Swati Jitendrakmar Patel | Artificial Intelligence | Best Researcher Award

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Ms. Swati Jitendrakmar Patel | Artificial Intelligence | Best Researcher Award

Software Engineer at Skyline Software Solutions, India

Ms. Swati Patel is an accomplished Data Analyst and Software Developer with extensive expertise in data science, machine learning, and software engineering. She has contributed significantly to academic research, publishing 8 journal papers, 2 IEEE conference papers, and 5 books on topics ranging from software security to predictive analytics. With strong analytical skills and a track record of developing efficient workflows and impactful applications, she has consistently delivered data-driven solutions for business optimization and research innovation.

Publication Profile

Google Scholar

Educational Details

Ms. Swati Patel holds a Master of Science in Advanced Computing Technologies from Birkbeck, University of London (2022-2023), where her projects included Principal Component Analysis on the Pima Indians Diabetes Dataset and predicting DDoS attacks using Darknet Time-Series data. She earned a Master’s degree in Computer Science from SES’s R. C. Patel Institute of Technology (NMU, India, 2012-2014), completing her thesis on software birthmark-based theft detection of JavaScript programs. Her Bachelor’s degree in Computer Engineering was completed at PSGVPM’s D. N. Patel College of Engineering, Shahada (NMU, India, 2008-2012), where she developed an Online Voting System using ASP.NET and SQL.

Professional Experience

Swati Patel is a Data Analyst and Entrepreneur with proven expertise in designing and optimizing workflows, data visualization, and software development. She served as a Data Analyst at SSP Group PLC, UK (2023-2024), where she created interactive Power BI dashboards to analyze sales data, optimized EPOS systems for data accuracy, and reduced data processing time by 30% through SQL optimization. As an entrepreneur, she successfully led Skyline Software Solutions (2014-2022), developing over 35 applications in Java, .NET, and other technologies. She managed end-to-end project execution, providing high-quality software solutions to diverse industries.

Research Interest

Swati’s research focuses on applied machine learning, natural language processing, and predictive modeling. She is particularly interested in statistical methods for data anomaly detection, speech-based health diagnostics, and secure systems in computing environments. Her work also extends to innovative clustering techniques for theft detection in software and dimensionality reduction in large datasets.

Author Metrics

  • Publications: 2 IEEE papers, 8 journal papers, 5 books.
  • Key Topics: Data science, machine learning, NLP, secure computing systems, predictive analytics.
  • Notable Works:
    • “A Review on Statistical Analysis-Based Approaches for Data Poison Detection Using Machine Learning.”
    • “Automated Depression Assessment from Speech Signals Using Pitch and Energy Features.”
    • “Long Short-Term Memory and Gated Recurrent Unit Networks for Accurate Stock Price Prediction.”
    • Books: COVID-19 Data Analysis for the United Kingdom and Data Exploration and Machine Learning using R.

Publication Top Notes

  • Software Birthmark Based Theft Detection of JavaScript Programs Using Agglomerative Clustering and Frequent Subgraph Mining
    • Authors: S.J. Patel, T.M. Pattewar
    • Conference: 2014 International Conference on Embedded Systems (ICES)
    • Pages: 63–68
    • Citations: 9
    • Year: 2014
    • Summary: This paper presents a novel method for detecting software theft in JavaScript programs using software birthmarks. The approach employs agglomerative clustering and frequent subgraph mining to identify similarities between programs, aiding in theft detection.
  • K-Means Clustering Algorithm: Implementation and Critical Analysis
    • Author: S. Patel
    • Publisher: Scholars’ Press
    • Citations: 8
    • Year: 2019
    • Summary: This publication provides an in-depth exploration of the K-means clustering algorithm, including its implementation and a critical analysis of its efficiency and limitations in clustering diverse datasets.
  • Software Birthmark Based Theft Detection of JavaScript Programs Using Agglomerative Clustering and Improved Frequent Subgraph Mining
    • Authors: S. Patel, T. Pattewar
    • Conference: 2014 International Conference on Advances in Electronics, Computers, and Communications
    • Citations: 7
    • Year: 2014
    • Summary: This paper builds upon earlier research by introducing an improved method for frequent subgraph mining, enhancing the detection accuracy of JavaScript program theft through advanced birthmark-based techniques.
  • Software Birthmark for Theft Detection of JavaScript Programs: A Survey
    • Authors: S.J. Patel, T.M. Pattewar
    • Journal: IJAFRC (International Journal of Advanced Foundation and Research in Computers)
    • Volume: 1, Issue 2, Pages: 29–38
    • Citations: 2
    • Year: 2014
    • Summary: This survey reviews existing methods for detecting software theft in JavaScript programs, with a focus on software birthmark techniques. It outlines challenges, solutions, and future research directions.
  • Emerging Trends in Computer Technology (NCETCT)
    • Authors: S.J. Patel, T.M. Pattewar
    • Journal: IJCA (International Journal of Computer Applications)
    • Conference Issue: NCETCT, Number 1
    • Year: 2014
    • Summary: This conference paper discusses advancements in computer technology with a focus on software security. It explores the use of software birthmarks as a means of detecting and preventing intellectual property theft in programming.

