Umesh Kumar Lilhore | Deep Learning | Best Researcher Award

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


 

 

 

Rafael Natalio Fontana Crespo | Artificial Intelligence | Young Scientist Award

Mr. Rafael Natalio Fontana Crespo | Artificial Intelligence | Young Scientist Award

PhD Student at Politecnico di Torino, Italy

Rafael Natalio Fontana Crespo is a dedicated and sociable Ph.D. student specializing in Computer and Control Engineering at Politecnico di Torino. With a strong academic background in mechatronics and practical experience in electrical energy analysis, he is passionate about tackling complex challenges through innovative solutions. 🌐💡

Publication Profile : 

Orcid

 

🎓 Educational Background :

Rafael is currently pursuing a Ph.D. in Computer and Control Engineering at Politecnico di Torino, Italy, since May 2022. He previously obtained a Master’s Degree in Mechatronic Engineering from the same institution, graduating with 110/110 cum laude in July 2022. His master’s thesis focused on designing and developing a distributed software platform for additive manufacturing. Rafael studied Electromechanical Engineering at the Universidad Nacional de Córdoba, Argentina, where he also completed a double degree program.

💼 Professional Experience :

Rafael gained practical experience during his internship at EPEC (Empresa Provincial de Energía de Córdoba) in Argentina, where he worked in the Statistics and Technical Department from May 2020 to May 2021. He was involved in analyzing thermal images of electrical components to prevent failures, contributing to the overall safety and efficiency of electrical systems.

📚 Research Interests : 

Rafael’s research interests lie at the intersection of computer engineering, control systems, and mechatronics, particularly focusing on additive manufacturing, machine learning applications in energy systems, and the optimization of neural networks.

📝 Publication Top Notes :

      1. Fontana Crespo, R.N., E. Patti, S. Di Cataldo, D. Cannizzaro. (2022). Design and Development of a Distributed Software Platform for Additive Manufacturing. Master’s Thesis, Politecnico di Torino.
      2. Fontana Crespo, R.N. (2023). Machine Learning in Energy Applications. Course Exam Paper, Politecnico di Torino.
      3. Fontana Crespo, R.N. (2023). IoT Platforms for Spatial Analytics in Smart Energy Systems. Course Exam Paper, Politecnico di Torino.
      4. Fontana Crespo, R.N. (2023). Optimized Execution of Neural Networks at the Edge. Course Exam Paper, Politecnico di Torino.
      5. Fontana Crespo, R.N. (2023). Adversarial Training of Neural Networks. Course Exam Paper, Politecnico di Torino.