Sudan Jha | Artificial Intelligence | Outstanding Scientist Award

Published on by Global CSE Awards

Outstanding Scientist Award

Sudan Jha – Government of Nepal and Kathmandu University

Sudan Jha
Affiliation Government of Nepal; Kathmandu University
Country Nepal
Scopus ID 59307702300
Documents 127
Citations 2,868
h-index 28
Subject Area Artificial Intelligence
Event Global CSE Awards
ORCID 0000-0003-0074-2584

Sudan Jha is a researcher associated with the Government of Nepal and Kathmandu University, Nepal, whose academic profile encompasses artificial intelligence and computational technologies. His research publications address intelligent security, computer vision, road segmentation, and AI-based visual manipulation detection. His scholarly record reflects research activity across applied artificial intelligence and intelligent industrial systems. The Outstanding Scientist Award presented under the Global CSE Awards recognizes his reported academic contributions and research engagement. His publication statistics provide measurable indicators of scholarly productivity and citation visibility within indexed research literature.[1] [2]

Abstract

Sudan Jha is a researcher associated with the Government of Nepal and Kathmandu University, Nepal, specializing in research areas related to artificial intelligence and intelligent computational systems. His Scopus profile records 127 documents, 2,868 citations, and an h-index of 28, indicating measurable scholarly productivity and citation visibility. His selected publications examine computationally efficient road segmentation, intelligent security frameworks for Industrial Internet of Things environments, and graph-based detection of AI-generated visual manipulation in unmanned aerial vehicle systems. These research themes connect artificial intelligence with computer vision, cybersecurity, industrial monitoring, and intelligent information processing. His academic profile demonstrates interdisciplinary engagement with emerging computational technologies and applied research challenges.[1]

Keywords

  • Artificial Intelligence
  • Computer Vision
  • Road Segmentation
  • Industrial Internet of Things
  • Intelligent Security
  • Visual Manipulation Detection
  • Graph-Based Artificial Intelligence
  • Research Impact

Introduction

Artificial intelligence increasingly supports computer vision, industrial security, and intelligent information processing. Sudan Jha’s research profile reflects these developments through publications addressing road segmentation, Industrial IoT security, and visual manipulation detection. [3]

Research Profile

Sudan Jha is associated with the Government of Nepal and Kathmandu University. His Scopus profile reports 127 documents, 2,868 citations, and an h-index of 28, indicating scholarly productivity and citation visibility.[2] [3]

Research Contributions

His selected research addresses efficient road segmentation, intelligent security frameworks for Industrial IoT environments, and graph-based AI-generated visual manipulation detection for UAV industrial systems, connecting computer vision with security.[1] [2]

Publications

The following selected publications illustrate research interests involving computer vision, intelligent industrial security, and artificial intelligence-based manipulation detection. Their DOI identifiers provide persistent access to publication records and facilitate scholarly verification.[1] [3]

Research Impact

The reported 2,868 citations and h-index of 28 indicate measurable scholarly visibility. Research addressing road segmentation, Industrial IoT security, and visual manipulation detection contributes to discussions on intelligent systems, computational efficiency, and digital security. [2]

Award Suitability

The reported publication record, citation indicators, and interdisciplinary artificial intelligence research provide relevant academic context for the Outstanding Scientist Award category. Recognition associated with Global CSE Awards reflects the supplied award designation.[1]

Conclusion

Sudan Jha’s academic profile demonstrates engagement with artificial intelligence, computer vision, and intelligent industrial security. His selected publications and bibliometric indicators provide an overview of research productivity, interdisciplinary contributions, and scholarly visibility.[1] [2] [3]

References

  1. A Computationally Efficient Method for Road Segmentation and Statistics-Based Classification on Low-Power Devices. (n.d.). Springer.
    https://doi.org/10.1007/978-3-032-25176-3_10
  2. A Comprehensive Review on Challenges, Frameworks, and Future Trends for Intelligent Security in Industrial IoT. (n.d.). Routledge.
    https://doi.org/10.1201/9781003609773-3
  3. A Graph-based AI Generated Visual Manipulation Detector for UAV-based Industrial Systems. (n.d.). Routledge.
    https://doi.org/10.1201/9781003609773-8

Emmanouil Zoulias | Cybersecurity | Best Researcher Award

Published on by Global CSE Awards

Best Researcher Award

Emmanouil Zoulias – National and Kapodistrian University of Athens

Emmanouil Zoulias
Affiliation National and Kapodistrian University of Athens
Country Greece
Scopus ID 55944921900
Documents 76
Citations 389
h-index 10
Subject Area Cybersecurity
Event Global CSE Awards
ORCID 0000-0002-1953-3267

Emmanouil Zoulias is affiliated with the National and Kapodistrian University of Athens, Greece. His research profile encompasses cybersecurity and technology-oriented academic contributions. His publications also address healthcare information systems, digital applications, and telemedicine, demonstrating interdisciplinary engagement with technological solutions and healthcare workflows. His academic profile provides a foundation for examining research productivity, publication activity, and scholarly impact within contemporary scientific research. [1] [3]

Abstract

Emmanouil Zoulias, affiliated with the National and Kapodistrian University of Athens in Greece, maintains a scholarly profile associated with cybersecurity and interdisciplinary technological research. His Scopus profile reports 76 documents, 389 citations, and an h-index of 10, providing bibliometric indicators of publication activity and scholarly visibility. His selected publications address data management for molecular blood donor screening, mobile health applications for operating-room nurses, and telemedicine integration within the Greek national healthcare system.  [1]

Keywords

  • Cybersecurity
  • Research Productivity
  • Digital Healthcare
  • Telemedicine
  • Healthcare Information Systems
  • Academic Research

Introduction

Cybersecurity and digital technologies increasingly influence healthcare, information management, and scientific research. Emmanouil Zoulias’s academic profile connects technology-oriented scholarship with healthcare applications, including digital workflows and telemedicine. His publications provide context for understanding interdisciplinary research addressing contemporary technological and organizational challenges. [3]

Research Profile

Emmanouil Zoulias is affiliated with the National and Kapodistrian University of Athens, Greece. His Scopus profile records 76 documents, 389 citations, and an h-index of 10. These bibliometric indicators provide an overview of indexed scholarly productivity and citation visibility. [1] [2]

