Vikram Narayandas | Computer Science | Outstanding Academic Achievement Award

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Assoc. Prof. Dr. Vikram Narayandas | Computer Science | Outstanding Academic Achievement Award

Associate professor | CVR college of Engineering | India

Assoc. Prof. Dr. Vikram Narayandas is an accomplished academic and researcher in the field of Computer Science and Engineering, specializing in Artificial Intelligence, Machine Learning, Internet of Things, Mobile Ad Hoc Networks, Cybersecurity, and Cloud Computing. With nearly two decades of teaching experience, he has served as Associate Professor and Assistant Professor in reputed engineering institutions, significantly contributing to curriculum development, student mentorship, and academic innovation. His research interests focus on integrating MANET with IoT for enhanced smart communication, energy-efficient networking technologies, anomaly detection systems, and IoT-based solutions for real-world applications. He has authored and co-authored several publications in peer-reviewed international journals and conferences, covering areas such as IoT communications, MANET protocols, and network security. In addition, he holds patents related to automated wireless network authentication, smart monitoring systems, and IoT-based safety mechanisms. He has also contributed to academia as a book author with a work dedicated to the role of MANET in IoT. Dr. Narayandas has received multiple awards and recognitions for teaching excellence, academic leadership, and contributions to technical education, including distinctions as a Cisco Networking Academy instructor, Fortinet instructor, and Palo Alto Networks Cybersecurity Academy trainer. He has actively participated as a resource person and coordinator in numerous workshops, faculty development programs, and conferences, sharing expertise on IoT, Linux, network security, and emerging technologies. Beyond teaching and research, he serves on editorial and review boards for several international journals, further supporting knowledge dissemination and academic collaboration. His blend of research, innovation, and teaching excellence reflects his commitment to advancing technological education and applied research in computer science. He has authored 6 documents with 23 citations across 23 citing documents, further reflecting the impact of his scholarly contributions.

Profile: Scopus | Orcid | Google Scholar

Featured Publications

Tiruvayipati, S., Yellasiri, R., Narayandas, V., Maruthavanan, A., & Meduri, A. (2024). Methodology for developing an IoT-based parking space counter system using XNO. Scalable Computing: Practice and Experience, 25(2).

Narayandas, V., Maruthavanan, A., & Dugyala, R. (2024). Integration of MANET and IoT for enhancing smart device communication infrastructure. International Advanced Research Journal in Science, Engineering and Technology (IARJSET), 11(1).

Narayandas, V., Maruthavanan, A., & Dugyala, R. (2023). Energy efficient better approach to mobile Ad hoc networking (BATMAN) using LoRa technology. International Journal of System Assurance Engineering and Management.

Narayandas, V. (2023). The chronicles of MANET in Internet of Things. Kindle Direct Publishing.

Narayandas, V., Maruthavanan, A., & Dugyala, R. (2023). Remote IoT correspondence for coordinating end-devices over MANET via energy-efficient LPWAN. International Journal of Nanotechnology, 20(5/6), 418–431.

 

Nooshin Nemati | Computer Science | Best Researcher Award

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Ms. Nooshin Nemati | Computer Science | Best Researcher Award

Ankara University, Turkey

Dr. Nooshin Nemati is a dedicated researcher in the fields of Artificial Intelligence, Deep Learning, and Medical Image Processing, currently pursuing her PhD in Computer Engineering at Ankara University, where she also contributes to multiple AI-based cancer detection projects. She holds a Master’s degree in Electrical and Electronics Engineering from Yuzuncu Yıl University and a Bachelor’s from Qazvin Azad University.

Profile:

Educational Background:

Nooshin Nemati is currently a PhD candidate in Computer Engineering at Ankara University. She earned her Master’s degree in Electrical and Electronics Engineering from Yuzuncu Yıl University with a completed her undergraduate studies at Qazvin Azad University in Iran.

Research Areas:

Her main research interests lie in Artificial Intelligence, Deep Learning, Medical Image Processing, and Computer Vision, particularly applied to cancer detection in histopathology images. She focuses on segmentation, classification, and detection tasks using advanced deep learning frameworks.

Projects and Contributions:

She has actively contributed to significant research initiatives such as the TUBITAK 1001 Project, focused on deep learning methodologies for breast cancer detection, and the BAP Project, which deals with cancer region detection in histopathology images. She has also been involved in the development of important datasets such as NuSeC and MiDeSeC, aimed at supporting machine learning in medical imaging. In addition, she has applied her technical skills in software development projects including system analysis and automation tools for banks.

Technical Skills:

Nooshin is proficient in AI, Machine Learning, Deep Learning, and programming frameworks such as ASP.NET and WordPress. She also holds certifications like Network+ and CCNA, showcasing her broad technical competence.

Citation Metrics:

  • Total Citations: 75

  • Citations Since 2020: 71

  • h-index: 6

  • h-index Since 2020: 5

  • i10-index: 3

  • i10-index Since 2020: 3

Publication Top Notes:

  • An imbalance-aware nuclei segmentation methodology for H&E stained histopathology images
    2023
    Citations: 22

  • Detection of colorectal cancer with vision transformers
    2022
    Citations: 11

  • Effect of color normalization on nuclei segmentation problem in H&E stained histopathology images
    2022
    Citations: 10

  • A hybridized deep learning methodology for mitosis detection and classification from histopathology images
    2023
    Citations: 8

  • CompSegNet: An enhanced U-shaped architecture for nuclei segmentation in H&E histopathology images
    2024
    Citations: 7