Naveen Kumar K | Artificial Intelligence | Best Researcher Award

Mr.  Naveen Kumar K | Artificial Intelligence | Best Researcher Award  

Mr.Naveen Kumar K,Indian Institute of Technoloy Hyderabad (IIT Hyderabad),India

 

Mr. Naveen Kumar K is affiliated with the Indian Institute of Technology Hyderabad (IIT Hyderabad), located in India. As of the latest update, Mr. Naveen Kumar K’s specific role or title was not provided. Generally, at institutions like IIT Hyderabad, individuals often hold positions related to teaching, research, administration, or technical support. For a more detailed bio, including academic background, research interests, or notable achievements,

Professional Profile:

🎓 Education:

PhD (Computer Science & Engineering),Indian Institute of Technology Hyderabad,Duration: Jan 2020 – Present,Research Area: Security and Privacy for Machine Learning,CGPA: 9.38 out of 10,Supervisor: Prof. C Krishna Moha,MTech (Computer Science & Engineering),Indian Institute of Technology Hyderabad,Duration: Jan 2019 – Dec 2019,Thesis Title: Defining Traffic States Using Spatio Temporal Traffic Graphs on Aerial Videos,CGPA: 8.65 out of 10,Supervisor: Prof. C Krishna Moha,BTech (Computer Science & Engineering), Indian Institute of Information Technology, Vadodara,Duration: July 2014 – May 2018,CGPA: 8.97 out of 10

💼 Professional Experience:

SahajAI (Bangalore): Optimized defence against poisoning attacks in federated learning for medical image classification (Oct 2023 – Mar 2024),Visiting Research Scholar – University of Agder, Norway: Optimized model poisoning attack in federated learning (Jan 2023 – July 2023),Visiting Research Scholar – Purdue University, USA: Mitigate data poisoning attacks in federated learning using a precision-guided approach (May 2022 – Sep 2022),TCS Research & Innovation Labs, Hyderabad: Non-convex optimization approach to mitigate data poisoning attacks in federated learning (Jan 2022 – Dec 2022),Visiting Research Scholar – Hiroshima University, Japan: Zero-shot 2D object detection in Autonomous Vehicles (Aug 2021 – Nov 2021)

🔧 Technical Skills:

  • Machine learning, deep learning (supervised and unsupervised), computer vision
  • Programming & Libraries: Python, TensorFlow, PyTorch, OpenCV

🏆Academic Achievements & Awards:

  • PhD Research Excellence Award 2024 (IIT Hyderabad)
  • Shortlisted for Google Research Week (2022, 2023)
  • Finalist in Nvidia AI Hackathon finals 2019
  • Selected for IITH-RU Project-Based Learning Program

Projects:

  • Medicine from the sky: AI-based real-time lightweight system for medical drone delivery
  • iV4V (Intelligent Voice for Vision): Audio assistance for visual impairment using AI
  • M2Smart: Smart Cities project based on sensing, network, and big data analysis

🔬 Research Focus:

Primary Research Interests:

  • Computer Vision
  • Machine Learning
  • Deep Learning
  • Federated Learning
  • Privacy and Security
  • Autonomous Vehicle Technology

Publications: 

  • Black-box adversarial attacks in autonomous vehicle technology
    • This paper likely explores vulnerabilities in autonomous vehicle systems when subjected to adversarial attacks that manipulate inputs in ways imperceptible to human senses but can mislead AI models.
  • Towards a Transitional Weather Scene Recognition Approach for Autonomous Vehicles
    • This research aims to improve autonomous vehicles’ ability to recognize and navigate through varying weather conditions, which are critical for safety and reliability.
  • The Impact of Adversarial Attacks on Federated Learning: A Survey
    • This survey paper discusses the vulnerabilities of federated learning systems to adversarial attacks, which is crucial for understanding security challenges in collaborative machine learning environments.
  • Defining traffic states using spatio-temporal traffic graphs
    • Focuses on utilizing spatio-temporal traffic graphs to define and analyze different traffic states, which could aid in optimizing traffic management and predicting congestion.
  • Open-air Off-street Vehicle Parking Management System Using Deep Neural Networks: A Case Study
    • Presents a case study on deploying deep learning techniques for managing open-air off-street vehicle parking, illustrating practical applications of AI in urban infrastructure.
  • Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning
    • Discusses methods to mitigate data poisoning attacks specifically targeted at federated learning setups, ensuring the integrity and reliability of collaborative machine learning models.
  • Towards Smarter Transport: Harnessing AI in Image Processing for Effective Vehicle Counting
    • Explores the application of AI in image processing for accurately counting vehicles, which is essential for traffic management and urban planning.
  • Revamping Federated Learning Security from a Defender’s Perspective: A Unified Defense with Homomorphic Encrypted Data Space
    • Proposes enhanced security measures for federated learning environments, focusing on leveraging homomorphic encryption to protect sensitive data during collaborative model training.

