Forecasts of the Amount Purchase Pork Meat by Using Structured and Unstructured Big Data

Ryu, Ga-Ae; Nasridinov, Aziz; Rah, HyungChul; Yoo, Kwan‐Hee

doi:10.3390/agriculture10010021

Cited by 14 publications

(17 citation statements)

References 15 publications

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“…Most classification algorithms only take structured data into account. In the processing of unstructured data, structured and unstructured information is generally combined [3,4] to reduce the disease-prediction risk. The combination of the information eases the cost of processing and reduces the redundant information.…”

Section: Introductionmentioning

confidence: 99%

Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control

Maheshwari

Mahmood

Sravanthi

et al. 2021

Journal of Nanomaterials

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Increasing the growth of big data, particularly in healthcare-Internet of Things (IoT) and biomedical classes, tends to help patients by identifying the disease early through methods for the analysis of medical data. Hence, nanotechnology-based IOT biosensors play a significant role in the medical field. Problem. However, the consistency continues to decrease where missing data occurs in such medical data from nanotechnology-based IOT biosensors. Furthermore, each region has its own special features, which further lowers the accuracy of prediction. The proposed model initially reconstructs lost or partial data in order to address the challenge of handling the medical data structures with incomplete data. Methods. An adaptive architecture is proposed to enhance the computing capabilities to predict the disease automatically. The medical databases are managed by unpredictable environments. This optimized paradigm for diagnosis produces the fuzzy, genetically categorized decision tree algorithm. This work uses a normalized classifier namely fuzzy-based decision tree (FDT) algorithm for classifying the data collected via nanotechnology-based IOT biosensors, and this helps in the identification of nondeterministic instances from unstructured datasets relating to the medical diagnosis. The FDT algorithm is further enhanced by using genetic algorithms for effective classification of instances. Finally, the proposed system uses two larger datasets to verify the predictive precision. In order to describe a fuzzy decision tree algorithm based upon the fitness function value, a modified decision classification rule is used. The structure and unstructured databases are configured for processing. Results and Conclusions. This evaluation of test patterns helps to track the efficiency of FDT with optimized rules during the training and testing stages. The proposed method is validated against nanotechnology-based IOT biosensors data in terms of accuracy, sensitivity, specificity, and F -measure. The results of the simulation show that the proposed method achieves a higher rate of accuracy than the other methods. Other metrics relating to the model with and without feature selection show an improved sensitivity, specificity, and F -measure rate than the existing methods.

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Section: Introductionmentioning

confidence: 99%

Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control

Maheshwari

Mahmood

Sravanthi

et al. 2021

Journal of Nanomaterials

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“…There have been several studies [12,[30][31][32][33] who predicted the prices of agricultural commodities using unstructured data, such as news articles, social network data, and others. Here, researchers have sought to predict prices using text analysis methods such as sentiment analysis and topic modeling.…”

Section: Prediction Of Agriculture Commodity Price Using Unstructured Datamentioning

confidence: 99%

“…In this approach, unstructured data and structured data are used together to achieve high accuracy in price prediction. For example, Ryu et al [33] introduced forecasts of the purchase amounts of pork using structured and unstructured data. Specifically, this research aimed to forecast consumption of pork using unstructured data, such as online text news, blogs, and television programs/shows.…”

Section: Prediction Of Agriculture Commodity Price Using Unstructured Data and Structured Datamentioning

confidence: 99%

Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea

et al. 2020

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Knowing the prices of agricultural commodities in advance can provide governments, farmers, and consumers with various advantages, including a clearer understanding of the market, planning business strategies, and adjusting personal finances. Thus, there have been many efforts to predict the future prices of agricultural commodities in the past. For example, researchers have attempted to predict prices by extracting price quotes, using sentiment analysis algorithms, through statistical information from news stories, and by other means. In this paper, we propose a methodology that predicts the daily retail price of pork in the South Korean domestic market based on news articles by incorporating deep learning and topic modeling techniques. To do this, we utilized news articles and retail price data from 2010 to 2019. We initially applied a topic modeling technique to obtain relevant keywords that can express price fluctuations. Based on these keywords, we constructed prediction models using statistical, machine learning, and deep learning methods. The experimental results show that there is a strong relationship between the meaning of news articles and the price of pork.

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“…As a result of the possibility of learning and generalizing data, the use of ANN gives better results than statistical methods. Neural modeling methods are used in classification, identification, and prediction, therefore, their potential is significant for practical application in broadly understood agriculture [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks to Analyze the Concentration of Ferulic Acid, Deoxynivalenol, and Nivalenol in Winter Wheat Grain

2020

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Biotic stress, which includes infection by pathogenic fungi, causes losses of wheat yield in terms of quantity and quality. Ear Fusarium is caused by strains of F. graminearum and F. culmorum, which can produce mycotoxins—deoxynivalenol (DON) and nivalenol (NIV). One of the wheat’s defense mechanisms against stressors is the activation of biosynthesis pathways of antioxidant compounds, including ferulic acid. The aim of the study was to conduct pilot studies on the basis of which neural models were created that would examine the impact of the variety and weather conditions on the concentration of ferulic acid, and link its content with the concentration of deoxynivalenol and nivalenol. The plant material was 23 winter wheat genotypes with different Fusarium resistance. The field experiment was conducted in 2011–2013 in Poland in three experimental combinations, namely: with full chemical protection; without chemical protection, but infested with natural disease (control); and in the absence of fungicidal protection, with artificial inoculation by genus Fusarium fungi. As a result of the pilot studies, three neural models—FERUANN analytical models (ferulic acid content), DONANN (deoxynivalenol content) and NIVANN (nivalenol content)—were produced. Each model was based on 14 independent features, 12 of which were in the form of quantitative data, and the other two were presented as qualitative data. The structure of the created models was based on an artificial neural network (ANN) of the multilayer perceptron (MLP) with two hidden layers. The sensitivity analysis of the neural network showed the two most important features determining the concentration of ferulic acid, deoxynivalenol, and nivalenol in winter wheat seeds. These are the experiment variant (VAR) and winter wheat variety (VOW).

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Forecasts of the Amount Purchase Pork Meat by Using Structured and Unstructured Big Data

Cited by 14 publications

References 15 publications

Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control

Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control

Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea

Application of Artificial Neural Networks to Analyze the Concentration of Ferulic Acid, Deoxynivalenol, and Nivalenol in Winter Wheat Grain

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