scholarly journals Fintech Predictive Modeling and Performance of Investment Firms in Kenya

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 1202-1212
Author(s):  
Elizabeth Ndichu Gitonga ◽  
Peter Wang’ombe Kariuki ◽  
Samuel Nduati Kariuki
Keyword(s):  
Predictive Modeling ◽  
Predictive Analytics ◽  
Machine Learning Techniques ◽  
Consumption Pattern ◽  
Mixed Method Research ◽  
Business Growth ◽  
Growth And Survival ◽  
Study Population ◽  
And Performance ◽  
Investment Firm

Predictive analytics is concerned with the prediction of future trends and outcomes. The approaches used to conduct predictive analytics can be classified into machine learning techniques and regression techniques. This study dteremined the influence of fintech predictive modeling on performance of investment firms in Kenya. The study population was 57 investment firms. The study employed mixed method research design by incorporating descriptive and explanatory research designs. Data was collected using questionnaires and an in-depth interview guide. Coefficient of fintech predictive modeling has a positive and significant effect on performance of investment firms. The study concluded that fintech predictive modeling allows investment firms to forecast business growth and customer behaviour chnages. It is important for an investment firm to be able to understand business growth by accurately forecasting future growth and survival. Moreover, it is of vital necessity to understand changes in customer buying/consumption behavior so as to develop products and services that suit their needs and preferences. As a result, predictive modeling is required to project future business growth and changes in customer consumption pattern.

scholarly journals The Effect of Corporate Social and environmental Responsibility on Leverage and Performance: Evidence from Jordan

2021 ◽  
Vol 10 (1) ◽  
pp. 48
Author(s):  
Lamees Talal Al-Radaydeh ◽  
Manal Sulieman Abughniem ◽  
Mohammad Adnan Hilal Al Aishat ◽  
Rania Al Omari
Keyword(s):  
Social Responsibility ◽  
Stock Exchange ◽  
Environmental Responsibility ◽  
Financial Leverage ◽  
The Social ◽  
Study Population ◽  
And Performance ◽  
Corporate Social ◽  
Negative Link ◽  
Environmental Activities

This study aimed to test the effect of corporate social responsibility through activities towards society and environmental activities on financial leverage and corporate performance, the study population consists of all companies listed on the Amman Stock Exchange. The study sample reached 187 companies for the period 2014-2017, to achieve the goals of the study; regression analysis was used to find out the effect of social  and environmental responsibility on leverage and performance, the study reached the results that the social responsibility towards the society affects the financial leverage of the company while the environmental activities did not affect the financial leverage, responsibility for social and environmental activities did not affect the performance of companies, and there was a positive correlation between social and environmental responsibility and financial leverage, and a negative link was found between financial leverage and performance.

scholarly journals TOTAL QUALITY MANAGEMENT IN AFFECTING COMPANY OPERATIONAL PERFORMANCE IN CONVECTION SMEs WITH ORGANIZATIONAL COMMITMENT AS A MEDIATOR

2021 ◽  
Vol 19 (3) ◽  
pp. 662-677
Author(s):  
Teuku Roli Ilhamsyah Putra ◽  
◽  
Mukhlis Yunus ◽  
Teuku Hafiz Fakhreza ◽  
◽  
...  
Keyword(s):  
Organizational Commitment ◽  
Quality Management ◽  
Total Quality Management ◽  
Structural Equation ◽  
Sampling Technique ◽  
Equation Model ◽  
Operational Performance ◽  
Total Quality ◽  
Study Population ◽  
And Performance

This study aims to prove the role of Total Quality Management (TQM) and organizational commitment in supporting the achievement of company operational performance. The population was all Convection SMEs in the city of Banda Aceh, consisted of 294 Convection SMEs. The sample was selected using a proportional sampling technique where sampling considers elements or categories in the study population. The sample taken was 15% of the population. One respondent represented one unit of SMEs, so the sample has amounted to 169 respondents. Data was collected using questionnaires. The model was analyzed with the Structural Equation Model Analysis. The result showed an influence between TQM on Organizational Commitment, TQM on Company Operational Performance, and Organizational Commitment on Company Operational Performance. This study also found that organizational commitment can act as a partial mediator to strengthen the effect of TQM on Company operational performance. The interesting issue in this study lies in the discussion of the organizational commitment to solving the inconsistency problems in the quality of convection production in SMEs. Furthermore, the other researchers can provide more concepts and variables to enrich this research model, like other mediators for TQM to achieve performance or even the commitment that can strengthen the TQM and performance. The model also can be a reference for practitioners to set their strategy in further, to go more productive.

