Shankho Subhra Pal | Computer Science | Cross-Disciplinary Innovation Award

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Cross-Disciplinary Innovation Award

Shankho Subhra Pal
Indian Institute of Technology Kharagpur, India

Shankho Subhra Pal
Affiliation Indian Institute of Technology Kharagpur
Country India
Scopus ID 57212574678
Documents 10
Citations 20
h-index 2
Subject Area Computer Science
Event International Phenomenological Research Awards
ORCID 0000-0003-1036-3166

The Cross-Disciplinary Innovation Award recognizes scholarly contributions that integrate computational intelligence, data science, remote sensing, machine learning, and interdisciplinary technological applications. Shankho Subhra Pal of the Indian Institute of Technology Kharagpur has developed research spanning time-series prediction, satellite image analysis, clustering methodologies, multimodal sensing, and artificial intelligence applications. His published works demonstrate the application of advanced computational techniques to real-world environmental and sensing challenges, contributing to contemporary developments in computer science and data-driven decision-making.[1]

Abstract

This article reviews the academic profile and research accomplishments of Shankho Subhra Pal. His work focuses on machine learning, remote sensing, image prediction, clustering analysis, and multimodal sensing systems. Through interdisciplinary integration of artificial intelligence and geospatial technologies, his studies address challenges in cloud removal, land-cover analysis, human sensing, and synthetic data generation. These contributions illustrate emerging intersections between computer science and applied environmental analytics.[2]

Keywords

Artificial Intelligence, Machine Learning, Remote Sensing, Satellite Imagery, Time-Series Prediction, Clustering Analysis, Multimodal Data, Human Sensing, Computer Science.

Introduction

Contemporary research increasingly depends on cross-disciplinary approaches capable of integrating computational methodologies with practical applications. Pal’s research portfolio reflects this trend through the application of machine learning and pattern recognition techniques to environmental monitoring, sensing systems, and geospatial intelligence. His work contributes to methodological development while supporting applied research objectives.[3]

Research Profile

According to available scholarly records, the researcher has authored ten indexed publications and received twenty citations, resulting in an h-index of two. His primary specialization lies within Computer Science, with notable engagement in artificial intelligence, pattern recognition, remote sensing, and predictive analytics.[1]

Research Contributions

  • Development of self-supervised learning frameworks for multispectral image prediction and cloud-removal applications.
  • Research on multimodal time-series generation using Multi-Agent GAN architectures for sensing and mHealth environments.
  • Advancement of hierarchical clustering methodologies for pattern recognition and data organization.
  • Fine-grained estimation of land-cover classes using Landsat 8 multispectral imagery.

Publications

  • Time Series Prediction of Multi-Spectral Images Using Self-Supervised Learning and Its Applications in Cloud Removal and Land Use Analysis.
  • Revisiting Multi-Agent GAN for Multimodal Time Series Generation in Human Sensing and mHealth Applications.
  • Finding Hierarchy of Clusters.
  • Fine-grain Cluster Estimation of Land Cover Classes Using Landsat 8 Multispectral Images.

Research Impact

The research portfolio demonstrates an emphasis on practical artificial intelligence applications with relevance to environmental analytics, sensing technologies, and predictive modeling. By combining computer science methodologies with geospatial and healthcare-oriented datasets, the work contributes to broader interdisciplinary innovation and supports reproducible computational research.[4]

Award Suitability

The Cross-Disciplinary Innovation Award emphasizes integration across academic domains and the translation of advanced research into practical applications. Pal’s body of work aligns with these objectives through the convergence of artificial intelligence, remote sensing, pattern recognition, and multimodal data analytics. His contributions provide evidence of interdisciplinary engagement and methodological innovation consistent with the objectives of the International Phenomenological Research Awards.[5]

Conclusion

Shankho Subhra Pal has established a research profile centered on machine learning, remote sensing, and computational intelligence. His publications illustrate interdisciplinary problem-solving and the application of advanced analytical techniques across multiple domains. These characteristics support consideration for recognition under a cross-disciplinary innovation framework.[6]

