Francisco Javier Lima Florido | Artificial Intelligence | Best Researcher Award

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Mr Francisco Javier Lima Florido | Artificial Intelligence | Best Researcher Award

Researcher in training , University of Malaga , Spain

Francisco Javier Lima Florido is an accomplished researcher whose work in Machine Learning, Deep Learning, and Natural Language Processing has significant practical and academic merit. His focus on multilingual dialogue systems, health applications, and automatic interpretation solutions speaks to his expertise and potential to impact society through technology. As a PhD student, he is still developing his academic career but has already made noteworthy contributions to the field through participation in significant projects.

Publication Profile
scopus

Education :

Francisco Javier Lima Florido holds a Bachelor’s degree in Software Engineering from the University of Málaga (2016). He also earned a Master’s degree in Software Engineering and Artificial Intelligence from the same institution in 2019. Currently, Francisco is pursuing a PhD in the Translation and Interpreting Department at the University of Málaga, where his research is primarily focused on the intersection of technology and language.

Experience:

Francisco has actively participated in various research projects throughout his academic career. Notably, he was involved in the VIP: Integrated Voice-Text System for Interpreters project. This project explored the integration of voice and text systems for interpreters. Presently, he is contributing to cutting-edge projects like the Neural-based multilingual dialogue systems for the development of health apps (focusing on triage in Spanish, English, and Arabic) and the MI4ALL – Automatic Interpretation for All Using a Deep Learning-based API transfer project. These initiatives demonstrate his extensive experience in developing machine learning models for natural language processing (NLP).

Research Focus:

His primary research interests lie in the application of Machine Learning and Deep Learning techniques to Natural Language Processing (NLP). He is particularly focused on the development of multilingual dialogue systems and automatic interpretation technologies. His work aims to enhance the functionality and accessibility of tools for interpreters and healthcare applications, with a special interest in bridging communication gaps in multilingual settings.

Skills:

Francisco is highly skilled in several areas within Software Engineering and Artificial Intelligence, with a strong emphasis on Machine Learning and Deep Learning. His technical expertise spans:

    • Natural Language Processing (NLP)
    • Multilingual Dialogue Systems
    • Deep Learning Algorithms
    • Machine Learning Model Development
    • Speech-to-Text Technologies
    • Python Programming and related frameworks (e.g., TensorFlow, PyTorch)

 

Publication :

Francisco Javier Lima Florido has contributed to several research projects and publications in the fields of Machine Learning, Deep Learning, and Natural Language Processing. Notably:​

  1. “Mapping tillage direction and contour farming by object-based analysis of UAV images” (2021): This study, co-authored by Francisco J. Lima-Cueto, Rafael Blanco-Sepúlveda, María L. Gómez-Moreno, José Dorado, and José M. Peña, was published in Computers and Electronics in Agriculture. ​

  2. “Using Vegetation Indices and a UAV Imaging Platform to Quantify the Density of Vegetation Ground Cover in Olive Groves (Olea Europaea L.) in Southern Spain” (2019): Authored by Francisco J. Lima-Cueto, Rafael Blanco-Sepúlveda, María L. Gómez-Moreno, and Federico B. Galacho-Jiménez, this paper appeared in Remote Sensing. ​

Additionally, Francisco Javier Lima Florido has been involved in research projects such as “VIP: Integrated Voice-Text System for Interpreters” and is currently participating in “Neural-based multilingual dialogue systems for the development of health apps: triage (Spanish – English/Arabic)” and the transfer project “MI4ALL – Automatic Interpretation For All Using a Deep Learning-based API”.

conclusion:

Francisco is highly deserving of consideration for the “Best Researcher Award.” His expertise in cutting-edge AI technologies, especially in the context of language translation and interpretation, holds immense potential for positive social impact. While there are areas for improvement, such as enhancing his publication record and broadening his collaborative network, his current research trajectory shows great promise. His ongoing contributions to AI research and application indicate that he is on a path to becoming a leading figure in the field.