The Relationship Between Dietary Habits and Body-Mass Index Using the Federal Airport Authority of Nigeria as the Sample

2002 ◽  
Vol 16 (3) ◽  
pp. 215-227 ◽  
Author(s):  
R.O. Abidoye ◽  
L.A. Madueke ◽  
G.O. Abidoye
Keyword(s):  
Blood Pressure ◽  
Diastolic Blood Pressure ◽  
Dietary Habits ◽  
Blood Cholesterol ◽  
Consumption Pattern ◽  
Feeding Patterns ◽  
High Systolic Blood Pressure ◽  
Cholesterol Levels ◽  
Acceptable Range ◽  
Study Population

This was a cross sectional survey of selected sample of staff of the Federal Airport Authority of Nigeria, Lagos, conducted in July to August, 2000. Feeding patterns observed among the sampled population showed that most (74.4%) ate three meals while 11.2% ate more than three meals daily. However, lunch was the most common meal eaten away from home by most (59.0%) of the respondents. Most of the respondents were observed to substitute snacks for their lunch (84.3%). Their food consumption pattern revealed that 23.2% consumed cereals daily while only 5.6% of the respondents consumed fruits and only 10.9% affirmed to consuming vegetables daily. Consumption pattern of other foods revealed that 15.6% consumed dairy products daily, meat/fish was daily consumed by only 16.2% and only 10.0% consumed fats and oils daily in the meals. Gender was also observed to influence feeding patterns of the sampled population studied. Most of the men consumed more meals per day than females. Only 46.4% of all the respondents had BMI values within normal acceptable range. Most of the respondents that were underweight were men (91.4%). Blood pressure measurements revealed that most of them had normal systolic (78.3%) and diastolic blood pressure (81.8%). Though 3.9% had severely high systolic blood pressure and 0.7% had severely high diastolic blood pressure. Only 20.5% of the study population had acceptable blood cholesterol levels of which only 61.1% had BMI values within the normal acceptable range. Only 0.8% of the study population had very high blood cholesterol levels with majority of the population 72.7% on the borderline. It is recommended that health and nutrition education be mounted and that periodic anthropometric measurements be used to evaluate the risk of some non-communicable diseases.

Predicting Secondary School Students' Performance Utilizing a Semi-supervised Learning Approach

2018 ◽  
Vol 57 (2) ◽  
pp. 448-470 ◽  
Author(s):  
Ioannis E. Livieris ◽  
Konstantina Drakopoulou ◽  
Vassilis T. Tampakas ◽  
Tassos A. Mikropoulos ◽  
Panagiotis Pintelas
Keyword(s):  
Supervised Learning ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Educational Data Mining ◽  
Semisupervised Learning ◽  
Research Field ◽  
Machine Learning Techniques ◽  
School Students ◽  
Supervised Learning Algorithms ◽  
And Performance

Educational data mining constitutes a recent research field which gained popularity over the last decade because of its ability to monitor students' academic performance and predict future progression. Numerous machine learning techniques and especially supervised learning algorithms have been applied to develop accurate models to predict student's characteristics which induce their behavior and performance. In this work, we examine and evaluate the effectiveness of two wrapper methods for semisupervised learning algorithms for predicting the students' performance in the final examinations. Our preliminary numerical experiments indicate that the advantage of semisupervised methods is that the classification accuracy can be significantly improved by utilizing a few labeled and many unlabeled data for developing reliable prediction models.

