Ayesha Banu | Computer Vision | Women Researcher Award

Published on by Global CSE Awards

Women Researcher Award

Ayesha Banu
Chittagong University of Engineering and Technology (CUET), Bangladesh

Ayesha Banu
Researcher Ayesha Banu
Affiliation Chittagong University of Engineering and Technology (CUET)
Country Bangladesh
Scopus ID 60706667500
Documents 3
Citations 0
h-index 0
Subject Area Computer Vision
Event Global CSE Awards

Ayesha Banu is a researcher affiliated with Chittagong University of Engineering and Technology (CUET), Bangladesh, whose documented publications address computer vision and deep learning. Her recent work includes image deraining and protein secondary structure prediction, reflecting applications of neural architectures, generative models, attention mechanisms, and sequence modeling across computational research domains. [1] [2] [3]

Abstract

Ayesha Banu is affiliated with Chittagong University of Engineering and Technology (CUET), Bangladesh, and has documented research spanning computer vision and deep learning. Her publication record includes studies on single-image deraining using Atrous U-Net and generative adversarial networks, an attention-enhanced GAN approach for image restoration, and E2BNet, a hybrid ESM2-BiLSTM model for eight-state protein secondary structure prediction. These works examine neural architectures for visual restoration and computational sequence analysis, using established evaluation measures and benchmark datasets. The supplied Scopus information records three documents, zero citations, and an h-index of zero, providing a concise bibliographic snapshot for her current academic profile. [1] [2] [3]

Keywords

Women Researcher Award; Ayesha Banu; Computer Vision; Deep Learning; Image Deraining; Generative Adversarial Networks; Atrous U-Net; Attention Mechanisms; ESM2; BiLSTM; Protein Secondary Structure Prediction; CUET; Global CSE Awards.

Introduction

Computer vision research increasingly uses deep learning to address image restoration and recognition problems under challenging visual conditions. Banu’s documented studies include generative adversarial networks, U-Net variants, attention mechanisms, and learning architectures for image deraining, alongside a hybrid ESM2-BiLSTM model for protein structure prediction. These works demonstrate interdisciplinary computational methods. [1] [2] [3]

Research Profile

The available publication record identifies Ayesha Banu with Chittagong University of Engineering and Technology in Bangladesh. The supplied Scopus record lists three documents, zero citations, and an h-index of zero. Her stated subject area is Computer Vision, while publications also connect computer vision methods with broader deep learning and computational biology. [1] [2] [3]

Research Contributions

Banu’s documented contributions include research on single-image deraining using Atrous U-Net and GAN architectures, attention-enhanced GAN-based restoration, and hybrid deep learning for eight-state protein secondary structure prediction. These studies apply convolutional, generative, attention-based, and sequential models to structured prediction and image restoration problems, with quantitative evaluation reported in publications. [1] [2] [3]

Publications

Three publications are identified for this profile. The 2024 IEEE conference paper addresses single-image deraining with Atrous U-Net and GAN. Two 2026 Discover Applied Sciences articles examine attention-enhanced GAN deraining and the E2BNet architecture combining ESM2 with BiLSTM for protein secondary structure prediction. Each publication provides a documented scholarly contribution relevant to research. [1] [2] [3]

Research Impact

The documented research impact can be described through publication outputs and methods presented in the cited studies. The deraining research reports image-quality evaluation using metrics including PSNR and SSIM, while the E2BNet study evaluates Q8 protein secondary structure prediction across benchmark datasets. The supplied profile currently records zero citations and h-index zero. [1] [2] [3]

Award Suitability

For a Women Researcher Award profile, the available evidence provides a documented basis for considering research activity, publication record, institutional affiliation, and subject-area relevance. The three identified publications demonstrate engagement with computational methods. Final award eligibility or selection should be determined by Global CSE Awards criteria and submitted supporting evidence. [1] [2] [3]

Conclusion

Ayesha Banu’s documented publication record reflects research involving computer vision, deep learning, image restoration, generative adversarial networks, attention mechanisms, and protein sequence modeling. The supplied bibliographic information establishes three publications and a CUET affiliation. Together, these records provide a concise scholarly profile for academic recognition and further assessment under applicable criteria. [1] [2] [3]

