Abstract-Research projects are graduation requirements for many university students. If students are arbitrarily assigned project supervisors without factoring in the students' preferences, they may be allocated supervisors whose research interests differ fro m theirs or who m they just do not enjoy working with. In this paper we present a genetic algorith m (GA ) for assigning project supervisors to students taking into account the students' preferences for lecturers as well as lecturers' capacities. Ou r work differs fro m several existing ones which tackle the student project allocation (SPA) problem. SPA is concerned with assigning research projects to students (and sometimes lecturers), wh ile our wo rk focuses on assigning supervisors to students. The advantage of the latter over the former is that it does not require pro jects to be available at the time of assignment, thus allowing the students to discuss their own pro ject ideas/topics with supervisors after the allocation. Experimental results show that our approach outperforms GAs that utilize standard selection and crossover operations. Our GA also compares favorably to an optimal integer programming approach and has the added advantage of producing mu ltip le good allocations, which can be d iscussed in order to adopt a final allocation.

No abstract

No abstract

Examinations are one of the most important activities that take place in institutions of learning. In many Nigerian universities, series of meetings are held to manually examine and approve computed student examination results. During such meetings, students" results are scrutinized. Reasonable explanations must be provided for any anomaly that is discovered in a result before the result is approved. This result approval process is prone to some challenges such as fatigue arising from the long duration of the meetings and wastage of manhours that could have been used for other productive tasks. The aim of this work is to build decision tree models for automatically detecting anomalies in students" examination results. The Waikato Environment for Knowledge Analysis (WEKA) data mining workbench was used to build decision tree models, which generated interesting rules for each anomaly. Results of the study yielded high performances when evaluated using accuracy, sensitivity and specificity. Moreover, a Windows-based anomaly detection tool was built which incorporated the decision tree rules.

No abstract

Reuse of software results in many gains such as reduced development time and overall cost, especially when it takes place in the early stages of software development. Retrieval is a crucial activity during software reuse. This work focuses on the retrieval of UML class diagrams using Genetic Algorithm (GA). It builds on our earlier work by describing a GA for determining class diagram similarity based on classifier features. Experimental results show that our newly proposed technique results in the retrieval of the most similar class diagrams from a repository.

No abstract

This paper presents the outcome of work conducted to develop models for the prediction of compressive strength (CS) of alkali-activated limestone powder and natural pozzolan mortar (AALNM) using hybrid genetic algorithm (GA) and support vector regression (SVR) algorithm, for the first time. The developed hybrid GA-SVR-CS1, GA-SVR-CS3, and GA-SVR-CS14 models are capable of estimating the one-day, three-day, and 14-day compressive strength, respectively, of AALNM up to 96.64%, 90.84%, and 93.40% degree of accuracy as measured on the basis of correlation coefficient between the measured and estimated values for a set of data that is excluded from training and testing phase of the model development. The developed hybrid GA-SVR-CS28E model estimates the 28-days compressive strength of AALNM using the 14-days strength, it performs better than hybrid GA-SVR-CS28C model, hybrid GA-SVR-CS28B model, hybrid GA-SVR-CS28A model, and hybrid GA-SVR-CS28D model that respectively estimates the 28-day compressive strength using three-day strength, one day-strength, all the descriptors and seven day-strength with performance improvement of 103.51%, 124.47%, 149.94%, and 262.08% on the basis of root mean square error. The outcome of this work will promote the use of environment-friendly concrete with excellent strength and provide effective as well as efficient ways of modeling the compressive strength of concrete.