Primary Factors Influencing the Decision to Vaccinate Against COVID-19 in the United States: A Predictive Analytics Approach (Preprint)

2021 ◽  
Author(s):  
Serkan Varol ◽  
Serkan Catma ◽  
Diana Reindl ◽  
Elizabeth Serieux
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Predictive Analytics ◽  
The United States ◽  
Machine Learning Techniques ◽  
Vaccine Hesitancy ◽  
Willingness To Accept ◽  
Perceptions And Attitudes ◽  
Learning Techniques ◽  
Vaccine Decision Making

BACKGROUND Vaccine refusal still poses a risk to reaching herd immunity in the United States. The existing literature focuses on identifying the predictors that would impact the willingness to accept (WTA) vaccines using survey data. These variables range from the socio-demographic characteristics of the participants to the perceptions and attitudes towards the vaccines so each variable’s statistical relationship with the WTA a vaccine can be investigated. However, while the results of these studies may have important implications for understanding vaccine hesitancy by offering interpretation of the statistical relationships, the prediction of vaccine decision-making has rarely been investigated OBJECTIVE We aimed to identify the factors that contribute to the prediction of COVID-19 vaccine acceptors and refusers using machine learning METHODS A nationwide survey was administered online in November, 2020 to assess American public perceptions and attitudes towards COVID-19 vaccines. Seven machine learning techniques were utilized to identify the model with the highest predictive power. Moreover, a set of variables that would contribute the most to the predictions of vaccine acceptors and refusers was identified using Gini importance based on Random Forest structure RESULTS The resulting machine learning algorithm has better prediction ability for willingness to accept (82%) versus reject (51%) a COVID-19 vaccine. In terms of predictive success, the Random Forest model outperformed the other machine learning techniques with a 69.52% accuracy rate. Worrying about (re) contracting Covid 19 and opinions regarding mandatory face covering were identified as the most important predictors of vaccine decision-making CONCLUSIONS The complexity of vaccine hesitancy needs to be investigated thoroughly before the threshold needed to reach population immunity can be achieved. Predictive analytics can help the public health officials design and deliver individually tailored vaccination programs that would increase the overall vaccine uptake.

scholarly journals Machine Learning for Benchmarking Models of Heating Energy Demand of Houses in Northern Canada

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1158
Author(s):  
Behrad Bezyan ◽  
Radu Zmeureanu
Keyword(s):  
Machine Learning ◽  
Energy Demand ◽  
Energy Use ◽  
Machine Learning Techniques ◽  
Northern Canada ◽  
Automation And Control ◽  
And Performance ◽  
Energy Signature ◽  
Heating Energy Demand ◽  
And Control

In most cases, the benchmarking models of energy use in houses are developed based on current and past data, and they continue to be used without any update. This paper proposes the method of retraining of benchmarking models by applying machine learning techniques when new measurements are made available. The method uses as a case study the measurements of heating energy demand from two semi-detached houses of Northern Canada. The results of the prediction of heating energy demand using static or augmented window techniques are compared with measurements. The daily energy signature is used as a benchmarking model due to its simplicity and performance. However, the proposed retraining method can be applied to any form of benchmarking model. The method should be applied in all possible situations, and be an integral part of intelligent building automation and control systems (BACS) for the ongoing commissioning for building energy-related applications.

scholarly journals Prediction of Fatal and Major Injury of Drivers, Cyclists, and Pedestrians in Collisions

2020 ◽  
Vol 32 (1) ◽  
pp. 39-53
Author(s):  
Dalia Shanshal ◽  
Ceni Babaoglu ◽  
Ayşe Başar
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Injury Severity ◽  
Predictive Analytics ◽  
Machine Learning Techniques ◽  
Lasso Regression ◽  
Severe Injuries ◽  
Factors Affecting ◽  
Spatio Temporal ◽  
Using Data

Traffic-related deaths and severe injuries may affect every person on the roads, whether driving, cycling or walking. Toronto, the largest city in Canada and the fourth largest in North America, aims to eliminate traffic-related fatalities and serious injuries on city streets. The aim of this study is to build a prediction model using data analytics and machine learning techniques that learn from past patterns, providing additional data-driven decision support for strategic planning. A detailed exploratory analysis is presented, investigating the relationship between the variables and factors affecting collisions in Toronto. A learning-based model is proposed to predict the fatalities and severe injuries in traffic collisions through a comparison of two predictive models: Lasso Regression and Random Forest. Exploratory data analysis results reveal both spatio-temporal and behavioural patterns such as the prevalence of collisions in intersections, in the spring and summer and aggressive driving and inattentive behaviours in drivers. The prediction results show that the best predictor of injury severity for drivers, cyclists and pedestrians is Random Forest with an accuracy of 0.80, 0.89, and 0.80, respectively. The proposed methods demonstrate the effectiveness of machine learning application to traffic and collision data, both for exploratory and predictive analytics.

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