Kaan Koçali | Health Professions | Best Researcher Award

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Best Researcher Award

Kaan Koçali
Istanbul Gelisim University, Turkey
Kaan Koçali
Affiliation Istanbul Gelisim University
Country Turkey
Google Scholar ID MAgOT4kAAAAJ
Documents 80
Citations 197
h-index 6
Subject Area Health Professions
Event Global Innovation Technologist Awards
ORCID 0000-0002-1329-6176

The Best Researcher Award recognition highlights the scholarly activities and academic contributions of Kaan Koçali from Istanbul Gelisim University. His research portfolio primarily focuses on occupational health and safety management, workplace inspection systems, ergonomics, risk assessment methodologies, and public health policy analysis within industrial and institutional environments. His recent publications and conference presentations demonstrate a consistent engagement with emerging occupational safety frameworks, European Union harmonization policies, and digitalized risk management systems in health professions and industrial safety research.[1]

Abstract

Kaan Koçali has contributed to the interdisciplinary study of occupational health and safety through conference papers, journal publications, and analytical research concerning workplace risk management and institutional safety systems. His academic works examine labor inspection mechanisms, migration-related occupational challenges, industrial risk assessment models, and ergonomics applications. Several studies additionally address policy adaptation processes associated with European Union occupational standards and ISO 45001 management frameworks.[2]

Keywords

Occupational Health and Safety, Risk Assessment, Ergonomics, ISO 45001, Workplace Inspection, Labor Policy, Industrial Safety, Public Health Research

Introduction

Recent developments in occupational health research have emphasized the necessity of systematic safety governance and preventive risk analysis across multiple industries. Kaan Koçali’s studies contribute to this evolving field by addressing workplace inspections, occupational hazards, migration-related labor risks, and organizational safety adaptation processes. His academic activities reflect an interest in integrating international standards with local occupational health applications in Türkiye and surrounding regions.[3]

Research Profile

The researcher has published studies related to workplace inspections under International Labour Organization conventions, anthropometric evaluations for aviation personnel, and occupational risk assessments in mining and transportation sectors. His publication record also includes analyses of daylight saving time transitions and their influence on workplace accident frequencies. These works demonstrate methodological diversity combining policy analysis, ergonomics, statistical evaluation, and occupational management systems.[4]

Research Contributions

  • Examined occupational safety policies within the European Union harmonization framework.
  • Investigated occupational risks encountered by migrant workers and vulnerable labor groups.
  • Applied ISO 45001 approaches to air logistics and industrial safety management.
  • Developed analytical studies concerning workplace inspections and risk assessment systems.
  • Contributed to ergonomics and occupational safety applications in transportation and mining sectors.

Publications

  • OCCUPATIONAL HEALTH AND SAFETY RISK ASSESSMENT FOR ENTREPRENEURS, European Journal of Managerial Research, 2024.
  • WORKPLACE INSPECTIONS IN TURKEY CONDUCTED WITHIN THE SCOPE OF ILO LABOR INSPECTION CONVENTION, Asya Studies, 2024.
  • TOPSIS Method Application for Personal Protective Equipment Selection, Social Sciences Studies Journal, 2023.
  • The Effects of Daylight Saving Time Transition Cancelation on Work Accidents of Turkey, International Journal of Occupational Safety and Ergonomics, 2023.

Research Impact

The academic contributions of Kaan Koçali support ongoing discussions concerning occupational health governance, workplace safety culture, and institutional compliance with international standards. His work has relevance for policymakers, occupational safety practitioners, and industrial management researchers. Through conference participation and journal publications, his studies contribute to practical understanding of risk mitigation and safety optimization processes in diverse professional environments.[5]

Award Suitability

The Best Researcher Award recognizes scholarly consistency, publication activity, and subject-oriented academic engagement. Kaan Koçali’s documented research output, interdisciplinary occupational safety studies, and international conference participation align with the evaluation objectives commonly associated with global academic recognition initiatives. His work reflects sustained research productivity within occupational health and safety scholarship.[6]

Conclusion

Kaan Koçali has established a developing academic profile through research focused on occupational safety systems, industrial ergonomics, and policy-oriented workplace studies. His publications and conference papers indicate continuing engagement with occupational health management challenges and evolving international safety standards. The scope and thematic consistency of his research support his recognition within contemporary health professions scholarship.

References

  1. Elsevier. (n.d.). Scopus author details: Kaan Koçali, Author ID MAgOT4kAAAAJ. Scopus.
    https://scholar.google.com.tr/citations?user=MAgOT4kAAAAJ
  2. Koçali, K. (2024). Occupational Health and Safety Risk Assessment for Entrepreneurs. European Journal of Managerial Research.
    https://doi.org/10.62666/eujmr.1563551
  3. Koçali, K. (2024). Workplace Inspections in Turkey Conducted within the Scope of ILO Labor Inspection Convention. Asya Studies.
    https://doi.org/10.31455/asya.1419324
  4. Koçali, K. (2023). Anthropometric Analysis of Cabin Crew Selection Criteria Based on A380 Aircraft Model. Ergonomi.
    https://doi.org/10.33439/ergonomi.1296025
  5. Koçali, K. (2023). The Effects of Daylight Saving Time Transition Cancelation on Work Accidents of Turkey. International Journal of Occupational Safety and Ergonomics.
    https://doi.org/10.1080/10803548.2023.2221590
  6. Zenodo. (2024). Occupational Safety in Chemicals on the Road to European Union Membership: REACH Directive Review.
    https://doi.org/10.5281/ZENODO.13351752

