Rashmi S | Machine Learning Techniques | Best Researcher Award

Posted on by Academic achievements

Mrs. Rashmi S | Machine Learning Techniques | Best Researcher Award

Rashmi S – Machine Learning Techniques | Senior Research Fellow at JSS Science and Technology University, India

Rashmi S. is an accomplished Ph.D. research scholar specializing in Computer Vision and Machine Intelligence. Her academic focus is particularly on medical image analysis, with a concentration on radiographic image annotation using AI and deep learning techniques. With approximately five years of experience in the tech industry as a Core Java Developer, Rashmi brings a unique blend of software development expertise and advanced research skills. She is currently working at the Pattern Recognition & Image Processing Lab at JSS Science and Technology University, Mysuru. Rashmi is driven by the ambition to enhance healthcare systems through innovative AI solutions, and her research contributions aim to create more accurate, automated systems for interpreting medical imagery.

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Education

Rashmi S. completed her Bachelor of Engineering (B.E.) in Computer Science and Engineering from SJCE, Mysore, graduating with a CGPA of 9.05. She then pursued her Master’s degree in Computer Engineering (M.Tech) from the same institution, achieving an outstanding CGPA of 9.77. Currently, she is pursuing her Ph.D. in Computer Science and Engineering at JSS S&TU, where she is expected to submit her thesis in September 2024. Her academic journey has been marked by a strong commitment to research excellence, particularly in Machine Learning and Deep Learning, both of which she applies in her medical image analysis research.

Experience

Rashmi S. has held various roles in both academic and industry settings, which have enriched her research and technical skills. She began her career in software engineering, working with Cisco Video Technology in Bengaluru, where she was involved in the development of Java-based software for Set-Top Boxes. She later moved on to Oracle India Pvt. Ltd. as an Application Engineer, working on software maintenance and the development of Oracle Projects Fusion, a project management tool. Rashmi’s academic career includes positions as a Junior Research Fellow and Senior Research Fellow at JSS Science and Technology University, where she currently conducts her doctoral research. Her professional journey in both the software industry and academia gives her a unique edge in developing and implementing cutting-edge research in healthcare.

Research Interests

Rashmi S. is primarily focused on Machine Learning, Deep Learning, and Image Processing, especially in the context of medical image analysis. Her research interests revolve around improving diagnostic tools through AI-powered systems. Specifically, her work addresses cephalometric landmark annotation in radiographs using both traditional machine learning algorithms and deep learning techniques. Rashmi has explored applications of EEG signal processing and computer vision in healthcare, striving to develop solutions that can automate the annotation of medical images for more accurate diagnoses. Her research aims to bridge the gap between artificial intelligence and clinical practices, potentially revolutionizing medical imaging and diagnostic procedures.

Awards

Rashmi S. has received several prestigious awards throughout her academic and professional career. She was awarded the UGC-NET Junior Research Fellowship in November 2021, which has enabled her to pursue her doctoral research in depth. She was also recognized with the Senior Research Fellowship by the University Grants Commission in February 2024. Additionally, Rashmi has been the recipient of several scholarships, including the MHRD & GATE Scholarships during her undergraduate and postgraduate studies. Her commitment to research excellence has also earned her multiple accolades for her academic performance, including being recognized for her outstanding contributions to machine learning in the medical field.

Publications

“Cephalometric Skeletal Structure Classification Using Convolutional Neural Networks and Heatmap Regression“, co-authored with P. Murthy, V. Ashok, and S. Srinath, published in SN Computer Science (2022). This study leverages convolutional neural networks (CNNs) and heatmap regression for advanced skeletal structure classification in cephalometric radiographs, with a focus on enhancing the accuracy of diagnostic tools in orthodontics.

“Extended Template Matching Method for Region of Interest Extraction in Cephalometric Landmarks Annotation“, co-authored with S. Srinath, R. Rakshitha, and B.V. Poornima, presented at the 2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical… This paper introduces an extended template matching method aimed at improving the extraction of regions of interest (ROIs) in cephalometric image annotation, a crucial step for automatic landmark detection.

