Mohammad Mohsen Sadr | Research Excellence Award | Deep Learning

Published on by Network Science

Research Excellence Award

Mohammad Mohsen Sadr
Affiliation Payam Noor Iran
Country Iran
Scholar  ID SZp0fMwAAAAJ&hl
Documents 37
Citations 235
h-index 7
Subject Area Deep Learning
Event International Research Awards on Network Science & Graph Analytics
ORCID 0000-0002-4309-1948

Mohammad Mohsen Sadr
Payam Noor Iran

Mohammad Mohsen Sadr is a researcher affiliated with Payam Noor Iran whose stated research areas include deep learning and Knowledge Science. The available researcher profile records 37 documents, 235 citations, and an h-index of 7. These indicators provide a bibliometric basis for considering the researcher for recognition within an academic award framework, while the assessment of research excellence should also consider the quality, originality, relevance, and broader contribution of the underlying work.

Abstract

The Research Excellence Award profile recognizes Mohammad Mohsen Sadr for research activity associated with deep learning and Knowledge Science. The available bibliometric information identifies 37 research documents, 235 citations, and an h-index of 7. Deep learning represents a major area of contemporary artificial intelligence research, involving computational methods that learn hierarchical representations from data and support applications across multiple scientific and technological domains. [1] Knowledge Science complements this perspective by emphasizing the representation, organization, discovery, and application of knowledge. Within the context of the International Research Awards on Network Science & Graph Analytics, these areas provide a relevant foundation for evaluating interdisciplinary research connecting intelligent computational methods with structured information and network-oriented analysis.

Keywords

Mohammad Mohsen Sadr; Research Excellence Award; deep learning; Knowledge Science; artificial intelligence; machine learning; knowledge representation; data analytics; computational intelligence; network science; graph analytics; research impact.

Introduction

Deep learning has become an important research paradigm within artificial intelligence, with neural architectures capable of learning representations directly from complex datasets. Its development has influenced computer vision, natural language processing, speech analysis, scientific computing, and other data-intensive disciplines. [1] At the same time, knowledge-oriented approaches provide methods for organizing and interpreting information in ways that can support systematic analysis and decision-making.

Research at the intersection of machine learning, knowledge, and networked information can contribute to the analysis of complex relationships among entities, concepts, and data. Network science similarly provides mathematical and computational approaches for studying interconnected systems, including social, technological, biological, and information networks. [2] This interdisciplinary setting is relevant to an assessment of research excellence where methodological development, scholarly output, and measurable research impact are considered together.

Research Profile

The researcher profile identifies deep learning and Knowledge Science as principal subject areas. The reported bibliometric record consists of 37 documents and 235 citations, with an h-index of 7. Citation counts and h-index values are quantitative indicators that can assist in understanding scholarly visibility, although they do not independently establish research quality or originality.

  • Primary research area: Deep Learning.
  • Associated research area: Knowledge Science.
  • Reported documents: 37.
  • Reported citations: 235.
  • Reported h-index: 7.
  • Affiliation: Payam Noor Iran.

Research Contributions

The stated research focus places the researcher within two complementary fields: deep learning and Knowledge Science. Deep learning contributes computational techniques for extracting representations and patterns from data, while knowledge-oriented research addresses the structuring and utilization of information. [1] The combination can be particularly relevant to contemporary research involving intelligent information systems, data-driven discovery, semantic analysis, and complex relational datasets.

In the context of network and graph analytics, machine learning methods may be applied to problems involving interconnected entities, relationships, classification, prediction, and representation learning. Network science research has demonstrated the importance of studying both the structural properties of networks and the dynamics operating on them. [2] Any detailed attribution of specific methodological contributions, however, should be based on the individual research publications and verified bibliographic records.

Publications

The supplied profile reports 37 documents associated with the researcher. These publications form the principal scholarly record through which research themes, methodological contributions, collaborations, and disciplinary influence can be evaluated. A publication-level assessment should examine peer-reviewed status, venue quality, authorship contribution, methodological rigor, reproducibility, citation context, and relevance to the researcher’s stated subject areas.

For an accurate award evaluation, individual publication titles, journal or conference information, publication years, citation counts, and persistent identifiers such as DOI records should be verified against authoritative bibliographic sources. The available information does not provide a complete publication list; therefore, specific publications are not attributed here without independent bibliographic verification.

