Umaa Mahesswari G | Artificial Intellugence Engineering | Best Researcher Award

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Ms. Umaa Mahesswari G | Artificial Intellugence Engineering | Best Researcher Award

Research Scholar at College of Engineering Guindy, Anna University, India

G. Umaa Mahesswari is a highly motivated research scholar from Chennai, India, with a passion for innovation and technology. With a solid foundation in Computer Science, her academic journey spans from her B.E. at R.M.K College to a Ph.D. at Anna University, specializing in Big Data Analytics. Umaa has worked on significant projects like IoT-based air pollution detection and digital inscription readers for ancient Tamil stone inscriptions. A recipient of the prestigious Anna Research Fellowship, she is dedicated to leveraging technology for social good. Her technical expertise and passion for research make her a valuable asset in the R&D domain.

Profile

Education šŸ“˜šŸŽ“

Bachelor of Engineering (B.E.) in Computer Science from R.M.K College of Engineering and Technology, Chennai (2015-2019), with a CGPA of 8.65.Master of Engineering (M.E.) in Computer Science with a Specialization in Big Data Analytics from College of Engineering, Guindy, Anna University, Chennai (2019-2021), with a CGPA of 9.07.Ph.D. in Computer Science from College of Engineering, Guindy, Anna University (2023-present).Completed the Google Data Analytics Professional Specialization Course via Coursera (2021), enhancing her skills in data-driven problem-solving.

Experience šŸ’¼šŸ“Š

Research Project Assistant at Tamil Virtual Academy (2022), working on the development of an inscription reader for ancient Tamil temple inscriptions, supervised by Dr. P. Uma Maheswari.Undergraduate project on IoT-based air pollution detection and analysis, focusing on real-time data collection and environmental monitoring.Postgraduate project on predictive analytics for plastic disposal by Walmart India, contributing to sustainable business solutions.Proficient in machine learning, data visualization, and SQL, with significant contributions to the field of Big Data Analytics.

Awards and Honors šŸ†šŸŽ–

Academic Topper in the third year of her B.E. program, demonstrating her dedication to academic excellence.Nominee for Best Outgoing Student Award at the end of her undergraduate studies.Prize winner in several intra-college cultural competitions, showcasing her versatility beyond academics.Anna Research Fellowship Holder from 2023 to 2025, awarded in recognition of her research potential in Big Data Analytics.Published academic papers in esteemed venues like Springer ICTIS and Computer Society of India, highlighting her contributions to the academic community.

Research Focus šŸ”šŸ’”

Umaa Mahesswariā€™s research focuses on leveraging Big Data Analytics, Machine Learning, and Deep Learning to solve pressing societal challenges. Her projects span diverse areas such as IoT-based environmental monitoring, predictive analytics for waste management, and digital solutions for heritage preservation (Tamil inscriptions). She aims to develop data-driven solutions that improve sustainability and drive innovation. Her ongoing Ph.D. research explores pattern recognition and data visualization techniques to extract meaningful insights from large datasets, with the goal of enhancing decision-making processes in both academic and practical settings.

Conclusion

G. Umaa Mahesswari is an exceptional candidate for the Best Scholar Award due to her innovative research, academic achievements, and technical expertise. By further broadening her research scope and international collaborations, she could elevate her already impressive profile and contribute even more profoundly to the global research community.

Publication Top Notes

SmartScanPCOS: A Feature-Driven Approach to Cutting-Edge Prediction of Polycystic Ovary Syndrome using Machine Learning and Explainable Artificial Intelligence

Journal: Heliyon
Published: October 2024
DOI: 10.1016/j.heliyon.2024.e39205
Contributors: G. Umaa Mahesswari; P Uma Maheswari

Mehdi Ghatee | Artificial Intelligence | Best Researcher Award

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Prof. Mehdi Ghatee | PArtificial Intelligence | Best Researcher Award

Full Professor at Amirkabir University of Technology, Iraq

This individual is a highly skilled project manager with expertise in intelligent transportation systems and infrastructure. They have led several high-profile ITS projects across Iran, including the Action Plan for Intelligent Transportation Systems in Shiraz and national projects involving intracity and intercity ITS. Their work focuses on optimizing transportation systems using advanced technologies such as artificial intelligence, neural networks, and data science. With a career spanning over two decades, they have contributed significantly to improving Iran’s public transportation and highway systems, as well as advising on critical petroleum transport safety issues.

Profile

EducationšŸŽ“

They hold advanced degrees in engineering and transportation systems, including specialized training in artificial intelligence and network optimization. Their academic journey began with a focus on optimizing transportation networks, and they later pursued studies that explored neural networks and intelligent transportation systems. Through continued education and professional development, they gained cutting-edge knowledge in data mining and its applications in transportation. Their interdisciplinary background merges technical expertise with the management of large-scale infrastructure projects.

ExperiencešŸ’¼

The individual has a rich professional background in transportation management. They have served as a project manager for various organizations, including the Shiraz Municipality, Tehran Deputy of Traffic, MSRT, and Tehran Control Traffic Company. Their leadership has been instrumental in developing intelligent transportation solutions and investigating hazardous material transportation risks. They also advised on BRT systems and public transportation initiatives. With over 15 years of experience in both the public and private sectors, theyā€™ve been at the forefront of implementing ITS and advising on petroleum-related transport and logistics.

