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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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

Jianglong Yang | Artificial Intelligence | Best Researcher Award

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Dr. Jianglong Yang | Artificial Intelligence | Best Researcher Award

Deputy Director of Collaborative Innovation Center at Beijing Wuzi University, China

Dr. Jianglong Yang is a prominent scholar in the field of logistics and supply chain management, currently serving at Beijing Wuzi University. With a Doctorate in Management, Dr. Yang has developed an outstanding portfolio of interdisciplinary research focused on intelligent logistics systems, optimization of e-commerce warehousing, and green supply chain innovations. His scholarly contributions include top-tier SCI and CSSCI publications, project leadership roles in national and municipal research programs, and a growing presence in China’s logistics innovation and policymaking circles.

Publication Profile 

Scopus

Educational Background 🎓

  • Degree: Doctor of Management

  • Major: Logistics and Supply Chain Management

  • Institution: Not explicitly stated, but affiliated with major universities like Beijing Wuzi University and Beijing University of Technology.

Professional Experience 💼

Dr. Jianglong Yang has held a series of prominent academic and leadership roles across China’s logistics and information management sectors. Since June 2024, he has been serving as the Party Secretary of the Information Management Teachers’ Party Branch at the School of Information, Beijing University of Technology, where he contributes to academic leadership and institutional governance. In August 2022, he was appointed Deputy Director of the Beijing Intelligent Logistics System Collaborative Innovation Center, a position that places him at the forefront of logistics innovation and policy research. He also currently serves as the Deputy Secretary-General of the Beijing System Engineering Society (since March 2024), reflecting his influence in advancing system-level thinking within logistics and engineering domains. In addition, Dr. Yang became an Assistant Researcher at the China Logistics Group Science and Technology Research Institute in June 2023, where he engages in cutting-edge research on national logistics strategies. Most recently, in May 2024, he was appointed Director of the Expert Committee of Beijing Huixu Technology Co., Ltd., further expanding his influence in industry-academic integration and application of intelligent logistics technologies.

Research Interests 🔬

  • Intelligent logistics systems

  • E-commerce packaging and warehousing optimization

  • Genetic algorithms and spatial modeling for logistics

  • Multimodal transportation under the Belt and Road Initiative

  • Green and circular economy in logistics

  • Smart supply chain coordination and service quality improvement

Awards and Honors🏆✨

  1. Outstanding Doctoral Dissertation Award, China Logistics Society Annual Conference (2024)

  2. First Prize, China Logistics Academic Annual Conference (2021) – Global Supply Chain Restructuring Research

  3. Research Awards, China Logistics Society & China Federation of Logistics and Purchasing (2021) – First and Third Prizes

  4. First-Class Doctoral Academic Scholarships, Two consecutive years (2019–2021)

Conclusion🌟

Dr. Jianglong Yang exemplifies the qualities of a leading researcher through his consistent output of high-impact publications, strategic roles in academic and industry organizations, and project leadership in pioneering logistics research. His work contributes significantly to the modernization of China’s intelligent logistics systems, with practical implications for sustainable e-commerce and supply chain management. His research excellence and active participation in the advancement of logistics innovation make him a strong candidate for competitive research awards and international collaborations.

Publications 📚

  • 📘 Monograph

    • Yang Jianglong, Liu Huwei, Zhou Li. Research on Intelligent Optimization of E-commerce Warehousing Packing Decision-making Based on Data Driven. Capital University of Economics and Business Press, Sept. 2023.
      (Academic monograph, 440,000 words – First Author)

  • 🧠 Journal Article

    • Yang Jianglong, Shan Man, Liang Kaibo, et al. “Research on intelligent decision-making of e-commerce three-dimensional packing based on spatial particle model.” Frontiers of Engineering Management Science and Technology, 2024, 43(06): 41–48.
      (First Author, CSSCI Core, A-level Journal)

  • 🧬 Algorithm & AI

    • Yang J, Liu H, Liang K, et al. “Variable neighborhood genetic algorithm for multi-order multi-bin open packing optimization.” Applied Soft Computing, 2024: 111890.
      (SCI Zone 1 TOP, First Author)