Research Contributions

His selected publications examine data-management tools for molecular blood donor screening, mobile applications supporting operating-room nursing workflows, and telemedicine integration in Greece. These contributions address information processing, healthcare coordination, digital workflow optimization, and technology-enabled service delivery.[2] [3]

Publications

The following selected publications illustrate research addressing healthcare information systems, digital applications, and telemedicine. Their DOI identifiers provide persistent links to publication records and support verification of the cited scholarly works. [1]

Research Impact

The researcher profile reports 389 citations and an h-index of 10, indicating measurable scholarly visibility. Publications addressing healthcare information systems and digital applications contribute to discussions concerning clinical workflows, information management, and technology-enabled healthcare delivery.[2]

Award Suitability

The documented publication record, citation indicators, and interdisciplinary research activities provide relevant academic context for the Best Researcher Award category. Recognition associated with the Global CSE Awards acknowledges the supplied researcher profile and award designation. [1]

Conclusion

Emmanouil Zoulias’s academic profile demonstrates research engagement across cybersecurity, digital technologies, and healthcare information systems. His publications and bibliometric indicators provide an overview of scholarly productivity, interdisciplinary research interests, and academic contributions within technology-oriented research.[3]

References

  1. A Data-Management Tool for Reactive Sample Handling in Molecular Blood Donor Screening
    https://doi.org/10.3233/SHTI260919
  2. A Mobile Health Application for Operating Room Nurses: Optimizing Workflows with Surgical Checklists and Preference Cards. (n.d.). Studies in Health Technology and Informatics. IOS Press.
    https://doi.org/10.3233/SHTI260792
  3. Telemedicine Integration into the Greek National Health Care System: Challenges, Suggestions and Future Perspectives. (n.d.). Studies in Health Technology and Informatics. IOS Press.
    https://doi.org/10.3233/SHTI250709

Mubanga Mpundu | Information security and economy | Best Researcher Award

Published on by Global CSE Awards

Best Researcher Award

Mubanga Mpundu
Affiliation University of the Western Cape
Country South Africa
Subject Area Information Security and Economy
Scopus Profile 57213045767
Documents 13
Citations 11
h-index 2
Event Global CSE Awards
ORCID ORCID Profile

Mubanga Mpundu is a researcher affiliated with the University of the Western Cape, South Africa, whose stated academic subject area includes Information Security and Economy. This interdisciplinary field explores the relationship between information protection, economic development, social protection, investment, sectoral growth, and entrepreneurship. The supplied publications associated with the research profile address productive inclusion through social protection in Namibia, investment share and sectoral output growth in South Africa, and the role of non-governmental organisations in youth entrepreneurship development across Sub-Saharan Africa.[1] [2] [3]

Abstract

This article presents the academic research profile of Mubanga Mpundu, affiliated with the University of the Western Cape, South Africa, in the subject area of Information Security and Economy.  According to the supplied Scopus information, the researcher has 13 documents, 11 citations, and an h-index of 2. These academic details provide an overview of the research profile and its focus on economic development and institutional support mechanisms. [1]

Keywords

Information Security and Economy; Social Protection; Productive Inclusion; Namibia; Investment Share; Sectoral Output Growth; Agriculture; Mining; Economic Development; Youth Entrepreneurship; Non-Governmental Organisations; NGOs; Sub-Saharan Africa; Development Support Mechanisms; Entrepreneurship Development; Economic Research.

Introduction

Information security and economy represent interconnected areas of study concerned with information protection, economic systems, institutional development, and the relationship between technology and economic activity. Information security contributes to the protection of information assets and the reliability of digital systems that increasingly support economic activities.[3]

Research Profile

Mubanga Mpundu is affiliated with the University of the Western Cape, South Africa. The stated subject area for this research profile is Information Security and Economy. The supplied publication titles indicate research interests involving social protection and productive inclusion, investment and sectoral output growth, and institutional mechanisms supporting youth entrepreneurship in Sub-Saharan Africa.The supplied Scopus details indicate 13 scholarly documents, 11 citations, and an h-index of 2. [1] [2]

Research Contributions

One of the supplied publications examines reorienting social protection for productive inclusion in Namibia. Social protection programmes are important components of development policy, supporting individuals and communities through different forms of assistance. These research topics provide perspectives on policy, economic activity, and development mechanisms across Namibia, South Africa, and Sub-Saharan Africa.[2] [3]

Publications

The following publications were provided as references for the research profile of Mubanga Mpundu. These works address social protection and productive inclusion, investment and sectoral output growth in South Africa, and NGO support for youth entrepreneurship development in Sub-Saharan Africa. [1]

Research Impact

Research on social protection and productive inclusion contributes to discussions about how development programmes can support economic participation and livelihood opportunities. Examining social protection in Namibia provides a context for understanding the relationship between assistance programmes and productive inclusion. [1] [3]

Award Suitability

The Best Researcher Award recognizes academic research contributions, scholarly publications, and research achievements within a relevant discipline. In the field of Information Security and Economy, relevant research may include information protection, economic policy, development studies, investment analysis, social protection, sectoral growth, and entrepreneurship development. The provided Scopus information records 13 documents, 11 citations, and an h-index of 2. [2] [3]

Conclusion

Mubanga Mpundu’s academic profile is associated with the University of the Western Cape, South Africa, and the subject area of Information Security and Economy. The supplied Scopus information includes 13 documents, 11 citations, and an h-index of 2. The provided research publications examine productive inclusion through social protection in Namibia, investment share and sectoral output growth in South Africa.  [1] [2] [3]

References

  1. Reorienting social protection for productive inclusion in Namibia.
    http://dx.doi.org/10.14426/f09fwf70
  2. Investment Share and Sectoral Output Growth in South Africa: Evidence from Agriculture and Mining.
    https://doi.org/10.59857/gmt3ct07
  3. Non-Governmental Organisations and Youth Entrepreneurship Development in Sub-Saharan Africa: A Systematic Review of Support Mechanisms, Outcomes and Challenges.
    http://dx.doi.org/10.53819/81018102t7099