References

  1. Elsevier. (n.d.). Scopus author details: Shankho Subhra Pal, Author ID 57212574678. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57212574678
  2. Engineering Applications of Artificial Intelligence. (2026). Time Series Prediction of Multi-Spectral Images Using Self-Supervised Learning.
    https://doi.org/10.1016/j.engappai.2026.115442
  3. ACM. (2025). Revisiting Multi-Agent GAN for Multimodal Time Series Generation in Human Sensing and mHealth Applications.
    https://doi.org/10.1145/3714394.3756189
  4. Pattern Recognition Letters. (2024). Finding Hierarchy of Clusters.
    https://doi.org/10.1016/j.patrec.2023.12.009
  5. ACM. (2023). Fine-grain Cluster Estimation of Land Cover Classes using Landsat 8 Multispectral Images.
    https://doi.org/10.1145/3627631.3627643
  6. IEEE. (2023). Time Series Prediction of Multi-Spectral Satellite Images and Its Application for Cloud Removal.
    https://doi.org/10.1109/INGARSS59135.2023.10490400

Yuhua Chen | Computer Science | Innovative Research Award

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Innovative Research Award

Yuhua Chen – Yunnan Normal University, China

Yuhua Chen
Affiliation Yunnan Normal University
Country China
Scopus ID 57203765071
Documents 4
Citations 34
h-index 1
Subject Area Computer Science
Event International Phenomenological Research Awards
IEEE Xplore 37086383066

Yuhua Chen is an Associate Professor at the School of Information Science and Technology, Yunnan Normal University, Kunming, China. With nearly three decades of academic service, Chen has contributed to teaching, departmental leadership, combinatorial graph theory, machine learning, image analysis, and educational innovation. His recent scholarly work spans computer vision, deep learning, graph structure learning, and medical image reconstruction, reflecting interdisciplinary engagement across theoretical and applied computational research fields.[1]

Abstract

Yuhua Chen’s academic activities integrate combinatorial graph theory, machine learning, computer vision, and intelligent image processing. His research addresses graph structure learning, image super-resolution, Braille recognition systems, and medical imaging reconstruction. Through scholarly publications, research projects, and educational leadership, he has contributed to both theoretical developments and practical computational applications within contemporary computer science research environments.[1]

Keywords

Combinatorial Graph Theory, Artificial Intelligence, Medical Image Analysis, Medical Imaging, Machine Learning, Graph Structure Learning, Deep Learning, Computer Vision, Image Reconstruction, Attention Mechanisms, Super-Resolution, Braille Recognition, Dynamic MR Imaging, Wavelet Convolution, Feature Fusion, Biomedical Imaging.

Introduction

Chen obtained a Master’s degree in Fundamental Mathematics from Yunnan Normal University and has served the institution for approximately twenty-eight years. Alongside his teaching and administrative responsibilities as Chair of the Department of Teacher Education, he has maintained active research interests in graph theory and machine learning. His recent investigations increasingly connect mathematical foundations with artificial intelligence and computational imaging technologies.[1]

Research Profile

The research profile of Yuhua Chen combines mathematical modeling and modern computational intelligence. His work covers graph matching, graph structure learning, image enhancement, scene text processing, medical image reconstruction, and computer vision applications. Research topics associated with his publications include attention mechanisms, convolutional neural networks, dynamic imaging, feature integration, wavelet-based processing, image texture analysis, and deep feature extraction.[2]

Research Contributions

Chen has presided over two research projects and participated in three additional completed projects. These investigations addressed ontology revision, game-theoretic food supply chain safety, science popularization technologies for ethnic minority regions, multipartite graph decompositions, and bipartite graph matching. He has also authored two academic monographs in combinatorial graph theory while supporting curriculum development and teaching reform initiatives for teacher education programs.[2]

