Towards Remote Electronic Voting Systems

During the last few years, a lot of research has been done to create voting protocols and election systems that facilitate voting via private computer networks, the Internet or remote mobile terminals. The interest in e-voting on one hand is founded in problems such as violence, intimidation, ballot stuffing, underage and multiple voting, complicity of security agencies, absence or late arrival of election materials etc which often characterise conventional voting systems. On the other hand, it is based upon interest and attention devoted to e-government, e-democracy, e-governance, etc. In this paper, a critical appraisal of e-voting variants; the benefits and risks associated with the various electronic voting methods and electronic voting systems were presented and exhaustively discussed

Determination of Customers’ Arrival and Service Patterns for a Restaurant Food Serving Process

Restaurant industry has become one of the most profitable industries in the world where incessant long waiting time may not only lead to customers’ dissatisfactions but also facilitate loosing of customers to other competitors. In this paper, in order to determine customers’ arrival patterns and service patterns which are critical factors in determining customers’ queue length and waiting time for a given restaurant, the food serving process employed at a named International Institute Restaurant (IIR), Ibadan, Nigeria, was used as a case study. Data were collected on customers’ number, customers’ inter arrival time and service time from Monday to Friday for a week. The data were analyzed statistically using the ARENA Input Analyzer to determine the arrival patterns and service patterns of customers within five working days of the week (Monday, Tuesday, Wednesday, Thursday and Friday). The results of the data analysis revealed that the arrival times of customers who patronized the IIR on Monday and Tuesday followed a Beta distribution. Furthermore, the arrival times of customers patronizing the IIR on Wednesday and Thursday followed a Weibull distribution while that of Friday assumed an Erlang distribution. Besides, the results of the data analysis revealed that the service times at IIR on Monday and Tuesday followed a Lognormal distribution. Beta, Lognormal and Weibull distributions were recorded in respect of service times characterizing the IIR on Wednesday, Thursday and Friday, respectively

An Enhanced Learning Technology System Architecture for Web-Based Instructional Design

Instructional design (ID) models are proven prescriptive techniques for qualitative lessons that could guarantee learning. Existing Learning Management Systems (LMS) miss-out the roles of this important quality control mechanism by providing a mere plane and passive platform for content authoring, thus becomes vulnerable for poor instructional design. This paper demonstrates an effort to ameliorate this limitation by extending the IEEE Learning Technology System Architecture (LTSA) with ID design processes. The Use Case diagram, Activities diagram and Entity Relation diagram for the extended LTSA are presented. The extended architecture was implemented on Moodle open sourced LMS which was extended and hosted live. Students’ impressions on the functionalities and operational effects of the platform were collated using online survey. The academic effects of the platform on the students’ performance were determined using the class mean. The value obtained was compared with that of the control group in the same session and those from the previous sessions. Consequently, this work demonstrates the feasibility of integrating ID models in E-learning. It also justifies its effects on the quality of learning

HISTOGRAM NORMALIZATION TECHNIQUE FOR PREPROCESSING MAMMOGRAPHIC IMAGES

images requires high computational capabilities. Pre-processing is one of the most important step in the mammogram analysis due to poor captured mammographic image qualities. Pre-processing is basically used to correct and adjust the mammogram image for further study and classification. Many image pre-processing techniques have been developed over the past decades to help radiologists in diagnosing breast cancer. Most studies conducted have proven that a pre-processed image is easier for radiologist to accurately detect breast cancer especially for dense breast. Different types of techniques are available for preprocessing of mammograms, which are used to improve image quality, remove noise, adjust contrast, enhance the image and preserve the edges within the image. This paper acquired 20 digital mammograms from Mammographic Image Analysis Society (MIAS) database and uses Histogram Normalization algorithm for pre-processing of the mammograms. A percentage of 95% was obtained. It was observed that the pre-processed mammographic images displayed breast abnormalities clearer with little or no noise