Nisar Hussain | Artificial Intelligence and Machine Learning | Best Researcher Award

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Mr.Nisar Hussain |Artificial Intelligence and Machine Learning|Best Researcher Award

Mr.  Nisar Hussain Instituto Politechnico Nacional, Mexico City, Mexico

Nisar Hussain is a researcher affiliated with the Instituto Politécnico Nacional (IPN) in Mexico City, Mexico. He is currently enrolled in the Doctorate in Computer Science program at IPN’s Centro de Investigación en Computación (CIC), focusing his research on offensive language detection and sentiment analysis in code-mixed text on social media.Throughout his academic career, Hussain has contributed to various studies in the field of Natural Language Processing (NLP). Notably, he co-authored the paper titled “ORUD-Detect: A Comprehensive Approach to Offensive Language Detection in Roman Urdu Using Hybrid Machine Learning–Deep Learning Models with Embedding Techniques,” published in the journal Information in February 2025.In addition to his work on offensive language detection, Hussain has explored other areas of NLP. He co-authored a study on guilt detection in text, which was published in Scientific Reports in July 2023.

Publication Profile

Google scholar

orcid

Education :

Ph.D. in Computer Science (2022-2025, Ongoing)
Instituto Politécnico Nacional, MéxicoMaster’s in Computer Science (2014-2017)
University of Agriculture, Faisalabad, PakistanBachelor of Science in Computer Science (BSCS) (2010-2014)
COMSATS University Islamabad, Sahiwal Campus

Experience :

With 4+ years of experience in developing and deploying ML and NLP systems, I have actively contributed to multiple projects, applying NLP techniques for real-world problem-solving. I have worked with large, complex datasets, implementing hybrid ML-DL approaches for automated text processing, sentiment analysis, and multilingual content understanding. My research collaborations span multiple institutions, focusing on AI-driven solutions for text analysis and detection tasks.

Research Focus :

I specialize in Natural Language Processing (NLP) and Machine Learning, with a particular emphasis on Offensive Language Detection and Sentiment Analysis of Code-Mixed Data. My research explores multilingual and low-resource language models, leveraging and fine-tuning mBERT, XLM-R, IndicBERT, and Google’s BERT-based models. I am particularly interested in hate speech detection, sentiment analysis, language identification, and emotion analysis in complex linguistic environments. My work integrates deep learning techniques, transformers, and hybrid ML-DL models to improve text processing and understanding in diverse multilingual contexts.

Awards:

Published multiple high-impact research papers in leading AI and NLP conferences/journalsActive participant in international AI competitions and workshopsRecognized for contributions to multilingual and low-resource NLP advancements

Publication :

  • Shaheen, M., Awan, S. M., Hussain, N., & Gondal, Z. A. (2019). Sentiment analysis on mobile phone reviews using supervised learning techniques. IJMECS, 11(7), 32.

 

  • Mehak, G., Qasim, A., Meque, A. G. M., Hussain, N., Sidorov, G., & Gelbukh, A. (2025, January). TechExperts (IPN) at GenAI Detection Task 1: Detecting AI-Generated Text in English and Multilingual Contexts. In Proceedings of the 1st Workshop on GenAI Content Detection (GenAIDetect) (pp. 161-165).

 

  • Hussain, N., Qasim, A., Mehak, G., Kolesnikova, O., Gelbukh, A., & Sidorov, G. (2025). Hybrid Machine Learning and Deep Learning Approaches for Insult Detection in Roman Urdu Text. AI, 6(2), 33. https://doi.org/10.3390/ai6020033

 

  • Qasim, A., Mehak, G., Hussain, N., Gelbukh, A., & Sidorov, G. (2025). Detection of Depression Severity in Social Media Text Using Transformer-Based Models. Information, 16(2), 114. https://doi.org/10.3390/info16020114

 

  • Manzoor, M. I., Shaheen, M., Khalid, H., Anum, A., Hussain, N., & Faheem, M. R. (2018). Requirement Elicitation Methods for Cloud Providers in IT Industry. IJMECS, 10(10).

