Ayesha Banu | Computer Vision | Women Researcher Award

Published on by Global CSE Awards

Women Researcher Award

Ayesha Banu
Chittagong University of Engineering and Technology (CUET), Bangladesh

Ayesha Banu
Researcher Ayesha Banu
Affiliation Chittagong University of Engineering and Technology (CUET)
Country Bangladesh
Scopus ID 60706667500
Documents 3
Citations 0
h-index 0
Subject Area Computer Vision
Event Global CSE Awards

Ayesha Banu is a researcher affiliated with Chittagong University of Engineering and Technology (CUET), Bangladesh, whose documented publications address computer vision and deep learning. Her recent work includes image deraining and protein secondary structure prediction, reflecting applications of neural architectures, generative models, attention mechanisms, and sequence modeling across computational research domains. [1] [2] [3]

Abstract

Ayesha Banu is affiliated with Chittagong University of Engineering and Technology (CUET), Bangladesh, and has documented research spanning computer vision and deep learning. Her publication record includes studies on single-image deraining using Atrous U-Net and generative adversarial networks, an attention-enhanced GAN approach for image restoration, and E2BNet, a hybrid ESM2-BiLSTM model for eight-state protein secondary structure prediction. These works examine neural architectures for visual restoration and computational sequence analysis, using established evaluation measures and benchmark datasets. The supplied Scopus information records three documents, zero citations, and an h-index of zero, providing a concise bibliographic snapshot for her current academic profile. [1] [2] [3]

Keywords

Women Researcher Award; Ayesha Banu; Computer Vision; Deep Learning; Image Deraining; Generative Adversarial Networks; Atrous U-Net; Attention Mechanisms; ESM2; BiLSTM; Protein Secondary Structure Prediction; CUET; Global CSE Awards.

Introduction

Computer vision research increasingly uses deep learning to address image restoration and recognition problems under challenging visual conditions. Banu’s documented studies include generative adversarial networks, U-Net variants, attention mechanisms, and learning architectures for image deraining, alongside a hybrid ESM2-BiLSTM model for protein structure prediction. These works demonstrate interdisciplinary computational methods. [1] [2] [3]

Research Profile

The available publication record identifies Ayesha Banu with Chittagong University of Engineering and Technology in Bangladesh. The supplied Scopus record lists three documents, zero citations, and an h-index of zero. Her stated subject area is Computer Vision, while publications also connect computer vision methods with broader deep learning and computational biology. [1] [2] [3]

Research Contributions

Banu’s documented contributions include research on single-image deraining using Atrous U-Net and GAN architectures, attention-enhanced GAN-based restoration, and hybrid deep learning for eight-state protein secondary structure prediction. These studies apply convolutional, generative, attention-based, and sequential models to structured prediction and image restoration problems, with quantitative evaluation reported in publications. [1] [2] [3]

Publications

Three publications are identified for this profile. The 2024 IEEE conference paper addresses single-image deraining with Atrous U-Net and GAN. Two 2026 Discover Applied Sciences articles examine attention-enhanced GAN deraining and the E2BNet architecture combining ESM2 with BiLSTM for protein secondary structure prediction. Each publication provides a documented scholarly contribution relevant to research. [1] [2] [3]

Research Impact

The documented research impact can be described through publication outputs and methods presented in the cited studies. The deraining research reports image-quality evaluation using metrics including PSNR and SSIM, while the E2BNet study evaluates Q8 protein secondary structure prediction across benchmark datasets. The supplied profile currently records zero citations and h-index zero. [1] [2] [3]

Award Suitability

For a Women Researcher Award profile, the available evidence provides a documented basis for considering research activity, publication record, institutional affiliation, and subject-area relevance. The three identified publications demonstrate engagement with computational methods. Final award eligibility or selection should be determined by Global CSE Awards criteria and submitted supporting evidence. [1] [2] [3]

Conclusion

Ayesha Banu’s documented publication record reflects research involving computer vision, deep learning, image restoration, generative adversarial networks, attention mechanisms, and protein sequence modeling. The supplied bibliographic information establishes three publications and a CUET affiliation. Together, these records provide a concise scholarly profile for academic recognition and further assessment under applicable criteria. [1] [2] [3]

References

  1. Droba, D. D., Banu, A., Sami, M. I., Hossain, R., & Chowdhury, M. (2026). Hybrid deep learning model (E2BNet) combining ESM2 and BiLSTM for 8-state protein secondary structure prediction. Discover Applied Sciences, 8, Article 681.
    https://doi.org/10.1007/s42452-026-08615-z
  2. Banu, A., & Hossain, R. (2026). Attention-enhanced GAN for single image deraining. Discover Applied Sciences.
    https://doi.org/10.1007/s42452-026-08519-y
  3. Banu, A., Anan, S., & Deb, K. (2024). Removing rain from single image using Atrous U-Net and GAN. In 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT). IEEE.
    https://doi.org/10.1109/ICEEICT62016.2024.10534531

Riad Hossain | Machine Learning | Innovative Research Award

Published on by Global CSE Awards

Innovative Research Award: Riad Hossain

Riad Hossain — East Delta University, Bangladesh

Riad Hossain
Name Riad Hossain
Affiliation East Delta University
Country Bangladesh
Scopus ID 59521054900
Documents 21
Citations 14
h-index 2
Subject Area Machine Learning
Event Global CSE Awards

Riad Hossain is a machine learning researcher affiliated with East Delta University, Bangladesh. The supplied Scopus record lists 21 documents, 14 citations, and an h-index of 2. His publication record includes research on explainable machine learning for Parkinson’s disease detection, Bengali speech analysis, and WiFi-based occupancy detection using CSI today.

