Satish Kabade | Computer Science and Artificial Intelligence | Best Industrial Research Award

Mr Satish Kabade | Computer Science and Artificial Intelligence | Best Industrial Research Award

Product Technical Expert, Communication Experts, United States

Satish Kabade is a seasoned IT Consultant and Solutions Architect with over 17 years of experience in software development, enterprise architecture, and cloud computing. He is renowned for his expertise in Microsoft .NET and Azure technologies, leading cross-functional teams to deliver scalable, high-performing solutions. Satish has been instrumental in integrating AI and Machine Learning into pension management systems, enhancing automation, risk analysis, and predictive analytics. His work includes developing AI-driven fraud detection algorithms, personalized retirement benefit recommendations, and AI-based chatbots for member inquiries. He holds certifications as an Azure Solution Architect, TOGAF 9 Certified Architect, and Certified Scrum Master. Satish is also a mentor, conducting workshops on design patterns, best coding practices, cloud migration strategies, and AI/ML implementation.

Profile

Google Scholar

Education 

Satish Kabade’s educational background reflects a strong foundation in technology and cloud computing. He completed a Post Graduate Program in Cloud Computing from Great Learning in 2021, equipping him with advanced knowledge in cloud technologies. Prior to this, he earned a Post Graduate Diploma in Computer Applications from CDAC, Pune, in 2006, which provided him with a comprehensive understanding of software development and computer science principles. His academic journey began with a Bachelor of Engineering in Mechanical Engineering from Shivaji University, Solapur, in 2004, showcasing his analytical and problem-solving skills. This diverse educational background has enabled Satish to bridge the gap between traditional engineering and modern IT solutions, making significant contributions to the integration of AI and cloud technologies in various domains, particularly in pension management systems.

Experience 

With over 17 years in the IT industry, Satish Kabade has amassed extensive experience in software development, enterprise architecture, and cloud computing. He has designed and developed full-stack solutions using .NET Core, C#, ASP.NET, and AWS cloud technologies, ensuring seamless integration between front-end and back-end components. Satish has leveraged AWS Cloud services such as EC2, S3, Lambda, and RDS to deploy, scale, and manage cloud-based applications, ensuring high availability and fault tolerance. His expertise extends to integrating AI and Machine Learning solutions into pension management systems, enhancing automation, risk analysis, and predictive analytics. Notably, he has developed AI/ML-based predictive analytics for retirement planning and investment forecasting, improving decision-making for pension fund administrators and members. Additionally, Satish has implemented AI-driven fraud detection algorithms for pension disbursements and payroll processing, minimizing risks and ensuring regulatory compliance.

Research Focus

Satish Kabade’s research focus centers on the integration of Artificial Intelligence (AI) and Machine Learning (ML) into pension management systems to enhance automation, risk analysis, and predictive analytics. He has developed AI/ML-based predictive analytics for retirement planning and investment forecasting, enabling improved decision-making for pension fund administrators and members. His work includes implementing AI-driven fraud detection algorithms for pension disbursements and payroll processing, minimizing risks and ensuring regulatory compliance. Satish has also designed and implemented Machine Learning models for personalized retirement benefit recommendations, leveraging historical contribution data and economic trends. Additionally, he has developed AI-based chatbots and virtual assistants for member inquiries, streamlining benefits administration and customer support. His research aims to improve the efficiency, security, and personalization of pension systems, contributing to the broader field of AI applications in financial services.

Publication Top Notes

  1. “AI-Driven Financial Management: Optimizing Investment Portfolios through Machine Learning”

    • Authors: T.V. Ambuli, S. Venkatesan, K. Sampath, Kabirdoss Devi, S. Kumaran

    • Published: August 2024

    • Conference: 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT)

    • Summary: This paper explores the application of AI and ML in optimizing investment portfolios, focusing on enhancing financial management strategies through advanced computational techniques.

  2. “A Machine Learning Model for Algorithmic Optimization of Superannuation Schemes”

    • Authors: Winfred Katile Mukunzi, Brian Wesley Muganda, Bernard Shibwabo

    • Published: October 2024

    • Summary: The study develops a machine learning-based recommendation model for optimal asset portfolio selection and allocation in superannuation schemes, addressing challenges in financial market uncertainties.

  3. AI-Driven Fraud Detection in Investment and Retirement Accounts
    Author: Ajay Benadict Antony Raju
    Published in: ESP International Journal of Advancements in Computational Technology, Volume 2, Issue 1, 2024

    Summary:
    This paper discusses the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in detecting fraudulent activities within investment and retirement accounts. It highlights the limitations of traditional fraud detection methods and emphasizes the advantages of AI and ML in analyzing large datasets to identify patterns indicative of fraudulent behavior. The study underscores the importance of integrating AI-driven approaches to enhance the security and integrity of financial systems.ESP Journals

  4.  Enhancing AI-Based Financial Fraud Detection with Blockchain
    Authors: Prof. Kumar Lui, Prof. Kusal Fisher, Prof. Shyam Raj
    Published in: International Journal of Holistic Management Perspectives, Volume 4, Issue 4, 2023

    Summary:
    This article explores the integration of Blockchain technology with AI-based financial fraud detection systems. It examines how blockchain’s decentralized and immutable nature can complement AI models to provide more robust and transparent fraud detection mechanisms. The paper discusses various use cases and the potential benefits of combining these technologies to combat financial fraud effectively.

