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.

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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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This paper addresses identification of implicit requirements (IMRs) in software requirements specifications (SRS). IMRs, as opposed to explicit requirements, are not specified by users but are more subtle. It has been noticed that IMRs are crucial to the success of software development. In this paper, we demonstrate a software tool called COTIR developed by us as a system that integrates Commonsense knowledge, Ontology and Text mining for early identification of Implicit Requirements. This relieves human software engineers from the tedious task of manually identifying IMRs in huge SRS documents. Our evaluation reveals that COTIR outperforms existing IMR tools. This demo paper would be useful to Software Engineers since it deals with automation in the requirements analysis phase, thus contributing to Requirements Engineering. It would interest AI scientists as it entails multi-disciplinary work encompassing text mining, ontology and commonsense knowledge. It makes a broader impact on Smart Cities, because automated identification of IMRs would offer inputs to Smart City Tools, where requirements may often be implicit given that Smart Cities are an emerging and growing paradigm.

Public health is now in danger because of the current monkeypox outbreak, which has spread rapidly to more than 40 countries outside of Africa. The growing monkeypox epidemic has been classified as a “public health emergency of international concern” (PHEIC) by the World Health Organization (WHO). Infection outcomes, risk factors, clinical presentation, and transmission are all poorly understood. Computer- and machine-learning-assisted prediction and forecasting will be useful for controlling its spread. The objective of this research is to use the historical data of all reported human monkey pox cases to predict the transmission rate of the disease. This paper proposed stacking ensemble learning and machine learning techniques to forecast the rate of transmission of monkeypox. In this work, adaptive boosting regression (Adaboost), gradient boosting regression (GBOOST), random forest regression (RFR), ordinary least square regression (OLS), least absolute shrinkage selection operator regression (LASSO), and ridge regression (RIDGE) were applied for time series forecasting of monkeypox transmission. Performance metrics considered in this study are root mean square (RMSE), mean absolute error (MAE), and mean square error (MSE), which were used to evaluate the performance of the machine learning and the proposed Stacking Ensemble Learning (SEL) technique. Additionally, the monkey pox dataset was used as test data for this investigation. Experimental results revealed that SEL outperformed other machine learning approaches considered in this work with an RMSE of 33.1075; a MSE of 1096.1068; and a MAE of 22.4214. This is an indication that SEL is a better predictor than all the other models used in this study. It is hoped that this research will help government officials understand the threat of monkey pox and take the necessary mitigation actions.

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