Irrelevant feature in heart disease dataset affects the performance of binary classification model. Consequently, eliminating irrelevant and redundant feature (s) from training set with feature selection algorithm significantly improves the performance of classification model on heart disease detection. Sequential feature selection (SFS) is successful algorithm to improve the performance of classification model on heart disease detection and reduces the computational time complexity. In this study, sequential feature selection (SFS) algorithm is implemented for improving the classifier performance on heart disease detection by removing irrelevant features and training a model on optimal features. Furthermore, exhaustive and permutation based feature selection algorithm are implemented and compared with SFS algorithm. The implemented and existing feature selection algorithms are evaluated using real world Pima Indian heart disease dataset and result appears to prove that the SFS algorithm outperforms as compared to exhaustive and permutation based feature selection algorithm. Overall, the result looks promising and more effective heart disease detection model is developed with accuracy of 99.3%.

Heart disease is one of the causes for death throughout the world. Heart disease cannot be easily identified by the medical experts and practitioners as the detection of heart disease requires expertise and experience. Hence, developing better performing models for heart disease detection using machine-learning algorithms is crucial for detecting heart disease in an early stage. However, employing machine learning algorithm involves determining the relationship between the heart failure dataset features. In this study, correlation analysis is employed to identify the relationship among the heart failure dataset features and a predictive model for heart failure detection is developed with K-nearest neighbor (KNN). Pearson correlation is employed to identify the relationship between the features in the heart failure dataset and the effect of strong correlation to the target feature on the performance of K-nearest neighbor (KNN) model is analyzed. The experimental result shows that highly correlated feature significantly affected the performance of K-nearest neighbor (KNN) for heart failure detection. Finally, the performance of KNN is evaluated and result reveals that the model has acceptable level of performance with highest accuracy of 97.07% on heart failure prediction.

No abstract

As Image size need to be reduced for the purpose of data storage and transmission for better utilization of the Bandwidth, Images are compressed using lossy and Lossless compression schemes. In this paper Fractal Image Compression, a lossy compression technique is being implemented on medical Images .Fractal Encoding involves partitioning the images into Range Blocks and Domain Blocks and each Range Block is mapped onto the Domain Blocks by using contractive transforms called the Affine Transforms. The Fractal encoding technique takes a longer encoding time and less decoding time. In the present paper Fractal Image Compression using quad-tree Partitioning technique is being implemented. on Medical Images like CT Of Bone and MR Images of Brain The Performance measures like Compression Ratio (CR), Peak Signal To Noise Ratio (PSNR), Mean Square Error (MSE), Encoding time and Decoding Time are determined for the Range Blocks of Sizes 2x2 and 4x4 respectively with different Threshold Values. Mat lab simulated results for these Performance Measures shows that for larger Range Block size, the PSNR Value decreases and CR increases for different threshold Values.

Artificial Intelligence has mostly penetrated in every field of technology and our lifestyle in numerous ways. The contribution of AI in the field of Civil engineering which mainly focuses on planning, design and construction is enormous. The main objective of this work is to develop a system that will automate the process of detecting errors in the engineering plans or drawings of structures. The work adapts convolution neural network technique with the help of Inception V3 model to automate detecting of multiple errors using Artificial Intelligence. AI technique has proven to be more effective, accurate and less time consuming against the existing manual verification technique