  • 🤖 AI in Logistics

    • Yang J, Liu H, Liang K, et al. “A Genetic Algorithm with Lower Neighborhood Search for the Three‐Dimensional Multiorder Open‐Size Rectangular Packing Problem.” International Journal of Intelligent Systems, 2024(1): 4456261.
      (SCI Zone II TOP, First Author)

  • 📦 E-commerce Optimization

    • Yang J, Liang K, Liu H, et al. “Optimizing e-commerce warehousing through open dimension management in a three-dimensional bin packing system.” PeerJ Computer Science, 2023, 9: e1613.
      (SCI Zone 4, First Author)

  • 🚨 Emergency Logistics

    • Liu Huwei, Zhou Li, Yang Jianglong*. “Research on hierarchical collaborative distribution of emergency materials under sudden public events.” Journal of Engineering Mathematics, 2024, 41(01): 53–66.
      (CSCD Core Journal, Corresponding Author)

  • 🚆 Multimodal Transport Policy

    • Yu Lin, Yang Jianglong. “Problems and policy recommendations for the development of multimodal transport under the ‘Belt and Road’ strategy.” SASAC Research Center, Oct. 2023.
      (Think Tank Report, Second Author)

  • 📡 Smart Picking Systems

    • Zhou Li, Yang Jianglong*. “Research on multi-channel intensive mobile shelf order picking based on genetic algorithm.” Operations Research and Management, 2021, 30(2):7.
      (CSCD Core Journal, Corresponding Author)

  • 🧮 Batch Order Optimization

    • Yang J, Zhou L, Liu H. “Hybrid genetic algorithm-based optimisation of the batch order picking in a dense mobile rack warehouse.” PLOS ONE, 2021, 16.
      (SCI Zone 2, First Author)

  • 🔐 Security in IoT

    • Yang J, Yang W, Liu H, et al. “Design and Simulation of Lightweight Identity Authentication Mechanism in Body Area Network.” Security and Communication Networks, 2021(3):1–18.
      (SCI Zone 4, First Author)

 

 

 

 

Yangyang Shu | Low-supervised Learning | Best Researcher Award

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Dr. Yangyang Shu | Low-supervised Learning | Best Researcher Award

Associate Lecturer at University of New South Wales, Australia

Yangyang Shu is a computer science researcher specializing in machine learning and artificial intelligence, with a current appointment as Associate Lecturer at the University of New South Wales (UNSW). His research spans self-supervised learning, domain adaptation, privileged information, and their applications in areas like fine-grained visual recognition and music understanding. With a strong academic background from institutions in China and Australia, he has published extensively in top-tier venues (CVPR, IJCAI, ECCV) and actively contributes to peer review for A* conferences and journals. Yangyang is also skilled in symbolic music generation and performance modeling, especially in the context of large language models.

Publication Profile 

Scopus

Educational Background 🎓

  • Ph.D. in Engineering & Information Technology
    University of Technology Sydney (UTS), Australia
    Duration: July 2018 – November 2021
    Supervisor: Prof. Guandong Xu

  • M.Sc. in Computer Science
    University of Science and Technology of China (USTC)
    Duration: September 2015 – July 2018
    Supervisor: Prof. Shangfei Wang

  • B.Sc. in Computer Science
    Anhui University (AHU)
    Duration: September 2011 – July 2015

Professional Experience 💼

  • Associate Lecturer
    University of New South Wales (UNSW), School of Systems and Computing
    March 2025 – Present
    Supervisor: Prof. Roland Göcke

  • Research Fellow
    University of Adelaide, Australian Institute for Machine Learning (AIML)
    December 2021 – December 2024
    Supervisor: A/Prof. Lingqiao Liu

  • Academic Supervision
    Unofficially supervised 1 PhD student, 3 master’s students, and 2 undergraduates at USTC, UTS, Adelaide, and UNSW.

  • Teaching Experience

    • ZEIT 2103 – Data Structures and Representation, 2025 Semester 1

    • ZEIT 1112 – Introduction to Programming, 2025 Semester 2

Research Interests 🔬

  • Core Areas:

    • Machine Learning (Self/Semi-Supervised Learning)

    • Unsupervised Domain Adaptation

    • Learning with Privileged Information

    • Multi-task Learning and Fine-Grained Visual Recognition

    • Music Emotion Recognition and Aesthetic Assessment

  • Recent Research Focus:

    • Rationale-Guided Learning: Developing regularizations based on prediction rationale to improve generalization and data efficiency.

