Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Zhang, Guanhong | Brown, Peter | Li, Guobin

Article Type: Research Article

Abstract: With the maturity of virtualization technology, cloud computing has brought us new computing and service models. Adopting a resource pool solution based on cloud computing architecture, the virtualized resources can be uniformly managed and deployed to achieve the purpose of automated and intelligent management of information systems. However, because cloud servers have some features that are different from ordinary hosts, existing intrusion detection technologies cannot be directly applied to cloud computing. This paper focuses on the real-time dispatching of people. The main innovation of this paper is to apply BP neural network technology to the real-time dispatching of intelligent people, …establish the time prediction model of intelligent dispatching of people based on BP neural network, and design the intelligent dispatching algorithm of people based on BP neural network, and use examples to analyze the opposition to verify the feasibility of the algorithm. The results show that the algorithm improves the classification accuracy of neural networks and shortens the training time of samples. It improves the efficiency of intelligent dispatch detection, the accuracy of results and the efficiency of explicit algorithm. Show more

Keywords: Intelligent Scheduling, BP neural network, Cloud Computing, Real-time

DOI: 10.3233/JIFS-179158

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2019

Authors: Zhao, Zhongying | Zhou, Hui | Zhang, Bijun | Ji, Fujiao | Li, Chao

Article Type: Research Article

Abstract: High influential users are playing an important role in promoting information propagation in social media. Thus, it has been a very interesting problem to identify influential users in social media, and attracted numerous researchers. A great deal of research work has been devoted to solving this problem. However, the existing methods mainly focus on the network topology, ignoring users’ behaviors. In this paper, we propose an High Influential Users Detection (HIUD) algorithm by analyzing users’ behaviors. To evaluate the performance of our algorithm, we carry out extensive experiments on Sina and Tencent microblogging data sets, and compare it with other …methods. The experimental results have shown that the HIUD achieves the best performance. Furthermore, we also make a spatial analysis on those high influential users with thermodynamic map. Show more

Keywords: Influential user detection, user behavior analysis, clustering, social media data mining

DOI: 10.3233/JIFS-182512

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2019

Authors: Mao, Hua | Zheng, Zhen

Article Type: Research Article

Abstract: In order to find a more efficient method for constructing a fuzzy concept lattice, This paper applies graph theory to a fuzzy formal context. A fuzzy formal context is represented by a weighted graph. A method for generating all crisp-fuzzy concepts by a weighted graph is proposed and the corresponding algorithm–Algorithm 1 is given in a fuzzy formal context. Through algorithm analysis, it is illustrated that Algorithm 1 is better than some of existed algorithms to obtain crisp-fuzzy concepts in a fuzzy formal context.

Keywords: Fuzzy formal concept, crisp-fuzzy concept, graph theory

DOI: 10.3233/JIFS-181642

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2019

Authors: Liu, Zimei | Xie, Yi | Zhang, Hao

Article Type: Research Article

Abstract:  Stampede accidents with serious injuries occur from time to time on escalators. Filed observation was conducted on four typical passenger behaviors during taking escalators, namely, walking behavior, subgroup behavior, overtaking behavior and waiting behavior. The effect of behavior characteristics on passenger safety was analyzed according to the observation data. Several scenarios were simulated to quantitatively study the impact of passenger behaviors on crowd stampede risk under different situations. The results show that: (1) the presence of subgroup behavior and overtaking behavior increases the crowd stampede risk by increasing the crowd density and the degree of congestion on the connection plane; …(2) the walking behavior reduces the crowd density; (3) the “walk left, stand right” rule decreases the evacuation efficiency; (4) waiting behavior of passengers on the connection plane significantly increases the crowd stampede risk. Management measures were proposed to promote the passenger safety and reduce the stampede injury on escalators. Show more

Keywords: Passenger safety, crowd stampede risk, passenger behavior, escalator, simulation

