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

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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.

Mingyue Cui | Computer Science and Technology | Best Researcher Award

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Mr. Mingyue Cui | Computer Science and Technology | Best Researcher Award

Dr. Mingyue Cui is a pioneering computer scientist whose multidisciplinary work bridges intelligent vehicles, biomedical computing, and real-time embedded systems. He earned his Ph.D. in Computer Science and Engineering from Sun Yat-sen University, with research affiliations at the Technical University of Munich. His scholarly journey reflects deep engagement in applied AI, autonomous driving, edge computing, and sensor data processing. Dr. Cui has authored over 20 high-impact papers in IEEE and AAAI venues and holds several national patents in autonomous systems and LiDAR compression. His innovation has been recognized through prestigious awards, including top honors in robotics and AI design competitions in China. Dr. Cui continues to advance research in scalable, low-cost AI for smart healthcare and mobility, driving collaborations across academia and industry.

Profiles

Google Scholar

Scopus

🎓 Education

Mingyue Cui holds a Ph.D. in Computer Science and Engineering from Sun Yat-sen University (2018–2022), with research conducted in partnership with the Technical University of Munich. His doctoral work, supervised by Prof. Kai Huang, focused on intelligent connected vehicles, emphasizing autonomous driving systems and biomedical signal processing. Prior to this, he completed a Master’s degree in Software Engineering (2015–2017) from the same university, authoring a thesis on real-time scene flow for embedded systems. His Bachelor’s degree in Software Engineering (2010–2014) was obtained from Chongqing Normal University, where he specialized in embedded software engineering. His academic training spans advanced topics like optimization theory, computational complexity, machine learning, and embedded systems.

🧪 Experience

Dr. Mingyue Cui has built a robust research profile with a focus on real-world applications of AI and embedded systems. His Ph.D. thesis explored intelligent connected vehicles, targeting the challenges of real-time computation and network reliability in autonomous driving. He led pioneering efforts in algorithm parallelization, edge computing for autonomous services, and quality of service assurance using low-cost embedded platforms. His current research has expanded to biomedical domains, particularly ECG signal processing and cardiovascular disease diagnostics. With over 20 academic publications and patents, Cui collaborates extensively with Prof. Kai Huang and research groups at both Sun Yat-sen University and the Technical University of Munich. In addition to his academic output, he actively contributes to competitive research through international robotics and AI competitions, where he has earned multiple first and second-place awards.

🏆 Awards and Honors 

Dr. Mingyue Cui’s research excellence is widely recognized through multiple awards. In 2023, he received a Bronze Award at the China College Students’ ‘Internet+’ Innovation Competition. He also secured the First Prize at the CCF Mobile Robot Challenge with a $10,000 grant, and a Second Prize in the International Running Intelligent Robot Competition. Earlier, he won the First Prize in the same international robotics event in 2019. In 2021, he earned the Second Prize in the World 5G Conference Application Design Competition. These accolades highlight his ability to translate complex theoretical work into high-impact innovations, especially in robotics, autonomous systems, and AI-powered design.

🔍 Research Focus 

Dr. Mingyue Cui’s research integrates real-time embedded systems, AI-driven autonomous vehicles, biomedical signal processing, and point cloud compression. His Ph.D. centered on Intelligent Connected Vehicles (ICV), where he developed methods for optimizing service offloading and computing efficiency while maintaining Quality of Service under network fluctuations. His recent research includes developing hybrid CNN-Transformer models for ECG denoising, distributed AI processors for seizure detection, and octree-based transformers for LiDAR compression. With applications spanning autonomous mobility to wearable health diagnostics, Cui’s work emphasizes scalable, cost-effective, and intelligent system architectures. He is also deeply involved in collaborative SLAM for multi-vehicle networks and cross-modal sensor fusion, pushing the boundaries of edge computing in real-time robotics and healthcare contexts.

📄 Publication Top Notes

1. Dense Depth-Map Estimation Based on Fusion of Event Camera and Sparse LiDAR

Cui et al., IEEE Transactions on Instrumentation and Measurement, 2022
This paper presents a novel method combining sparse LiDAR data with asynchronous event camera signals to estimate dense depth maps efficiently. The fusion approach leverages temporal resolution from event cameras and spatial accuracy from LiDAR to improve performance in dynamic environments.

2. Offloading Autonomous Driving Services via Edge Computing

Cui et al., IEEE Internet of Things Journal, 2020
A seminal work on optimizing the offloading of AI services in autonomous driving. It explores real-time system performance under various load conditions and proposes an adaptive framework to ensure service continuity with minimal latency.

3. OctFormer: Efficient Octree-Based Transformer for Point Cloud Compression

Cui et al., AAAI Conference on Artificial Intelligence, 2023
Proposes OctFormer, an efficient transformer architecture for compressing point cloud data using octree structures. It achieves local detail preservation with high compression ratios, enabling faster data transmission in autonomous systems.

4. OctFormer: Efficient Octree-Based Transformer for Point Cloud Compression

Cui et al., AAAI 2023
This paper introduces OctFormer, a novel transformer-based framework that leverages the octree structure for efficient point cloud compression. It enhances local feature extraction while achieving significant compression gains, facilitating faster 3D data transfer in autonomous systems.

