A New Paradigm for Development of Data Imputation Approach for Missing Value Estimation

Many real-world applications encountered a common issue in data analysis is the presence of missing data value and challenging task in many applications such as wireless sensor networks, medical applications and psychological domain and others. Learning and prediction in the presence of missing value can be treacherous in machine learning, data mining and statistical analysis. A missing value can signify important information about dataset in the mining process. Handling missing data value is a challenging task for the data mining process. In this paper, we propose new paradigm for the development of data imputation method for missing data value estimation based on centroids and the nearest neighbours. Firstly, identify clusters based on the k-means algorithm and calculate centroids and the nearest neighbour data records. Secondly, the nearest distances from complete dataset as well as incomplete dataset from the centroids and estimated the nearest data record which tends to be curse dimensionality. Finally, impute the missing value based nearest neighbour record using statistical measure called z-score. The experimental study demonstrates strengthen of the proposed paradigm for the imputation of the missing data value estimation in dataset. Tests have been run using different types of datasets in order to validate our approach and compare the results with other imputation methods such as KNNI, SVMI, WKNNI, KMI and FKNNI. The proposed approach is geared towards maximizing the utility of imputation with respect to missing data value estimation

XCovNet: An optimized xception convolutional neural network for classification of COVID-19 from point-of-care lung ultrasound images

Global livelihoods are impacted by the novel coronavirus (COVID-19) disease, which mostly affects the respiratory system and spreads via airborne transmission. The disease has spread to almost every nation and is still widespread worldwide. Early and reliable diagnosis is essential to prevent the development of this highly risky disease. The computer-aided diagnostic model facilitates medical practitioners in obtaining a quick and accurate diagnosis. To address these limitations, this study develops an optimized Xception convolutional neural network, called XCovNet, for recognizing COVID-19 from point-of-care ultrasound (POCUS) images. This model employs a stack of modules, each of which has a slew of feature extractors that enable it to learn richer representations with fewer parameters. The model identifies the presence of COVID-19 by classifying POCUS images containing Coronavirus samples, viral pneumonia samples, and healthy ultrasound images. We compare and evaluate the proposed network with state-of-the-art (SOTA) deep learning models such as VGG, DenseNet, Inception-V3, ResNet, and Xception Networks. By using the XCovNet model, the previous study\u27s problems are cautiously addressed and overhauled by achieving 99.76% accuracy, 99.89% specificity, 99.87% sensitivity, and 99.75% F1-score. To understand the underlying behavior of the proposed network, different tests are performed on different shuffle patterns. Thus, the proposed XCovNet can, in regions where test kits are limited, be used to help radiologists detect COVID-19 patients through ultrasound images in the current COVID-19 situation