Zhuldyz Tashenova | Computer Science | Innovative Research Award

Published on by Award and Honors

Innovative Research Award

Zhuldyz Tashenova
Gumilyov Eurasian National University, Kazakhstan

Zhuldyz Tashenova
Affiliation Gumilyov Eurasian National University
Country Kazakhstan
Scopus ID 55669178600
Documents 15
Citations 26
h-index 3
Subject Area Computer Science
Event International Award and Honors
ORCID 0000-0003-3051-1605

Zhuldyz Tashenova is a researcher in the field of computer science whose scholarly activities encompass cybersecurity, software security assessment, machine learning, computer vision, augmented reality applications, and information protection systems. Her academic output demonstrates an interdisciplinary approach that integrates emerging digital technologies with practical solutions for organizational security and data management. The Innovative Research Award recognizes contributions that support technological advancement and knowledge development through peer-reviewed research and innovation.[1]

Abstract

This article presents an overview of the academic achievements and research contributions of Zhuldyz Tashenova. Her work addresses contemporary challenges in cybersecurity, vulnerability assessment, machine learning, computer vision, and digital transformation. Through peer-reviewed publications, she has contributed to the development of methodologies that enhance security infrastructures, improve predictive analytics, and support innovative educational and technological applications.[2]

Keywords

Cybersecurity, Computer Science, Machine Learning, Computer Vision, Augmented Reality, Vulnerability Detection, Information Security, Data Protection.

Introduction

The growing complexity of digital ecosystems has intensified the need for advanced security mechanisms and intelligent computational solutions. Researchers in computer science increasingly focus on integrating machine learning, software analysis, and secure networking technologies to address evolving threats. Within this context, Zhuldyz Tashenova has contributed to studies that explore both theoretical frameworks and practical implementations across multiple domains of information technology.[3]

Research Profile

According to available scholarly records, Tashenova has authored fifteen indexed publications with twenty-six citations and an h-index of three. Her research profile reflects active engagement in cybersecurity, software vulnerability analysis, agricultural data analytics, and immersive technologies. These areas illustrate a commitment to interdisciplinary research and applied innovation.[1]

Research Contributions

  • Development of a multi-tier security model integrating human factors, identification mechanisms, and secure networking architectures.
  • Creation of SentinelCMS, a framework for proactive vulnerability detection using static taint analysis and bidirectional LSTM methods.
  • Application of machine learning techniques for early crop type classification using seasonal spectral features.
  • Research on augmented reality games supported by computer vision technologies to improve sports motivation.
  • Studies focused on enterprise personal data protection and information security management.

Publications

Representative publications include Design of a Multi-Tier Security Model Encompassing Human Factors, Identification Processes, and Secure Networking (2026), SentinelCMS: Proactive Vulnerability Detection in CMS Plugins Using Static Taint Analysis and Bidirectional LSTM (2026), Early Crop Type Classification Based on Seasonal Spectral Features and Machine Learning Methods (2026), and Development of Computer Vision-enabled Augmented Reality Games to Increase Motivation for Sports (2023). These publications demonstrate research diversity and practical relevance across multiple technological domains.[4]

Research Impact

The impact of Tashenova’s work can be observed through contributions to cybersecurity methodologies, machine learning applications, and digital innovation initiatives. Her studies provide practical frameworks that may support organizations in strengthening security infrastructures while also expanding opportunities for intelligent data-driven decision-making. The integration of emerging technologies across diverse application areas highlights the broader relevance of her scholarly efforts.[5]

Award Suitability

Zhuldyz Tashenova’s research portfolio aligns with the objectives of the Innovative Research Award by demonstrating sustained scholarly productivity, interdisciplinary collaboration, and engagement with contemporary technological challenges. Her contributions to cybersecurity, machine learning, and digital innovation illustrate a commitment to advancing scientific knowledge while addressing practical needs within modern information systems.[6]

Conclusion

The academic record of Zhuldyz Tashenova reflects meaningful contributions to computer science research, particularly in areas related to cybersecurity, machine learning, and digital technologies. Through peer-reviewed publications and applied research initiatives, she has contributed to the advancement of knowledge in fields that remain highly relevant to contemporary scientific and technological development.

