Deekshitha Kosaraju | Artificial Intelligence Award | Best Researcher Award

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Ms. Deekshitha Kosaraju | Artificial Intelligence Award | Best Researcher Award

LIMS Junior Developer, ALS Group USA, Corp., United States

Deekshitha Kosaraju is an accomplished Computer Science graduate from The University of Texas at Dallas, with a strong academic foundation and technical expertise in a variety of programming languages, frameworks, and cloud technologies. Her expertise spans Java, Python, JavaScript, and R, among others. Deekshitha is currently working as a Junior Developer at ALS Group USA, where she focuses on improving data integration and system efficiency. She is passionate about cloud computing, machine learning, and AI, and has published several papers on cutting-edge AI techniques, including explainable AI and quantum computing integration. 🎓👩‍💻📚

Publication Profile

Google Scholar

Education

Deekshitha Kosaraju graduated with a Bachelor of Science in Computer Science from The University of Texas at Dallas, maintaining a GPA of 3.6/4.0. During her time at university, she was honored with the Academic Excellence Scholarship. Her coursework included a wide range of subjects such as Data Structures, Machine Learning, Software Engineering, and Operating Systems. 🎓🏆

Experience

Deekshitha has gained invaluable professional experience through internships and full-time roles. Currently, she works as a Junior Developer at ALS Group USA, where she contributes to streamlining workflows, automating processes, and improving data transfer efficiency. She has previously interned at Radiant Digital, where she worked on low-code platforms and developed mobile applications that enhanced field coordination. In addition, her experience at Pearson as a Software Engineer Intern allowed her to improve user engagement and business outcomes through AI-driven applications. 💼💻

Awards and Honors

Deekshitha was awarded the Academic Excellence Scholarship during her time at The University of Texas at Dallas. Her achievements in academic and professional arenas reflect her dedication to excellence and innovation in the field of computer science. 🌟🏅

Research Focus

Deekshitha’s research primarily focuses on Artificial Intelligence, with specific attention to explainable AI, zero-shot learning, meta-learning, reinforcement learning, and AI’s integration with cloud computing and quantum technologies. She is also interested in exploring the applications of AI in various domains, such as healthcare and data analytics. Her research contributions include exploring how AI can enhance big data analytics and cloud computing innovations. 🤖📊

Conclusion

With a diverse set of technical skills and a passion for advancing AI and cloud technologies, Deekshitha Kosaraju continues to make impactful contributions to the field of Computer Science. She remains committed to expanding her knowledge in AI and exploring innovative solutions to real-world problems. 🌐🚀

Publications :

Shedding light on AI: exploring explainable AI techniques
International Journal of Research and Review, 2020
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Zero-Shot learning: teaching AI to understand the unknown
International Journal of Research and Review, 2021
DOI: 10.52403/ijrr.20211161

How meta learning enhances reinforcement learning in AI
Galore International Journal of Applied Sciences & Humanities, 2021
DOI: 10.52403/gijash.20210706

Crossing domains: the role of transfer learning in rapid AI prototyping and deployment
International Journal of Science & Healthcare Research, 2021
DOI: 10.52403/ijshr.20210464

Artificial intelligence in cloud computing: enhancements and innovations
Galore International Journal of Applied Sciences & Humanities, 2021
DOI: 10.52403/gijash.20211010

Quantum computing and artificial intelligence: a fusion poised to transform technology
International Journal of Research and Review, 2021
DOI: 10.52403/ijrr.20210974

The role of artificial intelligence in enhancing big data analytics
Galore International Journal of Applied Sciences and Humanities, 2021

Ao Guo | Artificial Intelligence | Best Researcher Award

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Mr. Ao Guo | Artificial Intelligence | Best Researcher Award

Master’s student, Xinjiang University, China

📚 Ao Guo is a dedicated postgraduate researcher at Xinjiang University with a focus on the innovation, optimization, and application of object detection technology. Currently pursuing a master’s degree in Electronic Information, Ao Guo has a robust background in computer vision, deep learning, pattern recognition, and image processing. He is committed to enhancing the accuracy and efficiency of object detection algorithms, contributing to both academia and industry.

Profile

Google Scholar

 

Education

🎓 Master’s Degree in Electronic Information – Xinjiang University, Urumqi, China
Ao Guo is advancing his studies in Electronic Information, focusing on the intersection of computer vision and deep learning to address real-world problems.

Experience

Ao Guo has been deeply involved in research aimed at optimizing deep learning models for intelligent weed management in agricultural environments. His work on a lightweight weed detection model, which incorporates global contextual features, is recognized for its high detection speed and accuracy, particularly suited for resource-constrained edge devices.

Research Interests

Ao Guo’s research interests encompass weed detection, deep learning, YOLO (You Only Look Once) models, attention mechanisms, and the development of lightweight networks. His innovative approach to integrating global information capture mechanisms into detection algorithms stands out in his field.

Awards

Ao Guo’s contributions to the field have been acknowledged through his publications and patent. Notably, he has published a paper in the highly reputed journal “Engineering Applications of Artificial Intelligence,” and he holds a patent for a lightweight weed detection method and device.

Publications

A lightweight weed detection model with global contextual joint features. Engineering Applications of Artificial Intelligence, 136, 108903. Link – Cited by: Article on Engineering Applications of Artificial Intelligence.

Ali Raza | artificial intelligence | Best Researcher Award

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Mr. Ali Raza | artificial intelligence | Best Researcher Award

Lecturer, The University of Lahore, Pakistan

Ali Raza is a dedicated research scholar specializing in data science, known for his expertise in machine learning and deep learning applications. With a strong academic background and extensive professional experience in software development, he has contributed significantly to research in artificial intelligence and health informatics.

Profile

Google Scholar

📚 Education:

Ali completed his Bachelor of Science in Computer Science at KFUEIT after graduating from Iqra Degree College with a degree in Pre-Engineering. He further pursued his passion for computer science by earning a Master’s degree in Computer Science from KFUEIT, where his research focused on novel approaches in deep learning for image detection.

💼 Experience:

Ali’s professional journey includes roles as a Research Assistant at KFUEIT, where he published research articles on artificial intelligence. He has also worked as a Desktop App Developer at DexDevs Company and as a Full Stack Python Developer at BuiltinSoft Company, gaining expertise in business application development and machine learning frameworks.

🔬 Research Interests:

Ali’s research interests revolve around data science, particularly in machine learning model optimization, health informatics, and artificial intelligence applications in diverse domains such as pregnancy health analysis and network security.

🏆 Awards:

Ali has contributed significantly to research, evident from his publications and contributions as a peer reviewer for IEEE Access and PLOS ONE, highlighting his recognition in the academic community.

📄 Publications:

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction, Plos one, 2022 (cited 46 times)

A novel deep learning approach for deepfake image detection, Applied Sciences, 2022 (cited 58 times)

Predicting employee attrition using machine learning approaches, Applied Sciences, 2022 (cited 44 times)

A novel methodology for human kinematics motion detection based on smartphones sensor data using artificial intelligence, Technologies, 2023 (cited 23 times)

Novel class probability features for optimizing network attack detection with machine learning, IEEE Access, 2023