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A hybrid of XGBoost and aspect-based review mining with attention neural network for user preference prediction Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-03-05 Chin-Hui Lai; Duen-Ren Liu; Kun-Sin Lien
With the rapid development of the internet, users tend to refer to the rating scores or review opinions on social platforms. Most recommendation systems use collaborative filtering (CF) methods to recommend items based on users’ ratings. The rating-based CF methods do not consider users’ review opinions on different aspects of items. The accuracy of the rating predictions can be effectively improved
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Remaining useful life prediction of integrated modular avionics using ensemble enhanced online sequential parallel extreme learning machine Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-02-27 Gao Zehai, Ma Cunbao, Zhang Jianfeng, Xu Weijun
Integrated modular avionics is the core system of modern aircraft, which hosts almost all kinds of electrical functions. The performance of integrated modular avionics has an immediate influence on flight mission. Remaining useful life prediction is an effective manner to guarantee the safety and reliability of airplane. To satisfy the real-time requirement of integrated modular avionics, the prediction
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Three-level and three-way uncertainty measurements for interval-valued decision systems Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-02-15 Shengjun Liao, Xianyong Zhang, Zhiwen Mo
Uncertainty measurements underlie the system interaction and data learning. Their relevant studies are extensive for the single-valued decision systems, but become relatively less for the interval-valued decision systems. Thus, three-level and three-way uncertainty measurements of the interval-valued decision systems are proposed, mainly by systematically constructing vertical-horizontal weighted entropies
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Reduced PAPR Model Predictive Control based FBMC/OQAM signal for NB-IoT paradigm Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-02-13 Pavika Sharma, Achyut Shankar, Xiaochun Cheng
The competent class of fifth-generation mobile network used in NB-IoT demands for an extended and efficient massive device to a device communication system that exhibits narrow band with a focus on maximum spectrum resource usage, time and frequency synchronization and minimum out of band leakage. Filter bank multi-carrier with offset quadrature amplitude modulation (FBMC/OQAM) based systems act as
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A study on the uncertainty of convolutional layers in deep neural networks Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-02-13 Haojing Shen, Sihong Chen, Ran Wang
This paper shows a Min–Max property existing in the connection weights of the convolutional layers in a neural network structure, i.e., the LeNet. Specifically, the Min–Max property means that, during the back propagation-based training for LeNet, the weights of the convolutional layers will become far away from their centers of intervals, i.e., decreasing to their minimum or increasing to their maximum
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Fault detection of railway freight cars mechanical components based on multi-feature fusion convolutional neural network Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-02-08 Tao Ye, Zhihao Zhang, Xi Zhang, Yongran Chen, Fuqiang Zhou
The fault detection of the mechanical components in railway freight cars is important to the safety of railway transportation. Owing to the small size of the mechanical components, a manual detection method has a low detection efficiency. In addition, traditional computer vision technology has difficulty detecting multiple categories of objects simultaneously. Inspired by the use of one-stage deep-learning-based
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Uncertainty measurement for a fuzzy set-valued information system Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-02-06 Zhaowen Li, Zhihong Wang, Qingguo Li, Pei Wang, Ching-Feng Wen
Uncertainty measurement (UM) can offer new visual angle for data analysis. A fuzzy set-valued information system (FSVIS) which means an information system (IS) where its information values are fuzzy sets. This article investigates UM for a FSVIS. First, a FSVIS is introduced. Then, the distance between two information values of each attribute in a FSVIS is founded. After that, the tolerance relation
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Vortex search optimization algorithm for training of feed-forward neural network Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-02-04 Tahir Sağ, Zainab Abdullah Jalil Jalil
Training of feed-forward neural-networks (FNN) is a challenging nonlinear task in supervised learning systems. Further, derivative learning-based methods are frequently inadequate for the training phase and cause a high computational complexity due to the numerous weight values that need to be tuned. In this study, training of neural-networks is considered as an optimization process and the best values
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Localizing pedestrians in indoor environments using magnetic field data with term frequency paradigm and deep neural networks Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-02-04 Imran Ashraf, Yousaf Bin Zikria, Soojung Hur, Ali Kashif Bashir, Thamer Alhussain, Yongwan Park
