Event-related potential (ERP) is a distinctive pattern of brain activity that is elicited by the brain’s sensitivity and cognition whereas P300 evoked potential changes in cognitive functions. Since P300 wave is a cognitive response across multiple brain channels correlated between the measured electroencephalogram (EEG) and deviant stimulus in a specific period, it requires a suitable signal processing application for interpretation. Moreover, multiple steps of data processing under neuroscience criteria make the P300 reflection difficult to analyze by common methods. Therefore, this study proposes the processing model for brainwave applications based on P300 peak signal detection in multiple brain channels. This study applies 64 channels ERP datasets throughout bandpass filter in fast Fourier transform (FFT) with the specific ranges of signal processing while ERP averaging is applied as a feature extraction method. Furthermore, the experimental metadata is applied with the filtered P300 peak signals in channel classification via a machine learning method, the Decision Tree. The experimental results indicate the accurate mental reflection of P300 evoked potential in different brain channels with high classification accuracy relying on the contrast condition throughout the original data source averaged across the individual electrodes.

Dynamic Host Configuration Protocol (DHCP) Internet Protocol (IP) address starvation is a method, used by attackers, to breakdown communication over IP network. In order to solve this problem, a method to detect and recover malicious IP address request by using Internet Control Message Protocol (ICMP) protocol has been proposed. However, the ICMP based was not be able to work faster in detecting and recovering the malicious request than the attack rate. This study proposed an ease and effective authentication method to emphasize on limiting the rate of IP addresses request by malicious client during the DHCP discovering process and prevent the DHCP server from being IP address starved. Experimental results revealed that the proposed method was not only limited to the IP addresses requested time by attackers but also able to prevent the DHCP server from facing the IP address starvation attack.

Beef cattle raising in Thailand has evolved from sub-office farming to more business farming. The cornerstone of beef cattle farming is profitable farming, which means production with a minimum number of units and the highest selling price in raising beef cattle. To achieve this, farmers need to learn and study many factors that will affect animal production. This study investigated. requirements analysis for implementation of farm management system by promote beef cattle feeding support system of smart farming based with ICT integrated. The pilot study revealed that the require of farm management and farm support system specification with stakeholder and user analysis results and also the design of beef cattle smart farming for Thai farmer's context.

This work proposes the new methodology for the vehicular network with fuzzy time windows. The Fuzzy technique is applied to produce an initial population and then the evolutionary algorithm is employed to improve the solution. In this work, the inter-route crossover, intra-route mutation, elitism strategy, and onlooker bee probability selection method were enhanced in the original processes of the evolutionary algorithm. The proposed algorithm is tested on 56 datasets of Solomon. The results from the proposed algorithm are shown in comparison with other algorithms in the literature. The findings from the computational results are very inspiring, it shows that the algorithm is very competitive. Comparing with the algorithm in the literature, the proposed algorithm obtains the best solution in terms of the coefficient of variation values for almost 40 instances from the 56 problem instances. In addition, the information regarding the p-value was resolved by the Wilcoxon signed-rank test for the considered testing instances that display statistically superior performance at the 95% significance level (α = 0.05) on comparing algorithms.

This study focused on the impact of local Arabica coffee on the level of attention of individual brain waves, and how coffee affects Human EEG Frequency. Local Arabica coffee is adopted in this study as a medium to wake up the Beta wave. The Personal brainwave data is then recorded through EEG equipment and classified. The result showed that local coffee is helping to improve people's attention level — the study conducted on fifty participants: twenty-five males and twenty-five females aged between twenty to thirty years old. Brainwaves or Electroencephalography are collected twice before and after drinking coffee to compare the effects of Arabica on human brain waves by using NeuroSky mindwave mobile. The paired sample t-test test was employed for comparing two groups of Beta brainwaves experiment. Besides, the k-means algorithm is used to perform data mining on brain waves, and the differential brain wave signal data is clustered and divided into three levels. The experimental results showed that there was a statistically significant difference between the two paired samples. Therefore, the results confirmed that local Arabica coffee has a direct impact on personal attention.