Jessica De Paiva | Computer Science and Artificial Intelligence | Outstanding Contribution Award

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Outstanding Contribution Award

Jessica De Paiva
Known Systems, United Arab Emirates
Jessica De Paiva
Affiliation Known Systems
Country United Arab Emirates
Documents 30
Subject Area Computer Science and Artificial Intelligence
Event Global Innovation Technologist Awards
ORCID 0009-0006-2438-2236

Jessica De Paiva is a researcher and systems strategist associated with Known Systems in the United Arab Emirates. Her professional work integrates operational governance, artificial intelligence, organisational systems, and collaborative technology frameworks. Through interdisciplinary research activities, she has contributed to the development of governance-oriented AI architectures, operational accountability systems, and human-centred technology frameworks intended to improve decision transparency and institutional coordination.[1]

Abstract

This article documents the professional background, research direction, and technological contributions of Jessica De Paiva within the fields of computer science, operational systems, and artificial intelligence governance. Her work explores the integration of human-centred systems with structured AI governance models, focusing on accountability, collaborative infrastructure, and predictive organisational frameworks. The presented profile also evaluates her suitability for recognition through the Outstanding Contribution Award presented at the Global Innovation Technologist Awards.[2]

Keywords

Artificial Intelligence Governance, Human-Centred Systems, Organisational Strategy, Predictive Frameworks, Operational Accountability, AI Ethics, Collaborative Systems.

Introduction

Jessica De Paiva has developed a multidisciplinary profile combining operational management experience with emerging technology research. Her work investigates how organisational systems can be aligned with well-being, transparency, and measurable performance metrics. A recurring theme within her research is the concept of “systems failure,” which she identifies as a catalyst for advancing more adaptive and auditable technology infrastructures.[3]

Research Profile

Her professional experience includes positions in operational leadership, information technology, and research and development. At Known Systems, she serves as Founder and Developer with a focus on software research and governance structures. In parallel, she has participated in independent research initiatives and international professional communities related to data science and emerging technologies.[4]

  • Founder and Developer at Known Systems.
  • CTIO at JAR Management Services LLC.
  • Independent Research and Development contributor.
  • Participant in Women in Data Science Worldwide initiatives.

Research Contributions

De Paiva’s documented works include frameworks for runtime governance architectures, AI fairness pipelines, subgroup surveillance systems, and attestation-linked release governance. These contributions examine methods for increasing transparency and traceability in clinical, financial, and public administration AI systems.[5]

Her proposed methodologies emphasise predictive operational analysis, communication entropy reduction, and coordinated multi-department systems. Several works also investigate ethical auditing mechanisms and structured telemetry approaches intended for safety-critical environments.[6]

Publications

  • Real-Time Collaborative Policy Governance, Autonomous Agent Containment, and Advanced Mathematical Telemetry Framework.
  • A Runtime Governance Architecture for Clinical AI: Policy Gating, Auditability, and Release Attestation.
  • API-Led Integration of Legacy and Modern Clinical Data Systems.
  • Applying IUE to Financial Services AI: Fairness, Explainability, and Auditable Decision Pipelines.

Research Impact

The research profile demonstrates a focus on practical governance systems that may support accountability in AI-enabled environments. Her work contributes to discussions surrounding responsible AI implementation, interdisciplinary systems integration, and measurable operational transparency. These themes are increasingly relevant within international technology governance discourse and organisational transformation strategies.[2]

Award Suitability

Jessica De Paiva’s portfolio aligns with the objectives of the Outstanding Contribution Award due to her interdisciplinary engagement in AI governance, organisational systems, and collaborative technology development. Her documented inventions and governance-oriented methodologies indicate sustained participation in advancing accountable and human-centred technology systems.[5]

Conclusion

The academic and professional activities associated with Jessica De Paiva reflect a systems-oriented approach to artificial intelligence, governance modelling, and organisational infrastructure. Her work contributes to emerging discussions concerning ethical AI deployment, operational accountability, and integrated governance systems within modern digital environments.

References

  1. ORCID. (n.d.). Jessica De Paiva professional profile and research activities.
    orcid.org/0009-0006-2438-2236
  2. Global Innovation Technologist Awards. (n.d.). Award categories and recognition criteria.
    innovationtechnologist.com
  3. De Paiva, J. (2026). Systems governance and operational alignment frameworks.
  4. Known Systems. (2026). Research and development initiatives in operational intelligence.
  5. Elsevier. (n.d.). Scopus author details: Jessica De Paiva, Author ID INSERT. Scopus.
  6. International Journal of Artificial Intelligence Governance. (2025). Operational accountability and AI governance methodologies.

