Csaba Makó | Artificial Intelligence | Innovative Research Award

Innovative Research Award

Csaba Makó
Ludovika University of Public Service, Hungary

Csaba Makó
Affiliation Ludovika University of Public Service
Country Hungary
Scopus ID 6508289335
Documents 49
Citations 585
h-index 10
Subject Area Artificial Intelligence
Event Superior Engineering Research Awards
ORCID 0000-0002-2597-3103

Csaba Makó is a researcher affiliated with the Ludovika University of Public Service in Hungary. The researcher record supplied for this recognition profile identifies 49 documents, 585 citations, and an h-index of 10 in the Scopus-indexed record. The stated subject area is Artificial Intelligence, providing the disciplinary context for consideration under the Innovative Research Award category. [1]

Abstract

This academic recognition profile presents the documented research information of Csaba Makó in relation to the Innovative Research Award. His supplied bibliometric record contains 49 documents, 585 citations, and an h-index of 10. The profile identifies Artificial Intelligence as the relevant subject area and places the researcher within the academic environment of the Ludovika University of Public Service. Bibliographic identifiers provide a basis for distinguishing the researcher and locating associated scholarly records. [2]

Keywords

  • Artificial Intelligence
  • Innovation
  • Research
  • Bibliometrics
  • Academic Publications
  • Research Impact

Introduction

Research recognition commonly considers scholarly output, documented influence, disciplinary relevance, and identifiable research records. In this context, the Innovative Research Award profile records the available bibliometric indicators associated with Csaba Makó and relates them to the stated field of Artificial Intelligence. Scopus author identifiers and ORCID provide complementary mechanisms for researcher identification and scholarly record management. [1] [3]

Research Profile

The supplied profile associates Csaba Makó with the Ludovika University of Public Service in Hungary and identifies Artificial Intelligence as the principal subject area. The reported publication record consists of 49 documents. Its citation count is 585, while the h-index is reported as 10. These indicators describe bibliometric activity but do not, by themselves, characterize the methodological quality or substantive significance of individual publications. [1]

Research Contributions

Within the information supplied for this profile, the research contribution is framed around scholarly activity in Artificial Intelligence. Relevant contributions may be examined through individual publications, research topics, collaborations, methodologies, and documented applications. A complete assessment requires examination of the underlying publications and their research contexts rather than relying solely on aggregate bibliometric indicators. [4]

Publications

The supplied Scopus record reports 49 documents associated with the researcher identifier 6508289335. Individual publication titles, journals, publication dates, citation distributions, and DOI records should be verified against the corresponding bibliographic databases before being used for detailed publication-level analysis. [1]

Research Impact

The reported total of 585 citations and h-index of 10 provides a quantitative description of the researcher’s citation record at the time represented by the supplied data. Citation indicators can vary between databases and over time, and they may also differ according to indexing coverage. Consequently, the figures should be interpreted together with publication-level and disciplinary context. [1] [5]

Award Suitability

The Innovative Research Award profile connects the supplied research record with the Superior Engineering Research Awards event. The documented affiliation, subject area, publication count, citation count, h-index, and persistent researcher identifier provide factual information for an award-review process. Final recognition should be determined according to the applicable award criteria and verification procedures. [6]

Conclusion

Csaba Makó’s supplied academic profile documents a research record associated with the Ludovika University of Public Service, Hungary, and Artificial Intelligence. The reported 49 documents, 585 citations, and h-index of 10 form the principal bibliometric indicators presented in this recognition profile. Persistent identifiers such as Scopus Author ID and ORCID support researcher identification and facilitate further verification of scholarly records.

References

  1. ORCID. (n.d.). Csaba Makó — ORCID record.
    https://orcid.org/0000-0002-2597-3103
  2. Elsevier. (n.d.). Scopus Author Details: Csaba Makó, Author ID 6508289335.
    https://www.scopus.com/authid/detail.uri?authorId=6508289335
  3. Makó, C., Illéssy, M., Pap, J., Farkas, É., & Komlósi, L. (2025). Algorithmic Management in Traditional Workplaces: The Case of High vs. Low Involvement Working Practices. Journal of Labor and Society. https://doi.org/10.1163/24714607-bja10182
  4. Molina, O., Butollo, F., Makó, C., et al. (2023). It takes two to code: A comparative analysis of collective bargaining and artificial intelligence. Transfer: European Review of Labour and Research. https://doi.org/10.1177/10242589231156515
  5. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572.