References

  1. Elsevier. (n.d.). Scopus author details: Zhuldyz Tashenova, Author ID 55669178600. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=55669178600
  2. Tashenova, Z. (2026). Design of a Multi-Tier Security Model Encompassing Human Factors, Identification Processes, and Secure Networking.
    DOI: https://doi.org/10.3390/info17060537
  3. Tashenova, Z. (2026). SentinelCMS: Proactive Vulnerability Detection in CMS Plugins Using Static Taint Analysis and Bidirectional LSTM.
    https://doi.org/10.3390/app16115471
  4. Tashenova, Z. (2026). Early Crop Type Classification Based on Seasonal Spectral Features and Machine Learning Methods.
    https://doi.org/10.3390/technologies14040221
  5. Tashenova, Z. (2023). Development of Computer Vision-enabled Augmented Reality Games to Increase Motivation for Sports.
    https://doi.org/10.14569/IJACSA.2023.0140428
  6. Journal of Theoretical and Applied Information Technology. (2022). Research and Development of Personal Data Protection Systems in Enterprises.

Alexandra Takou | Computer Science | Research Excellance Award

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 Dr. Alexandra Takou | Computer Science | Research Excellance Award

Post Doctoral Researcher | The University of  Thessaly | Greece

Dr. Alexandra Takou conducts research in hardware security, reliability-aware VLSI design, and fault-tolerant integrated circuits, with particular emphasis on hardware Trojans, electromagnetic and power grid based attacks, and soft error propagation mechanisms. Her work introduces sensitivity-aware and reliability-driven methodologies for Trojan design, placement, and security closure, addressing emerging threats in advanced semiconductor technologies. She has authored 5 peer-reviewed research documents published in reputable conferences and international journals, contributing novel approaches to EM-based attacks, SET-induced soft errors, and secure circuit design methodologies. Her publications have accumulated 7 citations, and she holds an h-index of 2, reflecting a focused and developing impact within the hardware security and electronic design automation research domains.

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Featured Publications

Dharmapuri Siri | Computer Science | Best Researcher Award

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Dr. Dharmapuri Siri | Computer Science | Best Researcher Award 

Associate Professor, at Gokaraju Rangaraju Institute of Engineering and Technology, India.

Dr. D. Siri is an accomplished academician and researcher specializing in Computer Science and Engineering. Currently serving as an Associate Professor at Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, she has over 14 years of teaching experience. Her academic journey includes a B.Tech in Information Technology and an M.Tech in Computer Science and Engineering from JNTU, Hyderabad. She earned her Ph.D. from JJT University, Rajasthan, focusing on software quality enhancement through machine learning techniques. Dr. Siri has contributed significantly to research in areas such as machine learning, deep learning, software engineering, and IoT. Her work has been published in esteemed journals and presented at international conferences. Beyond her academic pursuits, she holds a patent for a “Vehicle with Smart Biometric Device,” reflecting her innovative approach to technology. Her dedication to education and research continues to inspire students and colleagues alike.PMC+1ScienceDirect+1ScienceDirect

Professional Profile

Scopus

ORCID

Google Scholar

🎓 Education

Dr. D. Siri’s educational background is rooted in a strong foundation in computer science and engineering. She completed her B.Tech in Information Technology from Sreenivas Reddy Institute of Technology, Nizamabad, under JNTU, Hyderabad, with a 61.36% score. Pursuing further specialization, she obtained an M.Tech in Computer Science and Engineering from TRR Engineering College, Patancheru, achieving a 65% score. Her academic excellence culminated in a Ph.D. from JJT University, Rajasthan, in 2022, where her research focused on developing a bug prediction model for software quality using machine learning techniques. This comprehensive educational journey equipped Dr. Siri with the knowledge and skills to contribute meaningfully to the field of computer science and engineering.

💼 Experience

Dr. D. Siri’s professional experience spans over 14 years in the field of computer science and engineering education. She began her teaching career as an Assistant Professor in the Department of Information Technology at TRR Engineering College, Inole, Patancheru, from 2008 to 2013. Subsequently, she served as an Assistant Professor in the Department of Computer Science and Engineering at TRR College of Engineering, Inole, Patancheru, from 2013 to 2017. Her journey continued at Malla Reddy Engineering College for Women, Dulapally, Hyderabad, where she worked as an Assistant Professor from 2017 to 2019. Currently, Dr. Siri holds the position of Associate Professor in the Department of Computer Science Engineering at Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, since 2024. Throughout her career, she has been dedicated to imparting knowledge and fostering academic growth among students.

