Abdel Rahman Alkharabsheh | Artificial Intelligence | Best Researcher Award

Published on by Scientific World Research Awards
Abdel Rahman Alkharabsheh
Affiliation Higher Colleges of Technology Abu Dhabi
Country United Arab Emirates
Scopus ID 26321305000
Documents 12
Citations 47
h-index 4
Subject Area Artificial Intelligence
Event Scientific World Research Awards
ORCID 0000-0003-2837-6803

Best Researcher Award

Abdel Rahman Alkharabsheh –  Higher Colleges of Technology Abu Dhabi

This academic profile summarizes the research activities, scholarly publications, scientific impact, and professional contributions of Abdel Rahman Alkharabsheh. The article presents a concise overview of achievements relevant to Artificial Intelligence and associated computational disciplines while highlighting research productivity and suitability for recognition through the Scientific World Research Awards.[1]

Abstract

Abdel Rahman Alkharabsheh has contributed to Artificial Intelligence, cybersecurity, disaster management, and high-performance computing through interdisciplinary research. His scholarly work demonstrates practical applications of intelligent systems, computational optimization, and data-driven decision-making that support both academic advancement and real-world technological innovation.[1]

Keywords

Artificial Intelligence, Cybersecurity, Machine Learning, Disaster Management, Multi-Agent Systems, Parallel Computing, BLAST+, High Performance Computing, Threat Detection, Research Impact.

Introduction

Abdel Rahman Alkharabsheh conducts research focused on Artificial Intelligence, cybersecurity, parallel computing, and intelligent decision-support systems. His studies integrate computational efficiency with practical applications, emphasizing scalable algorithms, predictive analytics, and innovative technologies addressing contemporary scientific and engineering challenges across multidisciplinary research domains.[1][2]

Research Profile

His research profile demonstrates expertise in Artificial Intelligence, machine learning, cybersecurity analytics, disaster management, and parallel computing. Publications emphasize interdisciplinary collaboration, computational optimization, intelligent automation, and practical solutions supporting digital transformation while contributing to internationally indexed scientific literature.[1][2][3]

Research Contributions

Major contributions include improving sequence similarity search through parallel computing, developing AI-driven cybersecurity frameworks for threat prediction and attack classification, and proposing multi-agent systems supporting disaster detection, evacuation planning, and rescue coordination using intelligent computational approaches.[1][2][3]

Publications

His publication record includes research on BLAST+ performance optimization, AI-enabled cybersecurity frameworks, and intelligent multi-agent disaster response systems. These publications collectively demonstrate consistent scholarly engagement with computational intelligence, scalable algorithms, and practical engineering applications across emerging technological fields.[1][2][3]

Research Impact

The research has supported advancements in intelligent computing by improving computational efficiency, strengthening cybersecurity resilience, and enhancing disaster response methodologies. Citation metrics and interdisciplinary relevance indicate growing academic recognition and potential influence across Artificial Intelligence and computer science communities.[1][2]

Award Suitability

The researcher demonstrates consistent scholarly productivity, interdisciplinary innovation, and meaningful contributions to Artificial Intelligence. Research outcomes address important scientific and societal challenges, making the profile appropriate for consideration within the Scientific World Research Awards evaluation framework based on academic merit and research significance.[1][2][3]

Conclusion

Abdel Rahman Alkharabsheh has established a focused research portfolio integrating Artificial Intelligence with practical computational solutions. His publications, citation record, and interdisciplinary research contributions collectively reflect sustained academic development and continued potential for future scientific innovation and collaborative research excellence.[1][2][3]

References

    1. Performance Evaluation of BLAST Using Multi-Threading: A Parallel Computing Approach for Sequence Similarity Search.
      https://orcid.org/0000-0003-2837-6803
    2. Multi-agents system for early disaster detection, evacuation and rescuing.
      https://scholar.google.com/citations?user=tQk-NVYAAAAJ&hl=en
    3. AI-Driven Proactive Framework for Cybersecurity Threat Prediction, Detection, and Attack Classification.
      https://www.scopus.com/authid/detail.uri?authorId=26321305000

Raj Kumar | Artificial Intelligence | Innovative Research Award

Published on by Scientific World Research Awards

Innovative Research Award

Raj Kumar
Raj Kumar
Affiliation Jeju National University
Country South Korea
Scopus ID 60056121200
Documents 3
Citations 7
h-index 1
Subject Area Artificial Intelligence
Event Scientific World Research Awards
ORCID 0009-0008-6070-3855