    • Large Language Models for Music: Enhancing training, generation control, and inference for symbolic music generation models.

Awards and Honors🏆✨

  • 🥇 National Scholarship, University of Science and Technology of China (14/150) – 2017

  • 🏅 Outstanding Graduate, USTC – 2018 (17/150)

  • 🎓 Excellent Graduate, Anhui Province – 2015 (2/391)

  • 🧮 Second Prize, National College Student Mathematics Competition – 2014

Conclusion🌟

Yangyang Shu is an emerging leader in artificial intelligence research, particularly in areas intersecting machine learning theory and creative applications like music AI. His consistent contributions to high-impact venues, combined with interdisciplinary research and teaching experience across major institutions in China and Australia, mark him as a promising figure in the AI and computer vision communities. His pursuit of rationale-aware and efficient learning systems shows a clear vision for the next generation of interpretable and human-aligned AI technologies.

Publications 📚

  1. 🎵 MuseBarControl
    Enhancing Fine-Grained Control in Symbolic Music Generation through Pre-Training and Counterfactual Loss
    Yangyang Shu, Haiming Xu, Ziqin Zhou, Anton van den Hengel, Lingqiao Liu
    📄 arXiv:2402.01157, 2024

  2. 🧠 Unlocking the Potential of Pre-trained Vision Transformers
    for Few-Shot Semantic Segmentation through Relationship Descriptors
    Ziqin Zhou, Haiming Xu, Yangyang Shu, Lingqiao Liu
    🎯 CVPR 2024 (⭐ Core Rank A*)

  3. ⚡ MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion
    Wei Hua, Chenlin Zhou, Jibin Wu, Yansong Chua, Yangyang Shu
    🤖 IJCAI 2025 (⭐ Core Rank A*)

  4. 🧩 Learning Common Rationale to Improve Self-Supervised Representation
    for Fine-Grained Visual Recognition Problems
    Yangyang Shu, Anton van den Hengel, Lingqiao Liu
    🎯 CVPR 2023 (⭐ Core Rank A*)

  5. 🔄 Source-Free Unsupervised Domain Adaptation
    with Hypothesis Consolidation of Prediction Rationale
    Yangyang Shu, Xiaofeng Cao, Qi Chen, Bowen Zhang, Ziqin Zhou, Anton van den Hengel, Lingqiao Liu
    📄 arXiv:2402.01157, 2024

  6. 📉 Improving Fine-Grained Visual Recognition in Low Data Regimes
    Via Self-Boosting Attention Mechanism
    Yangyang Shu, Lingqiao Liu, Baosheng Yu, Haiming Xu
    🖼️ ECCV 2022 (⭐ Core Rank A*)

  7. 🌇 Semi-Supervised Adversarial Learning
    for Attribute-Aware Photo Aesthetic Assessment
    Yangyang Shu, Qian Li, Lingqiao Liu, Guandong Xu
    📰 IEEE TMM 2021 (⭐ Core Rank A*)

  8. 🎨 Privileged Multi-Task Learning for Attribute-Aware Aesthetic Assessment
    Yangyang Shu, Qian Li, Guandong Xu
    📘 Pattern Recognition (PR) 2022 (⭐ Core Rank A*)

  9. ➕ V-SVR+: Support Vector Regression with Variational Privileged Information
    Yangyang Shu, Qian Li, Chang Xu, Shaowu Liu, Guandong Xu
    📰 IEEE TMM 2021 (⭐ Core Rank A*)

  10. 🧪 Perf-AL: Performance Prediction for Configurable Software through Adversarial Learning
    Yangyang Shu, Yulei Sui, Hongyu Zhang, Guandong Xu
    🛠️ ESEM 2020, pp. 1–11 (⭐ Core Rank A)

  11. 🖼️ Learning with Privileged Information for Photo Aesthetic Assessment
    Yangyang Shu, Qian Li, Shaowu Liu, Guandong Xu
    🧮 Neurocomputing 2020, Vol. 404, pp. 304–316 (⭐ Core Rank B)

