-2025-0008).

Porwik, P., Orczyk, T., & Japkowicz, N. (2024). “Supervised and Unsupervised Analysis of Feature Drift in a New Type of Detector” in Preprint (Research Square)

Porwik, P., Orczyk, T., & Doroz, R. (2022). “A Stable Method for Detecting Driver Maneuvers Using a Rule Classifier” in Lecture Notes in Computer Science

Mebarka Allaoui | Machine Learning and AI Applications | Best Paper Award

Published on by Research Data Analysis

Dr. Mebarka Allaoui | Machine Learning and AI Applications | Best Paper Award

Bishop’s University | Canada

Dr. Mebarka Allaoui dedicated computer science researcher with a strong background in machine learning, manifold learning, and computer vision, this scholar holds a PhD in Computer Science focused on embedding techniques and their applications to visual data analysis. Their academic journey includes a master’s degree in industrial computer science and a bachelor’s degree in information systems, all completed with high distinction. Professionally, they have served as a Postdoctoral Fellow contributing to industry-funded research on anomaly detection, developing novel embedding, deep learning, and clustering methods to enhance the interpretability of latent representations and improve fraud detection in real-world financial datasets. Prior experience includes working as a computer engineer supporting system administration, software development, data analysis, and network configuration, alongside several teaching appointments delivering practical courses in software engineering, algorithmics, and web development. Their research contributions span dimensionality reduction, clustering, optimization, document analysis, and scientific information retrieval, with publications in reputable journals and conferences. Collaborative work further extends to studies on optimizers, object detection, and embedding initialization strategies. Recognized for high-quality academic performance and impactful research outputs, they continue to advance data-driven methodologies, aiming to bridge theoretical innovation with practical applications in intelligent systems and decision-support technologies.

Profile : Google Scholar

Featured Publications

Allaoui, M., Kherfi, M. L., & Cheriet, A. (2020). “Considerably improving clustering algorithms using UMAP dimensionality reduction technique” in International Conference on Image and Signal Processing, 317–325.

Drid, K., Allaoui, M., & Kherfi, M. L. (2020). “Object detector combination for increasing accuracy and detecting more overlapping objects” in International Conference on Image and Signal Processing, 290–296.

Allaoui, M., Belhaouari, S. B., Hedjam, R., Bouanane, K., & Kherfi, M. L. (2025). “t-SNE-PSO: Optimizing t-SNE using particle swarm optimization” in Expert Systems with Applications, 269, 126398.

Allaoui, M., Kherfi, M. L., Cheriet, A., & Bouchachia, A. (2024). “Unified embedding and clustering” in Expert Systems with Applications, 238, 121923.

Allaoui, M., Kherfi, M. L., & Cheriet, A. (2020). “International Conference on Image and Signal Processing” in Springer.

Ashutosh K Giri | Engineering | Best Researcher Award

Published on by Research Data Analysis

Dr. Ashutosh K Giri | Engineering | Best Researcher Award

Government Engineering College Bharuch | India

Dr. Ashutosh K. Giri is an Assistant Professor in the Department of Electrical Engineering at Government Engineering College, Bharuch, Gujarat, India, with over 20 years of teaching and research experience. He earned his Ph.D. from Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, focusing on control algorithms for power quality issues in wind-based distributed power generation systems. He also holds an M.E. in Electrical Engineering from L.D. College of Engineering, Gujarat University, and a B.Tech in Electrical Engineering from G.B. Pant University of Agriculture and Technology, Pantnagar. Dr. Giri’s research interests include renewable energy power converters, microgrid topology, operation and control, power quality enhancement, electric vehicle technologies, and adaptive control algorithms. He has published extensively in leading journals including IEEE, IET, Springer, Wiley, Elsevier, and Taylor & Francis, authored and edited multiple international books, and contributed to state- and national-level funded research projects. His work has been cited 478 times across 234 documents, with an h-index of 14, reflecting his significant impact in the field. Recognized among the top 2% of scientists globally, Dr. Giri continues to advance research and innovation in distributed generation systems and renewable energy, mentoring students and contributing to the scientific community through high-quality research and academic leadership.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Joshi, D., Deb, D., & Giri, A. K. (2025). “Metaheuristic adaptive input output feedback linearization control for BLDC motor drive” in IEEE Transactions on Consumer Electronics.

Kanojia, S. S., Suthar, B. N., & Giri, A. K. (2025). “Hybrid optimization approach for optimal location of electric vehicle charging station (EVCS) in distribution network considering voltage stability” in Smart Grids and Sustainable Energy.

Kundu, S., Giri, A. K., & Kadiyan, S. (2025). “An adaptive mixed-step size normalized least means fourth control approach for stand-alone power generation system considering dynamic conditions” in IEEE Journal of Emerging and Selected Topics in Power Electronics.