Pritom Jyoti Goutom | Artificial Intelligence | Best Researcher Award

Mr Pritom Jyoti Goutom | Artificial Intelligence | Best Researcher Award

 

Research Scholar and Project Associate I at Dibrugarh University ,India

Profile:

Scopus 

Education:

Pritom Jyoti Goutom is affiliated with the Centre for Computer Science and Applications at Dibrugarh University in Assam, India 🇮🇳. His research focuses on natural language processing (NLP), particularly related to the Assamese language 🗣️.

Some of his notable contributions include:

  1. Text Summarization 📄: He has co-authored papers on text summarization techniques using deep learning, such as “An Abstractive Text Summarization Using Deep Learning in Assamese” and “Text Summarization in Assamese Language Using Sequence to Sequence RNNs”【5​ (ORCID)​​ (Dibrugarh University)​2. Collaboration with Dr. Nomi Baruah 🤝: He often works with Dr. Baruah and others on projects aimed at improving NLP for low-resource languages.

For more details about his educational background and academic contributions, you can check his profile on academic platforms like ORCID【5​ (ORCID)

Professional Experience:

🎓 Research Scholar at Dibrugarh University
Specializing in Natural Language Processing (NLP) and AI-generated text in Assamese.
🔍 Research Focus: Text summarization, part-of-speech tagging, and fake news detection.

👨‍💻 Project Associate I, Dept. of Computer Science and Engineering
Contributing to cutting-edge projects and innovations in AI and NLP.

Research  Focus   :

  • Text Summarization: Developing algorithms for concise representation of Assamese text.
  • Machine Translation: Enhancing language conversion models for Assamese.
  • Sentiment Analysis: Analyzing opinions and emotions expressed in Assamese text.
  • Named Entity Recognition (NER): Identifying and categorizing entities in Assamese text.
  • Fake News Detection: Implementing models to identify misinformation in Assamese news sources.
  • Language Modeling: Building computational models to understand and generate Assamese text.

 

Contributions :

  • Co-authored research on abstractive text summarization using deep learning approaches, focusing on the nuances of the Assamese language.
  • Investigated LSTM and BiLSTM algorithms for fake news detection, enhancing accuracy and reliability in Assamese news sources.
  • Developed attention-based transformer models for text summarization in Assamese, improving content extraction and generation.
  • Worked on automatic spelling error identification using deep learning algorithms tailored for Assamese language nuances.

Citations:

Total Citations: 📈 13

Total Documents:  📂3

h-index: 🌟 1

Publication Top Notes:

  • Goutom, P. J., Baruah, N., & Sonowal, P. (2023). An abstractive text summarization using deep learning in Assamese. International Journal of Information Technology, 15(5), 2365-2372. (6 citations)

 

  • Phukan, R., Goutom, P. J., & Baruah, N. (2024). Assamese Fake News Detection: A Comprehensive Exploration of LSTM and Bi-LSTM Techniques. Procedia Computer Science, 235, 2167-2177.

 

  • Goutom, P. J., Baruah, N., & Sonowal, P. (2024). Attention-based Transformer for Assamese Abstractive Text Summarization. Procedia Computer Science, 235, 1097-1104.

 

  • Phukan, R., Neog, M., Goutom, P. J., & Baruah, N. (2024). Automated Spelling Error Detection in Assamese Texts using Deep Learning Approaches. Procedia Computer Science, 235, 1684-1694.

 

  • Goutom, P. J., & Baruah, N. (2023). Text summarization in Assamese language using sequence to sequence RNNs. Indian Journal of Science and Technology, 16(SP2), 22-29.