Alain Bernard Djimeli Tsajio | Apprentissage Machine (IA) | Best Researcher Award

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Prof. Alain Bernard Djimeli Tsajio | Apprentissage Machine (IA) | Best Researcher Award

Senior lecturer at University of Dschang, Cameroon.

Prof. Alain Bernard Djimeli Tsajio is a physicist, AI researcher, and telecommunications expert specializing in computer vision, machine learning, and cybersecurity. He holds a Ph.D. in Electronics from the University of Dschang and has over 20 years of experience in academia and research. As a faculty member at IUT Fotso Victor, University of Dschang, he has significantly contributed to curriculum development, student mentorship, and applied research in artificial intelligence. His work has been published in high-impact journals, and he actively collaborates on international research projects in AI-driven diagnostics, wireless networks, and intelligent security systems.

Publication Profile

Orcid

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Educational Details

  • Ph.D. in Physics (Electronics), 2016 – University of Dschang, Cameroon

  • Diplôme d’Études Approfondies (DEA) in Physics (Electronics), 2005 – University of Yaoundé I, Cameroon

  • Maîtrise in Physics (Electronics), 2003 – University of Yaoundé I, Cameroon

  • Licence in Physics, 2000 – University of Yaoundé I, Cameroon

  • Baccalauréat C, 1995 – Lycée Classique de Bangangté, Cameroon

Professional Experience

Prof. Djimeli Tsajio is a Senior Lecturer and Researcher in Telecommunications and Networks at the IUT Fotso Victor, University of Dschang, Cameroon. Since 2005, he has been responsible for course coordination, student mentoring, and curriculum development. His teaching expertise covers a broad range of topics, including:

  • Multimedia Protocols and Quality of Service

  • Wireless Networks and Emerging Technologies

  • Cybersecurity and Artificial Intelligence

  • Information Theory and Cryptography

  • Telecommunication Protocols and High-Speed Networks

He has also served as an instructor at the CISCO Networking Academy, a guest lecturer at multiple universities, and a thesis advisor for Master’s and Ph.D. students in AI and Cybersecurity. His administrative contributions include student academic counseling, exam grading, and research program coordination.

Research Interest

  • Precision Agriculture (disease detection in plants)

  • Medical Diagnostics (breast cancer detection, leukocyte counting)

  • Network Security and Cybersecurity

  • AI-Based Intelligent Agents

Top Noted Publication

  • Solitons and other solutions of the nonlinear fractional Zoomeron equation

    • Authors: E. Tala-Tebue, Z. I. Djoufack, A. Djimeli-Tsajio, A. Kenfack-Jiotsa

    • Journal: Chinese Journal of Physics

    • Volume: 56 (3), Pages: 1232-1246

    • Year: 2018

    • Citations: 26

    • Summary: This paper presents soliton solutions and other exact solutions to the nonlinear fractional Zoomeron equation, leveraging advanced mathematical techniques in nonlinear wave dynamics and fractional calculus.

  • Improved detection and identification approach in tomato leaf disease using transformation and combination of transfer learning features

    • Authors: A. B. Djimeli-Tsajio, N. Thierry, L. T. Jean-Pierre, T. F. Kapche, P. Nagabhushan

    • Journal: Journal of Plant Diseases and Protection

    • Volume: 129 (3), Pages: 665-674

    • Year: 2022

    • Citations: 14

    • Summary: This study introduces an AI-based method for plant disease detection, utilizing transfer learning and feature transformation techniques to improve tomato leaf disease identification.

  • Quantum breathers associated with modulational instability in 1D ultracold boson in optical lattices involving next-nearest neighbor interactions

    • Authors: Z. I. Djoufack, E. Tala-Tebue, F. Fotsa-Ngaffo, A. B. Djimeli-Tsajio, F. Kapche-Tagne

    • Journal: Optik

    • Volume: 164, Pages: 575-589

    • Year: 2018

    • Citations: 13

    • Summary: This work explores quantum breathers and modulational instability in ultracold bosonic systems under optical lattice constraints, contributing to quantum physics and nonlinear dynamics.