Conclusion

Ms. Swati Patel is a highly qualified and deserving candidate for the Best Researcher Award. Her combination of academic excellence, impactful research, and real-world contributions to artificial intelligence and software engineering set her apart as an innovative thinker. To maximize her potential and visibility, she could focus on strengthening her citation impact, pursuing international collaborations, and contributing to higher-impact conferences. Overall, her track record reflects dedication, skill, and the ability to drive meaningful advancements in her field.

 

 

 

Priyanka Das | Autonomous systems | Best Researcher Award

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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:

Modelling of ultra-wide stop-band frequency-selective surface to enhance the gain of a UWB antenna

  • Authors: P. Das, K. Mandal
  • Published in: IET Microwaves, Antennas & Propagation, Vol. 13, Issue 3, pp. 269-277 (2019).
  • Citation count: 62
  • Summary: This paper presents the design and modeling of an ultra-wide stop-band frequency-selective surface (FSS) to improve the gain of an ultra-wideband (UWB) antenna. The study demonstrates how FSS structures can suppress unwanted radiation and improve the antenna’s radiation efficiency and performance across a wide frequency range.

Single-layer polarization-insensitive frequency-selective surface for beam reconfigurability of monopole antennas

  • Authors: P. Das, K. Mandal, A. Lalbakhsh
  • Published in: Journal of Electromagnetic Waves and Applications, Vol. 34, Issue 1, pp. 86-102 (2020).
  • Citation count: 49
  • Summary: This research introduces a single-layer, polarization-insensitive FSS designed to enable beam reconfigurability for monopole antennas. The structure is simple and compact, showing significant versatility for applications in wireless communication systems.

Metamaterial loaded highly isolated tunable polarization diversity MIMO antennas for THz applications

  • Authors: P. Das, A.K. Singh, K. Mandal
  • Published in: Optical and Quantum Electronics, Vol. 54, Article 250 (2022).
  • Citation count: 26
  • Summary: The paper investigates a metamaterial-loaded MIMO antenna design that offers high isolation and tunable polarization diversity for terahertz (THz) applications. This design addresses challenges in THz communication by improving isolation and enabling efficient polarization management.

Beam-steering of microstrip antenna using single-layer FSS-based phase-shifting surface

  • Authors: P. Das, K. Mandal, A. Lalbakhsh
  • Published in: International Journal of RF and Microwave Computer-Aided Engineering (2021).
  • Citation count: 26
  • Summary: This work demonstrates a novel beam-steering technique for microstrip antennas using a single-layer FSS-based phase-shifting surface. The proposed method achieves efficient beam steering with minimal design complexity, making it suitable for modern wireless systems.

Gain enhancement of dual-band terahertz antenna using reflection-based frequency-selective surfaces

  • Authors: P. Das, G. Varshney
  • Published in: Optical and Quantum Electronics, Vol. 54, Article 161 (2022).
  • Citation count: 25
  • Summary: The paper focuses on using reflection-based FSS to enhance the gain of a dual-band terahertz antenna. The approach leverages FSS structures to improve radiation characteristics and efficiency, optimizing the antenna’s performance for dual-band THz applications.

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.

 

 

Deepak Kaul | AI Technology | Best Researcher Award

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Mr. Deepak Kaul | AI Technology | Best Researcher Award

Senior Software Engineer at Marriott International, Inc, United States
Summary:

Deepak Kaul is a highly qualified candidate for the Best Researcher Award, with over 19 years of experience in technical architecture and expertise in AI, ML, and cybersecurity. His career spans designing, implementing, and securing complex systems for major clients, with a strong track record in threat detection, predictive analytics, and real-time decision-making. His work in leveraging AI to enhance cybersecurity measures and optimize operations has led to impactful transformations in diverse industries, including hospitality, insurance, and telecommunications. Kaul’s innovative contributions and commitment to secure, scalable, and AI-driven solutions make him a noteworthy contender for this prestigious award.

