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)

 

 

 

 

Yousef Daradkeh | Computer Science | Best Academic Researcher Award

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Prof. Dr. Yousef Daradkeh | Computer Science | Best Academic Researcher Award

Professor at Prince Sattam Bin Abdulaziz University, Jordan

Prof. Dr. Yousef Daradkeh is a distinguished professor of Computer Engineering and Information Technology at the Department of Computer Engineering and Information, College of Engineering, Prince Sattam bin Abdulaziz University, Saudi Arabia. He holds a Doctor of Engineering Sciences in Computer Engineering and Information Technology and has over 19 years of extensive academic and administrative experience. Prof. Daradkeh is internationally recognized for his research and teaching in software engineering, computer networks, artificial intelligence, and cybersecurity. As an academic leader, researcher, author, and workshop facilitator, he has significantly contributed to scientific communities through over 128 peer-reviewed publications, multiple books, and active participation in international conferences and editorial boards.

Publication Profile 

Google Scholar

Educational Background 🎓

  • Ph.D. in Computer Engineering and Information Technology
    Belarusian State University of Informatics and Radioelectronics, 2006

    • Dissertation: Interpretation of Polymorphous Networks Models in Real-Time Allocation Systems

    • Accredited by the Higher Education Accreditation Commission (HEAC)

  • Postdoctoral Research Fellowships

    • University of Calgary, Canada (2007–2009) – Systems, Networks, and Devices of Telecommunications

    • University of New South Wales, Australia (2010) – Computer Science and Engineering

    • Massey University, New Zealand (2011) – School of Engineering and Advanced Technology

    • Istanbul Technical University, Turkey (2023–2024) – Electronics and Communication Engineering

  • M.Sc. in Software Engineering
    Belarusian National Technical University, 2003

    • Thesis: Mathematical and Software Optimization of Models of Power Equipment Operations

  • B.Sc. in Computer Engineering
    Technical University of Moldova, 2000

    • Graduation Project: Device for Transcendent Functions Calculation

Professional Experience 💼

  • Current Position:

    • Full Professor & Assistant Dean for Administrative Affairs
      Prince Sattam bin Abdulaziz University, Saudi Arabia

  • Previous Academic Roles:

    • University of Calgary, Canada – Postdoctoral Researcher & Faculty Developer

    • The University of Jordan – Faculty Member, Systems and Information Technology

    • Al-Balqa Applied University, Jordan – Faculty Member, Department of IT

    • Yarmouk University – Instructor, Computer and Information Centre

    • AL-Hussein Bin Talal University – Faculty Member, Engineering & IT

    • Jadara University – Faculty, Department of Software Engineering

    • Kazakh University of Economics, Finance, and International Trade – Online Course Lecturer

  • Other Roles:

    • Designed accredited computer engineering and IT courses

    • Conducted workshops on scientific writing and research methodology

    • Editorial and scientific committee member for numerous international journals and conferences

Research Interests 🔬

  • Wireless Networking and Telecom Systems

  • Modeling of Discrete and Polymorphous Systems

  • Computer Systems and Networks

  • Artificial Intelligence & Data Science

  • Machine Learning & Deep Learning

  • Vision Computing and Digital Image Processing

  • Cybersecurity and Information/Economic Security

  • Agent-Based and Context-Aware Software Engineering

  • E-Government and E-Learning Applications

  • Location-Based Services (LBS) and Geo Services

  • Knowledge Representation and Reasoning

  • Optimization and Real-Time Systems

  • Web and Java Development, Databases

Awards and Honors🏆✨

  • Higher Distinction Scientific Awards – From International Universities

  • Certificate of Appreciation – Ministry of Youth and Sport, Jordan (1999)

  • Faculty Teaching Certificate (FTC) – University of Calgary, Canada (2008)

  • Care Services Certificates – University of Calgary (2008)

  • IT Management Professional Award – Microsoft, Excellent Train, Jordan (2011)

  • Staff Development Certificate – University of Jordan (2012)

  • Life Ambassador First Aid Training Certificate – Riyadh

  • Certificate of English Language (CEL) – AMIDEAST, Jordan

  • Certificate of Russian & Romanian Languages – TUM, BNTU, BSUIR

Conclusion🌟

Prof. Dr. Yousef Daradkeh is a visionary academic leader and prolific researcher who has made significant contributions to the fields of computer engineering, software systems, and emerging technologies. His work continues to bridge theoretical advancements with practical applications, particularly in improving security, efficiency, and intelligence in digital systems. Through his extensive teaching, research, international collaborations, and service to the academic community, Prof. Daradkeh remains a highly influential figure in engineering education and innovation.

Publications 📚

  • 📡 6G mobile communication technology: Requirements, targets, applications, challenges, advantages, and opportunities
    🧾 Alexandria Engineering Journal 64, 245-274
    👥 Cited by: 360 | 📅 Year: 2023

  • 🧠 A hybrid deep learning-based approach for brain tumor classification
    🧾 Electronics 11 (7), 1146
    👥 Cited by: 248 | 📅 Year: 2022

  • 🚗 Key challenges, drivers and solutions for mobility management in 5G networks: A survey
    🧾 IEEE Access 8, 172534-172552
    👥 Cited by: 180 | 📅 Year: 2020

  • 🔍 Tools for fast metric data search in structural methods for image classification
    🧾 IEEE Access 10, 124738-124746
    👥 Cited by: 104 | 📅 Year: 2022

  • 🎗️ Intelligent hybrid deep learning model for breast cancer detection
    🧾 Electronics 11 (17), 2767
    👥 Cited by: 101 | 📅 Year: 2022

  • 🤖 Development of effective methods for structural image recognition using fuzzy logic
    🧾 IEEE Access 9, 13417-13428
    👥 Cited by: 98 | 📅 Year: 2021

  • 📷 Methods of classification of images based on statistical distributions
    🧾 IEEE Access 9, 92964-92973
    👥 Cited by: 90 | 📅 Year: 2021

  • ✈️ Handover management of drones in future mobile networks: 6G technologies
    🧾 IEEE Access 9, 12803-12823
    👥 Cited by: 89 | 📅 Year: 2021

  • 🧱 Classification of Images Based on a System of Hierarchical Features
    🧾 Computers, Materials & Continua 72 (1)
    👥 Cited by: 83 | 📅 Year: 2022

  • 🔧 Handover parameters optimisation techniques in 5G networks
    🧾 Sensors 21 (15), 5202
    👥 Cited by: 71 | 📅 Year: 2021

 

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).

 

 

 

Rafael Natalio Fontana Crespo | Artificial Intelligence | Young Scientist Award

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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.