Yanfeng Zhao | Computer Science | Best Scholar Award

Published on by Global Academic Awards

Best Scholar Award

Yanfeng Zhao
Xi’an Fanyi University, China

Yanfeng Zhao, affiliated with Xi’an Fanyi University, China, has been recognized in association with the Global Academic Awards for scholarly contributions in the field of Computer Science. The academic profile reflects a growing body of research activity with publications indexed in Scopus and measurable citation impact within the international research community.[1]

Yanfeng Zhao
Affiliation Xi’an Fanyi University
Country China
Scopus ID 58684155500
Documents 5
Citations 59
h-index 5
Subject Area Computer Science
Event Global Academic Awards
ORCID 0009-0004-2737-1124

The Best Scholar Award recognizes researchers demonstrating sustained academic engagement, publication activity, and scholarly visibility within their respective disciplines. Yanfeng Zhao’s research profile in Computer Science highlights contributions to contemporary technological and computational studies through peer-reviewed publications and citation-based academic influence.[2]

Abstract

This article presents an academic overview of Yanfeng Zhao in relation to the Best Scholar Award under the Global Academic Awards framework. The profile highlights scholarly metrics including publication records, citation performance, and subject specialization within Computer Science. Academic indicators sourced from Scopus demonstrate measurable research visibility and contribution to scientific discourse through indexed publications and interdisciplinary engagement.[1]

Keywords

Best Scholar Award, Yanfeng Zhao, Computer Science, Scopus Author Profile, Academic Recognition, Research Impact, Citation Analysis, Xi’an Fanyi University, Scholarly Publications, Global Academic Awards.

Introduction

Academic awards are frequently used to recognize scholarly productivity, research influence, and contributions to disciplinary advancement. In the context of higher education and scientific communication, citation metrics and indexed publications serve as indicators of academic engagement and visibility.[3]

The Best Scholar Award associated with Global Academic Awards acknowledges researchers demonstrating active participation in scientific publication and research dissemination. Yanfeng Zhao’s profile reflects academic activity in Computer Science, including contributions documented through internationally indexed databases and citation systems.[2]

Research Profile

Yanfeng Zhao is affiliated with Xi’an Fanyi University in China and is associated with research activities in Computer Science. The Scopus author profile records five indexed documents with a cumulative citation count of fifty-nine and an h-index value of five, indicating citation consistency across published work.[1]

  • Institutional Affiliation: Xi’an Fanyi University
  • Research Discipline: Computer Science
  • Indexed Publications: 5
  • Citation Count: 59
  • h-index: 5

Bibliometric indicators remain important tools for assessing publication performance and research dissemination in modern academic systems. The recorded metrics suggest emerging visibility within the scholarly literature of computing and related interdisciplinary studies.[4]

Research Contributions

Research contributions attributed to Yanfeng Zhao align with computational and information-oriented academic inquiry. Publications indexed within Scopus demonstrate participation in peer-reviewed scholarly communication and reflect engagement with evolving themes in Computer Science and technological studies.[1]

The researcher’s academic output contributes to broader discussions surrounding digital systems, computational methodologies, and interdisciplinary innovation. Citation accumulation further indicates that the published studies have attracted measurable scholarly attention from related research communities.[5]

  • Participation in peer-reviewed academic publishing
  • Contribution to Computer Science literature
  • Research dissemination through indexed platforms
  • Interdisciplinary scholarly engagement

Publications

The academic profile includes publications indexed in Scopus databases and associated scholarly repositories. Indexed research output contributes to citation-based evaluation systems frequently used in institutional and international academic assessments.[1]

  1. Research publications indexed in Scopus-related databases within Computer Science.
  2. Scholarly articles associated with interdisciplinary computational research and digital systems.
  3. Academic contributions demonstrating measurable citation performance in indexed literature.

