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Horse Racing is the favourite sports of Mauritians. This can be demonstrated by the presence of huge crowds at the Champ de Mars on racing days. Many people wait for the last moment to bet as they feel that the variation of odds has an influence on the winner of a race at the Champ de Mars. This is the motivation for this research. Thus, in this work, we have used artificial neural networks to predict...
This paper introduces to diagnosis of Dyslexia using computing system, considered people difficulties in reading, spelling, writing and speaking. Consequently, a computational analysis classifier will be achieved using dyslexia metrics techniques. Accordingly, Gibson test of brain skills will be used with effect of working memory, auditing (hearing and speech) and visual memory and cognition, visual...
A targeted approach to soot blow operation based on boiler operating conditions involves estimation of cleanliness factor. The present work introduces a way of estimating the cleanliness factor of thermal power plant reheater by artificial neural network (ANN). Three different algorithms used to train NN and their performance is compared. Based on the simulation studies it was observed that although...
This paper presents Artificial Neural Network (ANN) technique for predicting the output power from Grid-Connected Photovoltaic (GCPV) system. Different inputs are utilized in several models of ANN in order to obtain the output power. ANN parameters are chosen using trial-and-error method to find the optimal value of root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation...
The main objective of the spatial image classification is to extract information classes from a multiband raster spatial image. The network structure and number of inputs are the key factors in deciding the performance and accuracy of the traditional pixel based image classification techniques like Support Vector Machines (SVM), Artificial Neural Networks (ANN), Fuzzy logic, Decision Trees (DT) and...
In this paper an attempt has been made to predict the solar irradiance values for multiple look ahead time predictions with time intervals as small as fifteen minutes. The recurrent neural networks in the past have been implemented on datasets with an interval of at least 30 minutes. The recurrent neural network was trained using backpropagation through time and the prediction was done using only...
With the integration of EVs into the power grid, smart metering using machine-to-machine (M2M) communication is likely to play an important role in real-time energy management and control. Smart devices embedded with advanced metering infrastructure (AMI) can forecast the energy demand as well as perform energy pricing in real time. In this paper, an artificial neural network (ANN) based intelligent...
In this paper, we develop a projection neural network to solve the convex quadratic programming problem in support vector machine (SVM) learning. Then, we obtain a unique global solution for the proposed neural network. Furthermore, we prove that this network is completely stable and finite-time convergence. To present the feasibility and efficiency of the proposed neural network for solving the SVM...
To date, paper-based examinations are still in use worldwide on all levels of education levels (e.g. secondary, tertiary levels). However, literature regarding off-line automatic assessment systems employing off-line handwriting recognition is not numerous. This paper proposes an off-line automatic assessment system employing a hybrid feature extraction technique - a newly proposed Modified Direction...
In present paper, authors develop a model for estimation of earth slope stability based on the artificial neural networks. For this purpose, authors engage multi-layer feed-forward network with Levenberg-Marquardt learning algorithm and 14 hidden nodes, using existing experimental data, and the results of traditional limit equilibrium analyzes of 57 different cases according to the predefined experimental...
The aim of this work is to create a decision support system based on Adaptive Neuro-Fuzzy Inference System (ANFIS), which will be used for objective classification of employees in the employment process by analyzing available information about the candidates. Information about the candidates is extracted from the relevant documents on one company during the advertising for job in Information Technology...
Effective generation of hash function is very important for an achievement of a security of today networks. A cryptographic hash function is a transformation that takes an input and returns a fixed-size value, which is called the hash value. An artificial neural network (ANN), as a possible approach, could be used for the hash function generation. The performance of the ANN was validated by software...
This paper proposes an ensemble neural network (ENN) framework for robust automatic speech recognition (ASR). The proposed ENN framework can be divided into offline and online phases. In the offline phase, the ENN framework first applies an environment clustering technique to partition the training data into several subsets, where each subset characterizes specific local information of the entire...
We use query-by-example keyword spotting (QbyE-KWS) approach to solve the personalized wake-up word detection problem for small-footprint, low-computational cost on-device applications. QbyE-KWS takes keywords as templates, and matches the templates across an audio stream via DTW to see if the keyword is included. In this paper, we use neural networks as acoustic models to extract DNN/LSTM phoneme...
Software defect prediction (SDP) is a most dynamic research area in software engineering. SDP is a process used to predict the deformities in the software. To identifying the defects before the arrival of item or aimed the software improvement, to make software dependable, defect prediction model is utilized. It is always desirable to predict the defects at early stages of life cycle. Hence to predict...
At present, the issue of intrusion detection must be a hot point to all over the computer security area. In this paper, two novel intrusion detection techniques have been proposed. First, unlike the current existent detection methods, this paper combines the theories of both intuitionistic fuzzy sets (IFS) and artificial neural networks (ANN) together, which lead to much fewer iteration numbers, higher...
This article deals with the application of neural networks on the issues of steganalysis. The aim is to improve the detection capability of conventional steganalytical tools with using of ANN. We analyze the ability of the tool, which utilizes calibration and blockiness to detect the presence of steganography message in suspected image. We verify if deployment of neural network improves this detection...
In this work, we develop an artificial neural network model to predict the potential of solar power in Libya. We use multilayered, feed-forward, back-propagation neural networks for the mean monthly solar radiation using the data of 25 cities spread over Libya for the period of 6 years (2010–2015). Meteorological and geographical data (longitude, latitude, and altitude, month, mean sunshine duration,...
In this paper we present a novel architecture design called SpecNN for artificial neural networks. Our approach allows to consider prior probability distributions and leverage samples similarity to handle the problem of fine-grained samples, thus improving the classification accuracy. We present two different learning algorithms for SpecNN. SpecNN is especially useful in moot cases, when classification...
Biometrics play a crucial role in establishing an individuals identity. A signature is one of the most widely recognized way to authorize transactions and authenticate the human identity as compared to other electronic identification methods such as fingerprint and retina scans. Due to a huge demand for authentication, fast algorithms need to be assimilated for signature recognition and verification...
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