Galimkair Mutanov, Vladislav Karyukin^*, Zhanl Mamykova

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 913-930, 2021, DOI:10.32604/cmc.2021.017827 - 04 June 2021

Abstract The volume of social media data on the Internet is constantly growing. This has created a substantial research field for data analysts. The diversity of articles, posts, and comments on news websites and social networks astonishes imagination. Nevertheless, most researchers focus on posts on Twitter that have a specific format and length restriction. The majority of them are written in the English language. As relatively few works have paid attention to sentiment analysis in the Russian and Kazakh languages, this article thoroughly analyzes news posts in the Kazakhstan media space. The amassed datasets include texts… More >

Zhichao Hu¹, Xiangzhan Yu^1,*, Jiantao Shi¹, Lin Ye^1,2

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 695-711, 2021, DOI:10.32604/cmc.2021.017574 - 04 June 2021

Abstract With the continuous development of network technology, various large-scale cyber-attacks continue to emerge. These attacks pose a severe threat to the security of systems, networks, and data. Therefore, how to mine attack patterns from massive data and detect attacks are urgent problems. In this paper, an approach for attack mining and detection is proposed that performs tasks of alarm correlation, false-positive elimination, attack mining, and attack prediction. Based on the idea of CluStream, the proposed approach implements a flow clustering method and a two-step algorithm that guarantees efficient streaming and clustering. The context of an… More >

J. Hemalatha¹, S. Geetha², R. Geetha³, C. Balasubramanian⁴, Daniela Elena Popescu⁵, D. Jude Hemanth^6,*

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 283-300, 2021, DOI:10.32604/cmc.2021.017203 - 04 June 2021

Abstract A novel image reversible data-hiding scheme based on primitive and varying radix numerical model is presented in this article. Using varying radix, variable sum of data may be embedded in various pixels of images. This scheme is made adaptive using the correlation of the neighboring pixels. Messages are embedded as blocks of non-uniform length in the high-frequency regions of the rhombus mean interpolated image. A higher amount of data is embedded in the high-frequency regions and lesser data in the low-frequency regions of the image. The size of the embedded data depends on the statistics… More >

Muhammad Tayyab^1,*, Mohsen Marjani¹, N. Z. Jhanjhi¹, Ibrahim Abaker Targio Hashim², Abdulwahab Ali Almazroi³, Abdulaleem Ali Almazroi⁴

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1183-1200, 2021, DOI:10.32604/cmc.2021.017199 - 04 June 2021

Abstract Deep learning (DL) algorithms have been widely used in various security applications to enhance the performances of decision-based models. Malicious data added by an attacker can cause several security and privacy problems in the operation of DL models. The two most common active attacks are poisoning and evasion attacks, which can cause various problems, including wrong prediction and misclassification of decision-based models. Therefore, to design an efficient DL model, it is crucial to mitigate these attacks. In this regard, this study proposes a secure neural network (NN) model that provides data security during model training… More >

Haseeb Ahmad¹, Shahbaz Ahmad¹, Muhammad Asif¹, Mobashar Rehman^2,*, Abdullah Alharbi³, Zahid Ullah⁴

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1215-1232, 2021, DOI:10.32604/cmc.2021.016659 - 04 June 2021

Abstract Cricket databases contain rich and useful information to examine and forecasting patterns and trends. This paper predicts Star Cricketers (SCs) from batting and bowling domains by employing supervised machine learning models. With this aim, each player’s performance evolution is retrieved by using effective features that incorporate the standard performance measures of each player and their peers. Prediction is performed by applying Bayesian-rule, function and decision-tree-based models. Experimental evaluations are performed to validate the applicability of the proposed approach. In particular, the impact of the individual features on the prediction of SCs are analyzed. Moreover, the More >

Nasir Mahmood¹, Yaser Hafeez¹, Khalid Iqbal², Shariq Hussain³, Muhammad Aqib¹, Muhammad Jamal⁴, Oh-Young Song^5,*

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 873-893, 2021, DOI:10.32604/cmc.2021.016614 - 04 June 2021

Abstract Despite advances in technological complexity and efforts, software repository maintenance requires reusing the data to reduce the effort and complexity. However, increasing ambiguity, irrelevance, and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories. Thus, there is a need for a repository mining technique for relevant and bug-free data prediction. This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software. To predict errors in mining data, the Apriori algorithm was used to discover association rules… More >

Tina Babu¹, Deepa Gupta¹, Tripty Singh^1,*, Shahin Hameed², Mohammed Zakariah³, Yousef Ajami Alotaibi⁴

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 99-128, 2021, DOI:10.32604/cmc.2021.016341 - 04 June 2021

Abstract Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each magnification. This work presents a novel approach of robust magnification-independent colon cancer grading framework to distinguish colon biopsy images into four classes: normal, well, moderate, and poor. The contribution of this research is to develop a magnification invariant hybrid feature set comprising cartoon feature, Gabor wavelet, wavelet moments, HSV histogram, color auto-correlogram, color moments, and morphological features that can be used to characterize different grades. Besides, the classifier is modeled as a multiclass structure with… More >

K. Raju^*, M. Chinnadurai

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1057-1072, 2021, DOI:10.32604/cmc.2021.016068 - 04 June 2021

Abstract Security is a critical issue in cloud computing (CC) because attackers can fabricate data by creating, copying, or deleting data with no user authorization. Most of the existing techniques make use of password-based authentication for encrypting data. Password-based schemes suffer from several issues and can be easily compromised. This paper presents a new concept of hybrid metaheuristic optimization as an identity-based secure and optimal authentication (HMO-ISOA) scheme for CC environments. The HMO-ISOA technique makes use of iris and fingerprint biometrics. Initially, the HMO-ISOA technique involves a directional local ternary quantized extrema pattern–based feature extraction process… More >

Ishak Hashim¹, Nur Nabilah Che Draman², Samsul Ariffin Abdul Karim^3,*, Wee Ping Yeo⁴, Dumitru Baleanu^5,6,7

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 221-236, 2021, DOI:10.32604/cmc.2021.016006 - 04 June 2021

Abstract This paper discusses scattered data interpolation using cubic trigonometric Bézier triangular patches with continuity everywhere. We derive the condition on each adjacent triangle. On each triangular patch, we employ convex combination method between three local schemes. The final interpolant with the rational corrected scheme is suitable for regular and irregular scattered data sets. We tested the proposed scheme with 36,65, and 100 data points for some well-known test functions. The scheme is also applied to interpolate the data for the electric potential. We compared the performance between our proposed method and existing scattered data interpolation More >

Abdelaziz Alsubie¹, Mostafa Abdelhamid², Abdul Hadi N. Ahmed², Mohammed Alqawba³, Ahmed Z. Afify^4,*

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 213-232, 2021, DOI:10.32604/iasc.2021.018111 - 12 May 2021

Abstract In this paper, the estimation of the parameters of extended Marshall-Olkin inverse-Pareto (EMOIP) distribution is studied under complete and censored samples. Five classical methods of estimation are adopted to estimate the parameters of the EMOIP distribution from complete samples. These classical estimators include the percentiles estimators, maximum likelihood estimators, least squares estimators, maximum product spacing estimators, and weighted least-squares estimators. The likelihood estimators of the parameters under type-I and type-II censoring schemes are discussed. Simulation results were conducted, for various parameter combinations and different sample sizes, to compare the performance of the EMOIP estimation methods… More >