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Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

Determining the relevant information using financial measures is of great interest for various stakeholders to analyze the performance of the firm. This paper aims at identifying these financial measures (ratios) which critically affect the firm performance. The authors specifically focus on discovering the most prominent ratios using a two-step process. First, the authors use an exploratory factor analysis to identify the underlying dimensions of these ratios, followed by predictive modeling techniques to identify the potential relationship between measures and performance.

The study uses data of 25 financial variables for a sample of 1923 Indian manufacturing firms which exist continuously between 2011 and 2018. For prediction models, four popular decision tree algorithms [Chi-squared automatic interaction detector (CHAID), classification and regression trees (C&RT), C5.0 and quick, unbiased, efficient statistical tree (QUEST)] were investigated, and the information fusion-based sensitivity analyses were performed to identify the relative importance of these input measures.

Results show that C5.0 and CHAID algorithms produced the best predictive results. The fusion sensitivity results find that net profit margin and total assets turnover rate are the most critical factors determining the firm performance in an Indian manufacturing context. These findings may enable managers in their decision-making process and also have vital implications for investors in assessing the performance of the firm.

To the best of the authors’ knowledge, the current paper is the first to address the application of decision tree algorithms to predict the performance of manufacturing firms in an emerging economy such as India, with the latest data. This practical perspective helps the organizations in managing the critical parameters for the firm’s growth.

The purpose of the food traceability is used to retain the good quality of raw material supply, diminish the loss and reduced system complexity.

The proposed hybrid algorithm is for food traceability to make accurate predictions and enhanced period data. The operation of the internet of things is addressed to track and trace the food quality to check the data acquired from manufacturers and consumers.

In order to survive with the existing financial circumstances and the development of global food supply chain, the authors propose efficient food traceability techniques using the internet of things and obtain a solution for data prediction.

The operation of the internet of things is addressed to track and trace the food quality to check the data acquired from manufacturers and consumers. The experimental analysis depicts that proposed algorithm has high accuracy rate, less execution time and error rate.

Nowadays, the agricultural business environment is expended to the whole world. Transaction records in point of sales and customer relationship management (CRM) systems can be large-scale data for long-established global chain businesses. Thus, the purpose of this paper is to using a proposed data mining approach to discover valuable markets/customers of urban coffee shop industry (retailer) in current environment of Taiwan, which can implement the industry's data-driven marketing strategy on a CRM system.

In this research approach, Ward's method, C5.0 decision tree and a proposed model are applied for discovering valuable markets and mining useful customer rules.

These found markets and discovered rules can be applied on marketing information or CRM system for identifying valuable customers and target markets.

In this study, the CRM system can be the media for the data-driven marketing strategy in environment of Taiwan. The approach of this research can be applied on other businesses for their data-driven marketing strategies as well.

The purpose of this paper is to establish customers’ markets and rules of dynamic customer relationship management (CRM) systems for online retailers.

This research proposes a procedure to discover customers’ markets and rules, which adopts the recency, frequency, monetary value (RFM) variables, transaction records, and socioeconomic data of the online shoppers to be the research variables. The research methods aim at the supervised apriori algorithm, C5.0 decision tree algorithm, and RFM model.

This research discovered eight RFM markets and six rules of online retailers.

The proposed framework and research results can help retailer managers to retain and expand high value markets via their dynamic CRM and POS systems.

This research uses data mining technologies to extract high value markets and rules for marketing plans. The research variables are easy to obtain via retailers’ systems. The found customer values, RFM markets, shopping association rules, and marketing decision rules can be discovered via the framework of this research.

The aim of this paper is to research the correlation using artificial intelligent tools among international stock markets issuing for the companies.

The objective is to find out the correlation among markets so it can be used for trend prediction. The stock price data from various companies that have issued stock in different countries were used to produce analysis for predictive purposes. Various artificial intelligent tools were used and the predictive performance among them compared.

The finding is that the predictive results when using one market to predict another is above 50 percent and higher, which is much better than random walk.

The limitations are that only the raw market data are worked on, but there are many factors that could affect the short‐term trend of a stock.

This could benefit traders who are interested in trading international issuing stock by taking advantage of markets' different time zones.

The approach provides a methodology approach to predict the moving trend of a stock among international markets.

There is a need to predict whether the consumers liked the stay in the hotel rooms or not, and to remove the aspects the customers did not like. Many customers leave a review after staying in the hotel. These reviews are mostly given on the website used to book the hotel. These reviews can be considered as a valuable data, which can be analyzed to provide better services in the hotels. The purpose of this study is to use machine learning techniques for analyzing the given data to determine different sentiment polarities of the consumers.

Reviews given by hotel customers on the Tripadvisor website, which were made available publicly by Kaggle. Out of 10,000 reviews in the data, a sample of 3,000 negative polarity reviews (customers with bad experiences) in the hotel and 3,000 positive polarity reviews (customers with good experiences) in the hotel is taken to prepare data set. The two-stage feature selection was applied, which first involved greedy selection method and then wrapper method to generate 37 most relevant features. An improved stacked decision tree (ISD) classifier) is built, which is further compared with state-of-the-art machine learning algorithms. All the tests are done using R-Studio.

The results showed that the new model was satisfactory overall with 80.77% accuracy after doing in-depth study with 50–50 split, 80.74% accuracy for 66–34 split and 80.25% accuracy for 80–20 split, when predicting the nature of the customers’ experience in the hotel, i.e. whether they are positive or negative.

The implication of this research is to provide a showcase of how we can predict the polarity of potentially popular reviews. This helps the authors’ perspective to help the hotel industries to take corrective measures for the betterment of business and to promote useful positive reviews. This study also has some limitations like only English reviews are considered. This study was restricted to the data from trip-adviser website; however, a new data may be generated to test the credibility of the model. Only aspect-based sentiment classification is considered in this study.

Stacking machine learning techniques have been proposed. At first, state-of-the-art classifiers are tested on the given data, and then, three best performing classifiers (decision tree C5.0, random forest and support vector machine) are taken to build stack and to create ISD classifier.

The purpose of this study is to provide a data mining approach for classifying Taiwanese healthcare institutions based on customer value assessment. Each institution type has developed its own marketing strategy along with relationship management strategies.

Real transaction data from 88 pharmaceutical companies were the study samples. Expert interviews were conducted to analyze industry knowledge. The frequency, money, and contract term (FMC) model was developed to assess and segment the healthcare institutions. ANOVA and the Scheffe post-test were used to explore the test effects of each FMC indicator among the groups. The C5.0 decision tree was then used to generate the behavioral rules of various segmentations. Finally, this study combined the related variables with the purchasing behavioral rules to propose specific strategies for each type of healthcare institution.

A total of 663 health care institutions in Taiwan were divided into four types: loyalist, intellectualist, nitpicking, and churn. The terms frequency (F), money (M) and contract term (C) were all significant indicators for determining the differences among the four customer categories at the 0.01 level of significance. The behavioral rules related to the four categories were determined by using the C5.0 algorithm.

This FMC model can provide a strategic development method for the pharmaceutical industry to conduct market segmentation. The findings may assist pharmaceutical companies provide customized services to health care institutions and to manage their downstream demand effectively.