The World Health Organization (WHO) estimated that by the year 2030, lung disorders such as Chronic Obstructive Pulmonary Disease (COPD) would be one of the leading cause of death all over the world. Consequently, accurate and timely detection of lung diseases may prevent further death. It is therefore vital that the early detection may lead to treatment and prevention of mortality among patients. However, there are only a minimum number of experts or well-trained radiologists reading Chest X-Ray (CXR) that delays the timely diagnosis of lung diseases. In order to aid the radiologist in reading CXR images, a computer-aided tool is proposed. Before the processing of images, it needs to be segmented to make it easier for the machine to understand. This study is focused on developing a model that will segment the lung from CXR images. Using Residual U-Net (ResUnet) architecture based semantic segmentation, the researchers were able to develop and train a model using a set of 562 CXR images and lung mask images, 70% of the images were used as training data and 30% as test data. The model was trained with 40 epochs and a batch size of 16. Dice coefficient was used to assess the similarity of the segmented result and the ground truth mask. The developed model has achieved a Dice coefficient of 0.9860. The developed model can then be used in classifying lung diseases by focusing on the segmented image rather than focusing on the entire CXR image.
With the rapid depletion of IPv4 protocol in these recent years, the IETF introduced IPv6 as a solution to address the exhaustion, however, as a new protocol exists, new characteristics have been introduced and new threats have been discovered. Extension Headers are the new characteristics of IPv6 that have an emerging and re-emerging security threats that is needed to be taken into consideration during the full migration to the IPv6 network. This study revealed that up to this moment, the popular vendors are still vulnerable and doesn’t have any default protection to deal with extension headers’ Denial of Service Attack (DoS). Also, this study leads to the development of new security model which creates a new solution to address the emerging threats of IPv6 extension headers’ Denial of Service Attack. Moreover, the results of this study show that our proposed security model is more effective in terms of neutralizing the unwanted traffic causing evasion attack by filtering, rate-limiting and discarding the malformed packets of prohibited extension headers’ payload versus the traditional router protection.
Sentiment Analysis (SA) combines Natural Language Processing (NLP) techniques and text analytics to extract useful information from textual data. This study uses SA to estimate the Filipino internet customers' satisfaction related to the quality of the service provided by the Internet Service Providers (ISPs). Data were collected from Blog comments shared with online social media. Automatic word seed selection was applied using the word pair set {"Good" and "Slow"} as initial seed for the word dictionary. The Naïve Bayes method was used as a classifying tool to identify the dominant words used to express customers' sentiments and to determine the sentiment polarity of their opinions. The proposed automatic classifier successfully identifies positive and negative polarity of the blog sentences with a 91.50% accuracy in the training set. However, the results of the actual evaluation of the manually labelled test set show a drop in accuracy rate of 60.27%. Some of the reasons for this drop in accuracy are investigated in this paper.
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