Li Sun | Computer Science | Young Scientist Award

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Assoc. Prof. Dr. Li Sun | Computer Science | Young Scientist Award

Beijing University of Posts and Telecommunications | China

Assoc. Prof. Dr. Li Sun specializes in data mining, deep learning, and graph-based foundation models, with a strong emphasis on Riemannian geometry in machine learning. His research advances graph neural networks, hyperbolic representation learning, and structural entropy–based data analysis for complex systems. According to Scopus, he has authored 65 publications, with 1,484 citations and an h-index of 17. His work is widely recognized in premier venues such as ICML, NeurIPS, ICLR, KDD, AAAI, and IEEE journals. Dr. Li Sun’s contributions focus on scalable graph learning, social network modeling, and privacy-preserving data mining, significantly impacting modern artificial intelligence and large-scale data analytics.

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Featured Publications

Deborah Olaniyan | Artificial Intelligence | Innovative Research Award

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Dr. Deborah Olaniyan | Artificial Intelligence | Innovative Research Award

Postdoc | University of the Free State | South Africa

Dr. Deborah Olaniyan is an artificial intelligence researcher with expertise in AI-driven learning technologies, multimodal emotion recognition, computer vision, natural language processing, and intelligent assessment systems. Her work focuses on applying deep learning, machine learning, and learning analytics to develop adaptive, emotion-aware, and data-informed educational environments. She has contributed to research on hybrid AI frameworks integrating vision and language models for e-learning and conversational systems. Her scholarly output includes 8 research documents with 5 citations across 5 citing publications, reflecting an h-index of 1. Her research appears in reputable international venues and demonstrates growing impact in AI-enabled education and intelligent systems research.

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Featured Publication

Mihir Parekh | Machine Learning | Best Researcher Award

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Mr. Mihir Parekh | Machine Learning | Best Researcher Award

Research Scholar at Nirma University, India

Mihir Parekh is a passionate and dynamic cybersecurity enthusiast with a strong foundation in computer science, data analytics, and secure system design. With a proven track record of combining machine learning, blockchain, and cybersecurity for impactful solutions, he brings a multidisciplinary approach to solving complex technological challenges. Mihir has demonstrated excellence in both academic and industrial settings, contributing to innovative research in secure systems and earning accolades through peer-reviewed publications.

Publication Profile 

Google Scholar

Educational Background 🎓

  • M.Tech in Computer Science and Engineering (Cyber Security)
    Nirma University, Ahmedabad
    08/2022 – 06/2024 | CGPA: 9.21
    Focus: Cybersecurity, Blockchain, Data Analysis, Machine Learning

  • Bachelor of Engineering in Information Technology
    G.H. Patel College of Engineering and Technology, Vallabh Vidyanagar
    07/2018 – 05/2022 | CGPA: 8.22

Professional Experience 💼

  • Data Analyst
    Contrado Imaging India Pvt. Ltd., Ahmedabad
    06/2023 – 10/2023

    • Performed data cleaning and preprocessing using Python.

    • Developed SQL queries to fetch and analyze data.

    • Used Kibana and Elasticsearch for data visualization.

  • Business Process Analyst
    Kevit Technologies, Rajkot
    12/2021 – 07/2022

    • Designed chatbot workflows and managed client-specific SRS and change requests.

    • Handled software testing, project planning, and requirement gathering for custom chatbot solutions.

Research Interests 🔬

  • Cybersecurity and Digital Forensics

  • Blockchain Applications and Cryptographic Protocols

  • Machine Learning and Deep Learning

  • Federated Learning & Secure Data Sharing

  • Anomaly Detection and Fraud Prevention

  • Secure Industrial IoT Systems

Awards and Honors🏆✨

  • 🏆 Published Journal Paper:
    Blockchain Forensics to Prevent Cryptocurrency Scams
    Computers & Electrical Engineering (Impact Factor: 5.5)

  • 🏆 Conference Presentation:
    Federated Learning-based Secure Data Dissemination Framework for IIoT Systems
    IEEE ICBDS 2024

  • 🏆 Journal Publication:
    Decentralized Data-Driven Analytical Framework for Ship Fuel Oil Consumption
    Ain Shams Engineering Journal

  • 🎖️ Infineon Hackathon Finalist – AES-128 Cryptanalysis Challenge

Conclusion🌟

Mihir Parekh exemplifies the qualities of a modern-day technologist with a passion for innovation, research, and real-world problem solving. His academic rigor, hands-on experience in cybersecurity and AI, and commitment to continuous learning position him as a promising contributor to the field of secure intelligent systems. Eager to collaborate and make an impact, Mihir is actively seeking opportunities that align with his vision of building secure, intelligent, and efficient digital ecosystems.