References

  1. Droba, D. D., Banu, A., Sami, M. I., Hossain, R., & Chowdhury, M. (2026). Hybrid deep learning model (E2BNet) combining ESM2 and BiLSTM for 8-state protein secondary structure prediction. Discover Applied Sciences, 8, Article 681.
    https://doi.org/10.1007/s42452-026-08615-z
  2. Banu, A., & Hossain, R. (2026). Attention-enhanced GAN for single image deraining. Discover Applied Sciences.
    https://doi.org/10.1007/s42452-026-08519-y
  3. Banu, A., Anan, S., & Deb, K. (2024). Removing rain from single image using Atrous U-Net and GAN. In 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT). IEEE.
    https://doi.org/10.1109/ICEEICT62016.2024.10534531

Mojtaba Rafiee | Cryptography | Innovative Research Award

Published on by Global CSE Awards

Innovative Research Award

Mojtaba Rafiee
University of Isfahan, Iran

Mojtaba Rafiee
Affiliation University of Isfahan
Country Iran
Scopus ID 56689591300
Documents 5
Citations 53
h-index 4
Subject Area Cryptography
Event Global CSE Awards
ORCID 0000-0001-9365-1803

Mojtaba Rafiee is a researcher affiliated with the University of Isfahan whose scholarly work focuses on cryptography, privacy-preserving systems, and secure cloud computation. His research contributions emphasize functional encryption, encrypted set operations, and adaptive security models applicable to modern distributed systems and secure communication infrastructures.[1] His published studies demonstrate ongoing engagement with advanced cybersecurity challenges involving encrypted cloud datasets and secure multi-client cryptographic protocols.[2]

Abstract

Mojtaba Rafiee has contributed to research in cryptography and secure cloud computing through studies centered on functional encryption, private set operations, and adaptive security frameworks. His work addresses challenges related to data privacy, encrypted cloud datasets, and secure information sharing across distributed systems. By examining multi-adjustable join schemes and multi-client encryption models, his publications support the advancement of efficient privacy-preserving computation methodologies applicable to modern cybersecurity environments.[1][2] The scholarly impact of these studies reflects continued interest in practical cryptographic applications designed for scalable and secure computational infrastructures.[3]

Keywords

Cryptography, Functional Encryption, Secure Cloud Computing, Data Privacy, Encrypted Datasets, Multi-Client Encryption, Adaptive Security, Private Set Operations, Cybersecurity, Secure Computation.

Introduction

The increasing demand for secure digital communication has expanded research interest in cryptographic systems capable of preserving privacy within distributed computing environments. Mojtaba Rafiee’s work contributes to this field through studies focused on functional encryption and privacy-preserving cloud operations designed for modern computational infrastructures.[1]

Research Profile

Mojtaba Rafiee is affiliated with the University of Isfahan and specializes in cryptography and secure computation research. His publications investigate adaptive security mechanisms, encrypted cloud data processing, and functional encryption frameworks that support secure information exchange across distributed digital platforms.[2]

Research Contributions

His research contributions include the development of multi-adjustable join schemes and secure set intersection mechanisms applicable to encrypted cloud environments. These studies address data confidentiality challenges while maintaining computational efficiency and adaptable security structures for multi-client cryptographic applications.[1][4]

Publications

The publication record of Mojtaba Rafiee includes articles published in recognized journals such as IEEE Transactions on Dependable and Secure Computing, The Journal of Supercomputing, and The Computer Journal. These studies collectively examine encryption methodologies, secure cloud datasets, and adaptive privacy-preserving systems.[1][2]

  • Multi-Adjustable Join Schemes with Adaptive Indistinguishably Security
  • Flexible Multi-Client Functional Encryption for Set Intersection
  • Security of Multi-Adjustable Join Schemes: Separations and Implications
  • Private Set Operations over Encrypted Cloud Dataset and Applications

Research Impact

The documented citation record and publication activity indicate scholarly engagement within the field of cryptography. His studies contribute to advancing privacy-preserving computational methods and support ongoing academic discussion regarding secure cloud infrastructures and encrypted communication technologies.[3]

Award Suitability

Mojtaba Rafiee’s research profile aligns with the objectives of the Global CSE Awards due to his contributions to cryptography and secure computing methodologies. His publications address contemporary cybersecurity challenges while presenting practical frameworks for secure data sharing and encrypted cloud computation.[1]

Conclusion

The academic contributions of Mojtaba Rafiee reflect continued research activity in cryptography and secure cloud technologies. His studies provide relevant insights into adaptive encryption systems and privacy-preserving computation, supporting the broader advancement of dependable and secure digital communication infrastructures.[2]