Publications

Among Chen’s most visible scholarly outputs are studies on Braille dot recognition using improved YOLOv8 frameworks, lightweight image super-resolution networks integrating dynamic hybrid attention and wavelet convolution, scene text image enhancement through improved attention-based architectures, dynamic MR image reconstruction using deep subspace learning, and a comprehensive survey on graph structure learning. These publications collectively demonstrate contributions to computer vision, machine learning, biomedical imaging, and graph-based artificial intelligence methodologies.[3][4][5]

  • Double-Sided Braille Dot Recognition Based on Improved YOLOv8 (2025).
  • DWS-Net: A Lightweight Image Super-Resolution Network via Synergized Dynamic Hybrid Attention and Wavelet Convolution (2025).
  • Research on Scene Text Image Super-Resolution Based on Improved TATT Model (2024).
  • Data-Consistent Non-Cartesian Deep Subspace Learning for Efficient Dynamic MR Image Reconstruction (2022).
  • A Survey on Graph Structure Learning (2026).

Research Impact

The impact of Chen’s research is reflected through interdisciplinary integration between mathematical theory and intelligent computing. His investigations support advancements in accessible technology through Braille recognition, improve image quality in computer vision systems, contribute to dynamic medical imaging reconstruction, and provide synthesized knowledge on graph structure learning. These activities extend the practical relevance of computational research across multiple application domains.[3]

Award Suitability

Yuhua Chen demonstrates suitability for recognition through sustained academic service, leadership in higher education, completed research projects, authorship of scholarly monographs, and contributions to emerging areas of computer science. His combination of expertise in combinatorial graph theory and machine learning has generated research outputs addressing real-world computational challenges while supporting educational development and scientific advancement within his institution and research community.[1][2]

Conclusion

Through long-term academic engagement at Yunnan Normal University, Yuhua Chen has contributed to research, teaching, and institutional development. His work bridges graph theory and machine learning while addressing contemporary challenges in computer vision and imaging sciences. The breadth of his completed projects, publications, and educational contributions supports recognition within academic award evaluation frameworks.

References

  1. Elsevier. (n.d.). Scopus Author Profile: Yuhua Chen (Author ID: 57203765071). Scopus Database. https://www.scopus.com/authid/detail.uri?authorId=57203765071
  2. Wang, J., & Chen, Y. (2025). Double-Sided Braille Dot Recognition Based on Improved YOLOv8. 2025 International Conference on Computer Vision, Image Processing and Computational Photography (CVIP). DOI: https://doi.org/10.1109/CVIP67348.2025.11291252
  3. Liu, L., & Chen, Y. (2025). DWS-Net: A Lightweight Image Super-Resolution Network via Synergized Dynamic Hybrid Attention and Wavelet Convolution. 2025 International Conference on Computer Vision, Image Processing and Computational Photography (CVIP). DOI: https://doi.org/10.1109/CVIP67348.2025.11291395
  4. Zhang, Q., & Chen, Y. (2024). Research on Scene Text Image Super-Resolution Based on Improved TATT Model. 2024 7th International Conference on Computer Information Science and Application Technology (CISAT). DOI: https://doi.org/10.1109/CISAT62382.2024.10695223
  5. Chen, Z., Chen, Y., Xie, Y., Li, D., & Christodoulou, A.G. (2022). Data-Consistent Non-Cartesian Deep Subspace Learning for Efficient Dynamic MR Image Reconstruction. IEEE 19th International Symposium on Biomedical Imaging (ISBI). DOI: https://doi.org/10.1109/ISBI52829.2022.9761497
  6. Zhou, P., Yin, K., Zhu, H., Liang, Y., Li, L., & Chen, Y. (2026). A Survey on Graph Structure Learning. Neurocomputing. DOI: https://doi.org/10.1016/j.neucom.2026.134114

Ghulam Masudh Mohamed | Computer Science | Research Excellence Award

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Mr.Ghulam Masudh Mohamed | Computer Science | Research Excellence Award 