5. ECG Signal Denoising Based on Hybrid CNN-Transformer Network

Cui et al., Journal of Healthcare Engineering, 2023
This study proposes a deep hybrid model combining Convolutional Neural Networks (CNNs) and Transformers to denoise ECG signals. The model effectively suppresses motion artifacts and improves diagnostic signal quality, contributing to wearable and mobile health solutions.

6. Distributed Lightweight AI Processor for Real-Time Epileptic Seizure Detection

Cui et al., Biomedical Signal Processing and Control, 2022
Presents a low-latency, power-efficient edge processor design for seizure detection using EEG signals. The AI model is optimized for resource-constrained devices, enabling early and accurate detection in remote or wearable healthcare settings.

7. Cooperative SLAM for Multi-Vehicle Systems Based on Dynamic Bayesian Optimization

Cui et al., IEEE Access, 2021
This paper addresses collaborative simultaneous localization and mapping (SLAM) for autonomous vehicles. It proposes a Bayesian optimization strategy to dynamically adjust SLAM parameters across a vehicle fleet, enhancing map accuracy and robustness in changing environments.

8. Quality of Service-Oriented Computation Offloading for Autonomous Driving Applications

Cui et al., Sensors, 2020
Focuses on computation offloading strategies that prioritize QoS in vehicle-to-edge communication. It balances task latency and network reliability to ensure real-time performance for self-driving applications, even under fluctuating network conditions.

9. Real-Time Scene Flow Estimation for Stereo Vision Using Embedded GPU Platforms

Cui et al., International Conference on Embedded Systems and Applications, 2019
Develops a lightweight algorithm for estimating scene flow from stereo images, optimized for embedded GPU platforms. The approach supports real-time performance, enabling practical deployment in mobile robots and AR/VR applications.

10. LiDAR-Assisted Pedestrian Detection Based on Multi-Sensor Fusion with Deep Learning

Cui et al., Proceedings of the Chinese Conference on Intelligent Transportation, 2021
Integrates LiDAR data with camera input using a deep fusion network to enhance pedestrian detection accuracy in autonomous vehicles. The fusion technique improves robustness in low-light or occluded conditions.

Conclusion

Dr. M. Cui is a highly accomplished and forward-thinking researcher with:

  • A clear impact in autonomous systems and intelligent robotics,

  • Strong innovation credentials (patents and real-world applications),

  • Recognized technical contributions through competitive awards, and

  • A trajectory that continues to expand into biomedical applications.

He is highly suitable for a Best Researcher Award, especially in fields related to smart mobility, embedded systems, and AI-powered healthcare technologies.

Umar Islam | Computer Science | Best Researcher Award

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Mr. Umar Islam | Computer Science | Best Researcher Award

Senior Lecturer, IQRA National University Swat Campus, Pakistan

Mr. Umar Islam is a passionate and accomplished educator and researcher in the field of Computer Science, currently serving as a Lecturer at Iqra National University (INU) Swat Campus, Pakistan. With an impressive academic background spanning 18 years in Computer Science, Mr. Islam has become a recognized expert in AI, machine learning, blockchain security, IoT, bioinformatics, and financial analytics. His work has been published in over 15 research articles, including several in top-tier journals. A dedicated researcher, he focuses on real-time AI solutions, particularly in healthcare and cybersecurity. Mr. Islam is also a committed mentor, providing supervision and guidance to students in advanced topics such as Python programming, machine learning, and AI applications. His contributions to the academic community and his research endeavors demonstrate his commitment to pushing the boundaries of knowledge and solving real-world problems.

Profile

Education

Mr. Umar Islam has an extensive academic journey, earning 18 years of education in Computer Science. His academic path began with a Bachelor’s degree in Computer Science, followed by a Master’s degree, where he built the foundation of his knowledge in various aspects of computing. Mr. Islam’s thirst for knowledge and his passion for research led him to pursue advanced studies in areas like AI, machine learning, IoT, and cybersecurity, with a strong focus on applying these technologies to solve real-world challenges. His educational journey has equipped him with the skills to lead cutting-edge research projects and to innovate in fields like bioinformatics and financial analytics. Currently, he is working toward a PhD, which will further deepen his understanding and expertise in these areas. Through his education, Mr. Islam has gained a comprehensive understanding of theoretical and applied Computer Science, which he integrates into both his teaching and research.

Experience

With six years of teaching experience at the higher education level, Mr. Umar Islam has played a pivotal role in shaping the future of numerous students at Iqra National University (INU) Swat Campus. As a lecturer, he has delivered comprehensive lessons in Computer Science topics such as AI, machine learning, and cybersecurity. His commitment to academic excellence is reflected in his success as a supervisor, guiding students through complex topics like Python programming, e-learning analytics, and AI-driven applications. In addition to teaching, Mr. Islam has gained four years of extensive research experience, with a focus on AI applications in healthcare, cybersecurity, and blockchain security. He has led multiple research projects, producing groundbreaking results, and has contributed significantly to the academic community with over 15 published research articles. His academic experience extends beyond teaching, positioning him as a thought leader in his field.