Indoor environments are challenging for global navigation satellite systems and cripple its performance. Magnetic field data-based positioning and localization has emerged as a potential solution for ubiquitous indoor positioning and localization. The availability of embedded magnetic sensors in the smartphone simplifies the positioning without the additional cost of infrastructure. However, the data
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Adaptive robust local online density estimation for streaming data Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-02-03 Zhong Chen, Zhide Fang, Victor Sheng, Jiabin Zhao, Wei Fan, Andrea Edwards, Kun Zhang
Accurate online density estimation is crucial to numerous applications that are prevalent with streaming data. Existing online approaches for density estimation somewhat lack prompt adaptability and robustness when facing concept-drifting and noisy streaming data, resulting in delayed or even deteriorated approximations. To alleviate this issue, in this work, we first propose an adaptive local online
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Novel fusion strategies for continuous interval-valued q -rung orthopair fuzzy information: a case study in quality assessment of SmartWatch appearance design Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-02-01 Yi Yang, Zhen-Song Chen, Rosa M. Rodríguez, Witold Pedrycz, Kwai-Sang Chin
The notion of Yager’s q-rung orthopair fuzzy set (QROFS) have gained considerable and continuously increasing attention as a useful tool for imprecision and uncertainty representation due to its capability to discard the constraints on the membership and nonmembership functions as generally required by its intuitionistic fuzzy counterpart. Among the generalizations and variants established in the past
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Numerical solution for high-dimensional partial differential equations based on deep learning with residual learning and data-driven learning Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-30 Zheng Wang, Futian Weng, Jialin Liu, Kai Cao, Muzhou Hou, Juan Wang
Solving high-dimensional partial differential equations (PDEs) is a long-term computational challenge due to the fundamental obstacle known as the curse of dimensionality. This paper develops a novel method (DL4HPDE) based on residual neural network learning with data-driven learning elliptic PDEs on a box-shaped domain. However, to combine a strong mechanism with a weak mechanism, we reconstruct a
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Improving crowd labeling using Stackelberg models Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-26 Wenjun Yang, Chaoqun Li
Crowdsourcing systems provide an easy means of acquiring labeled training data for supervised learning. However, the labels provided by non-expert crowd workers (labelers) often appear low quality. In order to solve this problem, in practice each sample always obtains a multiple noisy label set from multiple different labelers, then ground truth inference algorithms are employed to obtain integrated
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Deep reinforcement learning based home energy management system with devices operational dependencies Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-24 Caomingzhe Si, Yuechuan Tao, Jing Qiu, Shuying Lai, Junhua Zhao
Advanced metering infrastructure and bilateral communication technologies facilitate the development of the home energy management system in the smart home. In this paper, we propose an energy management strategy for controllable loads based on reinforcement learning (RL). First, based on the mathematical model, the Markov decision process of different types of home energy resources (HERs) is formulated
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A scalable network intrusion detection system towards detecting, discovering, and learning unknown attacks Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-24 Zhao Zhang, Yong Zhang, Da Guo, Mei Song
Network intrusion detection systems (IDSs) based on deep learning have reached fairly accurate attack detection rates. But these deep learning approaches usually have been performed in a closed-set protocol that only known classes appear in training are considered during classification, the existing IDSs will fail to detect the unknown attacks and misclassify them as the training known classes, hence
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q -ROF-SIR methods and their applications to multiple attribute decision making Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-24 Hua Zhu, Jianbin Zhao, Hua Li
q-rung orthopair fuzzy set (q-ROFS) is a useful tool to express uncertain information. With the parameter q increasing, q-ROFSs have broader space for describing uncertain information than intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy sets (PFSs). This paper extends the superiority and inferiority ranking (SIR) methods to solve multiple attribute decision making (MADM) problems within the
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Sample-based online learning for bi-regular hinge loss Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-24 Wei Xue, Ping Zhong, Wensheng Zhang, Gaohang Yu, Yebin Chen
Support vector machine (SVM), a state-of-the-art classifier for supervised classification task, is famous for its strong generalization guarantees derived from the max-margin property. In this paper, we focus on the maximum margin classification problem cast by SVM and study the bi-regular hinge loss model, which not only performs feature selection but tends to select highly correlated features together
