SMS Spam Filtering Using Machine Learning Techniques: A Survey
Hedieh Sajedi,
Golazin Zarghami Parast,
Fatemeh Akbari
Issue:
Volume 1, Issue 1, December 2016
Pages:
1-14
Received:
28 September 2016
Accepted:
5 November 2016
Published:
5 December 2016
DOI:
10.11648/j.mlr.20160101.11
Downloads:
Views:
Abstract: Objective: To report a review of various machine learning and hybrid algorithms for detecting SMS spam messages and comparing them according to accuracy criterion. Data sources: Original articles written in English found in Sciencedirect.com, Google-scholar.com, Search.com, IEEE explorer, and the ACM library. Study selection: Those articles dealing with machine learning and hybrid approaches for SMS spam filtering. Data extraction: Many articles extracted by searching a predefined string and the outcome was reviewed by one author and checked by the second. The primary paper was reviewed and edited by the third author. Results: A total of 44 articles were selected which were concerned machine learning and hybrid methods for detecting SMS spam messages. 28 methods and algorithms were extracted from these papers and studied and finally 15 algorithms among them have been compared in one table according to their accuracy, strengths, and weaknesses in detecting spam messages of the Tiago dataset of spam message. Actually, among the proposed methods DCA algorithm, the large cellular network method and graph-based KNN are three most accurate in filtering SMS spams of Tiago data set. Moreover, Hybrid methods are discussed in this paper.
Abstract: Objective: To report a review of various machine learning and hybrid algorithms for detecting SMS spam messages and comparing them according to accuracy criterion. Data sources: Original articles written in English found in Sciencedirect.com, Google-scholar.com, Search.com, IEEE explorer, and the ACM library. Study selection: Those articles dealing...
Show More
Non Linear Cellular Automata Enhanced with Active Learning for Pattern Classification in Highly Dense Images
P. Kiran Sree,
Sssn Usha Devi N.
Issue:
Volume 1, Issue 1, December 2016
Pages:
15-18
Received:
27 November 2016
Accepted:
17 December 2016
Published:
16 January 2017
DOI:
10.11648/j.mlr.20160101.12
Downloads:
Views:
Abstract: This paper introduces a new approach to classify several high density images based on the properties of Non Linear Cellular Automata. We use a state-transition which consists of a set of disjoint trees rooted at cyclic states of unit cycle length thus forming a natural classifier. The framework proposed is strengthened with genetic algorithm to find the desired local rule of the modeling as a global state function.
Abstract: This paper introduces a new approach to classify several high density images based on the properties of Non Linear Cellular Automata. We use a state-transition which consists of a set of disjoint trees rooted at cyclic states of unit cycle length thus forming a natural classifier. The framework proposed is strengthened with genetic algorithm to fin...
Show More
A Novel Approach to Detect Text in Various Dynamic-Colour Images
S. Kannadhasan,
R. Rajesh Baba
Issue:
Volume 1, Issue 1, December 2016
Pages:
19-32
Received:
24 November 2016
Accepted:
24 December 2016
Published:
19 January 2017
DOI:
10.11648/j.mlr.20160101.13
Downloads:
Views:
Abstract: Detecting text in multi-colour images is an important prerequisite. The RBG image is converted into YUV image, after that the multidimensional filter is used to reduce the noise in the YUV image. Canny edge detection is used to measure the continuity of the edges in the images. A efficient text detection is proposed using stroke width transformation method based on contours which can effectively remove the interference of non-stroke edges in complex background and the importance of recent feature (inter-frame feature), in the part of caption extraction(detection, localization). The horizontal and vertical histogram basis is used to calculate the luminance and chrominance which defines the background. Moreover the morphological operation which removes non text areas in the boundaries. Since some background pixels can also have the similar colour, some false stroke areas or character pixels are possible to appear in the output image, which will degrade the recognition rate of OCR (optical character recognition). It exploits the temporal homogeneity of colour of text pixels to filter out some background pixels with similar colour. Optical character recognition enables us to successfully extract the text from an image and convert it into an editable text document. Experimental results evaluated on the Neural network classifier which performance training and testing methods. Training dataset show that our accession yields higher precision and performance compared with forefront methods. The experimental results demonstrate the proposed method will provides efficient result than the existing technique.
Abstract: Detecting text in multi-colour images is an important prerequisite. The RBG image is converted into YUV image, after that the multidimensional filter is used to reduce the noise in the YUV image. Canny edge detection is used to measure the continuity of the edges in the images. A efficient text detection is proposed using stroke width transformatio...
Show More
Adaptive Fuzzy Sliding Modes Observer for Phenol Biodegradation
Marco Antonio Márquez-Vera,
Julo César Ramos-Fernández,
Blanca Diana Balderrama-Hernández
Issue:
Volume 1, Issue 1, December 2016
Pages:
33-41
Received:
6 December 2016
Accepted:
6 January 2017
Published:
31 January 2017
DOI:
10.11648/j.mlr.20160101.14
Downloads:
Views:
Abstract: There exist processes difficult to control as the chemical ones, in this work the bacterial grow rate in a biotechnological process is controlled, to make it, a fuzzy model was proposed, this control uses the clustering technique to improve the membership functions for the antecedents rules and least squares for the consequents; the control work in an acceptable manner, but in practice it is common to find that the actuators cannot respond to the signal control due saturation or its frequency response; so, a predictive control was used to anticipate the control signal. A comparative Table shows the comparison between different control horizons. Finally the use of a model reference can reduce the control signal amplitude and reduce some criterion errors.
Abstract: There exist processes difficult to control as the chemical ones, in this work the bacterial grow rate in a biotechnological process is controlled, to make it, a fuzzy model was proposed, this control uses the clustering technique to improve the membership functions for the antecedents rules and least squares for the consequents; the control work in...
Show More