Conclusion

Satish Kabade is a highly capable technologist and applied researcher, especially in AI/ML integration within legacy government and pension systems. His work shows clear innovation, enterprise-scale application, and practical relevance, which are key strengths for industrial research recognition. However, for a Best Industrial Research Award, the lack of formal research dissemination (papers, presentations, patents) may be a limiting factor unless the award heavily favors applied over academic research.

Zdzislaw Kowalczuk | Artificial Intelligence Award | Best Researcher Award

Prof Zdzislaw Kowalczuk | Artificial Intelligence Award | Best Researcher Award

Prof Zdzislaw Kowalczuk , Gdansk University of Technology , Poland

Zdzisław Kowalczuk, Senior Member of IEEE, has been a Full Professor in automatic control and robotics at Gdańsk University of Technology since 1978. He has held visiting positions at University of Oulu, Australian National University, Technische Hochschule Darmstadt, and George Mason University. His research interests include robotics, control theory, AI, and system diagnostics. Kowalczuk has authored 30 books, over 120 journal papers, and 350+ conference publications, with over 3,300 Google Scholar citations and an H-index of 21. He is President of the Polish Consultants Society and founder of PWNT publishing house, receiving numerous awards, including the SEP Medal in 2014. 🎓📚🏅

 

Publication profile 

Google scholar

Academic Background 🎓

Zdzisław Kowalczuk (Senior Member, IEEE) has been with the Faculty of Electronics, Telecomm., and Informatics at Gdańsk University of Technology since 1978. He is a Full Professor in automatic control and robotics and the Chair of the Dept. of Robotics and Decision Systems. He has held visiting appointments at universities including Oulu, Australian National University, Technische Hochschule Darmstadt, and George Mason University.

Scientific Contributions 

His main scientific interests include robotics, control theory, system modeling, diagnostics, artificial intelligence, and control engineering. Kowalczuk has authored/co-authored about 30 books, over 120 journal papers, and over 350 conference publications. His Google Scholar citation index exceeds 3,300, with an H-index of 21.

Research Focus

Zdzisław Kowalczuk’s research focuses on several key areas within engineering and computer science. His primary interests include fault diagnosis and detection, particularly for automotive engines, and the development of intelligent systems for autonomous decision-making. He also explores system modeling and control theory, including the application of artificial intelligence and neural networks. Kowalczuk’s work extends to robotics, adaptive systems, and signal processing, with a notable focus on practical applications like leak detection in pipelines and thermal management in buildings. His contributions span theoretical foundations to real-world implementations, demonstrating a versatile and impactful research portfolio. 🚗🤖📊🔧

 

Publication Top Notes

Fault diagnicial intelligence, applications – J Korbicz, JM Koscielny, Z Kowalczuk, W Cholewa, Springer Science & Business Media, 2012, cited by 1064 📚osis: models, artif

Diagnostyka procesów: modele: metody sztucznej inteligencji: zastosowania – J Korbicz, JM Kościelny, Z Kowalczuk, W Cholewa, Wydawnictwa Naukowo-Techniczne, 2002, cited by 345 📘

Model based diagnosis for automotive engines-algorithm development and testing on a production vehicle – J Gerler, M Costin, X Fang, Z Kowalczuk, M Kunwer, R Monajemy, IEEE Transactions on Control Systems Technology, 1995, cited by 148 🚗

Thermal Barrier as a technique of indirect heating and cooling for residential buildings – M Krzaczek, Z Kowalczuk, Energy and Buildings, 2011, cited by 107 🏠

Model-based on-board fault detection and diagnosis for automotive engines – JJ Gertler, M Costin, X Fang, R Hira, Z Kowalczuk, Q Luo, Control Engineering Practice, 1993, cited by 100 🚗

Autonomous driver based on an intelligent system of decision-making – M Czubenko, Z Kowalczuk, A Ordys, Cognitive computation, 2015, cited by 98 🚘

Discrete approximation of continuous-time systems: a survey – Z Kowalczuk, IEE Proceedings G (Circuits, Devices and Systems), 1993, cited by 84 📈

Computational approaches to modeling artificial emotion–an overview of the proposed solutions – Z Kowalczuk, M Czubenko, Frontiers in Robotics and AI, 2016, cited by 75 🤖

Continuous-time approaches to identification of continuous-time systems – Z Kowalczuk, J Kozłowski, Automatica, 2000, cited by 49 ⏱️

Intelligent decision-making system for autonomous robots – Z Kowalczuk, M Czubenko, Uniwersytet Zielonogórski, 2011, cited by 38 🤖

Detecting and locating leaks in transmission pipelines – Z Kowalczuk, K Gunawickrama, Fault Diagnosis: Models, Artificial Intelligence, Applications, 2004, cited by 36 🛠️📈