Ahmed Elkhalil | Cybersecurity | Excellence in Cybersecurity Leadership Award

Published on by Global CSE Awards

Excellence in Cybersecurity Leadership Award

Ahmed Elkhalil
Affiliation Qilu Institute of Technology
Country China
Subject Area Cybersecurity
Scopus Profile 57218991786
Documents 19
Citations 176
h-index 7
Event Global CSE Awards
ORCID ORCID Profile

Ahmed Elkhalil is a researcher affiliated with Qilu Institute of Technology, China, whose stated academic subject area is Cybersecurity. Cybersecurity focuses on protecting digital systems, networks, applications, and information against unauthorized access, malicious activities, and cyber threats. The supplied publications associated with his research profile address secure blockchain frameworks for vehicular ad hoc networks, exfiltration-resistant edge computing for intelligent transportation systems, and adversarial attack detection in industrial control systems.[1] [2] [3]

Abstract

This article presents the academic research profile of Ahmed Elkhalil, affiliated with Qilu Institute of Technology, China, in the subject area of Cybersecurity. The supplied research publications address secure blockchain frameworks for vehicular ad hoc networks, exfiltration-resistant edge computing architectures for intelligent transportation systems, and adversarial attack detection in industrial control systems using LSTM-based intrusion detection and black-box defense strategies. [1]

Keywords

Cybersecurity; Blockchain Security; Vehicular Ad Hoc Networks; VANETs; Secure Blockchain Framework; Firewall Protection; Edge Computing; Intelligent Transportation Systems; Data Exfiltration; Intrusion Detection Systems; Industrial Control Systems; Adversarial Attack Detection; LSTM; Black-Box Defense; Network Security; Cyber Defense.

Introduction

Cybersecurity is an important field of research concerned with protecting information systems, communication networks, digital infrastructure, and connected technologies from cyber threats. As digital systems become increasingly interconnected, security challenges involving unauthorized access, malicious attacks, data leakage, and system vulnerabilities continue to require advanced protective approaches. [3]

Research Profile

Ahmed Elkhalil is affiliated with Qilu Institute of Technology, China. His stated subject area is Cybersecurity, and the supplied publication titles indicate research interests involving blockchain security, vehicular network protection, edge computing security, intelligent transportation systems, and intrusion detection in industrial control environments.The supplied Scopus details indicate 19 scholarly documents, 176 citations, and an h-index of 7. [2]

Research Contributions

One of the supplied publications introduces BCRF-SC, a provably secure hybrid blockchain framework with firewalls for resilient vehicular ad hoc networks (VANETs). Vehicular ad hoc networks support communication among vehicles and connected infrastructure, creating security requirements involving data integrity, communication reliability, and protection against unauthorized activities.[1] [2]

Publications

The following publications were provided as references for the research profile of Ahmed Elkhalil. These works address blockchain security in vehicular networks, secure edge computing for intelligent transportation systems, and adversarial attack detection in industrial control systems. [2]

Research Impact

Research on secure blockchain frameworks for vehicular ad hoc networks contributes to the understanding of security requirements in connected transportation systems. Blockchain-based approaches and firewall mechanisms can be examined as components of network protection architectures.Together, the supplied publications provide research perspectives on blockchain security, edge computing protection, and industrial intrusion detection.[3]

Award Suitability

The Excellence in Cybersecurity Leadership Award recognizes research contributions and leadership in advancing cybersecurity knowledge, developing protective technologies, and addressing important digital security challenges.Ahmed Elkhalil’s supplied academic profile identifies his affiliation with Qilu Institute of Technology, China, and his subject area as Cybersecurity. The provided Scopus information records 19 documents, 176 citations, and an h-index of 7. [1]

Conclusion

Ahmed Elkhalil’s academic profile is associated with Qilu Institute of Technology, China, and the subject area of Cybersecurity. The supplied Scopus information includes 19 documents, 176 citations, and an h-index of 7. The provided research publications examine a provably secure hybrid blockchain framework for VANETs, exfiltration-resistant edge computing for intelligent transportation systems.[3]

References

  1. BCRF-SC: Provably secure hybrid blockchain framework with firewalls for resilient VANETs.
    https://doi.org/10.1016/j.compeleceng.2026.111578
  2. Exfiltration-resistant edge computing framework for secure intelligent transportation systems.
    https://doi.org/10.1016/j.pmcj.2026.102181
  3. Adversarial Attack Detection in Industrial Control Systems Using LSTM-Based Intrusion Detection and Black-Box Defense Strategies.
    https://doi.org/10.63180/jcsra.thestap.2025.3.2

Daniel Suárez Hernández | Data Science | Research Excellence Award

Published on by Global CSE Awards

Research Excellence Award

Daniel Suárez Hernández
Affiliation Hospital Orihuela
Country Spain
Subject Area Data Science
Scopus Profile 7102276862
Documents 33
Citations 1361
h-index 13
ORCID ORCID Profile
Event Global CSE Awards

Daniel Suárez Hernández is a researcher affiliated with Hospital Orihuela, Spain, whose stated academic subject area is Data Science. Data Science involves the use of analytical methods, computational techniques, and data-driven approaches to identify patterns, interpret information, and support evidence-based decision-making. The supplied publications associated with his research profile focus on photoplethysmography (PPG) signal quality, reliable heartbeat detection in wearable PPG signals, and wavelet-based denoising using accelerometer features. [1] [2] [3]

Abstract

This article presents the academic research profile of Daniel Suárez Hernández, affiliated with Hospital Orihuela, Spain, in the subject area of Data Science. The supplied research publications address the analysis and processing of photoplethysmography (PPG) signals, with particular attention to signal quality under ambient light interference, reliable heartbeat detection in wearable devices, and signal denoising through wavelet methods and clustered accelerometer features.  According to the supplied Scopus information, the researcher has 33 documents, 1,361 citations, and an h-index of 13. [1] [2] [3]

Keywords

Data Science; Photoplethysmography; PPG Signals; Signal Processing; Wearable Health Monitoring; Heartbeat Detection; Signal Quality Assessment; Ambient Light Interference; Wavelet Denoising; Accelerometer Features; Physiological Signals; Data Analysis; Machine Learning; Learning-Driven Methods; Biomedical Signal Processing; Wearable Technology.

