Arti Singh | Machine learning | Best Researcher Award

Mrs. Arti Singh | Machine learning | Best Researcher Award

Assistant Professor at DYPIEMR, India

Mrs. Arti Singh is an accomplished academician and researcher with a robust background in Computer Science, Artificial Intelligence, and Data Science. She is currently serving as an Assistant Professor at Dr. D Y Patil Institute of Engineering Management and Research. With a passion for teaching and research, her expertise lies in machine learning, sentiment analysis, data science, and computational intelligence. Mrs. Singh has presented and published several research papers at national and international conferences. She is committed to continuous learning, having completed various industry-relevant certifications and training programs.

Publication Profile

Google Scholar

Educational Details

  • M.Tech in Computer Technology and Applications from National Institute of Technical Teachers’ Training and Research (RGPV, Bhopal) – 2016 (CGPA: 8.69)
  • B.E. in Computer Science Engineering from Sagar Institute of Research Technology and Science (RGPV, Bhopal) – 2014 (CGPA: 8.35)

Professional Experience

  • Assistant Professor in the Department of Artificial Intelligence and Data Science at Dr. D Y Patil Institute of Engineering Management and Research since July 1, 2022.
  • Lecturer in the Computer Department at Marathwada Mitra Mandal Polytechnic College.
  • Assistant Professor at Sri Sai Shail Manglam College, Singrauli (June 1, 2019, to June 30, 2021).
  • Resource Person for the B.C.A Vocational course at Babasaheb Bhimrao Ambedkar Bihar University, Muzaffarpur (May 30, 2017, to May 27, 2019).

Research Interest

  • Data Science
  • Machine Learning
  • Software Engineering
  • Operating Systems
  • Quantum Artificial Intelligence
  • Pattern Recognition
  • Computational Intelligence

Top Noted Publication

An Opinion Mining for Indian Premier League Using Machine Learning Techniques

  • Authors: KP Dubey, S Agrawal
  • Conference: 2019 4th International Conference on Internet of Things: Smart Innovation, Usage, and Application
  • Pages: 25
  • Year: 2019
  • Summary: This paper presents a sentiment analysis model for social media data related to the Indian Premier League (IPL). The authors employed machine learning techniques to classify public opinions, enabling better understanding of audience engagement and predicting trends in sports sentiment.

Comparing Classification and Regression Tree and Support Vector Machine for Analyzing Sentiments for IPL

  • Author: Arti Singh
  • Journal: International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC)
  • Volume: 4
  • Issue: 6
  • Pages: 172-175
  • Year: 2016
  • ISSN: 2321-8169
  • Summary: This study compares the performance of two machine learning algorithms, Classification and Regression Tree (CART) and Support Vector Machine (SVM), for sentiment analysis on IPL data. The research evaluates the accuracy and effectiveness of both approaches for sports sentiment analysis.

AI Application in Production

  • Author: Arti Singh
  • Publisher: Taylor & Francis
  • Book Title: Industry 4.0: Enabling Technologies and Applications
  • Chapter: AI Application in Production
  • Year: 2024
  • URL: Link to book
  • Summary: This book chapter explores the integration of Artificial Intelligence (AI) in manufacturing and production processes. It highlights AI-driven innovations, predictive maintenance, process optimization, and intelligent automation in modern industrial setups.

Automated Invoice Data Extraction: Advancements and Challenges in OCR-Based Approaches

  • Authors: Arti Singh, Sneha Kanwade, Siddhant Shendge, Amoksh Layane, Kohsheen Tikoo
  • Journal: International Journal of Scientific Research in Engineering and Management (IJSREM)
  • Volume: 8
  • Pages: 1-6
  • ISSN: 2582-3930
  • Year: 2024
  • Summary: This paper addresses the growing need for automated invoice data extraction using Optical Character Recognition (OCR) technologies. It discusses the latest advancements, the challenges faced, and potential solutions to enhance accuracy in invoice processing systems.

