Sara A. Shehab | Artificial Intelligence | Best Researcher Award

Published on 04/11/2025 by Popular Scientist

Assoc. Prof. Dr. Sara A. Shehab | Artificial Intelligence | Best Researcher Award

Faculty Of Computer And Artificial Intelligence | Egypt

Assoc. Prof. Dr. Sara A. Shehab is an Associate Professor in Computer Science at the University of Sadat City, Egypt, with expertise spanning artificial intelligence, bioinformatics, computational biology, quantum computing, and computer security. Her research focuses on developing intelligent algorithms for biological data analysis, optimization, and machine learning applications in medicine and environmental sustainability. She has contributed significantly to the advancement of multiple sequence alignment techniques, parallel and dynamic algorithms, and predictive modeling using machine learning. Her recent work explores deep learning for biomedical image analysis, explainable AI for green energy production, and hybrid optimization approaches for precision classification and prediction tasks. Dr. Shehab has published extensively in peer-reviewed international journals and conferences, collaborating with leading scholars in AI-driven bioinformatics and sustainable computing. She also serves as a reviewer for international journals and conferences, contributing to the academic community through quality evaluation and mentorship. Her professional experience includes leadership in e-learning, digital transformation, and program coordination within higher education, reflecting a strong integration of research, teaching, and institutional development. Through her interdisciplinary approach, she bridges artificial intelligence with biological and environmental sciences, fostering innovation in intelligent systems for healthcare, sustainability, and data-driven decision-making.

Profile: Google Scholar

Featured Publications

Shehab, S. A., Keshk, A., & Mahgoub, H. (2012). Fast dynamic algorithm for sequence alignment based on bioinformatics. International Journal of Computer Applications, 37(7), 54–61.

Ahmed, R. A. E. H., Shehab, S. A., Elzeki, O. M., & Darwish, A. (2024). An explainable AI for green hydrogen production: A deep learning regression model. International Journal of Hydrogen Energy, 83, 1226–1242.

Shehab, A. E. H. S., Mohammed, K. K., & Darwish, A. (2024). Deep learning and feature fusion-based lung sound recognition model to diagnose respiratory diseases. Soft Computing.

Shehab, A. E. H. S., & Darwish, A. (2023). Water quality classification model with small features and class imbalance based on fuzzy rough sets. Environment, Development and Sustainability.

Shehab, S., Shohdy, S., & Keshk, A. E. (2017). PoMSA: An efficient and precise position-based multiple sequence alignment technique. arXiv preprint arXiv:1708.01508.

Manjunath BR | Machine Learning | Best Researcher Award

Published on 31/10/202507/11/2025 by Popular Scientist

Prof. Dr. Manjunath BR | Machine Learning | Best Researcher Award

Professor | Tecnologico De Monterrey | Mexico

Prof. Dr. Manjunath BR is an accomplished academic leader and finance professional specializing in business analytics, financial modeling, econometrics, fintech, and artificial intelligence applications in finance. With extensive experience across academia and industry, he has contributed significantly to advancing data-driven financial education and research. His expertise spans financial analytics, investment management, corporate restructuring, and data visualization using advanced tools such as EViews, R, Python, Tableau, and Power BI. He has published extensively in ABDC, Scopus, UGC, and peer-reviewed journals, focusing on the intersection of finance, data science, and technology. As a researcher and educator, he integrates predictive analytics and machine learning into financial decision-making, contributing to the understanding of fintech adoption, banking innovations, and risk management. His academic leadership includes curriculum design, faculty development, and corporate collaborations to enhance experiential learning. He has served as a resource person for numerous international workshops and training programs on financial analytics, econometrics, and data visualization, empowering professionals and students with analytical and quantitative skills. Dr. Manjunath has authored and edited several books with leading global publishers, covering transformative areas such as AI in management education, blockchain economics, sustainable investment, and Quality 5.0 paradigms. He has also secured a patent for the application of AI in optimizing HR data management and authored a textbook on machine and deep learning. His professional journey embodies innovation, interdisciplinary scholarship, and a commitment to integrating technology with finance to foster global academic and industry excellence.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Raju, J. K., Manjunath, B. R., & Rehaman, M. (2018). An empirical study on the effect of gross domestic product on inflation: Evidence from Indian data. Academy of Accounting and Financial Studies Journal, 22(6), 1–11.

