Assistant Professor at Model Institute of Engineering and Technology, Jammu, India
Ms. Shiveta Bhat is an academician and researcher in the field of Electronics and Communication Engineering, known for her work in image fusion techniques, biomedical imaging, and optical communication networks. With a solid foundation in embedded systems and digital design, she combines practical engineering skills with deep research insights. She is currently contributing to academia through her teaching and research roles, demonstrating a consistent commitment to technological advancement and academic excellence.
profile
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Education
Ms. Bhat completed her Master of Engineering (M.E.) in Electronics and Communication from Chitkara University, Himachal Pradesh, between 2016 and 2019, graduating with a stellar CGPA of 9.15/10. Her postgraduate thesis focused on the “Development of Multi-Focus Image Fusion Technique,” which explored the synthesis of focused information from multiple images to create a single, high-quality output. She earned her Bachelor of Technology (B.Tech.) in Electronics and Communication from Kurukshetra University in 2015, achieving a score of 68.77%. Her early academic background includes secondary education under the J&K State Board with notable scores, particularly excelling in her Matriculation examinations.
Experience
Ms. Bhat has been actively involved in the academic and research community since 2016, starting as a Teaching Assistant at Chitkara University. During her tenure, she engaged in hands-on teaching, supervised student projects, and participated in curriculum development. In addition, she gained industry experience as a CSR executive at Dr. ITM Limited, Mohali from 2015 to 2016. Her teaching and research interests are further supported by practical training in Networking and Embedded Systems, undertaken at reputed institutions such as the National Informatics Centre and A2IT, Chandigarh.
Research Interest
Her primary research interests lie in image processing, especially in multi-focus image fusion, biomedical image analysis, and machine learning applications in medical diagnostics. She has a particular focus on integrating advanced computational models, such as neutrosophic logic and wavelet transforms, into image fusion tasks to enhance accuracy and performance. Her work has addressed real-world challenges like class imbalance in white blood cell classification and the development of efficient image segmentation techniques. She also shows an emerging interest in traffic grooming strategies within optical networks, expanding her research into communication systems.
Award
Throughout her career, Ms. Bhat has participated in various national and international conferences, workshops, and faculty development programs, reflecting her commitment to continuous professional growth. She contributed as a coordinator at the State Level Children’s Science Congress in 2016 and was actively involved in workshops focusing on FPGA design, optimization techniques, and satellite imaging. While no formal award names were listed in the documentation, her role in diverse academic and outreach programs suggests that she is a strong candidate for recognition based on her dedication, technical contributions, and engagement with the research community.
Publication
Ms. Bhat’s publication record reflects a steady contribution to high-impact areas of electronics and computer science. Her major publications include:
“Traffic grooming with greedy-based priority routing and wavelength assignment for passive optical networks,” Journal of Optical Communications, 2024.
“Multi-focus image fusion techniques: a survey,” Artificial Intelligence Review, 2021 — widely cited for its comprehensive analysis of fusion strategies.
“Multi-focus Image Fusion using Neutrosophic based Wavelet Transform,” Applied Soft Computing, 2021.
“Recent trends in computer assisted diagnosis (CAD) system for breast cancer diagnosis using histopathological images,” IRBM, 2019.
“Enhancing white blood cells classification using convolutional neural network with weighted loss,” AIP Conference Proceedings, 2024.
“Multi-Focus Image Fusion: Quantitative and Qualitative Comparative Analysis,” presented at ICRIC, Springer, 2019.
“Frequency Scaling Based Power Efficient Kannada Unicode Reader Design on 40nm and 28nm FPGA,” presented at INDIACOM, 2017.
These works have collectively been cited in scholarly articles addressing image processing, machine learning, and optical networks, underscoring her impact on these evolving domains.
Conclusion
Considering her robust academic record, impactful research, interdisciplinary publications, and active engagement in the academic community, Ms. Shiveta Bhat exemplifies the qualities of an exceptional researcher. Her contributions reflect innovation, rigor, and dedication to advancing knowledge in engineering and applied sciences. She is a deserving candidate for the Best Researcher Award, and her profile stands as a testament to scholarly excellence and potential for continued contributions to the field.