Dr. Deevyankar Agarwal

About

Email: deevyankar@gmail.com | Phone: +968 92043640 | Location: Muscat, Oman

Dr. Deevyankar Agarwal is a computer engineering academic and researcher based in Muscat, Oman. He is currently a Lecturer at the University of Technology & Applied Sciences (UTAS), previously known as the Higher College of Technology, where he has taught since 2013. He previously served as Senior Assistant Professor at JSS Academy of Technical Education in Noida, India (2010–2013).

His research focuses on Artificial Intelligence and Deep Learning, particularly applied to the healthcare sector. His Ph.D. from the University of Valladolid, Spain, centered on the development of deep learning models for Alzheimer’s diagnosis. He is known for his interdisciplinary work and commitment to advancing AI-driven education and innovation.

Projects & Current Research

Student Projects

• Real-time Vehicle Speed Detection: Utilizes YOLO-based object detection and frame-difference analysis to detect vehicles and calculate their speed from live video streams.
• Smart Neckband for Swimmers: A wearable device designed for swimmers that tracks location, detects anomalies like drowning or disorientation, and sends real-time alerts via IoT infrastructure.
• Smart Glasses for the Deaf: Converts real-time speech to text using voice recognition APIs and micro-displays.
• Attention-based Attendance System: Uses computer vision to monitor students’ attentiveness and mark attendance automatically, visualized via a custom teacher dashboard.
• AI-IoT Smart Fish Farming: Real-time monitoring of temperature, food levels, and fish health using sensors and AI-based analytics.

Ongoing Research

• Digitization of Building Permits using AI: Automating rule interpretation and compliance checking with NLP and ontology-based systems.
• Next-gen Clinical Decision Support System (CDSS) for Alzheimer’s: A multimodal AI model combining MRI, PET, genotyping, and blood biomarkers with LLMs for early-stage diagnosis and clinical chatbot assistance.

Executive Summary (CDSS Project): This project aims to develop a clinical decision system for Alzheimer’s diagnosis using multimodal data (MRI, PET, APOE4, blood biomarkers, cognitive scores). It incorporates uncertainty-aware AI models and fine-tuned language models to provide accurate, explainable predictions with a doctor-friendly chatbot interface. Outputs include four SCI-indexed publications and clinical-grade AI deployment to boost diagnostic precision.

Executive Summary (Building Permit Digitization): This research proposes an AI-driven system to streamline the construction request and building permit process in Muscat, focusing on integrating Building Information Modeling (BIM), cadastral maintenance, and advanced AI models to automate document verification, data classification, and request prioritization. In alignment with Oman Vision 2040, the system aims to enhance efficiency, reduce error rates, and improve user experience in public sector construction procedures. By leveraging machine learning for text and image processing and maintaining accurate cadastral data, the system will optimize handling of architectural documents and ensure regulatory compliance, contributing to a sustainable, technology-driven construction industry in Oman.

Teaching Philosophy & Experience

With over 23 years of experience, Dr. Agarwal believes in student-centered, inclusive, and applied learning. He integrates flipped classrooms, gamification, and AI-enhanced tools to boost engagement and understanding.

Research Focus & Contributions

• COVID-19 Diagnosis: Developed an EfficientNet-based CNN for automated diagnosis using radiographic imaging.
• Alzheimer’s Disease: Pioneered deep learning neuroimaging models for early diagnosis of Alzheimer's through MRI biomarkers.
• Cancer Detection: Applied ensemble ML techniques for improved lung and breast cancer classification from CT and mammogram data.
• Assistive Robotics & IoT: Focus on wearable exoskeletons, fatigue monitoring, and smart environments.

Dr. Agarwal’s work has over 600 citations (Google Scholar, h-index: 6). His research is featured on DBLP, ResearchGate, and ScholarGPS, and he collaborates with global AI experts like Isabel de la Torre Díez and Gonçalo Marques.

Selected Publications

• EfficientNet CNN for COVID-19 – Applied Soft Computing, 2020
• Transfer Learning for Alzheimer’s Detection – Sensors, 2021
• ML Models for Schizophrenia – Sensors, 2022
• 3D Neuroimaging CNNs – Mathematics, 2022
• Alzheimer’s Prediction – Journal of Medical Systems, 2023
• CT-based Lung Cancer Classification – Multimedia Tools & Applications, 2024
• ML for Breast Cancer Detection – IEEE Access, 2024

Books Published

• Deep Learning in Gaming and Animations: Principles and Applications
  Editors: Vikas Chaudhary, Moolchand Sharma, Prerna Sharma, Deevyankar Agarwal
  Publisher: CRC Press, 2021 | ISBN: 978-1032126098
  Description: This book provides a comprehensive overview of deep learning applications in gaming and animation. It covers fundamental concepts and progresses to advanced topics, illustrating how AI and machine learning are revolutionizing the industry.
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• Explainable Artificial Intelligence for Autonomous Vehicles: Concepts, Challenges, and Applications
  Editors: Kamal Malik, Moolchand Sharma, Suman Deswal, Umesh Gupta, Deevyankar Agarwal, Yahya Obaid Bakheet Al Shamsi
  Publisher: CRC Press, 2024 | ISBN: 978-1032655017
  Description: This book is a guide to developing and applying explainable artificial intelligence (XAI) in autonomous vehicles. It covers techniques to enhance safety, reliability, and user trust in autonomous systems.
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• Bioinformatics and Beyond: AI Applications in Healthcare
  Editors: Moolchand Sharma, Deepak Kumar Sharma, Deevyankar Agarwal, Khoula Al Harthy
  Publisher: CRC Press, 2025 | ISBN: 978-1003508403
  Description: This book explores the integration of AI in bioinformatics and healthcare, including data analysis, predictive modeling, and personalized medicine.
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