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Feature distribution-based label correlation in multi-label classification Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-24 Xiaoya Che, Degang Chen, Jusheng Mi
In multi-label classification, multiple label variables in output space are equally important and can be predicted according to a common set of input variables. To improve the accuracy and efficiency of multi-label learner, measuring and utilizing label correlation is the core breakthrough. Extensive research on label correlation focuses on the co-occurrence or mutual exclusion frequency of label values
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Problems selection under dynamic selection of the best base classifier in one versus one: PSEUDOVO Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-24 Izaro Goienetxea, Iñigo Mendialdua, Igor Rodríguez, Basilio Sierra
Class binarization techniques are used to decompose multi-class problems into several easier-to-solve binary sub-problems. One of the most popular binarization techniques is One versus One (OVO), which creates a sub-problem for each pair of classes of the original problem. Different versions of OVO have been developed to try to solve some of its problems, such as DYNOVO, which dynamically tries to
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L -fuzzifying approximation operators derived from general L -fuzzifying neighborhood systems Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-16 Lingqiang Li, Bingxue Yao, Jianming Zhan, Qiu Jin
For a completely distributive De Morgan algebra L, we develop a general framework of L-fuzzy rough sets. Said precisely, we introduce a pair of L-fuzzy approximation operators, called upper and lower L-fuzzifying approximation operators derived from general L-fuzzifying neighborhood systems. It is shown that the proposed approximation operators are a common extension of the L-fuzzifying approximation
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Routing protocol for low power and lossy network–load balancing time-based Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-14 Muneer Bani Yassien, Shadi A. Aljawarneh, Mohammad Eyadat, Eman Eaydat
Abstract Recently 6G/IoT emerged the latest technology of traditional wireless sensor network devices for 6G/IoT-oriented infrastructure. The construction of 6G/IoT utilizes the routing protocol for low power and lossy networks (RPL) protocol in the network layer. RPL is a proactive routing protocol with an IPV6 distance vector. The enormous number of connected smart devices and a huge amount of common
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Knowledge granularity reduction for decision tables Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-14 Guilong Liu, Yanbin Feng
Attribute reduction is a difficult topic in rough set theory and knowledge granularity reduction is one of the important types of reduction. However, up to now, its reduction algorithm based on a discernibility matrix has not been given. In this paper, we show that knowledge granularity reduction is equivalent to both positive region reduction and X-absolute reduction, and derive its corresponding
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$$L_{p}$$ L p -norm probabilistic K-means clustering via nonlinear programming Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-14 Bowen Liu, Yujian Li, Ting Zhang, Zhaoying Liu
Generalized fuzzy c-means (GFCM) is an extension of fuzzy c-means using \(L_{p}\)-norm distances. However, existing methods cannot solve GFCM with m = 1. To solve this problem, we define a new kind of clustering models, called \(L_{p}\)-norm probabilistic K-means (\(L_{p}\)-PKM). Theoretically, \(L_{p}\)-PKM is equivalent to GFCM at m = 1, and can have nonlinear programming solutions based on an efficient
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An automatic three-way clustering method based on sample similarity Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-14 Xiuyi Jia, Ya Rao, Weiwei Li, Sichun Yang, Hong Yu
The three-way clustering is an extension of traditional clustering by adding the concept of fringe region, which can effectively solve the problem of inaccurate decision-making caused by inaccurate information or insufficient data in traditional two-way clustering methods. The existing three-way clustering works often select the appropriate number of clusters and the thresholds for three-way partition
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Group decision making for internet public opinion emergency based upon linguistic intuitionistic fuzzy information Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-14 Yi Liu, Guiwu Wei, Haobin Liu, Lei Xu
With the wide use of network, the outbreak of network public opinion emergencies has changed from single to multiple. The goal of the current study is to construct the emergency group decision-making (EGDM) model for multiple network public opinion emergencies under the linguistic intuitionistic environment. First of all, we introduce a new version of Copula and Co-copula named extended Copula (EAC)
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A multiple-kernel clustering based intrusion detection scheme for 5G and IoT networks Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-14 Ning Hu, Zhihong Tian, Hui Lu, Xiaojiang Du, Mohsen Guizani
