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In this work, an artificial neural network is designed for predicting the heating and cooling loads for buildings. The paper develops two neural models that make use of a dataset with 8 input attributes with the output as a numeric value of the heating and cooling loads of the buildings. The predictive abilities of the neural nets are compared with linear regression under conventional validation,...
Feature selection is addressed an important problem in data mining. To be high dimension of the data obtained from the sources is encountered as an issue in many issues such as computation cost. For this reason, eliminating the unnecessary ones among these data and choosing the appropriate ones makes it possible to evaluate the information correctly. In this study, it is tried to suggest a method...
Automatic segmentation of the left ventricle (LV) can become a useful tool in echocardiography, for instance to provide automatic ejection fraction measurements or to initialize deformation imaging algorithms. Deep neural networks have recently shown very promising results for improving image classification and segmentation. These methods learn using only a set of input and output data, but require...
In this paper, a new hybrid model is proposed to control the DC/DC converter of Grid Connected Photovoltaic System. The approach used to build the developed controller is divided into three steps, the first step is to generate a data based on the fuzzy logic controller (FLC), the second step is to choose an artificial neural network (ANN) structure to model the FLC and the last step is the training...
In this paper, we propose a Wavelet Neural Network (WNN) classifier for breast cancer. WNN is a new kind of artificial neural network which is coming more popular these days. This method is based on the Wavelet Transform (WT) and classical neural networks. This paper explains how WNN classifies and uses formulas. The results of the experiments made to obtain the best results and the parameters affecting...
A spatial analysis of magnitude distribution is presented in this paper to identify the optimal number of clusters based on seismic data of all region in Indonesia. The data were obtained from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and United States Geological Survey's (USGS). Clustering process consist of two steps: finding the global optimum number of clusters...
The paper presents an approach to localize human body joints in 3D coordinates based on a single low resolution depth image. First a framework to generate a database of 80k realistic depth images from a 3D body model is described. Then data preprocessing and normalization procedure, and DNN and MLP artificial neural networks architectures and training are presented. The robustness against camera distance...
This paper presents a series of experiments on the classification of emergency phone conversation records using artificial neural networks (ANNs). Input data which were processed by ANNs were the features of callers and events taken from emergency phone calls. The authors analyzed four variants of classification: the groups of callers which have specified features, the groups of events which have...
An appreciable fraction of introns is thought to be involved in cellular functions, but there is no obvious way to predict which specific intron is likely to be functional. For each intron we are given a feature representation that is based on its evolutionary patterns. For a small subsets of introns we are also given an indication that they are functional. For all other introns it is not known whether...
Some of the best current face recognition approaches use feature extraction techniques based on either Principle Component Analysis (PCA), Local Binary Patterns (LBP), Autoencoder (non-linear PCA), etc. While each of these feature techniques works fairly well, we propose to combine multiple feature extractors with deep learning in a system so that the overall face recognition accuracy can be improved...
Fingerprinting based WLAN indoor positioning system (FWIPS) provides a promising indoor positioning solution to meet the growing interests for indoor location-based services (e.g., indoor way finding or geo-fencing). FWIPS is preferred because it requires no additional infrastructure for deploying an FWIPS — achieving the position estimation by reusing the available WLAN and mobile devices, and is...
Anaerobic ammonium oxidation (anammox) process has been recognized as efficient biological nitrogen removal process, which has the advantages of cost-effective and low energy compared to the conventional nitrification and denitrification processes. However, the efficient operation and control is limited due to the complexity of nonlinear and biochemical phenomena involved. This paper proposes an appropriate...
Every organism emits energy around it which comprises UV-radiation, EM-radiation, infrared and thermal radiation. This energy around human body represents health condition of the subject under study. These energy fields are called as aura of the body under consideration. Several types of equipments are there to capture such energy. Kirlian camera captures the distribution of energy radiation around...
Training of artificial neural networks (ANNs) using reinforcement learning (RL) techniques is being widely discussed in the robot learning literature. The high model complexity of ANNs along with the model-free nature of RL algorithms provides a desirable combination for many robotics applications. There is a huge need for algorithms that generalize using raw sensory inputs, such as vision, without...
Patient admitted with acute decompensated heart failure (ADHF) facing with high risk of mortality where 30 day mortality rates are reaching 10%. Identifying patient with high and low risk of mortality could improve clinical outcomes and hospital resources allocation. This paper proposed the use of artificial neural network to predict mortality for the patient admitted with ADHF. Results show that...
Audio Event Detection (AED) aims to recognize sounds within audio and video recordings. AED employs machine learning algorithms commonly trained and tested on annotated datasets. However, available datasets are limited in number of samples and hence it is difficult to model acoustic diversity. Therefore, we propose combining labeled audio from a dataset and unlabeled audio from the web to improve...
Independent Vector Analysis is a powerful tool for estimating the broadband acoustic transfer function between multiple sources and the microphones in the frequency domain. In this work, we consider an extended IVA model which adopts the concept of pilot dependent signals. Without imposing any constraint on the de-mixing system, pilot signals depending on the target source are injected into the model...
Automated, efficient and accurate classification of skin diseases using digital images of skin is very important for bio-medical image analysis. Various techniques have already been developed by many researchers. In this work, a technique based on meta-heuristic supported artificial neural network has been proposed to classify images. Here 3 common skin diseases have been considered namely angioma,...
In this work, the potential application of Artificial Neural Network (ANN) was studied to predict the absorption of Carbon Dioxide (CO2) in Ionic Liquid (IL) solutions over wide-ranging operating conditions. A few physical properties had been chosen as input data which were temperature, partial pressure of CO2, molecular weight, acentric value, critical temperature and critical pressure of IL. A sample...
This paper proposes Neural Network (NN) based data interpolation and design optimization algorithm for an Interior Permanent Magnet Synchronous Machine. Data interpolation using NN is suitable for estimating the performance of an electric machine, because NN is approximate function for representing nonlinear data. To utilize NN as an approximate function, training process is required. After training...
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