Maryam Kheirabadi | Computer Science | Women Researcher Award

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Women Researcher Award

Maryam Kheirabadi
Affiliation Islamic Azad University
Country Iran
Scopus ID 57210153054
Documents 16
Citations 710
h-index 9
Subject Area Computer Science
Event International Academic Achievements & Awards
ORCID 0000-0001-8980-4299

Maryam Kheirabadi
Islamic Azad University, Iran

The Women Researcher Award recognizes distinguished scholarly achievement, research excellence, scientific innovation, and sustained academic contributions by women researchers. Maryam Kheirabadi has established a research profile in computer science, artificial intelligence, medical image analysis, and intelligent healthcare systems through peer-reviewed publications indexed in Scopus. Her work demonstrates interdisciplinary applications of deep learning, convolutional neural networks, recurrent neural networks, and medical decision-support systems, contributing to diagnostic technologies and intelligent recommendation systems.[1]

Abstract

Maryam Kheirabadi’s scholarly work focuses on applying artificial intelligence techniques to healthcare, medical image processing, pattern recognition, and educational recommendation systems. Her publications emphasize convolutional neural networks, deep learning architectures, image classification, recurrent neural networks with attention mechanisms, and computer-assisted diagnosis of gastric diseases. These contributions illustrate the integration of computer science methodologies into practical healthcare applications while advancing intelligent computational models for diagnostic accuracy and automated decision support.[2]

Keywords

Artificial Intelligence, Deep Learning, Computer Science, Medical Image Analysis, Convolutional Neural Networks, Healthcare Informatics, Machine Learning, Clinical Decision Support

Introduction

Artificial intelligence has become an increasingly influential discipline for solving complex healthcare challenges. The combination of machine learning, deep neural networks, and biomedical image analysis has improved diagnostic accuracy, automated disease detection, and clinical decision support. Within this evolving research landscape, Maryam Kheirabadi has contributed to studies addressing intelligent diagnostic systems, neural network optimization, and educational recommender systems, demonstrating interdisciplinary collaboration between computer science and healthcare research.[2]

Research Profile

According to the supplied Scopus metrics, Maryam Kheirabadi has authored sixteen indexed publications, received more than seven hundred citations, and achieved an h-index of nine. Her research portfolio centers on artificial intelligence, image processing, neural networks, biomedical signal analysis, and intelligent software systems. Her publications demonstrate continued engagement with computational approaches that support clinical diagnosis and intelligent educational technologies.[1]

Research Contributions

  • Development of deep convolutional neural network models for gastric cancer diagnosis using tongue image analysis.
  • Research on intelligent educational recommender systems employing recurrent neural networks and attention mechanisms.
  • Advancement of MRI brain tumor classification through convolutional neural network architectures.
  • Application of biomedical image processing techniques for healthcare diagnostics.
  • Contribution to explainable and data-driven medical decision-support technologies.

Publications

  • Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep Convolutional Networks (2021). Journal of Information Systems and Telecommunication.
  • Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention Technique (2021). Applied Artificial Intelligence. DOI: https://doi.org/10.1080/08839514.2021.2005298
  • Increasing the Accuracy in the Diagnosis of Stomach Cancer Based on Color and Lint Features of Tongue (2021). Biomedical Signal Processing and Control. DOI: https://doi.org/10.1016/j.bspc.2021.102782
  • Y-net: A Reducing Gaussian Noise Convolutional Neural Network for MRI Brain Tumor Classification with NADE Concatenation (2021). Biomedical Physics and Engineering Express.
    DOI: https://doi.org/10.1088/2057-1976/ac107b

Research Impact

The reported citation record reflects meaningful scholarly visibility within computer science and healthcare-related artificial intelligence research. Her studies contribute to the development of intelligent diagnostic systems, automated disease classification, medical image interpretation, and educational recommendation technologies. These interdisciplinary contributions support ongoing research into machine learning applications that improve analytical performance and healthcare decision-making.[3]

Award Suitability

Maryam Kheirabadi demonstrates qualifications consistent with the objectives of the Women Researcher Award through sustained scientific publication, interdisciplinary innovation, measurable citation performance, and contributions to artificial intelligence applications in healthcare. Her research profile reflects active engagement in internationally indexed scholarship and supports recognition for advancing computational methods with practical societal applications.[1]

Conclusion

Maryam Kheirabadi’s publication portfolio illustrates the integration of artificial intelligence with healthcare and intelligent information systems. Her contributions to deep learning, neural network design, biomedical imaging, and recommendation systems have strengthened interdisciplinary research in computer science. The documented publication record, citation performance, and research focus collectively support recognition within academic achievement and research excellence programs.