“Lateral Cephalometric Landmark Annotation Using Histogram Oriented Gradients Extracted from Region of Interest Patches“, co-authored with S. Srinath, K. Patil, P.S. Murthy, and S. Deshmukh, published in Journal of Maxillofacial and Oral Surgery (2023). This research presents a novel approach for lateral cephalometric landmark annotation by extracting histogram-oriented gradients from ROIs, advancing the methods for more precise orthodontic assessments.

“A Novel Method for Cephalometric Landmark Regression Using Convolutional Neural Networks and Local Binary Pattern“, co-authored with V. Ashok, presented at the 5th International Conference on Computer Vision and Image Processing (2021). This paper explores a novel technique for landmark regression in cephalometric images using a combination of CNNs and local binary patterns, enhancing the automation of cephalometric analysis.

“Landmark Annotation Through Feature Combinations: A Comparative Study on Cephalometric Images with In-depth Analysis of Model’s Explainability“, co-authored with S. Srinath, S. Murthy, and S. Deshmukh, published in Dentomaxillofacial Radiology (2024). This comparative study examines various feature combinations for landmark annotation and provides an explainability analysis of the models used, aiming to make machine learning-based medical imaging more transparent and understandable.

“Recognition of Indian Sign Language Alphanumeric Gestures Based on Global Features“, co-authored with B.V. Poornima, S. Srinath, and R. Rakshitha, presented at the 2023 IEEE International Conference on Distributed Computing, VLSI… This paper investigates the use of global features for recognizing Indian Sign Language gestures, contributing to the development of gesture recognition systems in communication technologies.

“ISL2022: A Novel Dataset Creation on Indian Sign Language“, co-authored with R. Rakshitha, S. Srinath, and S. Rashmi, presented at the 2023 10th International Conference on Signal Processing and Integrated…. This paper presents the creation of the ISL2022 dataset, a significant step toward improving machine learning models for Indian Sign Language recognition, highlighting the importance of datasets in advancing language recognition research.

“Cephalometric Landmark Annotation Using Transfer Learning: Detectron2 and YOLOv8 Baselines on a Diverse Cephalometric Image Dataset“, co-authored with S. Srinath, S. Deshmukh, S. Prashanth, and K. Patil, published in Computers in Biology and Medicine (2024). This work leverages transfer learning techniques, using Detectron2 and YOLOv8 models, to annotate cephalometric landmarks on a diverse dataset, pushing the envelope for automated medical image analysis.

“Crack SAM: Enhancing Crack Detection Utilizing Foundation Models and Detectron2 Architecture“, co-authored with R. Rakshitha, S. Srinath, N. Vinay Kumar, and B.V. Poornima, published in Journal of Infrastructure Preservation and Resilience (2024). This research explores advanced crack detection techniques, using foundation models and Detectron2, to improve the detection of cracks in infrastructure.

“Enhancing Crack Pixel Segmentation: Comparative Assessment of Feature Combinations and Model Interpretability”, co-authored with R. Rakshitha, S. Srinath, N. Vinay Kumar, and B.V. Poornima, published in Innovative Infrastructure Solutions (2024). This paper focuses on crack pixel segmentation, offering insights into the comparative performance of various feature combinations and the interpretability of machine learning models used in infrastructure monitoring.

Conclusion

Rashmi S. has demonstrated exceptional skill and dedication to the field of Computer Vision and Machine Intelligence. With her substantial industry experience and strong academic background, Rashmi has contributed significantly to AI research in healthcare. Her work has the potential to revolutionize medical image analysis, offering more efficient and accurate diagnostic tools. Through her awards, publications, and ongoing research, Rashmi S. stands as an exemplary candidate for the Best Researcher Award, with the promise of continuing to make groundbreaking advancements in her field.

Subhrangshu Adhikary | Machine Learning | Young Scientist Award

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Mr Subhrangshu Adhikary | Machine Learning | Young Scientist Award

Mr Subhrangshu Adhikary, Spiraldevs Automation Industries Pvt. Ltd. India

Subhrangshu Adhikary is the Director of Spiraldevs Automation Industries Pvt. Ltd. and a PhD scholar at the National Institute of Technology, Durgapur. With a passion for leveraging scientific advancements to solve real-world problems, he aims to make a positive impact on society and the environment. Adhikary has a diverse skill set encompassing big data, AI, and IoT, and he is recognized for his leadership in both academia and industry. His work spans across research, innovation, and technology development, reflecting his commitment to advancing knowledge and practical solutions in computer science and engineering.

