Nasir Rashid | Medical Research | Best Researcher Award

Posted on by ScienceFather

Dr. Nasir Rashid | Medical Research | Best Researcher Award

Associate Professor National University of Sciences and Technology, Pakistan.

Nasir Rashid is a highly accomplished researcher and educator with a background in Mechatronics Engineering. He holds a PhD and an MSc in Mechatronics Engineering from the National University of Sciences and Technology, Islamabad, Pakistan, as well as a Bachelor’s degree in Mechanical Engineering from the University of Engineering & Technology, Lahore, Pakistan. Rashid’s research interests lie in Biomedical Engineering, Machine Learning, and Robotics, where he has made significant contributions. He has a strong track record of publications and has received the Best University Teacher Award in 2014 for his exceptional work in education. Rashid is known for his innovative approach to research and his commitment to advancing knowledge in his field. His skills in mechanical and electronic systems integration, programming, and problem-solving make him a valuable asset in both academic and industrial environments.

Professional Profiles:

Education:

Nasir Rashid holds a PhD in Mechatronics Engineering from the National University of Sciences and Technology, Islamabad, Pakistan, which he completed in 2019. Prior to this, he earned an MSc in Mechatronics Engineering from the same university in 2010. He also holds a Bachelor’s degree in Mechanical Engineering from the University of Engineering & Technology, Lahore, Pakistan, which he obtained in 1993. Nasir Rashid is a member of the Pakistan Engineering Council and IEEE. In 2014, he was honored with the Best University Teacher Award. His research interests include Biomedical Engineering, Machine Learning, and Robotics.

Research Experience:

Nasir Rashid has extensive research experience in the fields of Biomedical Engineering, Machine Learning, and Robotics. His research has focused on developing advanced technologies and methodologies to address complex challenges in healthcare and automation. Rashid has been involved in projects that aim to improve the accuracy and efficiency of medical diagnostics through the use of machine learning algorithms and robotic-assisted systems. His work has also explored the integration of artificial intelligence in biomedical devices to enhance their functionality and performance. Rashid’s research contributions have been widely recognized for their innovation and potential impact on the field.

Research Interest:

Nasir Rashid’s research interests encompass the fields of Biomedical Engineering, Machine Learning, and Robotics. He is particularly interested in exploring the intersection of these disciplines to develop innovative solutions that can improve healthcare outcomes and advance technology. His work aims to leverage the principles of mechatronics to design and develop novel biomedical devices and systems that can enhance medical diagnostics, treatment, and patient care. Rashid is committed to pushing the boundaries of knowledge in these areas and contributing to the advancement of science and technology for the betterment of society.

Award and Honors

Nasir Rashid has been honored with the Best University Teacher Award in 2014, recognizing his exceptional contributions to education and academia. This prestigious award highlights his dedication to teaching and mentoring students, as well as his commitment to advancing knowledge in his field. Rashid’s teaching methods and innovative approaches have been lauded by both students and colleagues, cementing his reputation as a respected educator. His receipt of this award reflects his outstanding achievements and leadership in the field of Mechatronics Engineering.

Skills:

Nasir Rashid possesses a diverse set of skills across various domains. With his background in Mechatronics Engineering, he is proficient in mechanical and electronic systems integration, automation, and control systems. His expertise extends to software development, particularly in the areas of machine learning and robotics. Rashid is skilled in programming languages such as C/C++, Python, and MATLAB, which he has used extensively in his research and projects. Additionally, he has strong analytical and problem-solving skills, which enable him to tackle complex engineering challenges effectively. Rashid’s interdisciplinary background and technical proficiency make him a valuable asset in both academic and industrial settings.

Publications:

  1. Emotion Fusion-Sense (Emo Fu-Sense) – A novel multimodal emotion classification technique
    • Journal: Biomedical Signal Processing and Control
    • Year: 2024
    • DOI: 10.1016/j.bspc.2024.106224
    • Source: Crossref
  2. Skeletal Keypoint-Based Transformer Model for Human Action Recognition in Aerial Videos
    • Journal: IEEE Access
    • Year: 2024
    • DOI: 10.1109/ACCESS.2024.3354389
    • Source: Crossref
  3. A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm
    • Journal: PLOS ONE
    • Year: 2023-09-08
    • DOI: 10.1371/journal.pone.0276133
    • Source: Crossref
  4. IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety
    • Journal: IEEE Access
    • Year: 2023
    • DOI: 10.1109/ACCESS.2023.3244008
    • Source: Crossref
  5. A Patch-Image Based Classification Approach for Detection of Weeds in Sugar Beet Crop
    • Journal: IEEE Access
    • Year: 2021
    • DOI: 10.1109/ACCESS.2021.3109015
    • Source: Crossref
  6. Advancements, Trends and Future Prospects of Lower Limb Prosthesis
    • Journal: IEEE Access
    • Year: 2021
    • DOI: 10.1109/ACCESS.2021.3086807
    • Source: Crossref
  7. Human activity recognition using 2D skeleton data and supervised machine learning
    • Journal: IET Image Processing
    • Year: 2019-11
    • DOI: 10.1049/iet-ipr.2019.0030
    • Source: Crossref
  8. Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis
    • Journal: BioMed Research International
    • Year: 2018
    • DOI: 10.1155/2018/2695106
    • Source: Crossref
  9. Efficient FIR Filter Implementations for Multichannel BCIs Using Xilinx System Generator
    • Journal: BioMed Research International
    • Year: 2018
    • DOI: 10.1155/2018/9861350
    • Source: Crossref