Umesh Kumar Lilhore | Deep Learning | Best Researcher Award

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Dr. Umesh Kumar Lilhore | Deep Learning | Best Researcher Award

Professor at Galgotias University, India

Dr. Umesh Kumar Lilhore is a seasoned Professor and Researcher in Computer Science and Engineering (CSE) at Galgotias University, Greater Noida, India. With over 18 years of experience in academia and research, he has established himself as an expert in Artificial Intelligence (AI), Deep Learning, and Environmental Studies. Dr. Lilhore has earned a Ph.D. and M.Tech in CSE, complemented by a Postdoctoral fellowship from the USA. He has published over 100 research articles in indexed journals, with more than 3,800 citations and an h-index of 29+, showcasing his impactful contributions to the academic community.

Publication Profile 

Scopus

Educational Background 🎓

  • Ph.D.: Computer Science and Engineering (Institution not specified)
  • M.Tech: Computer Science and Engineering (Institution not specified)
  • Postdoctoral Fellowship: USA (Institution not specified)

Professional Experience 💼

  • Designation: Professor, Computer Science and Engineering
  • Institution: Galgotias University, Greater Noida, India
  • Years of Experience: Over 18 years in teaching and research
  • Editorial Appointment: Editorial Board Member, Springer Journal: BMC Medical Informatics and Decision Making
  • Collaborations: National and international collaborations with institutions such as:
    • National University of Science and Technology Politehnica Bucharest
    • Pitesti University Center, Romania
    • University of Louisiana, USA
    • Arab Minch University

Research Interests 🔬

  • Artificial Intelligence (AI)
  • Deep Learning
  • Environmental Studies

Awards and Honors🏆✨

  • Patents:
    • 35 Indian patents
    • 2 UK design patents
  • Books Published: 10+ Scopus-indexed books
  • Projects: Completed AICTE-funded Air Quality Analysis project
  • Professional Memberships: IEEE, ACM

Contributions and Achievements

  • Published 51 SCI-indexed and 102 Scopus-indexed research papers.
  • Google Scholar citation index: 28+ with 3,800+ citations and an h-index of 29+.
  • Collaborated on research projects with prestigious international institutions.
  • Actively engaged in advancing AI and sustainability research.

Conclusion🌟

Dr. Umesh Kumar Lilhore exemplifies excellence in academia, research, and innovation. His prolific contributions to AI, Deep Learning, and Environmental Studies reflect his dedication to addressing critical global challenges. With a strong record of publications, patents, and collaborative projects, he has significantly advanced knowledge and applications in his field. Dr. Lilhore continues to inspire as a thought leader, mentor, and innovator in computer science and engineering.

Publications 📚

📄 Systematic Review on Cardiovascular Disease Detection and Classification
Authors: Pandey, V., Lilhore, U.K., Walia, R.
Journal: Biomedical Signal Processing and Control, 2025, 102, 107329.
📊 Citations: 0

📚 An Attention-Driven Hybrid Deep Neural Network for Enhanced Heart Disease Classification
Authors: Lilhore, U.K., Simaiya, S., Alhussein, M., Aurangzeb, K., Hussain, A.
Journal: Expert Systems, 2025, 42(2), e13791.
📊 Citations: 0

⚠️ Erratum: Hybrid CNN-LSTM Model with Efficient Hyperparameter Tuning for Prediction of Parkinson’s Disease
Authors: Lilhore, U.K., Dalal, S., Faujdar, N., Thangaraju, P., Velmurugan, H.
Journal: Scientific Reports, 2024, 14(1), 27077.
📊 Citations: 0

⚙️ Improving Efficiency and Sustainability via Supply Chain Optimization Through CNNs and BiLSTM
Authors: Dalal, S., Lilhore, U.K., Simaiya, S., Radulescu, M., Belascu, L.
Journal: Technological Forecasting and Social Change, 2024, 209, 123841.
📊 Citations: 0

❤️ Enhancing Heart Disease Classification with M2MASC and CNN-BiLSTM Integration for Improved Accuracy
Authors: Pandey, V., Lilhore, U.K., Walia, R., Baqasah, A.M., Algarni, S.
Journal: Scientific Reports, 2024, 14(1), 24221.
📊 Citations: 0

🧬 Intelligence Model on Sequence-Based Prediction of PPI Using AISSO Deep Concept with Hyperparameter Tuning Process
Authors: Thareja, P., Chhillar, R.S., Dalal, S., Baqasah, A.M., Algarni, S.
Journal: Scientific Reports, 2024, 14(1), 21797.
📊 Citations: 0

🔬 Optimizing Protein Sequence Classification: Integrating Deep Learning Models with Bayesian Optimization for Enhanced Biological Analysis
Authors: Lilhore, U.K., Simiaya, S., Alhussein, M., Dalal, S., Aurangzeb, K.
Journal: BMC Medical Informatics and Decision Making, 2024, 24(1), 236.
📊 Citations: 0