The 5G network provides higher bandwidth and lower latency for edge IoT devices to access the core business network. But at the same time, it also expands the attack surface of the core network, which makes the enterprise network face greater security threats. To protect the security of core business, the network infrastructure must be able to recognize not only the known abnormal traffic, but also
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Consensus model based on probability K-means clustering algorithm for large scale group decision making Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-14 Qian Liu, Hangyao Wu, Zeshui Xu
Nowadays, the increasing complexity of the social environment brings much difficulty in group decision making. The more uncertainty exists in a decision-making problem, the more collective wisdom is needed. Therefore, large scale group decision making has attracted a lot of researchers to investigate. Since the probabilistic linguistic terms have impressive performance in expressing DMs’ opinions,
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Hierarchical multi-attention networks for document classification Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-14 Yingren Huang, Jiaojiao Chen, Shaomin Zheng, Yun Xue, Xiaohui Hu
Research of document classification is ongoing to employ the attention based-deep learning algorithms and achieves impressive results. Owing to the complexity of the document, classical models, as well as single attention mechanism, fail to meet the demand of high-accuracy classification. This paper proposes a method that classifies the document via the hierarchical multi-attention networks, which
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Joint learning of author and citation contexts for computing drift in scholarly documents Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-14 J. Vijayarani, T. V. Geetha
Scholarly documents are sources of information on research topics written by academic experts. Topic drift in such scholarly documents is usually linked with the contextual variation in the title or abstract or entire document over time. However, topic distribution over words in different components of the document is non-uniform due to the varying impact of authors and citations, and their contribution
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Generating transferable adversarial examples based on perceptually-aligned perturbation Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-12 Hongqiao Chen, Keda Lu, Xianmin Wang, Jin Li
Neural networks (NNs) are known to be susceptible to adversarial examples (AEs), which are intentionally designed to deceive a target classifier by adding small perturbations to the inputs. And interestingly, AEs crafted for one NN can mislead another model. Such a property is referred to as transferability, which is often leveraged to perform attacks in black-box settings. To mitigate the transferability
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Clone detection in 5G-enabled social IoT system using graph semantics and deep learning model Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-10 Farhan Ullah, Muhammad Rashid Naeem, Leonardo Mostarda, Syed Aziz Shah
The protection and privacy of the 5G-IoT framework is a major challenge due to the vast number of mobile devices. Specialized applications running these 5G-IoT systems may be vulnerable to clone attacks. Cloning applications can be achieved by stealing or distributing commercial Android apps to harm the advanced services of the 5G-IoT framework. Meanwhile, most Android app stores run and manage Android
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Dynamic neural orthogonal mapping for fault detection Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-05 Zhengwei Hu, Jingchao Peng, Haitao Zhao
Dynamic principal component analysis (DPCA) and its nonlinear extension, dynamic kernel principal component analysis (DKPCA), are widely used in the monitoring of dynamic multivariate processes. In traditional DPCA and DKPCA, extended vectors through concatenating current process data point and a certain number of previous process data points are utilized for feature extraction. The dynamic relations
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Caps-OWKG: a capsule network model for open-world knowledge graph Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-04 Yuhan Wang, Weidong Xiao, Zhen Tan, Xiang Zhao
Knowledge graphs are typical multi-relational structures, which is consisted of many entities and relations. Nonetheless, existing knowledge graphs are still sparse and far from being complete. To refine the knowledge graphs, representation learning is utilized to embed entities and relations into low-dimensional spaces. Many existing knowledge graphs embedding models focus on learning latent features
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Adversarial examples: attacks and defenses in the physical world Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-04 Huali Ren, Teng Huang, Hongyang Yan
Deep learning technology has become an important branch of artificial intelligence. However, researchers found that deep neural networks, as the core algorithm of deep learning technology, are vulnerable to adversarial examples. The adversarial examples are some special input examples which were added small magnitude and carefully crafted perturbations to yield erroneous results with extremely confidence
