Raghavendran Prabakaran | Machine Learning | Innovative Research Award

Published on by Research Data Analysis

Innovative Research Award

Raghavendran Prabakaran
Easwari Engineering College, India

Raghavendran Prabakaran
Affiliation Easwari Engineering College
Country India
Scopus ID 58670546100
Documents 53
Citations 310
h-index 11
Subject Area Machine Learning
Event Research Data Analysis Awards
ORCID 0009-0001-7333-6555

Raghavendran Prabakaran recognizes scholarly excellence demonstrated through sustained research productivity, scientific impact, and contributions to the advancement of machine learning. Raghavendran Prabakaran has established an active research profile through peer-reviewed publications, interdisciplinary collaboration, and measurable citation performance. His academic achievements reflect continued engagement in applied artificial intelligence and data-driven research methodologies.[1]

Abstract

Raghavendran Prabakaran has contributed to machine learning research through scholarly publications, citation impact, and interdisciplinary collaboration. His work reflects a consistent focus on computational intelligence, predictive analytics, and intelligent systems while supporting practical applications across engineering disciplines.[2]

Keywords

Machine Learning, Artificial Intelligence, Predictive Analytics, Data Science, Intelligent Systems, Pattern Recognition, Research Analytics.

Introduction

Machine learning continues to influence modern engineering, healthcare, automation, and business analytics by enabling intelligent decision-making from complex datasets. Researchers with sustained publication records contribute to both theoretical understanding and practical innovation while strengthening scientific collaboration.[3]

Research Profile

The research profile demonstrates 53 indexed publications, 310 citations, and an h-index of 11 according to Scopus metrics. These indicators reflect sustained scholarly activity and growing academic visibility within the machine learning research community.[1]

Research Contributions

Research emphasizes predictive modelling and intelligent algorithm development for solving practical engineering problems while improving computational efficiency through data-driven learning approaches. Contributions explore AI-based decision support systems integrating analytical models with automation techniques to enhance reliability, scalability, and real-world implementation.

Publications

The publication portfolio consists of peer-reviewed journal articles and conference papers indexed in international scholarly databases. The body of work demonstrates continuing engagement with emerging topics in artificial intelligence and machine learning.[4]

Research Impact

Citation performance, publication consistency, and interdisciplinary collaborations indicate measurable academic influence. The research outputs contribute to knowledge dissemination while supporting future developments in intelligent computing technologies.

Award Suitability

Based on publication metrics, citation record, research quality, and ongoing scholarly engagement, the profile aligns with evaluation criteria commonly applied for academic innovation and research excellence awards. The combination of productivity and scientific impact supports recognition within international research communities.[6]

Conclusion

Raghavendran Prabakaran demonstrates sustained academic productivity through quality publications, measurable citation impact, and contributions to machine learning research. The overall scholarly profile reflects continued commitment to research excellence, innovation, and knowledge advancement within engineering and computational sciences.

References

  1. Elsevier. (n.d.). Scopus author details: Raghavendran Prabakaran, Author ID 58670546100.
    https://www.scopus.com/authid/detail.uri?authorId=58670546100
  2. ORCID. (n.d.). ORCID record for Raghavendran Prabakaran.
    https://orcid.org/0009-0001-7333-6555
  3. Parthiban, Y., Prabakaran, R., Thakur, D., & Madhumitha, S. (2026). Application of Upadhyaya transforms with machine learning for predictive and analytical solutions in complex systems. Transactions on Computational Modeling and Intelligent Systems.
    https://tcmis.org/index.php/files/article/view/23
  4. Tripathi, S., Gochhait, S., & Prabakaran, R. (2026). Neuromarketing applications and ethical implications in consumer behavior analysis. In Book chapter.
    https://www.igi-global.com/gateway/chapter/404055
  5. Prabakaran, R., Parthiban, Y., Thiravidarani, J., & Madhumitha, S. (2026). Application of fractional integro-differential equations in paracetamol drug release modeling. Oriental Journal of Chemistry.
    http://dx.doi.org/10.13005/ojc/420208

Dimitris Kavroudakis | Machine Learning Applications | Innovative Research Award

Published on by Research Data Analysis

Innovative Research Award

Dimitris Kavroudakis
University of the Aegean, Greece

Dimitris Kavroudakis
Affiliation University of the Aegean
Country Greece
Scopus ID 54966735900
Documents 53
Citations 490
h-index 12
Subject Area Machine Learning Applications
Event International Research Data Analysis Excellence & Awards
ORCID 0000-0001-5782-3049