Professional Profile:

👩‍🎓Education:

Deepak Kaul holds a Master of Computer Application degree from Punjab Technical University in Jalandhar, where he developed a solid foundation in computer science, software engineering, and systems architecture. His academic journey centered around mastering core programming languages, data structures, and algorithms, equipping him to navigate complex technology environments. Through his coursework and projects, Kaul built proficiency in designing and implementing scalable software architectures, fostering his long-standing interest in AI and ML. His commitment to lifelong learning is evident in his pursuit of multiple certifications, including TOGAF 9.2 for enterprise architecture, a Microservices Bootcamp from Coursera, and Cybersecurity Fundamentals from IBM Security Academy. This extensive educational background has empowered Kaul to bridge theoretical knowledge with practical expertise, facilitating his successful career in designing AI and cybersecurity systems that address critical challenges in modern enterprise environments.

🏢 Professional Experience:

Deepak Kaul has held pivotal roles in technology and security architecture, specializing in AI/ML, cybersecurity, and cloud-native solutions. Currently, he serves as a Solution Architect at Marriott International, where he has led the design of AI-powered platforms that enhance customer experiences and secure data across a suite of applications. Previously, as Solution Architect at Capgemini, he developed AI-driven insurance models and fraud detection systems, improving data security and operational efficiency for clients like USAA. Kaul’s earlier roles at Infosys and TCS solidified his expertise in developing and securing large-scale systems for industries ranging from banking to manufacturing. His work consistently emphasizes the integration of AI and cybersecurity, with measurable outcomes such as a 40% reduction in security breaches and enhanced system reliability. Kaul’s extensive experience in architecting secure, intelligent solutions has distinguished him as an expert in his field.

Awards and Honors:

Deepak Kaul’s excellence in AI and cybersecurity has earned him numerous awards, underscoring his impact on the technology landscape. He received the prestigious TCS Innovation Lead Award in 2021 for his leadership in developing AI-based cybersecurity solutions, significantly improving threat detection capabilities. Additionally, he was honored with the TCS Best Team Award for successfully implementing AI/ML-powered cybersecurity features across a high-profile cloud-based platform. Kaul’s contributions have been recognized through On-the-Spot Awards on two occasions, celebrating his exceptional performance in fraud detection and cybersecurity projects. These awards reflect his unwavering commitment to innovation and technical rigor in AI-driven security systems. Kaul’s accolades not only validate his expertise but also affirm his role as a thought leader in secure enterprise solutions, furthering his reputation for excellence in the domains of AI and cybersecurity.

Research Interests:

Deepak Kaul’s research interests center around the intersection of AI, ML, and cybersecurity, particularly in applications that drive secure enterprise solutions. His research focus includes developing AI-driven threat detection systems, predictive analytics, and adaptive AI frameworks for high-stakes decision-making environments. A notable project explores dynamic upselling in hotel reservation systems based on real-time cybersecurity risk scores, offering an innovative approach to enhancing customer service without compromising data security. Another study, Adaptive Neuro-Symbolic AI for Autonomous Decision-Making in High-Stakes Systems, delves into intelligent algorithms that support decision-making accuracy in mission-critical scenarios. His research not only advances cybersecurity but also addresses real-time analytics and scalability challenges, positioning AI as a transformative tool in security protocols. Kaul’s work is grounded in practical application, aimed at enhancing both operational efficiency and security standards across industries.

Skills:

Deepak Kaul possesses a comprehensive skill set that spans programming, AI/ML, cybersecurity, cloud architecture, and DevOps. He is proficient in programming languages such as Java, Python, and frameworks like AngularJS and Spring Boot, which enable him to build scalable, robust systems. His expertise in AI and ML includes proficiency in frameworks like TensorFlow, Keras, and PyTorch, essential for developing predictive models and real-time data analytics solutions. Kaul has a deep understanding of cybersecurity protocols and tools, including CyberArk, AWS KMS, Splunk, and the ELK Stack, to safeguard data integrity and ensure compliance. In cloud architecture, he has extensive experience with AWS services such as S3, DocumentDB, and SageMaker, enabling the creation of secure cloud-native applications. Additionally, his DevOps skills include setting up CI/CD pipelines with Jenkins and Git, ensuring streamlined development processes. Kaul’s skills make him a versatile and technically proficient architect.

Top Noted Publication:

Adaptive Neuro-Symbolic AI for Autonomous Decision-Making in High-Stakes Mission-Critical Systems

Authors: Deepak Kaul

Year: 2024

DOI: 10.21275/sr241019113405

Journal: International Journal of Science and Research (IJSR)

ISSN: 2319-7064

AI-Based Adaptive Loyalty Program Vulnerability Detection in Hotel Reservation Systems

Authors: Deepak Kaul

Year: 2023

Journal: International Journal of Innovative Research in Technology

ISSN: 2349-6002

Dynamic AI-Based Intrusion Detection for Quantum Computing Networks

Authors: Deepak Kaul

Year: 2023

Journal: IJIRT (International Journal of Innovative Research in Technology)

