Restricted Boltzmann machines (RBM) are probabilistic graphical models which are represented as stochastic neural networks. Increase in computational capacity and development of faster learning algorithms, led RBMs to become more useful for many machine learning problems. RBMs are the building blocks of many deep multilayer architectures like Deep Belief networks (DBN) and Deep Boltzmann Machin...

In this project we present a heuristic learning process by training ANN (artificial neural network) and KNN (k-nearest neighbor) using the best number of steps, gained from A*, from randomly generated states to the goal. After training ANN and KNN, the mixture of Experts is discussed and the empirical data are collected to demonstrate the feasibility and accuracy of combination of ANN and KNN i...

A novel objective methodology is proposed for the assessment of the physiological age of the heart which is based on extraction of wavelet features from sternal ballistocardiogram and classification of these features using neural networks and k-nearest neighbor classifiers. Some promising preliminary results suggest that the proposed methodology is feasible to be developed to a tool for assessm...

The paper focuses on predicting the Nifty 50 Index by using 8 Supervised Machine Learning Models. techniques used for empirical study are Adaptive Boost (AdaBoost), k-Nearest Neighbors (kNN), Linear Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), Stochastic Gradient Descent (SGD), Support Vector (SVM) and Decision Trees (DT). Experiments based historical data of Indian Sto...

RFID technology is one of the important technologies to determine the object locations. Distances are calculated with respect to calibration curves of RSSI amplitudes. The aim of this study is to determine the 2D position of mobile objects in the indoor environment. The importance of the work is to show that localization by using Artificial Neural Network plus Kalman Filtering is more accurate ...

The use of machine-learning techniques is becoming more and frequent in solving all those problems where it difficult to rationally interpret the process interest. Intrusion detection networked systems a problem which, although not fundamental measures that one able obtain from process, important an answer classification algorithm if network traffic characterized by anomalies (and hence, there ...

A new fault detection and identification approach is proposed. The kernel principal component analysis (KPCA) first applied to the data for reducing dimensionality, occurrence of faults determined by means two statistical indices, T2 Q. K-means clustering algorithm then adopted analyze perform clustering, according type fault. Finally, using a long short-term memory (LSTM) neural network. perfo...

In this paper an attempt has been made to the other corner of the power of neural networks. According to the neural network in the diagnosis of diseases, we use neural network models for diagnosing bipolar disorder; bipolar disorder is the common disorder of depression mood. We have used two neural network models: MLP & KNN. With different percentages of the implementation of neural network mod...