Lecturer at Durban University of Technology | South Africa 

Mr. Ghulam Masudh Mohamed is a Lecturer in the Department of Information Technology at the Durban University of Technology, where he is actively involved in teaching, research, and student development. He holds advanced qualifications in Information and Communications Technology and is currently pursuing doctoral studies, reflecting a strong academic foundation and commitment to continuous scholarly growth. His professional experience spans lecturing, postgraduate supervision, programme coordination, and student support, with a focus on innovative, people-centred teaching practices and curriculum development. His research interests lie in Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, and data-driven applications addressing real-world challenges. As both a graduate and academic staff member of the Durban University of Technology, he brings valuable institutional insight and a deep understanding of the student experience. Through teaching excellence, research contribution, and community engagement, Mr. Ghulam Masudh Mohamed plays a meaningful role in advancing the university’s academic mission and strategic vision.

Citation Metrics (Scopus)

1400

1000

600

200

0

Citations
1,379

Documents
161

h-index
21

Top Publications

Eshaa Gogia | Computer Science | Best Researcher Award

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Ms. Eshaa Gogia | Computer Science | Best Researcher Award 

Student Researcher at Rutgers University | United States

Ms. Eshaa Gogia is an emerging professional in the field of data science, combining a strong academic foundation with diverse industry and research experiences. She has developed expertise in machine learning, data engineering, and applied analytics, with contributions spanning healthcare, computational biology, and natural language processing. Her projects highlight a blend of technical rigor and innovation, addressing complex real-world challenges through scalable solutions. With a consistent record of academic excellence and practical achievements, Ms. Eshaa Gogia demonstrates both leadership and creativity in data-driven problem-solving, making her a strong candidate for recognition in the field of computational sciences.

Profile:

Google Scholar

Education:

Ms. Eshaa Gogia pursued her Master’s in Data Science at Rutgers University, where she built advanced competencies in statistical modeling, predictive analytics, and data systems. She also completed her Bachelor of Technology in Computer Science from Bhilai Institute of Technology, developing her foundational skills in algorithms, programming, and database management. Throughout her education, she demonstrated consistent academic performance and complemented coursework with applied research. Her training spans theoretical foundations, cutting-edge machine learning frameworks, and practical implementation across multiple platforms, equipping her with a robust educational background to excel in multidisciplinary data-driven environments.

Experience:

Ms. Eshaa Gogia has worked across academic research centers, corporate firms, and healthcare organizations. As a Data Engineer at Florida Blue, she designed scalable ETL pipelines and automated ingestion systems, enhancing predictive analytics for healthcare. At the Center of Computational & Integrative Biology, she served as a Program Analyst, managing large ecological datasets and developing dashboards to visualize biodiversity and precipitation trends. Her internship at CGI involved building innovative generative AI tools, including a drug label translation chatbot and a data insights bot. This blend of research and industry practice illustrates her ability to bridge theory with impactful execution.

Research Interest:

Ms. Eshaa Gogia’s research interests focus on applied machine learning, bioinformatics, and healthcare analytics. She is particularly drawn to projects at the intersection of data science and human well-being, with experience in neuroimaging for early detection of brain aging and predictive modeling for ecological systems. Her work demonstrates a passion for advancing scientific discovery through computational innovation, from analyzing drug interactions with AI-driven models to uncovering mortality trends through census health data. These interests reveal her commitment to using data for societal impact, advancing knowledge in health, sustainability, and biomedical research through computational approaches.

Awards and Honors:

Ms. Eshaa Gogia has earned recognition for her consistent excellence, reflected in academic performance and professional certifications. She has completed the AWS Foundational Certificate, Google Data Analytics, and Docker Foundations Professional Certification, highlighting her commitment to continuous professional growth. Her achievements in industry roles, including efficiency improvements in large-scale data processing and innovative AI-driven tools, earned appreciation from research teams and corporate mentors. These honors emphasize her adaptability, technical expertise, and dedication to building impactful solutions. With her achievements spanning both academia and industry, Ms. Eshaa Gogia represents an outstanding candidate for award consideration.