Raju, J. K., Manjunath, B. R., & Dhakal, M. H. (2015). Impact and challenges of merger and acquisition in Nepalese banking and financial institutions. Journal of Exclusive Management Science, 4(8), 25–33.

Raju, J. K., Manjunath, B. R., & G. M. M. N. (2015). Performance evaluation of Indian equity mutual fund schemes. Journal of Business Management & Social Sciences Research (JBM&SSR).

Manjunath, B. R., & Raju, J. K. (2020). Short-run performance evaluation of under-priced Indian IPOs. Law and Financial Markets Review.

Chaitra, R., Manjunath, B. R., & Rehaman, M. (2019). An analysis of pre and post-merger of Indian banks: An event analysis approach. International Journal for Research in Engineering Application & Management, 4.

Mostafa Gamal | Artificial Intelligence | Best Researcher Award

Published on 28/10/202531/10/2025 by Popular Scientist

Mr. Mostafa Gamal | Artificial Intelligence | Best Researcher Award

Egyptian Russian University | Egypt

Mr. Mostafa Gamal, is a dedicated researcher and academic specializing in artificial intelligence, machine learning, and natural language processing, with a particular focus on text summarization and semantic graph-based models. His research explores the integration of deep learning, swarm intelligence, and optimization algorithms to enhance automated summarization and intelligent decision-making systems. He has contributed to several high-impact journals, including IEEE Access, Results in Engineering, Discover Cities, and the International Journal of Data Science and Analytics, covering areas such as transformer architectures, reinforcement learning, and graph neural networks. Mr. Gamal’s work advances the field of AI through the development of novel, explainable, and efficient models for NLP applications and autonomous systems. Beyond research, he is actively involved in academic teaching and professional training, fostering AI literacy through programs with the Egyptian Russian University, Huawei Academy, and the Digital Egypt Cubs Initiative. His technical expertise spans TensorFlow, PyTorch, and Keras, alongside proficiency in Python and data analytics frameworks. With a strong foundation in applied AI, he bridges theoretical research with practical implementation, contributing to the development of intelligent systems that address real-world challenges. His scholarly and instructional activities reflect a commitment to advancing artificial intelligence education and applied innovation in computational sciences.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Abdul Salam, M., Gamal, M., Hamed, H. F. A., & Sweidan, S. (2025, December). GRAYSUM: Gray Wolf optimized multi-level semantic graph summarization. Results in Engineering, (2025), 107275.

Abdul Salam, M., Gamal, M., Hamed, H. F. A., & Sweidan, S. (2025, October). Abstractive text summarization using deep learning models: A survey. International Journal of Data Science and Analytics.

Gamal, M., & Ibrahim, O. A. (2025, October 24). Graph neural networks for real-time optimization of autonomous urban transit systems. Discover Cities.

Gamal, M. M., Abdul Salam, M., Sweidan, S., & Hamed, H. F. A. (2025, May 1). ACOSUM: Ant colony optimized multi-level semantic graph summarization. International Journal of Applied Intelligent Computing and Informatics.

Abdul Salam, M., Aldawsari, M., Gamal, M., Hamed, H. F. A., & Sweidan, S. (2024). Improving Arabic text summarization using advanced pre-trained models. Journal of Southwest Jiaotong University, 59(3), Article 5.