Awards & HonorsšŸ†

Over their career, the individual has been recognized for their contributions to transportation system innovation and project management excellence. Their leadership on intelligent transportation systems projects has earned them accolades from governmental bodies such as the MSRT and NIOPDC. Notable achievements include awards for the implementation of cutting-edge ITS technologies, as well as safety improvements in the transportation of hazardous materials. Their work has significantly enhanced public transit systems in major Iranian cities, particularly Tehran and Shiraz, positioning them as a key figure in national transportation projects.

Research Focus šŸ”¬

Their research primarily revolves around the application of artificial intelligence and neural networks in optimizing transportation networks. Starting with a focus on network optimization in 2004, they expanded their interests to intelligent transportation systems (ITS) and later neural networks and data mining. They aim to create efficient and safer transportation systems through the use of AI, exploring ways to minimize congestion, enhance road safety, and improve the transportation of goods, especially hazardous materials. Their work intersects with both public transit and highway infrastructure improvements through innovative tech solutions.

Conclusion

The candidate possesses outstanding credentials in intelligent transportation systems and AI-driven technologies, making them a strong contender for the Best Researcher Award. Their vast project management experience, coupled with a diverse research portfolio in AI, neural networks, and data science, aligns well with the award criteria. To further solidify their candidacy, expanding their international collaborations and academic contributions would provide a broader platform for their innovative work, making them a more recognized figure in the global research community.

Publication Top Notes
  • A systematic review on overfitting control in shallow and deep neural networks
    • Authors: MM Bejani, M Ghatee
    • Journal: Artificial Intelligence Review
    • Citations: 304
    • Year: 2021
    • Details: This review addresses various methods for controlling overfitting in neural networks, both shallow and deep, presenting strategies and techniques for improving generalization in machine learning models.
  • Computational methods for solving fully fuzzy linear systems
    • Authors: M Dehghan, B Hashemi, M Ghatee
    • Journal: Applied Mathematics and Computation
    • Citations: 298
    • Year: 2006
    • Details: This paper introduces novel computational techniques to solve fully fuzzy linear systems, which have applications in systems where uncertainty is modeled using fuzzy logic.
  • Solution of the fully fuzzy linear systems using iterative techniques
    • Authors: M Dehghan, B Hashemi, M Ghatee
    • Journal: Chaos, Solitons & Fractals
    • Citations: 171
    • Year: 2007
    • Details: Focuses on iterative techniques to solve fuzzy linear systems, providing an alternative to traditional crisp systems solutions.
  • A context-aware system for driving style evaluation by an ensemble learning on smartphone sensors data
    • Authors: MM Bejani, M Ghatee
    • Journal: Transportation Research Part C: Emerging Technologies
    • Citations: 153
    • Year: 2018
    • Details: Proposes an ensemble learning model for driving style evaluation using smartphone sensor data, improving transportation safety and behavior analysis.
  • Convolutional neural network with adaptive regularization to classify driving styles on smartphones
    • Authors: MM Bejani, M Ghatee
    • Journal: IEEE Transactions on Intelligent Transportation Systems
    • Citations: 84
    • Year: 2019
    • Details: Introduces CNNs with adaptive regularization techniques for classifying driving styles using mobile sensor data.
  • Hybrid of discrete wavelet transform and adaptive neuro-fuzzy inference system for overall driving behavior recognition
    • Authors: HR Eftekhari, M Ghatee
    • Journal: Transportation Research Part F: Traffic Psychology and Behavior
    • Citations: 83
    • Year: 2018
    • Details: Combines wavelet transforms and neuro-fuzzy systems to recognize driving behaviors, helping in real-time traffic safety assessments.
  • A similarity-based neuro-fuzzy modeling for driving behavior recognition applying fusion of smartphone sensors
    • Authors: HR Eftekhari, M Ghatee
    • Journal: Journal of Intelligent Transportation Systems
    • Citations: 68
    • Year: 2019
    • Details: Describes a neuro-fuzzy modeling approach for driving behavior detection using fused smartphone sensor data.
  • Three-phases smartphone-based warning system to protect vulnerable road users under fuzzy conditions
    • Authors: RB Zadeh, M Ghatee, HR Eftekhari
    • Journal: IEEE Transactions on Intelligent Transportation Systems
    • Citations: 65
    • Year: 2017
    • Details: Develops a warning system using fuzzy logic for protecting road users, especially pedestrians and cyclists.
  • An inference engine for smartphones to preprocess data and detect stationary and transportation modes
    • Authors: HR Eftekhari, M Ghatee
    • Journal: Transportation Research Part C: Emerging Technologies
    • Citations: 65
    • Year: 2016
    • Details: Introduces an inference engine to classify transportation modes from smartphone data, improving mobility tracking.
  • Optimal network design and storage management in petroleum distribution network under uncertainty
    • Authors: M Ghatee, SM Hashemi
    • Journal: Engineering Applications of Artificial Intelligence
    • Citations: 60
    • Year: 2009
    • Details: Proposes a model for optimizing petroleum distribution networks, considering uncertainty factors in supply chains.