Maryam Kheirabadi | Computer Science | Women Researcher Award

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Women Researcher Award

Maryam Kheirabadi
Affiliation Islamic Azad University
Country Iran
Scopus ID 57210153054
Documents 16
Citations 710
h-index 9
Subject Area Computer Science
Event International Academic Achievements & Awards
ORCID 0000-0001-8980-4299

Maryam Kheirabadi
Islamic Azad University, Iran

The Women Researcher Award recognizes distinguished scholarly achievement, research excellence, scientific innovation, and sustained academic contributions by women researchers. Maryam Kheirabadi has established a research profile in computer science, artificial intelligence, medical image analysis, and intelligent healthcare systems through peer-reviewed publications indexed in Scopus. Her work demonstrates interdisciplinary applications of deep learning, convolutional neural networks, recurrent neural networks, and medical decision-support systems, contributing to diagnostic technologies and intelligent recommendation systems.[1]

Abstract

Maryam Kheirabadi’s scholarly work focuses on applying artificial intelligence techniques to healthcare, medical image processing, pattern recognition, and educational recommendation systems. Her publications emphasize convolutional neural networks, deep learning architectures, image classification, recurrent neural networks with attention mechanisms, and computer-assisted diagnosis of gastric diseases. These contributions illustrate the integration of computer science methodologies into practical healthcare applications while advancing intelligent computational models for diagnostic accuracy and automated decision support.[2]

Keywords

Artificial Intelligence, Deep Learning, Computer Science, Medical Image Analysis, Convolutional Neural Networks, Healthcare Informatics, Machine Learning, Clinical Decision Support

Introduction

Artificial intelligence has become an increasingly influential discipline for solving complex healthcare challenges. The combination of machine learning, deep neural networks, and biomedical image analysis has improved diagnostic accuracy, automated disease detection, and clinical decision support. Within this evolving research landscape, Maryam Kheirabadi has contributed to studies addressing intelligent diagnostic systems, neural network optimization, and educational recommender systems, demonstrating interdisciplinary collaboration between computer science and healthcare research.[2]

Research Profile

According to the supplied Scopus metrics, Maryam Kheirabadi has authored sixteen indexed publications, received more than seven hundred citations, and achieved an h-index of nine. Her research portfolio centers on artificial intelligence, image processing, neural networks, biomedical signal analysis, and intelligent software systems. Her publications demonstrate continued engagement with computational approaches that support clinical diagnosis and intelligent educational technologies.[1]

Research Contributions

  • Development of deep convolutional neural network models for gastric cancer diagnosis using tongue image analysis.
  • Research on intelligent educational recommender systems employing recurrent neural networks and attention mechanisms.
  • Advancement of MRI brain tumor classification through convolutional neural network architectures.
  • Application of biomedical image processing techniques for healthcare diagnostics.
  • Contribution to explainable and data-driven medical decision-support technologies.

Publications

  • Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep Convolutional Networks (2021). Journal of Information Systems and Telecommunication.
  • Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention Technique (2021). Applied Artificial Intelligence. DOI: https://doi.org/10.1080/08839514.2021.2005298
  • Increasing the Accuracy in the Diagnosis of Stomach Cancer Based on Color and Lint Features of Tongue (2021). Biomedical Signal Processing and Control. DOI: https://doi.org/10.1016/j.bspc.2021.102782
  • Y-net: A Reducing Gaussian Noise Convolutional Neural Network for MRI Brain Tumor Classification with NADE Concatenation (2021). Biomedical Physics and Engineering Express.
    DOI: https://doi.org/10.1088/2057-1976/ac107b

Research Impact

The reported citation record reflects meaningful scholarly visibility within computer science and healthcare-related artificial intelligence research. Her studies contribute to the development of intelligent diagnostic systems, automated disease classification, medical image interpretation, and educational recommendation technologies. These interdisciplinary contributions support ongoing research into machine learning applications that improve analytical performance and healthcare decision-making.[3]

Award Suitability

Maryam Kheirabadi demonstrates qualifications consistent with the objectives of the Women Researcher Award through sustained scientific publication, interdisciplinary innovation, measurable citation performance, and contributions to artificial intelligence applications in healthcare. Her research profile reflects active engagement in internationally indexed scholarship and supports recognition for advancing computational methods with practical societal applications.[1]

Conclusion

Maryam Kheirabadi’s publication portfolio illustrates the integration of artificial intelligence with healthcare and intelligent information systems. Her contributions to deep learning, neural network design, biomedical imaging, and recommendation systems have strengthened interdisciplinary research in computer science. The documented publication record, citation performance, and research focus collectively support recognition within academic achievement and research excellence programs.

