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)

 

 

 

 

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

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).

 

 

 

Swati Jaiswal | Deep Learning | Women Researcher Award

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Mrs. Swati Jaiswal | Deep Learning | Women Researcher Award

Assistant Professor at DES Pune University, Pune, India

Swati Jaiswal, Ph.D. candidate at VIT Vellore, is an experienced Assistant Professor in Computer Engineering with over 14 years of academic and research expertise. Currently, she is serving at the School of Computer Engineering & Technology, DES Pune University. She has held various teaching and administrative roles across esteemed institutions like PCCOE, ZCOER, and SKNSITS, contributing significantly to academic development and research. Swati’s contributions span diverse fields like Machine Learning, Cybersecurity, Autonomous Vehicles, AI, and IoT, reflected in her numerous publications, patents, and book chapters 📚🔍. Swati’s dedication to research and teaching is complemented by a passion for developing innovative solutions to real-world problems 🤖💡.

Publication Profile : 

Google Scholar

Education🎓

Swati holds a Master’s in Computer Science & Engineering with 86% from RGPV, Bhopal (2012), and a BE in the same discipline with 80% (2009). She is currently pursuing a Ph.D. in the field of AI and Machine Learning at VIT Vellore, under the guidance of Dr. Chandra Mohan B. Her academic journey also includes certifications in various fields like Data Science, Machine Learning, and Software Testing 🎓📜.

Professional Experience💼

Swati began her career as an Assistant Professor at SAMCET Bhopal in 2009, where she coordinated seminars and workshops. Over the years, she worked at several prestigious institutions, including SKNSITS, ZCOER, and PCCOE, contributing to curriculum development, departmental coordination, and research activities. Since June 2024, she has been with DES Pune University, where she continues her academic journey while nurturing the next generation of engineers and researchers. Along with teaching, she has overseen various academic and administrative responsibilities, including time-table coordination, research guidance, and university exams 🏫📊.

Research Interests🔬

Her research primarily focuses on Machine Learning, Artificial Intelligence, Cybersecurity, Autonomous Systems, and Internet of Things (IoT). She has explored deep learning models for real-time systems, especially in autonomous driving, vehicle communication systems, and intelligent robotics. Additionally, Swati is passionate about the application of AI and ML in solving complex real-world problems such as fraud detection, data security, and predictive analytics 💻🔐🚗.

Publications Top Notes📚

  1. Jha, R. K., Kumar, A., Prakash, S., Jaiswal, S., Bertoluzzo, M., Kumar, A., Joshi, B. P., & … (2022). Modeling of the resonant inverter for wireless power transfer systems using the novel MVLT method. Vehicles, 4(4), 1277-1287. [34 citations]
  2. Kachhoria, R., Jaiswal, S., Khairnar, S., Rajeswari, K., Pede, S., Kharat, R., … (2023). Lie group deep learning technique to identify the precision errors by map geometry functions in smart manufacturing. The International Journal of Advanced Manufacturing Technology, 1-12. [12 citations]
  3. Kachhoria, R., Jaiswal, S., Lokhande, M., & Rodge, J. (2023). Lane detection and path prediction in autonomous vehicle using deep learning. In Intelligent edge computing for cyber physical applications (pp. 111-127). [11 citations]
  4. Swati Jaiswal, D. C. M. B. (2017). A survey: Privacy and security to Internet of Things with cloud computing. International Journal of Control Theory and Applications, 10(1), 487-500. [7 citations]
  5. Jaiswal, S., & Rodge, J. (2019). Comprehensive overview of neural networks and its applications in autonomous vehicles. In Computational Intelligence in the Internet of Things (pp. 159-173). [6 citations]
  6. Kati, S., Ove, A., Gotipamul, B., Kodche, M., & Jaiswal, S. (2022). Comprehensive overview of DDOS attack in cloud computing environment using different machine learning techniques. In Proceedings of the International Conference on Innovative Computing. [5 citations]
  7. Raut, R., Jadhav, A., Jaiswal, S., & Pathak, P. (2022). IoT-assisted smart device for blind people. In Intelligent Systems for Rehabilitation Engineering (pp. 129-150). [4 citations]
  8. Jaiswal, S., & Desai, M. (2019). Importance of information security and strategies to prevent data breaches in mobile devices. In Improving Business Performance Through Innovation in Digital Economy (pp. 215-225). [4 citations]
  9. Jaiswal, S., & Chandra, M. B. (2023). An efficient real-time decision-making system for autonomous vehicle using timber chased wolf optimization-based ensemble classifier. Journal of Engineering Science and Technology Review, 16(1), 75-84. [3 citations]
  10. Jaiswal, S., & Balasubramanian, C. M. (2023). An advanced deep learning model for maneuver prediction in real-time systems using alarming-based hunting optimization. International Journal of Advances in Intelligent Informatics, 9(2). [2 citations]
  11. Sorde, C., Jadhav, A., Jaiswal, S., Padwad, H., & Raut, R. (2023). Generative adversarial networks and its use cases. In Generative Adversarial Networks and Deep Learning (pp. 1-11). [2 citations]
  12. Rajeswari, K., Vispute, S., Maitre, A., Kharat, R., Aher, N., Vivekanandan, N., … (2023). Time series analysis with systematic survey on COVID-19 based predictive studies during pandemic period using enhanced machine learning techniques. iJOE, 19(07), 161. [2 citations]
  13. Jadhav, A., Raut, R., Jhaveri, R., Patil, S., Jaiswal, S., Katole, A., … (2021). A device for child safety and security. [2 citations]
  14. Jaiswal, S., Prakash, S., Gupta, N., & Rewadikar, D. (n.d.). Performance optimization in ad-hoc networks. International Journal of Computer Technology and Electronics Engineering. [2 citations]
  15. Jaiswal, S., & Mohan, B. C. (2024). Deep learning-based path tracking control using lane detection and traffic sign detection for autonomous driving. Web Intelligence, 22(2), 185-207. [1 citation]
  16. Raut, R., Jadhav, A., Jaiswal, S., Kathole, A., & Patil, S. (2023). Intelligent information system for detection of COVID-19 based on AI. In Proceedings of 3rd International Conference on Recent Trends in Machine Learning and Artificial Intelligence. [1 citation]
  17. Jaiswal, S., Sarkar, S., & Mohan, C. (2017). COT: Evaluation and analysis of various applications with security for cloud and IoT. In Examining Cloud Computing Technologies through Internet of Things (pp. 251-263). [1 citation]
  18. Prakash, S., Saxena, V., & Jaiswal, S. (2016). Smart grid: Optimized power sharing and energy storage system framework with recent trends and future ahead. In Handbook of Research on Emerging Technologies for Electrical Power Planning and Analysis (pp. 1-12). [1 citation]
  19. Jaiswal, S., Gupta, N., & Shrivastava, H. (2012). Enhancing the features of intrusion detection system by using machine learning approaches. International Journal of Scientific and Research Publications, 166. [1 citation]
  20. Kharat, R. S., Kalos, P. S., Kachhoria, R., Kadam, V. E., Jaiswal, S., Birari, D., … (2023). Thermal analysis of fuel cells in renewable energy systems using generative adversarial networks (GANs) and reinforcement learning. [No citation count]

