In this paper, we investigate the automatic recognition of emotion in text. We perform experiments with a new method of classification based on the PPM character-based text compression scheme. These experiments involve both coarse-grained classification (whether a text is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression based classification method is able to distinguish between emotional and nonemotional text with high accuracy, between texts involving Happiness and Sadness emotions (with 80% accuracy for Aman’s dataset and 76.7% for Alm’s datasets) and texts involving Ekman’s six basic emotions for the LiveJournal dataset (87.8% accuracy). Results also show that the method outperforms traditional feature-based classifiers such as Naïve Bayes and SMO in most cases in terms of accuracy, precision, recall and F-measure.
A (k,n)-arc is a set of k points of PG(2,q) for some n, but not n + 1 of them, are collinear. A (k,n)-arc is complete if it is not contained in a (k + 1,n)-arc. In this paper we construct complete (kn,n)-arcs in PG(2,5), n = 2,3,4,5, by geometric method, with the related blocking sets and projective codes.
We have studied Bayesian method in this paper by using the modified exponential growth model, where this model is more using to represent the growth phenomena. We focus on three of prior functions (Informative, Natural Conjugate, and the function that depends on previous experiments) to use it in the Bayesian method. Where almost of observations for the growth phenomena are depended on one another, which in turn leads to a correlation between those observations, which calls to treat such this problem, called Autocorrelation, and to verified this has been used Bayesian method.
The goal of this study is to knowledge the effect of Autocorrelation on the estimation by using Bayesian method. F
... Show MoreThis paper deals with prediction the effect of soil re-moulding (smear) on the ultimate bearing capacity of driven piles. The proposed method based on detecting the decrease in ultimate bearing capacity of the pile shaft (excluding the share of pile tip) after sliding downward. This was done via conducting an experimental study on three installed R.C piles in a sandy clayey silt soil. The piles were installed so that a gap space is left between its tip and the base of borehole. The piles were tested for ultimate bearing capacity according to ASTM D1143 in three stages. Between each two stages the pile was jacked inside the borehole until a sliding of about 200mm is achieved to simulate the soil re-moulding due to actual pile driving. The re
... Show MoreThis study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperatur
... Show MoreFeed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 m
... Show MoreUrban land uses are in a dynamic state that varies over time, the city of Karbala in Iraq has experienced functional changes over the past 100 years, as the city is characterized by the presence of significant tourist and socio-economic activity represented by religious tourism, and it occur due to various reasons such as urbanization. The purpose of this study is to apply a Markov model to analyze and predict the behavior of transforming the use of land in Karbala city over time. This can include the conversion of agricultural land, or other areas into residential, commercial, industrial land uses. The process of urbanization is typically driven by population growth, economic development, based on a set of probabilities and transitions bet
... Show MoreThis paper deals with prediction the effect of soil remoulding (smear) on the ultimate bearing capacity of driven piles. The proposed method based on detecting the decrease in ultimate bearing capacity of the pile shaft (excluding the share of pile tip) after sliding downward. This was done via conducting an experimental study on three installed R.C piles in a sandy clayey silt soil. The piles were installed so that a gap space is left between its tip and the base of borehole. The piles were tested for ultimate bearing capacity
according to ASTM D1143 in three stages. Between each two stages the pile was jacked inside the borehole until a sliding of about 200mm is achieved to simulate the soil remoulding due to actual pile driving. T