  • Formalization method of the UML statechart by transformation toward Petri Nets

    • Authors: T. Noulamo, E. Tanyi, M. Nkenlifack, J. P. Lienou, A. Djimeli

    • Journal: IAENG International Journal of Computer Science

    • Volume: 45 (4), Pages: 32

    • Year: 2018

    • Citations: 8

    • Summary: This paper focuses on software modeling and verification, proposing a methodology for transforming UML statecharts into Petri Nets for improved formal verification of software systems.

  • Analysis of interest points of curvelet coefficients contributions of microscopic images and improvement of edges

    • Authors: A. Djimeli, D. Tchiotsop, R. Tchinda

    • Journal: arXiv preprint

    • Year: 2013

    • Citations: 8

    • Summary: This study investigates curvelet transform techniques for microscopic image analysis, improving edge detection in biological and medical imaging.

Conclusion

Prof. Alain Bernard Djimeli Tsajio is a strong candidate for the Best Researcher Award, given his significant contributions to AI, cybersecurity, and telecommunications. His work in AI-driven plant disease detection and medical diagnostics highlights both technical excellence and real-world impact. Strengthening his global presence, industry partnerships, and citation impact could further solidify his position as a leading researcher in his field.

 

Swati Jitendrakmar Patel | Artificial Intelligence | Best Researcher Award

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Ms. Swati Jitendrakmar Patel | Artificial Intelligence | Best Researcher Award

Software Engineer at Skyline Software Solutions, India

Ms. Swati Patel is an accomplished Data Analyst and Software Developer with extensive expertise in data science, machine learning, and software engineering. She has contributed significantly to academic research, publishing 8 journal papers, 2 IEEE conference papers, and 5 books on topics ranging from software security to predictive analytics. With strong analytical skills and a track record of developing efficient workflows and impactful applications, she has consistently delivered data-driven solutions for business optimization and research innovation.

Publication Profile

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Educational Details

Ms. Swati Patel holds a Master of Science in Advanced Computing Technologies from Birkbeck, University of London (2022-2023), where her projects included Principal Component Analysis on the Pima Indians Diabetes Dataset and predicting DDoS attacks using Darknet Time-Series data. She earned a Master’s degree in Computer Science from SES’s R. C. Patel Institute of Technology (NMU, India, 2012-2014), completing her thesis on software birthmark-based theft detection of JavaScript programs. Her Bachelor’s degree in Computer Engineering was completed at PSGVPM’s D. N. Patel College of Engineering, Shahada (NMU, India, 2008-2012), where she developed an Online Voting System using ASP.NET and SQL.

Professional Experience

Swati Patel is a Data Analyst and Entrepreneur with proven expertise in designing and optimizing workflows, data visualization, and software development. She served as a Data Analyst at SSP Group PLC, UK (2023-2024), where she created interactive Power BI dashboards to analyze sales data, optimized EPOS systems for data accuracy, and reduced data processing time by 30% through SQL optimization. As an entrepreneur, she successfully led Skyline Software Solutions (2014-2022), developing over 35 applications in Java, .NET, and other technologies. She managed end-to-end project execution, providing high-quality software solutions to diverse industries.

Research Interest

Swati’s research focuses on applied machine learning, natural language processing, and predictive modeling. She is particularly interested in statistical methods for data anomaly detection, speech-based health diagnostics, and secure systems in computing environments. Her work also extends to innovative clustering techniques for theft detection in software and dimensionality reduction in large datasets.