 

 

 

Andrews Tang | Artificial Neural Networks | Best Researcher Award

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Mr. Andrews Tang | Artificial Neural Networks | Best Researcher Award

DIPPER Lab at KNUST, Ghana

👨‍🎓 Andrews Tang is a passionate computer engineering researcher from Kwame Nkrumah University of Science and Technology (KNUST) in Ghana. With a deep interest in deep learning and computer vision, Andrews has worked on impactful projects in areas such as agricultural quality control, food safety, and aviation safety. His work, which includes the development of deep learning models for detecting red palm oil adulteration and tomato condition assessment, aims to address critical challenges in the African context. He is a recipient of multiple academic honors, including the Excellent Student’s Award and has presented at international conferences such as AfricAI and Deep Learning Indaba. Andrews also contributes as a teaching assistant and mentor in his field, shaping the next generation of computer engineering students. 🚀

Publication Profile : 

Google Scholar

 

🎓 Educational Background :

🎓 Bachelor of Science in Computer Engineering
Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana (2018-2022)
• First Class Honours
• Cumulative Weighted Average (CWA): 76.18%
• Final CGPA: 3.74/4.0 (WES Evaluation)

💼 Professional Experience :

Andrews Tang has a strong academic foundation and extensive research experience at KNUST, where he has worked on groundbreaking projects with the DIPPER Lab and Responsible AI Lab (RAIL). As an Undergraduate Researcher and Research Assistant, he has tackled diverse challenges, from designing a decentralized food traceability system for Ghana’s agricultural supply chain to developing innovative deep learning models for detecting palm oil adulteration using GhostNet and SqueezeNet. In the field of Aviation Safety, Andrews is currently working as a Machine Learning Engineer, focusing on enhancing the accuracy of the Instrument Landing System (ILS) for low-visibility conditions, where his predictive models have improved flight safety protocols. His other work includes contributions to EEG report classification, sign language recognition, and mineral ore recovery predictions. Alongside his technical expertise, Andrews actively participates in mentorship and teaching roles, providing guidance in computer vision and secure network systems to undergraduate students at KNUST.

📚 Research Interests : 

🔍 Deep Learning
📷 Computer Vision
🧠 AI in Agriculture and Food Safety
🌐 Blockchain in IoT
✈️ Machine Learning for Aviation Safety

Awards & Honors:

🏆 Excellent Student’s Award (2020, 2022, 2023)
🏆 Best Poster Award, Deep Learning Indaba, Accra, 2023
🌍 Member, Black in AI Fellowship, 2024

📝 Publication Top Notes :

  • Tchao, E. T., Gyabeng, E. M., Tang, A., Benyin, J. B. N., Keelson, E., & Kponyo, J. J. (2022). “An Open and Fully Decentralized Platform for Safe Food Traceability.” 2022 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, pp. 487-493.
    DOI: 10.1109/CSCI58124.2022.00092
  • Tang, A., Agbemenu, A. S., Tchao, E. T., Keelson, E., Klogo, G. S., & Kponyo, J. J. (2024). “Assessing Blockchain and IoT Technologies for Agricultural Food Supply Chains in Africa: A Feasibility Analysis.” Heliyon, 10(4), e34584.
    DOI: 10.1016/j.heliyon.2024.e34584
  • Gyabeng, E. M., Tang, A., Agbemenu, A. S., Zaukuu, J. Z., Keelson, E., & Tchao, E. T. (2024). “AfroPALM – Afrocentric Palm Oil Adulteration Learning Models: An End-to-End Deep Learning Approach for Detection of Palm Oil Adulteration in West Africa.” LWT – Journal of Food Science and Technology. Preprint available at SSRN:
    https://ssrn.com/abstract=4917970
    Revised Manuscript Under Review By Elsevier LWT Journal.