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Transformation of occurred rainfall into runoff generated within a catchment is a complex natural phenomenon that passes through various inter-related processes and influenced by many topographic, geographic, geologic and sociologic factors. To develop a model that can reliably imbibe the complex Rainfall-Runoff interaction, two different approaches namely, conventional regression and Artificial Neural...
We address and compare two new frameworks for neural network (NN) computing-based feature enhanced (FE) fusion of remote sensing (RS) imagery acquired with different coherent radar sensing modalities. Both approaches exploit aggregation of the descriptive experiment design regularization (DEDR) based and the theoretical informatics inspired maximum entropy (ME) regularization paradigms for iterative...
Various researches on criminology provides us with a key piece of information about criminal psychology that, a criminal doesn't hover around unknown territory rather they commit crimes when opportunity provides in a concentrated or familiar area i.e. hotspots. So, a crime predicting model can be simulated using crime pattern theory which can analyze verified past crime data and predict future criminal...
Temperature plays a prominent role for living beings in the earth. People from various countries use to survive with different temperature based on the location of the country in the globe. Agriculture mainly depends on different temperature for crop cultivation and plantations. Many wildlife animals habituated to survive with particular temperature. Keeping all those things on hand, it is an impressing...
The requirement for data privacy is limiting to exploit the full potential of what modern data analytic capability could offer. To address such privacy concern, a number of techniques based on homomorphic encryption (HE) have been proposed to allow analytic computation, such as classification based on machine learning techniques, to run on encrypted data. However, these HE-based techniques suffer...
Systems based on artificial neural networks (ANNs) have achieved state-of-the-art results in many natural language processing tasks. Although ANNs do not require manually engineered features, ANNs have many hyperparameters to be optimized. The choice of hyperparameters significantly impacts models' performances. However, the ANN hyperparameters are typically chosen by manual, grid, or random search,...
Machine Learning (ML) techniques have allowed a great performance improvement of different challenging Spoken Language Understanding (SLU) tasks. Among these methods, Neural Networks (NN), or Multilayer Perceptron (MLP), recently received a great interest from researchers due to their representation capability of complex internal structures in a low dimensional subspace. However, MLPs employ document...
Inference systems basically aim to provide and present the knowledge (outputs) that decision-makers can take advantage of in their decision-making process. Nowadays one of the most commonly used inference systems for time series prediction is the computational inference system based on artificial neural networks. Although they have the ability of handling uncertainties and are capable of solving real...
This paper proposes a novel model called Space Time Features-based Recurrent Neural Network (STF-RNN) for predicting people next movement based on mobility patterns obtained from GPS devices logs. Two main features are involved in model operations, namely, the space which is extracted from the collected GPS data and also the time which is extracted from the associated timestamps. The internal representation...
The ability to adjust itself to users' profile is imperative in modern system, given that many people interact with a lot of information in different ways. The creation of adaptive systems is a complex domain that requires very specific methods and the integration of several intelligent techniques, from an intelligent systems development perspective. Designing an adaptive system requires planning...
In this paper, we present a new technique for model order reduction (MOR) that is based on an artificial neural network (ANN) prediction. The ANN-based MOR can be applied for different scale systems with substructure preservation. In the proposed technique, the ANN is implemented for predicting the unknown elements of the reduced order model. Prediction of the ANN architecture is based on minimizing...
Artificial neural network (ANN) is an important branch of artificial intelligence field. This paper reviews the classic models and implementation methods of ANN, and analyzes the key issues of implementation based on FPGA. Considering the demand of intelligent information processing in aviation field, this paper proposes an application assumption that combines traditional airborne computer systems...
Microarray data analysis directly relates with the state of disease through gene expression profile, and is based upon several feature extractions to classification methodologies. This paper focuses on the study of 8 different ways of feature selection preprocess methods from 4 different feature selection methods. They are Minimum Redundancy-Maximum Relevance (mRMR), Max Relevance (MaxRel), Quadratic...
We discuss the future of massively parallel computing from a fundamental architecture standpoint. Our central thesis is that various versions of Moore's Laws will all unavoidably break down over the next two to three decades, due to fundamental limitations imposed by the laws of physics (especially quantum mechanics). Therefore, the end to scaling-up von Neumann-based architectures by adding more...
This paper aim to investigate and to compare the capabilities of the Artificial Neural Networks (ANN) and the Factorial Experimental Planning (FEP) to measure the most significant variables on milling process that influence the cutting force. The force data were acquired by an experimental apparatus and the statistical inference of the force were set by the Root Mean Squared value. The FEP used the...
We present a novel physics-inspired neural network (Pi-NN) approach for compact modeling. Development of high-quality compact models for devices is a key to connect device science with applications. One recent approach is to treat compact modeling as a regression problem in machine learning. The most common learning algorithm to develop compact models is the multilayer perceptron (MLP) neural network...
This paper introduces a novel method, based on Gaussian Markov Random Field Model with back-propagation learning algorithm to retrieve multi-spectral satellite color imagery. The proposed method segregates the texture part and structure part of the imagery, and extracts features in the texture and structure parts separately. The extracted features are formed as a feature vector. The feature vector...
Intelligent Transportation Systems are an important aspect of our life and are going to become ubiquitous in the near future. Traffic flow prediction is a key component of any Intelligent Transportation Systems. This report uses Artificial Neural Network based models to predict short term traffic flow. Two new input parameters; temperature and truck flow has been introduced into a multi input parameters...
This paper discusses how to apply a hybrid computation process comprised by three techniques of Artificial Neural Network (ANN), Bayesian probability and Cellular Automata (CA), to establish simulating model which can embody the advantages of perceptron, stochastic and dynamic. Through our hybrid computation process, we can perform a series of dynamic simulations with high accuracy. Additionally,...
A stator winding fault in one phase of induction motor (IM) gives rise to higher harmonics distortion, increased torque ripple, temperature rise in the magnetic material, mechanical vibrations due to varying magnetic forces and magnetic noise. The fault leads to a change in the electromagnetic field generated in the motor as compared to the normal operation of motor. The copper losses generated in...
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