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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Rahman, K. | Khan, H. | Abdullah, S.
Article Type: Research Article
Abstract: The new emerged infectious disease that is known the coronavirus disease (COVID-19), which is a high contagious viral infection that started in December 2019 in China city Wuhan and spread very fast to the rest of the world. This infection caused millions of infected cases globally and still poses an alarming situation for human lives. Pakistan in Asian countries is considered the third country with higher number of cases of coronavirus with more than 649824. Recently, some mathematical models have been constructed for better understanding the coronavirus infection. Mostly, these models are based on classical integer-order derivative using real numbers …which cannot capture the fading memory. So at the current position it is a challenge for the world to understand and control the spreading of COVID-19. Therefore, the aim of our paper is to develop some novel techniques, namely complex Pythagorean fuzzy weighted averaging (abbreviated as CPFWA) operator, complex Pythagorean fuzzy ordered weighted averaging (abbreviated as CPFOWA) operator, complex Pythagorean fuzzy hybrid averaging (abbreviated as CPFHA) operator, induced complex Pythagorean fuzzy ordered weighted averaging (abbreviated as I-CPFOWA) operator and induced complex Pythagorean fuzzy hybrid averaging (abbreviated as I-CPFHA) operator to analysis the spreading of COVID-19. At the end of the paper, an illustrative the emergency situation of COVID-19 is given for demonstrating the effectiveness of the suggested approach along with a sensitivity analysis, showing the feasibility and reliability of its results. Show more
Keywords: Complex Pythagorean set, CPFWA operator, CPFOWA operator, CPFHA operator, I-CPFOWA operator, I-CPFHA operator
DOI: 10.3233/JIFS-212160
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3411-3427, 2022
Authors: Liu, Xiaole | Huang, Cheng | Fang, Yong
Article Type: Research Article
Abstract: A drive-by download is a method of hackers planting the Web Trojan, which exploits browser vulnerabilities to execute malicious software. Because people usually access web pages with various browsers daily, drive-by downloads have become one of the most common threats in recent years. Most previous studies utilize the abstract syntax tree(AST) with deep learning methods to detect such attacks, which achieved high accuracy but are time-consuming and challenging to explain. Also, some methods use dynamic analysis, which needs a specific environment and is time-consuming with the complex operation. In order to solve these problems, the paper proposes DDIML , an …explainable machine learning model based on novel features with static analysis. These features are extracted from five aspects: code obfuscation, URL redirection, special behaviors, encoding characters, and CSS attributes. The most popular machine learning algorithm, Random forest, is applied for building the classifier detection model. In addition, we use both local and global explanations to improve the model and prove that the proposed model could be trusted. The Experimental results show that our proposed model can efficiently detect drive-by downloads with a detection precision of 0.983 and a recall of 0.980. The average detection time for each sample is only 16.07ms in total. Show more
Keywords: static analysis, drive-by downloads, features, random forest, explanation
DOI: 10.3233/JIFS-212496
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3429-3442, 2022
Authors: Shahzad, Muhammad | Tahir, M. Atif | Khan, M. Atta | Jiang, Richard | Malick, Rauf Ahmed Shams
Article Type: Research Article
Abstract: Drug sensitivity prediction to a panel of cancer cell lines using computational approaches has been a challenge for two decades. With the emergence of high-throughput screening technologies, thousands of compounds and cancer cell lines panels with drug sensitivity data are publicly available at various pharmacogenomics databases. Analyzing these data is crucial to improve cancer treatment and develop new anticancer drugs. In this work, we propose EBSRMF : Ensemble Based Similarity-Regularized Matrix Factorization, which is a bagging based framework to improve the drug sensitivity prediction on the Cancer Cell Line Encyclopedia (CCLE) data. Based on the fact that similar drugs and …cell lines exhibit similar drug response, we have investigated cell line and drug similarity matrices based on gene expression profiles and chemical structure respectively. The drug sensitivity value is used as outcome values which are the half maximal inhibitory concentrations (IC50). In order to improve the generalization ability of the proposed model, a homogeneous ensemble based bagging learning approach is also investigated where multiple SRMF models are used to train N subsets of the input data. The outcome of each training algorithm is aggregated using the averaging method to predict the outcome. Experiments are conducted on two benchmark datasets: CCLE and GDSC. The proposed model is compared with state-of-the-art models using multiple evaluation metrics including Root Means Square Error (RMSE) and Pearson Correlation Coefficient (PCC). The proposed model is quite promising and achieves better performance on CCLE dataset when compared with the existing approaches. Show more
Keywords: Drug sensitivity, matrix factorization, cancer, ensemble learning, keyword five
DOI: 10.3233/JIFS-212867
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3443-3452, 2022
Authors: Wei, Mingrun | Wang, Hongjuan | Cheng, Ru | Yu, Yue | Wang, Lukun
Article Type: Research Article
