The most important factors that prevent pattern recognition from functioning rapidly and effectively are the noisy and inconsistent data in databases. This article presents a new data preparation method based on clustering algorithms for diagnosis of heart and diabetes diseases. In this method, a new modified K-means Algorithm is used for clustering based data preparation system for the elimination of noisy and inconsistent data and Support Vector Machines is used for classification. This newly developed approach was tested in the diagnosis of heart diseases and diabetes, which are prevalent within society and figure among the leading causes of death. The data sets used in the diagnosis of these diseases are the Statlog (Heart), the SPECT images and the Pima Indians Diabetes data sets obtained from the UCI database. The proposed system achieved 97.87 %, 98.18 %, 96.71 % classification success rates from these data sets. Classification accuracies for these data sets were obtained through using 10-fold cross-validation method. According to the results, the proposed method of performance is highly successful compared to other results attained, and seems very promising for pattern recognition applications.

This paper offers a hybrid approach that uses the artificial bee colony (ABC) algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications.

The rapid spread of SARS-CoV-2 with its mutating strains has posed a global threat to safety during this COVID-19 pandemic. Thus far, there are 123 candidate vaccines in human clinical trials and more than 190 candidates in preclinical development worldwide as per the WHO on 1 October 2021. The various types of vaccines that are currently approved for emergency use include viral vectors (e.g., adenovirus, University of Oxford/AstraZeneca, Gamaleya Sputnik V, and Johnson & Johnson), mRNA (Moderna and Pfizer-BioNTech), and whole inactivated (Sinovac Biotech and Sinopharm) vaccines. Amidst the emerging cases and shortages of vaccines for global distribution, it is vital to develop a vaccine candidate that recapitulates the severe and fatal progression of COVID-19 and further helps to cope with the current outbreak. Hence, we present the preclinical immunogenicity, protective efficacy, and safety evaluation of a whole-virion inactivated SARS-CoV-2 vaccine candidate (ERUCoV-VAC) formulated in aluminium hydroxide, in three animal models, BALB/c mice, transgenic mice (K18-hACE2), and ferrets. The hCoV-19/Turkey/ERAGEM-001/2020 strain was used for the safety evaluation of ERUCoV-VAC. It was found that ERUCoV-VAC was highly immunogenic and elicited a strong immune response in BALB/c mice. The protective efficacy of the vaccine in K18-hACE2 showed that ERUCoV-VAC induced complete protection of the mice from a lethal SARS-CoV-2 challenge. Similar viral clearance rates with the safety evaluation of the vaccine in upper respiratory tracts were also positively appreciable in the ferret models. ERUCoV-VAC has been authorized by the Turkish Medicines and Medical Devices Agency and has now entered phase 3 clinical development (NCT04942405). The name of ERUCoV-VAC has been changed to TURKOVAC in the phase 3 clinical trial.

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There is a large amount of information and maintenance data in the aviation industry that could be used to obtain meaningful results in forecasting future actions. This study aims to introduce machine learning models based on feature selection and data elimination to predict failures of aircraft systems. Maintenance and failure data for aircraft equipment across a period of two years were collected, and nine input and one output variables were meticulously identified. A hybrid data preparation model is proposed to improve the success of failure count prediction in two stages. In the first stage, ReliefF, a feature selection method for attribute evaluation, is used to find the most effective and ineffective parameters. In the second stage, a K-means algorithm is modified to eliminate noisy or inconsistent data. Performance of the hybrid data preparation model on the maintenance dataset of the equipment is evaluated by Multilayer Perceptron (MLP) as Artificial Neural network (ANN), Support Vector Regression (SVR), and Linear Regression (LR) as machine learning algorithms. Moreover, performance criteria such as the Correlation Coefficient (CC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are used to evaluate the models. The results indicate that the hybrid data preparation model is successful in predicting the failure count of the equipment.