2 research outputs found

    Artificial Neural Network Logic-Based Reverse Analysis with Application to COVID-19 Surveillance Dataset

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    The Boolean Satisfiability Problem (BSAT) is one of the crucial decision problems in the fields of computing science, operation research, and mathematical logic that is resolved by deciding whether or not a solution to a Boolean formula exists. When there is a Boolean variable allocation that induces the Boolean formula to yield TRUE, then the SAT instance is satisfiable. The main purpose of this chapter is to utilize the optimization capacity of the Lyapunov energy function of Hopfield neural network (HNN) for optimal representation of the Random Satistibaility for COVID-19 Surveillance Data Set (CSDS) classification with the aim of extracting the relationship of dominant attributes that contribute to COVID-19 detections based on the COVID-19 Surveillance Data Set (CSDS). The logical mining task was carried based on the data mining technique of the energy minimization technique of HNN. The computational simulations have been carried using the different number of clauses in validating the efficiency of the proposed model in the training of COVID-19 Surveillance Data Set (CSDS) for classification. The findings reveals the effectiveness and robustness of k satisfiability reverse analysis with Hopfield neural network in extracting the dominant attributes toward COVID-19 Surveillance Data Set (CSDS) logic

    Exploring the Effectiveness of Project-Based Learning Approach on Junior Secondary School Students' Academic Achievement in Descriptive Geometry in Katsina, Nigeria

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    This study examined the effectiveness of the project-based learning strategy on the academic achievement of junior secondary school students on descriptive geometry in dutsin-ma, Katsina State, Nigeria.  The study utilized the pretest, posttest and post posttest quasi experimental design involving two groups tagged ‘experimental’ and ‘control’.  The population of the study consists of all the public senior secondary school two (JSS2) students of the eleven (11) secondary schools in Dutsin-Ma  Zonal Quality Assurance with a total population of 34312 students. The sample size was 120 students. The instrument for the collection of data was Descriptive geometry achievement test (DGAT). It was validated by experts in measurement and evaluation and mathematics education with a reliability index of 0.88. Inferential statistic ANCOVA was used to test the hypotheses formulated at 0.05 level of significance. The study found among other things that discovery method enhanced students’ achievement in the Descriptive geometry taught during the period of this study. Recommendations such as encouraging teachers to adopt this strategy in their mathematics classroom were made. Adequate conclusion was equally reached