ANALYZING EMPLOYEE ATTRITION USING DECISION TREE ALGORITHMS

Employee turnover is a serious concern in knowledge based organizations. When employees leave an organization, theycarry with them invaluable tacit knowledge which is often the source of competitive advantage for the business. In order foran organization to continually have a higher competitive advantage over its competition, it should make it a duty to minimizeemployee attrition. This study identifies employee related attributes that contribute to the prediction of employees’ attritionin organizations. Three hundred and nine (309) complete records of employees of one of the Higher Institutions in Nigeriawho worked in and left the institution between 1978 and 2006 were used for the study. The demographic and job relatedrecords of the employee were the main data which were used to classify the employee into some predefined attrition classes.Waikato Environment for Knowledge Analysis (WEKA) and See5 for Windows were used to generate decision tree modelsand rule-sets. The results of the decision tree models and rule-sets generated were then used for developing a a predictivemodel that was used to predict new cases of employee attrition. A framework for a software tool that can implement therules generated in this study was also proposed.Keywords: Employee Attrition, Decision Tree Analysis, Data Minin

ORGANIZATIONAL CLIMATE, LEADERSHIP STYLE AND EMOTIONAL INTELLIGENCE AS PREDICTORS OF QUALITY OF WORK LIFE AMONG BANK WORKERS IN IBADAN, NIGERIA

The effects of organizational climate, leadership style and emotional intelligence on the quality of work life were investigated in this study. The participants were two hundred and fifty bank workers drawn from selected commercial banks within Ibadan metropolis. Three research questions and hypotheses were raised in the study. Four valid and standardized instruments were administered on the participants. Pearson product moment correlation, multiple regression analysis and analysis of variance were used to analyse data at 0.05 level of significance. The result shows that the three independent variables when combined were effective in predicting quality of work life. The three variables contributed significantly to quality of work life of the participants with leadership styles as the most potent predictor in the study.. the result also show there was also a significant difference in quality of work life among participants with Democratic, Autocratic and Laissez faire leadership with contributions of democratic style being the most potent. Based on the findings, it is suggested that management should take into cognizance the importance and roles of emotional intelligence and leadership styles in enhancing quality of work life among employee

Estimation of Human Health Risk Due to Heavy Metals around Schools and Auto-Mobile Workshops near Frequented Roads in Kaduna State, Nigeria

Heavy metals are widely known for their potential to cause carcinogenic and non-carcinogenic health risks. In this work, the carcinogenic and non-carcinogenic health risks associated with heavy metals in the vicinity of schools and auto mechanic workshops close to busy roads in Kaduna state was assessed using NEX CG EDXRF MODEL with brand name RIGAKU situated at a UTM Laboratory, Malaysia. The obtained heavy metals concentrations were used to estimate the health effects that might result from exposure to carcinogenic and non-carcinogenic chemicals for both the population ages using US EPA methodology. Findings indicated that in some locations the carcinogenic and non-carcinogenic hazards associated with exposure for residents was greater than the US EPA acceptable thresholds of 10-4 and 1 respectively. This indicated that the heavy metals may result to unacceptable carcinogenic and non-carcinogenic risks, which is an issue of concern in public health especially looking at the way school children play around these areas. The present study therefore provides scientific basis for strategies required to protect human and environmental health in schools and automobile workshops

EVALUATION OF TWO-STAGE SUBSURFACE FLOW CONSTRUCTED WETLANDS FOR ABATTOIR WASTEWATER MANAGEMENT

Abattoir wastewater is high in organic content, the waste recovery and treatment facility is expensive and this results in indiscriminate dumping into streams without adequate treatment. The effectiveness of using a two-stage subsurface flow constructed wetland to treat abattoir effluent was examined in this study. Diluted abattoir wastewater from Lafenwa Abattoir, Abeokuta, Ogun State, Nigeria was fed into a two-stage Vegetated Subsurface Bed Constructed Wetlands (VSBCW). The VSBCW consisted of 500 mm deep 10-15 mm diameter granite with 150 mm thick overlay of well graded sand planted with locally available Vetiveria nigritana. Grab samples were collected at selected points along Ogun river and measurement of physico-chemical parameters such as: Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD5), Electrical Conductivity (EC), Total Dissolved Solids (TDS) and Total Suspended Solid (TSS) of the influent and effluent from the VSBCW were carried out. Irrigation with water and diluted abattoir wastewater to examine the variation in plant growth rate was also investigated. The results revealed a pollution load reduction as the wastewater moves away from the discharge point but inadequate to meet the FEPA (1991) standard for wastewater discharge into rivers. The VSBCW was observed to reduce the concentration of BOD5, COD, EC, TDS and TSS in the abattoir wastewater by 88.71, 87.28, 45.72, 56.89 and 72.27 % respectively. The growth rate of the V. nigritana reduced by 1.9% when irrigated with abattoir wastewater. The study revealed that locally available V. nigritana in VSBCW is effective in abattoir wastewater treatment and could be use to curtail the pollution caused by discharge of untreated wastewater into rivers.     &nbsp