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Regularized based implicit Lagrangian twin extreme learning machine in primal for pattern classification Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-04 Umesh Gupta, Deepak Gupta
In this paper, we suggest a novel approach termed as regularized based implicit Lagrangian twin extreme learning machine in primal as a pair of unconstrained convex minimization problem (RILTELM) where regularization term is added to follow the structural risk minimization principle. Here, we consider 2-norm of the slack vector of variables to make the problem strongly convex which results in a unique
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Ensemble machine learning approach for classification of IoT devices in smart home Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-03 Ivan Cvitić, Dragan Peraković, Marko Periša, Brij Gupta
The emergence of the Internet of Things (IoT) concept as a new direction of technological development raises new problems such as valid and timely identification of such devices, security vulnerabilities that can be exploited for malicious activities, and management of such devices. The communication of IoT devices generates traffic that has specific features and differences with respect to conventional
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A credibility-based fuzzy programming model for the hierarchical multimodal hub location problem with time uncertainty in cargo delivery systems Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-03 Xiaoting Shang, Bin Jia, Kai Yang, Yaping Yuan, Hao Ji
This paper studies the fuzzy hierarchical multimodal hub location problem for cargo delivery systems. It differs from traditional hub location problem in two ways. First, this paper constructs a hierarchical multimodal hub-and-spoke distribution network for the cargo delivery systems, which involves two transportation modes (road and air), two types of hubs (ground and airport) and three corresponding
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Classifying imbalanced data using SMOTE based class-specific kernelized ELM Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-03 Bhagat Singh Raghuwanshi, Sanyam Shukla
In machine learning, a problem is imbalanced when the class distributions are highly skewed. Imbalanced classification problems occur usually in many application domains and pose a hindrance to the conventional learning algorithms. Several approaches have been proposed to handle the imbalanced learning. For example, Weighted kernel-based SMOTE (WKSMOTE) and SMOTE based class-specific extreme learning
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Attribution reduction based on sequential three-way search of granularity Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-03 Xun Wang, Pingxin Wang, Xibei Yang, Yiyu Yao
Most existing results about attribute reduction are reported by considering one and only one granularity, especially for the strategies of searching reducts. Nevertheless, how to derive reduct from multi-granularity has rarely been taken into account. One of the most important advantages of multi-granularity based attribute reduction is that it is useful in investigating the variation of the performances
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Attention-based context aggregation network for monocular depth estimation Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-03 Yuru Chen, Haitao Zhao, Zhengwei Hu, Jingchao Peng
Depth estimation is a traditional computer vision task, which plays a crucial role in understanding 3D scene geometry. Recently, algorithms that combine the multi-scale features extracted by the dilated convolution based block (atrous spatial pyramid pooling, ASPP) have gained significant improvements in depth estimation. However, the discretized and predefined dilation kernels cannot capture the continuous
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Algorithms of matrix recovery based on truncated Schatten p -norm Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-03 Chenglin Wen, Wenchao Qian, Qinghua Zhang, Feilong Cao
In recent years, algorithms to recovery low-rank matrix have become one of the research hotspots, and more corresponding optimization models with nuclear norm have also been proposed. However, nuclear norm is not a good approximation to the rank function. This paper proposes a matrix completion model and a low-rank sparse decomposition model based on truncated Schatten p-norm, respectively, which combine
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Class-weighted neural network for monotonic imbalanced classification Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-03 Hong Zhu, Han Liu, Aimin Fu
In real life scenarios, classification problems with the characters of monotonicity constraints and imbalanced class distribution widely exist. However, at present, the research on this kind of problem is still rare. Traditional algorithms designed only for monotonic classification and imbalanced classification are not available for monotonic imbalanced classification. So far, there is only one approach
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Entropy based optimal scale combination selection for generalized multi-scale information tables Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-02 Han Bao, Wei-Zhi Wu, Jia-Wen Zheng, Tong-Jun Li
In many real-life applications, data are often hierarchically structured at different levels of granulations. A multi-scale information table is a special hierarchical data set in which each object can take on as many values as there are scales under the same attribute. An important issue in such a data set is to select optimal scale combination in order to keep certain condition for final decision
