Yunchao Cao | Engineering | Innovative Research Award

Published on by Forensic Scientist Awards

Innovative Research Award

Yunchao Cao
Hunan University, China

Yunchao Cao
Affiliation Hunan University
Country China
Documents 1
Subject Area Engineering
Event International Forensic Scientist Awards
ORCID 0009-0003-6522-5745

Yunchao Cao is a researcher affiliated with Hunan University, China, whose reported research output addresses point cloud filtering for slope light detection and ranging (LiDAR) applications. The publication focuses on applying attention mechanisms and model scaling to PointNet++, a deep-learning architecture for processing three-dimensional point clouds. This research topic connects engineering, artificial intelligence, and geospatial data processing, with potential relevance to the analysis of complex terrain and slope environments. The recognition profile presents the available publication information without assuming unverified citation metrics or research achievements.

Abstract

Yunchao Cao’s reported research examines an attention-enhanced and scaled PointNet++ approach for filtering slope LiDAR point clouds. Point cloud filtering is an important processing stage in three-dimensional data analysis because unwanted measurements and irrelevant points can complicate the interpretation of terrain surfaces. Combining attention mechanisms with model scaling offers a methodological direction for investigating feature representation and computational performance. The associated article is listed in AI in Civil Engineering with DOI 10.1007/s43503-026-00110-z. Its publication metadata identifies a specific contribution to machine-learning-based engineering research, although detailed experimental results and independent impact indicators require verification from the full article. [1]

Keywords

Point cloud filtering; slope LiDAR; PointNet++; attention mechanism; model scaling; three-dimensional data; deep learning; civil engineering.

Introduction

LiDAR technology captures spatial information by measuring distances with laser pulses, producing point clouds that can support terrain mapping and engineering assessment. In slope-related applications, the quality of these datasets can influence subsequent modelling and interpretation. Deep-learning methods provide approaches for learning spatial features from point-based representations, while attention mechanisms can help models emphasize informative features. PointNet++ is designed to learn hierarchical representations of point clouds, making it a relevant architecture for this research direction. [2]

Research Profile

The available publication record places Cao’s documented research in engineering-oriented artificial intelligence, particularly the processing of three-dimensional spatial data. The reported study combines a point-cloud learning architecture with attention and scaling strategies. Further details concerning the researcher’s complete specialization, institutional research group, and broader project portfolio have not been independently established from the supplied information.

Research Contributions

The reported study identifies several areas of methodological interest:

  • Investigating point cloud filtering methods tailored to slope LiDAR data.
  • Incorporating an attention mechanism into a PointNet++-based processing approach.
  • Exploring model scaling as part of the architecture’s design.
  • Contributing to research at the intersection of three-dimensional data processing and civil engineering.

These points describe the study’s stated subject and approach; quantitative improvements over baseline methods should be assessed using the article’s reported experiments. [1]

Publications

One publication is provided in the supplied research record:

  1. “Point cloud filtering method for slope LiDAR based on attention mechanism and model scaling of PointNet++.” AI in Civil Engineering. Listed publication date: December 2026. [1]

As this profile is dated October 2026, the supplied December 2026 publication date is future-dated and should be checked against the publisher’s current record before final publication.

Research Impact

The documented research topic has potential relevance to terrain-data preparation, point-cloud analysis, and machine-learning applications in engineering. However, the supplied information does not establish citation counts, h-index, adoption by practitioners, or independently measured performance gains. These indicators should remain unreported until confirmed through reliable bibliographic databases or the published study. The listed document count is one, as provided for this recognition profile.

Award Suitability

The Innovative Research Award profile recognizes the methodological focus of Cao’s reported work on attention mechanisms, point-cloud processing, and model scaling. These subjects align with innovation-oriented engineering research because they investigate computational approaches to a practical data-processing problem. A complete award evaluation should additionally consider the originality of the method, experimental validation, comparison with established techniques, reproducibility, and demonstrated significance. This profile records the research area without implying an independently verified award decision or unsupported performance claims.

Conclusion

Yunchao Cao’s reported publication presents a research direction combining PointNet++, attention mechanisms, and model scaling for slope LiDAR point cloud filtering. The work connects artificial intelligence with engineering data processing and provides a defined basis for evaluating methodological innovation. Verification of publication timing, bibliographic identifiers, citation metrics, and experimental findings would strengthen the completeness and reliability of this academic recognition profile.

References

  1. Springer Nature. (2026). Point cloud filtering method for slope LiDAR based on attention mechanism and model scaling of PointNet++. AI in Civil Engineering. DOI: 10.1007/s43503-026-00110-z. Publication details and date should be confirmed against the publisher’s record.
    https://doi.org/10.1007/s43503-026-00110-z
  2. Qi, C. R., Yi, L., Su, H., & Guibas, L. J. (2017). PointNet++: Deep hierarchical feature learning on point sets in a metric space. Advances in Neural Information Processing Systems, 30.
  3. ORCID. (n.d.). Yunchao Cao: ORCID researcher identifier.
    https://orcid.org/0009-0003-6522-5745

Janet Mercy V | Engineering | Innovative Research Award

Published on by Forensic Scientist Awards

Innovative Research Award

Janet Mercy V
Holycross Engineering College, India

                Janet Mercy V
Affiliation Holycross Engineering College
Country India
Scopus ID 58175433900
Documents 1
Citations 1
h-index 1
Subject Area Engineering
Event International Forensic Scientist Awards

