With the advent of new technologies in the medical field, huge amounts of cancerous data have been collected and are readily accessible to the medical research community. Over the years, researchers have employed advanced data mining and machine learning techniques to develop better models that can analyze datasets to extract the conceived patterns, ideas, and hidden knowledge. The mined information can be used as a support in decision making for diagnostic processes. These techniques, while being able to predict future outcomes of certain diseases effectively, can discover and identify patterns and relationships between them from complex datasets. In this research, a predictive model for predicting the outcome of patients’ cervical cancer results has been developed, given risk patterns from individual medical records and preliminary screening tests. This work presents a Decision tree (DT) classification algorithm and shows the advantage of feature selection approaches in the prediction of cervical cancer using recursive feature elimination technique for dimensionality reduction for improving the accuracy, sensitivity, and specificity of the model. The dataset employed here suffers from missing values and is highly imbalanced. Therefore, a combination of under and oversampling techniques called SMOTETomek was employed. A comparative analysis of the proposed model has been performed to show the effectiveness of feature selection and class imbalance based on the classifier’s accuracy, sensitivity, and specificity. The DT with the selected features and SMOTETomek has better results with an accuracy of 98%, sensitivity of 100%, and specificity of 97%. Decision Tree classifier is shown to have excellent performance in handling classification assignment when the features are reduced, and the problem of imbalance class is addressed.

Recommender Systems (RSs) are termed as web-based applications that make use of filtering methods and several machine learning algorithms to suggest relevant user objects. It can be said that some techniques are usually adopted or trained to develop these systems that generate lists of suitable recommendations. Conventionally, RS uses a single rating approach to preference user recommendation over an item. Recently, multi-criteria technique has been identified as a new approach of recommending user items based on several attributes or features of user items. This new technique of item recommendation has been adopted to solve several recommendation problems compared to the single rating approach. Furthermore, the predictive performance of the multi-criteria technique when tested proves to be further efficient as compared to the traditional single ratings approach. This paper gives a comparative study between two models that are based on the features and architecture of fuzzy sets system and adaptive genetic algorithm. Genetic Algorithms (GAs) are robust and stochastic search techniques centered on natural selection and evaluation that are often applied when encountering optimization problems. Fuzzy logic (FL) on the other hand, is known for its wide application in diverse fields in science. This study aims to evaluate, analyze, and compare the predictive performance of both methods and present their results. The study has been accomplished using Yahoo! Movies dataset, and the results of the performance of each model have been presented in this paper. The results proved that both techniques have significantly enhanced the system's accuracy.

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Accurate prediction of crude oil price can successfully be used to deviate from the negative impact of the crude oil price hike. Limitations of previous studies prompt the proposal of an alternative approach for the hybridization of Cuckoo Search Algorithm with lévy flight and Back-propagation neural network (CSBP) for the estimation of crude oil price to enhance convergence speed accuracy. To evaluate the effectiveness of the proposed CSBP, we used ABC to train BP neural network (ABCNN) and Levenberg-Marquardt neural network (ABCLM) to develop an approach for crude oil price prediction. The outcomes of simulated comparative indicate that the proposed CSBP outperforms the ABCLM and ABCNN in both accuracy and convergence speed. Analysis of variance including post hoc multiple comparative test shows that the performance of the proposed CSBP is statistically significant than the comparison algorithms. The negative side of the crude oil prices in the global market can be defeated with proper planning based on the crude oil prices predicted by the CSBP. The CSBP proposed in this paper could provide a new strategy for risk managers and analysts to create an efficient risk management framework to formulate policies related to risk issues in the global crude oil market.

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