Abstract

Riad Hossain is identified in the supplied profile as a Machine Learning researcher associated with East Delta University, Bangladesh. His listed Scopus record contains 21 documents, 14 citations, and an h-index of 2. His research outputs include explainable machine learning for Parkinson’s disease detection from Bengali conversational speech, machine learning analysis of Bengali voice recordings, and CSI-based people counting in WiFi networks. These studies apply feature extraction, feature selection, classification, deep learning, and explainability across healthcare and wireless sensing applications. The publication record provides documented evidence of interdisciplinary research activity relevant to contemporary computer science and machine learning research domains. [1] [2] [3]

Keywords

  • Machine Learning
  • Explainable Artificial Intelligence
  • Parkinson’s Disease Detection
  • Bengali Speech Analysis
  • Voice-Based Machine Learning
  • WiFi Sensing
  • Channel State Information
  • Deep Learning

Introduction

Hossain’s documented research is situated within machine learning and its application to practical computational problems. The available publications address Bengali speech-based health analysis and WiFi sensing, demonstrating applications of computational methods to biomedical and networking contexts. These studies illustrate how machine learning techniques can process complex speech and wireless signal data. [1] [2] [3]

Research Profile

Hossain’s research profile centers on machine learning applications involving speech, healthcare analytics, and wireless sensing. His listed subject area is Machine Learning, while his publications demonstrate interdisciplinary work connecting artificial intelligence with biomedical signal analysis and computer networking. The record combines methodological development, feature engineering, classification, and data-driven evaluation domains. [1] [2] [3]

Research Contributions

The reported contributions include development of BenSParX, an explainable machine learning framework using Bengali conversational speech for Parkinson’s disease detection, and machine learning approaches for Bengali voice recordings. His work also addresses WiFi channel-state-information sensing for people counting, applying CNN and LSTM architectures to sequential wireless data environments. [1] [2] [3]

Publications

The supplied publication set comprises three research works. BenSParX presents a Bengali conversational speech framework for Parkinson’s disease detection; the ICCIT paper studies Parkinson’s detection from Bengali voice recordings; and the COMPAS paper investigates CSI-based people counting. Together, these publications represent applications of machine learning across healthcare and wireless sensing. [1] [2] [3]

Research Impact

The publications indicate research activity across two application domains: machine-learning-assisted health assessment and privacy-oriented wireless occupancy sensing. The reported studies use feature selection, acoustic measurements, classification algorithms, explainability, and deep learning methods. Their documented results provide research outputs that can support further investigation into Bengali speech analytics and WiFi sensing. [1] [2] [3]

Award Suitability

For the Global CSE Awards context, the supplied profile documents a machine-learning-focused research record, identifiable Scopus metrics, and publications with DOI-linked scholarly records. The evidence includes work in explainable artificial intelligence, speech-based disease detection, and wireless sensing. Award assessment should additionally follow the event’s published eligibility, nomination, and evaluation criteria. [1] [2] [3]

Conclusion

Riad Hossain’s supplied academic profile presents research activity in machine learning with publications spanning Bengali speech analysis, Parkinson’s disease detection, explainable models, and WiFi-based occupancy detection. The documented Scopus metrics and cited scholarly outputs provide identifiable evidence of research activity. The record can be reviewed alongside the award’s official criteria. [1] [2] [3]

 

References

    1. Hossain, R., Kabir, M. A., Mowla, A. I. G., Roy, A. C., & Ghosh, R. K. (2026). BenSParX: A robust explainable machine learning framework for Parkinson’s disease detection from Bengali conversational speech. Artificial Intelligence in Medicine, 182, 103538.DOI:
      https://www.sciencedirect.com/science/article/pii/S0933365726001909
    2. Mowla, A. I. G., Hossain, R., & Asaduzzaman. (2024). CSI-based people counting in WiFi networks: Leveraging occupancy detection. In 2024 IEEE International Conference on Computing, Applications and Systems (COMPAS).DOI:
      https://ieeexplore.ieee.org/abstract/document/10796139
    3. Hossain, R., Roy, A. C., & Mowla, A. I. G. (2024). Parkinson’s disease detection from Bengali voice recordings using machine learning approach. In 2024 27th International Conference on Computer and Information Technology (ICCIT).DOI:
      https://ieeexplore.ieee.org/abstract/document/11021761

Chetanpal Singh | Machine Learning | Innovative Research Award

Published on by Global CSE Awards

Innovative Research Award

Chetanpal Singh
RMIT, Australia
Chetanpal Singh
Affiliation RMIT
Country Australia
Scopus ID 57197208396
Documents 8
Citations 126
h-index 4
Subject Area Machine Learning
Event Global CSE Awards
ORCID 0000-0001-6246-444X

Chetanpal Singh is affiliated with RMIT in Australia. The profile records eight documents, 126 citations, and an h-index of four, with machine learning listed as the principal subject area. The highlighted research spans medical imaging, agricultural disease detection, and data visualization, with publications addressing applied computational methods across different domains.[1][2][3]

Abstract

Chetanpal Singh is an RMIT-affiliated researcher in Australia whose supplied profile identifies machine learning as a subject area. The record lists eight documents, 126 citations, and an h-index of four. His highlighted publications cover multimodal learning for cancer analysis, hybrid deep learning for cotton disease detection, and data visualization for audit efficiency and risk management. These works illustrate computational methods across healthcare, agriculture, and auditing. The profile is supported by Scopus and ORCID identifiers, while the cited publications provide traceable evidence of research activity. The information presented here is descriptive and should be considered alongside official award criteria and bibliographic records.[1][2][3]

Keywords

Machine learning; deep learning; multimodal learning; medical imaging; cancer detection; agricultural artificial intelligence; cotton disease detection; BERT; ResNet; particle swarm optimization; data visualization; audit efficiency; risk management; computer vision; research impact.