Abstract: Single image rain removal remains a crucial and challenging low-level image processing task while significantly for outdoor-based high-level computer vision tasks. Recently, deep convolutional neural networks (CNNs) have become the mainstream structure of removing rain streaks and obtained remarkable performance. However, most of the existing CNN-based methods mainly pay attention to completely removing rain streaks while neglecting the restoration of details after deraining, which suffer from poor visual performance. In this paper, we propose a deep residual attention and encoder-decoder network to overcome the above shortcoming. Specifically, we develop an excellent basic block that contains dual parallel paths which are …called rain removal network and detail restore network, respectively, to perform entirely and in-depth mapping relationships from rain to no-rain. The upper rain removal network is composed of dilated convolution and channel attention. This combination can explore the correlation between features from the dimensions of spatial and channel. Meanwhile, for the lower detail restore network, we construct a simple yet effective symmetrical encoder-decoder structure to prevent the loss of global structures information and encourage the details back. Furthermore, our network is end-to-end trainable, easy to implement and without giant parameter quantity. Extensive experiments on synthetic and real-world datasets have shown that our DRAEN achieves better accuracy and visual improvements against recent state-of-the-art methods. Show more
Keywords: Single image deraining, encoder-decoder network, image processing, feature fusion
DOI: 10.3233/JIFS-213134
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3453-3467, 2022
Authors: Yue, Shufang | Li, Ying | Zhao, Jianli
Article Type: Research Article
Abstract: In this paper, we provide a systematic characterization of finite BZMVdM -algebra by using semi-tensor product of matrices. The abstract operation law about logic of the finite algebra is transformed into the simple operation of concrete logical matrices. In addition, we study some properties of BZMVdM -algebra, such as homomorphism, isomorphism, and the product of the BZMVdM -algebra. Through logical matrix operation, the direct verifiable conditions for detecting the above properties are given.
Keywords: BZMVdM-algebra, semi-tensor product of matrices, isomorphism
DOI: 10.3233/JIFS-213173
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3469-3478, 2022
Authors: Wang, Hongxia | Ma, Wubin | Wang, Zhiru | Lu, Chenyang
Article Type: Research Article
Abstract: The prediction of residential building electricity consumption can help provide an early warning regarding abnormal energy use and optimize energy supply. In this study, a multiscale convolutional recurrent neural network (MCRNN) is proposed to predict residential building electricity consumption. The MCRNN model uses multiscale convolutional units to collect different information on environmental factors, such as temperature, air pressure, light, and uses a bidirectional recurrent neural network (Bi-RNN) to extract the long-term dependence information of these factors. In addition, a recurrent convolutional connection is used to filter the most useful multiscale and long-term information in the MCRNN model. The accuracy of …MCRNN is evaluated through an experiment using real data. The results show that MCRNN performs better than the other models. For instance, compared with the support vector regression (SVR) and random forest (RF) models, the MCRNN model has a 47.83% and 38.72% lower root mean square error (RMSE), respectively. The MCRNN model also shows a 37.81% and 70.38% higher accuracy, respectively, compared to the SVR and RF models. Show more
Keywords: Electricity consumption prediction, residential building, multiscale convolutional network, recurrent neural network
DOI: 10.3233/JIFS-213176
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3479-3491, 2022
Authors: Li, Helong | Liu, Shuli | Wang, Weizhong
Article Type: Research Article
Abstract: The Fine-Kinney model is a quantitative and effective method to identify and evaluate potential risks. The Fine-Kinney method has been widely used in practice, while the traditional Fine-Kinney method is difficult to access risk parameters precisely in practice. Besides, the current Fine-Kinney method fails to take into account the fact that decision makers are interrelated in practice. Further, the detailed relationships among the potential hazards cannot be reflected in the conventional Fine-Kinney method during the risk priority process, especially in the case of uncertain information. To compensate these deficiencies, this paper proposes an extended Fine-Kinney framework by integrating ORESTE (organísation, …rangement et Syn-thèse de données relarionnelles) (in French), Choquet integral, and Probabilistic Linguistic Term Sets (PLTSs). Firstly, the PLTSs are utilized to express the decision makers’ complex risk preference information. Then, the Choquet integral is used to integrate risk evaluation information, which can simulate the potential interaction relationships among individual risk evaluation of decision-makers. Next, an extended ORESTE based on the PLTSs method is used to obtain the priorities of potential hazards, in which distance measure of PLTSs is applied to replace Besson’s ranks. Moreover, the PIR (preference, indifference, and incomparability) structure is constructed to describe the detailed relationships between potential hazards. Finally, an illustrative example is described to illustrate the proposed risk evaluation method. After that, the rationality and efficiency of the proposed method are tested through the comparison with other similar methods. Show more
Keywords: Probabilistic linguistic term sets, Choquet integral, ORESTE method, Fine-Kinney method
DOI: 10.3233/JIFS-213326
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3493-3512, 2022
Authors: Bharathi, V. | Sakthivel, K.
