Dr. Rashid Mirzavand is a dedicated academic and researcher leading the Intelligent Wireless Technology Group at the University of Alberta. As an Assistant Professor in the Department of Electrical and Computer Engineering, he is passionate about fostering an inclusive environment that promotes equity, diversity, and excellence in research and education. His entrepreneurial spirit has led him to co-found and serve as CTO for three companies specializing in smart sensors, near-field measurement, and wireless power transfer technologies.
Dr. Mirzavand's research focuses on RF/microwave/mm-wave circuits, sensors, reconfigurable intelligent surfaces, and numerical methods. He is committed to advancing innovation and addressing real-world challenges, with a particular emphasis on promoting diversity and equity in STEM fields. With numerous awards, three granted and nine filed US patents, and over 180 publications to his credit, Dr. Mirzavand is a respected voice in his field. He is a registered member of Alberta's Association of Professional Engineers and Geoscientists.
Cucumber cultivation is a vital component of Pakistan's agricultural economy and is a key vegetable in the national diet. However, crop yield and quality are severely threatened by diseases like powdery mildew and downy mildew. Early and accurate disease detection is critical for implementing targeted treatment and preventing widespread infection. This study proposes a deep learning-based framework for the automated recognition of cucumber leaf diseases. We designed and trained a custom Convolutional Neural Network (CNN) from scratch and compared its performance against powerful pre-trained transfer learning models, including VGG16 and InceptionV3. The models were evaluated on a dataset of c... More >
Graphical Abstract
Free Access
|
Research Article
| 22 December 2024
| Cited:
3
,
9
In the healthcare sector, the application of deep learning technologies has revolutionized data analysis and disease forecasting. This is particularly evident in diabetes research, where in-depth analysis of Electronic Health Records (EHR) has unlocked new opportunities for early detection and effective intervention strategies. Our research presents an innovative model that synergizes the capabilities of Bidirectional Long Short-Term Memory Networks-Conditional Random Field (BiLSTM-CRF) with a fusion of XGBoost and Logistic Regression. This model is designed to enhance the accuracy of diabetes risk prediction by conducting an in-depth analysis of electronic medical records data. The first p... More >
Graphical Abstract
We use cookies
Our website uses cookies that are essential for its operation and additional cookies to track performance, or to improve and personalize our services. By clicking "Allow all cookies", you agree to the storing of cookies on your device. For more information on how we use cookies, please see our Cookie Policy.