The Innovative Research Award recognizes research contributions that demonstrate methodological development, technical inquiry, and potential value to scientific and engineering practice. This academic recognition profile presents the available research record of Janet Mercy V, affiliated with Holycross Engineering College in India. The documented publication concerns signal processing techniques for classifying normal and cancer cells, connecting computational methods with biomedical analysis. The profile summarizes the available bibliographic information and provides a research-focused overview without assuming achievements beyond the supplied evidence.[1]

Abstract

Janet Mercy V’s available research record includes a conference paper titled “Classification of Normal and Cancer Cells by Using Signal Processing Techniques—A Survey,” published in the proceedings of the 2022 1st International Conference on Computer, Power and Communications (ICCPC 2022). The paper addresses the use of signal processing techniques in distinguishing normal cells from cancer cells. This subject illustrates the intersection of engineering, computational analysis, and biomedical research. The available bibliographic record reports one document and one citation, with a Scopus h-index of 1. These indicators describe the indexed record supplied for this profile and should be interpreted in the context of its size.[1]

Keywords

Signal processing; cancer cell classification; biomedical engineering; computational analysis; engineering research; cell identification; scientific survey.

Introduction

Engineering methods support the development of analytical approaches for complex biological problems. Signal processing can help researchers examine measurable patterns and identify characteristics that may distinguish different cell classes. Research in this area requires careful consideration of signal representation, classification methodology, validation, and the limitations of available data. Janet Mercy V’s indexed conference-paper record relates to this broader interdisciplinary field. The supplied information establishes the publication topic but does not provide enough detail to attribute specific experimental results or clinical applications to the researcher.

Research Profile

The documented research topic is situated within engineering, with a particular connection to signal processing and biomedical classification. A survey-based study can organize existing approaches, compare methodological directions, and identify challenges for further investigation. In cancer-cell classification, relevant considerations may include signal quality, feature extraction, reproducibility, classification accuracy, and the availability of reliable reference data. These are general considerations for the field rather than confirmed methods or findings of the listed paper. Further assessment of the researcher’s specialization would benefit from the full paper and additional verified publications.

Research Contributions

The listed conference paper identifies a research direction focused on applying signal processing techniques to normal and cancer cell classification. Its survey framing indicates an examination of methodological approaches within the topic, although the supplied record does not disclose its detailed comparisons or conclusions. The research area is relevant to continued work on computational support for biomedical investigation. Establishing the paper’s precise original contribution requires examination of the full text, including its review methodology, cited evidence, and stated research gaps.[1]

Publications

The available Scopus record identifies the following conference paper:

  • “Classification of Normal and Cancer Cells by Using Signal Processing Techniques—A Survey.” G. Dency Flora and co-authors. In 2022 1st International Conference on Computer, Power and Communications (ICCPC 2022) Proceedings, 2022. The supplied record lists one citation. The complete author order and DOI should be verified against the official proceedings record before formal bibliographic reuse.[1]

Research Impact

The supplied bibliometric indicators are one indexed document, one citation, and an h-index of 1. These figures provide a limited snapshot of the researcher’s recorded publication activity rather than a comprehensive measure of research quality. Citation counts may change over time and depend on database coverage, publication age, and subject-area practices. A more complete assessment would consider the full publication record, methodological quality, relevance to the field, and independently verifiable applications or follow-up studies.

Award Suitability

The documented topic offers a relevant basis for considering the relationship between engineering techniques and biomedical analysis under an innovative research theme. Its potential suitability for the Innovative Research Award should be evaluated against the award’s official criteria, the paper’s verified content, the nominee’s specific contribution, and evidence of originality or practical significance. The available record alone does not establish an award decision or independently demonstrate novelty. A fair assessment should distinguish confirmed bibliographic details from claims requiring additional documentation.

Conclusion

Janet Mercy V’s available research profile is associated with engineering and a conference paper on signal-processing approaches to cancer-cell classification. The subject reflects an interdisciplinary research direction with relevance to computational and biomedical investigation. The indexed metrics provide an initial bibliometric reference, while a stronger evaluation of originality, impact, and individual contribution requires review of the full publication and supporting evidence. This profile therefore presents the documented record in a neutral academic format.

References

  1. Scopus. (n.d.). Author details: Janet Mercy V, Author ID 58175433900. Elsevier. Author profile and bibliographic record for the listed conference paper.
    https://www.scopus.com/authid/detail.uri?authorId=58175433900
  2. International Conference on Computer, Power and Communications. (2022). 2022 1st International Conference on Computer, Power and Communications (ICCPC 2022) Proceedings. Conference proceedings record associated with the supplied publication details. Verify the paper metadata against the official proceedings.
  3. Flora, G. D., Indurekaa, S. R., Dhivya, S. D., and co-authors. (2022). Classification of Normal and Cancer Cells by Using Signal Processing Techniques—A Survey. Conference paper listed in the ICCPC 2022 proceedings. A verified DOI or direct publisher URL was not included in the supplied record.
  4. International Forensic Scientist Awards. (n.d.). Official award website.
    forensicscientist.org

Saeid Pashazadeh | Computer Science and Artificial Intelligence | Forensic Science Communication Award

Published on by Forensic Scientist Awards

Forensic Science Communication Award

Saeid Pashazadeh
University of Tabriz, Iran

             Saeid Pashazadeh
Affiliation University of Tabriz
Country Iran
Scopus ID 24825064000
Documents 74
Citations 769
h-index 16
Subject Area Computer Science and Artificial Intelligence
Event International Forensic Scientist Awards
ORCID 0000-0002-8949-9180

The Forensic Science Communication Award recognizes research and scholarly contributions that support the communication, interpretation, and responsible application of scientific knowledge. This academic recognition profile presents Saeid Pashazadeh, affiliated with the University of Tabriz, Iran, whose reported research record includes publications spanning artificial intelligence, distributed computing, Internet of Things technologies, and intelligent systems. These areas provide relevant technological foundations for developing reliable computational methods and communicating complex research findings across scientific disciplines. The profile summarizes the supplied bibliometric indicators and selected publications in the context of the International Forensic Scientist Awards.