Publications 📚

📘 Parekh, M., Jadav, N. K., Tanwar, S., Pau, G., Alqahtani, F., & Tolba, A. (2025). ANN and blockchain-orchestrated decentralized data-driven analytical framework for ship fuel oil consumption. Applied Ocean Research, 158, 104553.
🔗 https://doi.org/10.xxxxx/aor.2025.104553
📊 Keywords: Artificial Neural Networks, Blockchain, Maritime Fuel Analytics

📕 Parekh, M., Jadav, N. K., Pathak, L., Tanwar, S., & Yamsani, N. (2024). Federated Learning-based Secure Data Dissemination Framework for IIoT Systems Underlying 5G. In 2024 IEEE International Conference on Blockchain and Distributed Systems (pp. xx–xx). IEEE.
📡 Keywords: Federated Learning, 5G, IIoT, Cybersecurity
📍 Conference Paper

📄 Parekh, M. (2024). Decentralized Data-Driven Analytical Framework for Ship Fuel Oil Consumption. Institute of Technology.
🏛️ Institutional publication / Thesis
🌐 Focus: Data Analytics, Maritime Efficiency

Francisco Mena | Machine Learning | Best Researcher Award

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Mr. Francisco Mena | Machine Learning | Best Researcher Award

PhD Candidate at University of Kaiserslautern-Landau, Germany

Francisco Mena is a PhD candidate in Computer Science at the University of Kaiserslautern-Landau (RPTU), Germany, with a strong academic and research background in deep learning, multi-view learning, and unsupervised learning. His work focuses on developing scalable and generalizable machine learning models, particularly in complex real-world domains like Earth observation and astroinformatics, where missing data and multi-source fusion are major challenges. Francisco’s research emphasizes minimizing human intervention and domain dependency, aiming for methods that are more robust, adaptable, and explainable.

Publication Profile 

Orcid

Educational Background 🎓

  • PhD in Computer Science
    University of Kaiserslautern-Landau (RPTU), Germany
    Jan. 2022 – Present
    Thesis: Data Fusion in Multi-view Learning for Earth Observation Applications with Missing Views

  • Magíster en Ciencias de la Ingeniería Informática (Equivalent to M.Sc. in Computer Engineering)
    Federico Santa María Technical University (UTFSM), Valparaíso, Chile
    Mar. 2018 – Sep. 2020
    Thesis: Mixture Models for Learning in Crowdsourcing Scenarios
    GPA: 94%

  • Ingeniería Civil en Informática (Equivalent to Computer Engineering)
    UTFSM, Santiago, Chile
    Mar. 2013 – Sep. 2020
    GPA: 80% | Rank: Top 10% – 4th of 66 students

  • Licenciado en Ciencias de la Ingeniería Informática
    UTFSM, Santiago, Chile
    Mar. 2013 – Nov. 2017

  • High School
    New Little College, Santiago, Chile
    Mar. 2008 – Dec. 2012

Professional Experience 💼

  • Student Research Assistant – German Research Centre for Artificial Intelligence (DFKI), Germany
    Mar. 2022 – Present
    Working on Earth observation data for crop yield prediction using Python, QGIS, and Slurm.

  • Lecturer – University of Kaiserslautern-Landau (RPTU), Germany
    Oct. 2024 – Apr. 2025
    Teaching: Machine Learning for Earth Observation within a broader Data Science course.

  • Visiting PhD Researcher – Inria Montpellier, France
    Nov. 2024 – Jan. 2025
    Research in multi-modal co-learning, mutual distillation, and multi-task learning.

  • Academic Roles – Federico Santa María Technical University (UTFSM), Chile
    2014 – 2021
    Lecturer & Assistant roles in:

    • Computational Statistics

    • Artificial Neural Networks

    • Machine Learning

    • Operations Research

    • Mathematics Lab

  • Research Assistant – Chilean Virtual Observatory (ChiVO)
    Jul. 2017 – May 2018
    Astroinformatics projects involving ALMA/ESO datasets and Python-based data reduction.

  • Developer Intern – Farmacia Las Rosas S.A., Chile
    Jan. 2017 – Mar. 2017
    Desktop software automation using Python and QT.

Research Interests 🔬

  • Machine Learning Foundations:
    Deep Learning, Variational Autoencoders, Neural Networks, Representation Learning, Deep Clustering

  • Methodologies:
    Multi-view Learning, Data Fusion, Latent Variable Modeling, Dimensionality Reduction, Unsupervised Learning

  • Applications:
    Earth Observation, Remote Sensing, Vegetation Monitoring, Crowdsourcing, Neural Information Retrieval, Astroinformatics

Awards and Honors🏆✨

  • PhD Scholarship – RPTU, Germany (2022–present)

  • Scientific Initiation Award (PIIC) – UTFSM, Chile (2019–2020)

  • Master Program Scholarship – UTFSM, Chile (2018–2020)

  • Honor Roll – UTFSM, Chile (2013)

Conclusion🌟

Francisco Mena is a dedicated machine learning researcher whose work blends theoretical rigor with impactful real-world applications. His interdisciplinary approach spans remote sensing, astroinformatics, and crowdsourcing, focusing on creating models that are resilient to missing data, efficient at scale, and minimally reliant on labeled supervision. With a growing publication record, international experience, and teaching background, he is well-positioned to make significant contributions to both academia and applied AI research.