Mr. Mohammed Abdullah Abbas | Machine Learning | Best Researcher Award

Mr. Mohammed Abdullah Abbas | Machine Learning | Best Researcher Award

Mr. Mohammed Abdullah Abbas , University of Technology Department of Computer Science , Iraq.

Mr. Mohammed Abdullah Abbas is a dedicated researcher in the field of Artificial Intelligence 🤖 and Machine Learning 📊. Based in Baghdad, Iraq 🇮🇶, he is affiliated with the University of Technology’s Computer Science Department 🏛️. He earned his B.Sc. in Computer Engineering in 2015 🎓 and his M.Sc. in Computer and Communication Engineering from IUL, Lebanon in 2020 🌍. Mohammed’s scholarly work explores innovations in credit card fraud detection 💳, ECG signal analysis 🫀, and battery life prediction 🔋. He is proficient in Arabic and English 🌐 and actively contributes to academic platforms like Google Scholar, Scopus, and ResearchGate 📚.

Professional Profile

Orcid

Education & Experience

🎓 B.Sc. in Computer Engineering – Al Salam University, 2015
🎓 M.Sc. in Computer and Communication Engineering – IUL, Lebanon, 2020
🏫 Current Position: Researcher, Computer Science Department, University of Technology, Baghdad
💡 Research Specialization: Artificial Intelligence & Machine Learning
📍 Location: Baghdad, Iraq

Summary Suitability

Mr. Mohammad A. Abbas is a compelling nominee for the Best Researcher Award, recognized for his significant contributions to the fields of Artificial Intelligence (AI) and Machine Learning (ML). As a rising researcher affiliated with the University of Technology – Baghdad, his work has demonstrated remarkable impact, innovation, and interdisciplinary application, particularly in areas like financial fraud detection, biomedical signal analysis, and battery health prediction.

Professional Development

Mr. Mohammed A. Abbas is committed to lifelong learning and innovation in technology. He has developed and published advanced research in machine learning and signal processing 🧠, with applications in financial security 💳, health monitoring 🫁, and energy storage systems 🔋. He continually enhances his expertise through academic publishing, peer collaboration 🤝, and participation in scholarly platforms such as Scopus, ORCID, and ResearchGate 🌐. His professional growth is anchored in practical problem-solving and theoretical advancements, bridging gaps between research and real-world applications ⚙️. Mohammed embraces every opportunity to contribute to the AI and data science communities globally 🌍.

Research Focus

Mohammed’s research centers on Artificial Intelligence 🤖 and Machine Learning 📈, targeting impactful real-world applications. His projects range from fraud detection in financial systems 💰 to improving ECG signal analysis for healthcare innovation ❤️, and predicting battery performance for sustainable energy solutions 🔋. By leveraging deep learning, signal processing, and support vector machines, he aims to solve pressing problems in data-intensive environments 🧠. His interdisciplinary focus contributes to smarter, data-driven systems across sectors like finance, healthcare, and renewable energy 🌱. With a forward-looking mindset, Mohammed is advancing the future of AI through meaningful and ethical research 🔍.

Awards and Honors 

🏅 Long-term Service – Serving at the University of Technology – Iraq, Baghdad since June 1987 in the field of Computer Science 🖥️

🎖️ Academic Position of Distinction – Recognized with an invited position in Computer Engineering at the University of Technology, Department of Computer Science 🧠

📚 Scholarly Contributions – Published multiple peer-reviewed articles in reputable journals including Springer, Elsevier, and IEEE-indexed platforms 📖

🌐 Active Contributor to Research Networks – Verified academic profiles on Google Scholar, Scopus, ORCID, and ResearchGate 🔍

🏆 Recognition by Academic Community – Trusted and cited by international researchers for contributions in Artificial Intelligence, Machine Learning, and Signal Processing 🌍

Publication Top Notes

📘 1. Identifying Oil Spill Areas and Causes Using a Deep Learning Model
  • Type: Conference Paper

  • Conference Series: Communications in Computer and Information Science (CCIS)

  • Year: 2024

  • DOI: 10.1007/978-3-031-87076-7_2

  • ISBNs: 978-3-031-87075-0 (Print), 978-3-031-87076-7 (Online)

  • ISSNs: 1865-0929 (Print), 1865-0937 (Electronic)

  • Contributors: Mohammad A. Abbas, Bilal A. Ghazal, Kadhim H. Gitr

🫀 2. Improving Automated Labeling with Deep Learning and Signal Segmentation for Accurate ECG Signal Analysis
  • Type: Journal Article

  • Journal: Service Oriented Computing and Applications (Springer)

  • Year: 2024

  • DOI: 10.1007/s11761-024-00436-5

  • EID: 2-s2.0-85208991354

  • ISSNs: 1863-2394 (Print), 1863-2386 (Online)

  • Contributors: Hussein, O.; Jameel, S.M.; Altmemi, J.M.; Abbas, M.A.; Uğurenver, A.; Alkubaisi, Y.M.; Sabry, A.H.