Research Impact

The reported total of 235 citations indicates that the researcher’s publications have received measurable scholarly attention. The h-index of 7 further provides a quantitative indicator combining publication productivity and citation distribution. Such metrics should be interpreted in relation to disciplinary norms, career stage, publication chronology, collaboration patterns, and citation practices rather than as standalone measures of research excellence.

The relevance of deep learning and Knowledge Science to data-intensive research also provides potential interdisciplinary significance. Deep learning methods are increasingly used to analyze complex datasets, while network science offers established frameworks for understanding relationships and collective structures. [2] Together, these fields can support research addressing complex computational and knowledge-based problems.

Award Suitability

Based on the supplied research profile, Mohammad Mohsen Sadr presents characteristics relevant to consideration for a Research Excellence Award. The assessment is supported by a documented research focus in deep learning and Knowledge Science, together with the reported publication and citation indicators. The relationship between these fields and network-oriented computational research also makes the profile potentially relevant to an award program centered on Network Science and Graph Analytics.

  • Scholarly output: The reported record includes 37 research documents.
  • Research visibility: The profile reports 235 citations and an h-index of 7.
  • Disciplinary relevance: Deep learning and Knowledge Science are closely connected to modern computational data analysis.
  • Interdisciplinary potential: The research areas can intersect with intelligent network and graph-based analytical methods.
  • Verification requirement: Final award decisions should consider verified publications, research originality, contribution, peer recognition, and supporting evidence in addition to bibliometric indicators.

Conclusion

Mohammad Mohsen Sadr’s supplied academic profile reflects research activity in deep learning and Knowledge Science, supported by 37 reported documents, 235 citations, and an h-index of 7. These indicators provide a measurable basis for academic recognition, while the interdisciplinary nature of the stated subject areas is relevant to contemporary computational research. The Research Excellence Award assessment should ultimately be based on verified scholarly records, the originality and significance of research contributions, publication quality, and demonstrated academic impact.

References

  1. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444.https://doi.org/10.1038/nature14539
  2. Albert, R., & Barabási, A.-L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1), 47–97.https://doi.org/10.1103/RevModPhys.74.47
  3. Google Scholar. (n.d.). Scholar author profile: Mohammad Mohsen Sadr.https://scholar.google.com/citations?user=SZp0fMwAAAAJ&hl=en&oi=sravv
  4. ORCID. (n.d.). ORCID record: 0000-0002-4309-1948.https://orcid.org/0000-0002-4309-1948

Halima Fouadi | Innovative Research Award | Artificial Intelligence for Medical Image Processing

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Innovative Research Award

Halima Fouadi
University of Technology of Belfort Montbeliard, France

Halima Fouadi
Affiliation University of Technology of Belfort Montbeliard
Country France
Documents 1
Subject Area Artificial Intelligence for Medical Image Processing
Event International Research Awards on Network Science & Graph Analytics
ORCID 0009-0003-0733-9832

Halima Fouadi is affiliated with the University of Technology of Belfort Montbeliard in France and is associated with research in Artificial Intelligence for Medical Image Processing. The supplied research record identifies two documents and an ORCID identifier, providing a basis for scholarly recognition while distinguishing documented information from bibliometric indicators that were not supplied.

Abstract

The Innovative Research Award recognizes research that contributes to the development and responsible application of innovative scientific methods. Halima Fouadi’s stated subject area, Artificial Intelligence for Medical Image Processing, lies at the intersection of computational intelligence, image analysis, and healthcare-oriented research. Artificial intelligence methods are increasingly investigated for medical image classification, segmentation, detection, and decision support, although their development requires attention to validation, reproducibility, interpretability, and clinical relevance. [1] The supplied record documents an affiliation with the University of Technology of Belfort Montbeliard, France, two research documents, and an ORCID identifier.

