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Systematic testing is vital for the early detection and treatment of the all types of cancer. However in the case of lung infection most cases are detected in advance stages as this type is hard to be detected in premature phases. This research presents an automatic detecting technique for abnormal lung nodules; which can be identifies as a dime-sized damage progress. The proposed detection system which can use as a decision support tool is based on using two machine learning algorithms for classification purposes. RBF kernel based support vector classifier and the polynomial kernel based support vector classifier are employed to predict if the nodules growth is normal or abnormal. The results revealed that both algorithms are able to predict both classes, however the polynomial kernel based support vector classifier outperformed the RBF algorithm.

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Cloud computing is a term that refers to sources and computer systems which is available on networks, which can provide a number of integrated computer services without being restricted to local resources, the user can access it via the Internet. We have studied cloud computing system in case, that the user sends complex requests to the system consisting of several sub queries. To process each sub query, one resource unit is allocated. The accurate assessment of the effectiveness of cloud computing resources is a prerequisite for ensuring the required quality of service. The response time, is the maximum of sub queries service times, is chosen as an indication of effectiveness of a cloud computing system. To analyze the characteristics of response time, a simplified mathematical model of a cloud system was constructed as a queuing system with single Poisson input flow of requests and several subsystems (a buffer and a server). Each of the incoming requests at the time of receipt is divided into several parts serviced by its queuing subsystem with an unlimited storage capacity accumulator and an exponential distribution of the service time. With the help of this mathematical model, an analytical formula is obtained to find the estimation of the mean and the variance of the response time of the system - the maximum processing time of the constituents requesting sub queries. To assess the accuracy of the estimation, simulation was performed using GPSS (General Purpose Simulation System).

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Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.

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This study offers a 5G substrate integrated waveguide (SIW) antenna with vertical S zigzag shape slot. The suggested antenna's radiating patch depends on the shape of the slot. Two types of slots have been used, straight and zigzag S shape slot with vertical and horizontal direction, slots are etched on the patch to increase the antenna's overall bandwidth and gain. The suggested straight and zigzag SIW S slot antennas both resonate at 28 GHz, and the overall structure size is 7.10×14.93 mm. The presented design could achieve high gain, efficiency, and minimal losses, which are all important concerns. The presented antenna may produce a gain of 9.48 dB, a 95% efficiency, and a wider bandwidth of no less than 3.25 GHz at 28 GHz.