Smita Nirkhi | Computer Science | Research Excellence Award

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Research Excellence Award

Smita Nirkhi

Symbiosis Institute of Technology, India

Smita Nirkhi
Affiliation Symbiosis Institute of Technology
Country India
Scopus ID 25927431900
Documents 45
Citations 79
h-index 5
Subject Area Computer Science
Event Top Teachers Awards
ORCID 0000-0002-1235-8865
Google Scholar VJxAP-wAAAAJ

Smita Nirkhi is an associate professor at Symbiosis Institute of Technology, Nagpur, India, working in artificial intelligence, machine learning, data mining, and digital forensics. Her Scopus record lists 45 documents, 79 citations, and an h-index of 5 within Computer Science [1]. This article reviews her research profile and suitability for the Research Excellence Award conferred through the Top Teachers Awards event [4].

Abstract

This article documents the scholarly record of Smita Nirkhi in computer science, emphasizing artificial intelligence, data mining, and digital forensics. Her indexed output and citation profile support a case for recognition in the Research Excellence Award category, evaluated here through verifiable bibliometric and professional evidence [2].

Keywords

  • Artificial Intelligence
  • Machine Learning
  • Data Mining
  • Digital Forensics
  • Cyber Security
  • Pattern Recognition

Introduction

Smita Nirkhi has more than two decades of teaching experience and a doctorate in Computer Science and Engineering from Nagpur University, awarded in 2017 for work on data mining techniques for digital forensics. Her research spans web mining, cyber security, pattern recognition, and machine learning, and she has held academic leadership roles including head of an artificial intelligence department and IQAC in-charge [1].

Research Profile

The Scopus author profile records 45 documents, 79 citations, and an h-index of 5, with Computer Science as the principal subject area [1]. The Google Scholar profile reports broader visibility, with 464 citations and an h-index of 10 across 112 documents [2]. Her ORCID record provides a persistent identifier for these outputs [3].

Research Contributions

Smita Nirkhi has published 87 research papers, including 25 international journal papers, 57 international conference papers, and 5 national conference papers. Her work includes a comparative study of authorship identification techniques for cyber forensics analysis and an early examination of artificial neural networks in data mining [5] [6]. She has also authored books on data mining in cyber forensics and machine learning.

Publications

Her publication record comprises journal and conference articles in data mining, forensics, and machine learning, indexed in Scopus and Google Scholar [1] [2]. Notable works include forensic authorship identification research and neural network applications in data mining, with DOIs available through the cited repositories [5].

Research Impact

Citation metrics indicate a developing international footprint, with Scopus reporting 79 citations and Google Scholar reporting 464 citations for the same author [1] [2]. Her funded projects include an AICTE research grant, and she has supervised numerous postgraduate and undergraduate projects, contributing to research capacity in her institution.

Award Suitability

The Research Excellence Award recognizes sustained scholarly contribution and measurable research impact. Smita Nirkhi’s indexed publication record, citation profile, funded research, and academic leadership collectively support her suitability for this recognition under the Top Teachers Awards event [4].

Conclusion

Smita Nirkhi presents a consistent record of research activity in computer science, supported by indexed publications, citation data, and professional service. Her profile aligns with the criteria of the Research Excellence Award, and her verified identifiers enable independent confirmation of the reported metrics [1] [2] [3].

References

  1. Elsevier. (n.d.). Scopus author details: Smita Nirkhi, Author ID 25927431900. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=25927431900
  2. Google Scholar. (n.d.). Citations profile: Smita Nirkhi. Google Scholar.
    https://scholar.google.com/citations?user=VJxAP-wAAAAJ&hl=en&oi=sra
  3. ORCID. (n.d.). ORCID record: Smita Nirkhi, 0000-0002-1235-8865. ORCID.
    https://orcid.org/0000-0002-1235-8865
  4. Top Teachers Awards. (n.d.). Award information and nomination details. Top Teachers.
    https://topteachers.net
  5. Nirkhi, S., & Dharaskar, R. V. (2013). Comparative study of authorship identification techniques for cyber forensics analysis. arXiv preprint arXiv:1401.6118.
    https://doi.org/10.48550/arXiv.1401.6118
  6. Nirkhi, S. (2010). Potential use of artificial neural network in data mining. Proceedings of the 2nd International Conference on Computer and Automation Engineering (ICCAE).
    https://doi.org/10.1109/ICCAE.2010.5451537