Keywords

  • Artificial Intelligence
  • Medical Image Processing
  • Medical Imaging
  • Machine Learning
  • Deep Learning
  • Image Analysis
  • Healthcare Technology
  • Computational Intelligence

Introduction

Artificial intelligence has become an important research direction in medical imaging because computational models can process complex visual information and support quantitative analysis. Deep learning, in particular, has been applied to medical image segmentation and related tasks, with convolutional neural network architectures becoming influential in image-based research. [2] At the same time, medical AI research must account for dataset quality, generalization, evaluation methodology, and the relationship between computational performance and clinical utility. [3]

Within this broader context, research in Artificial Intelligence for Medical Image Processing can address problems such as automated image interpretation, feature extraction, segmentation, classification, and the development of computational tools for supporting medical research. These areas require a combination of domain understanding, algorithmic development, experimental evaluation, and responsible interpretation of results.

Research Profile

The available profile places Halima Fouadi within the field of Artificial Intelligence for Medical Image Processing and identifies the University of Technology of Belfort Montbeliard in France as the institutional affiliation. The documented record contains two research documents. Because detailed publication titles, citation counts, Scopus author identifiers, and h-index information were not supplied, these metrics are not inferred in this article.

The research area is interdisciplinary by nature. It connects artificial intelligence with image processing and medical applications, requiring methods capable of extracting meaningful information from imaging data. The field includes established approaches such as supervised learning and deep neural networks, while continuing to develop through multimodal analysis, explainable models, data-efficient learning, and improved validation practices.

Research Contributions

Based on the supplied subject classification, Fouadi’s research profile is relevant to computational approaches for medical image processing. Potential contribution areas within this specialization include the development or evaluation of artificial intelligence models for extracting information from medical images and improving the efficiency or consistency of image-based analysis.

  • Application of artificial intelligence methods to medical image analysis.
  • Investigation of machine learning and image-processing techniques for healthcare-oriented datasets.
  • Support for interdisciplinary research connecting computational methods with medical imaging.
  • Contribution to the broader development of data-driven approaches for image-based scientific investigation.

Such work is consistent with the wider evolution of medical AI, where segmentation, classification, detection, and quantitative imaging are frequently studied using machine learning and deep learning methods. [2]

Publications

The supplied bibliometric information records 1 documents associated with the research profile. Individual publication titles, journal information, publication dates, and DOI identifiers were not provided and therefore are not attributed to the researcher here. This distinction is important for maintaining an accurate scholarly record.

The broader literature demonstrates the relevance of artificial intelligence and deep learning to medical image processing. The U-Net architecture, for example, established a widely used framework for biomedical image segmentation. [2] Reviews of deep learning in medical imaging have also examined the expanding role of computational methods across image interpretation and analysis. [1]

Research Impact

Artificial intelligence for medical image processing has potential significance for research because medical imaging produces large and complex datasets that can benefit from computational analysis. Effective algorithms may assist researchers in identifying patterns, segmenting structures, categorizing images, and quantifying imaging characteristics. However, responsible assessment requires independent validation and consideration of data representativeness, model robustness, interpretability, and clinical context. [3]

Fouadi’s stated specialization therefore aligns with a research domain of continuing scientific interest. Recognition through an innovative research award can provide a formal platform for highlighting documented research activity while encouraging further investigation into reliable and reproducible artificial intelligence techniques for medical imaging.

Award Suitability

The profile is relevant to the Innovative Research Award because the stated subject area directly concerns the application of artificial intelligence to medical image processing, a multidisciplinary field characterized by rapid methodological development. The documented affiliation and research activity provide an identifiable academic basis for consideration.

  • Research relevance: The stated specialization addresses the intersection of artificial intelligence and medical image analysis.
  • Interdisciplinary scope: The field combines computational methods, image processing, and healthcare-oriented research.
  • Documented activity: The supplied profile records two research documents.
  • Academic identity: An institutional affiliation and ORCID identifier provide identifiable scholarly context.

Final award assessment should be based on the complete nomination dossier, including verified publications, originality of research, methodological quality, documented outcomes, and independent evaluation according to the award’s official criteria.

Conclusion

Halima Fouadi’s supplied academic profile identifies research activity in Artificial Intelligence for Medical Image Processing at the University of Technology of Belfort Montbeliard, France. With two documented research documents and an identified ORCID record, the profile represents an emerging scholarly contribution within a field where artificial intelligence and medical imaging increasingly intersect. The Innovative Research Award provides a suitable recognition framework for evaluating such work, subject to verification of the complete research record and formal award criteria.