🔬 Research Interests

Dr. D. Siri’s research interests lie at the intersection of machine learning, deep learning, software engineering, and Internet of Things (IoT) applications. She has a keen interest in developing intelligent systems that enhance software quality and automate complex processes. Her work includes the development of bug prediction models using machine learning techniques, which aim to improve software reliability and performance. Additionally, Dr. Siri explores the application of deep learning models in various domains, such as underwater imagery for fish species identification and human activity recognition using accelerometer data. Her interdisciplinary approach seeks to address real-world challenges through innovative technological solutions.

🏆 Awards

Dr. D. Siri’s contributions to academia and research have been recognized through various accolades. Her innovative research in machine learning and software engineering has earned her invitations to present at international conferences, including the International Conference on Trends Recent Global Changes in Engineering, Management, Pharmacy, and Science (ICTEMPS-2018) and the International Conference on Recent Challenges in Engineering, Management, Science, and Technology (ICEMST-2021). These platforms have provided her with opportunities to share her insights and collaborate with fellow researchers. Furthermore, her work has been published in reputable journals such as IEEE Access and Heliyon, reflecting the impact and quality of her research. Dr. Siri’s dedication to advancing knowledge and fostering academic excellence continues to be acknowledged by the academic community.

📚 Top Noted Publications

Dr. D. Siri has an extensive publication record in esteemed journals and conferences, contributing significantly to the fields of machine learning, deep learning, and software engineering. Notable among her journal publications are:

1. Analyzing Public Sentiment on the Amazon Website: A GSK-Based Double Path Transformer Network Approach for Sentiment Analysis

  • Published in: IEEE Access, 2024

  • DOI: 10.1109/ACCESS.2024.3278901

  • Summary: This study introduces a novel transformer-based model for sentiment analysis of Amazon product reviews. The model employs a GSK-based double path architecture to capture both global and local contextual information, enhancing the accuracy of sentiment classification. The approach demonstrates significant improvements over traditional methods in processing and interpreting user sentiments.

2. Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism

  • Published in: IEEE Access, 2024

  • DOI: 10.1109/ACCESS.2024.3278902

  • Summary: This paper presents an integrated approach for diabetic retinopathy detection. It utilizes the IC2T model for effective image segmentation and the Rock Hyrax Swarm-Based Coordination Attention Mechanism for precise classification. The proposed method enhances the accuracy and reliability of automated diabetic retinopathy screening systems.

3. Enhanced Deep Learning Models for Automatic Fish Species Identification in Underwater Imagery

  • Published in: Heliyon, August 2024

  • DOI: 10.1016/j.heliyon.2024.e35217

  • Summary: This research develops a two-stage deep learning framework for identifying fish species in underwater images. The first stage applies an Unsharp Mask Filter (UMF) for image preprocessing, followed by a Region-based Fully Convolutional Network (R-FCN) for fish detection. The second stage enhances classification accuracy using an improved ShuffleNetV2 model integrated with a Squeeze and Excitation (SE) module, optimized by the Enhanced Northern Goshawk Optimization (ENGO) algorithm. The models achieve high performance metrics, including 99.94% accuracy.ScienceDirect+2PubMed+2PMC+2PubMed+2PMC+2ScienceDirect+2

4. Segment-Based Unsupervised Deep Learning for Human Activity Recognition Using Accelerometer Data and SBOA-Based Channel Attention Networks

  • Published in: International Research Journal of Multidisciplinary Technovation, 2024

  • DOI: 10.54392/irjmt2461

  • Summary: This paper proposes an unsupervised deep learning approach for human activity recognition (HAR) using accelerometer data. The method incorporates segment-based SimCLR with Segment Feature Decorrelation (SDFD) and utilizes the Secretary Bird Optimization Algorithm (SBOA) to enhance performance. The Channel Attention with Spatial Attention Network (CASANet) is employed to extract key features and spatial dependencies, achieving an average F1 score of 98% on the Mhealth and PAMAP2 datasets.Asian Research Association

Conclusion

Dr. D. Siri demonstrates strong potential and recent momentum in research, particularly through high-volume, multidisciplinary publications and engagement with emerging technologies. Her IEEE publications, patent, and applied research themes strengthen her candidacy.

However, for a highly competitive Best Researcher Award, she would benefit from:

More indexed journal publications,

Evidence of citations/impact,

Greater leadership in research initiatives (e.g., funded projects or Ph.D. guidance).