Introduction

Chetanpal Singh’s research profile is associated with machine learning and applied data-driven methods across medical imaging, agricultural disease detection, and data visualization. His listed publications include studies combining deep learning architectures, optimization, and analytical visualization. These works span healthcare, agriculture, and auditing applications, illustrating cross-domain use of computational methods research.[1][2][3]

Research Profile

Chetanpal Singh is affiliated with RMIT in Australia and is identified by Scopus Author ID 57197208396 and ORCID 0000-0001-6246-444X. The supplied profile records eight documents, 126 citations, and an h-index of four. His stated subject area is machine learning, with publications addressing artificial intelligence, deep learning, computer vision, and analytics.[1][2][3]

Research Contributions

The documented contributions represented by the supplied publications include multimodal deep learning for medical imaging, hybrid BERT-ResNet-PSO modelling for cotton disease recognition, and visualization-based approaches to audit efficiency and risk management. Together, these studies demonstrate applications of machine learning, neural architectures, optimization, and visual analytics to domain-specific problems and decision-support contexts.[1][2][3]

Publications

The publication record supplied for this profile contains three works. Singh and colleagues reported a graph-aware and sequence-aware multimodal framework for cancer analysis in Journal of Imaging in 2026. A 2025 Applied Sciences article addressed cotton plant disease detection using BERT-ResNet-PSO. A 2023 handbook chapter examined data visualization for auditing.[1][2][3]

Research Impact

The cited publications indicate research activity across several application domains rather than a single narrowly defined problem. The medical imaging study addresses cancer detection using graph, sequence, and multimodal learning; the agricultural study addresses cotton disease classification; and the auditing chapter discusses visualization for analytical procedures and risk management practices.[1][2][3]

Award Suitability

For an academic recognition profile, the documented record provides identifiable evidence through publications, author identifiers, citation information, and research topics. The listed work demonstrates application-oriented machine learning across healthcare, agriculture, and auditing. Any award assessment should additionally consider the event’s published eligibility criteria, nomination requirements, evidence standards, and comparison process.[1][2][3]

Conclusion

Chetanpal Singh’s research record presents a cross-domain machine learning profile supported by publications, author identifiers, and bibliometric information. The highlighted studies cover medical imaging, agricultural disease detection, and audit visualization. These materials can support a academic recognition profile, while final recognition depends on criteria and process of the awarding organization.[1][2][3]

References

  1. Singh, C., Wibowo, S., Grandhi, S., & Mandala, S. (2026). Graph-Aware and Sequence-Aware Multimodal Deep Learning Framework for Cancer Detection and Risk Analysis from Medical Imaging. Journal of Imaging, 12(9), 431.
    https://doi.org/10.3390/jimaging12090431https://www.mdpi.com/2313-433X/12/9/431
  2. Singh, C., Wibowo, S., & Grandhi, S. (2025). A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO. Applied Sciences, 15(13), 7075.
    https://doi.org/10.3390/app15137075https://www.mdpi.com/2076-3417/15/13/7075
  3. Ferdous, L. T., Singh, C., & Rana, T. (2023). A Picture Is Worth a Thousand Words: Audit Efficiency and Risk Management Through Data Visualization. In Handbook of Big Data and Analytics in Accounting and Auditing (pp. 17–39). Springer.
    https://doi.org/10.1007/978-981-19-4460-4_2Scopus record

 

Pabon Shaha | Artificial Intelligence | Best Academic Researcher Award

Published on by Global CSE Awards

Best Academic Researcher Award

Pabon Shaha — Bangladesh University, Bangladesh
Pabon Shaha
Affiliation Bangladesh University
Country Bangladesh
Scopus ID 58526083200
Documents 16
Citations 85
h-index 5
Subject Area Artificial Intelligence
Event Global CSE Awards

Pabon Shaha is a researcher whose documented scholarly work focuses on artificial intelligence, machine learning, feature optimization, cybersecurity, and data-driven approaches to health-related prediction. His publication record includes studies addressing chronic kidney disease diagnosis, DRDoS attack identification, and explainable artificial intelligence for cardiovascular risk assessment, demonstrating research activity across applied artificial intelligence domains. [1] [2] [3]

Abstract

Pabon Shaha’s documented research activity is centered on applied artificial intelligence and machine learning, with particular attention to feature optimization, cybersecurity, and health-oriented predictive analytics. His publications examine machine-learning methods for chronic kidney disease diagnosis, DRDoS attack identification, and explainable cardiovascular disease assessment. The available scholarly record indicates interdisciplinary application of computational methods to practical problems in healthcare and cybersecurity. His Scopus record lists 16 documents, 85 citations, and an h-index of 5, providing quantitative indicators of scholarly activity and citation impact. [1] [2] [3]

Keywords

Artificial Intelligence; Machine Learning; Feature Optimization; Explainable AI; Cybersecurity; DRDoS Detection; Chronic Kidney Disease; Cardiovascular Disease; Risk Assessment; Predictive Analytics. [1] [2] [3]

Introduction

Artificial intelligence and machine learning provide computational methods for analyzing complex datasets and developing predictive models across healthcare and cybersecurity. Shaha’s documented publications illustrate applications involving feature optimization, attack detection, disease diagnosis, and explainable predictive modeling. These studies position machine learning as a practical research tool for addressing data-intensive problems. [1] [2] [3]

Research Profile

The research profile represented by the supplied publication record is interdisciplinary within computer science, particularly artificial intelligence and machine learning. The documented work combines classification, feature optimization, dimensionality reduction, ensemble learning, and explainability techniques. Applications span healthcare and cybersecurity, indicating an emphasis on computational methods that can be evaluated through measurable predictive performance. [1] [2] [3]

Research Contributions

The documented contributions include investigating feature optimization for machine-learning-based chronic kidney disease diagnosis, evaluating machine-learning algorithms for DRDoS attack detection, and applying explainable artificial intelligence to cardiovascular disease detection and risk assessment. Together, these studies demonstrate the use of computational modeling, feature engineering, optimization, and interpretability techniques across distinct applied research challenges. [1] [2] [3]

Publications

The supplied publication record includes peer-reviewed and scholarly works addressing machine-learning applications in healthcare and cybersecurity. The 2022 study on chronic kidney disease examines feature optimization and ensemble classification, while the DRDoS study evaluates machine-learning classifiers with PCA-based feature reduction. A 2025 arXiv work examines explainable AI for cardiovascular disease detection and risk assessment. [1] [2] [3]

