Research on the Operation of Agricultural Products E-Commerce Platform Based on Cloud Computing

Liu, Lingxiao

doi:10.1155/2022/8489903

Cited by 3 publications

(1 citation statement)

References 28 publications

(31 reference statements)

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“…The burgeoning development of information technology has amplified the importance of big data in the realms of risk decision-making and safety management within the agricultural supply chains Dai and Liu, 2020;Wang and Wu, 2022). The comprehensive gathering, processing, and analytical examination of largescale data empower decision-makers to more precisely pinpoint potential risks and formulate more efficacious strategies for risk mitigation (Land and Siraj, 2021;Liu, 2022;Lu et al, 2022;Zhang et al, 2022). Such strategic advancements not only optimize resource distribution and augment the competitive edge of agricultural products but also enhance the capacity to manage unforeseen contingencies, thereby safeguarding the continuity and security of agricultural supply chain (Cao et al, 2022;Chen and Su, 2022;Cui and Gao, 2022;Hui, 2021;Lin and Hu, 2022;Nagendra et al, 2022;Xu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Big data-driven risk decision-making and safety management in agricultural supply chains

Han,

Pan,

Zhang

2024

Qual. Assur. Saf. Crops Foods

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In the era of digitization, the integration of big data technologies has become instrumental in advancing agri-cultural supply chain management and bolstering risk decision-making processes. Agricultural supply chains, critical to ensuring food security and bolstering rural economies, face vulnerabilities stemming from a myriad of internal and external elements, including natural disasters and market dynamics. Consequently, the urgency to adopt effective risk management strategies is paramount. Contemporary studies have explored the utilization of big data in decision-making processes specific to agricultural supply chain risks, predominantly concentrat-ing on preliminary risk prediction and characterization. Nonetheless, there exists a shortfall in comprehensively analyzing the intricate interplay among risk factors and establishing a holistic risk management decision-making framework based on such analyses. This research addresses these deficiencies through two principal investigative components. First, this research explores the analysis of risk factors and their interrelationships in the agricultural supply chain based on a decision tree algorithm with a transition structure. This algorithm enhances decision-makers’ understanding of risk factors and their interrelationships, and guide the implementation of effective risk mitigation measures and the formulation of contingency plans. Subsequently, the research constructs a corresponding data-driven multi-criteria decision-making method, assisting managers in balancing different risk management strategies in a volatile supply chain environment, considering costs, benefits, and feasibility to formulate the optimal strategy. The innovation of this research lies in the development of a novel risk analysis tool based on the transition decision tree algorithm. This is the first time that such advanced algorithms are applied to agricultural supply chain risk management, filling a gap in the current research. The outcomes of this study not only contribute to enhancing risk management practices within agricultural supply chains but also offer novel insights and methodological tools that are applicable in research and practices across related domains.

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Section: Introductionmentioning

confidence: 99%

Big data-driven risk decision-making and safety management in agricultural supply chains

Han,

Pan,

Zhang

2024

Qual. Assur. Saf. Crops Foods

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Role of artificial intelligence (AI) in poverty alleviation: a bibliometric analysis

Raghavendra,

Majhi,

Mukherjee

et al. 2023

VJIKMS

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Purpose This study aims to examine the current state of academic research pertaining to the role played by artificial intelligence (AI) in the achievement of a critical sustainable development goal (SDG) – poverty alleviation and describe the field’s development by identifying themes, trends, roadblocks and promising areas for the future. Design/methodology/approach The authors analysed a corpus of 253 studies collected from the Scopus database to examine the current state of the academic literature using bibliometric methods. Findings This paper identifies and analyses key trends in the evolution of this domain. Further, the paper distils the extant literature to unpack the intermediary mechanisms through which AI and related technologies help tackle the critical global issue of poverty. Research limitations/implications The corpus of literature used for the analysis is limited to English language studies from the Scopus database. The paper contributes to the extant research on AI for social good, and more broadly to the research on the value of emerging technologies such as AI. Practical implications Policymakers and government agencies will get an understanding of how technological interventions such as AI can help achieve critical SDGs such as poverty alleviation (SDG-1). Social implications The primary focus of this paper is on the role of AI-related technological interventions to achieve a significant social objective – poverty alleviation. Originality/value To the best of the authors’ knowledge, this is the first study to conduct a comprehensive bibliometric analysis of a critical research domain such as AI and poverty alleviation.

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User Behavior Analysis and Market Forecasting of Agricultural E-commerce Platforms

Zhu

2024

Applied Mathematics and Nonlinear Sciences

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In recent years, the burgeoning interest of enterprises in the e-commerce sector has underscored the necessity for effective analytical tools that monitor user engagement on agricultural products’ e-commerce platforms and provide precise market forecasts. To address this need, the current study proposes an analytical framework and develops a comprehensive market prediction system for agricultural products. Initially, behavioral analysis indices on the e-commerce platform are employed to delineate user behavioral patterns, which include traffic and purchase path analyses. Based on a case study, enhancements are made to the conventional RFM (Recency, Frequency, Monetary) customer behavior model, both strategically and structurally, thus enriching the analysis of user behavior on such platforms. Subsequently, a four-tiered e-commerce market prediction system is established, incorporating a multilayered deep learning network designed to anticipate market trend shifts. The analysis reveals that the application of deviation standardization processing significantly refines the representation of user behavior to the platform’s merchants. Moreover, the implementation of this market prediction system not only reduces processing time from 8 seconds to 4 seconds but also decreases the average relative error by 0.31%. These improvements highlight the system’s enhanced predictive accuracy, confirming its utility in navigating the complexities of the agricultural products market in the e-commerce domain.

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Research on the Operation of Agricultural Products E-Commerce Platform Based on Cloud Computing

Cited by 3 publications

References 28 publications

Big data-driven risk decision-making and safety management in agricultural supply chains

Big data-driven risk decision-making and safety management in agricultural supply chains

Role of artificial intelligence (AI) in poverty alleviation: a bibliometric analysis

User Behavior Analysis and Market Forecasting of Agricultural E-commerce Platforms

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