An Intelligent Foreign Exchange Robot (i-FOREXBOT) Development with Scale Conjugate Gradient Neural Network.

This paper describes an on-going work on the development of an intelligent foreign exchange forecast and decision support engine tagged i-FOREXBOT with an ultimate target of online deployment. FOREX market is a complex domain and an ability to forecast future price movements based on past data is highly fundamental to profitable trading by the practitioners. In order to assist traders to achieve minimal loss and high profitability, there are existing automated FOREX prediction robots which are based on technical data. Analysis using technical data involves the adoption of graphs and reoccurring patterns of price movement to forecast currency pair rate. Despite some positive results that have been recorded with this approach, technical analysis and the robots that are based on them only give scanty meaning to the market movements. The optional approach to market prediction being adopted by traders is fundamental analysis. In this method, the impact of fundamental data (such as cash flow, geopolitical factors, economic indices, government policies and news) on price movements are considered for trade forecasting. However, there are currently very little research efforts that focus on automation of FOREX prediction using fundamental data. Therefore, in this work, we are developing an online FOREX robot based on artificial neural network (ANN) and fundamental data to forecast the exchange rate of Great Britain Pound (GBP) and US dollar (USD) pair using six fundamental indices. The preliminary experimental results of the scale conjugate gradient ANN engine we developed is very encouraging and the platform promises to be a good and reliable tool for accurate exchange rate prediction when it is fully developed and deployed

E-PAYCHEQUE FRAMEWORK WITH CONTACT ELECTRONIC CARD AND FINGERPRINT BIOMETRIC FOR CASHLESS SMART CAMPUSES

Identity theft in financial transactions is a very rampant problem among students in most institutions of learning. Currently, in order to minimize financial theft, most schools dole out paper based “paycheques” to students, which usually contain the name, personal identification number (PIN) and the value of money deposited by the students to the cashier. However, anybody bearing the PIN of another student can conveniently assume the identity and defraud the legitimate owner. This often generates a lot of rancor among students and it is a major concern for management especially in the high schools. In this work, we developed an e-Paycheque framework for secured cashless campuses. To be recognized on the web application within the framework, each student must possess a Smart ID card that is preconfigured with their unique fingerprint template and the value of deposited cash for transaction purposes. Since no two humans have the same fingerprint, financial transactions will only be possible for the legitimate owners of cards on the platform. This will in no small measure curb identity theft with respect to financial transactions on the campuses and also fast track the pace of achievement of the cashless policy in Nigeri

Learning analytics: Data sets on the academic record of accounting students in a Nigerian University

This paper presents data on the academic performance of a particular set of accounting students from the year of inception into a Nigerian university to the year of graduation. Descriptive analysis was performed on the dataset and a regression model which is capable of making predictions was fitted to the dataset. From the dataset, 24 out of the students who started with a first class result (CGPA above 4.50) still maintained a first class result at graduation. 4 out of the students who started with a first class result dropped to second class upper division before graduation. 4 out of the students who started with a second class upper division result moved to first class result before graduation. 28 out of 35 students who started with a second class upper division maintained a second class upper division result at graduation. Definition of terms: CGPA: Cumulative Grade Point Average, GPA: Grade Point Average, First Class: CGPA between 4.50 and 5.0, Second Class Upper Division: CGPA between 3.50 and 4.49, 100 level result: First year result, Final CGPA: CGPA at graduation, Keywords: Academic performance, Accountants, Exploratory data analysis, University, Nigeri