Abstract

Saeid Pashazadeh’s supplied research profile records 74 documents, 769 citations, and an h-index of 16. The selected publications cover game-theoretic localization for Internet of Things healthcare applications, formal verification of crash-tolerant distributed systems, neural adaptation for unmanned aerial vehicle flight planning, and deep-learning-based voice analysis. Collectively, these works illustrate a research portfolio involving computational intelligence, algorithm design, and systems analysis. The award profile considers their relevance to interdisciplinary scientific communication without treating bibliometric indicators alone as proof of forensic specialization or award selection. [1]

Keywords

Forensic science communication; artificial intelligence; computer science; Internet of Things; game theory; distributed systems; formal verification; deep learning; neural adaptation; research impact.

Introduction

Scientific communication connects methodological advances with researchers, practitioners, and broader audiences. In computational research, this involves explaining algorithmic assumptions, evaluating evidence, and presenting technical outcomes in a reproducible and accessible form. Pashazadeh’s supplied publication records address several computational fields in which clarity, verification, and interpretation are important. His profile is presented for consideration in relation to the International Forensic Scientist Awards, with the distinction that the listed research topics do not independently establish direct forensic applications. [2]

Research Profile

The selected work reflects interests in intelligent algorithms, networked systems, computational modeling, and machine learning. These fields address challenges such as coordinating connected devices, checking distributed-system behavior, planning autonomous movement, and extracting patterns from voice data. Together, they demonstrate a cross-disciplinary technical orientation. The available information identifies the University of Tabriz as the researcher’s affiliation and Computer Science and Artificial Intelligence as the relevant subject area. [3]

Research Contributions

The supplied publication titles indicate several distinct contribution areas:

  • Game-theoretic localization methods for Internet of Things environments, including healthcare applications.
  • Cutoff theorems supporting model checking of crash-tolerant causal broadcast systems.
  • Intelligent three-dimensional flight-path planning for unmanned aerial vehicles using neural adaptation.
  • A comprehensive review of deep-learning approaches to voice-based gender detection.

These topics illustrate work across optimization, verification, autonomous systems, and machine learning. Their potential relevance to forensic practice would require assessment of specific applications, validation methods, and evidentiary requirements. [4]

Publications

  1. “Localization in the Internet of Things Based on Game Theory with Applications in the Healthcare.” Peer-to-Peer Networking and Applications, 2026.
  2. “Cutoff Theorems for the Model Checking of Crash-Tolerant Causal Broadcast.” Theory of Computing Systems, June 2026.
  3. “Intelligent 3-D Flight Path Planning for UAV Based on Epigenetic Learning Through Evolved Neural Adaptation (ELENA) Algorithm.” IEEE Open Journal of the Communications Society, 2026.
  4. “Voice-Based Gender Detection Using Deep Learning: A Comprehensive Review.” Book chapter, 2026.

Research Impact

The supplied Scopus indicators are 74 documents, 769 citations, and an h-index of 16. These measures offer a quantitative snapshot of publication and citation activity, but do not independently determine research quality, practical significance, or communication effectiveness. Citation totals can also vary over time as databases are updated. The figures should therefore be interpreted alongside article content, methodological rigor, reproducibility, and demonstrated application. [1]

Award Suitability

The research record offers material for evaluating technical scholarship and interdisciplinary communication. Relevant assessment criteria may include the clarity with which complex methods are explained, the accessibility of research findings, evidence of collaboration, and contributions to responsible scientific practice. Because the supplied publications primarily concern computational methods and intelligent systems, their specific relationship to forensic science communication should be established through documented applications or additional supporting evidence. This profile does not imply a confirmed award decision.

Conclusion

Saeid Pashazadeh’s reported research record combines publication activity with work across machine learning, distributed computing, and intelligent systems. The four selected publications provide concrete examples of this technical range. Consideration for the Forensic Science Communication Award should additionally examine evidence of communication quality and direct forensic relevance, ensuring that recognition reflects documented contributions as well as quantitative indicators.

