Obsa Gilo Wakuma | Artificial Intelligence | Best Researcher Award

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Dr Obsa Gilo Wakuma ย | Artificial Intelligence | Best Researcher Awardย 

ย Ass. Prof at Wallaga University, Ethiopia

Dr. Obsa Gilo Wakuma is a dedicated researcher and academician with a Ph.D. in Computer Science and Engineering from the Indian Institute of Technology Patna, where his thesis focused on “Deep Learning Approaches for Efficient Domain Adaptation.” He holds an M.Sc. in Computer Science (CGPA: 3.78) and a B.Sc. in Computer Science (CGPA: 3.56) from Wallaga University, Ethiopia.Dr. Obsa Gilo Wakuma continues to contribute to the academic and research community with his expertise in deep learning and domain adaptation, leveraging his strong background in computer science and engineering to drive innovative solutions.

๐ŸŽ“ Education:

  • Ph.D. in Computer Science and Engineering, IIT Patna (2024)
  • M.Sc. in Computer Science, Wallaga University (2018)
  • B.Sc. in Computer Science, Wallaga University (2014)
  • XII Class, Sibu Sire Preparatory School (2010)
  • X Class, Sibu Sire High School (2008)

๐Ÿ’ผ Work Experience:

Dr. Wakuma began his professional journey as a Recorder at Wallaga University’s main Registrar in Oromia, Ethiopia, from October 2014 to June 2015. He then served as a Laboratory Technician at Wallaga University’s Shambu campus until February 2016. From February 2016 to September 2018, he worked as a Graduate Assistant (GA-II) at Wallaga University, eventually becoming a Lecturer from February 2019 to September 2019. From September 2019 to December 2023, he was a Research Scholar at IIT Patna.

๐Ÿ“š Research Focus:

Dr. Wakuma’s research primarily revolves around deep learning and domain adaptation. His notable publications include articles in prestigious journals such as Expert Systems with Applications, Pattern Analysis and Applications, IEEE Access, and the Journal of Visual Communication and Image Representation. His work often explores robust unsupervised deep sub-domain adaptation and optimal transport for image classification.

๐Ÿ› ๏ธ Skills:

Dr. Wakuma possesses strong competencies in multiple languages, including English, Afaan Oromoo, and Amharic. His technical skills encompass programming languages such as Java, PHP, Python, C, C++, and R. He is proficient in databases like MySQL, PostgreSQL, HSQL, and SQLite, and has experience in web development using HTML, CSS, JavaScript, and Apache Web Server. Additionally, he is skilled in academic research, teaching, training, consultation, and community service.

Research and Publications

  1. Journal Articles: Published in prestigious journals such as “Expert Systems with Applications,” “Pattern Analysis and Applications,” “IEEE Access,” and “Journal of Visual Communication and Image Representation.” Topics covered include domain adaptation in sensor data, subdomain adaptation via correlation alignment, robust unsupervised deep sub-domain adaptation, and unsupervised sub-domain adaptation using optimal transport.
  2. Conference Proceedings: Presented at the IEEE 19th India Council International Conference (INDICON), discussing the integration of discriminate features and similarity preserving for unsupervised domain adaptation.

Conclusion

Given his strong academic background, extensive research publications, practical skills, and teaching experience, Obsa Gilo Wakuma is a highly suitable candidate for the Best Researcher Award. His contributions to the field of computer science, particularly in deep learning and domain adaptation, demonstrate a high level of expertise and impact, making him deserving of such recognition.