Raj Kumar is a research scholar at Jeju National University whose work focuses on artificial intelligence, machine learning, computer vision, renewable energy applications, and intelligent automation systems. His research portfolio demonstrates growing contributions to data-driven technologies and interdisciplinary AI solutions relevant to modern engineering challenges.[1]

Abstract

This article summarizes the academic profile and research achievements of Raj Kumar, a researcher specializing in artificial intelligence, machine learning, computer vision, and renewable energy applications. His scholarly activities encompass intelligent image analysis, solar energy forecasting, diffusion-based data augmentation, and machine learning-driven classification systems. Through journal publications, conference contributions, and interdisciplinary research initiatives, he has demonstrated engagement with emerging technological challenges. His work highlights the application of AI methodologies to practical engineering problems while contributing to advancements in intelligent automation, sustainable energy systems, and data-driven decision-making processes.[2]

Keywords

Artificial Intelligence; Machine Learning; Computer Vision; Solar Energy Forecasting; Deep Learning; Data Augmentation; Renewable Energy Systems.

Introduction

Artificial intelligence has become a major driver of innovation across engineering and energy domains. Raj Kumar’s research activities reflect this trend through investigations into machine learning algorithms, image-based analytics, and intelligent forecasting systems designed to improve operational efficiency and predictive accuracy.[2]

Research Profile

Currently affiliated with Jeju National University, Raj Kumar serves as a Research Scholar in a Machine Learning Laboratory. His expertise includes AI, computer vision, deep learning, reinforcement learning, image processing, and automation technologies. His ORCID record documents active engagement in research, education, and professional development activities.[1]

Research Contributions

His research contributions include diffusion-based image augmentation, class imbalance mitigation, solar panel fault detection, multimodal forecasting systems, and plant disease identification. These studies integrate advanced machine learning frameworks with real-world datasets to enhance analytical performance and predictive reliability.[3]

Publications

  • Hybrid Framework Combining Diffusion-Based Image Augmentation and Feature Level SMOTE for Addressing Extreme Class Imbalance (IEEE Access, 2025).
  • Multi-tier Data Augmentation and Balancing Framework Integrating Diffusion, Tomek Link, and SMOTE for Solar Panel Fault Detection (2026).
  • Fungal Blast Disease Detection in Rice Seed using Machine Learning (2021).

Research Impact

The documented publication record, citation activity, and interdisciplinary focus indicate meaningful participation in contemporary AI research. His work contributes to both theoretical development and practical deployment of machine learning approaches within energy and agricultural technology sectors.[4]

Award Suitability

Based on available scholarly records, Raj Kumar demonstrates active engagement in innovative artificial intelligence research, publication output, and emerging contributions to sustainable technology applications. These characteristics align with the objectives commonly associated with recognition programs that encourage research excellence and innovation.[5]

Conclusion

Raj Kumar’s academic profile reflects ongoing contributions to artificial intelligence, machine learning, and renewable energy research. His interdisciplinary approach and publication record support his recognition within emerging areas of technological innovation and applied engineering research.

References

  1. ORCID. (2026). Raj Kumar ORCID Record (0009-0008-6070-3855).
    https://orcid.org/0009-0008-6070-3855
  2. ORCID. (2026). Research Profile and Academic Activities of Raj Kumar.
    https://orcid.org/0009-0008-6070-3855
  3. Kumar, R., Kim, Y.-W., & Byun, Y.-C. (2025). Hybrid Framework Combining Diffusion-Based Image Augmentation and Feature Level SMOTE for Addressing Extreme Class Imbalance.
    https://doi.org/10.1109/ACCESS.2025.3600622
  4. Kumar, R., Kim, Y.-W., & Byun, Y.-C. (2026). Mitigating Dataset Imbalance Using Image-Based Stable Diffusion and Feature-Level SMOTE for Solar Panel Classification.
    https://doi.org/10.1016/j.egyr.2026.109055
  5. Elsevier. (n.d.). Scopus Author Details: Raj Kumar, Author ID 60056121200. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=60056121200

Ling Zhang | Artificial Intelligence | Best Researcher Award

Published on by Scientific World Research Awards

Best Researcher Award

Ling Zhang

Research Information
Affiliation Ocean University of China
Country China
Scopus ID 57851292900
Documents 56
Citations 537
h-index 13
Subject Area Artificial Intelligence
Event Scientific World Research Awards
ORCID 0000-0002-1679-7128

