Abstract-Academic advising of students is an expert task that requires a lot of time, and intellectual investments from the human agent saddled with such a responsibility. In addition, good quality academic advising is subject to availability of experienced and committed personnel to undertake the task. However, there are instances when there is paucity of capable human adviser, or where qualified persons are not readily available because of other pressing commitments, which will make system-based decision support desirable and useful. In this work, we present the design and implementation of an intelligent Course Advisory Expert System (CAES) that uses a combination of rule based reasoning (RBR) and case based reasoning (CBR) to recommend courses that a student should register in a specific semester, by making recommendation based on the student's academic history. The evaluation of CAES yielded satisfactory performance in terms of credibility of its recommendations and usability.

As water scarcity becomes a pending global issue, hygroscopic materials prove a significant solution. Thus, there is a good cause following the structure–performance relationship to review the recent development of hygroscopic materials and provide inspirational insight into creative materials. Herein, traditional hygroscopic materials, crystalline frameworks, polymers, and composite materials are reviewed. The similarity in working conditions of water harvesting and carbon capture makes simultaneously addressing water shortages and reduction of greenhouse effects possible. Concurrent water harvesting and carbon capture is likely to become a future challenge. Therefore, an emphasis is laid on metal–organic frameworks (MOFs) for their excellent performance in water and CO2 adsorption, and representative role of micro‐ and mesoporous materials. Herein, the water adsorption mechanisms of MOFs are summarized, followed by a review of MOF's water stability, with a highlight on the emerging machine learning (ML) technique to predict MOF water stability and water uptake. Recent advances in the mechanistic elaboration of moisture's effects on CO2 adsorption are reviewed. This review summarizes recent advances in water–harvesting porous materials with special attention on MOFs and expects to direct researchers’ attention into the topic of concurrent water harvesting and carbon capture as a future challenge.

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.

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Employability and Unemployment continues to be dire issues that Nigerian youth are faced with daily in a saturated employment market. Whereas, the use of workintegrated learning can help bridge the gap by increasing employability skills among students. The study examined the benefits of having a work-integrated learning (WIL) program for students in the construction field. Therefore, the study developed a framework for improving employability skills through a web-based work integrated learning database for construction students. Using a system block diagram, use case diagram and activity diagram, the study illustrated the functional requirement needed for the development of the WIL platform. The WIL platform is a web-based system pooling submission of available WIL positions from employers in construction businesses and former WIL students in order for prospective WIL students to access possible openings where they can learn in a workplace environment. The methodology of this research includes using the combination of HTML, CSS and the C-Sharp programming language for the interface design and server side scripting while MySQL was the database platform used for storing and retrieving the data used for the application. In conclusion, the study designed a WIL platform for construction students. The use of the WIL platform is intended to encourage employability of construction students by ensuring that they are adequately engaged in a work place training.

Abstract:Recently, most companies interact more with their customers through the social media, particularly Facebook and Twitter. This has made large amount of textual data freely available on the internet for competitive intelligence analysis, which is helping reposition more and more companies for better profit. In order to carry out competitive intelligence, financial institutions need to take note of and analyse their competitor's social media sites. This paper, therefore, aims to help the banking industry in Nigeria understand how to perform a social media competitive analysis and transform social media data into knowledge, which will form the foundation for decision-making and internet marketing of such institutions. The study describes an in-depth case study which applies text mining to analyse unstructured text content on Facebook and Twitter sites of the five largest and leading financial institutions (banks) in Nigeria: Zenith Bank, First Bank, United Bank for Africa, Access Bank and GTBank. Analysing the social media content of these institutions will increase their competitive advantage and also lead to more profit for the banking institutions in question. The results obtained from this research showed that text mining is able to reveal uncommon and non-trivial trend for competitive advantage from social media data, and also provide specific recommendations to help banks maximise their competitive edge.

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.