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Work stress induced weight gain in construction
Published in Imriyas Kamardeen, Work Stress Induced Chronic Diseases in Construction, 2021
Multiple analysis techniques were used to answer the research questions as outlined in the following: The prevalence of work stress induced weight gain was assessed using descriptive statistics, along with inferential techniques such as one-sample t-tests for population.Kruskal-Wallis H tests were performed to investigate how the differences in individual characteristics influence weight gain.Cluster analysis was performed to investigate the relationship of weight gain with job stressors, work stress, stress coping methods, mental disorders and job outcomes. Cluster analysis helps grouping of data based on their characteristics and enables the identification of most significant data characteristics that are pertinent to an outcome. The merits of this analytics technique were explained in Chapter 3.Pearson bi-variate correlation analysis was conducted to examine the association between weight gain and physical and mental health issues among construction professionals.
Computer Vision for Object Recognition and Tracking Based on Raspberry Pi
Published in Ibrahiem M. M. El Emary, Anna Brzozowska, Shaping the Future of ICT, 2017
Mean shift is a nonparametric feature-space analysis technique for locating the maxima of a density function. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining. The mean-shift tracker provides accurate identification of the location and it is computationally possible. The form that is used widespread for target representation is color histograms, due to its independence from scaling and for the purposes of rotation and its durability to partial blockage. To keep track of the target using the mean-shift algorithm, it repeats the following steps [7]: Choose a search window size and the initial site of the search window.Account for the mean site in the search window.Determine the center of the search window at the mean site computed in step 2.Repeat steps 2 and 3 until rapprochement (or until the mean location moves less than a predefined threshold).
Cluster analysis
Published in Catherine Dawson, A–Z of Digital Research Methods, 2019
Cluster analysis is a collection of data analysis techniques and procedures that help researchers to group, sort, classify and explore data to uncover or identify groups or structures within the data. Although cluster analysis has a long pre-digital history (see Everitt et al., 2011), the procedures and techniques have been included in this book because of technological developments in digital tools, software and visualisation packages, along with the increasing use of digital methods that use cluster analysis techniques and procedures, such as exploratory data mining (Chapter 12), data analytics (Chapter 10) and machine learning (Chapter 29).
Hydrogeochemical characteristics and multivariate statistical approach for monitoring groundwater quality scenario in the vicinity of industrial area of western Himalaya, India
Published in Chemistry and Ecology, 2023
Kshitindra Kumar Singh, Geeta Tewari, Mamta Bisht, R. K. Tiwary, Suresh Kumar, Kiran Patni, Aabha Gangwar, Bhawana Kanyal
Cluster analysis is defined as a class of tools which is used to categorise a set of objects into groups based on their similarities. It is a data reduction technique to create groups and subgroups that are more convenient to explain than individuals. This technique is used to find out the statistically different hydro-chemical groups that may be important in the geological context. Two types of cluster techniques have been identified: Q and R mode. In the R mode, a dendrogram is produced which shows similarity while in the Q mode, it is difficult to separate clusters [45–47]. Hierarchical agglomerative clustering (HAC) is a commonly used technique which gives a completely natural relationship between a sample and different parameters and is represented by a dendrogram. A dendrogram presents a picture of closely related groups. In this study, Hierarchical cluster analysis was applied using Ward’s method by SPSS 16.0 to check the variability among different water parameters.
New strategies on the application of artificial intelligence in the field of phytoremediation
Published in International Journal of Phytoremediation, 2023
Pratyasha Singh, Aparupa Pani, Arun S. Mujumdar, Shivanand S. Shirkole
Cluster analysis is a statistical method for grouping data or observations based on how similar they are or how close they are to one other. The data or observations are separated into homogeneous and distinct categories using cluster analysis. Cluster analysis revealed that when breeding seeds and plants get the largest yield, it is necessary to determine which section of the plant is most important, or which component has the greatest effect on the other parts. Cluster analysis of maize morphological features revealed that the area of the flag leaf, grain rows, peduncle length out of flag leaf, and ear comicalness index were among the 25 most important traits of this experience (Choukan et al.2005; Mousavi and Nagy 2021). Cluster analysis was found to be an effective tool to classify the amendments of compost and biochar based on the uptake of the heavy metals by root and shoot and the total heavy metals remaining in the soil (Bahrami et al.2021).
Driving behavior classification at signalized intersections using vehicle kinematics: Application of unsupervised machine learning
Published in International Journal of Injury Control and Safety Promotion, 2023
Nour O. Khanfar, Mohammed Elhenawy, Huthaifa I. Ashqar, Qinaat Hussain, Wael K. M. Alhajyaseen
To prepare the data for clustering, we standardized data to make classification of each observation analogous across different features. Observations in the dataset denotes a driving behavior event across the two different conditions. Each observation will be clustered using K-means. In cluster analysis, we used elbow method, which is a heuristic used in determining the number of clusters in a dataset. The method consists of plotting the explained variation as a function of the number of clusters and choosing the elbow of the curve as the optimal number of clusters () to use (Marutho et al., 2018). We used MathWorks’s MATLAB software to implement K-means clustering algorithm (MathWorks – MATLAB, 2021). Before we started with clustering driving behavior on the intersection level, we investigated K-means clustering on the condition level. As we note in the Descriptive Analysis section, clusters were found to be uncorrelated with the two different conditions at the signalized intersections. That said, we shall continue our clustering analysis ignoring the signal condition for the time being. We consider the signal conditions at the intersections and their impact on the cluster analysis once the framework is satisfactory and validated, as reported in Clustering Analysis of Signal Conditions section.