References

  1. Rafiee, M. (2023). Multi-Adjustable Join Schemes with Adaptive Indistinguishably Security. IEEE Transactions on Dependable and Secure Computing.
    https://ieeexplore.ieee.org/document/10363626
  2. Rafiee, M. (2023). Flexible multi-client functional encryption for set intersection. The Journal of Supercomputing.
    https://link.springer.com/article/10.1007/s11227-023-05129-y
  3. Rafiee, M., & Khazaei, S. (2021). Security of Multi-Adjustable Join Schemes: Separations and Implications. IEEE Transactions on Dependable and Secure Computing.
    https://ieeexplore.ieee.org/document/9366363
  4. Rafiee, M., & Khazaei, S. (2020). Private Set Operations over Encrypted Cloud Dataset and Applications.
    https://ieeexplore.ieee.org/document/9579286
  5. Elsevier. (n.d.). Scopus author details: Mojtaba Rafiee, Author ID 56689591300. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=56689591300

Ramchandra Mangrulkar | Cryptography | Best Researcher Award

Published on by Global CSE Awards

Dr. Ramchandra Mangrulkar | Cryptography | Best Researcher Award

Professor at SVKM’s Dwarkadas J. Sanghvi College of Engineering | India

Dr. Ramchandra Sharad Mangrulkar is a prolific researcher and academic leader whose contributions bridge advanced artificial intelligence research with practical, industry-ready solutions. leading international publishers, his scholarly output reflects both depth and breadth. He holds patents in deep learning-based applications and has provided consultancy in blockchain, high-performance computing, fraud detection, and cybersecurity, showcasing his commitment to solving real-world problems. As an IEEE Senior Member and editorial board member of reputed journals, Dr. Mangrulkar actively contributes to the global research community while mentoring doctoral scholars and fostering interdisciplinary collaborations. His research spans GPU-accelerated computing, secure AI, ethical applications of technology, and blockchain-based systems, all at the forefront of computer science. Through his innovative work, leadership, and dedication, Dr. Mangrulkar continues to advance secure, scalable, and ethical computing systems, making him an ideal candidate for prestigious recognition.

Professional Profiles

  Google scholar | Scopus | ORCID

Education

Dr. Ramchandra Sharad Mangrulkar has pursued an extensive academic journey that has provided him with a strong foundation in computer science and engineering. He completed his postgraduate degree from the prestigious National Institute of Technology, Rourkela, where he gained expertise in advanced computing methodologies and problem-solving strategies. His academic pursuit continued with a doctorate in Computer Science and Engineering from Sant Gadge Baba Amravati University, where his research was focused on innovative computational models and data-driven techniques. This combination of advanced qualifications has helped him specialize in areas such as predictive analytics, GPU-accelerated computing, and cybersecurity. His educational achievements reflect not only academic excellence but also a deep commitment to research-driven learning, providing him with the intellectual capacity to mentor future researchers. Through his education, Dr. Mangrulkar has developed a solid framework of technical and theoretical knowledge that has continuously guided his teaching, research, and consulting contributions.

Experience

Dr. Mangrulkar serves as a Professor in the Department of Information Technology at SVKM’s Dwarkadas J. Sanghvi College of Engineering, Mumbai, where he plays a pivotal role in teaching, mentoring, and guiding students in advanced domains of computing. With extensive academic and professional experience, he has contributed significantly to the growth of computer science research through his publications, consultancy, and supervisory roles. He has authored and co-authored numerous books with globally recognized publishers and has published more than a hundred research papers across reputed journals and conferences. His expertise extends into consultancy, where he has guided industries in adopting advanced frameworks for blockchain, data analytics, and high-performance computing. As an IEEE Senior Member and approved Ph.D. supervisor, he has mentored several doctoral scholars and graduate students. His leadership in academics, coupled with his strong research background, establishes him as a distinguished figure in the computer science engineering community.

Skills 

Dr. Mangrulkar possesses an extensive range of technical skills and domain expertise that span predictive analytics, artificial intelligence, cybersecurity, blockchain technology, and GPU-accelerated computing. His proficiency in developing high-performance computing solutions has made him a trusted consultant for organizations seeking innovation in research and industrial domains. He is highly skilled in ethical AI development, advanced cryptanalysis, data visualization, and network security frameworks. His capabilities include designing and implementing solutions for fraud detection, autonomous systems, and medical diagnostics, providing practical and impactful applications of his research. He has conducted cybersecurity audits, offered risk mitigation strategies, and guided enterprises in enhancing their IT infrastructure. His expertise extends to industry collaborations, where he has helped organizations implement blockchain frameworks, data-driven decision-making tools, and cloud-based GPU solutions. These multifaceted skills reflect his ability to bridge academic research with industrial innovation, demonstrating leadership in both technological development and applied problem-solving.

