Pattern Analysis and Intelligent Systems
Abdulbaghi Ghaderzadeh; sahar Hosseinpanahi; Sarkhel Taher kareem
Volume 7, Issue 2 , May 2021, , Pages 115-125
Abstract
Nowadays, spam is a major challenge regarding emails. Spam is a specific type of email that is sent to the network for malicious purposes. Spam plays an important role in stealing information and can include fake links to trick users. Machine learning and data mining techniques such as artificial neural ...
Read More
Nowadays, spam is a major challenge regarding emails. Spam is a specific type of email that is sent to the network for malicious purposes. Spam plays an important role in stealing information and can include fake links to trick users. Machine learning and data mining techniques such as artificial neural networks are the most applicable methods to detect spam. The multi-layer artificial neural network needs to select the most important features as inputs to reduce the output error for accurate spam detection. In the proposed method, a smart method based on swarm intelligence algorithms is used for feature selection. In this study, a binary version of Emperor Penguin Optimizer (EPO) is used to select more appropriate features. The proposed method uses the selected features for learning and classification in the spam detection process. Experiments in the MATLAB environment on the Spambase dataset show that with the increase in population the error in spam detection in Emails will decrease about 14.61% and with the increase in feature space, it will decrease about 43.85% in the best situation. Experiments show that the proposed method has less error in detecting spam compare to other methods, multilayer artificial neural network, recursive neural network, support vector machine, Bayesian network, and whale optimization algorithm. Experiments show that the error of spam detection in the proposed approach is about 23.57% less than the whale optimization algorithm. Empirical results, obtained through simulations on the Spambase dataset, show our approach outperforms the other existing methods on precision value.
Pattern Analysis and Intelligent Systems
Ali Hosseinalipour; Farhad Soleimanian Gharehchopogh; mohammad masdari; ALi Khademi
Volume 7, Issue 1 , February 2021, , Pages 81-92
Abstract
In recent years, social networks' growth has led to an increase in these networks' content. Therefore, text mining methods became important. As part of text mining, Sentiment analysis means finding the author's perspective on a particular topic. Social networks allow users to express their opinions and ...
Read More
In recent years, social networks' growth has led to an increase in these networks' content. Therefore, text mining methods became important. As part of text mining, Sentiment analysis means finding the author's perspective on a particular topic. Social networks allow users to express their opinions and use others' opinions in other people's opinions to make decisions. Since the comments are in the form of text and reading them is time-consuming. Therefore, it is essential to provide methods that can provide us with this knowledge usefully. Black Widow Optimization (BWO) is inspired by black widow spiders' unique mating behavior. This method involves an exclusive stage, namely, cannibalism. For this reason, at this stage, species with an inappropriate evaluation function are removed from the circle, thus leading to premature convergence. In this paper, we first introduced the BWO algorithm into a binary algorithm to solving discrete problems. Then, to reach the optimal answer quickly, we base its inputs on the opposition. Finally, to use the algorithm in the property selection problem, which is a multi-objective problem, we convert the algorithm into a multi-objective algorithm. The 23 well-known functions were evaluated to evaluate the performance of the proposed method, and good results were obtained. Also, in evaluating the practical example, the proposed method was applied to several emotion datasets, and the results indicate that the proposed method works very well in the psychology of texts.
Pattern Analysis and Intelligent Systems
Samira Amjad; Farhad Soleimanian Gharehchopogh
Volume 5, Issue 3 , August 2019, , Pages 181-194
Abstract
Because cyberspace and Internet predominate in the life of users, in addition to business opportunities and time reductions, threats like information theft, penetration into systems, etc. are included in the field of hardware and software. Security is the top priority to prevent a cyber-attack that users ...