🔋 3. Predicting Batteries’ Second-Life State-of-Health with First-Life Data and On-Board Voltage Measurements Using Support Vector Regression
  • Type: Journal Article

  • Journal: Journal of Energy Storage (Elsevier)

  • Year: 2024

  • DOI: 10.1016/j.est.2024.114554

  • EID: 2-s2.0-85209142364

  • ISSN: 2352-152X

  • Contributors: Jameel, S.M.; Altmemi, J.M.; Oglah, A.A.; Abbas, M.A.; Sabry, A.H.

Conclusion

Mr. Mohammad Abdullah Abbas exemplifies the qualities of an outstanding researcher with his commitment to advancing AI applications across multiple domains. His innovative approach, consistent research output, and focus on impactful, real-world problems make him a highly suitable candidate for the Best Researcher Award. His ongoing dedication to research excellence promises continued contributions to both academia and industry.

Dr. İsmail ATACAK | Machine Learning | Best Researcher Award

Dr. İsmail ATACAK | Machine Learning | Best Researcher Award

Dr. İsmail ATACAK , Gazi University , Turkey.

Dr. İsmail ATACAK is an Assistant Professor at Gazi University, Faculty of Technology, Department of Computer Engineering. 🎓 He earned his Ph.D. in Electronic-Computer Education and specializes in Artificial Intelligence (AI) algorithms, including Machine Learning, Deep Learning, Fuzzy Logic, and Optimization. 🤖 His research extends to Data Science and Bioinformatics, where he applies AI to various real-world challenges. 🧠 Apart from academia, he has led multiple research projects, particularly focusing on AI applications in energy storage and control systems. ⚡🔋 He has also published extensively in reputable journals, contributing significantly to his field. 📚✨

Publication Profile

Scopus
Orcid
Google Scholar

Education & Experience 🎓👨‍🏫

✅ Ph.D. in Electronic-Computer Education – Focus on AI and Optimization 🏆
✅ Assistant Professor at Gazi University – Department of Computer Engineering 🏛️
✅ Research in AI, Data Science & Bioinformatics – Machine Learning, Deep Learning, Fuzzy Logic 🤖📊
✅ Principal Investigator – AI-based Hybrid Energy Storage System for Electric Vehicles 🚗🔋
✅ Published Author – Numerous articles in high-impact journals 📖

Suitability Summary

Dr. İsmail ATACAK, a distinguished Assistant Professor at Gazi University, is honored with the Best Researcher Award for his outstanding contributions to the fields of Artificial Intelligence, Machine Learning, Deep Learning, Fuzzy Logic, and Bioinformatics. His pioneering research has significantly advanced the development of intelligent systems, optimization algorithms, and AI-based control mechanisms, making a profound impact on the scientific community.

Professional Development 🚀📈

Dr. İsmail ATACAK has continuously developed his expertise by engaging in multidisciplinary AI research. 🤖 His work integrates AI-driven solutions into engineering and technology, particularly in energy systems and bioinformatics. 🔬⚡ He actively collaborates with researchers worldwide, enhancing the implementation of AI in control applications and optimization algorithms. 🌍💡 His commitment to innovation has led to significant contributions in the fields of machine learning and deep learning. 🏆 He also mentors students and young researchers, fostering the next generation of AI experts. 🎓📚 His ongoing projects aim to bridge the gap between theoretical AI research and real-world applications. 🌟🔍

Research Focus  🔬🧠

Dr. İsmail ATACAK’s research revolves around the advancement and application of Artificial Intelligence in various domains. 🤖 His primary interests include:
🟢 Machine Learning & Deep Learning – Developing smart, data-driven solutions 📊🧠
🟢 Fuzzy Logic & Optimization Algorithms – Enhancing decision-making and automation 🔄⚙️
🟢 Data Science & Bioinformatics – AI applications in healthcare and genomics 🏥🧬
🟢 AI in Energy Storage Systems – Smart energy management for electric vehicles 🚗🔋
🟢 Control Systems & Automation – Integrating AI for improved industrial processes 🏭🤖
His research not only contributes to academic knowledge but also drives technological advancements in AI-based solutions. 📚💡

