Deepali Hirolikar | Machine Learning Award | Best Researcher Award

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Dr. Deepali Hirolikar | Machine Learning Award | Best Researcher Award

Dr. Deepali Hirolikar, PDEA,s College of Engineering, Manjari(Bk.), Pune, India

Dr. Deepali S. Hirolikar is the Head of the Department of Information Technology at PDEA’s College of Engineering, Pune, with 18 years of experience in academia. She holds a PhD in Information Technology from Shri JJT University, Rajasthan. Dr. Hirolikar has published numerous papers in national and international journals, focusing on topics such as IoT, cloud computing, and machine learning. She has also published a book on IoT security paradigms. As an active contributor to various workshops and conferences, she has received multiple accolades for her work. 🖥️📚🎓

Publication Profile

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Experience 🏆

Prof. Dr. Deepali S. Hirolikar has amassed over 18 years of experience in academia. She currently serves as the Head of the Information Technology Department and Assistant Professor at PDEA’s College of Engineering, Manjari, Pune, a position she has held since September 6, 2005. Before this, she was a Lecturer in the Computer Engineering Department at SRGSIOT, Hadapsar.

Education 📚

She completed her SSC at Keshavraj Vidyalaya, Latur in 1995 with distinction, and her HSC at Dayanand Science Junior College, Latur in 1997 with first class. She earned her Diploma in Computer Science Engineering from PLGP, Latur in 2000 with first class, and her BE in Computer Science and Engineering from Dr. BAMU, Aurangabad in 2004 with distinction. Prof. Dr. Hirolikar obtained her ME in Information Technology from UOP Pune, MIT College of Engineering, Pune in 2011 with first class, and her PhD in Information Technology from Shri JJT University, Rajasthan in 2021.

 

Research Focus

Deepali Hirolikar’s research primarily focuses on using metaheuristic methods and machine learning for efficiently predicting and classifying heart disease data. Her work includes the development and application of advanced algorithms to enhance the accuracy and efficiency of heart disease prediction models. By leveraging mathematical and engineering principles, she contributes to the field of medical data analysis, particularly in identifying patterns and improving diagnostic processes. Her research also spans the integration of machine learning techniques with medical datasets to facilitate better health outcomes.

Publication Top Notes

Metaheuristic Methods for Efficiently Predicting and Classifying Real Life Heart Disease Data Using Machine Learning

Weiwei Qian | Transfer learning | Best Researcher Award

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Dr. Weiwei Qian | Transfer learning | Best Researcher Award

Dr. Weiwei Qian, School of Artiffcial Intelligence, Nanjing University of Information Science and Technology, China

Dr. Weiwei Qian is an Associate Professor at Nanjing University of Information Science and Technology 🎓. His research focuses on equipment intelligent diagnosis and life prediction, particularly in the field of rotating machinery health monitoring under complex environments ⚙️. He has led numerous projects and published extensively in prestigious journals such as IEEE Transactions on Industrial Informatics and Pattern Recognition 📝. Dr. Qian’s innovative work includes the development of deep learning models for robust fault diagnosis, contributing significantly to the stable operation and maintenance of machinery in energy and power sectors 🔍.

 

Publication Profile:

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Experience:

Dr. Weiwei Qian leads research initiatives aimed at monitoring the health conditions of rotating machinery in complex energy and power environments 🔄. His focus is on developing precise, stable, and rapid intelligent systems for equipment health recognition, along with life prediction algorithms. This research is crucial for ensuring the stable and reliable operation of machinery, playing a vital role in intelligent operation and maintenance strategies ⚙️. Currently, Dr. Qian oversees several projects, including the Jiangsu Youth Fund and University General Fund, along with four horizontal projects. He also contributes to intelligent wind speed forecasting for the “smart weather and intelligent algorithm” wind farm project within his team 🌬️.

 

Research Focus:

Dr. Weiwei Qian’s research primarily focuses on intelligent fault diagnosis of machinery, especially bearings, under varying working conditions and data scarcity challenges 🛠️. His work spans across prestigious journals such as IEEE Transactions on Instrumentation and Measurement, Engineering Applications of Artificial Intelligence, and Applied Sciences. Dr. Qian’s expertise lies in developing advanced algorithms and models, including deep sparse topology networks and transfer learning methods, to enhance fault diagnosis accuracy and reliability. Through his contributions, he significantly advances the field of machinery health monitoring and plays a crucial role in ensuring the efficiency and reliability of industrial equipment in diverse operational environments ⚙️.

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