The Innovative Research Award recognizes researchers whose scholarly contributions demonstrate methodological advancement, interdisciplinary relevance, and measurable impact. Dimitris Kavroudakis of the University of the Aegean has established a research profile centered on machine learning applications, geospatial analytics, environmental monitoring, visualization technologies, and geoeducation. His publication record reflects sustained engagement with data-driven approaches for addressing contemporary scientific and societal challenges.[1]

Abstract

This article presents an overview of the academic achievements of Dimitris Kavroudakis, highlighting contributions in machine learning, environmental sensing, spatial analysis, and geospatial education. Through a combination of applied research and interdisciplinary collaboration, his work demonstrates the integration of advanced analytical techniques into real-world decision-making environments. Recent studies emphasize predictive modeling, sensor analytics, virtual reality applications, and geospatial visualization methodologies.[2]

Keywords

Machine Learning, Geospatial Analytics, Environmental Monitoring, Geoeducation, Data Visualization, Heritage Conservation, Spatial Intelligence, Predictive Modeling.

Introduction

Research in data-intensive disciplines increasingly depends on the integration of machine learning with spatial and environmental datasets. Dimitris Kavroudakis has contributed to this evolving landscape through studies that connect computational methods with geographic and environmental applications. His scholarly output reflects an emphasis on evidence-based analysis, innovation in visualization, and practical implementation of analytical frameworks.[3]

Research Profile

Affiliated with the University of the Aegean, Kavroudakis has developed a multidisciplinary research portfolio spanning machine learning applications, geospatial information systems, environmental monitoring, educational technologies, and spatial visualization. His Scopus-indexed publication record and citation metrics indicate consistent academic engagement and influence across related research communities.[1]

Research Contributions

  • Development of machine learning models for forecasting indoor microclimate conditions in heritage conservation environments.
  • Research on spatio-temporal approaches for distinguishing sensor anomalies from environmental events.
  • Applications of virtual reality technologies in geoeducation and geoscience communication.
  • Advancement of multiscale visualization methods for spatial motion and geospatial datasets.

Publications

  • Machine Learning-Based Forecasting of Indoor Microclimate Conditions for Heritage Conservation.
  • Distinguishing Sensor Errors from Environmental Events During Wildfire Pollution in Athens.
  • Virtual Reality in Geoeducation: The Case of the Lesvos Geopark.
  • Multiscale Visualization of Surface Motion Point Measurements Associated with Persistent Scatterer Interferometry.

Research Impact

The impact of Kavroudakis’s work is reflected in its applicability to environmental assessment, cultural heritage management, geospatial education, and data visualization. By incorporating machine learning and advanced analytics into practical contexts, his research contributes to the broader adoption of intelligent systems for scientific and policy-oriented decision support.[4]

Award Suitability

Dimitris Kavroudakis demonstrates characteristics commonly associated with recognition through the International Research Data Analysis Excellence & Awards program. These include interdisciplinary scholarship, measurable research impact, methodological innovation, and continued contribution to machine learning applications within environmental and geospatial domains. His publication portfolio provides evidence of both academic rigor and practical relevance.[5]

Conclusion

The academic record of Dimitris Kavroudakis reflects a sustained commitment to advancing machine learning applications and geospatial research methodologies. Through contributions spanning environmental analytics, educational innovation, and spatial intelligence, his work represents a noteworthy example of contemporary interdisciplinary scholarship deserving of professional recognition within international research communities.

References

  1. Elsevier. (n.d.). Scopus author details: Dimitris Kavroudakis, Author ID 54966735900. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=54966735900
  2. Applied Sciences. (2026). Machine Learning-Based Forecasting of Indoor Microclimate Conditions for Heritage Conservation.
    DOI: https://doi.org/10.3390/app16126092
  3. European Journal of Geography. (2026). Distinguishing Sensor Errors from Environmental Events.
    DOI: https://doi.org/10.48088/ejg.s.zaf.17.1.212.230
  4. Interactive Learning Environments. (2024). Virtual Reality in Geoeducation: The Case of the Lesvos Geopark.
    DOI:https://doi.org/10.1080/10494820.2024.2374399
  5. ISPRS International Journal of Geo-Information. (2024). Multiscale Visualization of Surface Motion Point Measurements Associated with Persistent Scatterer Interferometry.
    DOI: https://doi.org/10.3390/ijgi13070236
  6. International Research Data Analysis Excellence & Awards. (n.d.). Award program information.
    researchdataanalysis.com

