This work presents a parallel implementation of a graph-generating algorithm designed to be straightforwardly adapted to traverse large datasets. This new approach has been validated in a correlated scenario known as the word ladder problem. The new parallel algorithm induces the same topological structure proposed by its serial version and also builds the shortest path between any pair of words to be connected by a ladder of words. The implemented parallelism paradigm is the Multiple Instruction Stream - Multiple Data Stream (MIMD) and the test suite embraces 23-word ladder instances whose intermediate words were extracted from a dictionary of 183,719 words (dataset). The word morph quality (the shortest path between two input words) and the word morph performance (CPU time) were evaluated against a serial implementation of the original algorithm. The proposed parallel algorithm generated the optimal solution for each pair of words tested, that is, the minimum word ladder connecting an initial word to a final word was found. Thus, there was no negative impact on the quality of the solutions comparing them with those obtained through the serial ANG algorithm. However, there was an outstanding improvement considering the CPU time required to build the word ladder solutions. In fact, the time improvement was up to 99.85%, and speedups greater than 2.0X were achieved with the parallel algorithm.

Trust is a basic requirement for the acceptance and adoption of new services related to health care, and therefore, vital in ensuring that the integrity of shared patient information among multi-care providers is preserved and that no one has tampered with it. The cyber-health community in Nigeria is in its infant stage with health care systems and services being mostly fragmented, disjointed, and heterogeneous with strong local autonomy and distributed among several healthcare givers platforms. There is the need for a trust management structure for guaranteed privacy and confidentiality to mitigate vulnerabilities to privacy thefts. In this paper, we developed an efficient Trust Management System that hybridized Real-Time Integrity Check (RTIC) and Dynamic Trust Negotiation (DTN) premised on the Confidentiality, Integrity, and Availability (CIA) model of information security. This was achieved through the design and implementation of an indigenous and generic architectural framework and model for a secured Trust Management System with the use of the advanced encryption standard (AES-256) algorithm for securing health records during transmission. The developed system achieved Reliabity score, Accuracy and Availability of 0.97, 91.30% and 96.52% respectively.

Recommendation Systems are everywhere, from offline shopping malls to major e-commerce websites, all use recommendation systems to enhance customer experience and grow profit. With a growing customer base, the requirement to store their interest, behavior and respond accordingly requires plenty of scalability. Thus, it is very important for companies to select a scalable recommender system, which can provide the recommendations not just accurately but with low latency as well. This paper focuses on the comparison between the four methods KMeans, KNN, SVD, and SVD++ to find out the better algorithm in terms of scalability. We have analyzed the methods on different parameters i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Precision, Recall and Running Time (Scalability). Results are elaborated such that selection becomes quite easy depending upon the user requirements.

A significant number of Information Technology incidents are reported through email. To design and implement an intelligent incident management system, it is significant to automatically classify the reported incident to a given incident category. This requires the extraction of semantic content from the reported email text. In this research work, we have attempted to classify a reported incident to a given category based on its semantic content using ontology. We have developed an Incident Ontology that can serve as a knowledge base for the incident management system. We have also developed an automatic incident classifier that matches the semantical units of the incident report with concepts in the incident ontology. According to our evaluation, ontology-driven incident classification facilitates the process of Information Technology incident management in a better way since the model shows 100% recall, 66% precision, and 79% F1-Score for sample incident reports.

Information systems have come a long way in the 21st century, with search engines emerging as the most popular and well-known retrieval systems. Several techniques have been used by researchers to improve the retrieval of relevant results from search engines. One of the approaches employed for improving relevant feedback of a retrieval system is Query Expansion (QE). The challenge associated with this technique is how to select the most relevant terms for the expansion. In this research work, we propose a query expansion technique based on Azak & Deepak's WWQE model. Our extended WWQE technique adopts Candidate Expansion Terms selection with the use of in-links and out-links. The top two relevant Wikipedia articles from the user's initial search were found using a custom search engine over Wikipedia. Following that, we ranked further Wikipedia articles that are semantically connected to the top two Wikipedia articles based on cosine similarity using TF-IDF Vectorizer. The expansion terms were then taken from the top 5 document titles. The results of the evaluation of our methodology utilizing TREC query topics (126-175) revealed that the system with extended features gave ranked results that were 11% better than those from the system with unexpanded queries.