Research Journal of Mathematics and Computer Science


A Conceptual Design and Evaluation Framework for Mobile Persuasive Health Technologies (Usability Approach)

Research Article of Research Journal of Mathematics and Computer Science A Conceptual Design and Evaluation Framework for Mobile Persuasive Health Technologies (Usability Approach) Kasali, F. A.1, Awodele, O.2, Kuyoro, S.3, Akinsanya, A.4, Eze, M.5 1,2,3,4,5Department of Computer Science and Information Technology, Babcock University, Ogun State, Nigeria Persuasive techniques are recently being explored by computer science researchers as an effective strategy towards creating applications that are aimed at positive attitudinal changes especially in the health domain but finding effective evaluation approaches for these technologies remain an herculean task for all stakeholders involved and in order to overcome this limitation, the Persuasive System Design (PSD) model was designed but researchers claim that the model is too theoretical in nature and some of its design principles are too subjective as they cannot be measured quantitatively. Hence, the focus of this paper is to critically review the PSD model and popular models currently being used to evaluate the usability of information systems as usability has been identified as an important requirement currently used to evaluate the overall success of persuasive technologies. To achieve the stated objectives, the systematic review method of research was done to objectively analyze the PSD model, its applicability as an evaluation tool was tested on a popular mobile health application installed on the Samsung Galaxy Tablet using android Operation system. Exhaustive evaluation of the application was performed by 5 software usability researchers using the method of cognitive walkthrough. From the analysis, it was realized that the PSD model is a great tool at designing persuasive technologies but as an evaluation tool, it is too theoretical in nature, its evaluation strategies are too subjective in nature and the 28 principles described in it overlap with one another. As a result, the PSD model was extended with an integrated usability model and ...

A Logical Approach for Empirical Risk Minimization in Machine Learning for Data Stratification

Research Article of Research Journal of Mathematics and Computer Science A Logical Approach for Empirical Risk Minimization in Machine Learning for Data Stratification 1Taiwo, O. O., 2Awodele O., 3Kuyoro, S. O. 1,2,3Department of Computer Science, Babcock University, Ilishan-Remo, Ogun State, Nigeria The data-driven methods capable of understanding, mimicking and aiding the information processing tasks of Machine Learning (ML) have been applied in an increasing range over the past years in diverse areas at a very high rate, and had achieved great success in predicting and stratifying given data instances of a problem domain. There has been generalization on the performance of the classifier to be the optimal based on the existing performance benchmarks such as accuracy, speed, time to learn, number of features, comprehensibility, robustness, scalability and interpretability. However, these benchmarks alone do not guarantee the successful adoption of an algorithm for prediction and stratification since there may be an incurring risk in its adoption. Therefore, this paper aims at developing a logical approach for using Empirical Risk Minimization (ERM) technique to determine the machine learning classifier with the minimum risk function for data stratification. The generalization on the performance of optimal algorithm was tested on BayesNet, Multilayered perceptron, Projective Adaptive Resonance Theory (PART) and Logistic Model Trees algorithms based on existing performance benchmarks such as correctly classified instances, time to build, kappa statistics, sensitivity and specificity to determine the algorithms with great performances. The study showed that PART and Logistic Model Trees algorithms perform well than others. Hence, a logical approach to apply Empirical Risk Minimization technique on PART and Logistic Model Trees algorithms is shown to give a detailed procedure of determining their empirical risk function to aid the decision of choosing an algorithm to be the best fit classifier for data stratification. This therefore serves as a ...

Session Hijacking in Mobile Ad-hoc Networks: Trends, Challenges and Future

Research Article of Research Journal of Mathematics and Computer Science Session Hijacking in Mobile Ad-hoc Networks: Trends, Challenges and Future Kuyoro Shade O., Okolie Samuel O. and Oyebode Aduragbemi Department of Computer Science, Babcock University, Nigeria Technological advancement in the field of telecommunication has led to the creation of highly dynamic networks, one of such is Mobile Ad-Hoc Networks which can be described as an autonomous collection of devices (mobile devices) that offers dynamic topologies, no central administration, dynamic and ever mobile nodes and so on, information becomes easy to disseminate. Mobile Ad-Hoc Networks and the various features it provides affects the security of the network. A network with dynamic nodes and no central administration can be prone to network attacks one of such is session hijacking. Integrity is paramount in any network, session hijacking affects the integrity of data on a network, important information is leaked also due to the sensitive application of MANETs especially in the military these has to be avoided. This paper looks into session hijacking in MANETS, reviewed various existing solutions to find out gaps and also proposing an optimised IDS which offers more flexibility and also dynamic applications in any network traffic environment. Keywords: Session Hijacking; Intrusion Detection System, Information Sharing, Security, Confidentiality ...

Human Gait identification System

Research Article of Research Journal of Mathematics and Computer Science Human Gait identification System Ali Mohammed mohaisun 1, Muayad Sadik Croock 2 1 Informatics Institute for Postgraduate Studies, 2 University of Technology, Computer Engineering Department. Few biometrics can be used to recognize a person from distance without need for direct sharing or cooperation of that person, gait (walking behavior) is one of them. Walking behavior (gait) recognition includes specifying person identity by analyzing his walking style (walking manner). In this paper, a human gait identification system depending on extracted features, which are vertical hip, horizontal hip angle and slop of thigh. The first step of the proposed system is detecting the binary silhouette of a walking individual from the uploaded videos. Then, the gait cycle is allocated using aspect ratio method. Finally, the required features from each frame in gait cycle are extracted. Different image processing operation have been performed to extract the required features. The outcome of the proposal system reflects flexibility in term of inserting, searching, updating, deleting and matching. The proposed system is tested in terms of offered functions, human recognition and noise effects. The obtained results show the efficient performance of the system and high ability of covering the error caused by surrounding conditions. The system is evaluated using the matching rate with the threshold of 70%. The adding noise can degrade the matching rate, particularly for high variance values. This is because of the increasing of noise values that might be the reason of moving the object irregularly while capturing or unexpected changing in the effected surrounding conditions. Keywords: Human Gait, Gait Recognition, Biometric ...

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Research Journal of Mathematics and Computer Science

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