Research Focus

Mr. Umar Islam’s research is deeply focused on the intersection of artificial intelligence (AI), cybersecurity, healthcare, and financial analytics. One of his key research areas includes AI-driven solutions in healthcare, particularly the development of federated learning-based intrusion detection systems and epileptic seizure prediction models. He is also actively exploring AI in cybersecurity, specifically in blockchain security, to mitigate data tampering risks. His work in financial analytics uses AI and machine learning to predict market trends, including cryptocurrency values, demonstrating his interdisciplinary approach to solving real-world problems. In addition to these topics, Mr. Islam is involved in pioneering research in IoT security and bioinformatics. His research aims to address key global challenges such as healthcare delivery, data security, and economic stability through cutting-edge AI applications. His innovative contributions to various fields have resulted in multiple published articles in prestigious journals, demonstrating the far-reaching impact of his work.

Publication Top Notes

  • Detection of distributed denial of service (DDoS) attacks in IoT-based monitoring system of banking sector using machine learning models 🌐🔐📊
  • IOTA-Based Mobile Crowd Sensing: Detection of Fake Sensing Using Logit-Boosted Machine Learning Algorithms 🤖📱💡
  • Real-time detection schemes for memory DoS (M-DoS) attacks on cloud computing applications ☁️💻🛡️
  • Detection of renal cell hydronephrosis in ultrasound kidney images: a study on the efficacy of deep convolutional neural networks 🏥🧠📸
  • A novel anomaly detection system on the internet of railways using extended neural networks 🚆🔍⚙️
  • NeuroHealth guardian: A novel hybrid approach for precision brain stroke prediction and healthcare analytics 🧠💓📈
  • An intelligent approach for preserving the privacy and security of a smart home based on IoT using LogitBoost techniques 🏠🔐💡
  • Enhancing Economic Stability with Innovative Crude Oil Price Prediction and Policy Uncertainty Mitigation in USD Energy Stock Markets 💰📊📉
  • Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data 💾🔎👨‍💻
  • Empowering global ethereum price prediction with EtherVoyant: a state-of-the-art time series forecasting model ⛓️💹🔮

 

 

 

Mathieu Chartier | Computer Science and Artificial Intelligence | Best Researcher Award

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Mr. Mathieu Chartier | Computer Science and Artificial Intelligence | Best Researcher Award

PhD student, Poitiers University, France 

Mathieu Chartier is a digital humanities researcher, educator, and web professional based in Buxerolles, France. With expertise in natural language processing (NLP) and information retrieval, he bridges technology and history. Mathieu is an independent consultant at Internet-Formation, specializing in digital training, web marketing, and development. A multilingual scholar, he holds a strong academic background in humanities and digital tools, delivering courses on SEO, AI, and digital communication. As a prolific author, Mathieu has written several books and articles about web technologies and marketing. His current PhD research focuses on improving historical data analysis using AI.

Profile

Orcid

Education

Mathieu Chartier earned a Research Master’s in Ancient and Medieval Archaeology (2008) and a Professional Master’s in Information and Communication, Web Editorial Specialization (2009) from Poitiers University. Currently, he is pursuing a PhD in Digital Humanities, focusing on improving information retrieval in historical research through advanced NLP and large language models. Over the years, Mathieu has also acquired certifications in Google Ads and Google Analytics, enhancing his expertise in digital marketing. His interdisciplinary education combines humanities, web technology, and artificial intelligence.

Experience

With a career spanning over 15 years, Mathieu Chartier has held several key roles in academia and industry. As a freelancer, he leads Internet-Formation, providing training in web marketing, SEO, and digital communication. He has been an adjunct lecturer at institutions like the University of Poitiers and Paris-Sorbonne, teaching digital skills, including web marketing, SEO/SEA, and AI. Mathieu has authored multiple books on SEO and Google Ads and has worked as a web editor for the CNED. He has a deep understanding of web technologies, programming, and digital marketing.

Research Focus

Mathieu Chartier’s research in Digital Humanities focuses on enhancing historical data retrieval using Natural Language Processing (NLP) and Large Language Models (LLM). His work aims to develop innovative methods for historical inquiry, applying cutting-edge AI techniques to optimize information retrieval in history. Mathieu’s interdisciplinary approach blends technology and history, making significant contributions to both fields. His current research project, HiBenchLLM, investigates how to benchmark historical inquiries using LLMs, pushing the boundaries of digital history and artificial intelligence.

Publications

  • HiBenchLLM: Historical Inquiry Benchmarking for Large Language Models (2024) 📜🤖
  • Techniques de référencement web : audit et suivi SEO – 5th edition (2024) 📚💻
  • Google Ads : 60 fiches pour obtenir les certifications officielles (2022) 📘📈
  • Guide complet des réseaux sociaux (2013) 🌐📱
  • Le guide du référencement web (2013) 🔍🌍
  • Du bon usage des réseaux sociaux (BioContact n°313) 🗣️💬
  • Vie privée, l’enjeu du moment (BioContact n°272) 🔐📚
  • Media queries CSS3 pour le web mobile (Oracom, WebDesign magazine) 📱💻