Enhancing random forest classification with NLP in DAMEH: A system for DAta Management in eHealth Domain

Amato, Flora; Coppolino, Luigi; Cozzolino, Giovanni; Mazzeo, Giovanni; Moscato, Francesco; Nardone, Roberto

doi:10.1016/j.neucom.2020.08.091

Cited by 15 publications

(2 citation statements)

References 48 publications

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“…(2) The bootstrap methods, which are based on the RF algorithm, resample to generate multiple training sets, construct the supervised classification RF algorithm model, and use the mean squared error (MSE) of the out-of-bag (OOB) estimation method to evaluate the importance of the explanatory variables in the regression model. The mathematical definition of MSE is as follows [27]:…”

Section: Discussionmentioning

confidence: 99%

Characterization of Soil-Plant Leaf Nutrient Elements and Key Factors Affecting Mangoes in Karst Areas of Southwest China

Song

Huang

Yang

et al. 2022

Land

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Baise city is one of the largest producers of mangoes, with this agricultural industry located in the karst region of Southwest China. However, calcium-rich and alkaline soils, severe soil fragmentation, and poor water and fertilizer retention capacity contribute to low mango yields and are key issues that limit the development of the mango industry in karst areas. Our study objectives were to identify the soil factors that limit mango growth and yield in the karst region of Southwest China, and to determine how these growth- and production-limiting conditions vary between landscape positions. This study analyzed the differences in soil nutrient and element contents in mango leaves, and used a Random Forest algorithm to calculate the eigenvalues of the mango leaf and soil elemental indices in the different geomorphic parts (slopes, transition zone, passes, high-yielding depressions, and low-yielding depressions) of the karst peak-cluster depressions. The key factors affecting the mango leaves and soil were screened based on the diagnostic results and the eigenvalues. The results showed that for the elemental contents of Fe, Mg, Ca, and Mn in the mango leaves in the different geomorphic parts of the karst, the peak-cluster depressions were generally deficient and varied significantly. The contents of available B (AB), soil organic matter (SOM), pH, total nitrogen, available Fe, available Mn, alkaline hydrolysis nitrogen, exchangeable Ca (Caex), exchangeable Mg, and other indices in the soils differed significantly, and AB, available Zn, and available K (AK) showed low or very low content levels. In addition, the key soil factors limiting mango yield in the karst areas were AB, fulvic acid, SOM, Fe, Mn, Caex, soil water, and AK; and the key mango leaf factors were Ca, Mn, Fe, Zn, and Mg. Consequently, the characteristics of soil water content, pH, and soil organic matter may be the main drivers affecting the differences in the mango yield and the elemental characteristics. These findings suggest that the addition of organic fertilizer could improve the quality and yield of mangoes in karst areas.

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Section: Discussionmentioning

confidence: 99%

Characterization of Soil-Plant Leaf Nutrient Elements and Key Factors Affecting Mangoes in Karst Areas of Southwest China

Song

Huang

Yang

et al. 2022

Land

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“…An alternative to word embeddings, also used in this study, is the feature-based approaches [84] that break up text into individual words called bag of words (BOW) and treat each word (unigram) or a contiguous sequence of words (2, 3, or more (n) words together referred to as bigrams, trigrams, or n-grams, accordingly) as a potential feature. The splitting of text into words (tokens) is referred to as tokenization.…”

Section: Text Vectorization Techniquesmentioning

confidence: 99%

Explaining tourist revisit intention using natural language processing and classification techniques

et al. 2023

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Revisit intention is a key indicator of business performance, studied in many fields including hospitality. This work employs big data analytics to investigate revisit intention patterns from tourists’ electronic word of mouth (eWOM) using text classification, negation detection, and topic modelling. The method is applied on publicly available hotel reviews that are labelled automatically based on consumers’ intention to revisit a hotel or not. Topics discussed in revisit-annotated reviews are automatically extracted and used as features during the training of two Extreme Gradient Boosting models (XGBoost), one for each of two hotel categories (2/3 and 4/5 stars). The emerging patterns from the trained XGBoost models are identified using an explainable machine learning technique, namely SHAP (SHapley Additive exPlanations). Results show how topics discussed by tourists in reviews relate with revisit/non revisit intention. The proposed method can help hoteliers make more informed decisions on how to improve their services and thus increase customer revisit occurrences.

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Artificial Intelligence-Based Early Prediction Techniques in Agri-Tech Domain

Amato

Angrisani

et al. 2021

Advances in Intelligent Networking and Collaborative Systems

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Enhancing random forest classification with NLP in DAMEH: A system for DAta Management in eHealth Domain

Cited by 15 publications

References 48 publications

Characterization of Soil-Plant Leaf Nutrient Elements and Key Factors Affecting Mangoes in Karst Areas of Southwest China

Characterization of Soil-Plant Leaf Nutrient Elements and Key Factors Affecting Mangoes in Karst Areas of Southwest China

Explaining tourist revisit intention using natural language processing and classification techniques

Artificial Intelligence-Based Early Prediction Techniques in Agri-Tech Domain

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