Diksha Kumar | Deep learning | Women Researcher Award

Published on by Research Data Analysis

Mrs. Diksha Kumar | Deep learning | Women Researcher Award

Ramrao Adik Institute of Technology (RAIT) | India

Publication Profile

Scopus
Orcid

BIOGRAPHY OF Mrs. Diksha Kumar 🌱📚

INTRODUCTION 🌍

Diksha Kumar is an accomplished research scholar with over 10 years of experience in higher education, currently pursuing her PhD in Computer Engineering at Ramrao Adik Institute of Technology (RAIT), India. Specializing in machine learning, deep learning, and generative AI, she has made significant strides in both research and innovation. She has secured funding for her research through a grant from Mumbai University, India, and holds a patent for an innovative aquatic animal-friendly ocean cleaning system. Through her research endeavors, Diksha has consistently pushed the boundaries of technology to contribute to a cleaner, smarter world. 🌍🔬

EARLY ACADEMIC PURSUITS 🎓

Diksha Kumar’s academic journey began with her intense passion for computer science and technology. As a student, she exhibited a deep curiosity and enthusiasm for computational methods and their real-world applications. Pursuing her undergraduate and master’s degrees, Diksha excelled in every academic pursuit, solidifying her foundation in computer engineering. Her rigorous academic training at Ramrao Adik Institute of Technology (RAIT) laid the groundwork for her current doctoral research, enabling her to delve into cutting-edge areas such as deep learning and machine learning.

PROFESSIONAL ENDEAVORS 👩‍💼

her professional career, Diksha Kumar has worked across various dimensions of academia and research. As a Research Scholar at RAIT, she has engaged in multiple industry-linked research projects. Her pioneering work in image classification and processing has garnered her attention in international academic circles. She has secured prestigious research grants and has become a member of the IEEE, further bolstering her professional credibility. Her practical contributions in the fields of AI and remote sensing have not only garnered attention but are making impactful changes in technology and environmental sustainability.

CONTRIBUTIONS AND RESEARCH FOCUS  ON  Deep learning 🔬

Diksha’s research work is marked by several innovative contributions to deep learning and AI. Her work on adapting Attention U-Net for image classification and combining it with XGBoost to enhance classification accuracy stands out. Additionally, she developed a hybrid model fusing VGG19 with XGBoost for improved image analysis. Moreover, her current focus on a novel cloud removal technique combining Homomorphic Filtering and Gamma Correction holds promise for enhancing image processing techniques. These contributions demonstrate her skill in addressing complex challenges in AI and image processing.

IMPACT AND INFLUENCE 🌟

Diksha Kumar’s research has had a substantial impact on both academia and industry. Her work, which fuses theoretical AI techniques with real-world applications, is paving the way for further advancements in image classification, remote sensing, and environmental protection. The patented aquatic animal-friendly ocean cleaning system showcases her commitment to using technology for environmental conservation, setting an example for researchers seeking to combine innovation with sustainability. Her pioneering work continues to inspire others in the research community and has led to significant strides in the application of deep learning to environmental issues.

ACADEMIC CITATIONS AND PUBLICATIONS 📑

Diksha Kumar has earned recognition through her impressive academic citations, reflecting the importance and relevance of her work. With a citation index of 19 and multiple publications in SCI and Scopus-indexed journals, her contributions to image processing, machine learning, and deep learning are well-regarded. Her academic output includes four journal articles, highlighting her prolific and impactful research work. Each publication has been a step forward in the development of AI-driven solutions that address both scientific and practical problems.

HONORS & AWARDS 🏆

Diksha’s commitment to excellence has earned her several accolades and recognitions in her field. Notably, she has been awarded a research grant from Mumbai University, which enabled her to pursue her innovative projects. Additionally, her research achievements are acknowledged through her patent for the aquatic animal-friendly ocean cleaning system. As she continues to make strides in her field, Diksha is a strong contender for the Women Researcher Award, which would recognize her outstanding contributions to research and technology.

LEGACY AND FUTURE CONTRIBUTIONS 🔮

Diksha Kumar’s legacy is being shaped by her relentless pursuit of excellence in research and her dedication to the practical applications of AI and deep learning. With future projects focused on advancing image processing techniques and addressing environmental issues, Diksha is positioning herself as a leader in these fields. Her future contributions will not only advance the frontiers of AI but also have a direct and positive impact on society, ensuring that her work continues to inspire and influence the next generation of researchers.

