by Etuh E, Obiniyi A. A, Yusuf O. S and Akinyele O. A
Original Research
Business transaction since inception from trade by barter to modern e-commerce system constantly faced the challenges of how to minimise fraudulent activities around it. With the advent of Internet, trading has become a global activity where the location of one’s goods and services is no longer important provided there is a connection to the “market”. Serious attention must be given to the security challenges facing the “Market” arising from online authentication mechanisms. Prevalent ones today uses what the user knows (password) or what the user have (token) for authentication, there is a need for a more reassuring proof of identity to make cashless economy a huge success and ensure effective participation in the contemporary E-market. The research work- Secure Online Electronic Transaction (SOET) System integrates the functionality of biometric credential as authentication mechanism to ensure secured transactions over the network in an E-market of a cashless society.
Journal of Computer Sciences and Applications. 2017, 5(2), 83-89. DOI: 10.12691/jcsa-5-2-5
Pub. Date: August 15, 2017
19524 Views4892 Downloads
by Aboagye Emelia Opoku, Jianbin Gao, Dagadu Joshua Caleb and Qi Xia
Original Research
We explore the sparsity problem associated with recommendation system through the concept of transfer learning (TL) which are normally caused by missing and noisy ratings and or review helpfulness. TL is a machine learning (ML) method which aims to extract knowledge gained in a source task/domain and use it to facilitate the learning of a target predictive function in a different domain. The creation and transfer of knowledge are a basis for competitive advantage. One of the challenges prevailing in this era of big data is scalable algorithms that process the massive data in reducing computational complexity. In the RS field, one of the inherent problems researchers always try to solve is data sparsity. The data associated with rating scores and helpfulness of review scores are always sparse presenting sparsity problems in recommendation systems (RSs). Meanwhile, review helpfulness votes helps facilitate consumer purchase decision-making processes. We use online review helpfulness votes as an auxiliary in formation source and design a matrix transfer framework to address the sparsity problem. We model our Homogenous Fusion Transfer Learning approach based on Matrix Factorization HMT with review helpfulness to solve sparsity problem of recommender systems and to enhance predictive performance within the same domain. Our experiments show that, our framework Efficient Matrix Transfer Learning (HMT) is scalable, computationally less expensive and solves the sparsity problem of recommendations in the e-commerce industry.
Journal of Computer Sciences and Applications. 2017, 5(2), 76-82. DOI: 10.12691/jcsa-5-2-4
Pub. Date: July 18, 2017
8660 Views2134 Downloads1 Likes
by Anderson Rioba Ondieki, George Onyango Okeyo and Ann Kibe
Original Research
Stock Exchange Prediction using neural networks has been an interesting research problem whereby many researchers have developed a keen interest in prediction of future values and trends. Little research has been done to apply and improve prediction models based on newer and impactful variables to show that mining opinions and sentiments from the information shared in Twitter platform can be converted into statistical values and applied as inputs in a neural network together with other inputs to facilitate an improvement in the accuracy of predictions of stock prices and movements. In this research, two stocks were selected on the basis of their social media communication in twitter and this information was used as additional feature by deploying a supervised learning approach to compute daily company twitter sentiment score for improving prediction purposes in neural networks. The daily twitter sentiment scores were computed in a supervised learning algorithm by use of WordNet and Sentiwordnet lexicons for classification and scoring. Through experimentation with different sets of hidden layers and 70% training set. 15% validation set and 15 % test set, the research applied two Non Linear Autoregressive Neural Network with Exogenous Inputs (NARX) models which were trained using Levenberg-Marquadt back propagation. The results showed that adding lexicon based twitter sentiment scores as additional inputs to other company stock variables for stock price prediction improved the prediction accuracy and resulted to a more accurate NARX model.
Journal of Computer Sciences and Applications. 2017, 5(2), 64-75. DOI: 10.12691/jcsa-5-2-3
Pub. Date: July 10, 2017
17466 Views3082 Downloads
by Godwin Ansa, Haitham Cruickshank, Zhili Sun and Mazin Alshamrani
Original Research
Denial of Service (DoS) attacks are a major network security threat which affects both wired and wireless networks. The effect of DoS attacks is even more damaging in Delay Tolerant Networks (DTNs) due to their unique features and network characteristics. DTN is vulnerable to resource exhaustion and flooding DoS attacks. Several DoS mitigating schemes for wired and wireless networks have been investigated and most of them have been found to be highly interactive requiring several protocol rounds, resource-consuming, complex, assume persistent connectivity and hence not suitable for DTN. To mitigate the impact of resource exhaustion and flooding attacks in DTN, we propose a security scheme which integrates ingress filtering, rate limiting and light-weight authentication security mechanisms to monitor, detect and filter attack traffic. We propose three variants of light-weight bundle authenticators called DTNCookies. To make the proposed DTNCookies random and hard to forge, we exploit the assumption that DTN nodes are loosely time-synchronized to generate different nonce values in different timeslots for the computation and verification of our proposed DTNCookies. The results demonstrate the efficiency and effectiveness of the proposed scheme to detect and drop attack traffic. The simulation results also show good performance for the proposed scheme in terms of energy and bandwidth efficiency, high delivery ratio and low latency.
Journal of Computer Sciences and Applications. 2017, 5(2), 50-63. DOI: 10.12691/jcsa-5-2-2
Pub. Date: June 30, 2017
21914 Views3420 Downloads
by Adebola K. Ojo and Adesesan B. Adeyemo
Original Research
The ever-growing volume of published academic journals and the implicit knowledge that can be derived from them has not fully enhanced knowledge development but rather resulted into information and cognitive overload. However, publication data are textual, unstructured and anomalous. Analysing such high dimensional data manually is time consuming and this has limited the ability to make projections and trends derivable from the patterns hidden in various publications. This study was designed to develop and use intelligent text mining techniques to characterise academic journal publications. Journals Scoring Criteria by nineteen rankers from 2001 to 2013 of 50th edition of Journal Quality List (JQL) were used as criteria for selecting the highly rated journals. The text-miner software developed was used to crawl and download the abstracts of papers and their bibliometric information from the articles selected from these journal articles. The datasets were transformed into structured data and cleaned using filtering and stemming algorithms. Thereafter, the data were grouped into series of word features based on bag of words document representation. The highly rated journals were clustered using Self-Organising Maps (SOM) method with attribute weights in each cluster.
Journal of Computer Sciences and Applications. 2017, 5(2), 42-49. DOI: 10.12691/jcsa-5-2-1
Pub. Date: June 19, 2017
13673 Views3218 Downloads