References

  1. Elsevier. (n.d.). Scopus Author Details: Maryam Kheirabadi, Author ID 57210153054. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57210153054
  2. Kheirabadi, M., et al. (2021). Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep Convolutional Networks. Journal of Information Systems and Telecommunication.
    http://www.scopus.com/inward/record.url?eid=2-s2.0-85111735812&partnerID=MN8TOARS
  3. Kheirabadi, M., et al. (2021). Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention Technique. Applied Artificial Intelligence. DOI: https://doi.org/10.1080/08839514.2021.2005298
  4. Kheirabadi, M., et al. (2021). Increasing the Accuracy in the Diagnosis of Stomach Cancer Based on Color and Lint Features of Tongue. Biomedical Signal Processing and Control. DOI: https://doi.org/10.1016/j.bspc.2021.102782
  5. Kheirabadi, M., et al. (2021). Y-net: A Reducing Gaussian Noise Convolutional Neural Network for MRI Brain Tumor Classification with NADE Concatenation. Biomedical Physics and Engineering Express. DOI: https://doi.org/10.1088/2057-1976/ac107b

Qin Liu | Big Data | Editorial Board Member

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Assoc. Prof. Dr. Qin Liu | Big Data | Editorial Board Member

Assoc. Prof. Dr. Qin Liu | Big Data | Dean at Wuhan University of Technology | China

Big Data has become the defining pillar of the academic identity, research vision, and professional impact of Assoc. Prof. Dr. Qin Liu, a distinguished scholar in management science whose work integrates data-driven intelligence with innovation management, green entrepreneurship, and strategic decision-making. Assoc. Prof. Dr. Qin Liu received a Ph.D. degree in Management from Wuhan University, following a Master’s degree in Management and a Bachelor of Management from Wuhan University of Technology, forming a rigorous academic foundation that combines management theory, quantitative analysis, and applied policy research. Throughout his professional career, Assoc. Prof. Dr. Qin Liu has accumulated extensive experience in government industrial planning projects and enterprise management consulting initiatives, where Big Data methodologies have been applied to real-world policy evaluation, strategic planning, and organizational optimization. He has presided over major nationally funded projects, including a general project of the National Social Sciences Foundation and soft science projects supported by provincial science and technology departments, while also actively participating in multiple national, provincial, and municipal scientific research programs. The professional experience of Assoc. Prof. Dr. Qin Liu reflects a rare balance between academic leadership and applied consulting, enabling him to translate Big Data insights into actionable management and policy outcomes. The research interests of Assoc. Prof. Dr. Qin Liu center on Big Data–driven business analysis, intelligent management decision-making, green entrepreneurship, innovation management, digital empowerment, and sustainable industrial transformation, with particular emphasis on new energy vehicles, green innovation networks, and digital economy governance. His research skills include Big Data analytics, text mining, knowledge framework construction, multi-agent modeling, complex network analysis, policy evaluation, econometric modeling, and intelligent decision-support systems, all of which support interdisciplinary inquiry at the intersection of data science and management. In terms of academic achievements, Assoc. Prof. Dr. Qin Liu has published more than forty high-quality academic papers in leading international journals, authored one scholarly monograph, and edited three teaching textbooks, contributing substantially to both research advancement and talent cultivation. His publications demonstrate consistent influence in applying Big Data to energy policy, sustainability, innovation ecosystems, and intelligent manufacturing. Although formal awards and honors are embedded primarily within competitive national research grants and academic leadership roles, his recognition is evident through repeated selection as principal investigator for major projects and publication in top-tier journals. In conclusion, Assoc. Prof. Dr. Qin Liu stands as a forward-looking scholar whose sustained commitment to Big Data–enabled management science continues to shape intelligent decision-making, green innovation, and sustainable economic development in the digital era.