Publication Profile

Scopus

Strengths for the Award

  1. Significant Contributions to Research:
    • Subhrangshu Adhikary has made notable contributions in various fields including machine learning, big data, and biomedical signal processing. His research work covers a range of topics such as secure classification frameworks, document classification with vision transformers, and advanced cryptographic algorithms.
    • His papers, such as those published in Biomedical Signal Processing and Control and Machine Learning and Knowledge Extraction, demonstrate his expertise and innovative approach to complex problems.
  2. Innovative Intellectual Properties:
    • Adhikary holds multiple patents and copyrights, which showcase his ability to develop practical and innovative solutions. For instance, his patents related to fault-tolerant storage systems and decentralized sensor networks are indicative of his technical prowess and creativity.
  3. Awards and Recognition:
    • His achievements, including the Best Research Award at SMTST-2020 and several accolades from NPTEL, highlight his recognition within the scientific community. These awards underline his commitment to excellence and his impact on the field of computer science and engineering.
  4. Diverse Skill Set:
    • His broad technical skill set in areas such as big data, AI/ML, and IoT, combined with his experience in full-stack development and various programming languages, reflects a well-rounded expertise essential for cutting-edge research.
  5. Leadership and Experience:
    • As the Director of Spiraldevs Automation Industries Pvt. Ltd. and IT Head of Drevas Vision Pvt. Ltd., Adhikary has demonstrated strong leadership skills and practical experience in managing and executing technology projects. His roles in these positions complement his research activities, providing a solid foundation for innovative and impactful work.

Areas for Improvement

  1. Publication Impact:
    • While Adhikary has numerous publications, increasing the citation impact and visibility of his work in high-impact journals could further enhance his reputation in the research community. Expanding his research into more widely recognized venues could contribute to a greater influence in his field.
  2. Interdisciplinary Collaboration:
    • Further collaboration with researchers from different disciplines could broaden the scope and application of his work. Engaging in interdisciplinary projects may lead to more diverse and impactful research outcomes.
  3. Research Visibility:
    • Increasing engagement with broader research communities through conferences and workshops could raise awareness of his contributions. Presenting at international forums and participating in collaborative research initiatives may help elevate his profile.

Education

Subhrangshu Adhikary is pursuing a Doctorate in Computer Science and Engineering at the National Institute of Technology, Durgapur, since August 2022. He earned his Bachelor of Technology in Computer Science and Engineering from Dr. B.C. Roy Engineering College, achieving a CGPA of 9.20. His academic journey began at Bethany Mission School, where he completed his Class XII with an aggregate of 74% and Class X with a perfect CGPA of 10.0. His educational background underscores his strong foundation and continued pursuit of excellence in technology and research.

Experience

Subhrangshu Adhikary has been the Director of Spiraldevs Automation Industries Pvt. Ltd. since July 2020, driving innovation and technology solutions in West Bengal. He also serves as the IT Head of Drevas Vision Pvt. Ltd., a software outsourcing company, since September 2020. His roles involve overseeing technology development and management, showcasing his leadership and expertise in the tech industry. His experience encompasses both strategic direction and technical execution, highlighting his multifaceted capabilities in technology and business management.

Awards and Honors

Subhrangshu Adhikary has been honored with the Best Research Award at the SMTST-2020 Conference, recognizing his outstanding research contributions. He has also received multiple accolades from NPTEL, including NPTEL Star, NPTEL Believer, and NPTEL Enthusiast. These awards reflect his excellence in the field of computer science and engineering. Additionally, Adhikary has achieved significant recognition through various local, school, and college-level competition wins, and ranked AIR 108 in the PNTSE Olympiad in 2015.

Research Focus

Subhrangshu Adhikary’s research focuses on integrating big data, AI, and IoT to address complex problems. His work includes developing distributed fault-tolerant storage systems, decentralized sensor networks, and advanced cryptographic algorithms. He explores machine learning applications for environmental monitoring and biomedical diagnostics. His research aims to create sustainable and impactful solutions, enhancing both theoretical knowledge and practical applications in technology and data science.