Ling Zhang is a researcher affiliated with Ocean University of China whose scholarly work integrates artificial intelligence, radar signal processing, maritime surveillance, and autonomous marine systems. Her publication portfolio demonstrates contributions to high-frequency surface wave radar technologies, target detection, and intelligent ocean engineering applications.[1]

Abstract

Ling Zhang has developed a research portfolio focused on artificial intelligence applications in maritime sensing, radar target detection, signal processing, and autonomous vessel technologies. Her work addresses challenges associated with shipborne high-frequency surface wave radar systems, clutter suppression, motion compensation, direction finding, and intelligent detection frameworks. Through publications in leading engineering and remote sensing journals, she has contributed methodologies that combine machine learning, deep feature fusion, and advanced radar analytics. These studies support improved situational awareness, marine monitoring, and autonomous ocean operations while advancing interdisciplinary collaboration between artificial intelligence and marine engineering research.[2]

Keywords

Artificial Intelligence, HFSWR, Radar Signal Processing, Target Detection, Marine Engineering, Autonomous Vessels.

Introduction

The integration of artificial intelligence into ocean observation and radar systems has become increasingly important for maritime safety and environmental monitoring. Ling Zhang’s research aligns with these developments through investigations into intelligent sensing technologies and data-driven detection methods.[3]

Research Profile

Her research profile encompasses radar engineering, machine learning, remote sensing, ocean engineering, and autonomous navigation systems. Published studies demonstrate continuous engagement with marine surveillance and intelligent maritime technologies.[2]

Research Contributions

Key contributions include deep feature fusion for radar target detection, direction-finding correction techniques, clutter suppression frameworks, and AI-enhanced path-planning algorithms for unmanned surface vessels. These studies strengthen the accuracy and operational effectiveness of maritime monitoring systems.[4]

Publications

Selected publications appear in IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, Ocean Engineering, IEEE Access, and Engineering Applications of Artificial Intelligence, reflecting interdisciplinary research activity and international visibility.[5]

Research Impact

With 56 indexed documents, 537 citations, and an h-index of 13, Ling Zhang’s work demonstrates measurable academic influence and engagement within radar technology, marine engineering, and artificial intelligence research communities.

Award Suitability

The combination of sustained publication activity, interdisciplinary innovation, and contributions to intelligent maritime technologies supports consideration for recognition through the Scientific World Research Awards program.

Conclusion

Ling Zhang’s research reflects ongoing efforts to advance artificial intelligence-enabled radar systems and marine technologies. Her scholarly output contributes to improved sensing, detection, and autonomous operational capabilities within maritime environments.

References

  1. Elsevier. (n.d.). Scopus author details: Ling Zhang, Author ID 57851292900. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57851292900
  2. ORCID. (2026). Ling Zhang ORCID Record.
    https://orcid.org/0000-0002-1679-7128
  3. Wang, C., Zhang, L., et al. (2023). Accurate Direction Finding for Shipborne HFSWR Through Platform Motion Compensation.
    https://doi.org/10.1109/TGRS.2023.3328264
  4. Wu, T., Zhang, L., et al. (2025). Two-Stage Target Detection for Compact HFSWR With Space-to-Depth YOLOv8 and Multiframe ViT.
    DOI:10.1109/JSTARS.2025.3556138
  5. Lu, Y., Li, G., Zhang, L., et al. (2026). Orthogonal Momentum Progressive Subnetwork Representation Learning with Feature Fusion for Surface Wave Radar Target Detection.
    https://doi.org/10.1016/j.engappai.2026.114821

Jianxi Zhao | Artificial Intelligence | Best Researcher Award

Published on by Scientific World Research Awards

Mr. Jianxi Zhao | Artificial Intelligence | Best Researcher Award

Beijing Information Science and Technology University, China

Mr. Jianxi Zhao is an emerging researcher recognized for his contributions to computational statistics, recurrent event analysis, and advanced statistical modeling. Affiliated with Beijing Information Science & Technology University, he has developed expertise in handling complex quantitative data through innovative analytical methodologies. His scholarly work focuses on improving statistical accuracy in situations involving intermittently observed covariates and dynamic event-driven datasets. With multiple indexed publications and a steadily growing citation record, he has demonstrated academic consistency and research capability within the field of applied statistics. His research activities emphasize methodological precision, mathematical computation, and interdisciplinary problem-solving relevant to modern scientific investigations. Through collaborations with fellow researchers and participation in scholarly publishing, he continues to strengthen his professional visibility and academic impact. Mr. Jianxi Zhao’s dedication to statistical innovation and computational research reflects strong potential for future contributions to global scientific and analytical advancement.