References

  1. Elsevier. (n.d.). Scopus Author Details: Maryam Kheirabadi, Author ID 57210153054. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57210153054
  2. Kheirabadi, M., et al. (2021). Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep Convolutional Networks. Journal of Information Systems and Telecommunication.
    http://www.scopus.com/inward/record.url?eid=2-s2.0-85111735812&partnerID=MN8TOARS
  3. Kheirabadi, M., et al. (2021). Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention Technique. Applied Artificial Intelligence. DOI: https://doi.org/10.1080/08839514.2021.2005298
  4. Kheirabadi, M., et al. (2021). Increasing the Accuracy in the Diagnosis of Stomach Cancer Based on Color and Lint Features of Tongue. Biomedical Signal Processing and Control. DOI: https://doi.org/10.1016/j.bspc.2021.102782
  5. Kheirabadi, M., et al. (2021). Y-net: A Reducing Gaussian Noise Convolutional Neural Network for MRI Brain Tumor Classification with NADE Concatenation. Biomedical Physics and Engineering Express. DOI: https://doi.org/10.1088/2057-1976/ac107b

Divya Gautam | Computer Science | Innovative Research Award

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Innovative Research Award

Divya Gautam
Affiliation STME, SVKM’s NMIMS, Indore
Country India
Scopus ID 57315556700
Documents 17
Citations 81
h-index 4
Subject Area Computer Science
Event International Academic Achievements & Awards
ORCID 0000-0002-1395-997X

Divya Gautam
STME, SVKM’s NMIMS, Indore, India

The Innovative Research Award recognizes researchers whose scholarly contributions demonstrate originality, scientific rigor, and meaningful impact within their respective disciplines. Divya Gautam has established a research portfolio in computer science with particular emphasis on mobile ad hoc networks (MANET), denial-of-service attack detection, machine learning-based network security, and optimization techniques. Her publications have contributed to understanding secure communication mechanisms and intelligent intrusion detection methods for decentralized wireless environments.[1]

Abstract

Divya Gautam’s research primarily investigates cybersecurity challenges within mobile ad hoc networks, focusing on intelligent techniques for detecting and mitigating denial-of-service attacks. Her work integrates support vector machines, particle swarm optimization, and pattern-based analytical methods to improve network resilience and communication reliability. The resulting publications collectively contribute toward advancing practical security frameworks for decentralized wireless systems while supporting broader developments in computer science research.[2]

Keywords

Computer Science, Cybersecurity, Mobile Ad Hoc Networks, DoS Detection, Machine Learning, Network Security, Support Vector Machine, Optimization Algorithms

Introduction

Modern wireless communication environments require effective mechanisms for detecting malicious activities while maintaining reliable network performance. Mobile ad hoc networks remain particularly vulnerable because of their decentralized architecture and dynamic topology. Research conducted by Divya Gautam addresses these challenges through computational intelligence techniques capable of improving attack detection accuracy while reducing network disruption.[3]

Research Profile

According to Scopus, Divya Gautam has authored seventeen indexed publications with eighty-one citations and an h-index of four. Her scholarly activities are concentrated within computer science, particularly network security, intrusion detection, wireless communication, intelligent optimization algorithms, and secure distributed networking. These metrics indicate an active and developing research profile with contributions appearing in journals, conference proceedings, and scholarly book chapters.[1]

Research Contributions

  • Development of pattern-based techniques for detecting DoS and DDoS attacks.
  • Application of Support Vector Machine methodologies for intelligent intrusion detection.
  • Integration of Particle Swarm Optimization with machine learning for improved classification.
  • Comparative evaluation of attack mitigation techniques in MANET environments.
  • Contribution to secure wireless networking research through conference and journal publications.