DOI-linked academic documentation improves discoverability and accessibility within international research infrastructures. Persistent digital identifiers remain central to scholarly archiving and citation tracking systems.[6]

Research Impact

Citation-based metrics indicate that Yanfeng Zhao’s published work has generated academic engagement within the research community. Citation counts and the h-index are commonly utilized to evaluate scholarly influence, publication consistency, and visibility across disciplinary networks.[4]

The research profile demonstrates evidence of academic dissemination through indexed publications and references by subsequent scholarly works. Such indicators contribute to institutional reputation and broader international academic recognition.[2]

Award Suitability

The Best Scholar Award framework emphasizes publication quality, citation visibility, and scholarly participation in recognized research databases. Based on available academic indicators, Yanfeng Zhao demonstrates characteristics associated with emerging scholarly recognition in Computer Science.[1]

  • Documented research publications indexed in Scopus
  • Consistent citation performance
  • Academic participation in Computer Science research
  • International scholarly visibility through indexed databases

Recognition programs such as the Global Academic Awards contribute to visibility for researchers engaged in publication-oriented scholarship and interdisciplinary academic development.[7]

Conclusion

Yanfeng Zhao’s academic profile reflects active engagement in Computer Science research through indexed publications, citation activity, and measurable scholarly indicators. The documented metrics align with evaluation standards commonly associated with academic recognition initiatives and research distinction programs. Continued scholarly participation and publication dissemination are expected to further contribute to academic visibility and interdisciplinary research communication.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Yanfeng Zhao, Author ID 58684155500. Scopus. https://www.scopus.com/authid/detail.uri?authorId=58684155500
  2. Global Academic Awards. (n.d.). Academic recognition and international award programs. https://globalacademicawards.com/
  3. Hicks, D., Wouters, P., Waltman, L., de Rijcke, S., & Rafols, I. (2015). Bibliometrics: The Leiden Manifesto for research metrics. https://doi.org/10.1038/520429a
  4. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. https://doi.org/10.1073/pnas.0507655102
  5. Bornmann, L., & Daniel, H.-D. (2008). What do citation counts measure? A review of studies on citing behavior. https://doi.org/10.1002/asi.20831
  6. International DOI Foundation. (n.d.). The DOI System and digital scholarly identification.
  7. ORCID. (n.d.). Connecting research and researchers through persistent identifiers.

Anjan Kumar Reddy Auyadapu | Computer Science | Research Excellence Award

Published on by Global Academic Awards

Mr. Anjan Kumar Reddy Auyadapu | Computer Science | Research Excellence Award

Mr. Anjan Kumar Reddy Ayyadapu is a researcher and industry expert specializing in Artificial Intelligence, cloud security, and big data analytics. With 73 citations, an h-index of 5, and multiple publications, his work focuses on AI-driven incident response, multi-cloud security, and privacy-preserving techniques. He has contributed to advancing cybersecurity through machine learning and big data integration, alongside innovations in IoT, predictive analytics, and intelligent systems, supported by patents and conference research outputs.

Citation Metrics (Google Scholar)

100

80

60

40

20

0

Citations 73

Documents 22

h-index
5

Citations
Documents
h-index


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Featured Publications

Lisandra Díaz de la Paz | Data Science | Best Researcher Award

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Assoc. Prof. Dr. Lisandra Díaz de la Paz | Data Science | Best Researcher Award

Assoc. Prof. Dr. Lisandra Díaz de la Paz, Central University “Marta Abreu” of Las Villas, Cuba

Assoc. Prof. Dr. Lisandra Díaz de la Paz is a Cuban computer scientist and academic with a Ph.D. in Technical Sciences (2023), a Master’s (2011), and a Bachelor’s (2008) in Computer Science from the Central University “Marta Abreu” of Las Villas (UCLV). She serves as an Associate Professor and researcher specializing in databases, decision-support systems, data integration, metadata management, and artificial intelligence. With over 15 years of teaching experience, she has instructed various undergraduate and postgraduate courses in computer science and related fields. Dr. Díaz de la Paz has completed extensive postgraduate training in areas such as software engineering, machine learning, and data science. She currently leads the Information Systems discipline and serves as Vice Dean of Research and Postgraduate Studies at the MFC Faculty, UCLV. Her research focuses on data quality models, big data, Python programming, semantic web, LLMs, and generative AI. She is an active contributor to Cuba’s technological advancement.

Publication Profile

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Academic Background

Assoc. Prof. Dr. Lisandra Díaz de la Paz is a distinguished academic in the field of Computer Science with a robust educational foundation acquired from the Central University “Marta Abreu” of Las Villas (UCLV), Cuba. She earned her Bachelor’s degree in Computer Science in July 2008, followed by a Master’s degree in the same field in December 2011. Demonstrating a continuous commitment to academic excellence and research, she completed her Doctorate in Technical Sciences in November 2023. This progression reflects her deepening expertise and scholarly dedication within computing and technical disciplines. Her academic journey at UCLV has equipped her with strong theoretical and practical knowledge, forming the basis for her professional contributions as a university professor, researcher, and academic leader. Dr. Díaz de la Paz’s qualifications underpin her role in advancing research in artificial intelligence, databases, and data systems while mentoring the next generation of computing professionals in Cuba and beyond.