DOI: 10.3233/JIFS-179156

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2019

Authors: Heda, Zhang

Article Type: Research Article

Abstract: Machinery and equipment are widely used in modern large-scale production, while industrial large-scale production and the progress of science and technology make machinery and equipment more complex and large-scale. Traditional mechanical diagnosis technology cannot meet the actual diagnosis requirements. The residual life of the whole equipment is predicted. This is of great significance for improving the efficiency of equipment, enhancing reliability, reducing maintenance costs and prolonging service life. The advantages of artificial intelligence in solving the problems of remote control, fault diagnosis and non-linearity point out the direction of the development of mechanical fault diagnosis technology. The research shows that …the fault prediction and maintenance process based on the operating state of the device is summarized into three steps: data acquisition, data processing and equipment remaining life prediction. The comprehensive detection algorithm is used for diagnosis, and the diagnosis method is comprehensively analyzed. The research shows that after optimizing the network parameters through human intelligence, the network convergence speed is obviously accelerated, which can be used as a performance-based pattern recognition system for fault diagnosis of mechanical equipment. Show more

Keywords: Artificial intelligence, machinery and equipment, fault diagnosis

DOI: 10.3233/JIFS-179157

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2019

Authors: Libo, Zhou | Tian, Huang | Chunyun, Guan | Elhoseny, Mohamed

Article Type: Research Article

Abstract: A fundamental problem facing deep neural networks is that they require a large amount of data to keep the system efficient in complex applications. Promising results of this problem are made possible by using techniques such as data enhancement or transfer learning in large data sets. However, when the application provides limited or unbalanced data, the problem persists. In addition, the number of false positives generated by deep model training has a significant negative impact on system performance. This study aims to solve the problem of false positives and class imbalances by implementing an improved filter library framework for Cole …pest identification. The system consists of three main units: First, the primary diagnostic unit (boundary box generator) generates a bounding box containing the location of the infected area and class. Then, the promising box belonging to each category is used as an input to the secondary diagnostic unit (CNN filter bank) for verification. In the second unit, the misclassified samples are filtered by training for each category of independent CNN classifiers. The result of the CNN filter bank is to determine if a target belongs to the category because it is detected (true) or no (false), otherwise. Finally, an integrated unit combines the information of the autonomous unit and the secondary unit in the future while maintaining a true positive sample and eliminating false positives of misclassification in the first unit. By this implementation, the recognition rate of this method is about 96%, which is 13% higher than our previous work in the complex task of Cole disease and pest identification. In addition, our system is able to handle false positives generated by bounding box generators and class imbalances that occur on data sets with limited data. Show more

Keywords: Plant diseases, detection, deep neural networks, filter banks, false positives

DOI: 10.3233/JIFS-179155

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2019

Authors: Nannan, Zhang | Chao, Luo

Article Type: Research Article

Abstract:  This paper proposes a fuzzy cognitive map scheme for real-time online data prediction. The fuzzy cognitive maps (FCMs) are constructed on the basis of abstracting a numerical time series into a limited number of nodes (concepts), and used as a modeling tool for predicting time series. By representing time series in terms of information granules constructed in the space of amplitude and change of amplitude of the time series, a fuzzy cognitive map is dynamically constructed by using the set of information granule, where the particle swarm optimization (PSO) is utilized to study the parameters. In order to find better …weights in the global search process, each parameter of the particle swarm algorithm (PSO) is not set to a fixed value but adaptively changes. In this paper, a dynamic fuzzy C-means clustering algorithm is used to online adjust the cluster center and weight according to the impact of the incoming data at the current moment such that the model can capture real-time changes in the data information. The proposed approach is illustrated in detail by a series of experiments using a collection of publicly available data. Show more

Keywords: Fuzzy cognitive maps, information granules, particle swarm optimization (PSO), dynamic fuzzy C-means

DOI: 10.3233/JIFS-181064

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2019

Authors: Farouk, Ahmed | Zhen, Dou

Article Type: Editorial

Keywords: Big data analysis, scientific computing, data-driven service management, data processing for intelligent systems