Bhatt, K. A., Bhalja, B. R., & Giri, A. K. (2024). “Controlled single-phase auto-reclosing technique for shunt reactor compensated lines in electrical energy systems transmission lines” in International Journal of Ambient Energy.

Parmar, D. B., & Giri, A. K. (2024). “Integration of field-oriented and steady-state linear Kalman filter control in PMSG-based grid-connected system for improving voltage control and power balance operation” in Electrical Engineering.

Modafar Ati | Machine Learning and AI Applications | Best Researcher Award

Published on by Research Data Analysis

Assoc. Prof. Dr. Modafar Ati | Machine Learning and AI Applications | Best Researcher Award

Abu Dhabi University | United Arab Emirates

Assoc. Prof. Dr. Modafar Ati is an accomplished academic and researcher in Computer Science and Information Technology with extensive experience in teaching, curriculum development, and leadership. He holds a BSc in Electrical & Communication Engineering and a PhD in Electrical & Computing Engineering from Newcastle upon Tyne, UK. Dr. Ati has over three decades of experience in both academia and industry, serving in senior roles including Associate Professor at Abu Dhabi University, Acting Dean at Sohar University, and Assistant Dean at multiple institutions in Oman. His professional expertise spans software development, systems integration, networking, ICT, project management, Service-Oriented Architecture (SOA), Artificial Intelligence, Big Data, Cybersecurity, and eHealth smart systems. He has published numerous research articles focusing on AI-driven Chronic Disease Management and smart healthcare systems and has led research groups in eGovernment and ICT. Dr. Ati has received multiple grants and awards, including the Chancellor Innovation Award, and serves on editorial boards and as a senior reviewer for international journals and conferences. His teaching portfolio covers programming, network security, project management, human-computer interaction, and mobile application development. Dr. Ati’s contributions to education, research, and innovation reflect a strong commitment to advancing technology-driven solutions in healthcare, smart cities, and digital systems.

Profile : Google Scholar

Featured Publications

Hussein, A. S., Omar, W. M., Li, X., & Ati, M. (2012). “Efficient chronic disease diagnosis prediction and recommendation system” in 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, 209–214.

Mohamed, O., Kewalramani, M., Ati, M., & Al Hawat, W. (2021). “Application of ANN for prediction of chloride penetration resistance and concrete compressive strength” in Materialia, 17, 101123.

Mohamed, O. A., Ati, M., & Najm, O. F. (2017). “Predicting compressive strength of sustainable self-consolidating concrete using random forest” in Key Engineering Materials, 744, 141–145.

Hussein, A. S., Omar, W. M., Li, X., & Ati, M. (2012). “Accurate and reliable recommender system for chronic disease diagnosis” in Global Health, 3(2), 113–118.

Ati, M., Kabir, K., Abdullahi, H., & Ahmed, M. (2018). “Augmented reality enhanced computer aided learning for young children” in 2018 IEEE Symposium on Computer Applications & Industrial Electronics.

David Almorza | Statistical Analysis | Best Researcher Award

Published on by Research Data Analysis

Dr. David Almorza | Statistical Analysis | Best Researcher Award

University of Cádiz | Spain

Dr. David Almorza Gomar is a Profesor Titular in the Department of Statistics and Operational Research at the University of Cádiz, Spain. He earned his Ph.D. in Mathematics from the University of Cádiz in 1998, with a doctoral thesis on the characterization of the Pólya distribution. Over his academic career, he has authored more than 85 publications. According to Google Scholar, his work has been cited 379 times, and he holds an h-index of 11. His early research focused on environmental statistics, probability distributions and genetics, while more recently he has explored applied statistics in education, well‑being (happiness), and sports, particularly university football. Among his notable contributions is a 2022 study evaluating an experience of “academic happiness” through football, published in the International Journal of Environmental Research and Public Health, showing how rule changes promoted fair play and reduced disciplinary sanctions. He has also applied multivariate statistical techniques to maritime safety in a recent work on the Paris MoU inspections. For his scientific achievements, he has been recognized by the Genetic Society of Argentina with a medal. In sum, Dr. Almorza is a versatile statistician whose interdisciplinary research spans mathematics, genetics, education, and sport, contributing both theoretical insight and practical societal impact.

Profiles : Scopus | Orcid

Featured Publications

Bazco Nogueras, E., Sanagustín-Fons, V., & Almorza-Gomar, D. (2025). “Job stress in industrial company: The impact of gender, age and seniority on tension levels” in Anduli.

Almorza Salas, D. (2025). “La imprenta en Cádiz: 1960, 1961 y 1962” in Gaditana-Logía.

Prieto, J. M., Almorza, D., Amor-Esteban, V., & Endrina, N. (2025). “Review of ship risk analyses through deficiencies found in port state inspections” in Journal of Marine Science and Engineering.

