Process Framework for Subscriber Management and Retention in Nigerian Telecommunication Industry

in the global telecommunication industry. Hence, a dominant approach for subscriber management and retention is churn control, since it is cheaper to retain an existing subscriber than acquiring a new one. Predictive modeling employs the use of data mining techniques to identify patterns and provide a result that a group of subscribers are likely to churn in the near future. However, the effectiveness of subscriber retention strategy in an organization can be further boosted if the reason for churn and the timing of churn can also be predicted. In this paper, we propose a data mining process framework that can be used to predict churn, determine when a subscriber is likely to churn, provides the reason why a subscriber may churn, and recommend appropriate intervention strategy for customer retention using a combination of statistical and machine learning techniques. This experiment is carried out using data from a major telecom operator in Nigeria

A Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to Knowledge Acquisition

Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts’ opinion about the data description. The proposed approach is committed to modelling of a compact Fuzzy Rule-Based Expert Systems. It is also aimed at providing a platform for instant update of the knowledge-base in case new knowledge is discovered. The insight to the new approach strategies and underlining assumptions, the structure of FARME-D and its practical application in medical domain was discussed. Also, the modalities for the validation of the FARME-D approach were discussed

Application of k Means Clustering algorithm for prediction of Students Academic Performance

The ability to monitor the progress of students academic performance is a critical issue to the academic community of higher learning. A system for analyzing students results based on cluster analysis and uses standard statistical algorithms to arrange their scores data according to the level of their performance is described. In this paper, we also implemented k mean clustering algorithm for analyzing students result data. The model was combined with the deterministic model to analyze the students results of a private Institution in Nigeria which is a good benchmark to monitor the progression of academic performance of students in higher Institution for the purpose of making an effective decision by the academic planners.Comment: IEEE format, International Journal of Computer Science and Information Security, IJCSIS January 2010, ISSN 1947 5500, http://sites.google.com/site/ijcsis

Design and Implementation of Text To Speech Conversion for Visually Impaired People

A Text-to-speech synthesizer is an application that converts text into spoken word, by analyzing and processing the text using Natural Language Processing (NLP) and then using Digital Signal Processing (DSP) technology to convert this processed text into synthesized speech representation of the text. Here, we developed a useful text-to-speech synthesizer in the form of a simple application that converts inputted text into synthesized speech and reads out to the user which can then be saved as an mp3.file. The development of a text to speech synthesizer will be of great help to people with visual impairment and make making through large volume of text easie

Design of A Fuzzy Ranking System for Admission Processes in Higher School of Learning

An expert system is a computer program that contains some of the subject-specific knowledge, as well as the knowledge and analytical skills of one or more human experts and reasons with uncertainty and imprecise information. Currently, in Nigeria, there are very few institutions that use computerized admission systems. Most institutions are still using manual process of admission system. However, the major task is to determine whether a candidate is qualified or not based on the ordinary level (0' level) results requirements, the qualifying examination result cut off mark for their course of choice and other determinant factors. In this paper we introduced fuzzy harming distance function into candidates ranking and implemented it with Java Netbean IDE 6.0. The system was used to evaluate candidates' cr'edentials and every other determinant factor for admitting students. The results showed each candidate's chances of admission, while the system minimized the level of subjectivity in decision makin

Social Differentiation of Inter-word Yod Coalescence in Spoken Nigerian English

This study attempts to track the incidence of inter-word yod coalescence and possibility of its correlation with social factors in Nigerian English. Three hundred and sixty educated Nigerian speakers of English, evenly distributed into social variables of gender, age and social class, provided data for the study. They were guided to voice five utterances and a short passage into digital recording devices. Tokens of yod coalescence produced at different word boundaries were extracted and analysed statistically, using percentages and the univariate Analysis of Variance (ANOVA). The findings reveal a very low usage (3.6%) of inter-word yod coalescence. The process was, however, more prevalent among young speakers and members of high social class who seem to be importing it into the accent. This finding points in the direction of some ongoing innovation in the NigE accent, which possibly suggests the onset of socially conditioned phoneticphonological variation

The Impact of Some Socio-Economic Factors on Academic Performance: A Fuzzy Mining Decision Support System

Due to the reported impacts of some socio-economic factors on academic performance and nations’ education value, there is need for strong awareness to assist students in making the right decision. To this effect, this study proposes and designs student decision support system for determining the extent to which different levels of some socio-economic factors involvement can jointly affect academic performance. The factors are: Student’s interest, Relationship status, Entrepreneurial activities, Peer influence, Health and family background. The traditional decision support system architecture was extended in this study by introducing two components: Fuzzy engine and Mining Engine. Fuzzy engine was introduced to capture intra uncertainties in students' judgment about the data gathered and Mining engine to extract hidden and previously unknown interesting patterns from the dataset. The predictive model was established using fuzzy association rule mining technique. The dataset was gathered using one-on-one questionnaire interaction with students from 4 Universities in Nigeria. The system evaluates students' linguistic levels of involvement and predicts the possible class of honours for them with explicit interpretation of the fired patterns. This system will assist the students in decision making as to the extent they can be involved in some socio-economic activities relative to their family and health status in order to have their desired classes of honour

Application of Fizzy Logic in Decision Making on Student’s academic performance.

Decision making is a knowledge is a knowledge discovery in Fuzzy logic application. Therefore, this paper conceptually defined, explained, and implemented fuzzy logic to the model to system performance, specifically, students’ performance model is studied and the various results generated and the performance chart obtained from overall performance for each year for the consecutive eight years in making decisions for future academic performance are also obtained

Application of Fuzzy Association Rule Mining for Analysing Students Academic Performance

This study examines the relationship between students’ preadmission academic profile and academic performance. Data sample of students in the Department of Computer Science in one of Nigeria private Universities was used. The preadmission academic profile considered includes ‘O’ level grades, University Matriculation Examination (UME) scores, and Post-UME scores. The academic performance is defined using students’ Grade Point Average (GPA) at the end of a particular session. Fuzzy Association Rule Mining (FARM) was used to identify the hidden relationships that exist between students’ pre-admission profile and academic performance. This study hopes to determine the academic profile of students who are most admitted in the session. It determines students’ performance ratings as against their pre-admission academic profile. This can serve as a predictor for admission committee to enhance the quality of the new in-take and guide for the academic advise