scholarly journals A new model for automatic text classification

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Hekmat Moumivand ◽  
Rasool Seidi Piri ◽  
Fatemeh Kheiraei
Keyword(s):  
Text Categorization ◽  
Learning Algorithm ◽  
Text Processing ◽  
Second Phase ◽  
Text Documents ◽  
Automatic Text Classification ◽  
Tree Algorithms ◽  
Standard Text ◽  
Two Phases

AbstractIn this paper, a new method for automatic classification of texts is presented. This system includes two phases; text processing and text categorization. In the first phase, various indexing criteria such as bigram, trigram and quad-gram are presented to extract the properties. Then, in the second phase, the W-SMO machine learning algorithm is used to train the system. In order to evaluate and compare the results of the two criteria of accuracy and readability, Macro-F1 and Micro-F1 have been calculated for different indexing methods. The results of experiments performed on 7676 standard text documents of Reuters showed that our proposed method has the best performance compared to the W-j48, Naïve Bayes, K-NN and Decision Tree algorithms.

scholarly journals COVID-19 Pneumonia Level Detection using Deep Learning Algorithm

2020 ◽  
Author(s):  
Kayhan Ghafoor
Keyword(s):  
Lung Inflammation ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Second Phase ◽  
K Nearest Neighbor ◽  
Deep Learning Algorithm ◽  
Severe Stage ◽  
Efficient Detection ◽  
Two Phases

The first COVID-19 confirmed case is reported in Wuhan, China and spread across the globe with unprecedented impact on humanity. Since this pandemic requires pervasive diagnosis, it is significant to develop smart, fast and efficient detection technique. To this end, we developed an Artificial Intelligence (AI) engine to classify the lung inflammation level (mild, progressive, severe stage) of the COVID-19 confirmed patient. In particular, the developed model consists of two phases; in the first phase, we calculate the volume and density of lesions and opacities of the CT images of the confirmed COVID-19 patient using Morphological approaches. In the second phase, the second phase classifies the pneumonia level of the confirmed COVID-19 patient. To achieve precise classification of lung inflammation, we use modified Convolution Neural Network (CNN) and k-Nearest Neighbor (kNN). The result of the experiments show that the utilized models can provide the accuracy up to 95.65\% and 91.304 \% of CNN and kNN respectively.<br>

scholarly journals COVID-19 Pneumonia Level Detection using Deep Learning Algorithm

2020 ◽  
Author(s):  
Kayhan Ghafoor
Keyword(s):  
Lung Inflammation ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Second Phase ◽  
K Nearest Neighbor ◽  
Deep Learning Algorithm ◽  
Severe Stage ◽  
Efficient Detection ◽  
Two Phases

The first COVID-19 confirmed case is reported in Wuhan, China and spread across the globe with unprecedented impact on humanity. Since this pandemic requires pervasive diagnosis, it is significant to develop smart, fast and efficient detection technique. To this end, we developed an Artificial Intelligence (AI) engine to classify the lung inflammation level (mild, progressive, severe stage) of the COVID-19 confirmed patient. In particular, the developed model consists of two phases; in the first phase, we calculate the volume and density of lesions and opacities of the CT images of the confirmed COVID-19 patient using Morphological approaches. In the second phase, the second phase classifies the pneumonia level of the confirmed COVID-19 patient. To achieve precise classification of lung inflammation, we use modified Convolution Neural Network (CNN) and k-Nearest Neighbor (kNN). The result of the experiments show that the utilized models can provide the accuracy up to 95.65\% and 91.304 \% of CNN and kNN respectively.<br>

Differential Evolution-Based Optimization of Kernel Parameters in Radial Basis Function Networks for Classification

2013 ◽  
Vol 4 (1) ◽  
pp. 56-80 ◽  
Author(s):  
Ch. Sanjeev Kumar Dash ◽  
Ajit Kumar Behera ◽  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Neural Networks ◽  
Differential Evolution ◽  
Radial Basis Function ◽  
Basis Function ◽  
Learning Algorithm ◽  
Basis Functions ◽  
Second Phase ◽  
Rbf Neural Networks ◽  
Radial Basis ◽  
Two Phases

In this paper a two phases learning algorithm with a modified kernel for radial basis function neural networks is proposed for classification. In phase one a new meta-heuristic approach differential evolution is used to reveal the parameters of the modified kernel. The second phase focuses on optimization of weights for learning the networks. Further, a predefined set of basis functions is taken for empirical analysis of which basis function is better for which kind of domain. The simulation result shows that the proposed learning mechanism is evidently producing better classification accuracy vis-à-vis radial basis function neural networks (RBFNs) and genetic algorithm-radial basis function (GA-RBF) neural networks.

scholarly journals Optimizing Text Categorization for Indonesian Text Using Clustering Label Technique