Publications

  1. A Comparative Study of DoS Attack Detection and Mitigation Techniques in MANET (2020), Lecture Notes in Networks and Systems.DOI:
    10.1007/978-981-15-2071-6_50
  2. Fixed points results in b-metric spaces (2020), Materials Today: Proceedings.DOI:
    10.1016/j.matpr.2021.05.147
  3. Pattern Based Detection and Mitigation of DoS Attacks in MANET Using SVM-PSO (2020).DOI:
    10.1007/978-3-030-44758-8_16
  4. Detection of DoS attacks in MANET using LIBSVM (2019), International Journal of Engineering and Advanced Technology.
  5. Pattern based detection of DDoS attacks in MANET (2019), International Journal of Innovative Technology and Exploring Engineering.

Research Impact

The available publication record demonstrates continued engagement with contemporary cybersecurity challenges affecting wireless communication networks. Citation activity reflects scholarly visibility, while the combination of journal articles, conference papers, and book chapters indicates dissemination across multiple academic publishing platforms. The research has practical relevance for secure communication systems, intelligent network monitoring, and machine learning-based threat detection.[4]

Award Suitability

Based on the documented publication record, indexed research output, citation performance, and focus on innovative cybersecurity methodologies, Divya Gautam demonstrates characteristics commonly associated with recognition through an Innovative Research Award. Her research emphasizes methodological development, practical applicability, interdisciplinary integration, and contribution to secure wireless networking technologies.[5]

Conclusion

Divya Gautam’s academic portfolio reflects sustained contributions to computer science, particularly in network security and intelligent attack detection for mobile ad hoc networks. Through the integration of machine learning and optimization techniques, her publications support ongoing developments in cybersecurity research while contributing to the broader understanding of resilient wireless communication systems. The documented scholarly record provides a solid foundation for academic recognition within innovation-oriented research awards.

References

  1. Elsevier. (n.d.). Scopus Author Details: Divya Gautam, Author ID 57315556700. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57315556700
  2. Gautam, D., & Tokekar, V. (2020). A Comparative Study of DoS Attack Detection and Mitigation Techniques in MANET. Lecture Notes in Networks and Systems, Springer. DOI: https://doi.org/10.1007/978-981-15-2071-6_50
  3. Gautam, D., & Tokekar, V. (2020). Pattern Based Detection and Mitigation of DoS Attacks in MANET Using SVM-PSO. Intelligent Computing Applications for Sustainable Real-World Systems.DOI: https://doi.org/10.1007/978-3-030-44758-8_16
  4. Gautam, D., & Tokekar, V. (2020). Fixed Points Results in b-Metric Spaces. Materials Today: Proceedings.
    https://doi.org/10.1016/j.matpr.2021.05.147
  5. SVKM’s NMIMS. (n.d.). Faculty Profile: Dr. Divya Gautam, Assistant Professor, Computer Engineering.
    https://indore.nmims.edu/faculty/dr-divya-gautam/

Quang Minh Tran | Computer Science | Innovative Research Award

Published on by Academic Achievements

Innovative Research Award

Quang Minh Tran
Affiliation University of Wollongong
Country Australia
ORCID 0009-0007-9413-2600
Documents 2
Subject Area Computer Science
Event International Academic Achievements & Awards

Quang Minh Tran
Institution: University of Wollongong,

Quang Minh Tran is a researcher in the field of Computer Science whose recent work focuses on trustworthy artificial intelligence, deepfake audio detection, adversarial machine learning, and multimedia security. His research investigates the robustness of deep learning systems against sophisticated adversarial attacks while contributing to the development of reliable forensic methods for synthetic audio detection. These studies address important challenges in AI security, digital trust, and the protection of multimedia systems against manipulation.[1]

Abstract

This article presents an academic profile of Quang Minh Tran in recognition of research contributions to Computer Science, particularly in adversarial machine learning and deepfake audio detection. His work examines the resilience of artificial intelligence systems under universal adversarial perturbations while advancing forensic methods capable of identifying manipulated synthetic speech. The research contributes to improving the security, reliability, and robustness of AI-enabled multimedia technologies.[2]

Keywords

Computer Science, Artificial Intelligence, Deepfake Audio Detection, Adversarial Machine Learning, Multimedia Security, Universal Adversarial Perturbations, AI Robustness, Audio Forensics, Digital Trust, Machine Learning Security.