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IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-02 Dac-Nhuong Le, Velmurugan Subbiah Parvathy, Deepak Gupta, Ashish Khanna, Joel J. P. C. Rodrigues, K. Shankar
At present times, the drastic advancements in the 5G cellular and internet of things (IoT) technologies find useful in different applications of the healthcare sector. At the same time, COVID-19 is commonly spread from animals to persons, but today it is transmitting among persons by adapting the structure. It is a severe virus and inappropriately resulted in a global pandemic. Radiologists utilize
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A social immunity based approach to suppress rumors in online social networks Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-02 Santhoshkumar Srinivasan, Dhinesh Babu L D
Online social networks (OSNs) connect people around the globe under one virtual society. It helps people gather, communate and share their common interests. But many times, OSNs are also exploited and eventually become a major platform for rumor or false information propagation. Controlling such rumors in OSNs has been the most challenging research interest in recent days. Since OSNs are a platform
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A reinforcement learning optimization for future smart cities using software defined networking Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-02 Kulandaivel Rajkumar, Manikandan Ramachandran, Fadi Al-Turjman, Rizwan Patan
Nowadays smart cities towards software defined network (SDN) approach will become better flexibility and manageability. A stronger, more dynamic network is an SDN network, which is precisely what a smart city network must be if it wants to be viable on a real-world scale. SDN architecture is developed to implement a learning framework for network optimization. The proposed method is called mixed-integer
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Towards wide-scale continuous gesture recognition model for in-depth and grayscale input videos Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-02 Rihem Mahmoud, Selma Belgacem, Mohamed Nazih Omri
In recent years, gesture recognition in video sequences has aroused growing interest in the fields of computer vision and behavioral understanding, for example in the control of robots and video games, in the field of video surveillance, automatic video indexing or content-based video retrieval. Processing large-scale continuous gesture data with in-depth, grayscale input videos remains a primary challenge
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Ensemble adaptive convolutional neural networks with parameter transfer for rotating machinery fault diagnosis Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2021-01-02 Ke Zhao, Hongkai Jiang, Xingqiu Li, Ruixin Wang
There exist many rotating machinery parts, and many types of failure modes, including single failure modes and compound failure modes. This brings high requirements on the performance and generalization ability of fault diagnosis methods. Compared with single fixed model, ensemble model can gather the strengths of others to achieve more accurate identification performance and stronger generalization
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SemiDroid: a behavioral malware detector based on unsupervised machine learning techniques using feature selection approaches Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2020-11-24 Arvind Mahindru, A. L. Sangal
With the exponential growth in Android apps, Android based devices are becoming victims of target attackers in the “silent battle” of cybernetics. To protect Android based devices from malware has become more complex and crucial for academicians and researchers. The main vulnerability lies in the underlying permission model of Android apps. Android apps demand permission or permission sets at the time
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Hierarchical extreme learning machine with L21-norm loss and regularization Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2020-11-23 Rui Li, Xiaodan Wang, Yafei Song, Lei Lei
Recently, multilayer extreme learning machine (ELM) algorithms have been extensively studied for hierarchical abstract representation learning in the ELM community. In this paper, we investigate the specific combination of \(L_{21}\)-norm based loss function and regularization to improve the robustness and the sparsity of multilayer ELM. As we all known, the mean square error (MSE) cost function (or
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An improved density-based adaptive p -spectral clustering algorithm Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2020-11-21 Yanru Wang, Shifei Ding, Lijuan Wang, Ling Ding
As a generalization algorithm of spectral clustering, p-spectral clustering has gradually attracted extensive attention of researchers. Gaussian kernel function is generally used in traditional p-spectral clustering to construct the similarity matrix of data. However, the Gaussian kernel function based on Euclidean distance is not effective when the data-set is complex with multiple density peaks or
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Knowledge-driven graph similarity for text classification Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2020-11-19 Niloofer Shanavas, Hui Wang, Zhiwei Lin, Glenn Hawe
