EXPLOITING GENETIC ALGORITHM IN STRING CASH DISPENSE DISPUTE

The cash transaction dispense dispute seems to be an ugly daily experience to bank customers. Most times the customers’ debited funds would not be auto reversed within the 24 hours given by inter-switch. At this point, customers have to report officially to their banks to get the problem sorted out manually by filling the dispute form. In some cases, after filling the form, they have to wait for at least seven business working days before the un-dispensed debited funds are reversed. This might be very excruciating on the customer, especially when un-dispensed cash is the only money on him/her. In this paper, our core interest is to solve the problem of delay in reversing debited funds using a genetic algorithm approach based on numerical integration. The result of the new system revealed how dispensed disputes are resolved within seconds without delay using the optimal fitness function values of the genetic algorithm that validates the customers’ claims and makes refunds. The system is independent of inter-switch and its flexibility allows customers to report their dispensed dispute online, especially when it is not reversed within allotted seconds. The research paper data set and the results were tested and analyzed using MATLAB software application

Effect of fungi associated with foliar diseases of Ficus capensis on the proximate, anti-nutrient and mineral composition of leaves

 The Ficus capensis Thunb leaves are affected by some foliar diseases, which could reduce the nutrient benefits from these leaves. This study aimed at isolation and characterization of fungal pathogens associated with the plant's foliar diseases, analysis of the proximate and phytochemical composition of healthy and diseased fruits and leaves. The effect of isolated fungal species on these proximate and phytochemical contents of leaves was also investigated. Hendersonula sp., Didymeria conferta, Rhizopus stolonifer and Fusarium oxysporium were isolated and characterized as fungi associated with the leaves. Proximate analysis revealed the presence of protein, carbohydrate, lipid, ash, fiber, and moisture. Mineral nutrient analysis revealed the presence of copper, zinc, lead, manganese, magnesium, potassium, sodium, calcium, while that of anti-nutrients revealed saponin, alkaloid, flavonoid, tannin and cyanogenic glycoside, both in the diseased and the healthy leaves and fruits of the plant. The values of the food nutrients and minerals in healthy leaves were significantly different from those of the infected fruits and leaves, with the value of non-nutrients in infected leaves were higher than those in health ones. The non-nutrients in the infected leaves were higher than those in the healthy leaves and fruits of the plants, while the proximate and mineral composition of the supposedly healthy leaves was greater than those of diseased leaves.  &nbsp

Investigating large-scale brain dynamics using field potential recordings: Analysis and interpretation

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (EEG, MEG, ECoG and LFP) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide best-practice recommendations for the analyses and interpretations using a forward model and an inverse model. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems

DEVELOPMENT OF AN OPTIMIZED INTELLIGENT MACHINE LEARNING APPROACH IN FOREX TRADING USING MOVING AVERAGE INDICATORS

This research presents the development of an optimized intelligent machine learning approach in Forex trading using two variants of Moving Average indicators. The main aim of the Expert Advisor (EA) development is to introduce a new intelligent model for automated execution of trades in the Forex market, reducing potential losses due to human errors and sentimental factors in trading Forex. In developing this trading model, Momentum strategy was used since it takes advantage of market swings, along with Machine Learning - Genetic algorithm, being a type of supervised learning used in training the past historical data based on selected trading parameters in a Meta Trader 4 (MT4) platform. The new Expert Advisor –Exponential Moving Average (ESMA) was built using the MQL4 language which is based on C++ for programming specific trading strategies and easily facilitates automated trading. The result is an optimized intelligent trading system that implements the intersection of the two moving averages at various periods, to execute trades autonomously with a profit pass rate of 75% visible from the Optimization chart of the MetaTrader 4 (MT4) platfor

ADOPTING GENETIC ALGORITHM AS A SMART OPTION IN DEVELOPING AN INTELLIGENT DATA LOGGER FOR SETTLING CASH DISPENSE ALTERCATION

The customers of Nigerian banks are regularly faced with the issue of delay in reversing debited funds when dispense errors occur.  These customers in some cases wait for days, weeks and months to get their reversal and sometimes they are never reversed.  24 hours auto reverse of debited funds only occurs in one out of hundreds of transactions made by customers which makes the existing system unreliable and inefficient. In order to improve the present system and get rid of the manual method of filling out the dispense dispute form at the banking hall, an intelligent data logger for resolving cash dispense disputes in banking industry using a Genetic Algorithm (GA) was developed. The data for the system was collected through the application of questionnaires and personal interviews of bank staff and customers. The GA optimization methodology was applied for automating the reversing of debited funds with an online user interface that speedily resolves the issue of debited funds in microseconds. The automated platform developed was tested on a real-life application of cash transaction and optimized result achieved. The achieved results were validated using Genetic Algorithm’s fitness function on a MATLAB application while the dispense dispute e-form was developed and validated on the sublime text web application. The significant results obtained from the sampled banks indicated an optimized performance efficiency of 97% in resolving delay in reversing debited funds on dispense dispute

DEVELOPMENT OF AN INTELLIGENT WEB BASED DYNAMIC NEWS AGGREGATOR INTEGRATING INFOSPIDER AND INCREMENTAL WEB CRAWLING TECHNOLOGY

The World Wide Web is a rapidly growing and changing information source. This reality is gradually replacing the traditional way users obtain news or information. Traditionally, individuals get their news or information from print media, such as newspapers and magazines. Although, the advent of the internet has made things a lot easier by making this digitalized news accessible from anywhere in the world, either through news websites or dedicated applications. However, the growth and change rates make the task of finding relevant and recent information harder. Users are still faced with the challenges of visiting numerous websites just to get updated or informed on a specific type of news. This creates a problem as users have to always memorize different URLs and visit numerous websites just to view a specific type of news. Therefore, the need to develop an intelligent web based dynamic news aggregator that will provide a digital platform for individuals to easily find news pertaining to a particular topic in real time becomes imperative. It crawls the web, searches for news agencies and return a specific news of interest to the user. To address the shortcomings of existing news aggregators, this work was implemented by integrating the intelligent web based dynamic news aggregator, into an infospider web crawling technology. This is achieved applying a stochastic selector and incremental web crawling technology that crawls the entire seed urls. This system was implemented with the PHP scripting language developed to access the PHP-crawler using Aptana Studio as the Integrated Development Environment (IDE), Bootsrap3 and jQuery were used to provide a set of style sheets and JavaScript libraries to simplify the client-side scripting. The application was deployed and tested using the apache web server and a personal computer