An In-Depth Analysis of Sentiment Polarity Using Various Machine Learning Algorithms

  • Author: Arti Singh
  • Conference: 8th International Conference on ISDIA 2024
  • Volume: 1107
  • Pages: 157–167
  • Year: 2024
  • Summary: This research investigates the effectiveness of different machine learning algorithms for sentiment polarity detection. The study evaluates models such as SVM, Random Forest, and Extremely Randomized Trees to improve sentiment classification accuracy in social media data.

Comparative Study of Machine Learning Algorithms for Sentiment Polarity

  • Author: Arti Singh
  • Conference: IRF International Conference
  • Pages: 1-5
  • Year: 2017
  • Summary: The paper compares several machine learning techniques, including Naive Bayes, Decision Trees, and SVM, for sentiment polarity classification. It emphasizes the importance of selecting the appropriate algorithm for accurate sentiment detection in online text data.

Conclusion:

Mrs. Arti Singh is a strong candidate for the Best Researcher Award, given her consistent research output in machine learning and applied AI domains, industry-relevant research contributions, and dedication to academic excellence. Her work bridges the gap between theory and practice, making her a valuable contributor to the field of computational intelligence and Industry 4.0 applications.

With increased focus on high-impact journals, research funding, and industry collaborations, she has the potential to emerge as a leading figure in her field. Therefore, she is highly deserving of recognition through the Best Researcher Award.

 

 

Yousry AbdulAzeem | Artificial Intelligence | Best Researcher Award

Assoc. Prof. Dr. Yousry AbdulAzeem | Artificial Intelligence | Best Researcher Award

Associate Professor at School of Computational Sciences and Artificial Intelligence (CSAI), Zewail City of Science and Technology, Egypt

Assoc. Prof. Dr. Yousry AbdulAzeem is a computer science researcher and educator specializing in distributed database systems, artificial intelligence, and software performance engineering. With over two decades of academic and industry experience, he has contributed to database optimization, software modeling, and AI-driven data analysis. He is currently affiliated with Misr Higher Institute for Engineering and Technology, University of Science and Technology (Zewail City, Giza, Egypt), and The Higher Institute of Engineering, El-Shorouk Academy, Cairo, Egypt.

Publication Profile

Scopus

Google Scholar

Educational Details

  • Ph.D. in Automatic Control Engineering (2014) – Mansoura University, Egypt
    Thesis: Ranking of Distributed Uncertain Database Systems
  • M.Sc. in Automatic Control Engineering (2009) – Mansoura University, Egypt
    Thesis: Performance Evaluation of UML Software Models Based on LQN
  • B.Sc. in Computers and Systems Engineering (2004) – Mansoura University, Egypt (Very Good)

Professional Experience

Dr. Yousry AbdulAzeem is an Associate Professor of Computer Science and Engineering with extensive academic and industry experience. He is currently a full-time Associate Professor at the Misr Higher Institute for Engineering and Technology, Mansoura, Egypt, and also serves as an Adjunct Associate Professor at both the University of Science and Technology (Zewail City, Giza) and The Higher Institute of Engineering (El-Shorouk Academy, Cairo, Egypt).

From 2015 to 2022, he worked as an Assistant Professor at Taibah University, Saudi Arabia, where he taught courses in network security, programming, database systems, and artificial intelligence. Before this, he held academic roles at Mansoura University and Misr Higher Institute for Computers and Commerce.

In addition to his academic career, Dr. AbdulAzeem has industry experience as a Technical Director and Executive Manager at Trust IT (2007–2009) and a Senior Programmer & Project Administrator at Aflak Masr for Information Technology (2004–2006). He also has significant experience in academic mentoring and quality assurance, having served as the Academic Mentoring Manager at both Taibah University (2017–2022) and Misr Higher Institute for Engineering and Technology (2023–present).

Research Interest

  • Distributed Database Systems
  • Software Performance Engineering
  • Artificial Intelligence & Knowledge Representation
  • Data Governance & Security
  • Cloud Computing & Distributed Systems

Top Noted Publication

“A CNN-based framework for classification of Alzheimer’s disease” – Neural Computing and Applications (2021)

  • Citations: 127
  • Developed a deep learning framework for Alzheimer’s disease classification using CNNs.

“A congestion-aware clustering and routing (CCR) protocol for mitigating congestion in WSN” – IEEE Access (2019)

  • Citations: 61
  • Proposed a congestion-aware routing protocol for wireless sensor networks.