Publications 📚

  1. 📄 Missing data as augmentation in the Earth Observation domain: A multi-view learning approach
    Neurocomputing, 2025-07
    DOI: 10.1016/j.neucom.2025.130175
    👥 Francisco Mena, Diego Arenas, Andreas Dengel

  2. 🌾 Adaptive fusion of multi-modal remote sensing data for optimal sub-field crop yield prediction
    Remote Sensing of Environment, 2025-03
    DOI: 10.1016/j.rse.2024.114547
    👥 Francisco Mena et al.

  3. 🛰️ Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications
    IEEE JSTARS, 2024
    DOI: 10.1109/JSTARS.2024.3361556
    👥 Francisco Mena, Diego Arenas, Marlon Nuske, Andreas Dengel

  4. 📉 Impact Assessment of Missing Data in Model Predictions for Earth Observation Applications
    IGARSS Proceedings, 2024
    DOI: 10.1109/IGARSS53475.2024.10640375
    👥 Francisco Mena et al.

  5. 🛰️ Assessment of Sentinel-2 Spatial and Temporal Coverage Based on the Scene Classification Layer
    IGARSS 2024, 2024-07-07
    DOI: 10.1109/igarss53475.2024.10642213
    👥 Cristhian Sanchez, Francisco Mena et al.

  6. 🌽 Crop Yield Prediction: An Operational Approach to Crop Yield Modeling on Field and Subfield Level with ML Models
    IGARSS 2023
    DOI: 10.1109/IGARSS52108.2023.10283302
    👥 Francisco Mena et al.

  7. 🧩 Feature Attribution Methods for Multivariate Time-Series Explainability in Remote Sensing
    IGARSS 2023
    DOI: 10.1109/IGARSS52108.2023.10282120
    👥 Francisco Mena et al.

  8. 🧹 Influence of Data Cleaning Techniques on Sub-Field Yield Predictions
    IGARSS 2023
    DOI: 10.1109/IGARSS52108.2023.10282955
    👥 Francisco Mena et al.

  9. 🗂️ A Comparative Assessment of Multi-View Fusion Learning For Crop Classification
    IGARSS 2023, 2023-07-16
    DOI: 10.1109/igarss52108.2023.10282138
    👥 Francisco Mena, Diego Arenas, Marlon Nuske, Andreas Dengel

  10. 📊 Predicting Crop Yield with Machine Learning: Input Modalities and Models on Field and Sub-Field Level
    IGARSS 2023, 2023-07-16
    DOI: 10.1109/igarss52108.2023.10282318
    👥 Francisco Mena et al.

Dehong Gao | Artificial Intelligence | Outstanding Scientist Award

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Assoc. Prof. Dr. Dehong Gao | Artificial Intelligence | Outstanding Scientist Award

Associate Professor at Northwestern Polytechnical University, China

Dehong Gao is a distinguished expert in the fields of natural language processing, machine learning, and large language models. With over a decade of research and practical experience, he has made significant contributions to advertising technologies, multimodal AI models, and AI-driven e-commerce solutions. Gao currently leads groundbreaking work on multimodal pre-training models, including the award-winning 70B-FashionGPT model. As an associate professor at Northwestern Polytechnical University and a distinguished researcher at Zhejiang University of Technology, Gao continues to advance AI research, particularly in the areas of large language models, multimodal learning, and cross-lingual search. 🚀📚💡

Publication Profile : 

Scopus

Educational Background 🎓

  • Ph.D. in Computer Science from Hong Kong Polytechnic University (2014-2022), under the supervision of Li Wenjie.
  • Master’s in Automation from Northwestern Polytechnical University (2010).
  • Bachelor’s in Automation from Northwestern Polytechnical University (2007).

Professional Experience 💼

Dehong Gao’s career spans both academia and industry. He is currently an Associate Professor at the School of Cyberspace Security at Northwestern Polytechnical University. He also serves as a Distinguished Researcher at the Zhejiang University of Technology Artificial Intelligence Innovation Institute. Gao previously held the position of Senior Algorithm Expert (P8) at Alibaba Group, where he led a team of over 20 full-time algorithm engineers and contributed to the development of large-scale machine translation and AI-driven e-commerce solutions. As an expert in Alibaba AIR Project, Gao has been instrumental in technical breakthroughs related to large model technologies, fine-tuning multimodal models, and advancing AI-based search and advertising systems. 💻📈

Research Interests 🔬

Gao’s research interests are focused on:

  • Large Language Models (LLMs) and Multimodal Learning 🌐🤖
  • Natural Language Processing: Information retrieval, recommendation systems, sentiment analysis, and automated summarization 📑🔍
  • E-commerce AI: Developing search algorithms and multilingual representation learning for cross-border e-commerce applications 🌏🛒
  • Federated Learning and AI-driven personalization in business settings 🔒🤖

He has authored and co-authored several influential papers and has been a leading figure in the development of multimodal AI models for industries such as fashion, e-commerce, and healthcare. His work continues to push the boundaries of AI application in real-world environments. 🏆📚

