The objective of this research is to investigate the randomization of data on a computer based feature selection for diagnosing coronary artery disease. The randomization on Cleveland dataset was conducted because the performance value is different for each experiment. Assuming the performance values have a Gaussian probability distribution is a solution to handle different performance value provided by the process of randomizing dataset. The final performance is taken from the mean value of all performance value. In this research, computer based feature selection (CFS), medical expert based feature selection (MFS) and combined both of MFS and CFS (MFS+CFS) are also conducted to improve the performance of the classification algorithm. Also, this research found a different characteristic on Cleveland dataset from previous work. This difference obviously can affect the feature selection result and the final performance. In summary, the randomization dataset and computing the final performance can generally represent the performance of the classification algorithm.

In communication between planes and satellites, Optical Beamforming Networks (OBFNs), which rely on many small and flat Phased Array Antennas (PAAs), need to be tuned in order to receive signals from specific angles. In this paper, we develop a deep neural network representation of tuning OBFNs. The problem of tuning an OBFN is in many aspects similar to training a deep neural network. We present a way to exploit the special structure of OBFNs into deep neural network and an algorithm for tuning OBFNs based on feedback that can be easily measured in real system. Training data, which consists of full signals, can be measured, and therefore is used in this paper. For pilot signals, the desired signal is known explicitly. Given the configuration of OBFNs and all nominal parameters required, it was verified in simulation that the deep neural network can be used to tune large scale OBFNs for any desired delays.

<p>The use of mobile phone has been increasing nowadays in most part of the world and it has become the  phenomenon where people cannot live without. This study aims to reveal whether mobile phone use affects student’s academic achievement compared to other factors such as study program, student’s focus and gender. The frequent of mobile phone use and how excellence student’s academic performance will be analysed. A survey has been conducted to a large number of college students. A questionnaire was developed and delivered by online questionnaire to 513 students of Universitas Pertamina, Jakarta, Indonesia. Using Ordinary Least Square’s result statistical analysis, it can be concluded that gender and study program have significant effects to GPA, while the use of mobile phone and its effect of distracting student’s focus are not significant to GPA. Furthermore , female students significantly scored higher GPA result by 0.23 point than male students, cateris paribus. Then, students from social sciences have higher GPA results by 0.2 point than students from engineering sciences, cateris paribus. Generally, the results should be interesting for decision maker in academic field on how important to embrace mobile phone for learning style. </p>

Visible Light Communication (VLC) adalah suatu teknologi komunikasi yang memanfaatkan pancaran cahaya tampak untuk pengiriman dan penerimaan sinyal. Perkembangan VLC merupakan sebuah inovasi dalam sistem navigasi untuk melacak posisi dari sebuah objek dengan komunikasi cahaya tampak LED. LED digunakan untuk menentukan titik koordinat dari sebuah objek. Pada penelitian ini digunakan metode Particle Swarm Optimization (PSO) dan Genetic Algorithm (GA) untuk menentukan posisi robot terhadap posisi LED. Dari dua metode tersebut dapat diketahui keefisienan masing-masing metode serta mengetahui tingkat keakuratan posisi robot dalam menerima informasi data dari LED. Hasil analisis menunjukan jika nilai path loss yang diterima receiver semakin besar, maka nilai squared error semakin besar pula. Saat nilai squared error semakin besar, tingkat keakuratan penentuan posisi robot semakin kecil karena posisi robot semakin jauh dari titik referensi yaitu posisi LED. Dengan menggunakan metode PSO dan GA diketahui jika hasil pencarian nilai minimum error relatif sama baiknya. Perbedaan pada kedua metode terlihat pada proses komputasi, yaitu waktu komputasi serta banyaknya partikel atau generasi yang dihitung.

This paper proposes a fully convolutional variational autoencoder (VAE) for features extraction from a large-scale dataset of fire images. The dataset will be used to train the deep learning algorithm to detect fire and smoke. The features extraction is used to tackle the curse of dimensionality, which is the common issue in training deep learning with huge datasets. Features extraction aims to reduce the dimension of the dataset significantly without losing too much essential information. Variational autoencoders (VAEs) are powerfull generative model, which can be used for dimension reduction. VAEs work better than any other methods available for this purpose because they can explore variations on the data in a specific direction.