“Human action recognition based on transfer learning approach” – IEEE Access (2021)

  • Citations: 53
  • Applied deep transfer learning for human activity recognition in videos.

“An optimized transfer learning-based approach for automatic diagnosis of COVID-19 from chest X-ray images” – PeerJ Computer Science (2021)

  • Citations: 39
  • Introduced an optimized AI model for COVID-19 detection using X-ray images.

“Classification of breast cancer using a manta-ray foraging optimized transfer learning framework” – PeerJ Computer Science (2022)

  • Citations: 27
  • Developed a bio-inspired optimization technique for breast cancer classification.

Conclusion

Assoc. Prof. Dr. Yousry AbdulAzeem is a highly qualified and impactful researcher in Artificial Intelligence and Distributed Systems. His strong publication record, interdisciplinary expertise, and leadership roles make him a top contender for the Best Researcher Award.

To further strengthen his case, he could increase grant acquisitions, expand international collaborations, and focus on AI deployment in real-world applications. Given his high citation impact, research relevance, and academic leadership, he is a strong candidate deserving recognition.

 

 

 

 

Priyanka Das | Autonomous systems | Best Researcher Award

Ms. Priyanka Das | Autonomous systems | Best Researcher Award

Manufacturing Engineer at Ford Motor company

Summary:

Priyanka Das is a skilled robotics and controls engineer with expertise in autonomous systems, manufacturing automation, and advanced robotics. Currently a Manufacturing Controls Engineer at Ford Motor Company, she has previously contributed to innovative automation solutions at Tesla. A researcher and thought leader, Priyanka has authored multiple publications in controls engineering and localization techniques. Her technical acumen, combined with her passion for community engagement and STEM advocacy, underscores her commitment to advancing technology and empowering the next generation of engineers.

Professional Profile:

👩‍🎓Education:

Priyanka Das holds a Master of Engineering in Electrical Engineering, with a major in Robotics, from the University of Cincinnati (2019–2021). During her studies, she specialized in advanced topics such as Autonomous Vehicle (AV) Navigation and Controls, Simultaneous Localization and Mapping (SLAM), Kalman and Particle Filters, and Robot Operating System (ROS). She was awarded the prestigious Graduate Incentive Award (GIA) valued at $10,640 USD. Priyanka also earned her Bachelor of Technology in Electrical and Electronics Engineering from Vellore Institute of Technology, India (2015–2019), where she actively participated as a student representative and served as captain of the women’s sports team.

Professional Experience:

Priyanka is an accomplished engineer with experience in controls, robotics, and automation across leading organizations. She currently works as a Manufacturing Controls Engineer at Ford Motor Company (April 2024–Present), where she oversees the implementation and validation of assembly and machining controls for global Powertrain Operations (PTO) programs. Her responsibilities include leading engineering meetings, driving cross-functional collaboration with Tier I suppliers, and delivering new model programs across plants worldwide.

Previously, Priyanka worked as a Controls Engineer at Tesla Inc. (November 2021–February 2024). There, she developed automation programs for inverter and battery manufacturing lines, optimized robotic processes for improved production efficiency, and debugged control systems using advanced tools like Beckhoff and Siemens PLCs. She has also contributed to path-planning research and quadcopter localization as a volunteer Guided Navigation and Control Researcher at the University of Cincinnati’s RISC Lab. Earlier, she interned at the Tarapur Atomic Power Station, India, assisting with testing and calibration of power generation equipment and developing solutions for smart power management.

Research Interests:

Priyanka’s research interests lie in autonomous systems, advanced robotics, and controls engineering. She has a particular focus on developing robust localization techniques, path-planning algorithms, and machine learning models for GPS-denied environments. Her expertise spans fields like Sensor Fusion, SLAM, PID, and LQR controls.

Author Metrics and Awards

Priyanka Das has authored five research papers with significant contributions to the fields of robotics, controls engineering, and autonomous systems. Her work has been published in reputed journals like IJIRMPS and IJCEM, and her research is widely referenced in the academic community.

