A new intelligent methodology in bearing condition diagnosis analysis has been proposed to predict the status of rolling bearing based on vibration signals by multi class support vector machine (MSVM), a classification algorithm. Wavelet packet transform (WPT) is used for signal processing and standard statistical feature extraction process. Feature reduction is a method used to deselect the irrelevant features acquired from the large dataset. Recent survey shows feature reduction is used widely in the field of machine learning to discover the knowledge with reduced features. Rough set is hybridized with particle swarm optimization (PSO), an population based stochastic optimization technique, to reduce the features. The efficiency of classification algorithm is compared based on their classification accuracy before and after feature reduction. Four states of bearing health conditions such as normal, defective inner race, defective outer race and defective ball conditions are simulated and used in this proposed work.

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Early fault detection methodology in gear box diagnosis has been proposed to find the status of the gear based on vibration signals obtained from the experimental test rig. Signal processing categorized to time-frequency domain such as continues wavelet transform is used in the proposed work for statistical feature extraction. Feature selection method is used for selecting the extensive useful features among the extracted features to reduce the processing time. A famous optimization technique, Genetic algorithm (GA) and rough set based approach is used to select the best input features to reduce the computation burden. The efficiency of this feature selection method is evaluated based on the classification accuracy obtained from the proposed algorithms: back propagation neural network (BPNN) a famous artificial neural network algorithm and C4.5.Performance of classifiers are evaluated with the different signals acquired from the experimental test rig for different states of gears.

Breast cancer is the second most frequent malignant tumor in the world. Early findings of breast cancer can significantly improve treatment effectiveness. Manual methods of breast cancer diagnosis are prone to human fault and inaccuracy, and they take time. A computer-aided diagnosis can assist radiologists in making better choices by overcoming the disadvantages of manual methods. One of the significant steps in the breast cancer diagnosis process is feature selection. In recent decades, many studies have proposed numerous hybrid optimization methods to select the optimal features in the breast cancer detection system. However, many hybrid optimization algorithms are trapped in local optima and have slow convergence speed. Thus, it reduces the classification accuracy. For resolving these issues, this work proposes a hybrid optimization algorithm that combines the grasshopper optimization algorithm and the crow search algorithm for feature selection and classification of the breast mass with multilayer perceptron. The simulation is experimented with using MATLAB 2019a. The efficacy of the proposed hybrid grasshopper optimization-crow search algorithm with multilayer perceptron system is compared to multilayer perceptron based algorithms of enhanced and

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