Identification of pattern mining algorithm for rugby league players positional groups separation based on movement patterns

The application of pattern mining algorithms to extract movement patterns from sports big data can improve training specificity by facilitating a more granular evaluation of movement. Since movement patterns can only occur as consecutive, non-consecutive, or non-sequential, this study aimed to identify the best set of movement patterns for player movement profiling in professional rugby league and quantify the similarity among distinct movement patterns. Three pattern mining algorithms (l-length Closed Contiguous [LCCspm], Longest Common Subsequence [LCS] and AprioriClose) were used to extract patterns to profile elite rugby football league hookers (n = 22 players) and wingers (n = 28 players) match-games movements across 319 matches. Jaccard similarity score was used to quantify the similarity between algorithms’ movement patterns and machine learning classification modelling identified the best algorithm’s movement patterns to separate playing positions. LCCspm and LCS movement patterns shared a 0.19 Jaccard similarity score. AprioriClose movement patterns shared no significant Jaccard similarity with LCCspm (0.008) and LCS (0.009) patterns. The closed contiguous movement patterns profiled by LCCspm best-separated players into playing positions. Multi-layered Perceptron classification algorithm achieved the highest accuracy of 91.02% and precision, recall and F1 scores of 0.91 respectively. Therefore, we recommend the extraction of closed contiguous (consecutive) over non-consecutive and non-sequential movement patterns for separating groups of players

The use of match-based exact movement activities to classify elite rugby league players into positional groups

The cluster analysis of elite rugby league players identified groups of distinct playing positions that can be referred to as broad positional groups. However, the identified positional groups were based on traditional indicators (physical and technical–tactical) that provided no information about the exact match-based movement activities that led to such similarity grouping and the classification of elite rugby league players into these broad positional groups remains unexplored. Hence, this study finds the best model to classify elite rugby league players into positional groups, using data characterised by movement patterns to uncover the similar movement activities of distinct playing positions within a positional group. Key movement patterns for the positional group classification and differences between the groups were also investigated. A total of 18,173 unique movement patterns were derived from 422 players’ GPS data across the 2019 and 2020 seasons, where only 36 were identified as key patterns. The highest classification accuracy of 77.58% using all unique patterns and 74.5% accuracy using the key patterns was achieved, outperforming studies that used traditional indicators. Further analyses based on key patterns revealed differences between forwards and backs. These findings establish movement patterns as viable indicators to classify rugby league players into positional groups, enabling coaches and trainers to develop position-specific training programmes that cater to the unique physical demands of each position, leading to better player development and team performance. Movement patterns are therefore recommended as an alternative approach to quantifying players’ external loads and obtaining granular information

Development of an Alumni Feedback System for Curriculum Improvement in Building Technology Courses

In this fast-paced world, the needs of the world of work and the global market is changing at an unprecedented speed. Therefore, institutions of higher learning need to constantly adjust their programs to fit into these needs. The study aimed to develop an alumni feedback system for curriculum improvement in Building Technology courses. The study highlighted the benefits of an alumni feedback system compared to a manual questionnaire method or other methods of curriculum improvement. The web-based system was designed through use case and system block diagrams. Thereafter, the webbased system was programmed using HTML, CSS, MySQL and PHP. Screenshots of the web-based system was presented. The alumni feedback system comprises of background information of the alumni, perception test on the impact of the course content and a review of the course content for curriculum improvement. Since this is a preliminary study, future studies would be based on analyzing data obtained in the database in terms of the numerical and text data. This study can be adapted for other programmes for the purpose of curriculum improvement