Professional Role and Academic Specialization

Assoc. Prof. Dr. Lisandra Díaz de la Paz is a dedicated professor and researcher with a strong focus on the field of Computer Science. Currently holding the academic rank of Associate Professor, she plays a vital role in higher education by teaching, mentoring, and guiding students across multiple levels of university instruction. Her primary specialization lies in computing, where she has developed expertise in areas such as databases, data quality, artificial intelligence, decision-support systems, and big data technologies. As both an educator and researcher, she combines theoretical knowledge with practical applications, contributing to academic excellence and technological advancement. Her position as a faculty member enables her to engage in curriculum development, academic leadership, and innovative research initiatives. Dr. Díaz de la Paz’s dual role as a professor and researcher allows her to bridge the gap between knowledge creation and dissemination, making her an influential figure in the Cuban academic and scientific community.

Awards and Recognitions

Assoc. Prof. Dr. Lisandra Díaz de la Paz has received multiple prestigious awards in recognition of her contributions to computing and educational technologies. She was a co-author of the project “Algorithms and Tools for the Library Management System,” which earned the 2024 Provincial CITMA Award in Villa Clara. In 2021, she received the Provincial CITMA Award for her work on improving the accuracy and completeness of bibliographic records in MARC 21 format. In 2019, she received the Annual Award from the Minister of Higher Education for her research in database systems and computing. Her 2018 work on the ABCD Library Management System implementation across Cuban higher education institutions was recognized for its scientific and educational impact. She also received CITMA awards in 2016 and 2012 for her innovative contributions to active database rule maintenance and business rule implementation in relational databases, respectively—highlighting her sustained excellence in research and technical innovation.

Research Focus

Assoc. Prof. Dr. Lisandra Díaz de la Paz focuses her research primarily on data quality, metadata management, bibliographic systems, and decision support through data-driven computing. Her work encompasses key areas such as the completeness and accuracy of bibliographic records in MARC 21 format, ETL process optimization, metadata profiling, and author name disambiguation using ontologies and deep learning. She has also explored big data integration with NoSQL systems, MapReduce techniques for anomaly detection, and frameworks for metadata quality evaluation in the context of open science. Her contributions have practical applications in library science, digital repositories, and institutional decision-making, particularly within educational and academic information systems. Additionally, her interdisciplinary approach blends artificial intelligence, machine learning, semantic web technologies, and business intelligence, supporting national and international collaboration for improving data infrastructure. These efforts position her as a leading researcher in data-centric computing, database technologies, and intelligent information systems.

Publication Top Notes

  • 📘 Algorithm to correct instance-level anomalies in large data using MapReduce – Cited by 7 – 2016

  • 📗 Data quality analysis in ABCD suite sources – Cited by 7 – 2015

  • 📕 Techniques to capture changes and maintain updated data warehouse – Cited by 5 – 2015

  • 📙 Data market for decision-making on teaching/research staff at UCLV – Cited by 5 – 2013

  • 📒 Techniques to capture data changes (extended version) – Cited by 4 – 2015

  • 📓 Automation of data loading processes in HR data market at UCLV – Cited by 4 – 2014

  • 📘 Weights estimation in completeness measurement of bibliographic metadata – Cited by 3 – 2021

  • 🧠 Author name disambiguation using ontology & deep learning – Cited by 1 – 2022

  • 📊 CompMARC tool for measuring completeness in MARC 21 – Cited by 1 – 2016

  • 📚 Model for metadata quality evaluation: Proposal for open science – Published – 2024

  • 📝 Accuracy measurement of author names in MARC 21 records – Published – 2018

  • 📈 Optimal weight estimation for completeness in MARC 21 metadata – Published – 2017

  • 🔍 Metadata profiling tool in MARC 21 PMMarc v2.0 – Published – 2017

  • 💾 Method for selecting data model and NoSQL system in big data – Published – 2017

  • 🛠 Procedure to improve completeness in MARC 21 records – Published – 2017

Rania Hamdani | Computer science | Best Researcher Award

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Mrs. Rania Hamdani | Computer science | Best Researcher Award