DOI: 10.3233/JIFS-179109

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-5, 2019

Authors: Barzegar, Behnam | Motameni, Homayun | Movaghar, Ali

Article Type: Research Article

Abstract:  Energy consumption and performance metrics have become critical issues for scheduling parallel task-based applications in high-performance computing systems such as cloud datacenters. The duplication and clustering strategy, as well as Dynamic Voltage Frequency Scaling (DVFS) technique, have separately been concentrated on reducing energy consumption and optimizing performance parameters such as throughput and makespan. In this paper, a dual-phase algorithm called EATSDCD which is an energy efficient time aware has been proposed. The algorithm uses the combination of duplication and clustering strategies to schedule the precedence-constrained task graph on datacenter processors through DVFS. The first phase focuses on a smart combination …of duplication and clustering strategy to reduce makespan and energy consumed by processors in an effort to execute Directed Acyclic Graph (DAG) while satisfying the throughput constraint. The main idea behind EATSDCD intended to minimize energy consumption in the second phase. After determining the critical path and specifying a set of dependent tasks in non-critical paths, the slack time for each task in non-critical paths was distributed among all dependent tasks in that path. Then, the frequency of DVFS-enabled processors is scaled down to execute non-critical tasks as well as idle and communication phases, without extending the execution time of tasks. Finally, a testbed is developed and different parameters are tested on the randomly generated DAG to evaluate and illustrate the effectiveness of EATSDCD. It was also compared against duplication and clustering-based algorithms and DVFS-based algorithms. In terms of energy consumption and makespan, the results show that our proposed algorithm can save up to 8.3% and 20% energy compared against Power Aware List-based Scheduling (PALS) and Power Aware Task Clustering (PATC) algorithms, respectively. Furthermore, there is 16% improvement over Parallel Pipeline Latency Optimization (PaPilo) algorithm with Encur  = 1.2Enmin  (G). In comparison with Reliability Aware Scheduling with Duplication (RASD) algorithm, the execution time has been reduced in heterogeneous environments. Show more

Keywords: Green computing, cloud data centers, dynamic voltage and frequency scaling (DVFS), task duplication, energy consumption, slack time, throughput

DOI: 10.3233/JIFS-171927

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2019

Authors: Wang, Haofang | Yun, Ruan | Zhao, Ranhang | Qi, Zhen

Article Type: Research Article

Abstract: Flash flood is one of the most significant natural disasters in China, particularly in mountainous area, causing heavy economic damage and casualties of life. For numerous small hilly basins that need flash flood prevention and control with limited funds, it is necessary to give a priority order or to determine which basin needs to be harnessed firstly. Flash flood risk assessment is critical to an efficient flash flood management. Among many flash flood risk evaluation methods in literatures, variable fuzzy method (VFM) was chosen in this paper. To verify the results of VFM, fuzzy clustering analysis (FCA) is also used. …First, taking Licheng county with 119 small basins in China as an example, 9 indexes were identified among index system, based on disaster-breeding environment (or underlying surface conditions) of small basin in hilly region. Risk levels are divided into three grading levels such as high, medium and low. Second, VFM was introduced, and the flash flood risk grade eigenvalue (H) of each small basin was calculated. The results show that no small basin belongs to high risk level, 14 basins belong to low risk level, and the remaining 105 small basins belong to medium risk level. Third, FCA was used to verify the result of VFM. The results of two methods show that they are nearly in consistence. This paper shows that VFM is feasible for flash flood risk evaluation. Finally, the priorities for flash flood mitigation of 119 small watersheds in Licheng county are mapped out, which will provide effective help for flood disaster mitigation of small basin. Show more

Keywords: Variable fuzzy method, fuzzy clustering analysis, flash flood risk, disaster-breeding environment, small basin

DOI: 10.3233/JIFS-171089

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2019