๐Ÿ“œ Publications:

  • Unsupervised sub-domain adaptation using optimal transport
    O. Gilo, J. Mathew, S. Mondal, R.K. Sanodiya
    Journal of Visual Communication and Image Representation (2023)
    ๐Ÿ–ผ๏ธ๐Ÿ”„๐Ÿšš
  • Subdomain adaptation via correlation alignment with entropy minimization for unsupervised domain adaptation
    O. Gilo, J. Mathew, S. Mondal, R.K. Sanodiya
    Pattern Analysis and Applications (2024)
    ๐Ÿ“Š๐Ÿ”„๐ŸŒ
  • Integration of Discriminate Features and Similarity Preserving for Unsupervised Domain Adaptation
    O. Gilo, J. Mathew, S. Mondal
    2022 IEEE 19th India Council International Conference (INDICON) (2022)
    ๐Ÿ“š๐Ÿ”๐Ÿค
  • Kernelized Bures metric: A framework for effective domain adaptation in sensor data analysis
    O. Gilo, J. Mathew, S. Mondal
    Expert Systems with Applications (2024)
    ๐Ÿ“ˆ๐Ÿ”„๐Ÿ”ฌ
  • RDAOT: Robust Unsupervised Deep Sub-domain Adaptation through Optimal Transport for Image Classification
    O. Gilo, J. Mathew, S. Mondal, R.K. Sanodiya
    IEEE Access (2023)
    ๐Ÿ–ผ๏ธ๐Ÿ”„๐Ÿง 
  • Information Extraction For Afaan Oromo News Texts Using Hybrid Approach
    O. Gilo
    Journal of Innovation in Computer Science and Engineering (2019)
    ๐Ÿ“ฐ๐Ÿ”๐Ÿ‡ช๐Ÿ‡น
  • Unified Domain Adaptation with Discriminative Features and Similarity Preservation
    O. Gilo, J. Mathew, S. Mondal
    (Journal/Conference not specified)
    ๐Ÿ”„๐ŸŒ๐Ÿค

Nafis Uddin Khan | Artificial Intelligence | Best Researcher Award

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Dr Nafis Uddin Khan | Artificial Intelligence | Best Researcher Awardย 

Associate Professor at ย SR University Warangal India

Dr. Nafis Uddin Khan is an esteemed academic and researcher specializing in Information and Communication Technology. With a Ph.D. from the Atal Bihari Vajpayee – Indian Institute of Information Technology & Management, Gwalior, he has a profound expertise in computer vision, image processing, and fuzzy logic applications in signal and image processing. He currently serves as an Associate Professor at SR University Warangal.

Education ๐Ÿ“š

  • Ph.D. in Information and Communication Technology from Atal Bihari Vajpayee – Indian Institute of Information Technology & Management, Gwalior, India (2013).
  • M.Tech in Software Systems from Samrat Ashok Technological Institute, Vidisha, under Rajiv Gandhi Prodyogiki Vishwavidyalaya, Bhopal, India (2008).
  • B.E. in Electronics & Telecommunication Engineering from Jawaharlal Darda Institute of Engineering & Technology, Yavatmal, under Amravati University, Amravati, India (2003).
  • H.S.S.C. from Tiny Tots E. M. H. S. School, Seoni, M.P. Board, India (1997).
  • H.S.C. from Gyan Jyoti E. M. H. S. School, Nainpur, M.P. Board, India (1995).

Work Experience ๐Ÿ’ผ

  1. Associate Professor, School of CS & AI, SR University Warangal, Telangana, India (since August 2023).
  2. Assistant Professor (Senior Grade), Jaypee University of Information Technology, Solan, Himachal Pradesh, India (2017-2023).
  3. Assistant Professor (Senior Grade), Jaypee University of Engineering and Technology, Raghogarh, Madhya Pradesh, India (2013-2017).
  4. Lecturer, Anand Engineering College, Agra, Uttar Pradesh, India (2008-2009).
  5. Lecturer, Hitkarini College of Engineering and Technology, Jabalpur, Madhya Pradesh, India (2004-2005).

Skills & Certifications ๐Ÿ’ก

  • Programming: Matlab, C, C++, Python.
  • Technical Writing
  • Team Work and Leadership

Administrative Roles ๐Ÿข

  • Associate Dean โ€“ Admissions, SR University, Warangal (since January 2024).
  • Training and Placement – Faculty Coordinator, Jaypee University of Information Technology, Solan (2017-2023).
  • Coordinator of Student Activities and Placement Section in Internal Quality Assurance Cell (IQAC), Jaypee University of Information Technology, Solan (2017-2023).