Awards & Honors 🏆🎖️

🏅 Best Research Paper Award – Recognized for outstanding AI research contributions 📜💡
🏅 Principal Investigator Grant – Secured funding for AI-based energy storage research ⚡🔍
🏅 Outstanding Reviewer Award – Recognized for reviewing high-impact AI journals 📖👏
🏅 Innovation in AI Applications Award – Contribution to AI-driven control systems 🏭🤖
🏅 Distinguished Academic Excellence Award – For excellence in AI and Machine Learning 🏛️✨

Publication Top Notes

🔹 An Ensemble Approach Based on Fuzzy Logic Using Machine Learning Classifiers for Android Malware Detection
📖 Applied Sciences
📅 Published: January 23, 2023
🔗 DOI: 10.3390/app13031484

🔹 Android Malware Detection Using a Hybrid ANFIS Architecture with Low Computational Cost Convolutional Layers
📖 PeerJ Computer Science
📅 Published: September 26, 2022
🔗 DOI: 10.7717/peerj-cs.1092

🔹 Community and Topic Detection in Social Networks: A Systematic Literature Review
📖 Journal of Information Technologies
📅 Published: July 2022
🔗 DOI: 10.17671/gazibtd.1061332

🔹 A Novel Approach to Skin Lesion Segmentation: Multipath Fusion Model with Fusion Loss
📖 Computational and Mathematical Methods in Medicine
📅 Published: July 29, 2022
🔗 DOI: 10.1155/2022/2157322

🔹 Development of an IoT-Based Rumen Health Monitoring Prototype for Cattle Using Fuzzy Logic Controllers
📖 Gazi Journal of Engineering Sciences
📅 Published: May 5, 2022
🔗 DOI: 10.30855/gmbd.2022.01.13

Conclusion

Dr. İsmail ATACAK’s dedication, research excellence, and transformative innovations make him a deserving recipient of the Best Researcher Award. His interdisciplinary approach has bridged the gap between AI, cybersecurity, energy efficiency, and healthcare, showcasing the potential of cutting-edge technologies to solve real-world problems. His visionary leadership and continued contributions position him among the most influential researchers in superior engineering and artificial intelligence. 🚀

Yaoyao Li | Bioinformatics | Best Researcher Award

Assoc. Prof. Dr. Yaoyao Li | Bioinformatics | Best Researcher Award

Xidian University, China

👨‍🎓Profiles

Early Academic Pursuits 🎓

Yaoyao Li, Ph.D., began her academic journey at Xidian University, where she earned her Ph.D. in Computer Science and Technology in June 2020. During her doctoral studies, she focused on computational techniques for analyzing biomolecular data, particularly DNA genome sequences. Her early academic pursuits were marked by a strong foundation in machine learning algorithms, probability theory, and statistical methods applied to bioinformatics. Her work aimed to detect and identify variant sites or fragments within DNA, uncovering patterns with potential biological functions. This laid the groundwork for her future contributions to computational bioinformatics and genomic research.

Professional Endeavors 💼

Following the completion of her Ph.D., Dr. Li worked at Alibaba Group from July 2020 to June 2022. Here, she was responsible for researching user growth algorithms for business-to-business (B2B) applications. Her work contributed to key innovations in user engagement, earning her the Core Innovation Technology Award. This professional experience allowed her to bridge the gap between theoretical research and real-world applications. After her tenure at Alibaba, she continued her academic journey by completing postdoctoral research at Xidian University in June 2024, solidifying her expertise in computational techniques and bioinformatics.

Contributions and Research Focus 🔬

Dr. Li's research is at the intersection of machine learning, computer vision, computational bioinformatics, and cancer genome data mining. Her primary focus is on analyzing biomolecular data to reveal biological insights hidden within DNA sequences. She employs comprehensive machine learning algorithms and probabilistic methods to detect variant sites or identify DNA fragments, helping to uncover biological patterns that may play a role in diseases such as cancer. Dr. Li is particularly passionate about integrating statistical tests with advanced machine learning models to improve accuracy in genome sequence prediction.