Mrs. Rania Hamdani, University of Luxembourg, Luxembourg

Rania Hamdani is a research scientist specializing in operational research, data management, and cloud architecture for Industry 5.0. Based in Luxembourg, she is currently affiliated with the University of Luxembourg, where she explores advanced methodologies for integrating and managing heterogeneous data sources. She holds an engineering degree in Software Engineering and has extensive experience in software development, AI, and DevOps. Rania has worked on multiple industry and academic projects, publishing three research papers in Ontology-Driven Knowledge Management and Cloud-Edge AI. With a strong background in programming, cloud computing, and AI-driven solutions, she has contributed to platforms ranging from job recommendation systems to adaptive human-computer interaction systems. Her expertise includes Python, SpringBoot, Kubernetes, and Azure DevOps. She is also an active member of IEEE and other technical organizations, promoting innovation and knowledge-sharing in AI and cloud technologies. 🌍💻🔬

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🎓 Education

Rania Hamdani holds an Engineering Degree in Software Engineering from the National Higher School of Engineers of Tunis (2021–2024), where she specialized in advanced design, service-oriented architecture, object-oriented programming, database management, and operational research. Prior to this, she completed a two-year preparatory cycle at the Preparatory Institute for Engineering Studies of Tunis (2019–2021), undertaking intensive coursework in mathematics, physics, and technology to prepare for engineering studies. She also earned a Mathematics-specialized Baccalaureate from Pioneer High School Bourguiba Tunis (2015–2019), graduating with honors. Throughout her academic journey, she gained expertise in artificial intelligence, machine learning, cloud computing, and DevOps methodologies. Her education provided a solid foundation in programming languages, data processing techniques, and full-stack development. Additionally, she holds multiple Microsoft certifications in Azure fundamentals, AI, data security, and compliance, reinforcing her expertise in cloud-based solutions and AI-driven applications. 📚🎓💡

💼 Experience

Rania Hamdani is a research scientist at the University of Luxembourg, where she focuses on integrating and managing heterogeneous data sources for cloud-based decision-making. Previously, she was a research intern at the same institution, contributing to Ontology-Driven Knowledge Management and Cloud-Edge AI, with three published papers. She also worked as a part-time software engineer at CareerBoosts in Quebec (2021–2025), specializing in Python, Azure DevOps, Docker, and test automation. She gained industry experience through internships at Qodexia (Paris), Sagemcom (Tunisia), and Tunisie Telecom, working on smart recruitment platforms, employee management systems, and server monitoring solutions using SpringBoot, Angular, and PostgreSQL. Her technical expertise spans full-stack development, DevOps, AI-driven applications, and cloud computing. She has contributed to major projects, including an adaptive human-computer interaction system, a job recommendation system, and a problem-solving platform, demonstrating her versatility in research and software engineering. 🚀🖥️🔍

🏆 Awards & Honors

Rania Hamdani has been recognized for her outstanding contributions to AI-driven cloud computing and operational research. She received excellence awards during her engineering studies at the National Higher School of Engineers of Tunis and was among the top-performing students in her Mathematics-specialized Baccalaureate. Her research papers in Ontology-Driven Knowledge Management and Cloud-Edge AI have been acknowledged in academic circles, contributing to the advancement of Industry 5.0 technologies. She has also earned multiple Microsoft certifications in cloud and AI fundamentals, reinforcing her technical expertise. As an active member of IEEE and the Youth and Science Association, she has been involved in technology outreach and innovation-driven initiatives. Her leadership in ENSIT Junior Enterprise as a project manager further showcases her ability to lead and contribute to tech communities. These recognitions highlight her dedication to research, software development, and cloud-based AI applications. 🏅📜🌟

🔬 Research Focus

Rania Hamdani’s research focuses on operational research, data management, cloud-edge AI, and Industry 5.0 applications. She specializes in ontology-driven knowledge management, exploring methodologies for integrating heterogeneous data sources to optimize cloud-based decision-making processes. Her work includes artificial intelligence, machine learning, reinforcement learning, and human-computer interaction systems. She has contributed to projects involving job recommendation systems, adaptive human-computer interaction platforms, and cloud-based problem-solving platforms. Rania is particularly interested in scalable cloud architectures, leveraging technologies like FastAPI, Kubernetes, Docker, and Azure DevOps to build efficient AI-powered solutions. Her research also integrates graph databases, Apache Airflow, and big data analytics for enhanced data processing. By combining AI and cloud computing, she aims to develop innovative, data-driven solutions for automation, decision support, and optimization in various industrial applications. Her expertise bridges the gap between theoretical research and real-world software engineering. ☁️🤖📊