Huili Zhang | Computer Science and Artificial Intelligence | Innovative Research Award

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Innovative Research Award

Huili Zhang
Shanghai University, China

Huili Zhang
Affiliation Shanghai University
Country China
Scopus ID 58607120700
Documents 17
Citations 223
h-index 8
Subject Area Computer Science and Artificial Intelligence
Event Top Teachers Awards
ORCID 0000-0002-3336-1756

The Innovative Research Award recognizes researchers whose scholarly activities demonstrate originality, methodological rigor, and measurable impact within their respective disciplines. Huili Zhang of Shanghai University has established a research profile centered on artificial intelligence, medical image analysis, radiomics, and intelligent diagnostic systems. Through interdisciplinary collaboration and the application of advanced machine learning methods to healthcare challenges, Zhang has contributed to the development of computational frameworks that support disease detection, classification, and clinical decision-making.[1]

Abstract

Huili Zhang’s research integrates artificial intelligence and medical imaging technologies to improve diagnostic accuracy and predictive modeling in healthcare. Her publications address multimodal ultrasound analysis, radiomics, deep learning, and knowledge distillation techniques, emphasizing clinically relevant solutions for cancer diagnosis and treatment evaluation. The body of work demonstrates a consistent focus on translating computational innovation into practical medical applications.[2]

Keywords

Artificial Intelligence, Deep Learning, Medical Imaging, Radiomics, Ultrasound Diagnostics, Knowledge Distillation, Computer-Aided Diagnosis, Healthcare Analytics.

Introduction

The convergence of artificial intelligence and healthcare has created opportunities for improved diagnostic efficiency and personalized treatment strategies. Within this evolving landscape, Huili Zhang has contributed to research that applies machine learning and image-based analytics to complex clinical problems. Her studies demonstrate the growing importance of data-driven methodologies in modern medical practice.[3]

Research Profile

As a researcher affiliated with Shanghai University, Zhang has developed expertise in computer science and artificial intelligence with a strong emphasis on biomedical applications. Her scholarly record includes peer-reviewed publications focused on multimodal imaging, radiomics-based prediction models, and intelligent healthcare systems. The available bibliometric indicators demonstrate growing academic influence across interdisciplinary domains.[1]

Research Contributions

  • Development of multi-view and multimodal deep learning frameworks for liver cancer diagnosis using ultrasound imaging.
  • Advancement of generalized knowledge distillation approaches for medical image interpretation.
  • Creation of MRI-based radiomics models for differentiating spinal multiple myeloma from metastatic lesions.
  • Application of dual-modal ultrasound and molecular data integration for predicting chemotherapy response in breast cancer patients.
  • Research into deep learning radiomics for distinguishing benign and malignant breast conditions.

Publications

  1. Multi-view doubly supervised knowledge distillation for diagnosis of liver cancers with imbalanced ultrasound imaging modalities (2026).
  2. Multi-View Disentanglement-based Bidirectional Generalized Distillation for Diagnosis of Liver Cancers with Ultrasound Images (2024).
  3. Radiomics Model Based on MRI to Differentiate Spinal Multiple Myeloma from Metastases: A Two-center Study (2024).
  4. Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer (2023).
  5. Deep Learning Radiomics of Ultrasonography for Differentiating Sclerosing Adenosis from Breast Cancer (2023).