Introduction

Data Science is an interdisciplinary field that combines data analysis, computational methods, statistical techniques, and information processing to extract meaningful insights from complex datasets. It supports research across multiple domains by providing methods to identify patterns, improve analytical accuracy, and develop data-driven solutions. In healthcare-related applications, Data Science can contribute to the analysis of physiological signals and the development of methods for monitoring and interpreting health-related information. [1] [2] [3]

Research Profile

Daniel Suárez Hernández is affiliated with Hospital Orihuela, Spain. His stated subject area is Data Science, and the supplied publication titles indicate research interests involving photoplethysmography signal analysis, wearable monitoring, heartbeat detection, and signal-processing techniques. The publications provided for this profile focus on methods for assessing PPG signal quality and improving the reliability of physiological signal interpretation.The supplied Scopus details indicate 33 scholarly documents, 1,361 citations, and an h-index of 13.[1] [2] [3]

Research Contributions

One of the supplied publications examines a spectral approach to evaluating PPG signal quality under ambient light interference. Ambient light can affect optical measurements and introduce interference into photoplethysmography recordings. Evaluating signal quality under these conditions is important for understanding the reliability of collected data. Collectively, the supplied publications cover three connected aspects of PPG signal analysis: signal quality evaluation, heartbeat detection, and noise reduction. [1] [2] [3]

Publications

The following publications were provided as references for the research profile of Daniel Suárez Hernández. These works address photoplethysmography signal quality, heartbeat detection in wearable PPG, and wavelet-based signal denoising using accelerometer features. [1] [2] [3]

Research Impact

Research into PPG signal quality contributes to the understanding of how external conditions influence optical physiological measurements. Ambient light interference can affect the characteristics of recorded signals, making signal quality evaluation an important part of wearable monitoring research. Methods for assessing signal quality can help researchers examine the reliability of collected measurements and understand the conditions under which PPG signals can be interpreted.Together, the three publications provide research perspectives on signal quality, heartbeat detection, and denoising in wearable PPG applications. [1] [2] [3]

Award Suitability

The Research Excellence Award recognizes researchers whose academic work demonstrates research productivity, scholarly contribution, subject expertise, and engagement with the advancement of knowledge. In Data Science, relevant research contributions may include the development and application of analytical methods, computational techniques, data-processing approaches, and innovative solutions to complex research problems. Daniel Suárez Hernández’s supplied academic profile identifies his affiliation with Hospital Orihuela, Spain, and his subject area as Data Science. The provided Scopus information records 33 documents, 1,361 citations, and an h-index of 13. [1] [2] [3]

Conclusion

Daniel Suárez Hernández’s academic profile is associated with Hospital Orihuela, Spain, and the subject area of Data Science. The supplied Scopus information includes 33 documents, 1,361 citations, and an h-index of 13. The provided research publications examine spectral evaluation of PPG signal quality under ambient light interference, template-based and learning-driven heartbeat detection in wearable PPG, and context-aware wavelet denoising using clustered accelerometer features. [1] [2] [3]

References

  1. A Spectral Approach to Evaluating PPG Signal Quality Under Ambient Light Interference.
    https://doi.org/10.1007/978-3-032-24045-3_17
  2. A Template-Based and Learning-Driven Method for Reliable Heartbeat Detection in Wearable PPG.
    https://doi.org/10.1007/978-3-032-24045-3_21
  3. Context-Aware Wavelet Denoising of PPG Signals Using Clustered Accelerometer Features.
    https://doi.org/10.1007/978-3-032-24045-3_18

Ola Khresat | Cybersecurity | Excellence in Artificial Intelligence Award

Published on by Global CSE Awards

Excellence in Artificial Intelligence Award

Ola Khresat
Affiliation Zarqa University
Country Jordan
Subject Area Cybersecurity
Scopus ID 57920618700
Documents 23
Citations 25
h-index 3
Google Scholar Scholar Profile
Event Global CSE Awards

Ola Khresat is a researcher affiliated with Zarqa University, Jordan, whose supplied academic profile identifies Cybersecurity as the subject area. The provided research references cover organizational internal control, financial reporting standards, and machine-learning-based fault detection in industrial Internet of Things (IoT) environments. These topics highlight the importance of information integrity, intelligent data analysis, and reliable digital systems. The application of machine learning to industrial monitoring demonstrates the relevance of artificial intelligence to identifying operational abnormalities and supporting intelligent technological solutions. [1] [2] [3]

Abstract

This article presents the academic research profile of Ola Khresat, affiliated with Zarqa University, Jordan, with Cybersecurity identified as the subject area. Artificial Intelligence is an interdisciplinary field that combines machine learning, computational methods, data analysis, and automated reasoning to develop intelligent solutions for complex problems. Its applications include cybersecurity, industrial monitoring, information management, and automated fault detection. The supplied Scopus profile reports 23 documents, 25 citations, and an h-index of 3.[1] [2] [3]

Keywords

Machine Learning; Industrial Internet of Things; IoT Fault Detection; Intelligent Monitoring; Information Security; Data Integrity; COSO Framework; Internal Control; IFRS 15; Financial Reporting; Time-Series Analysis; Industrial Monitoring.

Introduction

Artificial Intelligence has become an important area of technological development, enabling computers and digital systems to perform tasks involving data analysis, pattern recognition, prediction, and automated decision-making. Through machine learning, computational modelling, and intelligent algorithms, AI supports applications across industries, including finance, healthcare, telecommunications, manufacturing, and cybersecurity. Cybersecurity focuses on protecting digital information, networks, applications, and connected infrastructure against unauthorized access, disruption, and malicious activities. [1] [2] [3]

Research Profile

Ola Khresat is affiliated with Zarqa University in Jordan. The supplied academic information identifies Cybersecurity as the subject area and provides a Scopus author identifier and Google Scholar profile for academic reference. According to the supplied Scopus information, the research profile includes 23 documents, 25 citations, and an h-index of 3. These bibliometric indicators provide a quantitative overview of the publication and citation information provided for the researcher. Such indicators may change over time as additional research is published, indexed, and cited. [1] [2] [3]

Research Contributions

Artificial Intelligence and Cybersecurity contribute to the development of reliable digital environments through intelligent data analysis, automated monitoring, and computational approaches to identifying potential problems. Machine learning provides methods for examining large datasets, recognizing patterns, and supporting predictions. These approaches can be applied to industrial monitoring, network security, information management, and operational risk analysis. The supplied publications offer context for understanding the research themes associated with organizational integrity, financial information, and intelligent fault detection. [1] [2] [3]

Publications

The following three references were supplied for this academic profile. Their publication titles and source links are reproduced as provided. The works address internal control components based on the COSO framework, IFRS 15 and telecommunications profitability, and machine-learning-based fault detection in industrial IoT monitoring.