Introduction

The increasing adoption of artificial intelligence has intensified concerns regarding the misuse of generative technologies, including deepfake audio. Detecting synthetic speech while maintaining robustness against adversarial attacks represents a significant challenge in AI security. Quang Minh Tran’s research explores these issues through systematic evaluation of deepfake detectors and vocoder fingerprint detectors, supporting the development of trustworthy AI systems suitable for practical deployment.[2]

Research Profile

  • Research field: Computer Science.
  • Primary interests include AI security and multimedia forensics.
  • Research emphasizes adversarial robustness of deep learning systems.
  • Investigates deepfake audio detection and vocoder fingerprint analysis.
  • Contributes to trustworthy artificial intelligence and secure multimedia applications.

Research Contributions

Quang Minh Tran has contributed to the evaluation of adversarial robustness in deepfake audio detection systems through comprehensive analysis of universal adversarial perturbations. His work investigates vulnerabilities in deep learning-based forensic models while identifying approaches that improve detector resilience. These contributions are relevant to cybersecurity, digital media authentication, trustworthy AI, and the broader development of reliable machine learning systems capable of operating under adversarial conditions.[2]

Publications

  • Evaluating Adversarial Robustness of Deepfake Audio Detectors and Vocoder Fingerprint Detectors Against Universal Adversarial Perturbations. Future Internet, 2026. DOI:10.3390/fi18070344
  • Evaluating Adversarial Robustness of Deepfake Audio Detectors and Vocoder Fingerprint Detectors Against Universal Adversarial Perturbations. Preprint, 2026. DOI:10.20944/preprints202606.0272.v1

Research Impact

The research addresses an increasingly important area of artificial intelligence by strengthening the understanding of adversarial vulnerabilities affecting deepfake detection technologies. The findings provide valuable insights for researchers, cybersecurity practitioners, and developers seeking to improve the resilience of AI-based forensic systems. This work contributes to ongoing efforts aimed at enhancing digital trust, secure communication, and responsible deployment of artificial intelligence.[2]

Award Suitability

Based on the available scholarly publications, Quang Minh Tran demonstrates emerging research contributions in artificial intelligence security, adversarial machine learning, and multimedia forensics. His work addresses contemporary challenges involving deepfake detection and AI robustness using rigorous scientific methodology. These contributions provide a sound academic basis for consideration within the Innovative Research Award category of the International Academic Achievements & Awards program.[1]

Conclusion

Quang Minh Tran’s research advances the field of Computer Science by addressing the robustness and security of artificial intelligence systems against adversarial manipulation. His investigations into deepfake audio detection and multimedia forensics contribute to the growing body of knowledge supporting trustworthy AI technologies. The combination of technical innovation, practical relevance, and scientific rigor reflects meaningful scholarly progress within the rapidly evolving domain of AI security.

References

  1. ORCID. (n.d.). Quang Minh Tran ORCID Record.
    https://orcid.org/0009-0007-9413-2600
  2. Future Internet. (2026). Evaluating Adversarial Robustness of Deepfake Audio Detectors and Vocoder Fingerprint Detectors Against Universal Adversarial Perturbations.
    https://doi.org/10.3390/fi18070344
  3. Preprints.org. (2026). Evaluating Adversarial Robustness of Deepfake Audio Detectors and Vocoder Fingerprint Detectors Against Universal Adversarial Perturbations.
    https://doi.org/10.20944/preprints202606.0272.v1

Md Minhajul Amin | Computer Science | Best Innovator Award

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Best Innovator Award

Md Minhajul Amin
Affiliation Intento Analytics
Country United States
Google Scholar 8Cc4X70AAAAJ
Documents 11
Citations 86
h-index 4
Subject Area Computer Science
Event International Academic Achievements & Awards

Md Minhajul Amin
Intento Analytics, United States

Md Minhajul Amin is a data analyst and researcher specializing in business analytics, artificial intelligence, healthcare informatics, project management, and big data applications. His multidisciplinary research integrates predictive analytics, machine learning, digital healthcare, and operational decision-making. Through academic publications, IEEE conference papers, industrial projects, and editorial activities, he has contributed to advancing evidence-based management and intelligent data-driven systems.[1]

Abstract

Md Minhajul Amin has established an interdisciplinary research portfolio connecting artificial intelligence, predictive analytics, healthcare systems, fraud detection, digital communication, and project management. His work emphasizes practical analytical solutions that improve organizational efficiency and support evidence-based decision-making across healthcare and business environments.[2]

Keywords

Business Analytics, Artificial Intelligence, Machine Learning, Healthcare Analytics, Telemedicine, Big Data, Project Management, Data Visualization, Predictive Analytics, Digital Transformation.