Publication Top Notes

  • “DitDViiPt PrivLet: A differential privacy and inverse wavelet decomposition framework for secure and optimized hemiplegic gait classification” 📝
  • “VisFormers—Combining Vision and Transformers for Enhanced Complex Document Classification” 📄
  • “Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing” 🌊
  • “Secret learning for lung cancer diagnosis—a study with homomorphic encryption, texture analysis and deep learning” 🩺
  • “Optimized EEG based mood detection with signal processing and deep neural networks for brain-computer interface” 🧠
  • “DeCrypt: a 3DES inspired optimised cryptographic algorithm” 🔐
  • “Improved Large-Scale Ocean Wave Dynamics Remote Monitoring Based on Big Data Analytics and Reanalyzed Remote Sensing” 🌍
  • “If Human Can Learn from Few Samples, Why Can’t AI? An Attempt On Similar Object Recognition With Few Training Data Using Meta-Learning” 🤖
  • “Evolutionary Swarming Particles To Speedup Neural Network Parametric Weights Updates” ⚙️
  • “Price Prediction of Digital Currencies using Machine Learning” 💹

Conclusion

Subhrangshu Adhikary is a highly deserving candidate for the Best Researcher Award due to his significant contributions to research, innovative intellectual properties, and demonstrated leadership in the technology sector. His impressive track record of awards, patents, and publications underscores his expertise and dedication to advancing knowledge in his field. Addressing areas for improvement, such as increasing the impact of his publications and enhancing interdisciplinary collaborations, could further solidify his standing as a leading researcher. Overall, Adhikary’s achievements and potential make him a strong contender for this prestigious award.

AHMADOU MUSTAPHA FONTON MOFFO | Machine Learning | Best Researcher Award

Posted on by Academic achievements

Dr. AHMADOU MUSTAPHA FONTON MOFFO | Machines Learning | Best Researcher Award 

Economist | UNESCO | Canada

Short Bio 🌟

Ahmadou Mustapha FONTON is a distinguished economist based in Montréal, Canada, with a Ph.D. in Economics from the Université du Québec à Montréal. Specializing in macroeconomics, financial economics, and applied econometrics, FONTON excels in leveraging machine learning and big data to inform policy decisions and develop robust risk models. His extensive professional experience includes roles at UNESCO and the Ministry of Scientific Research in Cameroon, reflecting his dedication to advancing economic research and policy.

Profile

Google Scholar

Strengths for the Award

  1. Extensive Expertise and Experience: Dr. Fonton brings a wealth of experience in both academic and non-academic settings. His role as an economist at UNESCO and previous positions demonstrate a solid track record in applied econometrics, macroeconomics, and financial economics. His contributions to data collection, statistical analysis, and policy evaluation underscore his broad expertise.
  2. Advanced Technical Skills: His proficiency with a diverse set of software tools (PYTHON, R, MATLAB, STATA, SPSS, etc.) and techniques, including machine learning and big data analysis, highlights his technical acumen. This expertise is critical for modern economic research, especially in forecasting and analyzing complex economic phenomena.
  3. Strong Research Output: Dr. Fonton’s publication record, including his recent work on machine learning in stress testing US banks, demonstrates his ability to contribute valuable insights to the field of economics. His working papers and conference presentations further reflect his active engagement in cutting-edge research.
  4. Academic and Teaching Experience: His roles as a research assistant and instructor at Université du Québec à Montréal and Institut Siantou Superieur show a strong background in teaching and mentoring. This experience is important for fostering new talent and advancing the field through education.
  5. International Perspective and Multilingual Skills: Dr. Fonton’s international experience, combined with his multilingual abilities (English, French, and Bamoun), provides him with a unique perspective on global economic issues. This is especially relevant in the context of UNESCO’s work and cross-border research collaborations.
  6. Policy Impact: His involvement in projects that influence policy, such as his work on forecasting time series for UNESCO and his previous consulting roles, indicates a strong capacity for translating research into practical recommendations. This aligns well with the goals of the Research for Best Researcher Award, which often emphasizes practical impacts of research.