☁️ Optimizing Energy Efficiency in MEC Networks: A Deep Learning Approach with Cybertwin-Driven Resource Allocation
Authors: Lilhore, U.K., Simaiya, S., Dalal, S., Baqasah, A.M., Algarni, S.
Journal: Journal of Cloud Computing, 2024, 13(1), 126.
📊 Citations: 0

🌾 Maize Leaf Disease Recognition Using PRF-SVM Integration: A Breakthrough Technique
Authors: Bachhal, P., Kukreja, V., Ahuja, S., Alroobaea, R., Algarni, S.
Journal: Scientific Reports, 2024, 14(1), 10219.
📊 Citations: 1

✅ Correction: Hybrid CNN-LSTM Model with Efficient Hyperparameter Tuning for Prediction of Parkinson’s Disease
Authors: Lilhore, U.K., Dalal, S., Faujdar, N., Thangaraju, P., Velmurugan, H.
Journal: Scientific Reports, 2024, 14(1), 9335.
📊 Citations: 0

 

 

 

Andrews Tang | Artificial Neural Networks | Best Researcher Award

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Mr. Andrews Tang | Artificial Neural Networks | Best Researcher Award

DIPPER Lab at KNUST, Ghana

👨‍🎓 Andrews Tang is a passionate computer engineering researcher from Kwame Nkrumah University of Science and Technology (KNUST) in Ghana. With a deep interest in deep learning and computer vision, Andrews has worked on impactful projects in areas such as agricultural quality control, food safety, and aviation safety. His work, which includes the development of deep learning models for detecting red palm oil adulteration and tomato condition assessment, aims to address critical challenges in the African context. He is a recipient of multiple academic honors, including the Excellent Student’s Award and has presented at international conferences such as AfricAI and Deep Learning Indaba. Andrews also contributes as a teaching assistant and mentor in his field, shaping the next generation of computer engineering students. 🚀

Publication Profile : 

Google Scholar

 

🎓 Educational Background :

🎓 Bachelor of Science in Computer Engineering
Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana (2018-2022)
• First Class Honours
• Cumulative Weighted Average (CWA): 76.18%
• Final CGPA: 3.74/4.0 (WES Evaluation)

💼 Professional Experience :

Andrews Tang has a strong academic foundation and extensive research experience at KNUST, where he has worked on groundbreaking projects with the DIPPER Lab and Responsible AI Lab (RAIL). As an Undergraduate Researcher and Research Assistant, he has tackled diverse challenges, from designing a decentralized food traceability system for Ghana’s agricultural supply chain to developing innovative deep learning models for detecting palm oil adulteration using GhostNet and SqueezeNet. In the field of Aviation Safety, Andrews is currently working as a Machine Learning Engineer, focusing on enhancing the accuracy of the Instrument Landing System (ILS) for low-visibility conditions, where his predictive models have improved flight safety protocols. His other work includes contributions to EEG report classification, sign language recognition, and mineral ore recovery predictions. Alongside his technical expertise, Andrews actively participates in mentorship and teaching roles, providing guidance in computer vision and secure network systems to undergraduate students at KNUST.

📚 Research Interests : 

🔍 Deep Learning
📷 Computer Vision
🧠 AI in Agriculture and Food Safety
🌐 Blockchain in IoT
✈️ Machine Learning for Aviation Safety

Awards & Honors:

🏆 Excellent Student’s Award (2020, 2022, 2023)
🏆 Best Poster Award, Deep Learning Indaba, Accra, 2023
🌍 Member, Black in AI Fellowship, 2024

📝 Publication Top Notes :

  • Tchao, E. T., Gyabeng, E. M., Tang, A., Benyin, J. B. N., Keelson, E., & Kponyo, J. J. (2022). “An Open and Fully Decentralized Platform for Safe Food Traceability.” 2022 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, pp. 487-493.
    DOI: 10.1109/CSCI58124.2022.00092
  • Tang, A., Agbemenu, A. S., Tchao, E. T., Keelson, E., Klogo, G. S., & Kponyo, J. J. (2024). “Assessing Blockchain and IoT Technologies for Agricultural Food Supply Chains in Africa: A Feasibility Analysis.” Heliyon, 10(4), e34584.
    DOI: 10.1016/j.heliyon.2024.e34584
  • Gyabeng, E. M., Tang, A., Agbemenu, A. S., Zaukuu, J. Z., Keelson, E., & Tchao, E. T. (2024). “AfroPALM – Afrocentric Palm Oil Adulteration Learning Models: An End-to-End Deep Learning Approach for Detection of Palm Oil Adulteration in West Africa.” LWT – Journal of Food Science and Technology. Preprint available at SSRN:
    https://ssrn.com/abstract=4917970
    Revised Manuscript Under Review By Elsevier LWT Journal.