Special Issue: Implementing e-commerce in agri-food sector with machine learning approaches for sustainable development

Guest Editors

Prof. Neyara Radwan

Industrial Engineering Dept., College of Applied Sciences, Al Maarefa University, Saudi Arabia & Faculty of Engineering, Suez Canal University, Egypt

Email: nrhassan@kau.edu.sa


Prof. Sasho Guergov
Professor in the Faculty of Industrial Technology, Technical University, Sofia, Bulgaria
Email: sguergov@tu-sofia.bg


Prof. Dimitrios A. Karras
National and Kapodistrian University of Athens (NKUA), Psachna, Evia, Hellas (Greece)
Email: dakarras@uoa.gr

Manuscript Topics

The way data is generated, stored, and consumed in an organization has changed. It enables us a new field of computer science tools. Machine learning is the science of getting computers to act without being explicitly programmed. It has emerged as a core technology in today's internet-centric interactive information-hungry world wide web. Machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome in the past decade. The development and application of adaptive modeling derive their power from computers' ability to find patterns in data. Over time, a machine learning algorithm can build a model to see from new data through observation.


Machine learning is used in many smart and advanced systems such as autonomous vehicles, intelligent assistive rehabilitation robots, virtual personal assistants, and home automation. It can be applied to agri-food e-commerce systems to build intelligent and smart products that meet customers' demands. Moreover, machine learning can be used in agri-food e-commerce to replicate farmers' knowledge and experience with data analysis to improve the quality of products.


E-commerce has become a key sales channel for agri- and consumer packaged goods and foodservice. In e-commerce, the system should have the ability to let users quickly find what they are looking for. Machine Learning is a promising way to meet this challenge. The core of machine learning techniques is smart training and manual techniques like text mining. The system can learn and extract advanced knowledge from an enormous corpus of raw data. Three types of machine learning applications, namely supervised, unsupervised, and reinforcement, are explored in the context of various stakeholders involved in the agri-food e-commerce ecosystem, through farmers, supply chain management agencies, consumers, environmental protection agencies, policymakers, and research institutes. With machine learning, computers can receive input data when there's no explicit programming on how to model or label the data. This results in computers that can proactively make decisions or provide recommendations based on the input, where uses like customer support chatbots come into play. Machine learning can be beneficial in an Agri-food e-commerce business since it can help you uncover hidden patterns, make accurate decisions, predict future outcomes, adapt to new inputs and outputs, and perform tasks quicker with greater accuracy. However, machine learning techniques applied to the site e-commerce have not yet been complemented with agri-food product knowledge and lack of product standardization, making it difficult for consumers to use keywords alone to find a specific product. In addition, the analysis and extraction of knowledge from data represented by images, videos, or sounds is a complex task and transforms results into tangible products that improve people's lives. Submissions are welcome for articles that examine scientific advice from various academic perspectives, including science and technology studies. Insights from practitioners and case studies are also welcome.


List of topics:
• The role of AI is machine learning, data mining, and semantic technologies in agri-food e-commerce.
• A holistic view towards applying AI to enable data-driven integration in e-commerce agri-food.
• Improve the user experience on Agri-food E-commerce platforms using machine learning.
• Classifying and understanding unstructured multimodal data for developing agri-food applications using machine learning.
• Machine learning improves search queries and delivers personalized products in the e-commerce agri-food sector.
• Predicting farmers' demand trends using machine learning in the e-commerce agri-food sector.
• Increasing agricultural yield using machine learning in the e-commerce agri-food sector.
• Improved food availability using machine learning in the e-commerce agri-food sector.
• Using machine learning, reducing price volatility through increased investment in knowledge and sustainable farming practices.
• Predicting crop yields to reducing food waste using machine learning in the e-commerce agri-food sector.
• Recent approaches for the problems of buying behavior prediction using machine learning in the e-commerce agri-food sector.
• Study on Collecting data to train agricultural models using machine learning in the e-commerce agri-food sector.


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Please submit your manuscript to online submission system
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Paper Submission

All manuscripts will be peer-reviewed before their acceptance for publication. The deadline for manuscript submission is 30 June 2024

Published Papers(0)