Irina Karabulatova | Artificial Intelligence | Best Researcher Award

Published on 23/08/202523/08/2025 by Popular Scientist

Prof. Dr. Irina Karabulatova | Artificial Intelligence | Best Researcher Award

Professor | Heilongjiang University | China

Prof. Dr. Irina S. Karabulatova is an internationally recognized philologist, linguist, and academician known for her pioneering research in applied linguistics, psycholinguistics, neurolinguistics, sociolinguistics, media linguistics, computational psycholinguistics, and digital humanities. She serves as Head of the Research Center for Digital Humanities at Heilongjiang University, Research Professor at RUDN-University, and Senior Researcher at Moscow State University’s Institute for Advanced Research in AI. She has trained more than sixty doctoral and PhD students from multiple countries, authored nearly five hundred publications across leading journals, and contributed to global knowledge in language, brain studies, NLP, emotional artificial intelligence, and cross-cultural communication. She is an elected member of the Russian and European Academies of Natural Sciences, with global academic influence spanning Russia, China, Europe, India, and Kazakhstan. Her career combines rigorous scientific scholarship with cultural preservation, academic leadership, and contributions to international collaboration in linguistics, AI, and intercultural communication.

Profile

Scopus

Orcid

Education

Prof. Dr. Irina Karabulatova graduated with honors in philology from Arkalyk State Pedagogical Institute with a specialization in Russian language and literature. Her early academic focus was on toponymy and linguistic geography, culminating in her candidate dissertation on hydronyms of the Russian Rim, which established her as a rising scholar in Russian linguistics. She later defended her doctoral dissertation at Kuban State University on Russian toponymy in an ethno-psycholinguistic framework, combining theoretical linguistics with cultural identity studies. Her academic formation was marked by recognition from leading foundations supporting research excellence in culture and creativity. She pursued multiple advanced qualifications in philology and linguistics and was awarded the titles of associate professor and later full professor by the Higher Attestation Commission of the Russian Federation. Over her academic journey, she complemented her education with international internships in migration studies, cultural sociology, translation studies, and digital technologies, strengthening her interdisciplinary expertise in linguistics and humanities.

Professional Experience

Prof. Dr. Irina Karabulatova’s professional career spans academia, research, and international collaboration. She began as lecturer and professor at Russian universities before holding leadership positions at Tyumen State University, Kazan Federal University, and RUDN-University. She has served as professor, head of departments, and director of interdisciplinary research centers, combining linguistics with digital technologies, cultural studies, and artificial intelligence. Internationally, she has been visiting professor at universities in Kazakhstan, Turkey, China, Italy, and the United States, delivering courses on psycholinguistics, intercultural communication, digital linguistics, and verification of manipulative media markers. She has held senior research positions at Moscow State University, MIPT, and institutes of the Russian Academy of Sciences. Currently, she leads the Research Center for Digital Humanities at Heilongjiang University, China, where her work integrates NLP, emotional intelligence, and neurocognitive modeling. Her teaching, research supervision, and scientific expertise reflect global recognition, with doctoral students guided under her supervision across multiple continents.

Awards and Honors

Prof. Dr. Irina Karabulatova has received numerous prestigious awards and honors recognizing her contributions to philology, cultural preservation, and interdisciplinary research. She is a laureate of the N. Roerich International Prize for preservation of cultural values and peacemaking, and an Honored Worker of Culture of Kazakhstan. She has been awarded multiple national and international prizes including the All-Russian public award “Success” for women leaders in science, culture, and education, as well as certificates of honor and gratitude from universities, regional authorities, and cultural institutions in Russia, Kazakhstan, and Israel. Her achievements are highlighted by listings in major encyclopedias documenting leading migrationologists and psycholinguists of Russia. She has been recognized by academic, cultural, and governmental institutions for her scholarly books, research contributions, and active promotion of intercultural understanding. Her honors reflect her pioneering role in applied linguistics, psycholinguistics, media linguistics, cultural heritage, and the development of interdisciplinary fields in the digital humanities.