 

Publication Top Notes

Adaptive human-computer interaction for industry 5.0: A novel concept, with comprehensive review and empirical validation

 

Dinar Ajeng Kristiyanti | Data Mining | Best Researcher Award

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Dr. Dinar Ajeng Kristiyanti | Data Mining | Best Researcher Award

Dr. Dinar Ajeng Kristiyanti, Universitas Multimedia Nusantara, Indonesia

Dr. Dinar Ajeng Kristiyanti is a passionate Lecturer and Assistant Professor with over a decade of experience in computer science. She holds a Bachelor’s and Master’s in Computer Science from Sekolah Tinggi Manajemen dan Informatika Nusa Mandiri and is pursuing her PhD at Institut Pertanian Bogor 🎓. Her research focuses on Sentiment Analysis, Machine Learning, and Data Mining 💻. Dr. Kristiyanti has published 20 national and 8 international papers 📑, earning recognition as a top 10 author in the SINTA Index (2020-2022). She is also a recipient of several awards for her academic excellence 🏅.

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Educational Background 🎓

Dr. Dinar Ajeng Kristiyanti has a strong academic foundation in computer science. She earned her Bachelor of Information Systems from Sekolah Tinggi Manajemen dan Informatika Nusa Mandiri (2011-2012) with a GPA of 3.76 📘. She continued her studies at the same institution, completing her Master’s in Computer Science (2012-2014) with an impressive GPA of 3.88 🏅. Currently, Dr. Kristiyanti is pursuing her Doctorate in Computer Science at Institut Pertanian Bogor (2020-present), further advancing her expertise in the field of data science and machine learning 💻.

 

Work Experience 🏫

Dr. Dinar Ajeng Kristiyanti has extensive teaching experience across several prestigious institutions. Since 2010, she has been a Lecturer at Universitas Bina Sarana Informatika, where she contributes to the fields of computer science and informatics. From 2015 to 2021, she also served as a Lecturer at Universitas Nusa Mandiri, imparting her knowledge to future professionals. In 2014, Dr. Kristiyanti was a Guest Lecturer at Universitas Budi Luhur, further expanding her academic reach. Her diverse teaching roles reflect her dedication to educating and mentoring students across various institutions 📚👩‍🏫.

 

Award History and Personal Achievements 🏆

Dr. Dinar Ajeng Kristiyanti has been recognized for her academic excellence and contributions to research. She ranked in the Top Ten Authors in the SINTA Science and Technology Index (2020-2022) for her performance at Universitas Bina Sarana Informatika and Universitas Nusa Mandiri 📊. She has also won awards for Best Paper and Presenter at various national and international seminars 🌍. Additionally, Dr. Kristiyanti was honored as the Best Graduate of her Master’s in Computer Science program at STMIK Nusa Mandiri 🎓. Her achievements reflect her dedication and impact in the field of computer science.

 

Publication Top Notes

  • Comparison of SVM & Naïve Bayes algorithm for sentiment analysis (2018) 📊 – Cited by 80
  • Sentiment analysis of smartphone product reviews using SVM-based PSO (2016) 📱 – Cited by 55
  • Prediction of Indonesia presidential election results using Twitter sentiment analysis (2019) 🇮🇩 – Cited by 50
  • Feature selection for cosmetic product review using GA, PSO, and PCA (2017) 💄 – Cited by 45
  • Comparison of Naïve Bayes and SVM using PSO for e-wallet review (2020) 💳 – Cited by 39
  • Sentiment analysis for Halodoc app using Naïve Bayes, SVM, and KNN (2021) 🩺 – Cited by 34
  • Sentiment analysis of cosmetic reviews using SVM and PSO (2015) 💅 – Cited by 32
  • Machine Learning for Beginners (2022) 📖 – Cited by 29
  • E-wallet sentiment analysis using Naïve Bayes and SVM (2020) 💼 – Cited by 25
  • Sentiment analysis of cosmetic product review using feature selection comparison (2015) 👗 – Cited by 25
  • Decision support system for employee bonus using AHP at Buah Hati Ciputat Hospital (2018) 🏥 – Cited by 24
  • Decision support system for employee selection with profile matching analysis (2017) 🧑‍💼 – Cited by 20
  • Web-based thesis monitoring system for Mercu Buana University (2020) 💻 – Cited by 16
  • Application of seasonal multiplicative decomposition for inventory forecasting at PT. Agrinusa (2020) 📦 – Cited by 13
  • Sentiment analysis of public acceptance of COVID-19 vaccines in Indonesia (2023) 💉 – Cited by 11
  • Feature selection using v-shaped transfer function for salp swarm algorithm in sentiment analysis (2023) 🐟 – Cited by 11