Introduction

Holding an M.S. in Information Systems from Central Michigan University, Md Minhajul Amin combines academic research with industry experience in data analytics. His research focuses on solving real-world organizational challenges through statistical analysis, visualization, artificial intelligence, and business intelligence technologies.[3]

Research Profile

  • IEEE conference contributor.
  • Associate Editor at IFR Discovery.
  • Editorial Board Member of The Science Post Journal.

Research Contributions

His publications cover ethical business analytics, AI-powered project management, fraud detection, cancer classification using machine learning, telemedicine implementation, customer segmentation, healthcare dashboards, RF communication systems, and network management technologies. His research demonstrates the practical integration of advanced analytics into healthcare, engineering, and organizational management.[4]

Publications

  • Ethical Challenges in Business Analytics.
  • Developing a Project Management Dashboard for Telehealth.
  • Business Analytics in the Era of Big Data.
  • AI-Powered Personalized Marketing.
  • Classification of Cancer Stages Using Machine Learning.

Research Impact

His scholarly output has attracted citations across business analytics, healthcare informatics, artificial intelligence, and project management disciplines. His professional activities further include patented AI-driven analytical devices and collaborative research addressing practical industry challenges.

Award Suitability

Considering his multidisciplinary research portfolio, peer-reviewed publications, IEEE conference participation, editorial responsibilities, industrial analytics experience, and measurable research impact, Md Minhajul Amin demonstrates qualifications aligned with recognition under an Innovative Research Award category.

Conclusion

Md Minhajul Amin continues contributing to applied research that bridges artificial intelligence, business analytics, healthcare systems, and project management. His academic achievements and industry experience illustrate the growing role of data-driven methodologies in addressing modern organizational and societal challenges.

External Links

References

    1. Amin, M. M., Munmun, Z. S., Atayeva, J., Ahmed, S. W., Shamim, I., & Akter, M. H. (2025).
      Developing a Project Management Dashboard for Telehealth Implementation.
      https://www.researchgate.net/publication/392591167_Developing_a_Project_Management_Dashboard_for_Telehealth_Implementation
    2. Google Scholar. (2026).
      Md Minhajul Amin – Google Scholar Profile.
      https://scholar.google.com/citations?user=8Cc4X70AAAAJ&hl=en
    3. ResearchGate. (2026).
      Md Minhajul Amin – Research Profile.
      https://www.researchgate.net/profile/Md-Minhajul-Amin
    4. LinkedIn. (2026).
      Md Minhajul Amin – Professional Profile.
      https://www.linkedin.com/in/md-minhajul-amin-cmu

Getachew Getu Enyew | Computer Science | Research Excellence Award

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Mr. Getachew Getu Enyew | Computer Science | Research Excellence Award

Information Network Security Administration (INSA) & Addis Ababa Science and Technology University | Ethiopia

Getachew Getu Enyew is an AI/ML Engineer and emerging researcher specializing in artificial intelligence, machine learning, and autonomous systems. He holds an M.Sc. in Electrical and Computer Engineering from Addis Ababa Science and Technology University, Ethiopia. His research focuses on intelligent decision-making systems, robotics perception, computer vision, and anomaly detection for real-world applications, particularly in cybersecurity and critical infrastructure. Currently working at the Information Network Security Administration (INSA), he develops AI-driven solutions for threat detection and leads MLOps integration for scalable deployment of machine learning models. He has authored multiple research papers on topics such as traffic accident prediction, industrial fault diagnosis, and AI-based intrusion detection, and has presented his work at national conferences. His contributions aim to advance safe, adaptive, and trustworthy AI systems with strong societal and industrial impact.

Featured Publications

Artificial Intelligence in Fault Diagnosis of Industrial Machinery: A Comprehensive Review
– Structural Control and Health Monitoring (2025) | Citations: 1

 

Mr. Md. Asraful Sharker Nirob | Data Science | Best Researcher Award

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Mr. Md. Asraful Sharker Nirob | Data Science | Best Researcher Award

Mr. Md. Asraful Sharker Nirob | Data Science – Researcher at Daffodil International University, Bangladesh

Md. Asraful Sharker Nirob is a highly motivated early-career researcher in computer science with a focus on machine learning, deep learning, and artificial intelligence applications. With a strong academic background and research involvement at the Health and Informatics Lab, he is driven to solve real-world problems in agriculture and healthcare using intelligent systems. His experience spans industry and academia, with a combination of technical expertise and leadership qualities that position him as a rising figure in applied AI research.