 

  • Hussain, N., & Anees, T. (2018). Development of a novel approach to search resources in IoT. International Journal of Advanced Computer Science and Applications, 9(9).

 

  • Faheem, M. R., Iftikhar, A., & Hussain, N. (2022). Automated Diagnosing of Eye Disease in Real Time. Journal of Computing & Biomedical Informatics, 3(1), 282-288.

 

  • Shaheen, M., Anees, T., Hussain, N., & Obaid, I. (2019, April). A Research on SOA in the IT Industry of Pakistan. In Proceedings of the 2019 ICCTA (pp. 149-154).

 

  • Meque, A. G. M., Hussain, N., Sidorov, G., & Gelbukh, A. (2023). Guilt Detection in Text: A Step Towards Understanding Complex Emotions. arXiv preprint arXiv:2303.03510.

 

  • Tash, M. S., Ahani, Z., Tonja, A., Gemeda, M., Hussain, N., & Kolesnikova, O. (2022, December). Word Level Language Identification in Code-mixed Kannada-English Texts using Traditional Machine Learning Algorithms. In Proceedings of the (ICON) (pp. 25-28).

 

 

 Conclusion

Given their strong publication record, hands-on experience with AI models, and focus on low-resource NLP, the candidate is highly competitive for the Best Researcher Award. Strengthening industry collaborations, increasing research impact, and securing grants will further enhance their research profile.

 

 

 

Shankar Patil | Deep Learning | Best Researcher Award

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Prof Dr. Shankar Patil | Deep Learning | Best Researcher Award

Prof Dr, Shankar Patil,Smt. Indira Gandhi College of Engineering, Ghansoli, India

Prof. Dr. Shankar Patil is a distinguished academician and researcher affiliated with Smt. Indira Gandhi College of Engineering in Ghansoli, India. With a robust background in engineering education and research, Dr. Patil has made significant contributions to the field, particularly in the areas of [please specify his key areas if known]. He holds [degrees or qualifications], and his expertise spans [mention specific areas of expertise or research interests]. Dr. Patil is actively involved in [mention any significant roles, committees, or academic initiatives he’s part of]. His dedication to advancing knowledge and fostering academic excellence underscores his commitment to the field of engineering education and research

Professional Profiles:

Scopus

Objective:

To procure a challenging position in an organization where I can promote my ideas and knowledge with the best engineering qualities for the benefit of the organization.

Education :

  • Ph.D. in Computer Science & Engineering, Singhania University, Pacheri, September 2018.
  • Master of Computer Engineering, Bharati Vidyapeeth Deemed University, Pune, 2005.
  • Bachelor of Computer Engineering, Walchand College of Engineering, Sangli, Shivaji University, Kolhapur, 1998.

Membership:

  • Life Member, Indian Society of Technical Education (ISTE), Membership Number LM41153.
  • Computer Society of India (CSI), Membership Number N1158683.
  • Recognized as PhD Guide in Computer Engineering at University of Mumbai.
  • Recognized as Post-Graduate Teacher at University of Mumbai.

Publications :

  • Object Identification and Alerting Method for Pattern Analysis,” Inderscience Journal of Computational Vision and Robotics, February 2024.
  • “Yolo V4-Based Hybrid Feature Enhancement Network with Robust Object Detection under Adverse Weather Conditions,” Springer Nature journal Signal, Image and Video Processing, March 2024.
  • “Caritas- ‘Serving Smiles’,” 2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), India, 2023.
  • “Online Exam Proctoring System Based on Computer Aided Design In Mechanical Engineering,” 2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), India, 2023.
  • “Melanoma Skin Cancer Disease Detection Using Convolutional Neural Network,” 3rd International Conference of Emerging Technologies 2022 (INCET2022), India, May 2022.