References

  1. Elsevier. (n.d.). Scopus author details: Saeid Pashazadeh, Author ID 24825064000. Scopus. Bibliometric figures in this profile are based on the supplied researcher data and should be checked against the live record.
    https://www.scopus.com/authid/detail.uri?authorId=24825064000
  2. International Forensic Scientist Awards. (n.d.). Official awards website.
    https://forensicscientist.org/
  3. ORCID. (n.d.). Saeid Pashazadeh researcher identifier.
    https://orcid.org/0000-0002-8949-9180
  4. “Localization in the Internet of Things Based on Game Theory with Applications in the Healthcare.” (2026). Peer-to-Peer Networking and Applications.
    https://doi.org/10.1007/s12083-026-02300-z
  5. “Cutoff Theorems for the Model Checking of Crash-Tolerant Causal Broadcast.” (2026). Theory of Computing Systems.
    https://doi.org/10.1007/s00224-026-10283-w
  6. “Intelligent 3-D Flight Path Planning for UAV Based on Epigenetic Learning Through Evolved Neural Adaptation (ELENA) Algorithm.” (2026). IEEE Open Journal of the Communications Society.
    https://doi.org/10.1109/OJCOMS.2026.3710732
  7. “Voice-Based Gender Detection Using Deep Learning: A Comprehensive Review.” (2026). Book chapter.
    https://doi.org/10.1007/978-3-032-27860-9_13

Gajendra Halmandge | Engineering | Best Researcher Award

Published on by Forensic Scientist Awards

Best Researcher Award

Gajendra Halmandge
Sharnbasva University, India

Gajendra Halmandge
Affiliation Sharnbasva University
Country India
Google Scholar ID ly0KZqMAAAAJ
Documents 11
Citations 10
h-index 2
Subject Area Engineering
Event International Forensic Scientist Awards
ORCID 0000-0002-4363-1860

Gajendra Halmandge is an engineering researcher affiliated with Sharnbasva University, India, whose scholarly work addresses structural engineering, reinforced concrete behaviour, seismic response, impact loading, and analytical assessment of multi-storey building systems. His documented research output includes studies on hybrid fiber-reinforced concrete beams, nonlinear time-history analysis, structural irregularity, and seismic performance of high-rise buildings. These contributions demonstrate an applied research orientation focused on understanding structural response under dynamic and extreme loading conditions. [1]

Abstract

This article presents an academic recognition profile of Gajendra Halmandge in consideration of the Best Researcher Award. His research activities are situated within engineering, particularly structural and civil engineering applications involving reinforced concrete systems, seismic loading, structural dynamics, and impact behaviour. Selected publications indicate continuing engagement with analytical and experimental approaches to structural performance assessment. [1] [2]

Keywords

Structural Engineering; Reinforced Concrete; Seismic Analysis; Hybrid Fiber-Reinforced Concrete; Impact Loading; Nonlinear Time-History Analysis; Research Recognition.

Introduction

Engineering research plays an important role in improving the reliability and resilience of built infrastructure. Research concerning concrete behaviour, dynamic loading, seismic response, and structural irregularities contributes to the broader understanding of how buildings and structural components perform under demanding service conditions. Halmandge’s published work reflects engagement with these areas through studies examining both material-level and system-level structural behaviour. [2]

Research Profile

The research profile is centered on structural engineering investigations involving reinforced concrete frames, high-rise structures, hybrid fiber-reinforced concrete, and seismic performance analysis. His work applies engineering modelling and comparative analysis to investigate the influence of loading conditions, boundary conditions, structural geometry, and irregularity on system response. The available scholarly record identifies 11 documents, 10 citations, and an h-index of 2 based on the supplied research metrics.

Research Contributions

  • Comparative investigation of hybrid fiber-reinforced concrete beams subjected to low-velocity impact loading under varying boundary conditions.
  • Analysis of mass and geometric regularity and irregularity in multi-storey moment-resisting RCC frames using nonlinear time-history methods.
  • Assessment of podium effects on high-rise buildings subjected to seismic loading.

Publications

Selected research publications include A Comparative Study of the Behaviour of Hybrid Fiber-Reinforced Concrete (HFRC) Beams Subjected to Low-Velocity Impact Loads Under Various Boundary Conditions, published in the Engineering and Technology Journal in 2024. [2] Other documented works address nonlinear time-history analysis of RCC frames and podium impact on high-rise structures under seismic loading. [3] [4]

Research Impact

The research impact of this work is reflected through its contribution to contemporary discussions on structural safety, material behaviour, and earthquake-resistant design. Studies of impact resistance and nonlinear seismic response are relevant to engineering efforts seeking improved structural resilience. Citation and publication indicators provide one measurable perspective on scholarly visibility, while the technical relevance of individual studies demonstrates the practical orientation of the research programme.

Award Suitability

Gajendra Halmandge’s research profile demonstrates suitability for recognition under the Best Researcher Award category based on documented scholarly publications, focused engineering research, and contributions addressing structural performance under dynamic and seismic conditions. His work represents an academically relevant combination of concrete technology, structural analysis, and infrastructure resilience, aligning with research-oriented recognition criteria used in international academic award programmes. [5]

Conclusion

The academic profile of Gajendra Halmandge reflects sustained research interest in structural engineering and reinforced concrete systems. His documented publications contribute to the examination of impact loading, seismic behaviour, structural irregularity, and high-rise building performance. These research activities provide a scholarly basis for consideration within the Best Researcher Award recognition category of the International Forensic Scientist Awards.

