-Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

Problem statement: Predicting the value for missing attributes is an important data preprocessing problem in data mining and knowledge discovery tasks. Several methods have been proposed to treat missing data and the one used more frequently is deleting instances containing at least one missing value of a feature. When the dataset has minimum number of missing attribute values then we can neglect the instances. But if it is high, deleting those instances may neglect the essential information. Some methods, such as assigning an average value to the missing attribute, assigning the most common values make good use of all the available data. However the assigned value may not come from the information which the data originally derived from, thus noise is brought to the data. Approach: In this study, k-means clustering is proposed for predicting missing attribute values. The performance of the proposed approach is analyzed with nine different methods. The overall analysis shows that the k-means clustering can predict the missing attribute values better than other methods. After assigning the missing attributes, the feature selection is performed with Bees Colony Optimization (BCO) and the improved Genetic KNN is applied for finding the classification performance as discussed in our previous study. Results: The performance is analyzed with four different medical datasets; Dermatology, Cleveland Heart, Lung Cancer and Wisconsin. For all the datasets, the proposed k-means based missing attribute prediction achieves higher accuracy of 94.60 %, 90.45 %, 87.51 % and 95.70 % respectively. Conclusion: The greater classification accuracy shows the superior performance of the k-means based missing attribute value prediction.

The work was motivated by the increasing awareness of the need for bone age assessment (BAA) schemes featuring an appropriate methodology for skeletal age estimation. The endocrinological problems in youngsters are already evident in many countries worldwide, varying in scale and intensity for different age groups and sexes. Change in lifestyles and eating habits of people also contribute to endocrine disorders, increasing the need for a system that predicts such problems well in advance. Skeletal bone age assessment is a procedure often used in the management and diagnosis of endocrine disorders. It also serves as an indication of the therapeutic effect of treatment. It is of much significance in pediatric medicine in the detection of hormonal growth or even genetic disorders. Bone age is assessed from the left-hand wrist radiograph and then compared with the chronological age. A discrepancy between the two indicates abnormalities. This paper consists of an overall review and technical assessments of various skeletal age assessment schemes in the literature. This review also recommends some research areas in this field and those leading to high efficiency are highlighted

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