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Md. Iqbal

Md. Iqbal

Professor · COMPUTER SCIENCE AND ENGINEERING
Editor
APID
412
Location
ROORKEE, UTTRAKHAND, India
Expertise
Machine Learning, Artificial Intelligence, Cloud Computing and Blockchain Technology
Areas of interest
Machine Learning, Artificial Intelligence, Cloud Computing and Blockchain Technology
Member since
November 2022

Biography

Dr. Md. Iqbal is a Professor in the Department of Computer Science and Engineering at Quantum University, Roorkee, Uttarakhand, India. He is an experienced academician and researcher with expertise in emerging areas of Computer Science, particularly Artificial Intelligence, Machine Learning, Blockchain, Cybersecurity, and Distributed Denial-of-Service (DDoS) attack detection. His academic profile indicates doctoral research in the area of reducing the impact of pandemics using Blockchain and Machine Learning technologies.

Dr. Iqbal is actively involved in teaching, research, project evaluation, and mentoring students. He has contributed to research publications in areas such as AI-based cybersecurity and intelligent transportation systems and has participated in academic and technical activities at Quantum University.

He has also served as a reviewer/evaluator for innovative student projects and technical events, supporting experiential learning and research-oriented education. His professional interests focus on applying emerging technologies to address contemporary challenges in cybersecurity, intelligent systems, and digital technologies.

Overall, Dr. Md. Iqbal is committed to academic excellence, research innovation, and developing industry-relevant technical skills among students at Quantum University.

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Publications

  1. A three-stage novel framework for efficient and automatic glaucoma classification from retinal fundus images
  2. Development of a Dual-Stream Vision Transformer with Cross-Attention Fusion (DS-ViT-CAF) for Detection of Sugarcane Leaf Diseases Using a Custom Dataset
  3. Explainable deep learning framework for multimodal emotion recognition using physiological signals
  4. GAINSeq: glaucoma pre-symptomatic detection using machine learning models driven by next-generation sequencing data
  5. Improved machine learning framework with feature engineering and SHAP analysis for predicting wine quality
  6. Optimized Deep Learning and Mobile Deployment for Real-Time Sugarcane Leaf Disease Diagnosis in Precision Agriculture