References

  1. Gajendra Halmandge. (n.d.). Research publication profile and scholarly output. Academic research records.
  2. Halmandge, G. (2024). A Comparative Study of the Behaviour of Hybrid Fiber-Reinforced Concrete (HFRC) Beams Subjected to Low-Velocity Impact Loads Under Various Boundary Conditions. Engineering and Technology Journal. DOI: 10.47191/etj/v9i01.24.
    https://doi.org/10.47191/etj/v9i01.24
  3. Halmandge, G. (2023). Nonlinear Time History Analysis Of Mass And Geometric Regular And Irregular Multi Storey Moment Resisting RCC Frames. Zenodo. DOI: 10.5281/ZENODO.8350539.
    https://doi.org/10.5281/ZENODO.8350539
  4. Halmandge, G. (2023). Investigation Podium Impact On High-Rise Building Subjected To Seismic Load. Zenodo. DOI: 10.5281/ZENODO.8307581.
    https://doi.org/10.5281/ZENODO.8307581
  5. International Forensic Scientist Awards. (n.d.). Research recognition and academic award programme.
    forensicscientist.org

Fahimeh Ghasemian | Computer Science | Research Excellence Award

Published on by Forensic Scientist Awards

Research Excellence Award

Fahimeh Ghasemian
Shahid Bahonar University of Kerman, Iran

Fahimeh Ghasemian, affiliated with Shahid Bahonar University of Kerman, Iran, is a researcher in the field of Computer Science with recognized scholarly contributions in artificial intelligence, machine learning, natural language processing, medical informatics, and computational optimization. Her academic record reflects interdisciplinary research activity across healthcare analytics, deep learning, and intelligent systems, supported by indexed publications, citation impact, and international research visibility.[1]

Fahimeh Ghasemian
Affiliation Shahid Bahonar University of Kerman
Country Iran
Scopus ID 57190766313
Documents 26
Citations 235
h-index 9
Subject Area Computer Science
Event International Forensic Scientist Awards
ORCID 0000-0002-2176-7089

Abstract

Fahimeh Ghasemian has established a research portfolio centered on artificial intelligence and data-driven computational methodologies, with particular emphasis on healthcare applications, medical imaging, and predictive analytics. Her work integrates deep learning architectures, natural language processing systems, machine learning models, and optimization algorithms to address challenges in disease diagnosis, prognosis, and intelligent healthcare management. Published research associated with her academic profile demonstrates interdisciplinary collaboration and methodological innovation across computer science and medical informatics domains.[2]

Keywords

  • Artificial Intelligence
  • Deep Learning
  • Natural Language Processing
  • Machine Learning
  • Medical Informatics
  • Social Network Analysis

Introduction

The increasing role of artificial intelligence in biomedical and healthcare research has created opportunities for interdisciplinary investigations integrating computational intelligence with clinical decision-making. Researchers in computer science are contributing to this transformation through the development of predictive algorithms, intelligent diagnostic systems, and advanced analytical models. Within this context, Fahimeh Ghasemian has contributed to research exploring machine learning applications in healthcare environments, including COVID-19 prognosis, CT image classification, and natural language processing systems for medical analysis.[3]

Her scholarly activities further extend into optimization algorithms and computational modeling, reflecting broader interests in intelligent systems and data-centric problem solving. The combination of applied healthcare analytics and theoretical algorithmic research demonstrates an interdisciplinary framework aligned with contemporary trends in artificial intelligence and computer engineering.[4]

Research Profile

Fahimeh Ghasemian is affiliated with Shahid Bahonar University of Kerman in Iran. Her academic background includes studies in Computer Engineering, including graduate and postgraduate qualifications from Amirkabir University of Technology and doctoral research at the University of Isfahan. Her research output indexed in Scopus includes journal articles, reviews, and interdisciplinary computational studies with citation activity across medical and engineering domains.[1]

The Scopus profile associated with Author ID 57190766313 indicates documented contributions across healthcare-oriented machine learning systems, predictive modeling, image analysis, and metaheuristic optimization algorithms. Citation metrics and h-index indicators demonstrate measurable scholarly engagement and international visibility within the scientific literature.[5]

Research Contributions

Among the notable areas of contribution is the application of machine learning for healthcare diagnostics and prognostic systems. Research publications involving COVID-19 mortality prediction and hospitalization duration employed data mining and machine learning approaches to support clinical analytics and decision-making processes.[6]

Additional contributions include deep learning frameworks for computerized tomography image classification. The AFEX-Net model introduced adaptive feature extraction strategies within convolutional neural network architectures, demonstrating the integration of artificial intelligence methods into medical image interpretation workflows.[7]

Her research also includes systematic reviews of machine learning models for image-based diagnosis and prognosis in COVID-19 contexts, contributing to evidence synthesis within medical informatics literature. These studies provide comparative perspectives on computational methodologies applied in healthcare analytics and intelligent diagnostics.[8]

Beyond healthcare applications, Fahimeh Ghasemian participated in the development of optimization approaches such as the Human Urbanization Algorithm, a metaheuristic framework designed for solving optimization problems through population-based search mechanisms.[9]

Publications

  • Possibilistic–Probabilistic Consumer Participation Modelling and Cybersecure Demand Response Enabled by Convolutional–Bidirectional Long Short‐Term Memory Forecasting, IET Smart Grid (2026).
  • AFEX-Net: Adaptive feature extraction convolutional neural network for classifying computerized tomography images, DIGITAL HEALTH (2024).
  • Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms, DIGITAL HEALTH (2023).
  • Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review, JMIR Medical Informatics (2021).
  • Natural Language Processing Systems for Diagnosing and Determining Level of Lung Cancer: A Systematic Review, Frontiers in Health Informatics (2021).