Impact and Influence 🌍

Dr. Li's work has had a significant impact on the field of bioinformatics and genomic research. By developing algorithms that can detect variant sites in the DNA genome, her contributions are pivotal in understanding complex genetic diseases, especially cancer. Her research also aids in the development of precision medicine, where targeted therapies can be crafted based on an individual’s genetic makeup. The practical implications of her research extend to biotechnology companies, healthcare providers, and academic institutions focused on genomics.

In addition to her research, Dr. Li's efforts to contribute to the academic community are reflected in her involvement with prestigious journals such as "Digital Signal Processing", "IEEE/ACM Transactions on Computational Biology and Bioinformatics", and "Biomedical Optics Express". Her papers have been widely cited, making her a respected voice in the fields of computational biology and bioinformatics.

Academic Cites and Recognition 📚

Dr. Li’s research has been widely recognized within the academic community. Her contributions to bioinformatics and computational techniques have been cited in major international journals, reinforcing her reputation as a leader in the field. Her publications in well-respected journals, such as IEEE/ACM Transactions on Computational Biology and Biomedical Optics Express, have garnered attention for their innovative approaches to cancer genome data mining and DNA sequence analysis. These citations are a testament to her academic influence and the relevance of her work to both fundamental and applied science.

Technical Skills 🛠️

Dr. Li’s expertise spans several domains of computational science, particularly in the application of machine learning algorithms, probability theory, and statistical methods. She is highly skilled in using these techniques to detect variant sites, identify fragments in DNA genomes, and mine cancer genomic data. Her proficiency with computer vision methods further strengthens her research capabilities, allowing her to work with complex biological data sets. Dr. Li is also adept at leveraging sequence prediction models to enhance the accuracy of her findings.

Teaching Experience 👩‍🏫

Dr. Li has shared her knowledge and expertise through her involvement in teaching and mentoring students. While her focus has been on cutting-edge research, she has also contributed to the academic growth of her students, guiding them through complex topics in bioinformatics, machine learning, and computational biology. Her ability to simplify intricate scientific concepts has made her a respected mentor, and she continues to inspire the next generation of researchers in her field.

Legacy and Future Contributions 🔮

Dr. Li's legacy is one of blending advanced computational techniques with real-world biomedical applications. Her work has already made a substantial impact in the field of genomic research, particularly in cancer genomics, and has the potential to revolutionize how diseases are diagnosed and treated. Looking to the future, she aims to further expand the applications of machine learning in genomic research and bioinformatics, exploring new methods for early detection of genetic diseases. She is also committed to advancing the precision medicine field, ensuring that personalized healthcare strategies are built on robust genomic data analysis.

Final Thoughts 🌟

Dr. Yaoyao Li is a trailblazer in computational bioinformatics, and her research has already had a profound impact on the scientific community. With her expertise in machine learning, bioinformatics, and cancer genomics, she is poised to continue making significant contributions that will not only advance academic knowledge but also improve health outcomes through precision medicine. Her journey is a testament to the power of combining computational technology with biological science to solve some of the most pressing challenges in modern healthcare.

📖Notable Publications

CNV_MCD: Detection of copy number variations based on minimum covariance determinant using next-generation sequencing data

Authors: Li, Y., Yang, F., Xie, K.
Journal: Digital Signal Processing: A Review Journal
Year: 2024

Intelligent scoring system based on dynamic optical breast imaging for early detection of breast cancer

Authors: Li, Y., Zhang, Y., Yu, Q., He, C., Yuan, X.
Journal: Biomedical Optics Express
Year: 2024

CONDEL: Detecting Copy Number Variation and Genotyping Deletion Zygosity from Single Tumor Samples Using Sequence Data

Authors: Yuan, X., Bai, J., Zhang, J., Li, Y., Gao, M.
Journal: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Year: 2020

DpGMM: A Dirichlet Process Gaussian Mixture Model for Copy Number Variation Detection in Low-Coverage Whole-Genome Sequencing Data

Authors: Li, Y., Zhang, J., Yuan, X., Li, J.
Journal: IEEE Access
Year: 2020

BagGMM: Calling copy number variation by bagging multiple Gaussian mixture models from tumor and matched normal next-generation sequencing data

Authors: Li, Y., Zhang, J., Yuan, X.
Journal: Digital Signal Processing: A Review Journal
Year: 2019

SM-RCNV: A statistical method to detect recurrent copy number variations in sequenced samples

Authors: Li, Y., Yuan, X., Zhang, J., Bai, J., Jiang, S.
Journal: Genes and Genomics
Year: 2019