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Security breach has been recorded in high volume and has compromised several Information Systems and critical applications as well. An Intrusion Detection is the process of analyzing the events occurring in an information system in order to detect different security threats and vulnerabilities. Research and development communities are putting their extra effort for optimizing Intrusion Detection System performance as network data traffic including vulnerabilities are found to be complex and have shown dynamic properties. The idea to explore if certain classifier perform better for certain attack classes constitutes the motivation for this research work. In this research, performance of a comprehensive set of potential classifiers using Knowledge Discovery and Data (KDD99) dataset has been evaluated. Based on evaluated results, maximum accurate classifier for high attack detection rate and low false alarm rate has been chosen and suitable classifier has been proposed. The comparison of simulation result indicates that noticeable performance improvement can be achieved with the proposed classifier to detect different kinds of network attacks and security vulnerabilities.

During the last few years, there has been a switch between traditional media consumption into online media. Millennials in Indonesia, which is currently the biggest age group on the internet, seem to regard social media like Youtube as their go-to online video platform. This caused marketers to compete with each other in creating the best video advertising to grab their attentions. On the other hand, few researchers have found that millenials are avoiding ads. This has become a problem for marketers, as video ads aim to grab the interest consumers to buy their products, ended up not being able to communicate their messages to the consumers. For this reason, it is important to analyze what kind of factors that will interest the millennials to accept online video advertising and if these factors impacted purchase intention. Survey had been conducted to 400 millenials in Jakarta, and the gathered data will be using exploratory factor analysis and path analysis, respectively, to find out how each factor impacted their attitudes and intention to purchase. The results of this research could help marketers in creating the best online video advertising for millennials by showing that ad entertainment, credibility, and emotional appeal simultaneously have significant impacts on attitudes toward social media advertising and purchase intention.

Onset detection is a primary task in audio processing for any higher-level audio processing such as music information retrieval or automatic speech recognition. Onset detection using data driven approach is hard due to labeled data scarcity. In this work we use some in build dataset for training our convolutional neural network (CNN) work and make some test data for Nepalese traditional music. The CNN with raw waveform of input audio signal performs well in this study for onset detection. This network performs well in diversified audio type where 50 millisecond windows are set in each audio file to identify the presence or absence of onset.

Medical image processing is one of the most challenging and emerging filed. Processing of medical image is one of the important tasks for the diagnosis of brain tumor. Image segmentation is required for detection of brain tumors, which is a quite complicated job if performed automatically. In recent time, scientists from various fields including medical, mathematical and computer science have collaborated to find out a better understanding of the disease and devise more cost-effective treatments. Due to advancements in the field of science and technology, we have innumerous methods for image segmentation which are used for the detection of brain tumor and to clearly recognize it from MRI imagery. Various methods and algorithms have been implemented for segmenting MRI imagery. This work implements particle swarm optimization technique to recognize brain tumor by characterizing MRI images. Machine learning algorithm is used for severity analysis of brain tumor.

Over the first few years, a wireless sensor network has a very important role over the networks. The primary features of WSN include satellite communication, broadcast channel, hostile environment, medical system and data gathering. There are a lot of attacks available in WSN. Wormhole attack is one of the severe attacks, which is smoothly resolved in networks but tough to observe. This survey paper is an experiment to observing threats and focuses on some different technique to detecting wormhole attacks in wireless sensor networks.