Top Noted Publication:

1. Optimizing Sensor Integration for Enhanced Localization in Underwater ROVs

  • Publication: International Journal of Scientific Research in Engineering and Management
  • Publication Date: December 26, 2024
  • DOI: 10.55041/ijsrem10901
  • Author(s): Priyanka Das
  • Summary:This study presents an optimized approach for integrating multiple sensors to enhance localization accuracy in remotely operated underwater vehicles (ROVs). It explores sensor fusion techniques, error mitigation strategies, and real-time localization improvements using AI-based models.

2. A Comparative Study of Kalman Filters and Particle Filters for Localization in Dynamic Settings for SLAM in Unknown Environments

  • Publication Type: Dataset
  • DOI: 10.5281/zenodo.14498207
  • Author(s): Priyanka Das
  • Summary:This dataset provides a comparative analysis of Kalman Filters and Particle Filters in dynamic environments, focusing on their performance in Simultaneous Localization and Mapping (SLAM) for robots operating in unknown terrains.

3. Advancing Simultaneous Localization and Mapping (SLAM) for Robots in Unstructured Terrain

  • Publication: Journal article
  • DOI: 10.36948/ijfmr.2020.v02i06.25432
  • Author(s): Priyanka Das
  • Summary:This paper investigates the latest advancements in SLAM techniques for robotic navigation in unstructured and unpredictable environments, with an emphasis on sensor fusion, real-time mapping, and adaptive algorithms.

4. Case Studies in ROV Development: Innovations in Underwater Exploration Technology

  • Publication Type: Dataset
  • DOI: 10.5281/zenodo.14434005
  • Author(s): Priyanka Das
  • Summary:A collection of case studies highlighting recent innovations in remotely operated vehicle (ROV) technology, including sensor integration, autonomy, and deep-sea exploration capabilities.

5. Challenges in Designing Motors for Remotely Operated Underwater Vehicles: A Focus on Hydrodynamics

  • Publication Type: Report
  • DOI: 10.5281/zenodo.14538286
  • Author(s): Priyanka Das
  • Summary:This report discusses the hydrodynamic challenges involved in designing efficient motors for underwater ROVs, examining propulsion efficiency, power consumption, and environmental factors affecting performance.

Conclusion:

Priyanka Das is an exceptional candidate for the Best Researcher Award, with notable strengths in impactful research, technical innovation, and industry experience. Her work in advanced robotics and FSS technology has made significant contributions to academia and industry alike. While there is room to expand her publication base and professional recognition, her dedication to engineering excellence and STEM advocacy makes her a strong contender for the award.

 

 

Govind Prasad Buddha | Machine Learning | Best Paper Award

Dr. Govind Prasad Buddha, Machine Learning, Best Paper Award

Doctorate at Bank of America, United States

Summary:

Dr. Govind Prasad Buddha is an accomplished software engineer and researcher with extensive experience in the fields of fraud detection, machine learning, and software engineering. He holds a Master’s degree in Computer Science from Andhra University and a Master of Technology (CST) from the University of Mysore. Currently pursuing his Doctor of Philosophy (Ph.D.) in Computer Science from LIUTEBM University, Dr. Buddha has made significant contributions to the development of innovative fraud detection systems during his tenure at Bank of America and Verizon Data Services India Pvt Ltd.

With roles ranging from Software Engineer to Feature Lead, Dr. Buddha has demonstrated expertise in leading diverse teams and spearheading projects aimed at enhancing fraud detection mechanisms for credit card and digital platforms. His research interests encompass fraud detection systems, machine learning applications, software engineering practices, data security, and big data analytics. Driven by a passion for innovation and a commitment to excellence, Dr. Buddha continues to advance the field of fraud detection through his research and practical contributions.

Professional Profile:

Google Scholar Profile

👩‍🎓Education & Qualification:

Master of Computer Science from Andhra University, completed in 1999-2001.

Master of Technology (CST) from the University of Mysore, completed in 2002-2004.

Doctor of Philosophy (CS) from LIUTEBM University, completed in 2020-2023.