Profile: Scopus

Featured Publications 

Liu, Q., Wen, X., & Cao, Q. (2023). Multi-objective development path evolution of new energy vehicle policy driven by big data: From the perspective of economic–ecological–social. Applied Energy. 
Liu, Q., Jia, M., & Xia, D. (2023). Dynamic evaluation of new energy vehicle policy based on text mining of PMC knowledge framework. Journal of Cleaner Production. 
Chen, J., & Liu, Q. (2023). The green consumption behavior process mechanism of new energy vehicles driven by big data: From a metacognitive perspective. Sustainability. 
Wang, S., & Liu, Q. (2023). Decentralized multi-agent collaborative innovation platform for new energy vehicle core technology breakthrough with digital empowerment: From the perspective of prospect theory. Heliyon. 
Li, H. Y., Liu, Q., & Ye, H. Z. (2023). Digital development influencing mechanism on green innovation performance: A perspective of green innovation network. IEEE Access. 
He, Z., & Liu, Q. (2023). The crossover cooperation mode and mechanism of green innovation between manufacturing and internet enterprises in digital economy. Sustainability. 
Liu, Q., & Chen, G. (2022). Research on government countermeasure optimization of multi-agent complex network game in intelligent vehicle cooperative manufacturing driven by big data. Proceedings of the International Conference on Electronics, Communications and Information Technology. 
Muazu, A., Yu, Q., & Liu, Q. (2023). Does renewable energy consumption promote economic growth? An empirical analysis of panel threshold based on 54 African countries. International Journal of Energy Sector Management. 

John Rowen Miano | Statistics | Best Researcher Award

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Mr. John Rowen Miano | Statistics | Best Researcher Award

Mr. John Rowen Miano | Statistics – Student at Cebu Technological University, Philippines

John Rowen Miano is an aspiring early-career researcher whose work sits at the intersection of computational biology and agricultural science. Based at Cebu Technological University, he is known for applying mathematical and computational tools to explore natural product chemistry, particularly in the field of agrochemical development. His independent research using molecular docking techniques to investigate plant-derived inhibitors has drawn academic interest and showcases his potential as a young innovator in sustainable agriculture. His initiative, curiosity, and analytical mindset distinguish him among his peers, making him a promising candidate for future academic and scientific excellence.

Profile Verified:

ORCID

Education:

John is currently pursuing his studies at Cebu Technological University under the Department of Mathematics and Statistics. His academic focus blends quantitative analysis with biological research, giving him a unique edge in computational studies. Through coursework and project-based learning, he has developed strong foundations in mathematics, statistics, and bioinformatics—skills that are critical for in silico research and predictive modeling. His education emphasizes both theoretical understanding and practical application, which is evident in his recent research outputs.

Experience:

John’s primary experience comes from his involvement as a student researcher at his university. During this time, he has conducted independent and guided research focused on plant-based antimicrobial agents. He has experience in molecular docking, virtual screening, database preparation, and software tools such as AutoDock and PyRx. His work has been presented at conferences and shared on academic platforms like Zenodo. He has collaborated with faculty for project feedback and scientific validation, and he is gradually building a network of fellow researchers within his institution.

Research Interests:

John’s research interests include molecular docking, phytochemistry, plant pathology, and the computational screening of bioactive compounds. He is particularly focused on identifying eco-friendly alternatives to synthetic agrochemicals by analyzing the inhibitory effects of natural phytochemicals against plant pathogens. His current study involves the evaluation of Euphorbia tirucalli compounds against Xanthomonas oryzae, the causative agent of bacterial leaf blight in rice. His broader interests also include artificial intelligence applications in drug discovery, sustainable agriculture, and the use of statistical models to predict pathogen resistance.

Awards:

As an emerging researcher, John has not yet received formal awards; however, he has been recognized at the university level for research presentation and participation. His poster presentation at a recent academic conference has gained early citations, demonstrating the relevance and growing academic attention toward his work. His nomination for the “Best Researcher Award” reflects both his existing achievements and the future potential that he holds as a developing scientific contributor.

Publications 📚:

  1. 🧪 Phytochemicals of Euphorbia tirucalli and their Inhibitory Potential against Xanthomonas oryzae Ddl Enzyme: An In silico Evaluation for Potential Agrochemical
    📅 Published: 2024 | Platform: Zenodo
    🔗 DOI: 10.5281/ZENODO.12183931
    📌 Cited by 2 articles

Conclusion:

John Rowen Miano is a highly motivated and intellectually capable young researcher. His contributions—although still at the early stage—exemplify innovation, relevance, and commitment to solving real-world agricultural problems. With a strong foundation in mathematical sciences and a growing body of work in computational biology, he is poised to become a key contributor to sustainable agrochemical discovery. His single-author research, proactive approach, and dedication to scientific exploration make him a strong nominee for the “Best Researcher Award” under an early-career or emerging talent category. He represents the next generation of researchers who merge computational power with natural science to address urgent agricultural and environmental challenges.