Research Impact

The research contributions associated with Fahimeh Ghasemian reflect the growing integration of computational intelligence into healthcare and biomedical sciences. Citation records indicate scholarly engagement with her work, particularly in studies involving machine learning-based diagnostics, COVID-19 predictive systems, and medical image analysis.[5]

Her publications in peer-reviewed journals and interdisciplinary outlets contribute to discussions on artificial intelligence methodologies applicable to clinical support systems, predictive healthcare analytics, and intelligent data processing. The combination of systematic reviews and methodological studies enhances the visibility and applicability of her research within computer science and health informatics communities.[8]

Award Suitability

Fahimeh Ghasemian demonstrates qualifications aligned with recognition in international scientific and research award contexts through her interdisciplinary scholarship, publication record, and contributions to intelligent healthcare systems. Her research activity integrates advanced computational methodologies with real-world healthcare applications, reflecting the objectives commonly associated with research excellence and scientific innovation awards.[2]

The diversity of her research topics, including artificial intelligence in medicine, machine learning, natural language processing, and optimization algorithms, further supports the relevance of her profile for academic recognition programs emphasizing scientific impact, interdisciplinary collaboration, and technological advancement.[9]

Conclusion

Fahimeh Ghasemian has contributed to the advancement of computer science and medical informatics through interdisciplinary investigations in artificial intelligence, machine learning, and intelligent healthcare systems. Her scholarly record includes research on predictive analytics, medical image classification, natural language processing, and optimization methodologies. Indexed publications, citation activity, and participation in computational healthcare research collectively demonstrate an established academic profile suitable for scholarly recognition within international research communities.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Fahimeh Ghasemian, Author ID 57190766313. Scopus.
    www.scopus.com/authid/detail.uri?authorId=57190766313
  2. ORCID. (n.d.). Fahimeh Ghasemian ORCID profile.
    orcid.org/0000-0002-2176-7089
  3. Ghasemian, F., et al. (2021). Natural Language Processing Systems for Diagnosing and Determining Level of Lung Cancer: A Systematic Review. Frontiers in Health Informatics.
  4. Ghasemian, F., et al. (2020). Human urbanization algorithm: A novel metaheuristic approach. Mathematics and Computers in Simulation.
    https://doi.org/10.1016/j.matcom.2020.05.023
  5. Scopus Preview. (n.d.). Citation metrics and indexed documents associated with Fahimeh Ghasemian.
    https://www.scopus.com/authid/detail.uri?authorId=57190766313
  6. Ghasemian, F., et al. (2023). Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms. DIGITAL HEALTH.
    https://doi.org/10.1177/20552076231170493
  7. Ghasemian, F., et al. (2024). AFEX-Net: Adaptive feature extraction convolutional neural network for classifying computerized tomography images. DIGITAL HEALTH.
    https://doi.org/10.1177/20552076241232882
  8. Ghasemian, F., et al. (2021). Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review. JMIR Medical Informatics.
    https://doi.org/10.2196/25181
  9. Elsevier. (2020). Human urbanization algorithm: A novel metaheuristic approach. Mathematics and Computers in Simulation.
    https://doi.org/10.1016/j.matcom.2020.05.023

Mehnaz Tabassum | Computer Science and Artificial Intelligence | Women Researcher Award

Published on by Forensic Scientist Awards

Dr. Mehnaz Tabassum | Computer Science and Artificial Intelligence | Women Researcher Award

University of Sydney | Australia

Dr. Mehnaz Tabassum is an accomplished researcher in Computational Neurosurgery and Health Innovation, with core expertise in medical image analysis, artificial intelligence, and brain tumor diagnostics. Her research integrates deep learning, radiomics, and neuroimaging to enhance the precision of tumor segmentation, classification, and recurrence prediction in neuro-oncology. Her scholarly contributions include 14 Scopus-indexed publications, with a total of 87 citations and an h-index of 4 (Scopus metrics). She has published in prestigious journals such as Cancers, European Radiology, and Neuro-Oncology Advances, and has presented her findings at leading international conferences including IEEE EMBC and IEEE ISBI. Dr. Mehnaz Tabassum’s recent research explores cross-modality medical image synthesis, MRI-to-PET generation using diffusion and GAN-based models, and meta transfer learning for brain tumor segmentation. Her innovative work advances computational solutions for precision medicine and AI-assisted neuroimaging. She has received multiple distinctions, including the Pro-Vice Chancellor’s Research Excellence Scholarship and the Henry Sutton Postgraduate Research Scholarship, alongside a Best Paper Award for excellence in scientific contribution. Her interdisciplinary research continues to impact the fields of AI-driven diagnostics, eye-tracking in medical imaging, and computational modeling for neurosurgical innovation, reflecting her commitment to advancing data-driven healthcare and translational neuroscience.

Profiles: Scopus | Google Scholar | ORCID | ResearchGate | Staff Page

Featured Publications

  • Tabassum, M., Suman, A. A., Suero Molina, E., Pan, E., Di Ieva, A., & Liu, S. (2023). Radiomics and machine learning in brain tumors and their habitat: A systematic review. Cancers, 15(8), Article 2034. https://doi.org/10.3390/cancers15082034

  • Ghose, P., Alavi, M., Tabassum, M., Ashraf Uddin, M., Biswas, M., Mahbub, K., … & Hassan, M. (2022). Detecting COVID-19 infection status from chest X-ray and CT scan via single transfer learning-driven approach. Frontiers in Genetics, 13, 980338. https://doi.org/10.3389/fgene.2022.980338

  • Moradizeyveh, S., Tabassum, M., Liu, S., Newport, R. A., Beheshti, A., & Di Ieva, A. (2024). When eye-tracking meets machine learning: A systematic review on applications in medical image analysis. arXiv preprint arXiv:2403.07834. https://arxiv.org/abs/2403.07834

  • Tabassum, M., Suman, A. A., Russo, C., Di Ieva, A., & Liu, S. (2023). A deep learning framework for skull stripping in brain MRI. Neurocomputing (Under review).