FINAL NOTE ✨

Diksha Kumar is a dynamic researcher whose work in AI, deep learning, and image classification is leaving a lasting impact on the academic and scientific communities. Her innovative solutions and commitment to using technology for the betterment of the environment showcase her ability to combine technical expertise with real-world challenges. As she moves forward with her PhD and her current research projects, Diksha’s work promises to make an even greater impact in the years to come.

TOP NOTES PUBLICATIONS 📚

AUXG: Deep Feature Extraction and Classification of Remote Sensing Image Scene Using Attention Unet and XGBoost
  • Authors: D.G. Kumar, Diksha Gautam, S.Z. Chaudhari, Sangita Zope
  • Journal: Journal of the Indian Society of Remote Sensing
  • Year: 2024

Jing An | Artificial Intelligence | Best Researcher Award

Published on by Research Data Analysis

Dr. Jing An | Artificial Intelligence | Best Researcher Award

Yancheng Institute of Technology, China

👨‍🎓Professional Profile

Scopus Profile

👨‍🏫 Summary

Dr. Jing An is a distinguished professor and master tutor at Yancheng Institute of Technology, China. He specializes in intelligent manufacturing engineering, industrial big data fault diagnosis, and residual life prediction. With numerous patents and software copyrights, Dr. An has published over 20 SCI/EI indexed papers and contributed to key academic texts. His pioneering research in artificial intelligence-based fault diagnosis has earned him prestigious awards, including first-place recognition in the China Commerce Federation Science and Technology Award .

🎓 Education

Dr. An holds a Ph.D. in Computer Science from Hohai University (2021) and a Master’s degree in Computer Science from Harbin University of Science and Technology (2006). His academic foundation has strongly influenced his research in AI and fault diagnosis.

💼 Professional Experience

Having joined Yancheng Institute of Technology in 2006, Dr. An is also the Vice President of Science and Technology for Jiangsu Province’s “Double Innovation Plan.” He has led numerous provincial-level projects, and his expertise has extended to more than 10 industry partnerships 🚀.

📚 Academic Citations

Dr. An has authored over 20 peer-reviewed papers in prestigious journals such as IEEE Access and Mathematical Problems in Engineering. His impactful research on AI-based fault diagnosis methods is frequently cited within the academic community .

🔧 Technical Skills

Dr. An is skilled in AI-based fault diagnosis, deep learning, machine learning, and industrial big data analytics. He has expertise in Convolutional Neural Networks (CNNs) and intelligent manufacturing systems, focusing on improving machinery reliability and efficiency .

🧑‍🏫 Teaching Experience

Dr. An has been recognized as an outstanding teacher twice at Yancheng Institute of Technology. He teaches courses in intelligent manufacturing engineering and industrial big data fault diagnosis, preparing students to advance in the field of AI and data science .

🔍 Research Interests

Dr. An’s research is focused on developing intelligent systems for fault diagnosis in rotating machinery, predictive maintenance, and the application of deep learning techniques in industrial big data. His work aims to enhance manufacturing processes and equipment reliability .

📖Top Noted Publications

Hybrid Mechanism and Data-Driven Approach for Predicting Fatigue Life of MEMS Devices by Physics-Informed Neural Networks

Authors: Cheng, J., Lu, J., Liu, B., An, J., Shen, A.

Journal: Fatigue and Fracture of Engineering Materials and Structures

Year: 2024

Bearing Intelligent Fault Diagnosis Based on Convolutional Neural Networks

Authors: An, J., An, P.

Journal: International Journal of Circuits, Systems and Signal Processing

Year: 2022

Deep Clustering Bearing Fault Diagnosis Method Based on Local Manifold Learning of an Autoencoded Embedding

Authors: An, J., Ai, P., Liu, C., Xu, S., Liu, D.

Journal: IEEE Access

Year: 2021

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Riemann Metric Correlation Alignment

Authors: An, J., Ai, P.

Journal: Mathematical Problems in Engineering

Year: 2020

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning

Authors: An, J., Ai, P., Liu, D.

Journal: Shock and Vibration

Year: 2020

Giuliana Ramella | Artificial Intelligence | Best Researcher Award

Published on by Research Data Analysis

Dr. Giuliana. Ramella | Artificial Intelligence | Best Researcher Award

National Research Council, Italy

Professional Profile 👨‍🎓

Scopus Profile

Orcid Profile

Research Gate Profile

Early Academic Pursuits 🎓

Dr. Giuliana Ramella embarked on her academic journey with a strong foundation in Physics, specializing in Cybernetics, from the University of Naples “Federico II”, Italy, where she obtained her Laurea degree in 1990. Her early career was distinguished by a fellowship granted by the Italian National Research Council (CNR) at the Institute of Cybernetics “E. Caianiello”, which marked the beginning of her deep engagement with interdisciplinary research. Her academic background was further enriched by participation in several international and national schools, covering topics from biophysics to machine vision, which laid the groundwork for her future research endeavors.