Automatic text classification using machine learning is significantly affected by the text representation model. The structural information in text is necessary for natural language understanding, which is usually ignored in vector-based representations. In this paper, we present a graph kernel-based text classification framework which utilises the structural information in text effectively through
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A robust spectral clustering algorithm based on grid-partition and decision-graph Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2020-11-19 Lijuan Wang, Shifei Ding, Yanru Wang, Ling Ding
Spectral clustering (SC) transforms the dataset into a graph structure, and then finds the optimal subgraph by the way of graph-partition to complete the clustering. However, SC algorithm constructs the similarity matrix and feature decomposition for overall datasets, which needs high consumption. Secondly, k-means is taken at the clustering stage and it selects the initial cluster centers randomly
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MAGDM-oriented dual hesitant fuzzy multigranulation probabilistic models based on MULTIMOORA Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2020-11-19 Chao Zhang, Deyu Li, Jiye Liang, Baoli Wang
In real world, multi-attribute group decision making (MAGDM) is a complicated cognitive process that involves expression, fusion and analysis of multi-source uncertain information. Among diverse soft computing tools for addressing MAGDM, the ones from granular computing (GrC) frameworks perform excellently via efficient strategies for multi-source uncertain information. However, they usually lack convincing
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Dynamic fusion for ensemble of deep Q-network Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2020-11-16 Patrick P. K. Chan, Meng Xiao, Xinran Qin, Natasha Kees
Ensemble reinforcement learning, which combines the decisions of a set of base agents, is proposed to enhance the decision making process and speed up training time. Many studies indicate that an ensemble model may achieve better results than a single agent because of the complement of base agents, in which the error of an agent may be corrected by others. However, the fusion method is a fundamental
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Automated worker skill evaluation for improving productivity based on labeled LDA Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2020-11-11 Kentaro Mori, Hiroshi Nakajima, Yutaka Hata
This paper proposed automated systems for analyzing elemental processes and for evaluating work skills. The systems use labeled latent Dirichlet allocation (L-LDA) to classify worker motions obtained from sensors into four elemental processes. L-LDA automatically learns characteristic motions, so there is no need to define and identify motion features. The proposed system predicts elemental processes
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Recurrence quantification analysis of EEG signals for tactile roughness discrimination Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2020-11-05 Golnaz Baghdadi, Mahmood Amiri, Egidio Falotico, Cecilia Laschi
Roughness recognition is an important function in the nervous system that facilitates our interactions with the environment. Previous studies have focused on the neuro-cognitive aspects and frequency-based changes in response to the roughness stimuli. In this study, we investigate the effect of different roughness levels on the nonlinear characteristics of EEG signals. Nine healthy subjects participated
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usfAD : a robust anomaly detector based on unsupervised stochastic forest Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2020-11-02 Sunil Aryal, K.C. Santosh, Richard Dazeley
In real-world applications, data can be represented using different units/scales. For example, weight in kilograms or pounds and fuel-efficiency in km/l or l/100 km. One unit can be a linear or non-linear scaling of another. The variation in metrics due to the non-linear scaling makes Anomaly Detection (AD) challenging. Most existing AD algorithms rely on distance- or density-based functions, which
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A novel feature learning framework for high-dimensional data classification Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2020-11-02 Yanxia Li, Yi Chai, Hongpeng Yin, Bo Chen
Feature extraction is an essential component in many classification tasks. Popular feature extraction approaches especially deep learning-based methods, need large training samples to achieve satisfactory performance. Although dictionary learning-based methods are successfully used for feature extraction on both small and large datasets, however, when dealing with high-dimensional datasets, a large
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Dynamic clustering collaborative filtering recommendation algorithm based on double-layer network Int. J. Mach. Learn. & Cyber. (IF 3.753) Pub Date : 2020-11-02 Jianrui Chen, Bo Wang, Zhiping Ouyang, Zhihui Wang
With the rapid development of internet economy, personal recommender system plays an increasingly important role in e-commerce. In order to improve the quality of recommendation, a variety of scholars and engineers devoted themselves in developing the recommendation algorithms. Traditional collaborative filtering algorithms are only dependent on rating information or attribute information. Most of