Prieto, J. M., Almorza, D., Amor-Esteban, V., Muñoz-Perez, J. J., & Jigena-Antelo, B. (2025). “Identification of risk patterns by type of ship through correspondence analysis of port state control: A differentiated approach to inspection to enhance maritime safety and pollution prevention” in Oceans.

Almorza, D., Prieto, J. M., Amor-Esteban, V., & Piniella, F. (2024). “Port state control inspections under the Paris Memorandum of Understanding and their contribution to maritime safety: Additional risk classifications and indicators using multivariate techniques” in Journal of Marine Science and Engineering.

Hisao Nakai | Public Health Analytics | Best Researcher Award

Published on by Research Data Analysis

Dr. Hisao Nakai | Public Health Analytics | Best Researcher Award

Faculty of Nursing, University of Kochi | Japan

Dr. Hisao Nakai is an Associate Professor in the Faculty of Nursing at the University of Kochi, specializing in disaster nursing, disaster management, and GIS‑based risk assessment. Holding a Ph.D. in Health Sciences from Kanazawa University, he has published 28 documents, which have been cited 107 times by 96 documents, resulting in an h-index of 7. His research centers on vulnerable populations—such as medically dependent children, older adults, and people with mental illness—and uses participatory ICT/IoT systems like K‑DiPS, which he helped develop, to support personalized disaster readiness, evacuation planning, power backup, and business continuity. He has applied GIS to simulate evacuation routes (e.g., for tsunamis) and assess environmental risks, especially for those with special needs. Academically, he has held roles at Kanazawa Medical University and Kochi University and has received awards including a Japanese Public Health Society poster prize. He has also led national surveys on disaster information sharing among caregivers of children requiring medical care. Through his interdisciplinary, community‑oriented work, Dr. Nakai is advancing equitable disaster resilience for people often overlooked in traditional emergency planning, combining practical applications with innovative research in disaster preparedness and nursing science.

Profile : Scopus

Featured Publications

Nakai, H., Oe, M., Nagayama, Y., Tokuoka, M., Yamaguchi, C., & Tanaka, K. (2025). “A retrospective study on evacuation and long-term displacement among home-visit psychiatric nursing service users in the aftermath of the 2024 Noto Peninsula Earthquake” in International Journal of Environmental Research and Public Health.

Oe, S., Nakai, H., Nagayama, Y., Oe, M., & Yamaguchi, C. (2025). “Factors associated with worsening post-earthquake psychiatric symptoms in patients receiving psychiatric visiting nurse services during the 2024 Noto Peninsula Earthquake: A retrospective study” in Psychiatry International.

Nitta, Y., Hashimoto, R., Shimizu, Y., Nakai, Y., & Nakai, H. (2025). “Adherence to outpatient care among individuals with pre‐existing psychiatric disorders following the 2024 Noto Peninsula Earthquake: A retrospective study” in Psychiatry and Clinical Neurosciences Reports.

Nakai, H., Oe, M., & Nagayama, Y. (2024). “Factors related to evacuation intention when a Level 4 evacuation order was issued among people with mental health illnesses using group homes in Japan: A cross-sectional study” in Medicine.

Nakai, H., Ishii, K., & Sagino, T. (2024). “Turnover intention among staff who support older adults living alone in Japan: A cross-sectional study” in Social Sciences.

Hongyan Jin | Bioinformatics | Young Researcher Award

Published on by Research Data Analysis

Ms. Hongyan Jin | Bioinformatics | Young Researcher Award

Tarim University | China

Ms. Hongyan Jin dedicated and accomplished biologist with a strong foundation in plant molecular biology, bioinformatics, and physiological research. Holds a Master’s degree in Biology from Tarim University, with a cumulative GPA of 87.64, earning recognition as an Outstanding Graduate and recipient of multiple university-level scholarships. Academic training includes advanced courses in plant physiology, molecular and cell biology, biochemistry, bioinformatics, and biostatistics. Experienced in genome-wide identification and analysis of gene families, particularly the SnRK2 gene family in Populus euphratica, and proficient in total RNA extraction and qRT-PCR techniques. Research focuses on plant responses to abiotic stresses, integrating computational and experimental approaches to explore stress-responsive gene functions. Published research in the International Journal of Molecular Sciences, highlighting expertise in genome-wide bioinformatics analysis and molecular plant biology. Fluent in English (CET-6) and holds a Mandarin proficiency certificate, demonstrating strong communication skills. Recognized for academic excellence, diligence, and integrity, with a proactive and detail-oriented approach to research and professional responsibilities. Committed to continuous learning and tackling scientific challenges with rigor and persistence. Possesses a clear understanding of professional knowledge, strong integrative abilities, and a collaborative mindset, making significant contributions to plant biology and stress response research.