Research Impact

The supplied metrics report 16 documents, 85 citations, and an h-index of 5, which provide quantitative indicators of the researcher’s documented scholarly activity. The publications also address practical problems in healthcare and cybersecurity, including disease prediction and network attack detection. Such application-oriented research can contribute methods and evidence for subsequent investigations in these areas. [1] [2] [3]

Award Suitability

The documented research record aligns with an academic recognition category focused on artificial intelligence and applied computer science. Evidence includes publications using machine learning for healthcare diagnosis, cybersecurity attack identification, feature optimization, and explainable AI. The reported scholarly metrics and multidisciplinary applications provide identifiable evidence that can be considered during an award evaluation process. [1] [2] [3]

Conclusion

Pabon Shaha’s documented scholarly work demonstrates research activity in artificial intelligence and machine learning, with applications spanning healthcare and cybersecurity. The selected publications illustrate work on feature optimization, attack detection, and explainable predictive modeling, while the supplied bibliometric indicators provide additional measures of research activity. [1] [2] [3]

References

  1. Hossain, M. M., Swarna, R. A., Mostafiz, R., Shaha, P., Pinky, L. Y., Rahman, M. M., Rahman, W., Hossain, M. S., Hossain, M. E., & Iqbal, M. S. (2022). Analysis of the performance of feature optimization techniques for the diagnosis of machine learning-based chronic kidney disease. Machine Learning with Applications, 9, 100330.
    https://www.sciencedirect.com/science/article/pii/S2666827022000421
  2. Shaha, P., Khan, M. S. I., Rahman, A., Hossain, M. M., Mammun, G. M., & Nasir, M. K. (2025). A prevalent model-based on machine learning for identifying DRDoS attacks through features optimization technique. Statistics, Optimization and Information Computing, 13(1), 409–433.
    https://iapress.org/index.php/soic/article/view/2042
  3. Sourov, M. E. A., Hossen, M. S., Shaha, P., Hossain, M. M., & Iqbal, M. S. (2025). An explainable AI-enhanced machine learning approach for cardiovascular disease detection and risk assessment. arXiv.
    https://arxiv.org/abs/2507.11185

Shamsuddeen Umaru Adamu | Cybersecurity | Best Researcher Award

Published on by Global CSE Awards

Best Researcher Award

Shamsuddeen Umaru Adamu — Kaduna State University

Shamsuddeen Umaru Adamu
Affiliation Kaduna State University
Country Nigeria
Scopus ID 59104600400
Documents 2
Citations 6
h-index 1
Subject Area Cybersecurity
Event Global CSE Awards
ORCID 0000-0001-8227-220X

Shamsuddeen Umaru Adamu is a researcher affiliated with Kaduna State University, Nigeria, whose documented scholarly work is associated with cybersecurity and related computational research. His research record includes studies addressing cybersecurity frameworks in higher education and natural language processing involving Hausa-language data, reflecting interdisciplinary engagement with security, technology, and African language resources. [1] [2]

Abstract

Shamsuddeen Umaru Adamu is a cybersecurity researcher affiliated with Kaduna State University, Nigeria. His documented research interests include cybersecurity frameworks in higher education and computational language technologies involving Hausa, demonstrating an intersection of information security, artificial intelligence, and African language resources. His publication record includes work examining the adoption and effectiveness of cybersecurity frameworks within higher education institutions, together with contributions to sentiment analysis using low-resource Hausa tweet data. These studies address practical and methodological challenges relevant to secure digital environments and inclusive language technologies. His Scopus record reports two documents, six citations, and an h-index of one, providing measurable evidence of scholarly activity.

Keywords

Cybersecurity, Cybersecurity Frameworks, Higher Education Security, Information Security, Hausa Natural Language Processing, Sentiment Analysis, Low-Resource Languages, African Language Technology, Computational Research, Cybersecurity Research. [1] [2]

Introduction

Cybersecurity research in higher education addresses institutional protection, governance, risk management, and effective security practices across increasingly digital academic environments. Adamu’s documented work examines cybersecurity framework adoption and effectiveness, while related research explores Hausa-language sentiment analysis using low-resource social-media data. These topics demonstrate engagement with contemporary computational challenges. [1] [2]

Research Profile

Adamu’s research profile is centered on cybersecurity, with documented scholarly activity extending into natural language processing and sentiment analysis. His work connects information security concerns with computational methods and African language resources, indicating an interdisciplinary research orientation. The available publication record provides evidence of research activity across security and language-technology themes. [1] [2]

Research Contributions

The documented contributions include examination of cybersecurity framework adoption and effectiveness in higher education institutions, alongside participation in research addressing sentiment analysis for Hausa, a relatively low-resource African language. Together, these works contribute to discussions of institutional cybersecurity and computational approaches for improving language-data analysis in underrepresented linguistic contexts. [1] [2] [3]

Publications

The available publication record includes a systematic literature review concerning cybersecurity framework adoption in higher education and two Scopus-indexed records associated with HausaNLP and SemEval-2023 Task 12. These publications reflect research activity spanning cybersecurity, natural language processing, sentiment analysis, and low-resource African language technologies, with the cited records providing bibliographic verification. [1] [2] [3]

Research Impact

The available Scopus indicators report two documents, six citations, and an h-index of one, providing a quantitative snapshot of Adamu’s indexed research activity. His work on cybersecurity frameworks is relevant to institutional security discussions, while HausaNLP research supports broader efforts to develop computational resources and analytical methods for African low-resource languages. [1] [2]

Award Suitability

Adamu’s documented research activity aligns with the Best Researcher Award through its focus on cybersecurity and related computational research. His publications address applied security questions and low-resource language technology, demonstrating scholarly engagement across complementary areas of computer science. The available bibliographic record and citation indicators provide supporting evidence for recognition consideration. [1] [2] [3]

