A Frame Work for Customer Relationship Management in Nigerian Banks Using Data Analytics

One of the most crucial challenges that Nigeria banks have to face is in the jurisdiction of customers’ satisfaction. Customers’ satisfaction has become one of the most important factors of success in today’s banking industry in Nigeria. Today Nigeria banks customer’s increases every day, as it is essential for many Nigerian to have proper savings with any bank of their choice; if the performance of bank falls short of their expectations, the very survival of such bank would be difficult. In this paper, a   framework for customer relationship management for Nigeria banks using big data analytics approach was developed. Qualitative research was used to identify customer satisfaction through customer management system information publish annually.  The data were collected from complaint data for financial report 2017 from the Customer Relationship Management System for WEMA Bank Plc. The data were analyzed using excel spread sheet and later converted into CSV and ARFF file format respectively. Data were  exported into WEKA for data analytics which then generated results. The formulated hypotheses are subjected to empirical test using Logistic regression and Machine learning. This new strategy provided solution of these problems identified. Keywords: Data Analytics, Linear regression, Banking,  Customer Satisfactio

Cybercrimes in Nigeria: Analysis, Detection and Prevention

Over the years, the alarming growth of the internet and its wide acceptance has led to increase in security threats. In Nigeria today, several internet assisted crimes known as cybercrimes are committed daily in various forms such as fraudulent electronic mails, pornography, identity theft, hacking, cyber harassment, spamming, Automated Teller Machine spoofing, piracy and phishing. Cybercrime is a threat against various institutions and people who are connected to the internet either through their computers or mobile technologies. The exponential increase of this crime in the society has become a strong issue that should not be overlooked. The impact of this kind of crime can be felt on the lives, economy and international reputation of a nation. Therefore, this paper focuses on the prominent cybercrimes carried out in the various sectorsin Nigeria and presents a brief analysis of cybercrimes in tertiary institutions in Ekiti-State. In conclusion, detection and prevention techniques are highlighted in order to combat cybercrimes in Nigeria

Development of mobile-interfaced machine learning-based predictive models for improving students' performance in programming courses

Student performance modelling (SPM) is a critical step to assessing and improving students' performances in their learning discourse. However, most existing SPM are based on statistical approaches, which on one hand are based on probability, depicting that results are based on estimation; and on the other hand, actual influences of hidden factors that are peculiar to students, lecturers, learning environment and the family, together with their overall effect on student performance have not been exhaustively investigated. In this paper, Student Performance Models (SPM) for improving students' performance in programming courses were developed using M5P Decision Tree (MDT) and Linear Regression Classifier (LRC). The data used was gathered using a structured questionnaire from 295 students in 200 and 300 levels of study who offered Web programming, C or JAVA at Federal University, Oye-Ekiti, Nigeria between 2012 and 2016. Hidden factors that are significant to students' performance in programming were identified. The relevant data gathered, normalized, coded and prepared as variable and factor datasets, and fed into the MDT algorithm and LRC to develop the predictive models. The developed models were obtained, validated and afterwards implemented in an Android 1.0.1 Studio environment. Extended Markup Language (XML) and Java were used for the design of the Graphical User Interface (GUI) and the logical implementation of the developed models as a mobile calculator, respectively. However, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE) and the Root Relative Squared Error (RRSE) were the metrics used to evaluate the robustness of MDT and LRC models. The evaluation results obtained indicate that the variable-based LRC produced the best model in terms of MAE, RMSE, RAE and the RRSE having yielded the least values in all the evaluations conducted. Further results obtained established the strong significance of attitude of students and lecturers, fearful perception of students, erratic power supply, university facilities, student health and students' attendance to the performance of students in programming courses. The variable-based LRC model presented in this paper could provide baseline information about students' performance thereby offering better decision making towards improving teaching/learning outcomes in programming courses

Approaches to Machine Translation: A Review

Translation is the transfer of the meaning of a text from one language to another. It is a means of sharing information across languages and therefore essential for addressing information inequalities. The work of translation was originally carried out by human translators and its limitations led to the development of machine translators. Machine Translation is a subfield of computational linguistics that investigates the use of computer software to translate text or speech from one natural language to another. There are different approaches to machine translation. This paper reviews the two major approaches (single vs. hybrid) to machine translation and provides critique of existing machine translation systems with their merits and demerits. Several application areas of machine translation and various methods used in evaluating them were also discussed. Our conclusion from the reviewed literatures is that a single approach to machine translation fails to achieve satisfactory performance resulting in lower quality and fluency of the output. On the other hand, a hybrid approach combines the strength of two or more approaches to improve the overall quality and fluency of the translation

A Neural Network-based System Identification Model to Predict Output Current and Voltage of Solar Photovoltaic Panels

Solar irradiance is the energy per unit area received by the Sun as electromagnetic radiation. It is one of the most important renewable energy sources. Photovoltaic or other solar technologies are used to generate power more accurately than direct sun irradiation. Solar irradiance research and measurement have a variety of critical applications, including forecasting power generation from solar power plants, climate modeling, and weather forecasting. This paper presents a neural network-based system identification model developed using measured parameters from solar panels with various wattage specifications, namely, 10W, 20W, 40W, and 100W. The parameters that were measured to train the ANN model for the prediction of the output current and voltage include the angle of panel orientation, panel temperature, ambient temperature, irradiance, and wattage. Several training experiments were conducted and the best ANN model produced at 500 epochs gave an accuracy of 99.81% and a loss of 0.1940. The model was deployed into an intelligent Web App that was also developed in this study. This app could be a potential tool for renewable energy engineers and researchers

System Identification Model to Predict Output Current and Voltage of Solar Photovoltaic Panels

Solar irradiance is the energy per unit area received by the Sun as electromagnetic radiation. It is one of the most important renewable energy sources. Photovoltaic or other solar technologies are used to generate power more accurately than direct sun irradiation. Solar irradiance research and measurement have a variety of critical applications, including forecasting power generation from solar power plants, climate modeling, and weather forecasting. This paper presents a neural network-based system identification model developed using measured parameters from solar panels with various wattage specifications, namely, 10W, 20W, 40W, and 100W. The parameters that were measured to train the ANN model for the prediction of the output current and voltage include the angle of panel orientation, panel temperature, ambient temperature, irradiance, and wattage. Several training experiments were conducted and the best ANN model produced at 500 epochs gave an accuracy of 99.81% and a loss of 0.1940. The model was deployed into an intelligent Web App that was also developed in this study. This app could be a potential tool for renewable energy engineers and researchers