Professional Experience:  

Dr. Govind Prasad Buddha has a diverse professional experience spanning various roles and responsibilities. He has been actively engaged in the field of software engineering and technology management. His career highlights include:

  • Serving as a Software Engineer III at Bank of America in Newark, DE, USA, since September 2022. In this role, he is involved in leading the Venom Detection automation team and overseeing Merchant Fraud Detection, among other responsibilities.
  • Previously, he worked as a Feature Lead at Bank of America in India from October 2018 to June 2022, where he played a pivotal role in crafting merchant fraud rule engines and implementing machine learning models for real-time fraud detection.
  • Before joining Bank of America, Dr. Buddha worked as a Senior Analyst and Team Lead in the same organization in India from September 2010 to December 2015, where he contributed to various projects involving credit card claims resolution, fraud detection, and system enhancements.
  • Prior to his tenure at Bank of America, he gained valuable experience at Verizon Data Services India Pvt Ltd in Hyderabad, IN, serving as an Analyst from June 2007 to September 2010. During his time there, he worked as a lead developer for telecom mediation systems and played a crucial role in developing critical mediation reports.
  • Earlier in his career, Dr. Buddha worked as an Associate Analyst at Convergys in Hyderabad, IN, from June 2005 to June 2007, where he was involved in telecom billing applications and developed background jobs for billing systems, among other responsibilities.

Throughout his career, Dr. Govind Prasad Buddha has demonstrated a strong commitment to excellence in software engineering, project management, and technical leadership, contributing significantly to the success of the organizations he has been associated with.

Research Interest:

Fraud Detection Systems: Dr. Buddha is interested in developing innovative fraud detection systems for financial institutions, particularly focusing on credit card and digital transaction fraud. His research explores the application of machine learning algorithms and real-time data analysis techniques to enhance fraud detection accuracy and efficiency.

Machine Learning Applications: He is keen on exploring the practical applications of machine learning in various domains, including fraud detection, customer behavior analysis, and predictive analytics. His research aims to leverage advanced machine learning models to uncover valuable insights from large-scale datasets and improve decision-making processes.

Software Engineering Practices: Dr. Buddha investigates software engineering practices and methodologies aimed at improving the quality, reliability, and maintainability of software systems. His research interests include software design patterns, code refactoring techniques, and agile development methodologies.

Data Security and Privacy: He is interested in studying data security and privacy concerns in software systems, especially in the context of financial transactions and sensitive personal information. His research focuses on developing robust security measures and privacy-preserving techniques to safeguard user data and prevent unauthorized access.

Big Data Analytics: Dr. Buddha explores the challenges and opportunities associated with big data analytics in the context of fraud detection and financial services. His research aims to develop scalable and efficient algorithms for processing and analyzing large volumes of data to extract actionable insights and detect fraudulent activities in real-time.

Publication Top Noted:

  • Authors: M Nalluri, SRB Reddy, R Pulimamidi, GP Buddha
  • Journal: Tuijin Jishu/Journal of Propulsion Technology
  • Volume: 44
  • Issue: 5
  • Pages: 2505-2513
  • Year: 2023
  • Citations: 36

Title: Behaviour based credit card fraud detection design and analysis by using deep stacked autoencoder based Harris grey wolf (HGW) method

  • Authors: GPB GRADXS, N RAO
  • Journal: SJIS-P
  • Volume: 35
  • Issue: 1
  • Pages: 1-8
  • Year: 2023
  • Citations: 10

Title: AI-Enabled Health Systems: Transforming Personalized Medicine And Wellness

  • Authors: R Pulimamidi, GP Buddha
  • Journal: Tuijin Jishu/Journal of Propulsion Technology
  • Volume: 44
  • Issue: 3
  • Pages: 4520-4526
  • Year: 2023
  • Citations: 8

Title: The Future of Healthcare: Artificial intelligence’s Role in Smart Hospitals and Wearable Health Devices

  • Authors: DRP Govind Prasad Buddha
  • Journal: Tuijin Jishu/Journal of Propulsion Technology
  • Volume: 44
  • Issue: 5
  • Pages: 2498-2504
  • Year: 2023
  • Citations: 7

Title: Electronic system for authorization and use of cross-linked resource instruments

  • Authors: GP Buddha, SP Kumar, CMR Reddy
  • Publication: US Patent Application
  • Application Number: 17/203,879
  • Year: 2022
  • Citations: 7