Conclusion

Shamsuddeen Umaru Adamu’s documented scholarly profile reflects research activity in cybersecurity and computational language technologies. His work addresses cybersecurity frameworks in higher education and sentiment analysis for Hausa-language data, connecting practical security concerns with emerging computational research. The available Scopus indicators and indexed publications provide a basis for academic recognition. [1] [2]

References

  1. A systematic literature review on the adoption and effectiveness of cybersecurity frameworks in higher education institutions. (n.d.). Scopus.
    https://www.scopus.com/inward/record.url?eid=2-s2.0-105029652508&partnerID=MN8TOARS
  2. HausaNLP at SemEval-2023 Task 12: Leveraging African Low Resource TweetData for Sentiment Analysis. (n.d.). Scopus.
    https://www.scopus.com/inward/record.url?eid=2-s2.0-85159598145&partnerID=MN8TOARS
  3. HausaNLP at SemEval-2023 Task 12: Leveraging African Low Resource TweetData for Sentiment Analysis. (n.d.). Scopus.
    https://www.scopus.com/inward/record.url?eid=2-s2.0-85161065173&partnerID=MN8TOARS

Sergio Montes | Software Engineering | Innovative Research Award

Published on by Global CSE Awards

Innovative Research Award

Sergio Montes
UNIVERSIDAD REY JUAN CARLOS, Spain

Sergio Montes
Affiliation UNIVERSIDAD REY JUAN CARLOS
Country Spain
Scopus ID 57219722553
Documents 10
Citations 30
h-index 3
Subject Area Software Engineering
Event Global CSE Awards
ORCID 0000-0002-2564-4465

Sergio Montes is a software engineering researcher whose documented scholarly work addresses software repositories, Android development, open-source software licensing, and software heritage. His indexed research profile comprises 10 documents, 30 citations, and an h-index of 3, providing measurable indicators of research activity and scholarly visibility within software engineering. [1]

Abstract

Sergio Montes’s research profile reflects scholarly activity in software engineering, particularly repository analysis, Android development, open-source software licensing, and software preservation. His documented record includes 10 publications, 30 citations, and an h-index of 3. Recent research examines corporate contributions to Android development through repository analysis and systematic identification of free and open-source software licenses. Related work considers the Software Heritage license dataset and its role in studying software artifacts. These topics connect empirical software engineering with software ecosystems, licensing practices, repository mining, and digital preservation. Collectively, the research demonstrates a coherent interest in understanding software development practices and reusable software knowledge. [1] [2] [3]

Keywords

  • Software Engineering
  • Android Development
  • Software Repository Analysis
  • Open Source Software
  • Software Licensing
  • Software Heritage
  • Empirical Software Engineering

Introduction

Modern software engineering increasingly relies on empirical analysis of repositories, development ecosystems, and open-source software. Repository mining can reveal development patterns and organizational contributions, while systematic analysis of software licenses supports clearer understanding of reuse and distribution. Montes’s research engages with these themes through studies of Android development and software licensing. [1] [2]

Research Profile

The research profile centers on software engineering research involving software repositories, mobile application development, open-source licensing, and software preservation. The available publications indicate an empirical and analytical orientation, with research examining repository evidence, license classification, and structured software datasets. These interests place the work at the intersection of software development, open-source ecosystems, and software engineering research. [1] [2] [3]

Research Contributions

Montes’s documented contributions include empirical examination of corporate participation in Android development, systematic identification and classification of free and open-source software licenses, and work involving a software heritage license dataset. These studies contribute analytical evidence and structured resources for investigating software development organizations, licensing practices, and long-term software research. [1] [2] [3]

Publications

The selected publications demonstrate a consistent focus on empirical software engineering and open-source software ecosystems. The Android repository study investigates corporate contributions, while the systematic literature review examines methods for identifying and classifying free and open-source licenses. Research on the Software Heritage license dataset further supports analysis of software artifacts and licensing information. [1] [2] [3]

Research Impact

The available bibliometric profile records 30 citations across 10 documents and an h-index of 3, indicating measurable scholarly visibility. The research topics also address practical issues in software engineering, including understanding corporate development activity, license identification, and preservation-oriented software datasets. These indicators provide quantitative and thematic evidence of research relevance. [1] [2]

Award Suitability

The documented profile is relevant to an Innovative Research Award because it combines software engineering research with empirical investigation of contemporary software ecosystems. Work involving Android repositories, open-source license classification, and software heritage datasets demonstrates engagement with significant software development and knowledge-management questions. The publication record and citation indicators provide additional evidence for consideration. [1] [2] [3]

Conclusion

Sergio Montes’s research demonstrates a focused contribution to software engineering through studies of Android development, open-source licensing, repository analysis, and software heritage. His documented bibliometric record provides measurable research visibility, while the selected publications demonstrate topical consistency. Collectively, these characteristics provide a reasonable scholarly basis for consideration for the Innovative Research Award. [1] [3]

References

  1. Montes, S., et al. (2026). Exploratory study of corporate contributions in Android development through repository analysis. Journal of Systems and Software.
    https://doi.org/10.1016/j.jss.2026.113005
  2. Montes, S., et al. (2025). Identification and classification of free, open source software licenses: A systematic literature review. Journal of Systems and Software.
    https://doi.org/10.1016/j.jss.2025.112628
  3. Montes, S., et al. (2023). The Software Heritage license dataset (2022 edition). Empirical Software Engineering.
    https://doi.org/10.1007/s10664-023-10377-w

Heungseob Kim | Reliability Engineering | Innovative Research Award

Published on by Global CSE Awards

Innovative Research Award

Heungseob Kim — Changwon National University, South Korea

Heungseob Kim
Affiliation Changwon National University
Country South Korea
Scopus ID 57191904084
Documents 14
Citations 455
h-index 8
Subject Area Reliability Engineering
Event Global CSE Awards
ORCID 0000-0003-0090-5670

Heungseob Kim is a researcher affiliated with Changwon National University whose documented research spans reliability engineering, underwater vehicle path planning, and logistics-robot optimization. His recent publications address reliability modeling, current-aware navigation, and multi-robot task scheduling, providing a multidisciplinary profile relevant to contemporary engineering research and technological development. [1][2]