Article Type: Research Article
Abstract: This paper focuses on a wheeled mobile robot that utilizes the Adaptive Nonlinear Sliding mode control technique for trajectory tracking and obstacle avoidance control. A parallel wheeled differential drive Mobile robot’s trajectory tracking control problem is investigated. For diverse initial conditions, the robot must follow a given course to reach it’s destination. To monitor and identify the obstacles in the path, an obstacle micro-controller is fixed to decide quick crash avoidance and follow the obstacle limit at a predetermined distance. It depends on the robot’s vector connections. An Adaptive Nonlinear Sliding Mode (ANSM) control concept is used for continuous trajectory …tracking or object monitoring in the path to avoid it. A Lapunov function control gives stability to each controller. The proposed simulation results demonstrate that the mobile robot can be applied to guarantee its protected development in an obscure obstacle environment. In detail, the proposed control gives another, more straightforward methodology with application esteems for tracking critical thinking in an obscure obstacle environment. Finally, based on these above characteristics, the proposed control strategy’s efficiency, simplicity, and accuracy prove. The steady-state error and mean squared error of the proposed ANSM are 6% and 0.07db, respectively. Show more
Keywords: Adaptive nonlinear sliding, trajectory tracking, collision avoidance, Lyapunov function
DOI: 10.3233/JIFS-213588
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3513-3525, 2022
Authors: Xuan, Cho Do | Huong, D.T. | Nguyen, Toan
Article Type: Research Article
Abstract: Detecting and warning Advanced Persistent Threat (APT) malware in Endpoint is essential because the current trend of APT attacker groups is to find ways to spread malware to users and then escalate privileges in the system. In this study, to improve the ability to detect APT malware on Endpoint machines, we propose a novel intelligent cognitive calculation method based on a model combining graph embeddings and Attention using processes generated by executable files. The proposed intelligent cognitive computation method performs 3 main tasks: i) extracting behaviors of processes; ii) aggregating the malware behaviors based on the processes; iii) detecting APT …malware based on behavior analysis. To carry out the task (i), we propose to use several data mining techniques: extracting processes from Event IDs in the operating system kernel; extracting abnormal behaviors of processes. For task (ii), a graph embedding (GE) model based on the Graph Convolutional Networks (GCN) network is proposed to be used. For task (iii), based on the results of task (ii), the paper proposes to use a combination of the Convolutional Neural Network (CNN) network and Attention network (called CNN-Attention). The novelty and originality of this study is an intelligent cognitive computation method based on the use, combination, and synchronization of many different data mining techniques to compute, extract, and represent relationships and correlations among APT malware behaviors from processes. Based on this new intelligent cognitive computation method, many meaningful anomalous features and behaviors of APT malware have been synthesized and extracted. The proposals related to data mining methods to extract malware’s features and the list of malware’s behaviors provided in this paper are new information that has not been published in previous studies. In the experimental section, to demonstrate the effectiveness of the proposed method in detecting APT malware, the study has compared and evaluated it with other approaches. Experimental results in the paper have shown the outstanding efficiency of the proposed method when ensuring all metrics from 96.6% or more (that are 2% to 6% higher than other approaches). Experimental results in the paper have proven that our proposed method not only has scientifically significant but also has practical meaning because the method has helped to improve the efficiency of analyzing and detecting APT malware on Endpoint devices. In addition, this research result also has opened up a new approach for the task of detecting other anomalies on the Endpoint such as malware, unauthorized intrusion, insider, etc. Show more
Keywords: APT, APT malware detection on Endpoint, event ID, behavior profile, deep learning, process profile, graph analysis, selecting and exacting features, abnormal behavior
DOI: 10.3233/JIFS-220233
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3527-3547, 2022
Authors: Salih, Mahmood M. | Al-Qaysi, Z.T. | Shuwandy, Moceheb Lazam | Ahmed, M.A. | Hasan, Kahlan F. | Muhsen, Yousif Raad
Article Type: Research Article
Abstract: To date, for the purpose of solving the complex problems in the area of expert system, Multi criteria decision making is the best technique to offer the suitable solution. In the academic literature, the MCDM methods suffered from many challenges. The most important challenges are uncertainty and vagueness. One of the latest MCDM method, called the fuzzy decision by opinion score method (FDOSM). However, there are still some vagueness issues around these methods (mention some of them). According to the advantage of the Fermatean fuzzy set in solving these issues, in this research extends FDOSM into Fermatean-FDOSM so as to …effectively benchmark the real-life problem. In this study, we present our methodology in two phases. The first phase presents the mathematical model of Fermatean-FDOSM which is composed of three stages of FDOSM. The second phase applied the new extension to benchmark the COVID-19 machine learning methods. The finding of Fermatean-FDOSM after comparing the result with the basic FDSOM and TOPSIS, is more logical and undergoing a systematic ranking. In the validation process, objective validation is applied to validate the final result of Fermatean-FDOSM. The result of Fermatean-FDOSM is valid, and more logical and in line with decision makers’ opinions. Show more
Keywords: Fuzzy decision by opinion score method (FDOSM), machine learning, Fermatean fuzzy, COVID-19, multi-criteria decision-making
DOI: 10.3233/JIFS-220707
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3549-3559, 2022
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