Profile:

Orcid | Scopus | Google Scholar

Education:

Nirob completed his Bachelor of Science in Computer Science and Engineering from a well-regarded university in Bangladesh, graduating with a CGPA of 3.70 out of 4.00. Throughout his academic journey, he maintained a high level of performance, actively engaged in research projects, and participated in student organizations. His academic background provided him with a solid foundation in software engineering, machine learning, data science, and algorithmic problem-solving.

Experience:

He is currently serving as a Research Assistant at the Health and Informatics Lab, where he works on AI-based models for disease diagnosis and predictive analysis. His responsibilities include dataset curation, preprocessing, and implementing neural network models using frameworks such as TensorFlow and PyTorch. Previously, he worked as a Junior Software Engineer, contributing to responsive web application development using React.js, JavaScript, and version control systems. He also gained administrative and communication experience as a Student Associate at the university’s career development center.

Research Interest:

Nirob’s research interests lie in machine learning 🤖, computer vision 👁️, deep learning 🧠, and natural language processing 💬. He is particularly passionate about explainable AI, neural network architectures, and hybrid deep learning models. His projects often explore the intersection of AI and practical domains like agriculture and medical imaging, with a goal to enhance classification accuracy, interpretability, and real-world impact.

Award:

Md. Asraful Sharker Nirob is a strong candidate for the Best Researcher Award due to his excellent research contributions, early career productivity, and innovative use of AI in solving domain-specific problems. His technical skills, multi-disciplinary collaborations, leadership in co-curricular activities, and strong academic record make him a well-rounded researcher. His dedication to publishing high-quality research and building accessible datasets adds further weight to his nomination.

Publications:

  • “XSE-TomatoNet” 🍅 – MethodsX, 2025
    Introduced an explainable AI approach using EfficientNetB0; cited for improving agricultural diagnostics.
  • “COLD-12” 🌿 – Franklin Open, 2025
    Hybrid CNN model for cotton disease detection; praised for high accuracy and innovation in multi-level features.
  • “Dragon Fruit Dataset” 🍓 – Data in Brief, 2023
    Created a comprehensive fruit maturity grading dataset; referenced in multiple dataset-driven research works.
  • “Brain Tumor Classification with Explainable AI” 🧠 – ECCE Conference, 2025
    Proposed a multi-scale attention fusion model; appreciated for bridging AI with medical imaging.
  • “Credit Card Fraud Detection” 💳 – IJRASET, 2024
    Leveraged behavioral biometrics and Random Forest; cited in interdisciplinary cybersecurity studies.
  • “Lemon Leaf Dataset” 🍋 – Data in Brief, 2024
    Shared high-quality annotated lemon disease dataset; reused in computer vision-based plant disease research.
  • “Sugarcane Disease Classification” 🌱 – IEEE STI Conference, 2024
    Applied hybrid DL model to agriculture; useful in smart farming systems and cited in precision agri-tech work.

Conclusion:

In summary, Md. Asraful Sharker Nirob demonstrates the qualities of a dedicated and impactful young researcher. With a diverse publication portfolio, technical depth, and ongoing contributions to pressing real-world problems, he stands out as a deserving nominee for the Best Researcher Award. His strong academic background, innovation in AI applications, and leadership in student activities indicate a promising future in academia and research. 🌟

 

 

 

Dr. Uddalak Mitra | Machine learning | Best Researcher Award

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Dr. Uddalak Mitra | Machine learning | Best Researcher Award 

Dr. Uddalak Mitra, JIS College of Engineering, India

Dr. Uddalak Mitra is an Assistant Professor at JIS College of Engineering, affiliated with MAKAUT University, Kolkata, West Bengal. He holds a Ph.D. in Bioinformatics from Visva-Bharati University, Santiniketan, India. With expertise in bioinformatics, computational biology, machine learning, and deep learning, Dr. Mitra focuses on applying AI-driven methods to agriculture and medical diagnosis. He has published over 22 research articles and holds 9 patents under process. Actively mentoring students across academic levels, he also serves as a reviewer for reputed international journals. His research bridges biological sequence analysis and clinical applications, aiming to advance scientific and healthcare innovations.

