14 research outputs found
MUSCLES ACTIVATIONS DURING "SHOULDER MOUNT" POLE ACROBATIC EXERCISE
No full textAuthor: Bc. Modinat Sanni Supervisor: Ing. Miroslav Vilímek, PhD Title: MUSCLES ACTIVATION DURING "SHOULDER MOUNT"POLE ACROBATIC EXERCISE Purpose: This study is an empirical - theoretical study presents the literature review regarding to the topic of the shoulder function anatomy, kinesiology, biomechanics, non-traumatic injuries of the shoulder and their prevention by using the available literatures. Further, the study also compares by surface electromyography the amplitudes, shapes and durations of myoelectric signals of m. latissimus dorsi, m. pectoralis maior, m. biceps brachii, m. infraspinatus and m. supraspinatus of dominant shoulder in two healthy individuals during acrobatic exercise on vertical pole known as "Shoulder Mount". The purpose was monitor the changes in two different conditions; i.e. kinesiotape and elbow brace and compare with the control condition for the reason of finding out their ability to affect the myoelectric activities of selected muscles. Further, the Shoulder Mount exercise had recorded by six Qualisys cameras for motion analyses. Methods and materials: The potentially eligible scientific articles perform a search of studies on the topic of kinesiotapes and tennis elbow brace as measured by EMG mainly on myoelectric activity of the shoulder complex were seared from..
A FUZZY-BASED BUSINESS DECISION MAKING SYSTEM: FROM A MULTI-OBJECTIVE PERSPECTIVE
No full textIn order to provide essential managerial services for making critical business-biased decisions, there is need for accurate data. A business activity hinged on an effective administrative course of action will not only portray the manager of the business as adept but also help advance the financial interests of the organization, while minimizing its losses in this respect. In this paper, a decision making model for controlling business activities is developed, using a fusion of linear programming methods and a set of fuzzy membership functions. In the research conducted, it is revealed that: to improve the effectiveness of a model used for making multiple objective decisions for business related activities, the use of a fuzzy method is more effective than the use of a non-fuzzy method in minimizing the objective functions. It was also discovered that when computing the objective functions of a problem, a more precise result can be obtained by fortifying a linear programming model, with a technique for managing imprecise data
A Full-text Website Search Engine Powered by Lucene and The Depth First Search Algorithm
No full textThe impact of the tax reform act of 1986 on the black population with particular reference to the individual income tax, 1987
No full textThe primary intent of this degree paper is to discuss some of the probable impact of the Tax Reform Act of 1986 on Blacks. An attempt has been made to show the economic status of the Black population and to explain the probable impact of the Tax Reform Act of 1986 on it. The old tax system had several shortcomings which prompted the need for tax reform. Among the several reasons for a new tax system were lack of efficiency and equity in the old system. Black politicians advocated more support for the equity side due to the disproportional distribution of the Black population, the distribution of income (majority of Black incomes are found in the lower income brackets and only about 20 percent of their income comes from property while majority of it comes from employment). Also, proportionally, more Black female head of households are found in that category (head of household) than White females. These circumstances would put the Black population in a position to bear more of the tax burden proportionally, than the White population. The major findings of the study were that (1) the restructuring of the tax rates could lower the progressivity of the tax system (this could probably have a disproportional adverse impact on the Black population), and (2) that the increase in the standard deduction and personal exemptions would probably compensate for this adverse impact on Blacks and other minority groups. It was concluded that the impact of the overall tax system on the tax burdens of the Black population, depends on the extent to which the system stresses progressive versus regressive taxes. There is still room for improvement and reform, and that through more research, income tax reforms can be made better. The main sources of information for this paper include The Atlanta Journal and Constitution, Consumer Reports, National Tax Journal, publications from the Bureau of the Census, and the Internal Revenue Service. Also, a wide variety of primary and secondary information was used
Performance Evaluation of Manhattan and Euclidean Distance Measures For Clustering Based Automatic Text Summarization
No full textIn the past few years, there has been an explosion in the amount of text data from a variety of sources. This volume of text is a valuable source of information and knowledge which needs to be effectively summarized to be useful. In this paper, automatic text summarization with K-means clustering techniques is presented by employing two different distance measurement methods (Euclidean and Manhattan). The dataset extracted from African prose was preprocessed using stopwords removal and tokenization. The preprocessed document is converted into vector representation using tf-idf technique and k-means clustering is applied using Euclidean and Manhattan distance measures to generate summary. There are different distance measures for k-means which has been used in several works. However, there is dearth of work on the performance evaluation of these distance measures in text summarization. The experimental analysis was performed on Waikato Environment for Knowledge Analysis (WEKA). The results obtained showed that the Euclidean variation produced an extractive summary of sentences amounting to 72% from three different clusters while the Manhattan variation produced an extractive summary of sentences that made up 94% of the total document all in one cluster using compression ratio as the performance metric. Keywords— Text summarization, Euclidean distance, k-means clustering, Manhattan distanc