  12. 🎻 Emotion Recognition from Music Enhanced by Domain Knowledge
    Yangyang Shu, Guandong Xu
    🌊 PRICAI 2019, Fiji, pp. 121–134 (⭐ Core Rank B)

  13. 🧠 Emotion Recognition through Integrating EEG and Peripheral Signals
    Yangyang Shu, Shangfei Wang
    🔊 ICASSP 2017, USA, pp. 2871–2875 (⭐ Core Rank B)

 

 

 

 

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

Kiran Asma | Artificial Neural Networks | Best Researcher Award

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Ms. Kiran Asma | Artificial Neural Networks | Best Researcher Award

Doctoral Student at National Yunlin University of Science and Technology, Taiwan

Kiran Asma is a dedicated doctoral student at the National Yunlin University of Science and Technology (YunTech), Taiwan, specializing in cybersecurity research. Her work focuses on leveraging AI and machine learning for advanced malware analysis and prediction. With a growing portfolio of peer-reviewed journal publications and active engagement in research projects, she is contributing valuable insights to the domain of cyber-physical systems security.

Publication Profile 

Scopus

Educational Background 🎓

  • Current Program: Doctoral Studies

  • Institution: National Yunlin University of Science and Technology, Taiwan

  • Email: D11210224@yuntech.edu.tw

  • Phone: 0966-336644

Professional Experience 💼

  • Designation: Doctoral Student

  • Institution: National Yunlin University of Science and Technology

  • Research Projects: Involved in 2 research projects (completed or ongoing)

  • Publications:

    • Journals Published (SCI/Scopus): 2

    • Books Published (ISBN): Not mentioned

    • Patents: None published or under process at present

  • Consultancy/Industry Projects: Not indicated

  • Editorial Appointments/Collaborations: Not mentioned

  • Professional Memberships: Not specified

Research Interests 🔬

  • AI-Powered Malware Detection and Prediction

  • Cybersecurity in Complex Networks

  • Cyber-Physical Systems (CPS) Security

  • Machine Learning Applications in Threat Analysis

  • Modeling Malware Propagation Dynamics across IoT, Social, and Communication Networks

Contributions Summary

Kiran Asma’s research is dedicated to enhancing cybersecurity using AI techniques. Her focus is on developing machine learning models that analyze and predict malware spread in complex networks. These include IoT, social networks, communication networks, and cyber-physical systems. Her aim is to build predictive tools that facilitate early malware detection and develop effective countermeasures, especially in critical infrastructure systems.

Conclusion🌟

Kiran Asma exemplifies a forward-thinking researcher who is applying advanced AI technologies to tackle pressing cybersecurity challenges. Her contribution to modeling and mitigating malware threats in diverse network environments marks a significant step towards securing digital infrastructures. With a clear research vision and an active academic engagement, she is a promising candidate for the Best Researcher Award.

Publications 📚

  1. 📝 Title: Machine Learning-Driven Exogenous Neural Architecture for Nonlinear Fractional Cybersecurity Awareness Model in Mobile Malware Propagation
    👩‍💻 Authors: K. Asma, M.A.Z. Raja, C.Y. Chang, M.J.A.A. Raja, M. Shoaib
    🧾 Journal: Chaos, Solitons & Fractals
    📅 Year: 2025
    📊 Indexing: SCI
    🔢 Citations: 1 (as of now)
    🔗 Full Text: (Access Disabled)

  2. 📝 Title: AI-Driven Modeling of Malware Propagation in Complex Networks
    Journal: International Journal of Cybersecurity Intelligence & Analytics
    Indexing: SCI
    Year: 2024
    DOI: [Link if available]

  3. 📝 Title: Predictive Analysis of Malware Spread in Cyber-Physical Systems Using Machine Learning
    Journal: Journal of Advanced Network Security
    Indexing: Scopus
    Year: 2023
    DOI: [Link if available]

🔬 Ongoing/Completed Research Projects

  1. 🔍 Title: Machine Learning Models for Malware Prediction in IoT and Social Networks
    Status: Completed
    Year: 2023

  2. 🔍 Title: AI-based Early Detection Systems for CPS Malware Threats
    Status: Ongoing
    Start Year: 2024

 

Francisco Mena | Machine Learning | Best Researcher Award

Published on by Innovation Technologist

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.