Professional Profile:

GOOGLE SCHOLAR

ORCID

SCOPUS

Summary of Suitability for Best Researcher Award:

Dr. Uddalak Mitra is a highly suitable candidate for the Best Researcher Award due to his impactful contributions in the fields of bioinformatics, machine learning, and deep learning, with applications in healthcare and agriculture. With over 22 publications, including in SCI and Scopus-indexed journals, and 9 patents under process, he has demonstrated consistent research productivity and innovation. His interdisciplinary approach—bridging computational biology with AI-driven diagnostics—has advanced scientific understanding and clinical applications. As an active mentor and reviewer, Dr. Mitra exemplifies both academic excellence and leadership in research.

🎓 Education

  • 📘 Ph.D. in Bioinformatics
    🏫 Visva-Bharati University, Santiniketan, India
    🧬 Specialized in computational biology, machine learning, and their applications in bio-sciences.

💼 Work Experience

  • 👨‍🏫 Assistant Professor
    🏢 JIS College of Engineering, affiliated with MAKAUT University, Kolkata, West Bengal
    📆 Teaching & mentoring students (Ph.D., Master’s, UG)
    🔬 Active in interdisciplinary research combining ML/DL with bioinformatics and medical diagnostics
    🧠 Reviewer for international peer-reviewed journals

🏆 Achievements

  • 📚 22+ research publications in journals, conferences & book chapters

  • 🔍 5 papers in SCI/Scopus-indexed journals

  • 🧪 9 patents published or under process

  • 🧠 Research focus on AI-based biological sequence analysis & clinical diagnosis

  • 🤝 Member of IFERP & ISTE

🥇 Awards & Honors

  • 🏅 Award Nomination: Best Researcher Award (2025)

  • 📈 Citation Index:

    • h-index: 3

    • i10-index: 1

  • 🌐 Recognized for advancing AI-driven innovations in science and medicine

Publication Top Notes:

Ml-powered handwriting analysis for early detection of Alzheimer’s disease

CITED:11

PEER: a direct method for biosequence pattern mining through waits of optimal k-mers

CITED:6

Leveraging AI and Machine Learning for Next-Generation Clinical Decision Support Systems (CDSS)

CITED:4

An efficient tactic for analysis and evaluation of malware dump file using the volatility tool

CITED:3

Tandem repeat interval pattern identifies animal taxa

CITED:1

Jacob Mbarndouka Taamté | Machine learning | Best Researcher Award

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Dr. Jacob Mbarndouka Taamté | Machine learning | Best Researcher Award

Dr. Jacob Mbarndouka Taamté | Machine learning – Research Officer at Institute of Geological and Mining Research, Cameroon

Jacob Mbarndouka Taamté is an accomplished research scientist specializing in electronics, electrical engineering, automation, instrumentation, and industrial maintenance. Based in Cameroon, his work has been instrumental in the development of low-cost, innovative devices for monitoring air quality, environmental radiation, and nuclear safety. Taamté holds a Ph.D. in Physics, with a focus on Electrical and Electronic Systems, and has made significant contributions to the field of environmental monitoring through cutting-edge technology. His academic and professional journey is marked by numerous achievements, including being awarded the Best Young Researcher of Cameroon in 2024. He is also an active member of several international research initiatives and has presented his findings at numerous conferences, advancing global discussions on sustainable technology and environmental protection.

Profile:

Orcid

Education:


Jacob Mbarndouka Taamté’s academic journey is defined by rigorous studies in the fields of physics, electrical engineering, and industrial production. He completed his Ph.D. in Physics, specializing in Electrical and Electronic Systems, at the University of Yaoundé I in 2022. Prior to this, he earned his Master’s in Science from the University of Ngaoundéré, where he also completed his Bachelor’s and UDT degrees, specializing in industrial maintenance and production. His educational background, spanning over a decade, has provided him with a solid foundation in the development and application of advanced technologies aimed at solving complex industrial and environmental challenges.

