A new anomalous text detection approach using unsupervised methods

Amouee, Elham; Zanjireh, Morteza Mohammadi; Bahaghighat, Mahdi; Ghorbani, Mohsen

doi:10.2298/fuee2004631a

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…In the current study, the performance of our proposed algorithm was evaluated on static data as well. In future works, we study the performance and effectiveness of the proposed method on dynamic data found on more complex outlier removal algorithm found on the machine learning and deep learning algorithms [36][37][38][39][40][41][42][43].…”

Section: Discussionmentioning

confidence: 99%

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

Khorasani¹,

Zanjireh²,

Bahaghighat³

et al. 2022

Computer Systems Science and Engineering

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With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster's inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.

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Section: Discussionmentioning

confidence: 99%

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

Khorasani¹,

Zanjireh²,

Bahaghighat³

et al. 2022

Computer Systems Science and Engineering

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“…Consequently, it is possible that the presented algorithm cannot find some global extrema. Therefore, we can use this algorithm along with GA or unsupervised learning models [36] to solve it for future studies. In the future, we first consider a solution to the problem with the Best-Fit algorithm presented in this study.…”

Section: The Time Complexitymentioning

confidence: 99%

A Time-Efficient and Exploratory Algorithm for the Rectangle Packing Problem

Bozorgi¹,

Zanjireh²,

Bahaghighat³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Today, resource waste is considered as one of the most important challenges in different industries. In this regard, the Rectangle Packing Problem (RPP) can affect noticeably both time and design issues in businesses. In this study, the main objective is to create a set of non-overlapping rectangles so that they have specific dimensions within a rectangular plate with a specified width and an unlimited height. The ensued challenge is an NP-complete problem. NP-complete problem, any of a class of computational problems that still there are no efficient solution for them. Most substantial computer-science problems such as the traveling salesman problem, satisfiability problems (sometimes called propositional satisfiability problem and abbreviated SAT or B-SAT), and graph-covering problems are belong to this class. Essentially, it is complicated to spot the best arrangement with the highest rate of resource utilization by emphasizing the linear computation time. This study introduces a time-efficient and exploratory algorithm for the RPP, including the lowest front-line strategy and a Best-Fit algorithm. The obtained results confirmed that the proposed algorithm can lead to a good performance with simplicity and time efficiency. Our evaluation shows that the proposed model with utilization rate about 94.37% outperforms others with 87.75%, 50.54%, and 87.17% utilization rate, respectively. Consequently, the proposed method is capable to of achieving much better utilization rate in comparison with other mentioned algorithms in just 0.023 s running-time, which is much faster than others.

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“…On the other hand, there are some approaches to predict BTC price based on Natural Language Processing (NLP) and sentiment analysis. Today, NLP as an AI (artificial intelligence) technology and Deep learning [9,2] are used together in advanced text mining/analytic tools [23,4,26,8,7]. These approaches get social media text data from Twitter, Facebook, and etc., as the input and try to draw a link between the content of daily messages and the BTC price.…”

Section: Introductionmentioning

confidence: 99%

Bitcoin daily close price prediction using optimized grid search method

Rostami¹,

Bahaghighat

Zanjireh

2021

Acta Universitatis Sapientiae, Informatica

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Cryptocurrencies are digital assets that can be stored and transferred electronically. Bitcoin (BTC) is one of the most popular cryptocurrencies that has attracted many attentions. The BTC price is considered as a high volatility time series with non-stationary and non-linear behavior. Therefore, the BTC price forecasting is a new, challenging, and open problem. In this research, we aim the predicting price using machine learning and statistical techniques. We deploy several robust approaches such as the Box-Jenkins, Autoregression (AR), Moving Average (MA), ARIMA, Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), and Grid Search algorithms to predict BTC price. To evaluate the performance of the proposed model, Forecast Error (FE), Mean Forecast Error (MFE), Mean Absolute Error (MAE), Mean Squared Error (MSE), as well as Root Mean Squared Error (RMSE), are considered in our study.

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A new anomalous text detection approach using unsupervised methods

Cited by 4 publications

References 33 publications

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

A Time-Efficient and Exploratory Algorithm for the Rectangle Packing Problem

Bitcoin daily close price prediction using optimized grid search method

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