2021 ◽  
Vol 12 (3) ◽  
pp. 1483-1491
Author(s):  
Syopiansyah Jaya Putra Et.al
Keyword(s):  
Text Categorization ◽  
Intelligent System ◽  
Text Processing ◽  
Cluster Formation ◽  
Clustering Algorithms ◽  
Experimental Result ◽  
Text Documents ◽  
A Value ◽  
Digital Format ◽  
Clustering Quality

Text Categorization plays an important role for clustering the rapidly growing, yet unstructured, Indonesian text in digital format. Furthermore, it is deemed even more important since access to digital format text has become more necessary and widespread. There are many clustering algorithms used for text categorization. Unfortunately, clustering algorithms for text categorization cannot easily cluster the texts due to imperfect process of stemming and stopword of Indonesian language. This paper presents an intelligent system that categorizes Indonesian text documents into meaningful cluster labels. Label Induction Grouping Algorithm (LINGO) and Bisecting K- means are applied to process it through five phases, namely the pre-processing, frequent phrase extraction, cluster label induction, content discovery and final cluster formation. The experimental result showed that the system could categorize Indonesian text and reach to 93%. Furthermore, clustering quality evaluation indicates that text categorization using LINGO has high Precision and Recall with a value of 0.85 and 1, respectively, compare to Bisecting K-means which has a value of 0.78 and 0.99. Therefore, the result shows that LINGO is suitable for categorizing Indonesian text. The main contribution of this study is to optimize the clustering results by applying and maximizing text processing using Indonesian stemmer and stopword.

scholarly journals Support Vector Machines and Kernel Functions for Text Processing

2013 ◽  
Vol 20 (3) ◽  
pp. 130 ◽  
Author(s):  
Celso Antonio Alves Kaestner
Keyword(s):  
Text Classification ◽  
Learning Algorithm ◽  
Text Processing ◽  
Dimensional Space ◽  
Kernel Functions ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Automatic Text Classification ◽  
Automatic Text

This work presents kernel functions that can be used in conjunction with the Support Vector Machine – SVM – learning algorithm to solve the automatic text classification task. Initially the Vector Space Model for text processing is presented. According to this model text is seen as a set of vectors in a high dimensional space; then extensions and alternative models are derived, and some preprocessing procedures are discussed. The SVM learning algorithm, largely employed for text classification, is outlined: its decision procedure is obtained as a solution of an optimization problem. The “kernel trick”, that allows the algorithm to be applied in non-linearly separable cases, is presented, as well as some kernel functions that are currently used in text applications. Finally some text classification experiments employing the SVM classifier are conducted, in order to illustrate some text preprocessing techniques and the presented kernel functions.

scholarly journals Comparative analysis of the assortment structure of poplar clones I-214 and Pannonia

2020 ◽  
pp. 31-46
Author(s):  
Milorad Danilovic ◽  
Dragan Rakovic ◽  
Dusan Isajev ◽  
Slavica Antonic
Keyword(s):  
Data Collection ◽  
Second Phase ◽  
Detailed Measurement ◽  
Populus Euramericana ◽  
Poplar Clone ◽  
Poplar Clones ◽  
Poplar Trees ◽  
Significant Difference ◽  
Two Phases

Poplars occupy about 31.4 million ha in the world, while in Serbia poplars spread over the area of 48.000 ha. The subject of this research are artificially raised poplar plantations, consisting of poplar clone I-214 (Populus?euramericana (Dode) Guinier cl. I-214) and poplar clone Pannonia (Populus?euramericana (Dode) Guinier cl. Pannonia). Field activities of collecting data required for this research were conducted in two phases. The first phase of data collection included measurement of tree diameter. Also, the numbering, marking and recording of poplar rows, as well as each poplar in the row, was conducted. The second phase of data collection was conducted after the felling of trees that were selected for detailed measurement of the elements required for theoretical cross cutting. In accordance with the general principles of cross cutting, as well as the principles of maximum financial effect, the qualitative partition of trunks into several variants was performed. The classification of wood assortments was performed on the basis of SRPS wood standards. The share of technical wood for veneer (F and L class) in the analyzed poplar trees clone I-214 is 47.54% of the total volume of wood assortments. When it comes to the clone Pannonia, logs for cutting (quality class II), have the greatest share in total volume of wood assortments with 44.08. There is no statistically significant difference between the total volume and the value of the assortments of the two analyzed poplar clones, except when it comes to assortments for chemical exploitation where statistical differences exist.

