Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.

The advent of the internet has facilitated greater access to the myriad of job opportunities available globally. Currently there exist many job application submission portals that are being used for online job recruitment purposes. However, the task of many of these job submission portals is limited to matching the professional and academic qualifications of applicants with the requirements of employers and several organisations and does not involve the ranking of applicants' credentials according to their relative suitability for the jobs applied for. In this paper, we describe the implementation of fuzzy expert system (FES) tool for selection of qualified job applicants with the aim of minimising the rigour and subjectivity associated with the candidate selection process. A performance evaluation of the FES tool that was conducted confirmed the viability of a FES-based approach in handling the fuzziness that is associated with the problem of personnel recruitment.

Data is useless if information or knowledge that can be used for further reasoning cannot be inferred from it. Cluster analysis, based on some criteria, shares data into important, practical or both categories (clusters) based on shared common characteristics. In research, clustering and classification have been used to analyze data, in the field of machine learning, bioinformatics, statistics, pattern recognition to mention a few. Different methods of clustering include Partitioning (K-means), Hierarchical (AGNES), Density-based (DBSCAN), Grid-based (STING), Soft clustering (FANNY), Model-based (SOM) and Ensemble clustering. Challenges and problems in clustering arise from large datasets, misinterpretation of results and efficiency/performance of clustering algorithms, which is necessary for choosing clustering algorithms. In this paper, application of data clustering was systematically discussed in view of the characteristics of the different clustering techniques that make them better suited or biased when applied to several types of data, such as uncertain data, multimedia data, graph data, biological data, stream data, text data, time series data, categorical data and big data. The suitability of the available clustering algorithms to different application areas was presented. Also investigated were some existing cluster validity methods used to evaluate the goodness of the clusters produced by the clustering algorithms.

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Abstract-ManyAfrican academic institutions have adopted the use of elearning systems, since they enable students to learn at their own pace, time, and without restriction to the classroom. However, evidence of usability evaluation of e-learning systems in Africa is mostly lacking in the literature. This paper reports the experimental heuristic evaluation of the e-learning system of a Nigerian University. The objective is to demonstrate the application of expertbased usability evaluation techniques such as Heuristic evaluation for assessing the attributes of existing e-learning systems. The study revealed that while the e-learning system has strong credentials in terms of support for Web 2.0 activities, good learning content and boasts of useful e-learning features, improvements are necessary in other areas such as interactive learning, assessment and feedback, and quality of learning content. The study adds to the body of extant knowledge in the area of usability evaluation of e-learning systems in African institutions.

A Text-to-speech synthesizer is an application that converts text into spoken word, by analyzing and processing the text using Natural Language Processing (NLP) and then using Digital Signal Processing (DSP) technology to convert this processed text into synthesized speech representation of the text. Here, we developed a useful text-to-speech synthesizer in the form of a simple application that converts inputted text into synthesized speech and reads out to the user which can then be saved as an mp3.file. The development of a text to speech synthesizer will be of great help to people with visual impairment and make making through large volume of text easier.

Malaria is an infectious disease that affects close to half a million individuals every year and Plasmodium falciparum is a major cause of malaria. The treatment of this disease could be done effectively if the essential enzymes of this parasite are specifically targeted. Nevertheless, the development of the parasite in resisting existing drugs now makes discovering new drugs a core responsibility. In this study, a novel computational model that makes the prediction of new and validated antimalarial drug target cheaper, easier, and faster has been developed. We have identified new essential reactions as potential targets for drugs in the metabolic network of the parasite. Among the top seven (7) predicted essential reactions, four (4) have been previously identified in earlier studies with biological evidence and one (1) has been with computational evidence. The results from our study were compared with an extensive list of seventy-seven (77) essential reactions with biological evidence from a previous study. We present a list of thirty-one (31) potential candidates for drug targets in Plasmodium falciparum which includes twenty-four (24) new potential candidates for drug targets.

Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.