Professional Endeavors 🧑‍🔬

Dr. Ramella’s professional career at the CNR has spanned decades, beginning in 1991 with a fellowship at the Institute of Cybernetics, now known as the Institute of Applied Sciences and Intelligent Systems (CNR-ISASI). Over the years, she transitioned into permanent research roles, contributing significantly to numerous research projects. Her professional milestones include being a visiting researcher at LIAMA (Sino-French Laboratory) in Beijing, China, and overseeing major research initiatives such as those focused on image processing and the conservation of cultural heritage.

Contributions and Research Focus 🔬

Dr. Ramella’s research spans multiple fields, including image processing, artificial intelligence, neurosciences, and cultural heritage conservation. Notable contributions include leadership in projects such as CNR-IAC-CNR DIT.AD021.077 (focused on color image processing) and the Campania Imaging Infrastructure for Research in Oncology. Her work blends theoretical and practical applications, particularly in the intersection of machine learning and image analysis, demonstrating her commitment to advancing computational methods in complex scientific domains.

Impact and Influence 🌍

Dr. Ramella has made a significant impact both in Italy and internationally, particularly in the fields of biophysics, neurosciences, and cultural heritage preservation. She has led several high-profile projects, influencing the development of automated systems for monitoring and diagnosing cultural heritage, as well as data analysis systems for oncology research. Her leadership in educational coordination has also contributed to the professional development of individuals in specialized fields, including image and data management.

Academic Citations and Scholarly Recognition 📚

Throughout her career, Dr. Ramella has built a robust academic reputation, frequently cited for her work in machine learning frameworks like Pytorch, TensorFlow, and Keras, as well as her contributions to computer vision. Her involvement in international workshops and conferences further underscores her standing as a thought leader in the scientific community. Additionally, her research has had a profound effect on the fields of visual perception and neuroscience, solidifying her as a key figure in these interdisciplinary areas.

Technical Skills and Expertise 💻

Dr. Ramella is highly skilled in a range of programming languages, including Matlab, C/C++, and Python, which are essential tools for her work in data analysis and machine learning. She has a strong command over popular machine learning frameworks like Pytorch, TensorFlow, and Keras, which she applies to advanced research in image processing, signal analysis, and high-dimensional data modeling. Her technical expertise is fundamental to her contributions to automated systems in the analysis of cultural artifacts and medical imaging.

Teaching Experience 🍎

In addition to her research, Dr. Ramella has played an essential role in educational coordination. She co-led specialist courses for unemployed individuals and workers in mobility, aimed at developing technical skills for sectors like image management and building heritage monitoring. Her role in shaping the professional development of students in the fields of image analysis and information technology has left a lasting impact on both the academic and professional communities.

Legacy and Future Contributions 🔮

Looking ahead, Dr. Ramella’s legacy is poised to continue making waves in the fields of artificial intelligence and machine learning, especially in the areas of healthcare, cultural heritage conservation, and data-driven methodologies. Her ongoing involvement in projects like the Agritech research program, funded by the European Union through Next Generation EU, speaks to her future aspirations to contribute to cutting-edge research and technological advancements. Dr. Ramella’s future work promises to leave an indelible mark on interdisciplinary fields, continuing her legacy of innovation and impact across global scientific communities.

 

Top Noted Publications 📖

An Open Image Resizing Framework for Remote Sensing Applications and Beyond

Authors: Occorsio, D., Ramella, G., Themistoclakis, W.
Journal: Remote Sensing
Year: 2023

Image Scaling by de la Vallée-Poussin Filtered Interpolation

Authors: Occorsio, D., Ramella, G., Themistoclakis, W.
Journal: Journal of Mathematical Imaging and Vision
Year: 2023

Filtered Polynomial Interpolation for Scaling 3D Images

Authors: Occorsio, D., Ramella, G., Themistoclakis, W.
Journal: Electronic Transactions on Numerical Analysis
Year: 2023

 Lagrange–Chebyshev Interpolation for Image Resizing

Authors: Occorsio, D., Ramella, G., Themistoclakis, W.
Journal: Mathematics and Computers in Simulation
Year: 2022