Research Focus

Dr. Mangrulkar’s research work demonstrates a strong focus on GPU-accelerated computing, blockchain frameworks, ethical artificial intelligence, and advanced cybersecurity solutions. He has completed diverse projects on predictive healthcare models, electronic health record security, and intelligent decision systems for medical diagnostics, all of which have had significant societal relevance. His ongoing research includes high-performance vector search applications, cryptanalysis attacks using deep neural networks, and customized generative adversarial networks for image synthesis. His scholarly contributions are evident from his wide publication record, multiple book authorships, and ongoing guidance of research scholars. His work reflects a balance between theoretical exploration and applied innovation, with clear emphasis on responsible computing practices. He actively collaborates with students and researchers, fostering an environment of knowledge exchange and innovation. His research continues to evolve with global technological trends, maintaining a strong impact within academic circles and extending into industry-driven problem-solving applications.

Awards 

Dr. Mangrulkar has been recognized across academia and industry for his outstanding contributions to computer science and engineering. He has authored and edited books with renowned publishers, published high-impact research, and contributed as a reviewer to international journals. His achievements include collaborative patents, such as an innovation on deep learning-based language translation, demonstrating his ability to transform research into applied technological solutions. He holds the distinction of being a Senior Member of IEEE, which reflects his global standing in the professional community. He has received appreciation for his consultancy work in cybersecurity, blockchain adoption, and high-performance computing solutions for enterprises. His leadership roles in academia, coupled with his guidance to research scholars and students, underline his contribution to nurturing future innovators. Recognition through citations, industry projects, and academic visibility highlights his influence, making him a highly respected professional dedicated to advancing computer science research and practice.

Publication Top Notes

Title: Design and implementation of smart HealthCare system using IoT
Journal/Conference: International Conference on Innovations in Information, Embedded and Systems
Citations: 106

Title: Intrusion detection system using random forest on the NSL-KDD dataset
Journal/Conference: Emerging Research in Computing, Information, Communication and Applications
Citations: 96

Title: Routing protocol for delay tolerant network: A survey and comparison
Journal/Conference: International Conference on Communication Control and Computing Technologies
Citations: 84

Title: Few shot learning for medical imaging
Journal/Conference: Machine Learning Algorithms for Industrial Applications (Book Chapter)
Citations: 58

Title: Comparison of tabular synthetic data generation techniques using propensity and cluster log metric
Journal/Conference: International Journal of Information Management Data Insights, 3 (2), 100177
Citations: 45

Title: TaxoDaCML: Taxonomy based Divide and Conquer using machine learning approach for DDoS attack classification
Journal/Conference: International Journal of Information Management Data Insights, 1 (2), 100048
Citations: 43

Title: Trust based secured adhoc On demand Distance Vector Routing protocol for mobile adhoc network
Journal/Conference: Sixth International Conference on Wireless Communication and Sensor Networks
Citations: 36

Title: Cyber security and digital forensics: challenges and future trends
Journal/Conference: John Wiley & Sons (Book Publication)
Citations: 23

Title: Improving Route Selection Mechanism using Trust Factor in AODV Routing Protocol for MaNeT
Journal/Conference: International Journal of Computer Applications, 7 (10), 36–39
Citations: 22

Title: Future Trends in 5G and 6G: Challenges, Architecture, and Applications
Journal/Conference: CRC Press (Book Publication)
Citations: 19

Conclusion

Dr. Ramchandra Sharad Mangrulkar is a distinguished researcher and academic leader whose career reflects a rare combination of scholarly excellence, technological innovation, and industry collaboration.  his contributions have significantly advanced the fields of artificial intelligence, high-performance computing, blockchain technologies, and cybersecurity. He has also demonstrated innovation through patents and consultancy projects that apply advanced research to real-world challenges. As an IEEE Senior Member, editorial board member, and mentor to doctoral scholars, Dr. Mangrulkar has shown strong leadership in shaping the global research community. His work bridges theoretical research with practical applications, ensuring secure, scalable, and ethical computing systems. Based on his research productivity, technological innovations, leadership roles, and industry collaborations, Dr. Mangrulkar is an ideal candidate for the Best Researcher Award in Computer Science & Engineering, with contributions of both academic significance and practical impact.