  • Afrin, F., Al-Amin, M., & Tabassum, M. (2015). Comparative performance of using PCA with K-means and fuzzy C means clustering for customer segmentation. International Journal of Scientific and Technology Research, 4(8), 70–74.

Weimin Huang | Engineering | Best Researcher Award

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Assist. Prof. Dr. Weimin Huang | Engineering | Best Researcher Award

Shandong University of Science and Technology | China

Dr. Weimin Huang, Academic Associate Professor at the College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, is a leading expert in mechanical manufacturing and automation, with a focus on high-speed cutting technology, friction and fatigue wear mechanisms, and advanced agricultural machinery design. He earned his Ph.D. in Mechanical Manufacturing and Automation from Shandong University, establishing a strong foundation for his research and academic contributions. Dr. Weimin Huang has successfully led over 10 major research projects, including funding from the National Natural Science Foundation of China, and the Natural Science Foundation of Shandong Province, and has directed more than 20 industry-sponsored consultancy projects, effectively translating scientific insights into practical engineering solutions. His pioneering work on surface texture preparation via ball-end milling has significantly enhanced wear resistance and tribological performance of mechanical components, while his studies on sliding fatigue wear mechanisms have improved the durability and efficiency of industrial and agricultural equipment. He has published 37 Scopus-indexed journal articles, with 311 citations and an H-index of 11. Through his sustained research, innovation, and applied engineering contributions, Dr. Weimin Huang has established himself as a prominent scholar and a driving force in advancing mechanical manufacturing technologies.

Profile: Scopus

Featured Publications

1. Wang, G., Li, H., Wang, Z., & Jiang, D. (2025, May). Research on surface integrity and corrosion performance in high-speed ball-end milling of NiTi shape memory alloys.

2. Yang, J., Gong, C., Li, A., & Wang, P. (2025, March). Research on NiTi shape memory alloy electrolyte based on optimization of corrosion performance.

3. Huang, W., Huang, Y., Li, A., & Wang, G. (2024, November). Generation mechanism and anti-friction effect evaluation of continuous micro-groove texture machined by ball-end milling process.

4. Gao, L., Zhou, X., Huang, W., & Xia, H. (2024, February). Generation method and antifriction performance evaluation of discrete micro-pit surface texture based on high speed ball-end milling process.

5. Wang, G., Gong, C., Yang, J., & Wang, P. (2024, February). Electrochemical reaction mechanism of milled surface of NiTi shape memory alloy.

6. Gao, L., Wang, J., Huo, H., & Wang, Z. (2024, February). Residual height of surface topography in milling nickel-titanium shape memory alloy using a small-diameter cutter.

Tian Zhang | Engineering | Best Researcher Award

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Dr. Tian Zhang | Engineering | Best Researcher Award

Xi’an University of Architecture and Technology | China

Dr. Zhang Tian, a Master’s student in Structural Engineering at Xi’an University of Architecture and Technology, has built an impressive academic and research profile distinguished by consistent excellence, leadership, and early scholarly impact. He completed his undergraduate studies at Huanghuai University, where he was recognized as a “Three Good Student” for four consecutive years, awarded multiple academic scholarships, and graduated as an Outstanding Graduate. His achievements also include winning the third prize in the Challenge Cup of the School of Civil Engineering and being honored as an Outstanding Communist Youth League Member, distinctions that reflect his ability to combine academic rigor with innovation and service. At the graduate level, he has continued to excel, receiving an academic scholarship in 2022–2023 while advancing research in seismic-resistant structures, sustainable construction materials, and structural design optimization, areas vital to the development of safe and environmentally responsible infrastructure. Despite being in the early stage of his research career, Dr. Zhang Tian has already made notable scholarly contributions, with 6 publications indexed in Scopus, accumulating 69 citations from 68 documents, and achieving an h-index of 5. These metrics demonstrate that his work is not only visible but also valued within the global academic community. Combining strong academic performance, proven research productivity, and a clear vision for advancing structural engineering, Dr. Zhang Tian exemplifies the qualities of an emerging scholar whose contributions are poised to strengthen the safety, resilience, and sustainability of modern construction.

Profile: Scopus

Featured Publications

Xu, Y., Xu, Z.-D., Hu, H., Guo, Y.-Q., Huang, X.-H., Zhang, Z.-W., Zhang, T., & Xu, C. (2025). Experiment, simulation, and theoretical investigation of a new type of interlayer connections enhanced viscoelastic damper. International Journal of Structural Stability and Dynamics, 25(5), Article 2550045.

Yuan Rao | Computer Science | Best Researcher Award

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Dr. Yuan Rao | Computer Science | Best Researcher Award

Lecturer | School of Artificial Intelligence, Guangzhou University | China

Yuan Rao is a dedicated researcher and lecturer specializing in media forensics and AI security. She has contributed significantly to the field of image forgery detection, focusing on self-supervised learning and domain adaptation techniques. Currently a Lecturer at the School of Artificial Intelligence, Guangzhou University, Yuan Rao combines academic excellence and practical expertise, earning recognition through various awards and high-impact publications.