Author Metrics

  • Publications: 2 IEEE papers, 8 journal papers, 5 books.
  • Key Topics: Data science, machine learning, NLP, secure computing systems, predictive analytics.
  • Notable Works:
    • “A Review on Statistical Analysis-Based Approaches for Data Poison Detection Using Machine Learning.”
    • “Automated Depression Assessment from Speech Signals Using Pitch and Energy Features.”
    • “Long Short-Term Memory and Gated Recurrent Unit Networks for Accurate Stock Price Prediction.”
    • Books: COVID-19 Data Analysis for the United Kingdom and Data Exploration and Machine Learning using R.

Publication Top Notes

  • Software Birthmark Based Theft Detection of JavaScript Programs Using Agglomerative Clustering and Frequent Subgraph Mining
    • Authors: S.J. Patel, T.M. Pattewar
    • Conference: 2014 International Conference on Embedded Systems (ICES)
    • Pages: 63–68
    • Citations: 9
    • Year: 2014
    • Summary: This paper presents a novel method for detecting software theft in JavaScript programs using software birthmarks. The approach employs agglomerative clustering and frequent subgraph mining to identify similarities between programs, aiding in theft detection.
  • K-Means Clustering Algorithm: Implementation and Critical Analysis
    • Author: S. Patel
    • Publisher: Scholars’ Press
    • Citations: 8
    • Year: 2019
    • Summary: This publication provides an in-depth exploration of the K-means clustering algorithm, including its implementation and a critical analysis of its efficiency and limitations in clustering diverse datasets.
  • Software Birthmark Based Theft Detection of JavaScript Programs Using Agglomerative Clustering and Improved Frequent Subgraph Mining
    • Authors: S. Patel, T. Pattewar
    • Conference: 2014 International Conference on Advances in Electronics, Computers, and Communications
    • Citations: 7
    • Year: 2014
    • Summary: This paper builds upon earlier research by introducing an improved method for frequent subgraph mining, enhancing the detection accuracy of JavaScript program theft through advanced birthmark-based techniques.
  • Software Birthmark for Theft Detection of JavaScript Programs: A Survey
    • Authors: S.J. Patel, T.M. Pattewar
    • Journal: IJAFRC (International Journal of Advanced Foundation and Research in Computers)
    • Volume: 1, Issue 2, Pages: 29–38
    • Citations: 2
    • Year: 2014
    • Summary: This survey reviews existing methods for detecting software theft in JavaScript programs, with a focus on software birthmark techniques. It outlines challenges, solutions, and future research directions.
  • Emerging Trends in Computer Technology (NCETCT)
    • Authors: S.J. Patel, T.M. Pattewar
    • Journal: IJCA (International Journal of Computer Applications)
    • Conference Issue: NCETCT, Number 1
    • Year: 2014
    • Summary: This conference paper discusses advancements in computer technology with a focus on software security. It explores the use of software birthmarks as a means of detecting and preventing intellectual property theft in programming.

Conclusion

Ms. Swati Patel is a highly qualified and deserving candidate for the Best Researcher Award. Her combination of academic excellence, impactful research, and real-world contributions to artificial intelligence and software engineering set her apart as an innovative thinker. To maximize her potential and visibility, she could focus on strengthening her citation impact, pursuing international collaborations, and contributing to higher-impact conferences. Overall, her track record reflects dedication, skill, and the ability to drive meaningful advancements in her field.

 

 

 

Omar Soufi | Intelligence Artificial | Best Researcher Award

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Dr. Omar Soufi | Intelligence Artificial | Best Researcher Award

Doctorate at Mohammed V University of Rabat Mohammadia School of Engineering, Morocco

Summary:

Dr. Omar Soufi is an expert in artificial intelligence and data science, with a specialized focus on remote sensing and geographic information systems (GIS). He completed his Ph.D. in Computer Engineering with a concentration in Artificial Intelligence at EMI Rabat in 2023, where his research centered on enhancing satellite image quality using deep learning techniques. With extensive experience in both academia and industry, Dr. Soufi has led numerous projects in AI, data science, and business intelligence. His contributions to the field include developing geospatial platforms for natural disaster risk management and implementing innovative solutions for satellite image processing. Dr. Soufi is currently advancing his research and professional endeavors as a postdoctoral researcher at CRTS Rabat, where he continues to explore the frontiers of AI and remote sensing.