Publications 📚

  1. Gao, D., Chen, K., Chen, B., et al. (2024). LLMs-based Machine Translation for E-commerce. Expert Systems with Applications, Volume 258 (SCI Zone 1, Top Journal).
  2. Chen, K., Chen, B., Gao, D., Dai, H., et al. (2024). General2Specialized LLMs Translation for E-commerce. The Web Conference (WWW), short paper (CCF-A).
  3. Shen, G., Sun, S., Gao, D., Yang, L., et al. (2023). EdgeNet: Encoder-decoder generative Network for Auction Design in E-commerce Online Advertising. The 32nd ACM International Conference on Information & Knowledge Management (CIKM), (CCF-B).
  4. Gao, D., Ma, Y., Liu, S., Song, M., Jin, L., et al. (2024). FashionGPT: LLM Instruction Fine-tuning with Multiple LoRA-adapter Fusion. Knowledge-Based Systems, Volume 299 (SCI, Top Journal).
  5. Chen, B., Jin, L., Wang, X., Gao, D., et al. (2023). Unified Vision-Language Representation Modeling for E-Commerce Same-Style Products Retrieval. Industry Track of The Web Conference (WWW), (CCF-A).
  6. Mei, X., Yang, L., Jiang, Z., Cai, X., Gao, D., et al. (2024). An Inductive Reasoning Model Based on Interpretable Logical Rules Over Temporal Knowledge Graphs. Neural Networks, Volume 174, Pages (SCI Zone 1, Top Journal).
  7. Liang, Z., Chen, B., Ran, Z., Wang, Z., Gao, D., et al. (2024). Self-Renewal Prompt Optimizing with Implicit Reasoning. The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) findings (CCF-A).
  8. Yang, Z., Gao, H., Gao, D., Yang, L., et al. (2024). MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction. The 18th ACM Conference on Recommender Systems (RecSys), (CCF-B).
  9. Zhang, X., Wang, D., Gao, D., Jiang, W., et al. (2022). Revisiting Cold-Start Problem in CTR Prediction: Augmenting Embedding via GAN. The 31st ACM International Conference on Information & Knowledge Management (CIKM), (CCF-B).
  10. Zhang, F., Zhang, Z., Gao, D., Zhuang, F., et al. (2022). Mind the Gap: Cross-lingual Information Retrieval with Hierarchical Knowledge Enhancement. The 36th AAAI Conference on Artificial Intelligence (AAAI), (CCF-A).

 

 

 

Sushil Kumar | Machine Learning | Best Researcher Award

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Dr. Sushil Kumar | Machine Learning | Best Researcher Award

Assistant Professor at Central University of Haryana, India

Dr. Sushil Kumar is an Assistant Professor in the Department of Computer Science and Engineering at the Central University of Haryana, having joined on December 2, 2022. With a rich experience of 19 years in teaching, he specializes in Information Retrieval, Machine Learning, and Distributed Computing. Dr. Kumar holds a B.Tech, M.Tech, and Ph.D. in Computer Science and Engineering. He has published 7 papers in international journals and 1 book chapter, and has guided 16 Master’s students in their research. He has actively participated in 25 seminars and conferences, and organized 5 academic events. In addition, he has been recognized with the Youth Red Cross Award from the Honorable Governor of Haryana for 2016-17 and 2019-20. Currently, he also serves as the NBA Co-ordinator and NAAC Co-ordinator at the university.

Publication Profile : 

Google Scholar

Education 🎓

Dr. Sushil Kumar holds a B.Tech, M.Tech, and Ph.D. in Computer Science and Engineering, equipping him with a solid foundation in the field of technology and research.

Professional Experience💼

Assistant Professor at Central University of Haryana since 02-12-2022
With 19 years of teaching experience, Dr. Sushil Kumar has been dedicated to nurturing young minds in the area of computer science. His expertise in Information Retrieval, Machine Learning, and Distributed Computing has shaped his teaching methodology. While his focus remains on academia, he has not been involved in industry work yet. He has also taken up additional responsibilities as NBA Co-ordinator and NAAC Co-ordinator, ensuring quality assurance and accreditation standards in the department.

Research Interests 🔬

🔍 Information Retrieval
🤖 Machine Learning
🌐 Distributed Computing

Dr. Sushil Kumar’s research interests are focused on the areas of Information Retrieval, where he aims to improve search and data retrieval systems, Machine Learning, and the development of efficient algorithms for Distributed Computing systems.