Research Impact

The research output attributed to Zhang reflects a commitment to improving diagnostic workflows through advanced computational techniques. By combining machine learning, radiomics, and multimodal imaging data, her work contributes to enhanced disease characterization and supports evidence-based clinical decision-making. Citation activity and publication placement indicate recognition within the scientific community.[4]

Award Suitability

Huili Zhang’s research portfolio aligns with the objectives of the Innovative Research Award due to its interdisciplinary nature, methodological innovation, and relevance to healthcare technology. The integration of artificial intelligence with clinical imaging illustrates a forward-looking approach that addresses contemporary challenges in medical diagnostics while contributing to scientific advancement.[5]

Conclusion

Huili Zhang represents a growing cohort of researchers leveraging artificial intelligence to transform healthcare diagnostics. Her contributions to medical imaging, radiomics, and deep learning demonstrate both scholarly rigor and practical relevance. These achievements support recognition through the Innovative Research Award and reflect continued potential for future scientific impact.

References

  1. Elsevier. (n.d.). Scopus author details: Huili Zhang, Author ID 58607120700. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58607120700
  2. Zhang, H. (2026). Multi-view doubly supervised knowledge distillation for diagnosis of liver cancers with imbalanced ultrasound imaging modalities.
    DOI: https://doi.org/10.1016/j.engappai.2026.115252
  3. Zhang, H. (2024). Multi-View Disentanglement-based Bidirectional Generalized Distillation for Diagnosis of Liver Cancers with Ultrasound Images.
    DOI: https://doi.org/10.1016/j.ipm.2024.103855
  4. Zhang, H. (2024). Radiomics Model based on MRI to Differentiate Spinal Multiple Myeloma from Metastases: A Two-center Study.
    DOI: https://doi.org/10.1016/j.jbo.2024.100599
  5. Zhang, H. (2023). Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer.
    DOI: https://doi.org/10.1016/j.acra.2023.03.036
  6. Zhang, H. (2023). Deep Learning Radiomics of Ultrasonography for Differentiating Sclerosing Adenosis from Breast Cancer.
    DOI: https://doi.org/10.3233/CH-221608

Weidong Ji | Computer Science | Editorial Board Member

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Dr. Weidong Ji | Computer Science | Editorial Board Member

Harbin Normal University | China

Dr. Weidong Ji is a distinguished Chinese scholar known for his innovative contributions to artificial intelligence in education, learning analytics, and intelligent recommendation technologies, with a strong research presence reflected across international journals and conferences. His work integrates machine learning, knowledge graphs, deep learning models, and cognitive theories to enhance personalized learning, online education systems, student engagement assessment, and data-driven educational decision-making. With a Scopus record of 39 documents, 102 citations from 100 citing documents, and an h-index of 6, Dr. Ji’s scholarly influence continues to grow as his recent works advance emerging domains such as quantum-constructivism-based knowledge tracing, lightweight human–computer interaction models, and user-preference-driven recommendation algorithms. Google Scholar metrics (if available) would further extend his citation visibility, demonstrating the expanding global use of his models in adaptive learning, course recommendations, and real-time student behavior analysis. He also contributes to the academic community through participation in peer-review processes and editorial activities that support the development of high-quality research in AI-driven educational technology. Dr. Ji’s recent publications showcase cutting-edge computational frameworks such as neural knowledge graph reasoning, lightweight vision models for engagement analytics, and personalized prediction architectures for sparse learning environments. His contributions position him as an emerging leader at the intersection of educational psychology and computational intelligence, emphasizing practical applicability, algorithmic efficiency, and innovative pedagogical design, demonstrating excellence aligned with this award category as an Editorial Board Member.

Publication Profile

Scopus

Featured Publications

  • Authors unavailable. (2025). Knowledge graph convolutional networks with user preferences for course recommendation. Scientific Reports.

  • Authors unavailable. (2025). CQSA-KT: Research on personalized knowledge tracing based on quantum-constructivism in sparse learning environments. Knowledge Based Systems.

  • Authors unavailable. (2025). LightNet: A lightweight head pose estimation model for online education and its application to engagement assessment. Journal of King Saud University Computer and Information Sciences.