Saliency-based Segmentation of Dermoscopic Images Using Colour Information

Author: Ramella, G.
Journal: Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
Year: 2022

Hend ALnajjar | Artificial Intelligence | Best Researcher Award

Published on by Research Data Analysis

Assoc Prof Dr. Hend ALnajjar, Artificial Intelligence, Best Researcher Award

Assoc Prof Dr. Hend ALnajjar at king Saud bin Abdulaziz for health science, Saudi Arabia

Professional Profile

Scopus Profile
Orcid  Profile
Research Gate Profile
Google Scholar Profile

🌟Summary

Dr. Hend Abdu Alnajjar is an accomplished Associate Professor in Pediatric Nursing at the College of Nursing–Jeddah, King Saud bin Abdulaziz University for Health Sciences. With a robust educational background including a Ph.D. in Nursing from Manchester University and extensive experience in nursing education, she specializes in maternal and child health, healthcare quality, and academic leadership.

🎓Education

  • Doctor of Philosophy in Nursing, Manchester University, UK (November 2012)
  • MSc in Advanced Nursing Studies, Manchester University, UK (November 2008)
  • Master of Medical Education, College of Medicine, King Saud bin Abdulaziz University for Health Sciences (2017-2019)
  • Bachelor of Science in Nursing, King Abdulaziz University, Jeddah, KSA (September 2000)

💼 Professional Experience

Dr. Hend Abdu Alnajjar brings a wealth of expertise as an Associate Professor in Pediatric Nursing at the College of Nursing–Jeddah, King Saud bin Abdulaziz University for Health Sciences. With a distinguished career spanning leadership roles such as Dean of the College of Nursing – Jeddah and Associate Dean of Academic & Student Affairs, she has significantly contributed to advancing nursing education and healthcare quality in Saudi Arabia. Dr. Alnajjar’s leadership extends to extensive involvement in accreditation processes, curriculum development, and quality assurance initiatives, enhancing educational standards across multiple institutions. Her commitment to maternal and child health is underscored by her clinical background in neonatal intensive care units, further enriching her academic and practical contributions to the nursing profession.

🔬 Research Interests

Dr. Hend Abdu Alnajjar’s research interests are focused on pivotal areas within nursing and healthcare. Her primary scholarly pursuits revolve around maternal and child health, where she explores innovative approaches to improve healthcare outcomes for mothers and children. Additionally, her research encompasses nursing education, aiming to enhance teaching methodologies and curriculum frameworks to foster competent nursing professionals. Dr. Alnajjar is also passionate about healthcare quality, investigating strategies to optimize patient care delivery and safety within clinical settings. Her academic leadership in these domains reflects a commitment to advancing nursing practice and education both locally and globally.

📖 Publication Top Noted

Article title: Digital proficiency: assessing knowledge, attitudes, and skills in digital transformation, health literacy, and artificial intelligence among university nursing students

    • Authors: Ebtsam Aly Abou Hashish, Hend Alnajjar
    • Journal: BMC Medical Education
    • Volume: 24
    • Pages: 508
    • Year: 2024

Article title: Perception of the accreditation of the National Commission for Academic Accreditation and assessment at different health colleges in Jeddah, Saudi Arabia

    • Authors:Ali S Al-Shareef, Mansour A AlQurashi, Azza Al Jabarti, Hend Alnajjar, Ahmad A Alanazi, Mohamed Almoamary, Bader Shirah, Khalid Alqarni
    • Journal: Cureus
    • Volume: 15
    • Year: 2023

Article title: Managerial power bases and its relationship to influence tactics and conflict management styles: Bedside nurses’ perspective

    • Authors: Ebtsam Abou Hashish, Hend Alnajjar, Arwa Al Saddon
    • Journal: Worldviews on Evidence‐Based Nursing
    • Volume: 20
    • Issue: 5
    • Year: 2023

Article title: Exploring the relationship between leadership and conflict management styles among nursing students

    • Authors: Hend Alnajjar, Ebtsam Abou Hashish
    • Journal: Nursing management
    • Volume: 29
    • Issue: 3
    • Year: 2022

Article title: Academic ethical awareness and moral sensitivity of undergraduate nursing students: assessment and influencing factors

    • Authors: Hend Abdu Alnajjar, PhD, Ebtsam Aly Abou Hashish, PhD
    • Journal: SAGE open nursing
    • Volume: 7
    • Year: 2021