Profile

Scopus

Google Scholar

Strengths for the Awards

  • Outstanding Research Contributions 
    • Yuan Rao has published numerous high-impact research articles in leading journals and conferences such as:
      • IEEE Transactions on Pattern Analysis and Machine Intelligence
      • Pattern Recognition
      • IEEE International Conference on Computer Vision (ICCV)
    • Her work on JPEG-resistant image forgery detection and AI security vulnerabilities reflects groundbreaking innovation in media forensics.
  • Leadership in Projects 
    • She has successfully undertaken four research projects as Principal Investigator (PI), funded by prestigious bodies such as:
      • National Natural Science Foundation of China
      • Guangdong Basic and Applied Research Foundation
      • Science and Technology Foundation of Guangzhou

🎓 Education

  • PhD in Information and Communication Engineering (2014.9 – 2021.6)
    • Sun Yat-sen University
    • Supervisor: Professor Ni Jiangqun
  • Master’s in Communication and Information Systems (2011.9 – 2014.6)
    • Jinan University
    • Supervisor: Professor Junkai Huang
  • Bachelor’s in Telecommunications Engineering and Management (2007.9 – 2011.6)
    • Beijing University of Posts and Telecommunications

 Work Experience

  • Lecturer (2021.6 – Present)
    • School of Artificial Intelligence, Guangzhou University
    • Yuan Rao conducts research, supervises projects, and inspires students in the evolving fields of AI and media forensics.

🔍 Research Interests On Computer Science

Yuan Rao’s primary research interests focus on:

  • Media Forensics: Detecting and localizing image forgeries using deep learning and self-supervised techniques.
  • AI Security: Exploring vulnerabilities and robustness of AI models in practical scenarios.

🏆 Awards

  • 🥇 Champion and 3rd Place in “Guangzhou Pazhou Algorithm Competition” – Algorithm Safety Track, 2023
  • 🥇 First Prize in the National “Challenge Cup” Black Science and Technology Special Competition, 2023
  • 🏅 6th Rank out of 1471 in “Secure AI Challenger Program: Tamper Detection for Forged Images,” 2020

📚 Publications

1. A Deep Learning Approach to Detection of Splicing and Copy-Move Forgeries in Images

  • Authors: Y. Rao, J. Ni
  • Publication Year: 2016
  • Citations: 590

2. Deep Learning Local Descriptor for Image Splicing Detection and Localization

  • Authors: Y. Rao, J. Ni, H. Zhao
  • Publication Year: 2020
  • Citations: 104

3. Multi-semantic CRF-based Attention Model for Image Forgery Detection and Localization

  • Authors: Y. Rao, J. Ni, H. Xie
  • Publication Year: 2021
  • Citations: 53

4. Block-based Convolutional Neural Network for Image Forgery Detection

  • Authors: J. Zhou, J. Ni, Y. Rao
  • Publication Year: 2017
  • Citations: 46

5. Self-supervised Domain Adaptation for Forgery Localization of JPEG Compressed Images

  • Authors: Y. Rao, J. Ni
  • Publication Year: 2021
  • Citations: 29

6. Towards JPEG-Resistant Image Forgery Detection and Localization via Self-Supervised Domain Adaptation

  • Authors: Y. Rao, J. Ni, W. Zhang, J. Huang
  • Publication Year: 2022
  • Citations: 13

7. High-accuracy Current Sensing Circuit with Current Compensation Technique for Buck–Boost Converter

  • Authors: Y. Rao, W.L. Deng, J.K. Huang
  • Publication Year: 2015
  • Citations: 5

8. A Trigger-Perceivable Backdoor Attack Framework Driven by Image Steganography

  • Authors: W. Tang, J. Li, Y. Rao, Z. Zhou, F. Peng
  • Publication Year: 2024
  • Citations: – (Recent publication, citation data unavailable)

9. Dig a Hole and Fill in Sand: Adversary and Hiding Decoupled Steganography

  • Authors: W. Tang, H. Yang, Y. Rao, Z. Zhou, F. Peng
  • Publication Year: 2024
  • Citations: – (Recent publication, citation data unavailable)

10. Exploring the Vulnerability of Self-supervised Monocular Depth Estimation Models

  • Authors: R. Hou, K. Mo, Y. Long, N. Li, Y. Rao
  • Publication Year: 2024
  • Citations: – (Recent publication, citation data unavailable)

11. MKD: Mutual Knowledge Distillation for Membership Privacy Protection

  • Authors: S. Huang, Z. Liu, J. Yu, Y. Tang, Z. Luo, Y. Rao
  • Publication Year: 2023
  • Citations: – (Recent publication, citation data unavailable)

12. Deep Multi-image Hiding with Random Key

  • Authors: W. Zhang, W. Tang, Y. Rao, B. Li, J. Huang
  • Publication Year: 2023
  • Citations: – (Recent publication, citation data unavailable)

Research Projects

Yuan Rao has led multiple projects as Principal Investigator (PI):

  1. National Natural Science Foundation of China: Research on Self-supervised Transfer Learning Based Image Forgery Forensics.
  2. Guangdong Basic and Applied Basic Research Foundation: Image Forgery Localization Combining Self-supervised Learning and Knowledge Distillation.
  3. Science and Technology Foundation of Guangzhou: Verifiable Robustness of Object Detection Models Using Domain Knowledge.
  4. Science and Technology Foundation of Guangzhou: Short-term Heavy Precipitation Forecasting with Super-resolution Reconstruction.

📝 Conclusion

Yuan Rao is a prominent researcher and educator in media forensics and AI security. With a robust academic background, impressive research achievements, and numerous accolades, she is committed to advancing technologies that ensure the integrity and security of digital media.