Abstract

Heungseob Kim’s research profile reflects work across reliability engineering, autonomous underwater navigation, and logistics robotics. His publications include an exact reliability model for mixed redundant systems with heterogeneous components, a current-aware travel-time formulation for three-dimensional underwater path planning, and an optimization framework for allocating and scheduling logistics-robot tasks. These studies combine mathematical modeling, optimization, computational methods, and engineering applications to address reliability, navigation efficiency, and automated operations. The documented research demonstrates methodological breadth while maintaining a consistent emphasis on quantitative decision-making and system performance. [1][2][3]

Keywords

Reliability Engineering; Reliability Modeling; Mixed Redundancy; Heterogeneous Components; Phase-Type Distribution; Continuous-Time Markov Chains; Underwater Vehicles; 3D Path Planning; Ocean-Current-Aware Navigation; Logistics Robots; Task Allocation; Scheduling; Optimization; Mathematical Programming. [1]

Introduction

Heungseob Kim’s published research addresses engineering problems requiring mathematical formulation, computational optimization, and system-level analysis. His recent work considers underwater navigation under spatially varying ocean currents, reliability of heterogeneous redundant systems, and coordinated logistics-robot operations. Together, these studies illustrate applications of quantitative methods to complex engineering systems and decision problems. [2]

Research Profile

The research profile is centered on reliability engineering and extends into optimization-driven robotics and autonomous navigation. The reported studies employ structured reliability models, grid-based path planning, mathematical programming, and scheduling techniques. This combination indicates an interdisciplinary research orientation connecting reliability analysis, operations research, robotics, and engineering system optimization. [3]

Research Contributions

The documented contributions include a cell-transition travel-time cost for ocean-current-aware underwater routing, an exact reliability formulation incorporating heterogeneous components and sequencing, and a five-step framework for logistics-robot allocation and scheduling. These contributions emphasize adaptable mathematical formulations that can support established search, reliability, optimization, and scheduling procedures. [1][2]

Publications

The selected publications demonstrate research activity across three complementary engineering domains. The underwater-vehicle study appeared in Drones in 2026, the reliability study appeared in Mathematics in 2026, and the logistics-robot optimization study appeared in Mathematics in 2025. Collectively, they provide evidence of recent scholarly output and methodological diversity. [1][3]

Research Impact

The supplied bibliometric profile records 14 documents, 455 citations, and an h-index of 8. These indicators provide quantitative evidence of scholarly visibility, while the selected publications show application-oriented research across reliability, autonomous navigation, and logistics automation. Citation metrics should be interpreted alongside publication quality, methodological contribution, and research context. [2]

Award Suitability

The documented research is relevant to an Innovative Research Award because the selected studies introduce or apply structured computational approaches to challenging engineering problems. Evidence includes current-aware path-cost formulation, exact heterogeneous reliability modeling, and scalable robot task optimization. These works provide identifiable methodological contributions suitable for scholarly recognition. [3]

Conclusion

Heungseob Kim’s documented research demonstrates a broad engineering focus supported by mathematical modeling, optimization, and computational analysis. The selected publications address reliability, underwater navigation, and logistics robotics, while the supplied bibliometric indicators show established scholarly visibility. On the available evidence, the profile presents a coherent basis for consideration for research recognition. [1][2][3]

References

  1. Kim, H., Yu, S., & Choi, B. (2026). A cell-based ocean-current-aware travel-time cost formulation for offline 3D path planning of underwater vehicles. Drones, 10(8), 621.
    https://doi.org/10.3390/drones10080621
  2. Kim, H. (2026). Exact reliability model for a mixed redundant system with heterogeneous components and component sequencing. Mathematics, 14(16), 2925.
    https://doi.org/10.3390/math14162925
  3. Choi, B., Kim, M., & Kim, H. (2025). An optimization framework for allocating and scheduling multiple tasks of multiple logistics robots. Mathematics, 13(11), 1770.
    https://doi.org/10.3390/math13111770

Muhammad Azeem Akbar | Software Engineering | Best Researcher Award

Published on by Global CSE Awards

Best Researcher Award

 Muhammad Azeem Akbar
Affiliation LUT University
Country Pakistan
Scopus ID 57200183503
Documents 174
Citations 3,300
h-index 31
Subject Area Software Engineering
Event Global CSE Awards
ORCID 0000-0002-4906-6495

Muhammad Azeem Akbar is a software engineering researcher affiliated with LUT University, whose scholarly profile encompasses software development, emerging computing paradigms, blockchain-based systems, and quantum-classical software integration. His documented research activity includes 174 publications, approximately 3,300 citations, and an h-index of 31, providing a substantial basis for academic recognition in software engineering.

Abstract

Muhammad Azeem Akbar is a software engineering researcher affiliated with LUT University, Pakistan, whose scholarly work addresses contemporary challenges in software development and emerging computational technologies. His research profile records 174 documents, approximately 3,300 citations, and an h-index of 31. His publications examine software development reliability, blockchain-based development, quantum software engineering, and hybrid quantum-classical applications. Recent work includes frameworks for predicting success in blockchain software projects and approaches for connecting classical and quantum software development practices. These contributions demonstrate sustained engagement with evolving software engineering methods, interdisciplinary computing technologies, and systematic approaches to improving software development processes and outcomes. [1] [2] [3]

Keywords

Software Engineering, Software Development, Blockchain, Quantum Software Engineering, Quantum Computing, Hybrid Quantum-Classical Systems, Software Quality, Emerging Technologies, Software Project Success, Computational Systems.