ISSN: 2349-6002

AI-Driven Decentralized Authentication System Using Homomorphic Encryption

Authors: Deepak Kaul

Year: 2021

Journal: International Journal of Advanced Research in Engineering and Technology (IJARET)

AI-Driven Dynamic Upsell in Hotel Reservation Systems Based on Cybersecurity Risk Scores

Authors: Deepak Kaul

Year: 2021

Journal: International Journal of Computer Engineering and Technology (IJCET)

Dynamic Cybersecurity Strategies for AI-Enhanced E-Commerce: A Federated Learning Approach to Data Privacy

Authors: Deepak Kaul

Year: 2019

Journal: Applied Research in Artificial Intelligence and Cloud Computing

Conclusion:

Deepak Kaul’s extensive experience and innovative contributions to AI and cybersecurity place him among the top candidates for the Best Researcher Award. His work demonstrates a strong commitment to advancing technology and security solutions across sectors. With his achievements, coupled with potential growth areas in research publication and outreach, Kaul’s candidacy for this award reflects both his current impact and his promising potential for continued advancements in AI-driven cybersecurity.

 

Pablo Arnau González | Artificial Intelligence | Best Researcher Award

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Dr. Pablo Arnau González, Artificial Intelligence, Best Researcher Award

Doctorate at Universidad de Valencia, Spain

Summary:

Dr. Pablo Arnau González is a researcher with expertise in artificial intelligence and machine learning. He has made significant contributions to the fields of sentiment analysis, affective computing, and biometrics. Dr. González has conducted research on adapting conversational toolkits for sentiment analysis tasks and developing methodologies for identifying affect levels from EEG signals and visual stimuli. He has also contributed to the development of adaptive intelligent tutoring systems. With a background in computing and artificial intelligence, Dr. González has published several papers in reputable conferences and journals.

Professional Profile:

Scopus Profile

Orcid Profile

Google Scholar Profile

👩‍🎓Education & Qualification:

PhD in Computing and Artificial Intelligence

  • University of the West of Scotland, 2020

Graduado o Graduada en Ingeniería Informática de Gestión y Sistemas de Información

  • Universitat de València, 2015

Professional Experience:

Dr. Pablo Arnau González has a diverse professional background, including positions in consulting, industry, and academia. Here is a summary of his previous professional positions:

  • 2022 – 2023: Postdoctoral Fellow (Margarita Salas) at Universitat de València.
  • 2021: Senior Consultant at SDG Consulting.
  • 2019 – 2021: Analyst at Cecotec Innovaciones, S.L.
  • 2019: Technologist in Digital Health at the University of the West of Scotland.

Research Interest:

Artificial Intelligence: Exploring AI algorithms and techniques for various applications such as sentiment analysis, affective computing, and intelligent tutoring systems.

Machine Learning: Investigating machine learning models and methodologies for processing EEG signals and visual stimuli to identify affect levels and subject identification.

Natural Language Processing: Adapting conversational toolkits and architectures for tasks like sentiment analysis, chatbots, and conversational agents.

Biometrics: Researching the use of EEG signals and image-evoked affect for biometric authentication and identification systems.

Adaptive Intelligent Tutoring Systems: Developing systems that can assess affective and behavioral responses to adaptively tailor educational content and interactions.

Publication Top Noted:

On adapting the DIET architecture and the Rasa conversational toolkit for the sentiment analysis task

  • Authors: M Arevalillo-Herráez, P Arnau-González, N Ramzan
  • Journal: IEEE Access
  • Year: 2022
  • Volume: 10
  • Pages: 107477-107487
  • Citation count: 11

A Method to Identify Affect Levels from EEG signals using two-dimensional Emotional Models

  • Authors: P Arnau-González, N Ramzan, M Arevalillo-Herráez
  • Conference: The 2016 European Simulation and Modelling Conference
  • Year: 2016
  • Pages: 299-303
  • Citation count: 8

Image-evoked affect and its impact on EEG-based biometrics

  • Authors: P Arnau-González, S Katsigiannis, M Arevalillo-Herráez, N Ramzan
  • Conference: 26th IEEE International Conference on Image Processing
  • Year: 2019
  • Citation count: 7

Single-channel EEG-based subject identification using visual stimuli

  • Authors: S Katsigiannis, P Arnau-González, M Arevalillo-Herráez, N Ramzan
  • Conference: 2021 IEEE EMBS International Conference on Biomedical and Health Informatics
  • Year: 2021
  • Citation count: 5

Affective and Behavioral Assessment for Adaptive Intelligent Tutoring Systems

  • Authors: L Marco-Giménez, M Arevalillo-Herráez, FJ Ferri, S Moreno-Picot, …
  • Conference: UMAP (Extended Proceedings)
  • Year: 2016
  • Citation count: 5