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 ๐Ÿค– and Machine Learning ๐Ÿ“Š. Based in Baghdad, Iraq ๐Ÿ‡ฎ๐Ÿ‡ถ, he is affiliated with the University of Technologyโ€™s Computer Science Department ๐Ÿ›๏ธ. He earned his B.Sc. in Computer Engineering in 2015 ๐ŸŽ“ and his M.Sc. in Computer and Communication Engineering from IUL, Lebanon in 2020 ๐ŸŒ. Mohammedโ€™s scholarly work explores innovations in credit card fraud detection ๐Ÿ’ณ, ECG signal analysis ๐Ÿซ€, and battery life prediction ๐Ÿ”‹. He is proficient in Arabic and English ๐ŸŒ and actively contributes to academic platforms like Google Scholar, Scopus, and ResearchGate ๐Ÿ“š.

Professional Profile

Orcid

Education & Experience

๐ŸŽ“ B.Sc. in Computer Engineering โ€“ Al Salam University, 2015
๐ŸŽ“ M.Sc. in Computer and Communication Engineering โ€“ IUL, Lebanon, 2020
๐Ÿซ Current Position: Researcher, Computer Science Department, University of Technology, Baghdad
๐Ÿ’ก Research Specialization: Artificial Intelligence & Machine Learning
๐Ÿ“ 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 โ€“ 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 ๐Ÿง , with applications in financial security ๐Ÿ’ณ, health monitoring ๐Ÿซ, and energy storage systems ๐Ÿ”‹. He continually enhances his expertise through academic publishing, peer collaboration ๐Ÿค, and participation in scholarly platforms such as Scopus, ORCID, and ResearchGate ๐ŸŒ. His professional growth is anchored in practical problem-solving and theoretical advancements, bridging gaps between research and real-world applications โš™๏ธ. Mohammed embraces every opportunity to contribute to the AI and data science communities globally ๐ŸŒ.

Research Focus

Mohammedโ€™s research centers on Artificial Intelligence ๐Ÿค– and Machine Learning ๐Ÿ“ˆ, targeting impactful real-world applications. His projects range from fraud detection in financial systems ๐Ÿ’ฐ to improving ECG signal analysis for healthcare innovation โค๏ธ, and predicting battery performance for sustainable energy solutions ๐Ÿ”‹. By leveraging deep learning, signal processing, and support vector machines, he aims to solve pressing problems in data-intensive environments ๐Ÿง . His interdisciplinary focus contributes to smarter, data-driven systems across sectors like finance, healthcare, and renewable energy ๐ŸŒฑ. With a forward-looking mindset, Mohammed is advancing the future of AI through meaningful and ethical research ๐Ÿ”.

Awards and Honorsย 

๐Ÿ… Long-term Service โ€“ Serving at the University of Technology โ€“ Iraq, Baghdad since June 1987 in the field of Computer Science ๐Ÿ–ฅ๏ธ

๐ŸŽ–๏ธ Academic Position of Distinction โ€“ Recognized with an invited position in Computer Engineering at the University of Technology, Department of Computer Science ๐Ÿง 

๐Ÿ“š Scholarly Contributions โ€“ Published multiple peer-reviewed articles in reputable journals including Springer, Elsevier, and IEEE-indexed platforms ๐Ÿ“–

๐ŸŒ Active Contributor to Research Networks โ€“ Verified academic profiles on Google Scholar, Scopus, ORCID, and ResearchGate ๐Ÿ”

๐Ÿ† Recognition by Academic Community โ€“ Trusted and cited by international researchers for contributions in Artificial Intelligence, Machine Learning, and Signal Processing ๐ŸŒ

Publication Top Notes

๐Ÿ“˜ 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

๐Ÿซ€ 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.; UฤŸurenver, A.; Alkubaisi, Y.M.; Sabry, A.H.

๐Ÿ”‹ 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. ฤฐsmail ATACAK | Machine Learning | Best Researcher Award

Dr. ฤฐsmail ATACAK | Machine Learning | Best Researcher Award

Dr. ฤฐsmail ATACAK , Gazi University , Turkey.

Dr. ฤฐsmail ATACAK is an Assistant Professor at Gazi University, Faculty of Technology, Department of Computer Engineering. ๐ŸŽ“ 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. ๐Ÿค– His research extends to Data Science and Bioinformatics, where he applies AI to various real-world challenges. ๐Ÿง  Apart from academia, he has led multiple research projects, particularly focusing on AI applications in energy storage and control systems. โšก๐Ÿ”‹ He has also published extensively in reputable journals, contributing significantly to his field. ๐Ÿ“šโœจ

Publication Profile

Scopus
Orcid
Google Scholar

Education & Experience ๐ŸŽ“๐Ÿ‘จโ€๐Ÿซ

โœ… Ph.D. in Electronic-Computer Education โ€“ Focus on AI and Optimization ๐Ÿ†
โœ… Assistant Professor at Gazi University โ€“ Department of Computer Engineering ๐Ÿ›๏ธ
โœ… Research in AI, Data Science & Bioinformatics โ€“ Machine Learning, Deep Learning, Fuzzy Logic ๐Ÿค–๐Ÿ“Š
โœ… Principal Investigator โ€“ AI-based Hybrid Energy Storage System for Electric Vehicles ๐Ÿš—๐Ÿ”‹
โœ… Published Author โ€“ Numerous articles in high-impact journals ๐Ÿ“–