Profile : Scopus

Featured Publications

(2025). The identification and characterization of the PeGRF gene family in Populus euphratica Oliv. heteromorphic leaves provide a theoretical basis for the functional study of PeGRF9. International Journal of Molecular Sciences.

(2025). Genome-Wide Identification of the SnRK2 Gene Family and Its Response to Abiotic Stress in Populus euphratica

(2024).Comparative Genomics Analysis of the Populus Epidermal Pattern Factor (EPF) Family Revealed Their Regulatory Effects in Populus euphratica Stomatal Development

Abdelouahad Achmamad | Engineering | Best Researcher Award

Published on by Research Data Analysis

Dr. Abdelouahad Achmamad | Engineering | Best Researcher Award

Universite De Mans | France

Dr. Abde Louahad Achmamad is an embedded software engineer and researcher specializing in intelligent systems, AI algorithms, biomedical signal processing, and advanced embedded technologies for electrical, biomedical, and IoT applications. He holds a PhD in Electrical Engineering, complemented by master’s degrees in biomedical engineering and electrical/electronic engineering, supported by earlier qualifications in electromechanics and electronics. His professional experience spans biomedical R&D, embedded system design, AI-based medical diagnostics, MRI segmentation, IoT health monitoring, wearable sensor development, and security applications for STM32 microcontrollers. He has worked with leading institutions and companies across France and Morocco, including STMicroelectronics, Akkodis, Diabtech, Université de Rouen, ENSIAS, and multiple research laboratories. His scientific output includes 102 citations by 95 documents, 16 publications, and an h-index of 6, reflecting contributions in EMG-based diagnostics, phonoangiography, neuromuscular disorder detection, sensor fusion, and few-shot learning for medical imaging. He has also reviewed more than 200 scientific manuscripts for major journals from Springer, Elsevier, and MDPI, demonstrating recognized expertise in signal processing and embedded systems. His research interests include embedded AI, biomedical instrumentation, wearable sensors, IoT-based healthcare, security-focused embedded design, and intelligent diagnostic systems. Dr. Achmamad remains dedicated to advancing high-impact engineering solutions that enhance healthcare, sensing technologies, and intelligent embedded platforms.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Achmamad, A., & Jbari, A. (2020). A comparative study of wavelet families for electromyography signal classification based on discrete wavelet transform. Bulletin of Electrical Engineering and Informatics.

Achmamad, A., Belkhou, A., & Jbari, A. (2020). Fast automatic detection of amyotrophic lateral sclerosis disease based on Euclidean distance metric. In 2020 International Conference on Electrical and Information Technologies .

Belkhou, A., Achmamad, A., & Jbari, A. (2019). Classification and diagnosis of myopathy EMG signals using the continuous wavelet transform. In 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science .

Belkhou, A., Achmamad, A., & Jbari, A. (2019). Myopathy detection and classification based on the continuous wavelet transform. Journal of Communications Software and Systems.

Abdelouahad, A., Belkhou, A., Jbari, A., & Bellarbi, L. (2018). Time and frequency parameters of sEMG signal–force relationship. In 2018 International Conference on Optimization and Applications .

Federica Ciminelli | Healthcare Data Analysis | Women Researcher Award

Published on by Research Data Analysis

Dr. Federica Ciminelli | Healthcare Data Analysis | Women Researcher Award

Ospedale sacro cuore don Calabria | Italy

Dr. Federica Ciminelli is an Italian physician specialized in Infectious and Tropical Diseases, holding a postgraduate degree from the Second University of Naples after completing her medical training with distinction at the University of Bologna. She has worked extensively in primary care, infectious diseases, global health, and migrant health, serving in roles such as USCA physician, medical coordinator for migrant reception programs, and clinician in continuity-of-care services across Tuscany and Veneto. Her international experience includes Erasmus-based clinical rotations in Lisbon and a clinical research internship at St. Kizito Hospital in Uganda, where she contributed to data collection for her thesis on community-acquired pneumonia in low-resource settings. Her academic output includes 18 citations, 18 documents, 4 indexed documents, and an h-index of 2. She has collaborated with global health institutions, participated in congress organization on conflicts of interest in medicine, and engaged in long-term volunteer work with homeless communities and survivors of trafficking. Her research interests span infectious diseases, global health, health inequities, migrant access to care, and community medicine. She has received top-level academic evaluations in both her medical degree and specialization. Dr. Ciminelli remains committed to evidence-based medicine, equitable healthcare, and international collaboration.

Profile : Scopus

Featured Publications

Effectiveness of dihydroartemisinin-piperaquine for treating Plasmodium falciparum malaria from sub-Saharan Africa: a retrospective study, 2025

Prognostic Value of Creatinine Levels at Admission on Disease Progression and Mortality in Patients with COVID-19—An Observational Retrospective Study, 2023