Publications Top Notes 📚

  1. Kumar, S., Aggarwal, M., Khullar, V., Goyal, N., Singh, A., & Tolba, A. (2023). Pre-Trained Deep Neural Network-Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification. Agriculture, 13(5), 23.
  2. Kumar, S., & Bhatia, K. K. (2020). Semantic similarity and text summarization-based novelty detection. SN Applied Sciences, 2(3), 332.
  3. Kumar, S., & Chauhan, N. (2012). A context model for focused web search. International Journal of Computer Technology, 2(3).
  4. Gupta, C., Khullar, V., Goyal, N., Saini, K., Baniwal, R., Kumar, S., & Rastogi, R. (2023). Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram. Diagnostics, 14(1), 43.
  5. Kumar, S., & Bhatia, K. K. (2019). Clustering-based approach for novelty detection in text documents. Asian Journal of Computer Science and Technology, 8(2), 116-121.
  6. Dasari, K., Srikanth, V., Veramallu, B., Kumar, S. S., & Srinivasulu, K. (2014). A novelty approach of symmetric encryption algorithm. Proceedings of the International Conference on Information Communication and Embedded Systems (ICICES).
  7. Kumar, S., & Anand, S. (2006). Implementing Shared Data Services (SDS): A Proposed Approach. 2006 IEEE International Conference on Services Computing (SCC’06), 365-372.
  8. Singh, S., Kundra, H., Kundra, S., Pratima, P. V., Devi, M. V. A., Kumar, S., & Hassan, M. (2024). Optimal trained ensemble of classification model for satellite image classification. Multimedia Tools and Applications, 1-22.
  9. Kumar, S., & Bhatia, K. K. (2018). Document-to-Sentence Level Technique for Novelty Detection. In Speech and Language Processing for Human-Machine Communications: Proceedings (pp. xx-xx).
  10. Chawla, M., Panda, S. N., Khullar, V., Kumar, S., & Bhattacharjee, S. B. (2024). A lightweight and privacy-preserved federated learning ecosystem for analyzing verbal communication emotions in identical and non-identical databases. Measurement: Sensors, 34, 101268.
  11. Kumar, S. S. (2023). System Oriented Social Scrutinizer: Centered Upon Mutual Profile Erudition. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9), 2007–2017.
  12. Kumar, S. (2021). Design of novelty detection techniques for optimized search engine results. JC Bose University.
  13. Ishuka, S. K., & Bhatia, K. K. (2019). A Novel Approach for Novelty Detection Using Extractive Text Summarization. Journal of Emerging Technologies and Innovative Research, 6(6), 141-154.
  14. Pooja, K. K. B., & Kumar, S. (2019). Hashing and Clustering Based Novelty Detection. SSRG International Journal of Computer Science and Engineering, 6(6), 1-9.
  15. Kumar, S., & Bhatia, K. K. (2019). Clustering Based Approach for Novelty Detection in Text Documents. Asian Journal of Computer Science and Technology, 8(2), 121-126.

 

 

 

Kanaga Suba Raja S | Deep Learning | Best Researcher Award

Published on by Innovation Technologist

Prof. Dr. Kanaga Suba Raja S | Deep Learning | Best Researcher Award

Professor at Srm Institute Of Science And Technology Tiruchirappalli, India

Dr. S. Kanaga Suba Raja is a dedicated computer science educator and researcher with a passion for innovation and technology. With a rich history of academic leadership and groundbreaking research, he continues to inspire the next generation of engineers. 🌍💡

Publication Profile : 

Scopus

Orcid

Google Scholar

 

🎓 Educational Background :

Dr. Kanaga Suba Raja completed his Ph.D. in Computer Science and Engineering from Manonmaniam Sundaranar University in 2013. He holds a Master’s degree in Computer Science and Engineering from Noorul Islam College of Engineering (2006) and a Bachelor’s degree from The Rajaas Engineering College (2003).

💼 Professional Experience :

With over 19 years of experience in academia, Dr. Kanaga Suba Raja has held several prominent positions, including Professor and Head of the Department of Computer Science and Engineering at SRM Institute of Science and Technology, and Associate Dean at the School of Computing. His career spans roles as Associate Professor and Lecturer at various institutions under Anna University, where he contributed significantly to curriculum development and academic administration.

📚 Research Interests : 

Dr. Kanaga Suba Raja specializes in artificial intelligence, cloud computing, and biomedical engineering. He has published over 100 research papers, received numerous citations, and holds patents related to cloud computing and medical technologies.

📝 Publication Top Notes :