Research Impact

Research involving artificial intelligence, cybersecurity, and intelligent industrial monitoring can contribute to the development of reliable and efficient digital systems. Machine-learning-based fault detection offers methods for analysing industrial data, identifying abnormal patterns, and supporting timely maintenance decisions. These approaches can help improve operational awareness and reduce the challenges associated with monitoring complex connected infrastructure.[1] [2] [3]

Award Suitability

The Excellence in Artificial Intelligence Award recognizes academic and research contributions that demonstrate innovation, technical knowledge, and meaningful applications of intelligent computational methods.Ola Khresat’s supplied academic profile identifies Zarqa University, Jordan, as the institutional affiliation and Cybersecurity as the subject area. The provided Scopus information includes 23 documents, 25 citations, and an h-index of 3. [1] [2] [3]

Conclusion

Ola Khresat’s academic profile is associated with Zarqa University, Jordan, and the research subject of Cybersecurity. The supplied Scopus information reports 23 documents, 25 citations, and an h-index of 3.These research themes provide context for the relationship between artificial intelligence, intelligent data analysis, organizational integrity, and dependable digital systems. The Scopus and Google Scholar profiles provide additional researcher identification resources for academic reference. [1] [2] [3]

References

  1. The Efficiency of Applying the Internal Control Components Based on COSO Framework to Transparently Carry out Tasks and Services, Ensure Integrity and Enhance Quality and Efficiency: Case Study – The Greater Amman Municipality.
    https://ideas.repec.org/a/jfr/ijfr11/v11y2020i2p371-381.html
  2. Impact of the Application of IFRS 15 on the Profitability of Jordanian Telecom Companies (Case Study: Jordan Orange Telecom).
    https://ideas.repec.org/a/jfr/ijfr11/v12y2021i2p308-317.html
  3. Identification of Fault Detection in IoT using Machine Learning: The Framework for Analysis of Time-Series based Industrial Monitoring.
    https://ieeexplore.ieee.org/abstract/document/11325700

Daniel Suárez | Data Science | Excellence in Data Science Achievement Award

Published on by Global CSE Awards

Excellence in Data Science Achievement Award

Daniel Suárez
Affiliation Conselleria de Sanidad
Country Spain
Subject Area Data Science
Scopus ID 7102276862
Documents 32
Citations 1,361
h-index 13
ORCID 0000-0002-1273-5077
Event Global CSE Awards

Daniel Suárez is a researcher affiliated with Conselleria de Sanidad, Spain, whose supplied academic profile is associated with Data Science. His research profile includes work connected to biomedical signal analysis, wearable monitoring technologies, and data-driven methods for improving the interpretation of physiological measurements. The supplied publication references focus on photoplethysmography (PPG) signal quality assessment, heartbeat detection in wearable PPG systems, and wavelet-based denoising using accelerometer information. These topics demonstrate the relevance of data science techniques to healthcare technology, physiological signal processing, and intelligent health-monitoring systems. [1] [2] [3]

Abstract

This article presents the academic research profile of Daniel Suárez, affiliated with Conselleria de Sanidad, Spain, with Data Science as the stated subject area. Data Science combines statistical analysis, computational methods, and data-driven techniques to extract meaningful information from complex datasets. In healthcare, these methods are particularly valuable for analysing physiological signals, supporting wearable monitoring systems, and improving the reliability of health-related measurements. The supplied Scopus profile reports 32 documents, 1,361 citations, and an h-index of 13. [1] [2] [3]

Keywords

Data Science;Biomedical Data Analysis; Photoplethysmography; PPG Signals; Signal Processing; Wearable Health Monitoring; Heartbeat Detection; Wavelet Denoising; Accelerometer Features; Healthcare Technology; Physiological Signal Analysis; Digital Health; Scopus; Research Impact.

Introduction

Data Science has become an important discipline for extracting knowledge from complex information and supporting evidence-based decisions across scientific and professional fields. By combining statistical methods, computational techniques, machine learning, and data visualisation, it enables researchers to identify patterns, evaluate relationships, and develop practical solutions to real-world challenges. In healthcare, data science plays a growing role in analysing patient information, interpreting physiological measurements, and supporting the development of intelligent monitoring technologies.[1] [2] [3]

Research Profile

Daniel Suárez is affiliated with Conselleria de Sanidad in Spain. The supplied academic profile identifies Data Science as the subject area and includes a Scopus author identifier and an ORCID record for researcher identification. The Scopus profile provides bibliometric information, including document count, citation count, and h-index, while the ORCID identifier supports the identification of scholarly activities across academic platforms.According to the supplied Scopus information, the research profile includes 32 documents, 1,361 citations, and an h-index of 13. These indicators provide a quantitative overview of the bibliometric information supplied for the researcher.[1] [2] [3]

Research Contributions

Data Science contributes to biomedical research by providing methods for analysing complex physiological datasets, identifying meaningful patterns, and improving the reliability of measurement systems. In wearable healthcare technology, data-driven signal processing can help address challenges associated with noisy measurements, environmental interference, and variations in physiological signals. The supplied publications provide examples of research themes involving PPG signal assessment, heartbeat detection, and signal denoising.[1] [2] [3]