Professional Profile

Education

Jianxi Zhao has established a solid academic background in statistics, computational mathematics, and data-oriented scientific research. Associated with Beijing Information Science & Technology University, he has developed expertise in advanced statistical methodologies, recurrent event analysis, and mathematical modeling. His educational foundation emphasizes quantitative reasoning, analytical computation, and applied statistical interpretation, enabling him to address complex research challenges effectively. Through continuous academic engagement, he has strengthened his understanding of survival analysis, time-varying coefficient models, and intermittently observed covariate techniques. His scholarly preparation reflects dedication to methodological precision and scientific innovation. The combination of theoretical knowledge and computational capability has supported his contributions to statistical sciences and interdisciplinary analytical studies. His educational journey highlights a commitment to rigorous research practices, academic discipline, and the advancement of modern computational statistics for practical and scientific applications.

Professional Experience

Mr. Jianxi Zhao has gained valuable academic and research experience through active involvement in computational statistics and analytical modeling studies. His professional activities include conducting statistical investigations, contributing to scholarly publications, and collaborating with researchers in quantitative science disciplines. Working within the research environment of Beijing Information Science & Technology University, he has participated in projects focusing on recurrent event data, predictive modeling, and applied statistical methodologies. His experience reflects competence in handling complex datasets, developing mathematical frameworks, and interpreting analytical outcomes for scientific purposes. He has also contributed to collaborative research networks involving multiple co-authors and interdisciplinary perspectives. Through publication activities and academic engagement, he has strengthened his professional reputation within computational and statistical research communities. His growing experience demonstrates dedication to scientific inquiry, problem-solving, and the application of innovative statistical techniques in contemporary research environments.

Research Interest

The research interests of Jianxi Zhao primarily focus on computational statistics, recurrent event analysis, survival data modeling, and time-varying coefficient methodologies. His scholarly attention is directed toward developing advanced statistical approaches capable of addressing incomplete or intermittently observed covariate information in complex datasets. He is particularly interested in improving analytical accuracy and predictive reliability within biomedical statistics, longitudinal data interpretation, and mathematical computation. His work explores innovative techniques that enhance the understanding of event-driven data structures and dynamic statistical relationships. In addition, he demonstrates interest in interdisciplinary applications where computational modeling supports scientific and technological advancements. His research orientation combines theoretical development with practical implementation, contributing to the evolution of modern statistical science. By investigating sophisticated analytical frameworks, he aims to provide meaningful solutions for complex quantitative challenges across academic and applied research domains.

Award and Honor

Mr. Jianxi Zhao has earned academic recognition through his impactful research contributions in computational statistics and applied data analysis. His scholarly publications, citation record, and collaborative research activities reflect growing recognition within the scientific community. With indexed publications and measurable citation impact, he has demonstrated the quality and relevance of his research work in statistical modeling and recurrent event analysis. His contributions have strengthened his professional standing as an emerging researcher in computational and mathematical sciences. Participation in collaborative academic studies and publication in recognized scientific platforms further highlights his dedication to research excellence. Although publicly available information regarding formal awards remains limited, his academic performance, research productivity, and methodological contributions represent significant professional achievements. His growing citation influence and consistent engagement in advanced statistical research position him as a promising contributor to future scientific innovation and scholarly development within the international research landscape.

Conclusion

Mr. Jianxi Zhao demonstrates strong potential in computational statistics through impactful research, scholarly dedication, and analytical expertise. His growing academic influence and innovative statistical contributions support continued success in advanced scientific research.