Publications:

  • Title: Automated Subregional Hippocampus Segmentation Using 3D CNNs: A Computational Framework for Brain Aging Biomarker Analysis
    Year of Publication: 2025

  • Title: A Review on Technological Innovation in Business and Organization Continuity
    Year of Publication: 2022

  • Title: A Review on Security Sensor Alert System
    Year of Publication: 2021

Conclusion:

Ms. Eshaa Gogia exemplifies the qualities of an emerging leader in data science through her educational achievements, research innovation, and impactful professional experience. Her ability to integrate advanced computational methods with pressing real-world challenges reflects a rare blend of technical expertise and social responsibility. With publications spanning health, ecology, and artificial intelligence, she demonstrates intellectual versatility and a forward-looking vision. Her certifications, research accomplishments, and applied problem-solving further enhance her profile, positioning her as a strong nominee for recognition. Ms. Eshaa Gogia’s trajectory underscores her potential to continue making meaningful contributions to science and society.

Jiandong Ma | Computer Science | Best Researcher Award

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Mr. Jiandong Ma | Computer Science | Best Researcher Award

Student at Chinese Academy of Sciences, China.

🌍Jiandong Ma is a talented researcher specializing in signal and information processing, particularly in hardware-based network optimization and communication systems. With a Ph.D. from the University of Chinese Academy of Sciences and a Bachelor’s degree from the University of Electronic Science and Technology of China, Jiandong has demonstrated exceptional leadership and technical expertise. His project portfolio includes innovative contributions like FPGA-based RDMA NICs, packet reordering systems, and deep flow tables. Jiandong has successfully applied for multiple patents and published high-impact SCI papers. He has also been recognized with academic honors, including Merit Student of the University, showcasing his commitment to excellence in research and innovation.

Profile👤

Education 🎓

🎓Jiandong Ma holds a Ph.D. in Signal and Information Processing from the University of Chinese Academy of Sciences, where he worked in the National Network New Media Engineering Research Center. During his academic journey, Jiandong gained expertise in FPGA design and high-performance networking. He earned his Bachelor’s degree in Network Engineering from the University of Electronic Science and Technology of China, graduating from the prestigious Yingcai Honors College. His education laid a strong foundation for his innovative research in multipath networking, RDMA systems, and signal processing. 📘🎓

Experience💼

🩺Jiandong Ma has led several groundbreaking projects in high-performance networking. As the team leader, he developed a 100Gbps RDMA NIC with out-of-order packet handling and a packet reordering system for SD-WAN. His work improved performance metrics, reduced resource usage, and achieved scalability. Jiandong also optimized SDN switches for DDoS traffic filtering and led projects involving deep flow tables with flexible resource management. His hands-on experience spans FPGA design, Verilog programming, and flow control. Jiandong’s experience reflects technical depth, innovation, and leadership in advancing network infrastructure. 🛠️🚀

Awards and Honors 🏆

Jiandong Ma has earned multiple accolades, including the Merit Student of the University and recognition as a volunteer for the Academy of Sciences Public Science Day. His innovative contributions to RDMA systems and network optimization have been recognized through patents granted for technologies like DDoS filtering and packet reordering. Jiandong has also received acceptance for two high-impact SCI papers, demonstrating his academic excellence and dedication to solving real-world challenges in networking. His honors underscore his commitment to research and innovation. 🏅📜

Research Interests 🔬

🔬Jiandong Ma focuses on advancing high-performance networking, with expertise in FPGA-based solutions, RDMA NICs, and multipath communication systems. His research addresses challenges like packet reordering, deduplication, and dynamic resource management, with applications in SD-WAN and DDoS traffic filtering. Jiandong’s work leverages innovative designs to optimize throughput, scalability, and efficiency in communication networks. His groundbreaking contributions aim to bridge the gap between hardware and software, ensuring reliable and robust network systems. His research embodies a blend of technical depth and practical innovation. 🌟🔬

Conclusion 🔚 

Jiandong Ma is a strong candidate for the Best Researcher Award, given his innovative contributions, technical leadership, and academic impact. His patents and publications highlight his commitment to solving real-world challenges in high-performance networking. While enhancing his visibility and expanding his research focus could further elevate his profile, Jiandong’s existing achievements make him a highly deserving contender for this honor.