Research Focus ๐Ÿ”ฌ

  • Statistical Image Processing
  • Medical Image Processing
  • Fuzzy Logic based Applications in Signal and Image Processing
  • Soft Optimization Techniques in Signal and Image Processing

Professional Contributions ๐Ÿ“ˆ

  • Enhanced sharp edge features and reduced Gaussian noise using singular value decomposition on optimal anisotropic diffused images.
  • Developed a fuzzy-based diffusion coefficient function for selective smoothing of impulsive noise.
  • Explored soft computing-based optimization techniques for speckle reduction in medical ultrasound and X-ray images.

Workshops and Conferences ๐Ÿ—“๏ธ

  • Coordinated multiple workshops and short-term programs on AI, signal processing, and leadership in education.
  • Served as an Invited Session Chair and Organizing Committee Member in various international conferences, including the IEEE International Conference on Signal Processing, Computing and Control.

Memberships ๐Ÿค

  • Active participation in academic and professional organizations through organizing and coordinating roles in numerous conferences and workshops.

 

Publications ๐Ÿ“š

  1. Fuzzy C-Means Clustering Based Selective Edge Enhancement Scheme for Improved Road Crack Detection ๐Ÿ›ค๏ธ
    • Authors: Bhardwaj, M., Khan, N.U., Baghel, V.
    • Journal: Engineering Applications of Artificial Intelligence
    • Year: 2024
  2. Fuzzy Based Self-Similarity Weight Estimation in Non-Local Means for Gray-Scale Image De-Noising ๐Ÿ–ค
    • Authors: Thakur, N., Khan, N.U., Sharma, S.D.
    • Journal: Digital Signal Processing: A Review Journal
    • Year: 2024
  3. Road Crack Detection Using Pixel Classification and Intensity-Based Distinctive Fuzzy C-Means Clustering ๐Ÿ›ค๏ธ
    • Authors: Bhardwaj, M., Khan, N.U., Baghel, V.
    • Journal: Visual Computer
    • Year: 2024
  4. A Comparative Survey on Histogram Equalization Techniques for Image Contrast Enhancement ๐Ÿ“Š
    • Authors: Malik, A., Khan, N.U.
    • Journal: Lecture Notes in Electrical Engineering
    • Year: 2024
  5. A Two Phase Ultrasound Image De-Speckling Framework by Nonlocal Means on Anisotropic Diffused Image Data ๐Ÿฉบ
    • Authors: Thakur, N., Khan, N.U., Sharma, S.D.
    • Journal: Informatica (Slovenia)
    • Year: 2023
  6. An Efficient Fuzzy Inference System Based Approximated Anisotropic Diffusion for Image De-Noising ๐Ÿ–ฅ๏ธ
    • Authors: Thakur, N., Khan, N.U., Sharma, S.D.
    • Journal: Cluster Computing
    • Year: 2022
  7. Digital Image Enhancement and Reconstruction ๐Ÿ“˜
    • Authors: Rajput, S.S., Khan, N.U., Singh, A.K., Arya, K.V.
    • Journal: Digital Image Enhancement and Reconstruction
    • Year: 2022
  8. Brain Tumor Image Segmentation Using K-Means and Fuzzy C-Means Clustering ๐Ÿง 
    • Authors: Bhardwaj, M., Khan, N.U., Baghel, V., Vishwakarma, S.K., Bashar, A.
    • Journal: Digital Image Enhancement and Reconstruction
    • Year: 2022
  9. Improved Road Crack Detection Using Histogram Equalization Based Fuzzy-C Means Technique ๐Ÿ›ค๏ธ
    • Authors: Bhardwaj, M., Khan, N.U., Baghel, V.
    • Conference: PDGC 2022 – 7th International Conference on Parallel, Distributed and Grid Computing
    • Year: 2022
  10. Cuckoo Search Optimized Histogram Equalization for Low Contrast Image Enhancement ๐Ÿฆ
    • Authors: Thakur, N., Khan, N.U., Sharma, S.D.
    • Conference: PDGC 2022 – 7th International Conference on Parallel, Distributed and Grid Computing
    • Year: 2022

 

 

Pritha N | Deep learning | Best Researcher Award

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Mrs Pritha Nย  | Deep learningย  |ย ย Best Researcher Awardย 

 

 

Assistant professor at ย Panimalar Engineering College,ย India

 

๐Ÿ‘จโ€๐ŸซN. Prithaย  With over 16.8 years of teaching experience, this dedicated educator specializes in Information and Communication Engineering. They hold a PhD from Anna University, Chennai.

