ArticleSensors (Basel, Switzerland)2020
IoT-Blockchain Enabled Optimized Provenance System for Food Industry 4.0 Using Advanced Deep Learning.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- Towards a Sustainable Food Supply Chain: Digital Transformation via Blockchain, IoT and AI.Food science & nutrition · 2026Review
- Traceability and Anti-Counterfeiting in Agri-Food Supply Chains: A Review of RFID, IoT, Blockchain, and AI Technologies.Sensors (Basel, Switzerland) · 2026Review
- Blockchain-enabled traceability evaluation framework for mineral resource development and utilization: a fuzzy comprehensive assessment approach.Scientific reports · 2026Article
- Big data analytics in food industry: a state-of-the-art literature review.NPJ science of food · 2025Review
- Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety.Foods (Basel, Switzerland) · 2025Review
- A novel dilated weighted recurrent neural network (RNN)-based smart contract for secure sharing of big data in Ethereum blockchain using hybrid encryption schemes.PeerJ. Computer science · 2025Article
- A Longitudinal Analysis of the Impact of Digital Technologies on Sustainable Food Production and Consumption in the European Union.Foods (Basel, Switzerland) · 2024Article
- Automated detection and recognition system for chewable food items using advanced deep learning models.Scientific reports · 2024Article
- The nexus between green intellectual capital, blockchain technology, green manufacturing, and sustainable performance.Environmental science and pollution research international · 2024Article
- A Conceptual Model Relationship between Industry 4.0-Food-Agriculture Nexus and Agroecosystem: A Literature Review and Knowledge Gaps.Foods (Basel, Switzerland) · 2024Review
- Internet of things enabled deep learning methods using unmanned aerial vehicles enabled integrated farm management.Heliyon · 2023Article
- Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis.Foods (Basel, Switzerland) · 2023Review
- A critical analysis of the integration of blockchain and artificial intelligence for supply chain.Annals of operations research · 2023Article
- Applications of deep learning into supply chain management: a systematic literature review and a framework for future research.Artificial intelligence review · 2023Article
- A triple helix model for the diffusion of Industry 4.0 technologies in firms in the Marche Region.Open research Europe · 2023Article
- A Step toward Next-Generation Advancements in the Internet of Things Technologies.Sensors (Basel, Switzerland) · 2022Review
- A Refined Supervision Model of Rice Supply Chain Based on Multi-Blockchain.Foods (Basel, Switzerland) · 2022Article
- Rural Workplace Sustainable Development of Smart Rural Governance Workplace Platform for Efficient Enterprise Performances.Journal of environmental and public health · 2022Article
- Digital Strategy Decision Support Systems: Agrifood Supply Chain Management in SMEs.Sensors (Basel, Switzerland) · 2021Article
- Current State of Metabolomics Research in Meat Quality Analysis and Authentication.Foods (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Agriculture and livestock play a vital role in social and economic stability. Food safety and transparency in the food supply chain are a significant concern for many people. Internet of Things (IoT) and blockchain are gaining attention due to their success in versatile applications. They generate a large amount of data that can be optimized and used efficiently by advanced deep learning (ADL) techniques. The importance of such innovations from the viewpoint of supply chain management is significant in different processes such as for broadened visibility, provenance, digitalization, disintermediation, and smart contracts. This article takes the secure IoT-blockchain data of Industry 4.0 in the food sector as a research object. Using ADL techniques, we propose a hybrid model based on recurrent neural networks (RNN). Therefore, we used long short-term memory (LSTM) and gated recurrent units (GRU) as a prediction model and genetic algorithm (GA) optimization jointly to optimize the parameters of the hybrid model. We select the optimal training parameters by GA and finally cascade LSTM with GRU. We evaluated the performance of the proposed system for a different number of users. This paper aims to help supply chain practitioners to take advantage of the state-of-the-art technologies; it will also help the industry to make policies according to the predictions of ADL.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.