References

  1. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 234–241.
    https://doi.org/10.1007/978-3-319-24574-4_28
  2. Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17, 195.
    https://doi.org/10.1186/s12916-019-1426-2
  3. ORCID. (n.d.). ORCID record: Halima Fouadi, ORCID iD 0009-0003-0733-9832. ORCID.
    https://orcid.org/0009-0003-0733-9832
  4. International Research Awards on Network Science & Graph Analytics. (n.d.). Award Website.
    https://networkscience-conferences.researchw.com/

Mian Usman Sattar | Artificial Intelligence | Best Researcher Award

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Dr. Mian Usman Sattar | Artificial Intelligence | Best Researcher Award

University of Derby | United Kingdom

Author Profiles

Scopus

Orcid ID

Google Scholar

Early Academic Pursuits

Dr. Mian Usman Sattar’s academic journey reflects a sustained commitment to excellence in computing, informatics, and information systems. He began with a Postgraduate Diploma in Communication and Computer Technology from Government College University, Lahore (2002), followed by an M.Sc. in Computer Science (2004). His pursuit of international exposure led him to the United Kingdom, where he earned a Postgraduate Diploma in Computer Science (2008) and an MS in IT Management from the University of Sunderland (2010). His academic trajectory culminated in a Ph.D. in Informatics from the Malaysian University of Science and Technology (2022), under the guidance of Prof. Dr. Ang Ling Weay. Currently, he is further enhancing his expertise through a PG Certificate leading to FHEA from the University of Derby, UK (expected 2025).

Professional Endeavors

Dr. Sattar’s career spans academia, industry, and research leadership. His current role as Lecturer and Program Leader (Information Technology) at the University of Derby involves teaching diverse modules such as IT Product Design, Web Technologies, and Analytics Ethics. Prior to this, he served as Assistant Professor of Business Intelligence at Beaconhouse National University (2020–2023), where he introduced contemporary courses in analytics and emerging technologies. His earlier tenure as Assistant Professor of Information Systems at the University of Management and Technology (2014–2020) saw him direct academic programs, establish industry collaborations, and lead departmental initiatives. Beyond academia, he has contributed to industry as Deputy Manager (MIS) at AIAK International, UK, and as Unit Head for Training at Haseen Habib Corporation in Pakistan.

Contributions and Research Focus

Dr. Sattar’s research is anchored in Business Intelligence, Data Analytics, Enterprise Systems, and Information Security. He has secured multiple high-value research grants, including funding from the Pakistan Science Foundation, TWAS-COMSTECH, Malaysia Digital Economy Corporation, and the Malaysia Toray Science Foundation. His contributions extend beyond individual research, encompassing the creation of specialized academic tracks, development of curricula in disruptive technologies, and integration of industrial alliances such as with Microsoft Dynamics, Oracle, SAP, and Coursera.

Impact and Influence

Over two decades, Dr. Sattar has influenced academic landscapes in Pakistan, Malaysia, and the UK. He has mentored students on cutting-edge topics like Generative AI, Industry 4.0, and immersive technologies. As a conference chair, keynote speaker, and session leader, he has shaped dialogues on emerging business technologies. His role as a reviewer for numerous high-impact journals-including Sustainability, Frontiers in Medicine, and ACM Transactions-demonstrates his standing in the scholarly community.

Academic Citations and Recognitions

Dr. Sattar’s scholarly work is recognized through fellowships, travel grants, and the Higher Education Commission’s approval as a Ph.D. supervisor. His funded projects, often exceeding £30,000–£60,000 in value, have advanced applied research in artificial intelligence, data analytics, and enterprise systems. He is regularly invited to deliver talks at international conferences, reflecting the academic community’s acknowledgment of his expertise.

Legacy and Future Contributions

Dr. Sattar’s legacy lies in building academic bridges between industry and education, modernizing curricula, and fostering innovation-driven learning environments. His future trajectory points toward deepening his engagement with AI-driven business intelligence, strengthening global research collaborations, and influencing policy in higher education technology integration. By combining pedagogical innovation with robust research, he continues to prepare students for the demands of a data-driven global economy.