Introduction

Muhammad Azeem Akbar’s research is situated within software engineering and addresses challenges emerging from rapidly evolving computational technologies. His recent studies consider blockchain software development, quantum-classical integration, and software engineering practices for novel computing environments. These topics reflect the discipline’s movement toward increasingly heterogeneous and technologically complex development ecosystems. [1] [2] [3]

Research Profile

The research profile combines empirical software engineering with emerging technology domains. Recorded bibliometric indicators include 174 documents, approximately 3,300 citations, and an h-index of 31. His work demonstrates particular engagement with software development processes and the engineering implications of blockchain and quantum computing technologies, supported by recent peer-reviewed publications. [1] [2]

Research Contributions

Key contributions include investigation of success prediction in blockchain-based software development, development of concepts connecting classical and quantum software engineering, and examination of engineering lessons from hybrid quantum-classical image classification. Together, these studies address practical development challenges while extending software engineering considerations into emerging computational environments. [1] [2] [3]

Publications

Selected publications illustrate the breadth of Akbar’s current research agenda. These include a study of success probability prediction for blockchain-based software development, a framework titled C2|Q⟩ for bridging classical and quantum software development, and research examining installation, evaluation, and software engineering lessons associated with hybrid quantum-classical image classification. [1] [2] [3]

Research Impact

The documented citation profile indicates substantial scholarly visibility, with approximately 3,300 citations and an h-index of 31 across 174 documents. Beyond bibliometric measures, the research addresses practical questions concerning reliable software development and the adoption of emerging computational technologies, creating potential relevance for researchers and practitioners working across contemporary software engineering environments. [1] [2]

Award Suitability

The Best Researcher Award recognizes a profile demonstrating sustained research activity, scholarly influence, and engagement with relevant advances in a discipline. Akbar’s publication record, citation indicators, and research on blockchain and quantum-oriented software engineering provide relevant evidence of continued contribution to software engineering research and its evolving technological frontiers. [1] [2] [3]

Conclusion

Muhammad Azeem Akbar presents an established software engineering research profile supported by a substantial publication and citation record. His work addresses emerging areas including blockchain software development and quantum-classical computing, while maintaining a focus on software engineering challenges. The available evidence provides a coherent basis for consideration for the Best Researcher Award. [1] [2] [3]

References

  1. Akbar, M. A., et al. (2026). Success probability prediction framework for blockchain-based software development. Information and Software Technology. Elsevier.
    https://www.sciencedirect.com/science/article/pii/S095058492600114X?via%3Dihub
  2. Akbar, M. A., et al. (2026). C2|Q⟩: A robust framework for bridging classical and quantum software development. ACM. RCR Report.
    https://doi.org/10.1145/3833408
  3. Akbar, M. A., et al. (2025). Hybrid quantum-classical image classification: Installation, evaluation and software engineering lessons. Association for Computing Machinery.
    https://doi.org/10.1145/3803437.3807521
  4. Elsevier. (n.d.). Scopus author details: Muhammad Azeem Akbar, Author ID 57200183503. Scopus.
    https://www.scopus.com/pages/authors/57200183503
  5. ORCID. (n.d.). Muhammad Azeem Akbar: ORCID record. ORCID.
    https://orcid.org/0000-0002-4906-6495

Yan Zhang | Software Engineering | Best Researcher Award

Published on by Global CSE Awards

Best Researcher Award

Yan Zhang — Tsinghua University
Yan Zhang
Affiliation Tsinghua University
Country Japan
Google Scholar ID 6K7pE3YAAAAJ
Documents 9
Citations 26
h-index 3
Subject Area Software Engineering
Event Global CSE Awards

Yan Zhang is identified in the supplied research record as a researcher affiliated with Tsinghua University, with research interests represented through publications concerning visual perception, color vision deficiency, augmented reality, human-computer interaction, and vehicle-pedestrian communication. The available bibliographic information provides the basis for this academic recognition profile and its associated assessment.[1][2]

Abstract

This article presents a scholarly profile of Yan Zhang, whose record identifies Tsinghua University as the institutional affiliation and reports nine documents, 26 citations, and an h-index of 3. The selected publications address representative surface color perception, context-aware assistance for color vision deficiency using large language models and augmented reality, and pedestrian perceptions of external human-machine interfaces in virtual reality. Collectively, these studies connect visual perception, accessibility, human-computer interaction, artificial intelligence, and intelligent transportation. The profile is presented neutrally for academic recognition, emphasizing documented outputs, research themes, publication venues, citation indicators, and suitability for consideration within an award evaluation process. [1]

Keywords

  • Software Engineering
  • Computer Vision
  • Color Perception
  • Color Vision Deficiency
  • Augmented Reality
  • Large Language Models
  • Human-Computer Interaction
  • Vehicle-Pedestrian Interaction

Introduction

Yan Zhang’s research record reflects work spanning software engineering, human-computer interaction, computer vision, color perception, augmented reality, and vehicle-pedestrian interaction. The listed publications examine how people interpret visual information and technology-mediated signals, connecting empirical studies with interactive systems. These themes provide a multidisciplinary basis for evaluating research activity and assessment. [1][2]

Research Profile

The supplied profile identifies Yan Zhang with Tsinghua University and reports nine documents, 26 citations, and an h-index of 3. The publications include interdisciplinary studies involving perception, color vision deficiency, large language models, augmented reality, and external human-machine interfaces, indicating engagement with applied and human-centered computing research and scholarly innovation.[2]

Research Contributions

Zhang’s listed contributions address representative color perception in natural materials, context-aware assistance for color vision deficiency, and pedestrian perceptions of vehicle interfaces. Collectively, these studies investigate visual cognition, accessibility, augmented reality, and human-vehicle communication. Their methodological diversity illustrates research connecting perceptual evidence with emerging interactive technologies and practical user needs. [3]

Publications

The identified publication record includes a 2023 Scientific Reports article on representative surface color, a 2024 IEEE conference contribution integrating large language models with augmented reality for color vision deficiency, and a 2025 IEEE Access study examining eHMI placement and vehicle type. Together, these works demonstrate interdisciplinary activity across domains.[1]

Research Impact

The supplied metrics report 26 citations and an h-index of 3 across nine documents. These indicators provide a quantitative snapshot of scholarly visibility, while the publications show relevance across perception, accessibility, artificial intelligence, augmented reality, and intelligent transportation. Impact should be interpreted alongside publication quality, collaboration, venue, and citation development.[1][2]