Publications Top Noted

  • Title: A time-varying coefficient rate model with intermittently observed covariates for recurrent event data
    Authors: Jianxi Zhao et al.
    Year: 2025

Ilya Levin | Artificial Intelligence | Research Excellence Award

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Prof. Ilya Levin | Artificial Intelligence | Research Excellence Award

Professor | Holon Institute of Technology | Israel

Prof. Ilya Levin is a researcher at Holon Institute of Technology, specializing in artificial intelligence, machine learning, and educational technology. His research focuses on computational thinking, neural networks, and AI-driven learning systems. He has strong expertise in algorithm design and interdisciplinary innovation. His contributions are recognized through impactful publications and academic work. According to Scopus, he has 552 citations, 88 documents, and an h-index of 13, reflecting his significant impact in AI and education research.

 

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Featured Publications


Culture and Society in the Digital Age

– Information, 2021 (Citations: 275)


Massive open online courses – the modern concept in education and learning

– Vestnik Tomskogo State University, 2014 (Citations: 133)


Teaching machine learning in elementary school

– International Journal of Child-Computer Interaction, 2022 (Citations: 82)

 

Muhammad Furqan Zia | Artificial Intelligence | Young Scientist Award

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Mr. Muhammad Furqan Zia | Artificial Intelligence | Young Scientist Award

Researcher | Université du Québec à Trois-Rivières | Canada

Mr. Muhammad Furqan Zia is an emerging researcher at Université du Québec à Trois-Rivières, specializing in artificial intelligence and semantic communication. His research focuses on explainable AI and intelligent communication systems, addressing transparency and efficiency challenges. He has skills in AI modeling, data analysis, and system design, contributing to scholarly publications. His work is gaining recognition in the research community. According to Scopus, he has 5 citations, 4 documents, and an h-index of 2, reflecting his growing academic impact.

 

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An Advanced Non-Orthogonal Multiple Access Security Technique for Future Wireless Communication Networks

– RS Open Journal on Innovative Communication Technologies, 2020 (Citations: 27)

 

Andi Chen | Artificial Intelligence | Research Excellence Award

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Dr. Andi Chen | Artificial Intelligence | Research Excellence Award

Vice President of the Student Union | Nanjing University | China

Dr. Andi Chen is an emerging researcher in computer science and artificial intelligence, with a strong focus on machine learning, deep learning architectures, and pattern recognition. His research interests center on hybrid quantum-inspired neural networks, particularly the integration of ResNet and DenseNet models to improve feature representation, classification performance, and computational efficiency in complex data environments. He demonstrates solid research skills in AI algorithm design, deep neural network modeling, pattern recognition, data analysis, and experimental evaluation, with applications relevant to intelligent systems and next-generation computing. Dr. Chen’s scholarly contributions include publications in reputable venues such as Neurocomputing, reflecting growing visibility in the AI research community. While no major awards or funded projects are currently reported, his work shows strong potential for future recognition. According to Scopus, his research profile records 3 documents, 1 citation, and an h-index of 1. In conclusion, Dr. Chen’s research trajectory highlights promising contributions to advanced AI methodologies and quantum-inspired intelligent computing.

 

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Image Compression and Reconstruction Based on Quantum Network


– IEEE International Parallel and Distributed Processing Symposium, 2024 (Citations: 5)


Quantum Sparse Coding and Decoding Based on Quantum Network


– Applied Physics Letters, 2024 (Citations: 1)

 

Mingshou An | Artificial Intelligence | Excellence in Research Award

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Mr. Mingshou An | Artificial Intelligence | Excellence in Research Award

Lecturer | Xi’an Technological University | China

Mr. Mingshou An is an emerging researcher affiliated with Dong-A University, Busan, South Korea, recognized for his contributions to computer science and intelligent image processing. His research primarily focuses on deep learning, image denoising, medical and natural image enhancement, convolutional neural networks, U-Net architectures, and multi-scale attention mechanisms, with a strong emphasis on improving image quality and model efficiency. He possesses solid research skills in machine learning, deep neural network design, algorithm optimization, data preprocessing, model evaluation, and scientific computing, supported by hands-on experience in developing advanced attention-based architectures. His notable work includes an open-access publication on Multi-scale Attention Dense U-Net for image denoising, reflecting innovation in AI-driven image restoration. According to Scopus, Mr. An has 11 research documents, 15 citations , and an h-index of 3, demonstrating growing academic impact. While formal awards and honors are not yet listed, his citation growth indicates rising recognition. In conclusion, Mr. Mingshou An represents a promising researcher whose work contributes meaningfully to the advancement of intelligent imaging and deep learning applications.