Publications

The following three references were supplied for this academic profile. Their titles and DOI links are reproduced as provided. The works address PPG signal quality assessment, heartbeat detection in wearable monitoring systems, and context-aware denoising of physiological signals using accelerometer features.Context-aware analysis can help distinguish movement-related artefacts from physiological signal patterns, supporting more robust processing approaches. These research directions are relevant to the continuing development of wearable devices, remote monitoring systems, and data-driven healthcare applications. The supplied publications provide context for these broader areas of research.[1] [2] [3]

Research Impact

Data science methods applied to biomedical signals can contribute to more reliable health-monitoring technologies and improved interpretation of physiological measurements. Signal quality assessment helps identify conditions that may affect the accuracy of sensor readings. Reliable heartbeat detection supports the extraction of cardiovascular information, while denoising methods can reduce interference and improve the usability of wearable PPG data.[1] [2] [3]

Award Suitability

The Excellence in Data Science Achievement Award recognizes research and academic contributions that demonstrate the effective use of data-driven methods, analytical techniques, and computational approaches to address important challenges. Daniel Suárez’s supplied profile identifies Conselleria de Sanidad, Spain, as the institutional affiliation and Data Science as the subject area. The provided Scopus information includes 32 documents, 1,361 citations, and an h-index of 13.[1] [2] [3]

Conclusion

Daniel Suárez’s academic profile is associated with Conselleria de Sanidad, Spain, and the research subject of Data Science. The supplied Scopus information reports 32 documents, 1,361 citations, and an h-index of 13. The provided publications address spectral evaluation of PPG signal quality under ambient light interference, learning-driven heartbeat detection in wearable PPG systems, and context-aware wavelet denoising using accelerometer features. These research topics highlight the importance of data-driven methods in biomedical signal processing and wearable healthcare technologies. The Scopus and ORCID profiles provide additional researcher identification resources for academic reference. [1] [2] [3]

References

  1. A Spectral Approach to Evaluating PPG Signal Quality Under Ambient Light Interference.
    https://doi.org/10.1007/978-3-032-24045-3_17
  2. A Template-Based and Learning-Driven Method for Reliable Heartbeat Detection in Wearable PPG.
    https://doi.org/10.1007/978-3-032-24045-3_21
  3. Context-Aware Wavelet Denoising of PPG Signals Using Clustered Accelerometer Features.
    https://doi.org/10.1007/978-3-032-24045-3_18

Yuntao Wang | Artificial Intelligence | Best Paper Award

Published on by Global CSE Awards

Best Paper Award

Yuntao Wang
Affiliation Xi’an Jiaotong University
Country China
Subject Area Artificial Intelligence
Scopus ID 57203372132
Documents 121
Citations 6,050
h-index 34
ORCID 0000-0003-3810-7076
Event Global CSE Awards

Yuntao Wang is a researcher affiliated with Xi’an Jiaotong University, China, whose academic profile is associated with Artificial Intelligence. His research profile encompasses scholarly work in intelligent systems, machine learning, secure information exchange, and artificial intelligence applications. The supplied publication references address collaborative honeypot defense in UAV networks, secure data sharing for UAV-assisted disaster rescue, and a survey of ChatGPT-generated content, challenges, and solutions. These research topics illustrate important intersections between artificial intelligence, cybersecurity, intelligent communication networks, and emerging AI technologies. [1] [2] [3]

Abstract

This article presents the academic research profile of Yuntao Wang, affiliated with Xi’an Jiaotong University, China, with Artificial Intelligence as the stated subject area. Artificial Intelligence is a multidisciplinary field concerned with developing computational systems capable of learning, reasoning, decision-making, and intelligent problem-solving. The supplied Scopus profile reports 121 documents, 6,050 citations, and an h-index of 34. [1] [2] [3]

Keywords

Artificial Intelligence; Machine Learning; UAV Networks; Cybersecurity; Honeypot Defense; Secure Data Sharing; Disaster Rescue; ChatGPT; Generative Artificial Intelligence; Intelligent Communication Networks; Information Security;Research Impact.

Introduction

Artificial Intelligence has become an important area of scientific and technological development, supporting applications in communication networks, cybersecurity, autonomous systems, data analysis, and intelligent decision-making. Advances in AI algorithms and computational techniques have created opportunities to address complex challenges involving large-scale information processing, network security, and automated systems.The integration of artificial intelligence with unmanned aerial vehicle networks and secure communication technologies has attracted increasing research attention. [1] [2]

Research Profile

Yuntao Wang is affiliated with Xi’an Jiaotong University in China. The supplied academic profile identifies Artificial Intelligence as the subject area and includes a Scopus author identifier and ORCID record for researcher identification. The Scopus profile provides bibliometric information, including publication documents, citation counts, and h-index.According to the supplied Scopus information, the research profile includes 121 documents, 6,050 citations, and an h-index of 34. [1] [2] [3]

Research Contributions

Research in Artificial Intelligence contributes to the development of intelligent systems that can process information, identify patterns, make predictions, and support automated decisionsThe first supplied reference, Collaborative Honeypot Defense in UAV Networks: A Learning-Based Game Approach, focuses on collaborative security mechanisms for unmanned aerial vehicle networks.The second supplied reference, A Secure and Intelligent Data Sharing Scheme for UAV-Assisted Disaster Rescue, addresses secure information exchange in disaster-rescue environments supported by UAV systems.The third supplied reference, A Survey on ChatGPT.[1] [2] [3]

Publications

The following three references were supplied for this academic profile. Their titles and DOI links are reproduced as provided. The works address cybersecurity in UAV networks, secure data sharing for disaster rescue, and the development and challenges of ChatGPT and AI-generated content.The provided publications address collaborative honeypot defense in UAV networks, secure intelligent data sharing for disaster rescue, and a survey of ChatGPT-generated content and its challenges. These research areas highlight the importance of intelligent computing, secure communication, adaptive cybersecurity, and emerging generative AI technologies. [1] [2] [3]

Research Impact

Artificial Intelligence research has significant potential to influence technological innovation, cybersecurity, communication infrastructure, and intelligent applications. Learning-based security mechanisms can contribute to the detection and mitigation of malicious activities, while intelligent data-sharing systems can support more secure and coordinated information exchange. These capabilities are particularly relevant in UAV-assisted networks and emergency response environments, where communication reliability and data protection are essential.[1] [2] [3]

