Efficient Similarity Measures for Texts Matching

Calculation of similarity measures of exact matching texts is a critical task in the area of pattern matching that needs a great attention. There are many existing similarity measures in literature but the best methods do not exist for closeness measurement of two strings. The objective of this paper is to explore the grammatical properties and features of generalized n-gram matching technique of similarity measures to find exact text in electronic computer applications. Three new similarity measures have been proposed to improve the performance of generalized n-gram method. The new methods assigned high values of similarity measures and performance to price with low values of running time. The experiment with the new methods demonstrated that they are universal and very useful in words that could be derived from the word list as a group and retrieve relevant medical terms from database . One of the methods achieved best correlation of values for the evaluation of subjective examination

Effective similarity measures in electronic testing at programming languages

The purpose of this study is to explore the grammatical proper ties and features of generalized n-gram matching technique in electronic test at programming languages. N-gram matching technique has been success fully employed in information handling and decision support system dealing with texts but its side effect is size n which tends to be rather large. Two new methods of odd gram and sumsquare gram have been proposed for the improvement of generalized n-gram matching together with the modification of existing methods. While generalized n-grams matching is easy to generate and manage, they do require quadratic time and space complexity and are therefore ill-suited to the proposed and modified methods which work in quadratic in nature. Experiments have been conducted with the two new methods and modified ones using real life programming code assignments as pattern and text matches and the derived results were compared with the existing methods which are among the best in practice. The results obtained experimentally are very positive and suggested that the proposed methods can be successfully applied in electronic test at programming languages

A Framework for The Design of Speech-Enabled Self-Care EHealth Systems

The Internet provides a wide range of health information and services which consumers access for self-care and to participate in a more informed way in their healthcare when they see their physician. This information and services are however, delivered in text form and therefore, does not cater for the needs of the non-computer literate, the visually impaired and the blind. This paper presents a framework for the design of speech-enabled self-care e-Health systems. Reasoning-induced disease diagnosis which existing speech-based disease screening systems lack has been incorporated into the framework to enable systems based on the framework diagnose more than one type of disease. Based on the framework, speechbased self-care e-Health system (SSeS) prototype application was developed. The originality of this framework is that it is speech-based. This takes care of the health needs of the category of people earlier identified and the underserved people, majority of those who are domiciled in Africa

CK-RAID: Collaborative Knowledge Repository for Intrusion Detection System

Intrusion Detection Systems (IDSs) are an integral part of an organization\u27s infrastructure. Without an IDS facility in place to monitor network and host activities, attempted and successful intrusion attempts may go unnoticed. This study proposed a Collaborative Knowledge Repository Architecture for Intrusion Detection (CK-RAID). It is based on a distributed network of computer nodes, each with their individual IDS with a centralized knowledge repository system, and firewall acting as a defence. When an unfamiliar attack hits any node, the first step the intrusion monitor takes is to request from Knowledge Repository Server the most effective intrusion response. To improve performance, Intrusion Update module collaborates with IDSs sensor and log by updating their expert rule and intrusion information respectively and removing the old intrusion signature from the knowledge base with the aid of Intrusion Detector Pruning. To ensure security of information exchange, RSA encryption and Digital Signature were used to encode information during transit. The result showed that CK-RAID had a detection rate of 97.2%, compared with Medoid Clustering, Y-means, FCM and K-means that have an accuracy of 96.38%, 87.15%, 82.13% and 77.25% respectively. Therefore, CK-RAID can be deployed for efficient detection of all categories of intrusion detection and response

A linguistic variable of product-related question answering review system

E-commerce platforms like Amazon, Jumia, Airbnb, Alibaba, eBay and JD.com amongst others play a huge role in linking sellers of products to interested buyers. Consumers generally ask questions in order to know if the product of interest will meet their needs. Relying on existing product reviews posted online by other consumers who have once purchased and used the product becomes a viable option to waiting for direct answers from the community question answer system. They are usually forced to spend time manually wading through these numerous customer reviews, a task which is cumbersome. This motivated to use Bi-directional Auto-regressive Transformer (BART) model for both question-answerability classification and answer generation. A dataset provided by Gupta et al. and conducted on Amazon Mechanical Turk was used for the experiment. It was observed that BART outperformed the heuristic baselines on all the metrics considered with existing models of long short term memory, convolutional neural network and bidirectional encoder representative from transformers. Based on the BART performance, a fuzzy based rule was introduced with system parameters defined for the classification of product-related question answering review into linguistic variables for good purchase decision making. The classification is in line with well-formed responses to product-related questions based on existing customer reviews using BART heuristic baselines

A Users' Awareness Study and Influence of Socio-Demography Perception of Anti-Phishing Security Tips

Security tips are now used as a method of priming online users from falling prey for fraudulent scams. These security tips usually come as email, SMS or online posts where they can be easily accessed by the users. In this work, phishing attacks are simulated with varying cues that are available in such fraudulent email messages, SMS and web pages were used to investigate the effectiveness of the security tips used by Nigerian banks to prime their customers of online threats. A total of 427 respondents, purposively selected from three tertiary institutions in Ogun State, participated in the study. Each respondent was asked to identify five messages with varying phishing cues to evaluate their understanding of the security tips messages. The results which were computed at 95% Confidence Interval, indicated that 58.91% failed on the first attribute, 58.59% failed on the second attribute while 58.73% failed on the third attribute. 74.24% of the participant could not correctly identify a fake email message (fourth attribute) while 76.71% could not correctly identify a phished bank verification number update message (fifth attribute). Using the Mann Whitney Test, the result further showed that overall, those who failed the test are significantly more than those who passed. Moreover, a regression model is proposed to evaluate the influence of the socio demographic factors used in the study. This result indicated that gender, academic qualification and user's computer knowledge significantly influences their ability to recognize phished messages