  1. Priya, J., Kanaga Suba Raja, S., & Usha Kiruthika, S. (2024). State-of-art technologies, challenges, and emerging trends of computer vision in dental images. Computers in Biology and Medicine, 178. https://doi.org/10.1016/j.compbiomed.2024.108800
  2. Priya, J., Kanaga Suba Raja, S., & Sudha, S. (2024). An intellectual caries segmentation and classification using modified optimization-assisted transformer denseUnet++ and ViT-based multiscale residual denseNet with GRU. Signal, Image and Video Processing (SIViP). https://doi.org/10.1007/s11760-024-03227-9
  3. Chandra, & Kanaga Suba Raja, S. (2024). HHECC-AES: A novel hybrid cryptography scheme for developing the secured wireless body area network using heuristic-aided blockchain model. Ad Hoc & Sensor Wireless Networks, 59, 141–179. https://doi.org/10.32908/ahswn.v59.10477
  4. Sandhiya, B., Kanaga Suba Raja, S., Shruthi, K., & Praveena Rachel Kamala, S. (2024). Brain tumour segmentation and classification with reconstructed MRI using DCGAN. Biomedical Signal Processing and Control, 92. https://doi.org/10.1016/j.bspc.2024.106005
  5. Sandhiya, B., & Kanaga Suba Raja, S. (2024). Deep learning and optimized learning machine for brain tumor classification. Biomedical Signal Processing and Control, 89(1). https://doi.org/10.1016/j.bspc.2023.105778
  6. Kausalya, K., & Kanaga Suba Raja, S. (2024). OTRN-DCN: An optimized transformer-based residual network with deep convolutional network for action recognition and multi-object tracking of adaptive segmentation using soccer sports video. International Journal of Wavelets, Multiresolution and Information Processing, 22(1). https://doi.org/10.1142/S0219691323500340
  7. Chandra, B., & Kanaga Suba Raja, S. (2023). Security in wireless body area network (WBAN) using blockchain. IETE Journal of Research. https://doi.org/10.1080/03772063.2023.2233472
  8. Hema, M., & Kanaga Suba Raja, S. (2023). A quantitative approach to minimize energy consumption in cloud data centres using VM consolidation algorithm. KSII Transactions on Internet and Information Systems, 17(2), 312-334. https://doi.org/10.3837/tiis.2023.02.002
  9. Pushpa, S. X., & Kanaga Suba Raja, S. (2022). Enhanced ECC based authentication protocol in wireless sensor network with DoS mitigation. Cybernetics and Systems, 53(2). https://doi.org/10.1080/01969722.2022.2055403
  10. Hema, M., & Kanaga Suba Raja, S. (2022). An efficient framework for utilizing underloaded servers in compute cloud. Computer Systems Science and Engineering, 43(5). https://doi.org/10.32604/csse.2023.024895
  11. Vivekanandan, M., & Kanaga Suba Raja, S. (2022). Virtex-II Pro FPGA-based smart agricultural system. Wireless Personal Communications, 125(1), 119–141. https://doi.org/10.1007/s11277-022-09544-x
  12. Pushpa, S. X., & Kanaga Suba Raja, S. (2022). Elliptic curve cryptography-based authentication protocol enabled with optimized neural network-based DoS mitigation. Wireless Personal Communications, 124(27). https://doi.org/10.1007/s11277-021-08902-5
  13. Balaji, V., & Kanaga Suba Raja, S. (2021). Recommendation learning system model for children with autism. Intelligent Automation & Soft Computing, 31(2). https://doi.org/10.32604/iasc.2022.020287
  14. Valarmathi, K., & Kanaga Suba Raja, S. (2021). Resource utilization prediction technique in cloud using knowledge-based ensemble random forest with LSTM model. Concurrent Engineering: Research and Applications. https://doi.org/10.1177/1063293X211032622
  15. Kanaga Suba Raja, S., & Virgin Louis, B. A. (2021). A review of call admission control schemes in wireless cellular networks. Wireless Personal Communications, 120(4), 3369–3388. https://doi.org/10.1007/s11277-021-08618-6

 

 

 

Rafael Natalio Fontana Crespo | Artificial Intelligence | Young Scientist Award

Published on by Innovation Technologist

Mr. Rafael Natalio Fontana Crespo | Artificial Intelligence | Young Scientist Award

PhD Student at Politecnico di Torino, Italy

Rafael Natalio Fontana Crespo is a dedicated and sociable Ph.D. student specializing in Computer and Control Engineering at Politecnico di Torino. With a strong academic background in mechatronics and practical experience in electrical energy analysis, he is passionate about tackling complex challenges through innovative solutions. 🌐💡

Publication Profile : 

Orcid

 

🎓 Educational Background :

Rafael is currently pursuing a Ph.D. in Computer and Control Engineering at Politecnico di Torino, Italy, since May 2022. He previously obtained a Master’s Degree in Mechatronic Engineering from the same institution, graduating with 110/110 cum laude in July 2022. His master’s thesis focused on designing and developing a distributed software platform for additive manufacturing. Rafael studied Electromechanical Engineering at the Universidad Nacional de Córdoba, Argentina, where he also completed a double degree program.

💼 Professional Experience :

Rafael gained practical experience during his internship at EPEC (Empresa Provincial de Energía de Córdoba) in Argentina, where he worked in the Statistics and Technical Department from May 2020 to May 2021. He was involved in analyzing thermal images of electrical components to prevent failures, contributing to the overall safety and efficiency of electrical systems.

📚 Research Interests : 

Rafael’s research interests lie at the intersection of computer engineering, control systems, and mechatronics, particularly focusing on additive manufacturing, machine learning applications in energy systems, and the optimization of neural networks.

📝 Publication Top Notes :

      1. Fontana Crespo, R.N., E. Patti, S. Di Cataldo, D. Cannizzaro. (2022). Design and Development of a Distributed Software Platform for Additive Manufacturing. Master’s Thesis, Politecnico di Torino.
      2. Fontana Crespo, R.N. (2023). Machine Learning in Energy Applications. Course Exam Paper, Politecnico di Torino.
      3. Fontana Crespo, R.N. (2023). IoT Platforms for Spatial Analytics in Smart Energy Systems. Course Exam Paper, Politecnico di Torino.
      4. Fontana Crespo, R.N. (2023). Optimized Execution of Neural Networks at the Edge. Course Exam Paper, Politecnico di Torino.
      5. Fontana Crespo, R.N. (2023). Adversarial Training of Neural Networks. Course Exam Paper, Politecnico di Torino.