 

 

Supritha Nagendra | Big data analytics | Best Researcher Award

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Mrs. Supritha Nagendra | Big data analytics | Best Researcher Award

Mrs. Supritha Nagendra | Big data analytics- Research scholar at BMSIT&M, India

Supritha N is a dedicated academic professional with extensive experience in the field of Computer Science and Engineering (CSE). With a career spanning over 8.6 years in teaching and research, she has worked across several reputed institutions, contributing immensely to the academic and research community. Currently pursuing her Ph.D. at BMSIT&M, she has established herself as an innovative researcher with a focus on Big Earth Analytics, remote sensing, and active learning models. Supritha’s dedication to her work and her ability to blend academic and research excellence make her a deserving candidate for various accolades in the field of research and education.

Profile:

Orcid

Education:

Supritha holds a robust academic background, with a Ph.D. in Computer Science and Engineering from BMSIT&M. She completed her M.Tech. in the same field from A.P.S.C.E, securing an impressive 76%. Prior to this, she earned her B.E. from JVIT, with a 71% score. Her academic journey also includes a strong foundation in basic sciences, as evidenced by her outstanding performance during her P.U.C. and SSLC years, where she achieved high marks in her respective board examinations. Supritha’s consistent academic performance highlights her strong analytical skills, determination, and commitment to continuous learning.

Experience:

Supritha N brings over 8.6 years of professional experience, primarily in teaching and research roles. Her career began as a Lecturer at JVIT, where she worked from 2009 to 2011, before transitioning to a more extensive role as an Assistant Professor. She served at East West Institute of Technology from 2017 to 2021 and at RNS Institute of Technology from 2021 to 2024. Throughout her tenure in these positions, Supritha has mentored students, taught various undergraduate and postgraduate courses, and played a pivotal role in guiding student projects. Her comprehensive teaching portfolio includes subjects such as Digital Design and Computer Organization, Big Data Analytics, Microprocessors and Embedded Systems, and Cryptography, among others. Her experience in teaching has been marked by her innovative approach to complex subjects, enabling students to grasp critical concepts with ease.

Research Interests:

Supritha’s research interests lie at the intersection of Big Data Analytics, Remote Sensing, and Artificial Intelligence. She is particularly focused on developing resource-efficient models for Big Earth Spatial Data Management and Analytics (BESDMA). Her Ph.D. research aims to design sparse image representation models to improve remote sensing applications and resource efficiency in Earth observation systems. Additionally, Supritha has worked on various projects related to machine learning and active learning, emphasizing the importance of quality-sensitive solutions in data processing and analysis. Her innovative work in these fields has led to the publication of several impactful papers, furthering knowledge in her areas of research.

Awards:

Supritha N has earned numerous accolades throughout her career, underscoring her dedication and excellence in both research and teaching. She was awarded the Best Paper Award for her paper on “Object Placement Using Augmented Reality” at the 2nd National Conference on Computing Technology in 2022. Additionally, her work on “Communication for Motor Neuron Disease Patients via Eye Blink to Voice Recognition” received the Best Paper Award at the 14th National Conference in 2019. Her contributions to research have been recognized not only in academic circles but also through her active participation in national and international conferences and her ability to translate her research into real-world applications.

Publications:

Supritha N has authored and co-authored several research papers, many of which have been published in esteemed journals and conferences. Some of her key publications include:

  1. “Deep Spatio-textural feature driven multi-constraints pool-based active learning model for resource-efficient big earth observations” (2025), International Journal of Remote Sensing 🌍📚 [Cited by: 15 articles]
  2. “A Comprehensive Study on Satellite based Data Communication for Big Earth Observation Systems” (2024), IEEE International Conference on Knowledge Engineering and Communication Systems 🌐📈 [Cited by: 7 articles]
  3. “A Comparative Study of The CNN Based Models Used for Remote Sensing Image Classification” (2023), IJEER Journal 🛰️📄 [Cited by: 10 articles]
  4. “The Scope of Artificial Intelligence in Agriculture and Healthcare Sectors – A Comprehensive Review” (2022), Advanced Engineering Sciences 🤖🌾 [Cited by: 5 articles]

Conclusion:

Supritha N’s journey in academia and research reflects a high level of commitment, excellence, and passion for innovation. Her career trajectory, marked by substantial teaching experience and groundbreaking research, places her in a strong position for recognition in the field of Computer Science and Engineering. Her research work, particularly in Big Earth Analytics, remote sensing, and AI-driven models, has not only advanced academic understanding but also shown promise for real-world applications. With multiple publications, awards, and a demonstrated commitment to mentoring students, Supritha N is a deserving nominee for the Best Researcher Award. Her work continues to inspire peers and students alike, and she remains focused on pushing the boundaries of knowledge in her field.