Suitability Summary

Dr. ฤฐsmail 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 ๐Ÿš€๐Ÿ“ˆ

Dr. ฤฐsmail ATACAK has continuously developed his expertise by engaging in multidisciplinary AI research. ๐Ÿค– His work integrates AI-driven solutions into engineering and technology, particularly in energy systems and bioinformatics. ๐Ÿ”ฌโšก He actively collaborates with researchers worldwide, enhancing the implementation of AI in control applications and optimization algorithms. ๐ŸŒ๐Ÿ’ก His commitment to innovation has led to significant contributions in the fields of machine learning and deep learning. ๐Ÿ† He also mentors students and young researchers, fostering the next generation of AI experts. ๐ŸŽ“๐Ÿ“š His ongoing projects aim to bridge the gap between theoretical AI research and real-world applications. ๐ŸŒŸ๐Ÿ”

Research Focus ย ๐Ÿ”ฌ๐Ÿง 

Dr. ฤฐsmail ATACAK’s research revolves around the advancement and application of Artificial Intelligence in various domains. ๐Ÿค– His primary interests include:
๐ŸŸข Machine Learning & Deep Learning โ€“ Developing smart, data-driven solutions ๐Ÿ“Š๐Ÿง 
๐ŸŸข Fuzzy Logic & Optimization Algorithms โ€“ Enhancing decision-making and automation ๐Ÿ”„โš™๏ธ
๐ŸŸข Data Science & Bioinformatics โ€“ AI applications in healthcare and genomics ๐Ÿฅ๐Ÿงฌ
๐ŸŸข AI in Energy Storage Systems โ€“ Smart energy management for electric vehicles ๐Ÿš—๐Ÿ”‹
๐ŸŸข Control Systems & Automation โ€“ Integrating AI for improved industrial processes ๐Ÿญ๐Ÿค–
His research not only contributes to academic knowledge but also drives technological advancements in AI-based solutions. ๐Ÿ“š๐Ÿ’ก

Awards & Honors ๐Ÿ†๐ŸŽ–๏ธ

๐Ÿ… Best Research Paper Award โ€“ Recognized for outstanding AI research contributions ๐Ÿ“œ๐Ÿ’ก
๐Ÿ… Principal Investigator Grant โ€“ Secured funding for AI-based energy storage research โšก๐Ÿ”
๐Ÿ… Outstanding Reviewer Award โ€“ Recognized for reviewing high-impact AI journals ๐Ÿ“–๐Ÿ‘
๐Ÿ… Innovation in AI Applications Award โ€“ Contribution to AI-driven control systems ๐Ÿญ๐Ÿค–
๐Ÿ… Distinguished Academic Excellence Award โ€“ For excellence in AI and Machine Learning ๐Ÿ›๏ธโœจ

Publication Top Notes

๐Ÿ”น An Ensemble Approach Based on Fuzzy Logic Using Machine Learning Classifiers for Android Malware Detection
๐Ÿ“– Applied Sciences
๐Ÿ“… Published: January 23, 2023
๐Ÿ”— DOI: 10.3390/app13031484

๐Ÿ”น Android Malware Detection Using a Hybrid ANFIS Architecture with Low Computational Cost Convolutional Layers
๐Ÿ“– PeerJ Computer Science
๐Ÿ“… Published: September 26, 2022
๐Ÿ”— DOI: 10.7717/peerj-cs.1092

๐Ÿ”น Community and Topic Detection in Social Networks: A Systematic Literature Review
๐Ÿ“– Journal of Information Technologies
๐Ÿ“… Published: July 2022
๐Ÿ”— DOI: 10.17671/gazibtd.1061332

๐Ÿ”น A Novel Approach to Skin Lesion Segmentation: Multipath Fusion Model with Fusion Loss
๐Ÿ“– Computational and Mathematical Methods in Medicine
๐Ÿ“… Published: July 29, 2022
๐Ÿ”— DOI: 10.1155/2022/2157322

๐Ÿ”น Development of an IoT-Based Rumen Health Monitoring Prototype for Cattle Using Fuzzy Logic Controllers
๐Ÿ“– Gazi Journal of Engineering Sciences
๐Ÿ“… Published: May 5, 2022
๐Ÿ”— DOI: 10.30855/gmbd.2022.01.13

Conclusion

Dr. ฤฐsmail 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. ๐Ÿš€

Yaoyao Li | Bioinformatics | Best Researcher Award

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

Xidian University, China

๐Ÿ‘จโ€๐ŸŽ“Profiles

Early Academic Pursuits ๐ŸŽ“

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 ๐Ÿ’ผ

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 ๐Ÿ”ฌ

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 ๐ŸŒ

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 individualโ€™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 ๐Ÿ“š

Dr. Liโ€™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 ๐Ÿ› ๏ธ

Dr. Liโ€™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 ๐Ÿ‘ฉโ€๐Ÿซ

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 ๐Ÿ”ฎ

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 ๐ŸŒŸ

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.

๐Ÿ“–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