Read More
Because cyberspace and Internet predominate in the life of users, in addition to business opportunities and time reductions, threats like information theft, penetration into systems, etc. are included in the field of hardware and software. Security is the top priority to prevent a cyber-attack that users should initially be detecting the type of attacks because virtual environments are not monitored. Today, email is the foundation of many internet attacks that have happened. The Hackers and penetrators are using email spam as a way to penetrate into computer systems junk. Email can contain viruses, malware, and malicious code. Therefore, the type of email should be detected by security tools and avoid opening suspicious emails. In this paper, a new model has been proposed based on the hybrid of Scatter Searching Algorithm (SSA) and K-Nearest Neighbors (KNN) to email spam detection. The Results of proposed model on Spambase dataset shows which our model has more accuracy with Feature Selection (FS) and in the best case, its percentage of accuracy is equal to 94.54% with 500 iterations and 57 features. Also, the comparison shows that the proposed model has better accuracy compared to the evolutionary algorithm (data mining and decision detection such as C4.5).
Pattern Analysis and Intelligent Systems
Saman Khalandi; Farhad Soleimanian Gharehchopogh
Volume 4, Issue 3 , August 2018, , Pages 167-184
Abstract
With the fast increase of the documents, using Text Document Classification (TDC) methods has become a crucial matter. This paper presented a hybrid model of Invasive Weed Optimization (IWO) and Naive Bayes (NB) classifier (IWO-NB) for Feature Selection (FS) in order to reduce the big size of features ...
Read More
With the fast increase of the documents, using Text Document Classification (TDC) methods has become a crucial matter. This paper presented a hybrid model of Invasive Weed Optimization (IWO) and Naive Bayes (NB) classifier (IWO-NB) for Feature Selection (FS) in order to reduce the big size of features space in TDC. TDC includes different actions such as text processing, feature extraction, forming feature vectors, and final classification. In the presented model, the authors formed a feature vector for each document by means of weighting features use for IWO. Then, documents are trained with NB classifier; then using the test, similar documents are classified together. FS do increase accuracy and decrease the calculation time. IWO-NB was performed on the datasets Reuters-21578, WebKb, and Cade 12. In order to demonstrate the superiority of the proposed model in the FS, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been used as comparison models. Results show that in FS the proposed model has a higher accuracy than NB and other models. In addition, comparing the proposed model with and without FS suggests that error rate has decreased.
Pattern Analysis and Intelligent Systems
Zahra Shahpar; Vahid Khatibi; Asma Tanavar; Rahil Sarikhani
Volume 2, Issue 4 , November 2016, , Pages 31-38
Abstract
In recent years, utilization of feature selection techniques has become an essential requirement for processing and model construction in different scientific areas. In the field of software project effort estimation, the need to apply dimensionality reduction and feature selection methods has become ...
Read More
In recent years, utilization of feature selection techniques has become an essential requirement for processing and model construction in different scientific areas. In the field of software project effort estimation, the need to apply dimensionality reduction and feature selection methods has become an inevitable demand. The high volumes of data, costs, and time necessary for gathering data , and also the complexity of the models used for effort estimation are all reasons to use the methods mentioned. Therefore, in this article, a genetic algorithm has been used for feature selection in the field of software project effort estimation. This technique has been tested on well-known data sets. Implementation results indicate that the resulting subset, compared to the original data set, has produced better outcomes in terms of effort estimation accuracy. This article showed that genetic algorithms are ideal methods for selecting a subset of features and improving effort estimation accuracy.
Pattern Analysis and Intelligent Systems
Mozhgan Rahimirad; Mohammad Mosleh; Amir Masoud Rahmani
Volume 1, Issue 2 , May 2015, , Pages 1-8
Abstract
With the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. One of the major problems in text classification relates to the high dimensional feature spaces. Therefore, the main goal of text classification is to ...
Read More
With the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. One of the major problems in text classification relates to the high dimensional feature spaces. Therefore, the main goal of text classification is to reduce the dimensionality of features space. There are many feature selection methods. However, only a few methods are utilized for huge text classification problems. In this paper, we propose a new wrapper method based on Particle Swarm Optimization (PSO) algorithm and Support Vector Machine (SVM). We combine it with Learning Automata in order to make it more efficient. This helps to select better features using the reward and penalty system of automata. To evaluate the efficiency of the proposed method, we compare it with a method which selects features based on Genetic Algorithm over the Reuters-21578 dataset. The simulation results show that our proposed algorithm works more efficiently.