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Featured Publications


A Method of Visual Positioning of Tank Truck Openings via Two-Stage Fine-Tuning


– Book Chapter, 2025 | DOI: 10.1007/978-3-031-94962-3_14


An Enhanced LSTM with Hippocampal-Inspired Episodic Memory for Urban Crowd Behavior Analysis


– Electronics (Journal), 2025 | DOI: 10.3390/electronics15010101


A Method of Image Denoising via Dense Attention DnCNN


– Book Chapter, 2024 | DOI: 10.1007/978-981-97-4182-3_43


Fusion Self-Attention Feature Clustering Mechanism Network for Person ReID


– Book Chapter, 2024 | DOI: 10.1007/978-981-99-9416-8_55


A Study on Deep Learning Algorithm for Fire Detection based on Attention BiFPN


– Journal of Korean Institute of Information Technology, 2024 | DOI: 10.14801/jkiit.2024.22.9.1

 

Wiktor Jakowluk | Robotics and Automation | Research Excellence Award

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Assist. Prof. Dr. Wiktor Jakowluk | Robotics and Automation | Research Excellence Award

Assistant Professor | Bialystok University of Technology | Poland

Assist. Prof. Dr. Wiktor Jakowluk is an emerging scholar at the Bialystok University of Technology whose research focuses on advanced system identification, optimal input signal design, and application-oriented modeling for dynamic and control systems. His work explores closed-loop identification, application-oriented spectrum design, and robust modeling approaches that support modern predictive control and intelligent automation. His research interests include dynamic system identification, experiment design, adaptive control strategies, fractional-order modeling, and data-driven optimization for engineering processes. Dr. Jakowluk’s research skills span mathematical modeling, simulation-driven validation, algorithmic optimization, MATLAB-based system analysis, and the development of innovative methodologies for identifying nonstationary or complex dynamic structures. Although no formal awards or grants are listed, his scholarly impact within the control engineering community is demonstrated through international collaborations, peer-reviewed publications, and contributions to open-access research. According to Scopus, he has 60 citations, 15 indexed documents, and an h-index of 4, reflecting steady and growing influence in the fields of system identification and control engineering. His work continues to advance practical and application-oriented identification techniques that support reliable, efficient, and high-performance control systems. Dr. Jakowluk’s research trajectory highlights his commitment to bridging theory and engineering practice, contributing valuable methods that strengthen modeling accuracy and intelligent system design.

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Featured Publications


Plant friendly input design for parameter estimation in an inertial system with respect to D-efficiency constraints

– Entropy 16(11), 5822–5837, 2014 (11 citations)


Design of an optimal input signal for plant-friendly identification of inertial systems

– Przegląd Elektrotechniczny 85(6), 125–129, 2009 (11 citations)


Optimal input signal design for fractional-order system identification

– Bulletin of the Polish Academy of Sciences: Technical Sciences 67(1), 37–44, 2019 (10 citations)


Free final time input design problem for robust entropy-like system parameter estimation

– Entropy 20(7), 528, 2018 (10 citations)


Design of an optimal excitation signal for identification of inertial systems in time domain

– Przegląd Elektrotechniczny 85(6), 125–129, 2009 (9 citations)

 

 

Jorge Francisco Aguirre-Sala | Artificial Intelligence | Breakthrough Research Award

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Dr. Jorge Francisco Aguirre-Sala | Artificial Intelligence | Breakthrough Research Award

Profesor-Investigador | Universidad Autónoma de Nuevo León | Mexico

Dr. Jorge Francisco Aguirre-Sala is a leading scholar in digital democracy, civic participation, and the ethical–political implications of emerging technologies, recognized for his influential contributions across Latin America. His research focuses on electronic democracy, citizen engagement through social media, digital governance, crime prevention using ICT, hermeneutics, and the ethical challenges of artificial intelligence. He is skilled in interdisciplinary analysis, qualitative political research, evaluative methodologies, and the integration of ecological ethics with digital policy. His body of work spanning topics such as liquid democracy, participatory budgeting, and digital transformation of the state has earned him strong academic impact and international visibility. Dr. Aguirre-Sala has received multiple recognitions for his contributions to political philosophy, digital participation models, and public policy innovation. According to Scholar metrics, he has 786 citations, 27 documents, and an h-index of 15, reflecting sustained scholarly influence across his fields of expertise. His work continues to advance democratic quality by bridging technology, ethics, and participatory governance, offering forward-looking insights into how digital tools reshape citizenship and state–society relations.

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