Policy framework for adoption of bring your own device (BYOD) by institutions in Nigeria

Mobile computing makes access to data and services available anytime and anywhere. The recent increase in the number of mobile devices like smartphones and tablets has given rise to a phenomenon known as “IT Consumerization” that focuses on satisfying the needs of the consumers to improve their productivity for the benefit of their organization. Recent report from mobile trends indicates that in 2014 alone, manufacturers will ship more than a billion Android devices. It is estimated that seven out of every ten employees (7/10) will use their mobile devices for work in corporate environments. Mobile devices according to studies are known to be more vulnerable compared to laptops and PCs due to their small size, mobility and general lack of protection against viruses and malware. The use of these devices therefore can impact negatively on corporate networks unless properly and effectively managed. Organizations are now adopting a program known as „bring your own device‟ (BYOD) that will enable them capture, register, and manage the mobile devices that connect and use their corporate infrastructure to guarantee the security of the infrastructure and data of the organization. They achieve this by putting in place strategies and policies that involves all stakeholders. This paper surveys literature to extract useful information that serve to enlighten the community of workers and IT leaders on the current and rapid growing phenomenon of BYOD, including the strategies for deployment, BYOD models, benefits, security threats on corporate and user data and infrastructure. The study presents guidelines and a framework for adoption of BYOD by institutions of higher learning in Nigeria in order to improve learning and provide a better workplace. The study will enable IT leaders formulate policies and guidelines that will guarantee smooth adoption and usage of BYOD by their various organizations

Development of Prepaid Electricity Payment System for a University Community Using the LUHN Algorithm

This work presents a University Community based electricity prepaid billing system. Generally in Nigeria, electricity customers face a lot of problems with respect to their electricity bills from the distribution companies. The challenges they face include wrongly calculated bills as a result inaccurate reading of meters, general human errors in bill preparation among others. In some other semi-automated systems in which prepaid meters are used, consumers waste much time in purchasing utility units for electricity. This is the case presently at the university community we are considered in this work. This paper presents the design and implementation of a combination of a web-based and SMS alert prepaid electricity system called for the community. The implementation of the system was done using C# programming language and Microsoft SQL Server as the database platform. The system incorporates the Luhn algorithm for generating pins for use on the simulated prepaid meters. The system is able to run on the university intranet and can also serve as internet based application

Machine learning approach for identifying suspicious uniform resource locators (URLs) on Reddit social network

The applications and advantages of the Internet for real-time information sharing can never be over-emphasized. These great benefits are too numerous to mention but they are being seriously hampered and made vulnerable due to phishing that is ravaging cyberspace. This development is, undoubtedly, frustrating the efforts of the Global Cyber Alliance – an agency with a singular purpose of reducing cyber risk. Consequently, various researchers have attempted to proffer solutions to phishing. These solutions are considered inefficient and unreliable as evident in the conflicting claims by the authors. Against this backdrop, this work has attempted to find the best approach to solving the challenge of identifying suspicious uniform resource locators (URLs) on Reddit social networks. In an effort to handle this challenge, attempts have been made to address two major problems. The first is how can the suspicious URLs be identified on Reddit social networks with machine learning techniques? And the second is how can internet users be safeguarded from unreliable and fake URLs on the Reddit social network? This work adopted six machine learning algorithms – AdaBoost, Gradient Boost, Random Forest, Linear SVM, Decision Tree, and Naïve Bayes Classifier – for training using features obtained from Reddit social network and for additional processing. A total sum of 532,403 posts were analyzed. At the end of the analysis, only 87,083 posts were considered suitable for training the models. After the experimentation, the best performing algorithm was AdaBoost with an accuracy level of 95.5% and a precision of 97.57%.publishedVersio

Motivations and Social Media Influencing Online Purchase Intention in India

The purpose of the study is to examine the influence of hedonic and utilitarian motivation on Indian consumer's online purchase intention. Second, this study tried to explore if social media mediates the influence of hedonic and utilitarian motivation on online purchase intention. A sample of 282 valid online buyers were collected who intentionally involved in online shopping for the last one year. Structural equation modelling is used to analyse data and to examine the research hypotheses. Results of the study revealed that utilitarian and hedonic motivation positively influenced online purchase intention (β= +0.26, p=.03 & β= +0.03, p=.643). This study also indicated that hedonic and utilitarian motivation has significant positive relationship with social media (β= +0.28, p=***), (β= +0.45, p=***). The current research model will give fresh insights of understanding of consumer's motivation and role of social media in online purchase intention. Research implications, limitations, and scope of research are discussed

Motivations and Social Media Influencing Online Purchase Intention in India

The purpose of the study is to examine the influence of hedonic and utilitarian motivation on Indian consumer’s online purchase intention. Second, this study tried to explore if social media mediates the influence of hedonic and utilitarian motivation on online purchase intention. A sample of 282 valid online buyers were collected who intentionally involved in online shopping for the last one year. Structural equation modelling is used to analyse data and to examine the research hypotheses. Results of the study revealed that utilitarian and hedonic motivation positively influenced online purchase intention (β= +0.26, p=.03 & β= +0.03, p=.643). This study also indicated that hedonic and utilitarian motivation has significant positive relationship with social media (β= +0.28, p=***), (β= +0.45, p=***). The current research model will give fresh insights of understanding of consumer’s motivation and role of social media in online purchase intention. Research implications, limitations, and scope of research are discussed

Evaluation of Voltage Stability Indices

Voltage security/stability appraisal and control are not regarded as new issues. Nevertheless, they rather gained unusual attention to preserve the stability of power transmission networks and evade repeat of major power outages as experienced in some countries(like United States, Canada, Belgium, Sweden, Tokyo, Tennessee). Voltage stability evaluation is indispensable in monitoring power system stability. For ten (10) years, the Nigeria National Gird (NNG) has experienced a total of 29.3 collapses. This work demonstrates a comparison of six voltage stability indices referred to as a line (i.e., Lmn, FVSI, LQP, Lp, NVSI, and NLSI_1); it shows their advantages and disadvantages. The effectiveness of these indices is evaluated via numerical studies in the IEEE 14-bus test system under diverse loading situations. From the study of these indices, a suitable index would be chosen to monitor the Nigerian power system