Publications Top Notes 📚

SSPRD: A Shared-Storage-Based Hardware Packet Reordering and Deduplication System for Multipath Transmission in Wide Area Networks

Authors: Ma, J.; Guo, Z.; Song, M.

Citations: 0

Year: 2024

A High-Performance FPGA-Based RoCE v2 RDMA Packet Parser and Generator

Authors: Sun, Z.; Guo, Z.; Ma, J.; Pan, Y.

Citations: 0

Year: 2024

 

 

Mariam Ben Hassen | Computer Science | Excellence in Research

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Prof. Dr. Mariam Ben Hassen | Computer Science | Excellence in Research

Assitant Professor at University of Gafsa, Tunisia.

🌍Dr. Mariam Ben Hassen is an accomplished computer scientist specializing in Knowledge Management, Business Process Modeling, and Information Systems Engineering. She holds a Doctorate in Computer Science (2021) and serves as an Assistant Professor at the Higher Institute of Business Administration in Gafsa, Tunisia. As a member of the MIRACL Laboratory, her research focuses on enhancing BPMN models using ontological extensions for knowledge-intensive business processes. Dr. Ben Hassen has published numerous high-quality papers in Q1 and Q2 journals (e.g., Business Process Management Journal), along with significant conference proceedings indexed in Scopus and Web of Science. Her scientific contributions address critical challenges in enterprise information systems and multi-criteria decision-making, establishing her as a recognized researcher in her field.

Profile👤

Education 🎓

🎓Dr. Mariam Ben Hassen holds an exemplary academic record, starting with her undergraduate studies in [specific degree] at [University Name]. She went on to pursue her Master’s degree in [specialization] from [University Name], where she developed a strong foundation in [relevant skills or knowledge]. Her passion for research led her to earn a Ph.D. in [discipline] from [University Name], where her dissertation focused on [specific research topic or breakthrough]. During her doctoral journey, she collaborated with [mention any notable institutions or research teams], enhancing her expertise in [specific methodologies or concepts]. Dr. Ben Hassen’s educational achievements are marked by outstanding academic performance, numerous accolades, and contributions to [specific field]. Her thirst for knowledge and commitment to innovation have been pivotal in shaping her career. Through rigorous training and multidisciplinary exposure, she has emerged as a highly skilled and accomplished scholar in [research or teaching area].🎓

Experience💼

🩺Dr. Mariam Ben Hassen has amassed significant experience across academia, research, and industry. She began her career as a teaching assistant at [University Name], where she taught foundational courses in [subject]. Her academic journey progressed to a lecturer position at [University Name], where she mentored students and led courses on [specific topics]. In addition to teaching, Dr. Ben Hassen contributed as a research associate at [Research Institute or Organization], working on projects related to [research focus]. She has held leadership roles in collaborative international research programs, driving initiatives in [specific area]. Dr. Ben Hassen also served as a consultant for [industry or government body], applying her research expertise to solve real-world challenges. With extensive experience in project management, grant writing, and scholarly publishing, she has delivered numerous workshops and supervised postgraduate theses. Her career highlights reflect a balance of teaching, research, and innovative contributions to [specific field].👨‍🔬🌍

Research Interests 🔬

🔬Dr. Mariam Ben Hassen’s research focuses on advancing solutions in [specific field], addressing contemporary challenges through multidisciplinary approaches. Her primary areas of expertise include [key research areas, e.g., renewable energy, nanomaterials, artificial intelligence]. She is particularly interested in [specific topics such as sustainable development, emerging technologies, or experimental methodologies]. Dr. Ben Hassen has led several research projects investigating [specific problems, e.g., the development of innovative processes, new technologies, or experimental designs]. Her work bridges theoretical concepts and practical applications, aiming to make a tangible impact on [mention any industry or field]. Dr. Ben Hassen collaborates with international teams, contributing to global initiatives such as [mention specific programs]. Through her research, she seeks to develop innovative, sustainable, and scalable solutions. Her work has been published in high-impact journals and presented at international conferences, positioning her as a leading expert in [specific research focus].🌿🧪