Conclusion ✅

Dr. Dinar Ajeng Kristiyanti’s strong academic credentials, prolific research output, and numerous recognitions make her highly suitable for the Best Researcher Award. Her expertise in computer science, coupled with her dedication to innovation and teaching, align well with the award’s criteria, making her a strong candidate for this prestigious recognition.

 

 

 

Noor .A. Rashed | Computer Science Award | Women Researcher Award

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Dr . Noor .A. Rashed | Computer Science Award | Women Researcher Award

Dr. Noor Rashid, Iraq

Dr. Noor Rashid is a Ph.D. candidate at the University of Technology, Baghdad, specializing in Computer Science. She earned her master’s degree from the University of al-Anbar in 2018. Her research covers areas such as Artificial Intelligence, secure data systems, machine learning, data mining, image processing, and project management automation. Her current focus is on optimization algorithms, particularly multi-objective optimization (2022-2023). Dr. Rashid has contributed significantly to the field, including her recent publication on evolutionary and swarm-based algorithms. She continues to advance AI and optimization research in her academic journey.

 

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Employment

Dr. Noor Rashid is currently employed at the University of Technology, Baghdad, Iraq, in the Department of Computer Science. As a dedicated researcher and educator, she contributes to the university’s mission by advancing studies in Artificial Intelligence, secure data systems, and optimization algorithms. Her role involves teaching and mentoring students while conducting innovative research in multi-objective optimization and machine learning. Dr. Rashid’s work continues to impact both the academic community and the broader technological landscape through her involvement in cutting-edge computer science projects.

 

Education and Qualifications 🎓📜

Dr. Noor Rashid is currently pursuing her Ph.D. in Computer Science at the University of Technology, Baghdad, Iraq, from November 2021 to November 2024. Her doctoral research focuses on advanced areas such as optimization algorithms and Artificial Intelligence, contributing to cutting-edge technological advancements. Prior to this, Dr. Rashid earned her master’s degree from the College of Computer Science and Information Technology at the University of al-Anbar in 2018. Her academic background equips her with a strong foundation in secure data, machine learning, and project management systems, preparing her for continued success in the field.

 

Research Focus 🎯🔬

Dr. Noor Rashid’s research primarily focuses on Artificial Intelligence (AI), particularly in machine learning, optimization algorithms, and data mining. Her studies delve into complex areas such as multi-objective optimization and evolutionary algorithms, aiming to solve real-world computational problems. Additionally, Dr. Rashid has worked extensively on medical image processing, applying AI techniques like ANN and SVM to detect and classify diseases like diabetic retinopathy. Her research bridges the gap between AI and healthcare, making significant contributions to secure data, networks, and advanced algorithmic developments. 🚀🧠

 

Publication Top Notes

  • Diagnosis retinopathy disease using GLCM and ANN – N. Rashed, S. Ali, A. Dawood – J. Theor. Appl. Inf. Technol 96, 6028-6040, 2018 (Cited by: 4) 📖
  • Unraveling the Versatility and Impact of Multi-Objective Optimization: Algorithms, Applications, and Trends for Solving Complex Real-World Problems – N.A. Rashed, Y.H. Ali, T.A. Rashid, A. Salih – arXiv preprint, 2024 (Cited by: 2) 🌐
  • Advancements in Optimization: Critical Analysis of Evolutionary, Swarm, and Behavior-Based Algorithms –  Rashed, Y.H. Ali, T.A. Rashid – Algorithms 17(9), 416, 2024 📑
  • ANN and SVM to recognize Texture features for spontaneous Detection and Rating of Diabetic Retinopathy –  Rashed (Upcoming) 🔍