Professional Profile:

👩‍🎓Education:

  • Ph.D. in Computer Engineering: Artificial Intelligence (2023)
    • Institution: EMI Rabat
    • Dissertation: Approche par Deep Learning au profit de la télédétection spatiale : Amélioration de la qualité d’images satellites et du procédé du capteur d’étoile.
  • Engineering Degree in Computer Science (2020)
    • Institution: EMI Rabat
    • Option: Ingénierie et Qualité Logicielle.
  • Engineering Degree in Information Systems Engineering (2020)
    • Institution: Polytechnique Grenoble, ENSIMAG
  • Fundamental License in Mechanical Engineering (2014)
    • Institution: ARM Merkèns
  • Diplôme des Études Universitaires (2015)
    • Institution: ARM Merkèns
  • Baccalauréat (2011)
    • Institution: 1ER LMR
    • Option: Sciences de Vie et de Terre

🏢 Professional Experience:

Dr. Omar Soufi is currently a Postdoctoral Researcher in Computer Engineering at CRTS Rabat, a position he has held since February 2024. In this role, he leads projects focused on artificial intelligence and data science, particularly for satellite image processing and spatial data analysis. Since 2022, Dr. Soufi has also been the Head of the Geomatics & Decision-Making Tools Department at CRTS Rabat, where he manages geomatics projects, develops decision-making tools, and oversees the implementation of geospatial platforms.

Previously, from 2020 to 2022, Dr. Soufi served as the Head of the Business Intelligence & Decision-Making Tools Department at CRTS Rabat. In this capacity, he directed business intelligence projects, developed data analytics solutions, and optimized decision-making processes. From 2017 to 2020, he was the Chief of Project at the Decision Support Center, managing decision support projects, implementing big data architectures, and developing e-learning platforms.

Dr. Soufi’s earlier professional experience includes serving as a Project Manager in the IT Department at CRTS Rabat from 2016 to 2017. He led IT projects, developed web applications, and implemented distributed data processing systems. His internships include a PFE internship on the super resolution of satellite images using deep learning (February 2020 – July 2020), an engineering internship on the development of a space station management platform at CRERS Rabat (July 2019 – August 2019), and an internship on the development of an agricultural campaign bulletin diffusion platform at CRTS Rabat (July 2019 – August 2019).

Research Interests

Dr. Omar Soufi’s research interests are centered on artificial intelligence, data science, remote sensing, and geographic information systems (GIS). His work focuses on applying deep learning techniques to improve the quality of satellite images and developing intelligent systems for spatial data analysis and geospatial applications. He is particularly interested in enhancing accessibility to high-resolution satellite imagery and advancing spacecraft attitude control using AI.

Top Noted Publication:

  • Study of Deep Learning-Based Models for Single Image Super-Resolution
    • Authors: O. Soufi, F.Z. Belouadha
    • Published in: Revue d’Intelligence Artificielle, 2022
    • Link: DOI: 10.18280/ria.360616
  • FSRSI: New Deep Learning-Based Approach for Super-Resolution of Multispectral Satellite Images
    • Authors: O. Soufi, F.Z. Belouadha
    • Published in: Ingénierie des Systèmes d’Information, 2023
    • Link: DOI: 10.18280/isi.280112
  • Deep Learning Technique for Image Satellite Processing
    • Authors: O. Soufi, F.Z. Belouadha
    • Published in: Intell Methods Eng Sci, 2023
  • Enhancing Accessibility to High-Resolution Satellite Imagery: A Novel Deep Learning-Based Super-Resolution Approach
    • Authors: O. Soufi, F.Z. Belouadha
    • Published in: Journal of Environmental Treatment Techniques, 2023
  • An Intelligent Deep Learning Approach to Spacecraft Attitude Control: The Case of Satellites
    • Authors: O. Soufi, F.Z. Belouadha
    • Status: Under review, 2023