Areas for Improvement

  1. Broader Publication Record: While Dr. Fonton has a notable publication in the International Review of Financial Analysis and several working papers, increasing his publication count in high-impact journals could strengthen his profile further. Broadening his research topics or collaborating on interdisciplinary studies might also enhance his visibility in different research circles.
  2. Increased Collaboration and Networking: Engaging in more collaborative research projects and expanding his network within the global research community could open up additional opportunities for impactful research and visibility. This could involve co-authoring papers with researchers from diverse backgrounds or participating in more international conferences.
  3. Focus on Long-term Projects: While Dr. Fonton’s work on various projects is commendable, focusing on longer-term research initiatives might yield more significant and sustained contributions to the field. Developing comprehensive research programs or longitudinal studies could be beneficial.
  4. Enhanced Public Engagement: Increasing efforts to communicate his research findings to the public and policymakers could amplify the impact of his work. This might include writing policy briefs, engaging in media outreach, or participating in public lectures and forums.

Education 🎓

  • 2023: Ph.D. in Economics, Université du Québec à Montréal, Canada
  • 2010: M.Sc. in Economics, Université Catholique de Louvain, Belgium
  • 2005: B.Sc. in Statistics, ISSEA Yaoundé, Cameroon
  • 2000: Certificate in Mathematics, Cameroon

Experience 💼

2023–Present: Economist-Statistician, UNESCO Institute of Statistics, Canada
Leading data collection and processing for Science and Culture Annual Surveys, developing new survey instruments, and producing statistical reports.

2012–2017: Coordinator of Statistical Projects, Ministry of Scientific Research, Cameroon
Directed national statistical surveys, analyzed data on Research and Development, and assisted in organizing expert meetings and seminars.

2009–2012: Economist, Ministry of Economy and Planning, Cameroon
Monitored macroeconomic indicators and developed socio-economic analyses to guide policy decisions.

2008: Credit Analyst, Afriland First Bank, Cameroon
Analyzed credit portfolios and managed risk assessments to support the bank’s credit-granting process.

Research Interests 🔍

Main Interests:

  • Econometrics (Forecasting, Machine Learning, Big Data Analysis)

Secondary Interests:

  • Macroeconomics
  • Microeconometrics
  • Finance

FONTON’s research integrates advanced econometric models with machine learning techniques to explore macro-financial linkages and evaluate economic policies.

Award 🏅

Ahmadou Mustapha FONTON has been recognized for his contributions to economic research and policy development through various grants and academic accolades. His innovative work in econometrics and machine learning positions him as a leading candidate for prestigious research awards.

Publications 📚

  1. “A machine learning approach in stress testing US bank holding companies” – Accepted for publication in International Review of Financial Analysis (2024). Read Here

Conclusion

Dr. Ahmadou Mustapha FONTON is a highly qualified candidate for the Research for Best Researcher Award. His extensive experience in econometrics, macroeconomics, and financial economics, coupled with his technical skills and policy impact, positions him as a strong contender. His research contributions, combined with his international perspective and teaching experience, align well with the objectives of the award. Addressing the areas for improvement, such as increasing his publication record and expanding his collaborative efforts, could further enhance his candidacy. Overall, Dr. Fonton’s profile reflects a distinguished researcher with a promising trajectory in the field of economics.

Paulo Vinicius Moreira Dutra | Artificial Intelligence and Machine Learning | Best Researcher Award

Posted on by Academic achievements

Mr.Paulo Vinicius Moreira Dutra | Artificial Intelligence and Machine Learning | Best Researcher Award

Master Federal University of Juiz de Fora Brazil

Paulo Vinícius Moreira Dutra is a dedicated computer science professor specializing in system development, software engineering, and digital games. With over a decade of teaching experience, Paulo has made significant contributions to various educational institutions, including the Instituto Federal de Educação Ciência e Tecnologia Sudeste de Minas Gerais.

Profile

ORCiD

Education

🎓 Paulo holds a Master’s degree in Computer Science from the Universidade Federal de Juiz de Fora (2023), with a focus on artificial intelligence. He also has a specialization in Computer Programming (2008), Higher Education Teaching (2017), and Digital Game Development (2018), as well as a bachelor’s degree in System Development Technology (2006).