Professional Profile:

๐ŸŽ“ Education:

๐ŸŽ“ PhD in Information and Communication Engineering, Anna University
๐ŸŽ“ M.E. in Applied Electronics, Sathyabama University (CGPA: 8.1)
๐ŸŽ“ B.E. in Electronics & Communication Engineering, Adhiparasakthi College of Engineering (76%)
๐ŸŽ“ Diploma in Electronics & Communication Engineering, Bakthavatchalam Polytechnic (83%)

๐Ÿ’ผ Work Experience:

  • ๐Ÿซ Lecturer at Adhiparasakthi College of Engineering (5.11 years)
    ๐Ÿซ Assistant Professor (HOD Incharge) at John Bosco Engineering College (9 months)
    ๐Ÿซ Assistant Professor at Panimalar Engineering College (10 years)

๐Ÿ† Awards and Honors:

๐Ÿ… 2nd Topper in FDP on Embedded, IoT, AI, & HPC (2021)
๐Ÿ… 5% Topper and Silver certification in NPTEL’s Machine Learning course (2023)

 

๐Ÿ”ฌ Research Focus:

N. Prithaโ€™s research spans several critical areas in electronics and communication engineering. Her primary focus includes the design and optimization of RF and Microwave Engineering systems, exploring innovative techniques in Machine Learning, Deep Learning, and Artificial Neural Networks. She has contributed significantly to the development of multiband antennas for wireless applications, anomaly detection models in sensor networks, and enhancing the efficiency of digital multipliers like Wallace Tree and Dadda multipliers. Her work emphasizes low power, high-speed, and area-efficient solutions, contributing to advancements in embedded systems, IoT, and AI-driven applications.

Publications:ย 

 

Naveen Kumar K | Artificial Intelligence | Best Researcher Award

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

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:

๐ŸŽ“ 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

๐Ÿ’ผ 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)

๐Ÿ”ง Technical Skills:

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

๐Ÿ†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

๐Ÿ”ฌ Research Focus:

Primary Research Interests:

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

Publications:ย 

  • 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

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Mr Pritom Jyoti Goutom | Artificial Intelligence | Best Researcher Award

 

Research Scholar and Project Associate I at Dibrugarh University ,India

Profile:

Scopusย 

Education:

Pritom Jyoti Goutom is affiliated with the Centre for Computer Science and Applications at Dibrugarh University in Assam, India ๐Ÿ‡ฎ๐Ÿ‡ณ. His research focuses on natural language processing (NLP), particularly related to the Assamese language ๐Ÿ—ฃ๏ธ.

Some of his notable contributions include:

  1. Text Summarization ๐Ÿ“„: 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”ใ€5โ€‹ (ORCID)โ€‹โ€‹ (Dibrugarh University)โ€‹2. Collaboration with Dr. Nomi Baruah ๐Ÿค: 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 ORCIDใ€5โ€‹ (ORCID)

Professional Experience:

๐ŸŽ“ Research Scholar at Dibrugarh University
Specializing in Natural Language Processing (NLP) and AI-generated text in Assamese.
๐Ÿ” Research Focus: Text summarization, part-of-speech tagging, and fake news detection.

๐Ÿ‘จโ€๐Ÿ’ป Project Associate I, Dept. of Computer Science and Engineering
Contributing to cutting-edge projects and innovations in AI and NLP.

Research ย Focusย  ย :

  • 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: ๐Ÿ“ˆ 13

Total Documents:ย  ๐Ÿ“‚3

h-index: ๐ŸŒŸ 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.