Award Suitability

Based on the supplied record, Yan Zhang demonstrates a multidisciplinary research profile with publications addressing contemporary problems in visual perception, accessibility, interactive artificial intelligence, and human-vehicle communication. The documented outputs and citation indicators provide evidence for consideration in a researcher recognition process, subject to verification of affiliation, metrics, and eligibility.[2]

Conclusion

Yan Zhang’s documented research combines empirical perception studies with emerging computing and interaction technologies. The available record indicates contributions across color perception, assistive systems, augmented reality, and vehicle-pedestrian interfaces. With nine reported documents, 26 citations, and an h-index of 3, the profile presents a foundation for academic recognition and evaluation.[3]

References

  1. Zhang, Y., & Motoyoshi, I. (2023). Perceiving the representative surface color of real-world materials. Scientific Reports, 13, 6300.
    https://doi.org/10.1038/s41598-023-33563-8. Nature article.
  2. Morita, S., Zhang, Y., Yamauchi, T., Chen, S., Li, J., & Tei, K. (2024). Towards context-aware support for color vision deficiency: An approach integrating LLM and AR. In 2024 IEEE 13th Global Conference on Consumer Electronics (pp. 188–189). Institute of Electrical and Electronics Engineers.
    https://doi.org/10.1109/GCCE62371.2024.10761017. IEEE Xplore.
  3. Zheng, N., Li, J., Zhang, Y., & Tei, K. (2025). Exploring the impact of eHMI display location and vehicle type on pedestrian perceptions: A VR user study. IEEE Access, 13, 4947–4956.
    https://doi.org/10.1109/ACCESS.2025.3526172. IEEE Xplore.

Youness Javid | Reliability | Innovative Research Award

Published on by Global CSE Awards

Innovative Research Award

Youness Javid — Kharazmi University, Iran

Youness Javid
Affiliation Kharazmi University
Country Iran
Scopus ID 49561332700
Documents 19
Citations 273
h-index 8
Subject Area Reliability
Event Global CSE Awards

Youness Javid is a researcher affiliated with Kharazmi University whose indexed scholarly record includes research activity in reliability and related analytical applications. The available profile information records 19 documents, 273 citations, and an h-index of 8. His recent collaborative publication applies optimized machine learning and multi-criteria decision analysis to high-risk pregnancy prediction, illustrating an interdisciplinary research direction involving computational methods and applied decision support. [1][2]

1. Abstract

Youness Javid is affiliated with Kharazmi University and has an indexed research record comprising 19 documents, 273 citations, and an h-index of 8. His research profile includes work relevant to reliability and computational analysis. A 2026 Scientific Reports article involving Javid applies Taguchi optimization, supervised machine learning, and TOPSIS-based model selection to high-risk pregnancy prediction using Iranian hospital data. [1]

2. Keywords

Youness Javid; Kharazmi University; reliability; machine learning; predictive modeling; Taguchi optimization; TOPSIS; multi-criteria decision making; high-risk pregnancy prediction; computational analysis; clinical decision support; research impact. [2]

3. Introduction

Research on complex prediction problems increasingly combines statistical optimization, machine learning, and structured decision analysis. Javid’s recent publication demonstrates this interdisciplinary approach by examining high-risk pregnancy prediction through optimized preprocessing, feature selection, classifier tuning, and multi-criteria ranking. The study used hospital-based Iranian data and evaluated several supervised learning models. [1]

4. Research Profile

The available Scopus information identifies Javid through author identifier 49561332700 and records 19 documents, 273 citations, and an h-index of 8. The supplied subject classification is Reliability. These indicators provide a quantitative view of indexed scholarly activity, while the recent publication illustrates engagement with applied machine learning and analytical research questions. [2]

5. Research Contributions

Javid’s recent collaborative research contributes an integrated framework for high-risk pregnancy prediction by combining supervised classifiers with Taguchi-based optimization and TOPSIS-based model ranking. The study compares demographic, pregnancy-related, and complete-case feature groups and evaluates KNN, Random Forest, Decision Tree, SVM, and MLP models using multiple performance criteria. [1]

6. Publications

A documented recent publication is “High-risk pregnancy prediction using Taguchi-optimized machine learning methods and TOPSIS-based model selection,” published in Scientific Reports in 2026. The article lists Maryam Mousavi Nogholi, Ashkan Mozdgir, and Youness Javid as authors and reports analysis of clinical data from 62 pregnant women, with model optimization and multi-criteria evaluation. [1]

7. Research Impact

The supplied Scopus record reports 273 citations across 19 indexed documents, indicating measurable scholarly visibility. The h-index of 8 further reflects a sustained citation record across multiple publications. The recent Scientific Reports study also demonstrates potential applied relevance by addressing early identification of high-risk pregnancies through computational prediction methods. [1][2]

8. Award Suitability

Based on the supplied evidence, Javid presents characteristics relevant to an innovative research recognition: an indexed publication record, measurable citation activity, and recent work integrating optimization, machine learning, and decision analysis. His documented research applies computational techniques to a clinically significant prediction problem, providing a reasonable scholarly basis for consideration under an innovation-focused award. [2]

9. Conclusion

Youness Javid’s supplied research profile combines established indexed scholarly activity with recent interdisciplinary computational research. The available evidence records 19 documents, 273 citations, and an h-index of 8, while his 2026 publication demonstrates the application of optimized machine learning and TOPSIS to a complex healthcare prediction problem. [1][2]

11. References

  1. Mousavi Nogholi, M., Mozdgir, A., & Javid, Y. (2026). High-risk pregnancy prediction using Taguchi-optimized machine learning methods and TOPSIS-based model selection. Scientific Reports, 16, 23966.
    https://doi.org/10.1038/s41598-026-55011-z
  2. Scopus. (2026). Youness Javid: Scopus Author Profile, Author ID 49561332700. Elsevier.
    https://www.scopus.com/pages/authors/49561332700