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Analysis of Ontology-Based Semantic Association Rule Mining
Published in Archana Patel, Narayan C. Debnath, Bharat Bhushan, Semantic Web Technologies, 2023
Analyzing trends from the huge transactional data is challenging and important in business, medical, banking, academics, and so on. Trend analysis helps to take decisions that necessitate the understanding and analysis of complex data. The association rule mining helps to identify the trends by discovering the relations among data. The relations between the biomedical entities are helpful to understand the relationship between the diseases and their corresponding symptoms including their treatment. The trend breakthrough in customer’s loan may help the bank managers to decide whether the loan can be sanctioned or not. In business, the relations among the items have been used in marketing to promote product sales. The association mining algorithms are used to generate all the possible relations among the entities and the interesting relations can be derived using support and confidence metrics. The association rules are expressed in the form of antecedent and consequent which consists of the items list. For example, the rule depicted in Figure 14.1 shows that 5% of antivirus software is bought with the purchase of a computer and printer, and 60% of the customers who purchased antivirus software also bought a computer and printer.
Air cargo forecasting
Published in Peter S. Morrell, Thomas Klein, Moving Boxes by Air, 2018
Peter S. Morrell, Thomas Klein
Trend analysis techniques use time series and attempt to fit a trend line through historical data, whether on an annual or monthly basis. This line is then projected into the future depending on the equation that best describes the historical data. Statistical techniques can be used ranging from simple averages to the more complex exponential smoothing. There is no attempt to understand the causes of traffic trends, and these methods are not reliable beyond five or so years into the future. Apart from these problems they work on the assumption that past trends will continue into the future. This has not been the case in the past especially with the average capacity of flights which increased quite fast in the 1970s and 1980s only to flatten out in the 1990s and increase again significantly in the 2000s due to more cargo-friendly wide-body passenger aircraft. Similarly trend projections would not have taken into account the rapid growth in low-cost airlines over the past two decades in Europe.
Design of water quality monitoring networks
Published in R. N. Chowdhury, Geomechanics and Water Engineering in Environmental Management, 2017
Nilgun B. Harmancioglu, Necdet Alpaslan, Vijay P. Singh
Water quality monitoring practices are basically designed to achieve specific purposes which lead to various types of monitoring, i.e., trend monitoring, biological monitoring, ecological monitoring, compliance monitoring, and the similar. Among these types, collection of data for purposes of assuring compliance with standards has probably been the oldest practice. In the past, these activities were carried out in a problem, project, or user-oriented framework, as discussed in the previous section. Recently, however, as the emphasis is shifted more to water quality management and control efforts in a larger perspective, the major concern has become the assessment of the quality of surface waters in a wide area or a river basin. In achieving this specific purpose, trend monitoring is required to evaluate both changing quality conditions and the results of control measures.
Unveiling the influence of urban revitalization on pedestrian flow and conduct: a case study in New Cairo, Egypt
Published in HBRC Journal, 2023
Indjy M. Shawket, Rasha M. Shaban
This research aims to study human needs in urban places and identify pedestrian priorities through a stakeholder survey. The survey will include a diverse group with 60% female and 40% male representation, aged 18 to 65 years old. Participants will rank elements on a scale of 1 to 13, helping understand factors influencing pedestrian path choices. The survey will be distributed online, via social media, and through face-to-face interactions to ensure a representative sample. Statistical analysis, using excel, will be used to identify patterns and trends. The results will guide urban planners in designing pedestrian paths that meet user needs, contributing to livable cities. An equation of factors influencing pedestrian paths (pp), as shown in Figure 1, will be generated based on the findings:
Analysis of spatio-temporal variation of hydroclimatic variables of the Krishna river basin under future scenarios
Published in International Journal of River Basin Management, 2022
Chanapathi Tirupathi, Thatikonda. Shashidhar
The trend is a pattern of gradual change in the data points of a series in a certain direction. It may be upward or downward or no change over time. The trend analysis is used to quantify or to find the patterns in a data series over time. In this study, a non-parametric statistical test, known as Mann-Kendall (MK) test is used to assess, if there are any monotonic (upward or downward) trend in data series (Bisht et al., 2018; Kendall, 1975; Kundu et al., 2015; Mann, 1945; Praveenkumar and Jothiprakash, 2020; Zhao et al., 2015). The monotonic trend means a gradual change in a series either upward or downward direction over time. The main advantage of this test is that it is distribution-free and is applicable to all the distributions. The Null hypothesis (H0) considered in this study is there is no trend in the series. The initial assumption is that null hypothesis (H0) is true. The MK test has its own parameters to reject or accept the null hypothesis. Further details of the Mann Kendall (MK) test and Sen’s slope were provided in the supplementary materials (Appendix A. 3).
Wind speed forecasting using deep learning and preprocessing techniques
Published in International Journal of Green Energy, 2023
Figure 24 shows the result of evoking EWT1D() with mode 6 for wind speed data from site 3, yielding six subseries: one residual and five IMFs. The lowest frequency is represented by the first IMF, while the highest frequency is represented by the highest IMF. The residual is shown as a trend. A trend is defined as a general direction of data over a long period of time. It can be a long-term increase (upward), decrease (downward) or horizontal (stationary) (Hyndman and Athanasopoulos 2018). The residual plot shows an upward trend between 1000 and 2500 samples.