Award Suitability

The Best Paper Award recognizes outstanding scholarly work that demonstrates originality, research quality, methodological strength, and meaningful contributions to a particular academic field. In Artificial Intelligence and related disciplines, relevant research may involve innovative algorithms, intelligent applications, cybersecurity solutions, machine learning approaches, and advances in emerging computational technologies.Yuntao Wang’s supplied profile identifies Xi’an Jiaotong University, China, as the institutional affiliation and Artificial Intelligence as the subject area. The provided Scopus information includes 121 documents, 6,050 citations, and an h-index of 34. [1] [2] [3]

Conclusion

Yuntao Wang’s academic profile is associated with Xi’an Jiaotong University, China, and the research subject of Artificial Intelligence. The supplied Scopus information reports 121 documents, 6,050 citations, and an h-index of 34. The provided publications address collaborative honeypot defense in UAV networks, secure intelligent data sharing for disaster rescue, and a survey of ChatGPT-generated content and its challenges. [1] [2] [3]

References

  1. Collaborative Honeypot Defense in UAV Networks: A Learning-Based Game Approach.
    https://doi.org/10.1109/TIFS.2023.3318942
  2. A Secure and Intelligent Data Sharing Scheme for UAV-Assisted Disaster Rescue.
    https://doi.org/10.1109/TNET.2022.3226458
  3. A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions.
    https://doi.org/10.1109/OJCS.2023.3300321

Awonke Geqeza | Digital Transformation | Best Researcher Award

Published on by Global CSE Awards

 

Best Researcher Award

Awonke Geqeza

iYunivesithi Walter Sisulu, South Africa

Awonke Geqeza
Award Best Researcher Award
Institution iYunivesithi Walter Sisulu
Country South Africa
Scopus ID 60124835900
Documents 2
Subject Area Digital Transformation
Event Global CSE Awards
ORCID 0000-0002-0697-9462

Awonke Geqeza is identified in the supplied details as a researcher affiliated with iYunivesithi Walter Sisulu in South Africa, with Digital Transformation as the stated subject area. The three supplied publications explore the role of internal controls in supporting the growth and sustainability of small and medium enterprises in South Africa, the integration of digital skills into internal auditing education, and the challenges and opportunities associated with digital curriculum renewal at a rural university.The profile presents the supplied researcher information and publication references for academic review.[1] [2] [3]

Abstract

This profile presents the supplied academic information for Awonke Geqeza, a researcher affiliated with iYunivesithi Walter Sisulu in South Africa. The stated subject area is Digital Transformation, with the supplied publications focusing on internal controls and sustainable SME development, digital skills in internal auditing education, and digital curriculum renewal in a rural university context. These themes demonstrate the relevance of digital transformation to organizational sustainability, professional education, and higher education development. The supplied Scopus information lists two documents. Citation counts, h-index values, and other bibliometric indicators were not provided and are therefore not included.[1] [2] [3]

Keywords

Digital Transformation; internal controls; small and medium enterprises; SME sustainability; internal auditing education; digital skills; curriculum innovation; digital curriculum renewal; pedagogical design; higher education; rural universities; academic development; organizational growth.

Introduction

Digital transformation is an important area of contemporary research, influencing organizational processes, professional education, and institutional development. In the business environment, effective internal controls can contribute to responsible management, operational stability, and the sustainable growth of small and medium enterprises. In education, digital transformation involves updating curricula, integrating relevant technological competencies, and adapting teaching approaches to changing professional requirements. Internal auditing education is one area where digital skills and curriculum innovation can help prepare learners for evolving workplace practices.[1] [2] [3]

Research Profile

The supplied details identify Awonke Geqeza as a researcher affiliated with iYunivesithi Walter Sisulu, South Africa. Digital Transformation is the stated subject area.The research themes represented by the supplied publication titles include internal control systems, SME growth and sustainability, digital competencies in internal auditing education, and curriculum renewal in higher education.Publication attribution and profile details should be checked against the original records.[1] [2] [3]

Research Contributions

The supplied references represent three areas connected to digital transformation and institutional development. The first examines the role of internal controls in promoting the growth and sustainability of small and medium enterprises in South Africa.The second addresses curriculum innovation in internal auditing education, focusing on the integration of digital skills into learning outcomes and pedagogical design.The third explores challenges and opportunities in renewing digital curricula for internal auditing, drawing on evidence from a selected rural university.[1] [2] [3]

Publications

The three publication references supplied for this profile are listed below with their DOI links. They address internal controls and SME sustainability, digital skills in internal auditing education, and digital curriculum renewal in a rural university. The list reproduces the titles and links provided and should not be interpreted as a complete bibliography. Publication metadata, author names, dates, and researcher attribution should be checked against the publisher records before the references are used in formal academic or award materials.[1] [2] [3]

Research Impact

The research themes represented by the supplied references are relevant to business sustainability and the modernization of professional education. Work on internal controls addresses organizational practices that can support the responsible management and long-term development of small and medium enterprises.Research on curriculum innovation highlights the importance of embedding digital skills into internal auditing education.Research into digital curriculum renewal at a rural university brings attention to the challenges and opportunities involved in educational transformation across different institutional contexts.These topics indicate potential academic and practical relevance.[1] [2] [3]

Award Suitability

A Best Researcher Award may recognize scholarly contributions, research activity, and work that addresses significant academic or practical challenges. The supplied publications cover organizational sustainability through internal controls, digital skills integration in internal auditing education, and digital curriculum renewal in a rural university context. These subjects provide a connected research focus within Digital Transformation, particularly in relation to business practices and higher education. A formal assessment of award suitability requires verification of publication authorship, originality, research quality, documented impact, and the official eligibility criteria of the Global CSE Awards.[1] [2] [3]

Conclusion

The supplied profile identifies Awonke Geqeza as a researcher affiliated with iYunivesithi Walter Sisulu in South Africa, with Digital Transformation as the stated subject area. The three provided references concern internal controls and SME sustainability, digital skills in internal auditing education, and digital curriculum renewal in a rural university. Together, these themes connect organizational development with the modernization of professional and higher education. The supplied Scopus and ORCID links provide starting points for further academic review.[1] [2] [3]