Research Focus

Prof. Dr. Irina Karabulatova’s research focus spans applied linguistics, psycholinguistics, neurolinguistics, sociolinguistics, migration studies, computational linguistics, NLP, sentiment analysis, emotionology, and digital humanities. She is credited with founding new applied branches of linguistics such as digital linguomigration, predictive onomastics, digital folkloristics, and academic emotionology. Her work investigates neuro-psycholinguistic mechanisms of communication, emotional intelligence, and emotional artificial intelligence. She has analyzed the transformation of linguistic consciousness among diasporas, bilingualism and ASD differentiation, social schizophrenia in aggressive media environments, and manipulative markers in mass communication. Her interdisciplinary projects address verification and detection of manipulative discourse, psycholinguistic expertise of digital texts, and digital profiling for sociocultural security. She integrates AI and machine learning into linguistics to model language, brain, and cognition interactions. Her current research emphasizes the preservation of ethnocultural codes, neurocognitive modeling of bilinguals, and the development of computational methods for automatic analysis of potentially dangerous discourses in media and intercultural communication.

Publication

Neuromorphic Elements as a First Step Towards Sociomorphic Systems
Year: 2025

The interpretations of Russian traditional song folklore in the Internet virtual space in the aspect of digital linguistic folklore studies
Year: 2025

Sociomorphic Neuromodeling in Academic Emotionology as an Integration of Neurocognitive and Psycholinguistic Knowledge in Artificial Intelligence
Year: 2025

Metaphorical Terminology in Ancient Texts of Traditional Chinese Medicine: Problems of Understanding and Translation
Year: 2024

Modeling the Socio-Economic and Demographic Development of Transborder Regions (The Example of the Russian-Chinese Border Territories)
Year: 2024

Conclusion

Prof. Dr. Irina S. Karabulatova is highly suitable for recognition through a research award, given her groundbreaking contributions, global academic engagement, and leadership in emerging fields of linguistics and digital humanities. Her innovative approaches to language, cognition, and artificial intelligence mark her as a distinguished scholar whose work continues to shape contemporary linguistic science and intercultural studies, making her a deserving candidate for international academic honors.

Ali Raza | Computer Science and Artificial Intelligence | Young Scientist Award

Published on 08/03/202508/03/2025 by Popular Scientist

Mr. Ali Raza | Computer Science and Artificial Intelligence | Young Scientist Award

lecturer The University Of Lahore Pakistan

Ali Raza is a passionate researcher, educator, and developer specializing in computer science. With a strong academic background and extensive experience in machine learning, deep learning, and computer vision, he has contributed significantly to cutting-edge research. Currently serving as a Lecturer at the University of Lahore, Ali has also worked as a Visiting Lecturer at KFUEIT and a Full Stack Python Developer in the software industry. His expertise lies in AI-driven solutions, research writing, and technological advancements in artificial intelligence.

Profile

Google Scholar

Education 🎓

MS Computer Science (2021-2023) | Khwaja Fareed University of Engineering and Information Technology (KFUEIT), CGPA: 3.93
BS Computer Science (2017-2021) | KFUEIT, CGPA: 3.47

Professional Experience 💼

Lecturer | University of Lahore (2024 – Present)
Visiting Lecturer | KFUEIT (2022 – 2023)
Full Stack Python Developer | BuiltinSoft Software Industry (2020 – 2021)

Research Interests 📈

Ali Raza’s research focuses on artificial intelligence, machine learning, deep learning, and computer vision. He is particularly interested in developing AI-driven solutions for medical imaging, agricultural applications, and energy consumption prediction. His contributions span multiple domains, showcasing his ability to integrate AI with real-world challenges.

Awards & Certifications 🏆

Best Researcher Award | ScienceFather (26/06/2024)
Use of Generative AI in Higher Education | Punjab Higher Education Commission
Machine Learning with Python (ML0101EN) | IBM Developer Skills Network