Pablo Arnau González | Artificial Intelligence | Best Researcher Award

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Dr. Pablo Arnau González, Artificial Intelligence, Best Researcher Award

Doctorate at Universidad de Valencia, Spain

Summary:

Dr. Pablo Arnau González is a researcher with expertise in artificial intelligence and machine learning. He has made significant contributions to the fields of sentiment analysis, affective computing, and biometrics. Dr. González has conducted research on adapting conversational toolkits for sentiment analysis tasks and developing methodologies for identifying affect levels from EEG signals and visual stimuli. He has also contributed to the development of adaptive intelligent tutoring systems. With a background in computing and artificial intelligence, Dr. González has published several papers in reputable conferences and journals.

Professional Profile:

Scopus Profile

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👩‍🎓Education & Qualification:

PhD in Computing and Artificial Intelligence

  • University of the West of Scotland, 2020

Graduado o Graduada en Ingeniería Informática de Gestión y Sistemas de Información

  • Universitat de València, 2015

Professional Experience:

Dr. Pablo Arnau González has a diverse professional background, including positions in consulting, industry, and academia. Here is a summary of his previous professional positions:

  • 2022 – 2023: Postdoctoral Fellow (Margarita Salas) at Universitat de València.
  • 2021: Senior Consultant at SDG Consulting.
  • 2019 – 2021: Analyst at Cecotec Innovaciones, S.L.
  • 2019: Technologist in Digital Health at the University of the West of Scotland.

Research Interest:

Artificial Intelligence: Exploring AI algorithms and techniques for various applications such as sentiment analysis, affective computing, and intelligent tutoring systems.

Machine Learning: Investigating machine learning models and methodologies for processing EEG signals and visual stimuli to identify affect levels and subject identification.

Natural Language Processing: Adapting conversational toolkits and architectures for tasks like sentiment analysis, chatbots, and conversational agents.

Biometrics: Researching the use of EEG signals and image-evoked affect for biometric authentication and identification systems.

Adaptive Intelligent Tutoring Systems: Developing systems that can assess affective and behavioral responses to adaptively tailor educational content and interactions.

Publication Top Noted:

On adapting the DIET architecture and the Rasa conversational toolkit for the sentiment analysis task

  • Authors: M Arevalillo-Herráez, P Arnau-González, N Ramzan
  • Journal: IEEE Access
  • Year: 2022
  • Volume: 10
  • Pages: 107477-107487
  • Citation count: 11

A Method to Identify Affect Levels from EEG signals using two-dimensional Emotional Models

  • Authors: P Arnau-González, N Ramzan, M Arevalillo-Herráez
  • Conference: The 2016 European Simulation and Modelling Conference
  • Year: 2016
  • Pages: 299-303
  • Citation count: 8

Image-evoked affect and its impact on EEG-based biometrics

  • Authors: P Arnau-González, S Katsigiannis, M Arevalillo-Herráez, N Ramzan
  • Conference: 26th IEEE International Conference on Image Processing
  • Year: 2019
  • Citation count: 7

Single-channel EEG-based subject identification using visual stimuli

  • Authors: S Katsigiannis, P Arnau-González, M Arevalillo-Herráez, N Ramzan
  • Conference: 2021 IEEE EMBS International Conference on Biomedical and Health Informatics
  • Year: 2021
  • Citation count: 5

Affective and Behavioral Assessment for Adaptive Intelligent Tutoring Systems

  • Authors: L Marco-Giménez, M Arevalillo-Herráez, FJ Ferri, S Moreno-Picot, …
  • Conference: UMAP (Extended Proceedings)
  • Year: 2016
  • Citation count: 5