Tree-based homogeneous ensemble model with feature selection for diabetic retinopathy prediction
No full textDiabetic Retinopathy (DR) is a condition that emerges from prolonged diabetes, causing severe damages to the eyes. Early diagnosis of this disease is highly imperative as late diagnosis may be fatal. Existing studies employed machine learning approaches with Support Vector Machines (SVM) having the highest performance on most analyses and Decision Trees (DT) having the lowest. However, SVM has been known to suffer from parameter and kernel selection problems, which undermine its predictive capability. Hence, this study presents homogenous ensemble classification methods with DT as the base classifier to optimize predictive performance. Boosting and Bagging ensemble methods with feature selection were employed, and experiments were carried out using Python Scikit Learn libraries on DR datasets extracted from UCI Machine Learning repository. Experimental results showed that Bagged and Boosted DT were better than SVM. Specifically, Bagged DT performed best with accuracy 65.38 %, f-score 0.664, and AUC 0.731, followed by Boosted DT with accuracy 65.42 %, f-score 0.655, and AUC 0.724 when compared to SVM (accuracy 65.16 %, f-score 0.652, and AUC 0.721). These results indicate that DT's predictive performance can be optimized by employing the homogeneous ensemble methods to outperform SVM in predicting DR
Parameter tuning in KNN for software defect prediction: an empirical analysis
No full textSoftware Defect Prediction (SDP) provides insights that can help software teams to allocate their limited resources in developing software systems. It predicts likely defective modules and helps avoid pitfalls that are associated with such modules. However, these insights may be inaccurate and unreliable if parameters of SDP models are not taken into consideration. In this study, the effect of parameter tuning on the k nearest neighbor (k-NN) in SDP was investigated. More specifically, the impact of varying and selecting optimal k value, the influence of distance weighting and the impact of distance functions on k-NN. An experiment was designed to investigate this problem in SDP over 6 software defect datasets. The experimental results revealed that k value should be greater than 1 (default) as the average RMSE values of k-NN when k>1(0.2727) is less than when k=1(default) (0.3296). In addition, the predictive performance of k-NN with distance weighing improved by 8.82% and 1.7% based on AUC and accuracy respectively. In terms of the distance function, kNN models based on Dilca distance function performed better than the Euclidean distance function (default distance function). Hence, we conclude that parameter tuning has a positive effect on the predictive performance of k-NN in SDP
Empirical analysis of tree-based classification models for customer churn prediction
No full textCustomer churn is a vital and reoccurring problem facing most business industries, particularly the telecommunications industry. Considering the fierce competition among telecommunications firms and the high expenses of attracting and gaining new subscribers, keeping existing loyal subscribers becomes crucial. Early prediction of disgruntled subscribers can assist telecommunications firms in identifying the reasons for churn and in deploying applicable innovative policies to boost productivity, maintain market competitiveness, and reduce monetary damages. Controlling customer churn through the development of efficient and dependable customer churn prediction (CCP) solutions is imperative to attaining this goal. According to the outcomes of current CCP research, several strategies, including rule-based and machine-learning (ML) processes, have been proposed to handle the CCP phenomenon. However, the lack of flexibility and robustness of rule based CCP solutions is a fundamental shortcoming, and the lopsided distribution of churn datasets is deleterious to the efficacy of most traditional ML techniques in CCP. Regardless, ML-based CCP solutions have been reported to be more effective than other forms of CCP solutions. Unlike linear-based, instance-based, and function-based ML classifiers, tree-based ML classifiers are known to generate predictive models with high accuracy, high stability, and ease of interpretation. However, the deployment of tree-based classifiers for CCP is limited in most cases to the decision tree (DT) and random forest (RF). Hence, this research investigated the effectiveness of tree-based classifiers with diverse computational properties in CCP. Specifically, the CCP performances of diverse tree-based classifiers such as the single, ensemble, enhanced, and hybrid tree-based classifiers are investigated. Also, the effects of data quality problems such as the class imbalance problem (CIP) on the predictive performances of tree-based classifiers and their homogeneous ensemble variants on CCP were assessed. From the experimental results, it was observed that the investigated tree-based classifiers outperformed other forms of classifiers such as linear-based (Support Vector Machine (SVM)), instance-based (K-Nearest Neighbour (KNN)), Bayesian-based (Naïve Bayes (NB)) and function-based (MultiLayer Perceptron (MLP)) classifiers in most cases with or without the CIP. Also, it was observed that the CIP has a significant effect on the CCP performances of investigated tree-based classifiers, but the combination of a data sampling technique and a homogeneous ensemble method can be an effective solution to CIP and also generate efficient CCP models
Application of Shuffled Frog-Leaping Algorithm for Optimal Software Project Scheduling and Staffing
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