Accurate Detection and Quantization of Leaf- Diseases through Soft Computing

2018 ◽  
Vol 1 (1) ◽  
pp. 236-247
Author(s):  
Divya Srivastava ◽  
Rajitha B. ◽  
Suneeta Agarwal
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Agricultural Production ◽  
Bacterial Blight ◽  
Early Stage ◽  
Second Phase ◽  
Computationally Efficient ◽  
Stage Of Disease ◽  
Accurate Detection ◽  
Two Phases

Diseases in leaves can cause the significant reduction in both quality and quantity of agricultural production. If early and accurate detection of disease/diseases in leaves can be automated, then the proper remedy can be taken timely. A simple and computationally efficient approach is presented in this paper for disease/diseases detection on leaves. Only detecting the disease is not beneficial without knowing the stage of disease thus the paper also determine the stage of disease/diseases by quantizing the affected of the leaves by using digital image processing and machine learning. Though there exists a variety of diseases on leaves, but the bacterial and fungal spots (Early Scorch, Late Scorch, and Leaf Spot) are the most prominent diseases found on leaves. Keeping this in mind the paper deals with the detection of Bacterial Blight and Fungal Spot both at an early stage (Early Scorch) and late stage (Late Scorch) on the variety of leaves. The proposed approach is divided into two phases, in the first phase, it identifies one or more disease/diseases existing on leaves. In the second phase, amount of area affected by the disease/diseases is calculated. The experimental results obtained showed 97% accuracy using the proposed approach.

Representação e simbolismo: artes visuais na fronteira Brasil/Bolívia

2020 ◽  
Vol 4 (3) ◽  
pp. 286
Author(s):  
Paulo César Antonini de Souza ◽  
Derick Trindade Bezerra
Keyword(s):  
Visual Arts ◽  
Social Order ◽  
Research Field ◽  
Border Region ◽  
Second Phase ◽  
Two Dimensional ◽  
Santa Cruz ◽  
Cultural Conditions ◽  
Two Phases ◽  
America Latina

ResumoTendo por campo de investigação o Festival da América do Sul Pantanal (FASP) em 2018, na cidade de Corumbá (Brasil), objetiva-se identificar a materialidade e conceitos que permeiam as manifestações artísticas bidimensionais nesta região de fronteira, a partir da percepção de artistas da Bolívia. A pesquisa se organizou em duas fases: na primeira foi realizado um levantamento em plataformas online de produções acadêmicas em artes visuais, com foco no trabalho bidimensional, utilizando os descritores “arte popular” e “estética latina” resultando em três artigos. Na segunda fase foram selecionados dois trabalhos de uma artista da Bolívia, participante da mostra “Conexão Santa Cruz”, realizada durante o FASP 2018, que foram analisados em seus níveis representacional e simbólico. Pela interpretação das imagens foi possível construir uma leitura sobre a perspectiva da artista a respeito de suas condições culturais dentro da ordenação social em que se encontra situada.Palavras-chave: Artes Visuais. Arte Popular. Arte Regional. América Latina. Representation and symbolism: visual arts on the Brazil/Bolivia frontierAbstractHaving as research field the Festival da América do Sul Pantanal (FASP) in 2018, in the city of Corumbá (Brazil), the objective is to identify the materiality and concepts that permeate the two-dimensional artistic manifestations in this border region, from the perception of artists from Bolivia. The research was organized in two phases: in the first, a survey was carried out on online platforms of academic productions in visual arts, focusing on two-dimensional work, using the descriptors “arte popular” and “estética latina” resulting in three articles. In the second phase, two works were selected by an artist from Bolivia, participating in the exhibition “Conexão Santa Cruz”, held during FASP 2018, which were analyzed at their representational and symbolic levels. Through the interpretation of the images, it was possible to construct a reading on the artist’s perspective regarding her cultural conditions within the social order in which she is located.Keywords: Visual Arts. Folk Art. Regional Art. Latin America.Representación y simbolismo: artes visuales en la frontera de Brasil/BoliviaResumenTeniendo como campo de investigación el Festival de Sudamérica Pantanal (FASP) en 2018, en la ciudad de Corumbá (Brasil), el objetivo es identificar la materialidad y conceptos que permean las manifestaciones artísticas bidimensionales en esta región fronteriza, desde la percepción de artistas de Bolivia. La investigación se organizó en dos fases: en la primera, se realizó una encuesta en plataformas online de producciones académicas en artes visuales, con foco en el trabajo bidimensional, utilizando los descriptores “arte popular” y “estética latina” dando como resultado tres artículos. En la segunda fase, dos obras fueron seleccionadas por un artista de Bolivia, participante de la exposición “Conexão Santa Cruz”, realizada durante FASP 2018, que fueron analizadas en sus niveles representativos y simbólicos. A través de la interpretación de las imágenes, fue posible construir una lectura sobre la perspectiva de la artista sobre sus condiciones culturales dentro del orden social en el que se ubica.Palabras clave: Artes Visuales. Arte Popular. Arte Regional. América Latina.

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