Experience:


Jacob Taamté’s professional career spans several years in both academia and research. Since 2021, he has served as a Research Officer at the Research Center for Nuclear Science and Technology (CRSTN) at the Institute of Geological and Mining Research (IRGM) in Cameroon, where he continues to contribute to innovative research on environmental monitoring and radiation protection. He also teaches Electronics and Electrical Engineering at The Armandins Higher Institute in Yaoundé, Cameroon, guiding students in practical applications of his research. Before his current roles, Taamté worked as a teacher and supervisor in scientific clubs, mentoring young minds and promoting scientific inquiry. His work extends beyond research, as he actively engages in the development of programs aimed at promoting sustainable technological solutions in his region.

Research Interest:


Jacob Taamté’s primary research interests lie in the areas of environmental monitoring, nuclear instrumentation, and sustainable technology. He is particularly focused on the development of low-cost electronic devices for real-time monitoring of air quality, water quality, soil health, and environmental radiation. His work integrates the use of microcontrollers, embedded systems, and machine learning to design smart devices that provide real-time data for public health and safety. Taamté’s research in this domain has led to practical applications, such as radiation protection systems and air quality monitoring devices, which have been widely recognized for their impact on public health and safety, especially in Cameroon and other African countries.

Award:


Jacob Mbarndouka Taamté has earned numerous accolades for his groundbreaking research and contributions to the scientific community. In 2024, he was awarded the Special Prize at the National Technology Days in Cameroon for his innovative research in environmental monitoring. He also received the Best Young Researcher of Cameroon Award the same year, recognizing his outstanding contributions to research and technology. Additionally, Taamté was honored with the Best Young Professional Radiation Protection Scientist Award in 2022 by the International Radiation Protection Association (IRPA), reflecting his exceptional work in the field of environmental radiation measurement. His achievements underscore his leadership in scientific research and his dedication to improving public health through technology.

Publications:


Jacob Taamté has authored several influential publications in renowned scientific journals, contributing significantly to the fields of environmental monitoring, radiation protection, and low-cost technological innovations. Below are some of his key publications:

  1. Taamté, J. M., Danwé, Y. F., Folifack Signing, V. R., Gondji, D. S., Koyang, F., & Saïdou. (2025). Design of a low-cost water quality assessment device based on a reference instrument. Urban Water Journal, 1–22. [Cited by: 15]
  2. Taamte, J. M., Tchuente Siaka, Y. F., Nducol, N., Yakum-Ntaw Younui, S., Ahmadou, G., Etende Essama, R. C., … Saïdou. (2025). Smart electronic device for air quality and exposure risk assessment. Smart Science, 1–15. [Cited by: 12]
  3. Folifack Signing, V. R., Taamté, J. M., & Saïdou. (2024). IoT-based Monitoring System and Air Quality Prediction Using Machine Learning for a Healthy Environment in Cameroon. Environmental Monitoring and Assessment, 198(12). [Cited by: 25]
  4. Taamté, J. M., Kountchou Noube, M., Folifack Signing, V. R., Yerima Abba Hamadou, et al. (2024). Real-time air quality monitoring based on locally developed unmanned aerial vehicle and low-cost smart electronic device. Journal of Instrumentation, 19 P05036. [Cited by: 18]
  5. Taamté, J. M., Koyang, F., Gondji, D. S., Oumar Bobbo, M., et al. (2022). Low-cost radon monitoring with validation by a reference instrument. Instrumentation Science and Technology. [Cited by: 22]
  6. Taamté, J. M., Kountchou Noubé, M., Bodo Bertrand, et al. (2021). Low-cost air quality monitoring system design and comparative analysis with a conventional method. International Journal of Energy and Environmental Engineering, 10(4). [Cited by: 10]

Conclusion:


Jacob Mbarndouka Taamté stands out as a researcher whose work combines scientific excellence, innovative problem-solving, and a commitment to societal impact. His research has not only contributed to the advancement of environmental monitoring technology but has also provided practical solutions to pressing global challenges such as radiation protection and public health. Through his numerous accolades, publications, and active participation in international projects, Taamté has established himself as a leader in his field. His dedication to advancing scientific knowledge, particularly in developing affordable technologies for environmental monitoring, makes him a deserving candidate for the Best Researcher Award.

Rashmi S | Machine Learning Techniques | Best Researcher Award

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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.

AHMADOU MUSTAPHA FONTON MOFFO | Machine Learning | Best Researcher Award

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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

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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.