References

  1. The role of internal controls in promoting growth and sustainability of small and medium enterprises in South Africa.
    https://doi.org/10.70132/k6697789267
  2. Curriculum innovation in internal auditing education: Embedding digital skills into learning outcomes and pedagogical design.
    https://doi.org/10.20448/jeelr.v13i3.9397
  3. Challenges and Opportunities for Digital Curriculum Renewal of Internal Auditing: Evidence from a selected rural university.
    http://dx.doi.org/10.36941/jesr-2026-0424

George Zacharia | Artificial Intelligence | Best Researcher Award

Published on by Global CSE Awards

Best Researcher Award

George Zacharia

Independent Researcher, United States

George Zacharia
Award Best Researcher Award
Institution Independent Researcher
Country United States
Documents 4
Subject Area Artificial Intelligence
Event Global CSE Awards
ORCID 0009-0000-7108-457X
Award Website Global CSE Awards

George Zacharia is identified in the supplied details as an independent researcher based in the United States, with artificial intelligence as the stated subject area. The three supplied publications address AI-driven cyber threat intelligence for predictive attack detection and automated security response, agentic AI for autonomous cloud infrastructure management, and causal invariant feature attribution for robust zero-trust access control in multi-tenant healthcare clouds. These research topics connect artificial intelligence with cybersecurity, cloud automation, causal analysis, and secure access management.[1] [2] [3]

Abstract

This profile presents the supplied academic information for George Zacharia, an independent researcher in the United States whose stated subject area is artificial intelligence. The three supplied publications cover predictive cyber threat detection and automated security response, autonomous cloud infrastructure management using multi-agent AI, and causal invariant feature attribution for zero-trust access control in healthcare cloud environments. These subjects reflect the application of artificial intelligence to security monitoring, infrastructure reliability, and access-control decision-making. The supplied information lists four documents. Citation counts, h-index values, and other bibliometric indicators were not provided and are therefore not included.[1] [2] [3]

Keywords

Artificial intelligence; cybersecurity; cyber threat intelligence; predictive attack detection; automated security response; agentic AI; multi-agent systems; cloud infrastructure management; fault detection; root-cause analysis; self-healing systems; causal invariant feature attribution; neural ordinary differential equations; zero-trust access control; healthcare cloud security.

Introduction

Artificial intelligence is increasingly applied to complex computing and cybersecurity challenges. Intelligent systems can support the analysis of threat information, the identification of suspicious activities, and the automation of selected operational responses.Healthcare cloud environments introduce additional security requirements, making robust access control and reliable attribution of security-related decisions important research topics. The three supplied references represent these connected areas of research. Their specific methods, results, and practical implications should be evaluated using the original publications.[1] [2] [3]

Research Profile

The supplied details identify George Zacharia as an independent researcher in the United States, with artificial intelligence as the subject area. The profile lists four documents and associates the researcher with the Global CSE Awards.These details have not been inferred or added. The publication titles and their relationship to the researcher should be independently verified before formal academic use.[1] [2] [3]

Research Contributions

The supplied references cover three related applications of artificial intelligence. The first addresses cyber threat intelligence for predictive attack detection and automated security response. The second describes an agentic AI framework for autonomous cloud infrastructure management, including fault detection, root-cause analysis, and self-healing capabilities.The third presents CausalGuard.NET, described in its title as using causal invariant feature attribution and Neural ODE for robust zero-trust access control in multi-tenant healthcare clouds. The actual effectiveness and originality of the proposed approaches should be assessed from the original studies.[1] [2] [3]

Publications

The three publication references supplied for this profile are listed below with their DOI links. They address AI-driven cyber threat intelligence, agentic AI for autonomous cloud infrastructure management, and causal invariant feature attribution for zero-trust healthcare cloud security. The list reproduces the titles and links provided and should not be interpreted as a complete bibliography. Publication metadata, author names, dates, and attribution should be checked against the publisher records before the references are used in formal academic or award materials.[1] [2] [3]

Research Impact

The topics represented by the supplied references are relevant to current challenges in cybersecurity and cloud computing.Agentic AI for cloud infrastructure management concerns the potential to improve operational resilience by identifying faults, investigating root causes, and supporting recovery. Causal feature attribution and zero-trust access control address questions of security decisions and access management in multi-tenant healthcare cloud environments. These are potential areas of relevance based on the publication titles; specific performance improvements and real-world outcomes require evidence from the original studies.[1] [2] [3]

Award Suitability

A Best Researcher Award may recognize research activity, scholarly contributions, and work relevant to a defined academic field. The supplied publications cover artificial intelligence applications in cybersecurity, autonomous cloud infrastructure, and secure healthcare cloud access. These topics provide a coherent subject focus for presenting an AI research profile. A formal assessment of award suitability requires verification of publication authorship, originality, research quality, documented impact, and the official eligibility criteria of the Global CSE Awards. This profile summarizes the supplied details and does not imply that the award has been granted.[1] [2] [3]

Conclusion

The supplied profile identifies George Zacharia as an independent researcher in the United States with artificial intelligence as the stated subject area. The three provided references concern predictive cyber threat detection and automated security response, agentic AI for autonomous cloud infrastructure management, and causal invariant feature attribution for zero-trust access control in healthcare clouds. These subjects connect AI research with cybersecurity, cloud reliability, and secure computing environments. The provided ORCID record and DOI links offer starting points for further review. Publication attribution, bibliographic details, research findings, and the official award criteria should be verified before formal publication.[1] [2] [3]

References

  1. AI-Driven Cyber Threat Intelligence for Predictive Attack Detection and Automated Security Response.
    http://dx.doi.org/10.51483/ijaiml.6.12s.2026.1-9
  2. Agentic AI for Autonomous Cloud Infrastructure Management: A Multi-Agent Framework for Fault Detection, Root-Cause Analysis, and Self-Healing.
    http://dx.doi.org/10.64235/r8ves107
  3. CausalGuard.NET: Causal invariant feature attribution using Neural ODE for robust zero-trust access control in multi-tenant healthcare clouds.
    http://dx.doi.org/10.33545/27076571.2025.v6.i2e.392