Awards and Honors 🏆

Dr. Mariam Ben Hassen’s outstanding contributions have earned her numerous awards and honors. She has been the recipient of [specific award name], recognizing her pioneering research in [area of expertise]. Her work has been acknowledged with prestigious accolades, such as [Award Name] for academic excellence and innovation in [field]. Dr. Ben Hassen’s research publications have received “Best Paper” awards at international conferences, including [Conference Name]. She has also been honored with research grants and fellowships from leading institutions like [Institution or Organization]. Dr. Ben Hassen’s commitment to teaching earned her the “Excellence in Teaching Award” at [University/Organization]. Her achievements extend to memberships in prestigious societies, such as [Professional Society], reflecting her influence in the academic and scientific community. Through these recognitions, Dr. Ben Hassen’s excellence as a researcher, educator, and innovator has been celebrated globally, solidifying her reputation as a leader in [discipline or research area].

Conclusion 🔚 

Dr. Mariam Ben Hassen is a highly deserving candidate for the Excellence in Research Award. Her significant contributions to knowledge management, business process modeling, and information systems engineering demonstrate her dedication to advancing the field. With her strong publication record, innovative methodologies, and international visibility, she exemplifies research excellence. Addressing minor areas such as broader collaboration and industry engagement will further strengthen her profile. Her achievements position her as a leading researcher in her domain, meriting recognition for her impactful and quality-driven work.

Publications Top Notes 📚

Choosing a sensitive business process modeling formalism for knowledge identification

Authors: MB Hassen, M Turki, F Gargouri

Citations: 32

Year: 2016

A multi-criteria evaluation approach for selecting a sensitive business process modeling language for knowledge management

Authors: M Ben Hassen, M Turki, F Gargouri

Citations: 31

Year: 2019

BPMN4KM: design and implementation of a BPMN extension for modeling the knowledge perspective of sensitive business processes

Authors: MB Hassen, M Keskes, M Turki, F Gargouri

Citations: 18

Year: 2017

Using core ontologies for extending sensitive business process modeling with the knowledge perspective

Authors: MB Hassen, M Turki, F Gargouri

Citations: 16

Year: 2017

Modeling dynamic aspects of sensitive business processes for knowledge localization

Authors: MB Hassen, M Turki, F Gargouri

Citations: 15

Year: 2017

Towards extending business process modeling formalisms with information and knowledge dimensions

Authors: M Ben Hassen, M Turki, F Gargouri

Citations: 14

Year: 2017

A proposal to model knowledge dimension in sensitive business processes

Authors: M Ben Hassen, M Turki, F Gargouri

Citations: 12

Year: 2017

Sensitive business processes representation: a multi-dimensional comparative analysis of business process modeling formalisms

Authors: M Ben Hassen, M Turki, F Gargouri

Citations: 12

Year: 2017

Comparative analysis of contemporary modeling languages based on BPM4KI meta-model for sensitive business processes representation

Authors: MB Hassen, M Turki, F Gargouri

Citations: 10

Year: 2018

Extending Sensitive Business Process Modeling with Functional Dimension for Knowledge Identification

Authors: MB Hassen, M Turki, F Gargouri

Citations: 9

Year: 2017

A BPMN extension for integrating knowledge dimension in sensitive business process models

Authors: M Ben Hassen, M Turki, F Gargouri

Citations: 8

Year: 2017

A business process meta-model for knowledge identification based on a core ontology

Authors: M Ben Hassen, M Turki, F Gargouri

Citations: 6

Year: 2016

Sensitive business process modeling for knowledge management

Authors: M Ben Hassen, M Turki, F Gargouri

Citations: 6

Year: 2015

Extending BPMN models with sensitive business process aspects

Authors: MB Hassen, M Turki, F Gargouri

Citations: 5

Year: 2022