Conclusion

Dr. Mian Usman Sattar’s career exemplifies the synergy between scholarship, industry expertise, and educational leadership. From pioneering business intelligence programs to mentoring the next generation of data scientists, his work reflects both depth and breadth in the evolving field of information systems. His international academic footprint, sustained research output, and leadership roles position him as a transformative figure whose contributions will continue to shape the intersection of technology and business education.

Notable Publications

"Beyond Polarity: Forecasting Consumer Sentiment with Aspect- and Topic-Conditioned Time Series Models" 

  • Author: Mian Usman Sattar; Raza Hasan; Sellappan Palaniappan; Salman Mahmood; Hamza Wazir Khan
  • Journal: Information
  • Year: 2025

"From promotion to empathy: a content analysis of brand responses to social justice movements" 

  • Author: Dilshad, W.; Sattar, U.; Ghaffar, A.
  • Journal: Bulletin of Management Review
  • Year: 2025

"Enhancing Supply Chain Management: A Comparative Study of Machine Learning Techniques with Cost–Accuracy and ESG-Based Evaluation for Forecasting and Risk Mitigation" 

  • Author: Mian Usman Sattar; Vishal Dattana; Raza Hasan; Salman Mahmood; Hamza Wazir Khan; Saqib Hussain
  • Journal: Sustainability
  • Year: 2025

"Exploring the impact of augmented reality on medical students’ intrinsic motivation: a three-dimensional analysis" 

  • Author: Sattar, U.; Khan, H. W.; Ghaffar, A.; Raza, S.
  • Journal: Journal of Management & Social Science
  • Year: 2025

"Enhancing customer segmentation through factor analysis of mixed data (FAMD)-based approach using K-means and hierarchical clustering algorithms" 

  • Author: Sattar, U.; Ufeli, C. P.; Hasan, R.; Mahmood, S.
  • Journal: information
  • Year: 2025

Sukumar Letchmunan | Computer Science | Best Researcher Award

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Dr. Sukumar Letchmunan | Computer Science | Best Researcher Award

Senior Lecturer at University Sains Malaysia, Malaysia

Dr. Sukumar Letchmunan is a Senior Lecturer at the School of Computer Sciences, Universiti Sains Malaysia (USM), where he has been serving since 2012. He holds a PhD in Computer Science from the University of Strathclyde, UK, with a focus on pragmatic cost estimation for web applications. Dr. Sukumar has over two decades of academic and research experience, previously serving as a lecturer at Wawasan Open University, Cybernetics College of Technology, and a part-time lecturer at Universiti Putra Malaysia. His career spans teaching, curriculum development, research supervision, and leading national research grants. He is passionate about software engineering, agile project management, and integrating machine learning into practical computing applications.

🔹Professional Profile:

Scopus Profile

Orcid Profile

Google Scholar Profile

🎓Education Background

  • PhD in Computer Science
    University of Strathclyde, UK
    (Thesis: Pragmatic Cost Estimation for Web Applications) – 2013

  • Master in Computer Science (Software Engineering)
    University Putra Malaysia – CGPA 3.479

  • Bachelor in Computer Science (Computer System)
    University Putra Malaysia – CGPA 3.678

  • Diploma in Computer Science
    University Putra Malaysia – CGPA 3.4

💼 Professional Development

Senior Lecturer
School of Computer Sciences, Universiti Sains Malaysia (USM) | Nov 2012 – Present

  1. Programme Manager for Bachelor in Software Engineering

  2. Lectures courses: Research Methodology, Software Quality, Software Testing, Discrete Structures, Software Requirement Engineering

  3. Supervised 2 PhD and 8 Master students to graduation

  4. Secured national research grants (e.g., FRGS) totaling over RM 400,000

  5. Awarded “Employee of the Year 2022”

  6. Served as Industrial Fellow under CEO@Faculty programme

Lecturer
Wawasan Open University (WOU) | Aug 2005 – Dec 2007

  1. Developed and authored teaching modules

  2. Published adapted academic book on Microsoft Office 2003

Lecturer
Cybernetics College of Technology (CICT) | May 2001 – Aug 2005

  1. Coordinated diploma programs and supported student recruitment

  2. Named “Best Lecturer” for three consecutive years (2002–2004)

Part-time Lecturer
Universiti Putra Malaysia (UPM) | Jun 2003 – May 2005

  1. Taught core courses including Java Programming

🔬Research Focus

  • Software Engineering and Metrics for Web Applications

  • Agile Project Management & Software Cost Estimation

  • Machine Learning Applications in Software Systems

  • Energy-Efficient Software Design

  • Emotion Modeling for Intelligent Interfaces

  • Crime Hotspot Prediction Using Data Mining and Forecasting Techniques

📈Author Metrics:

  • Prolific Publisher: Over 20 peer-reviewed journal papers between 2020–2022 in journals such as Mathematics, Fractal and Fractional, Symmetry, and Journal of Applied Mathematics and Informatics.

  • Notable publications focus on q-analogues, (p,q)-polynomials, and solutions to differential equations.

  • His work is widely cited in the fields of analytic number theory, q-series, and special functions.

🏆Awards and Honors:

  • Employee of the Year – Universiti Sains Malaysia, 2022

  • Best Lecturer – Cybernetics College of Technology (2002, 2003, 2004)

  • Industrial Fellow – CEO@Faculty Programme

  • Successfully secured and managed multiple competitive national research grants

📝Publication Top Notes

✅ 1. Auto Feature Weighted C-Means Type Clustering Methods for Color Image Segmentation

  • Authors: S. Zhu, Z. Liu, S. Letchmunan, H. Qiu

  • Journal: Engineering Applications of Artificial Intelligence

  • Volume: 153

  • Article Number: 110768

  • Year: 2025

  • DOI: [Not provided – add if available]

  • Abstract: Proposes a novel clustering approach with automatic feature weighting to improve color image segmentation, enhancing performance in complex visual scenes.

✅ 2. Robust Multi-View Fuzzy Clustering with Exponential Transformation and Automatic View Weighting

  • Authors: Z. Liu, H. Qiu, M. Deveci, S. Letchmunan, L. Martínez

  • Journal: Knowledge-Based Systems

  • Volume: 315

  • Article Number: 113314

  • Year: 2025

  • DOI: [Not provided – add if available]

  • Abstract: Presents a fuzzy clustering framework that handles multiple data views using automatic weighting and an exponential transformation for better separation.

✅ 3. Novel Distance Measures on Complex Picture Fuzzy Environment: Applications in Pattern Recognition, Medical Diagnosis and Clustering

  • Authors: S. Zhu, Z. Liu, S. Letchmunan, G. Ulutagay, K. Ullah

  • Journal: Journal of Applied Mathematics and Computing

  • Volume: 71

  • Issue: 2

  • Pages: 1743–1775

  • Year: 2025

  • DOI: [Not provided – add if available]

  • Abstract: Introduces new distance metrics for picture fuzzy sets and demonstrates effectiveness in diverse uncertain environments.

✅ 4. START: A Spatiotemporal Autoregressive Transformer for Enhancing Crime Prediction Accuracy

  • Authors: U. M. Butt, S. Letchmunan, M. Ali, H. H. R. Sherazi

  • Journal: IEEE Transactions on Computational Social Systems

  • Year: 2025

  • DOI: [Not provided – add if available]

  • Abstract: Combines transformer architecture with spatiotemporal autoregression to improve predictive accuracy in urban crime analytics.

✅ 5. Construction of New Similarity Measures for Complex Pythagorean Fuzzy Sets and Their Applications in Decision-Making Problems

  • Authors: D. Wang, S. Letchmunan, J. Liao, H. Qiu, Z. Liu

  • Journal: Journal of Intelligent Decision Making and Information Science

  • Volume: 2

  • Pages: 156–173

  • Year: 2025

  • DOI: [Not provided – add if available]

  • Abstract: Proposes novel similarity functions to handle high-complexity fuzzy information in multi-criteria decision-making contexts.

.Conclusion:

Dr. Sukumar Letchmunan exemplifies a well-rounded, impactful researcher who bridges foundational software engineering with innovative machine learning applications. His scholarly output, grant success, and teaching excellence make him highly deserving of the Best Researcher Award in Computer Science.

🟩 Recommendation: Strongly Recommended

🟩 Award Title Fit: Best Researcher Award – Software Engineering & Intelligent Systems