Experience

💼 Paulo has extensive experience in both academia and industry. He has taught at the Faculdade de Filosofia, Ciências e Letras Santa Marcelina and currently serves as a professor at the Instituto Federal do Sudeste de Minas Gerais. His professional journey also includes a role as a systems analyst at Dvallone Tecidos Ltda, where he developed applications using Delphi, Advpl, and C#.

Research Interest

🔍 Paulo’s research interests lie in system development, software engineering, digital games, databases, machine learning, and reinforcement learning. His work often explores the intersection of artificial intelligence and game development, focusing on procedural content generation and educational applications.

Awards

🏆 Paulo has been recognized for his contributions to computer science education and research. His innovative approach to teaching and his impactful research projects have earned him accolades and nominations in various academic circles.

Publications

📝 Paulo has published several research articles and papers in esteemed journals and conferences. Notable publications include:

  1. “ARTOOLKIT: UMA BIBLIOTECA PARA CONSTRUÇÃO DE APLICAÇÕES EM REALIDADE AUMENTADA” (2016). Published in DUC IN ALTUM (Muriaé). Link.
  2. “Desenvolvimento de um framework para construção de aplicações desktop em java utilizando swing” (2011). Published in Duc in Altum (Muriaé).
  3. “A mixed-initiative design framework for procedural content generation using reinforcement learning” (2024). Accepted for publication in ENTERTAINMENT COMPUTING.
  4. “Procedural Content Generation using Reinforcement Learning and Entropy Measure as Feedback” (2022). Presented at the 21st Brazilian Symposium on Computer Games and Digital Entertainment (SBGames). Link.

 

Jeanfranco David Farfan Escobedo | Machine Learning | Young Scientist Award

Posted on by Academic achievements

Mr. Jeanfranco David Farfan Escobedo | Machine Learning | Young Scientist Award

Jeanfranco David Farfan at Escobedo State University of Campinas, Brazil

Jeanfranco David Farfan Escobedo is a PhD candidate in Computer Science at the University of Campinas (UNICAMP), Brazil, specializing in deep learning techniques for uncertainty reduction in oil reservoir simulations. He holds an M.Sc. in Computer Science from UNICAMP with a thesis in Conversational Systems and a B.Sc. in Computer and Systems Engineering from Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Peru, focusing on Computer Vision. Jeanfranco’s professional journey includes roles as a researcher at Shell Oil Company, Brazil, and teaching positions at UNICAMP and UTEC, Peru. He has received prestigious awards such as the Shell Oil Company Industry Research Scholarship and has contributed to significant publications in applied computing and artificial intelligence journals. His research timeline demonstrates continuous engagement in advancing deep learning, natural language processing, and computer vision fields.

Author Profile

Google Scholar Profile

Education

Jeanfranco David Farfan Escobedo is currently pursuing a PhD in Computer Science at the University of Campinas (UNICAMP), Brazil. He earned his Master of Science degree in Computer Science from UNICAMP, focusing on Conversational Systems. Previously, he obtained a Bachelor of Science in Computer and Systems Engineering from Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Peru, with a thesis in Computer Vision.

Research Focus

Jeanfranco’s research primarily revolves around applying deep learning techniques to reduce uncertainty in oil reservoir simulations. Additionally, he explores topics in natural language processing, focusing on conversational systems, and computer vision for tasks like image recognition.

Professional Journey

Jeanfranco has accumulated diverse professional experiences. He currently works as a researcher at Shell Oil Company in Brazil, specializing in utilizing deep learning for improving oil reservoir simulations. He has also served as a Teaching Assistant at UNICAMP, where he supported courses in Algorithms and Computer Programming. Furthermore, he has taught Machine/Deep Learning at the Artificial Intelligence University of Engineering and Technology (UTEC) in Peru.

Honors & Awards

Jeanfranco has received several notable awards, including the Shell Oil Company Industry Research Scholarship in 2021, the Sinch Latin America Industry Research Scholarship in 2019, and first place in the AgroHack hackathon for developing a plant disease monitoring app in 2018.

Publications Noted & Contributions

Jeanfranco has contributed significantly to academic publications, including:

Research Timeline

Jeanfranco’s research journey spans from his undergraduate studies through to his current doctoral research. He has consistently explored cutting-edge topics in deep learning, natural language processing, and computer vision, contributing to advancements in these fields.