Journal of Intelligent & Fuzzy Systems - Volume 40, issue 3

Authors: Sun, Rui | Han, Meng | Zhang, Chunyan | Shen, Mingyao | Du, Shiyu

Article Type: Research Article

Abstract: High utility itemset mining (HUIM) with negative utility is an emerging data mining task. However, the setting of the minimum utility threshold is always a challenge when mining high utility itemsets (HUIs) with negative items. Although the top-k HUIM method is very common, this method can only mine itemsets with positive items, and the problem of missing itemsets occurs when mining itemsets with negative items. To solve this problem, we first propose an effective algorithm called THN (Top-k High Utility Itemset Mining with Negative Utility). It proposes a strategy for automatically increasing the minimum utility threshold. In order to solve …the problem of multiple scans of the database, it uses transaction merging and dataset projection technology. It uses a redefined sub-tree utility value and a redefined local utility value to prune the search space. Experimental results on real datasets show that THN is efficient in terms of runtime and memory usage, and has excellent scalability. Moreover, experiments show that THN performs particularly well on dense datasets. Show more

Keywords: Utility mining, high utility itemsets mining, top-k high utility itemsets, negative utility

DOI: 10.3233/JIFS-201357

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5637-5652, 2021

Authors: Sun, Xiaofei | Li, Jianming | Ma, Jialiang | Xu, Huiqing | Chen, Bin | Zhang, Yuefei | Feng, Tao

Article Type: Research Article

Abstract: Chromosome visualization has been used in human chromosome analysis and is a crucial step in clinical diagnosis and drug development. An important step in chromosome visualization is the extraction of chromosomes from chromosome images obtained by light microscopy. Chromosomes often overlap in a complex and variable manner, resulting in significant challenges in chromosome segmentation. The process of chromosome visualization requires manual intervention and is tedious. A method based on a neural network is proposed for the automatic segmentation of overlapping chromosome images to speed up the workflow of visualizing chromosomes. Three improved dilated convolutions are used in the chromosome image …segmentation models based on U-Net. The proposed models successfully segment overlapping chromosomes in two publicly available overlapping chromosome data sets. Our models have better performance than existing overlapping chromosome segmentation methods based on U-Net. In summary, it is demonstrated that the improved dilated convolutions can be used for the automatic segmentation of overlapping chromosome images. The proposed improved dilated convolutions have a stable performance improvement, can be easily extended to the segmentation of multiple overlapping chromosomes, and are suitable as general neural network operations to replace standard convolutions in any network. Show more

Keywords: Overlapping chromosomes, image segmentation, improved dilated convolution, artificial intelligence, light microscopy

DOI: 10.3233/JIFS-201466

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5653-5668, 2021

Authors: Jingni, Guo | Junxiang, Xu | Zhenggang, He

Article Type: Research Article

Abstract: The construction of the Sichuan-Tibet railway is encountered with some problems such as complicated geological conditions, bad climate, active plate movement, and sensitive ecological environment. Therefore, scientific and reasonable site selection is an essential guarantee for the smooth construction of the Sichuan-Tibet railway. Through constructing weighted scoring function and intuitionistic fuzzy similarity model and researching the dynamic intuitionistic fuzzy multi-attribute decision-making method considering time factor, the location decision of client-supplied goods and materials support center for Sichuan-Tibet railway construction can be complete, and the research theories and methods of location problem worldwide can be analyzed. Given the route direction and …engineering construction of the Ya’an-Linzhi section of the Sichuan-Tibet railway, this paper aims to set up seven client-supplied goods and materials support centers as alternative site selection schemes, which integrates six factors (transportation, geological conditions, climate environment, site selection characteristics, engineering construction, and communication conditions) and constructs 12 index systems for client-supplied goods and materials support center location selection. Combining with the index system, the intuitionistic fuzzy decision-making matrices for four periods are established. Besides, using a dynamic intuitionistic multi-attribute decision-making method, the weighted results of similarity decision-making matrices are compared, and the location schemes of client-supplied goods and materials support centers are sequenced. The results demonstrate that Linzhi is the best site selection scheme for the construction client-supplied goods and materials support center of Ya’an to Linzhi section of the Sichuan-Tibet Railway, providing reference significance for supporting the construction of the Sichuan-Tibet Railway Project. Show more

Keywords: Sichuan-tibet railway, client-supplied goods and materials, location decision, dynamic multiple attribute decision, intuitionistic fuzzy set, similarity degree

DOI: 10.3233/JIFS-201572

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5669-5679, 2021

Authors: Imran, | Ahmad, Shabir | Kim, Do Hyeun

Article Type: Research Article

Abstract: Mountains are attraction spots for tourists, and tourism contributes to the country’s gross domestic product. Mountains have many benefits such as biodiversity, tourism, and the supplication of food, to name a few. However, there are challenges to protect mountain lives from hazards such as fire caused by tourist activities in mountains. The in-time fire detection and notification to the authorities have always been the central point in literature studies, and different studies have been carried out to optimize the notification time. In this paper, we model the fire detection and notification as a real-time internet of things application and uses …task orchestration and task scheduling mechanism to provide scalability along with optimal latency. The proposed fire detection and prediction mechanism detect mountain fire at the earliest stage and provide predictive analysis to prevent damage to mountain life and tourists. The architecture is based on microservice-based IoT task orchestration mechanism and device virtualization, which is not only lightweight but also handles a single problem in parallel chunks, thus optimizes the latency. The in-time information about the fire is used for predictive analysis and notified to safety authorities which helps them to make a more informed decisions to minimize the damage caused by mountain fire. The performance of the proposed mechanism is evaluated in terms of different measures such as RMSE, MAPE, MSE, and MAPE. The proposed work approaches the fire detection and notification as a collection of tasks, and thus those tasks are selected for deployment which are guaranteed to be executed and have minimum latency. This idea of pre-planing the latency and task execution is the first attempt to the best of the authors’ knowledge. Show more

Keywords: Internet of things, fire safety, fire detection, fire notification, predictive analysis, microservices, fire tracking, virtual objects

DOI: 10.3233/JIFS-201614

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5681-5696, 2021

Authors: Ding, Xiong | Lu, Yan

Article Type: Research Article

Abstract: In order to solve some optimization problems with many local optimal solutions, a microbial dynamic optimization (MDO) algorithm is proposed by the kinetic theory of hybrid food chain microorganism cultivation with time delay. In this algorithm, it is assumed that multiple microbial populations are cultivated in a culture system. The growth of microbial populations is not only affected by the flow of culture fluid injected into the culture system, the concentration of nutrients and harmful substances, but also by the interaction between the populations. The influence of culture medium which is injected regularly will suddenly increase the concentration of nutrients …and toxic substances, it will suddenly increase the impact on the population. These characteristics are used to construct absorption operators, grabbing operators, hybrid operators, and toxin operators; the global optimal solution of the optimization problem can be quickly solved by these operators and the population growth changes. The simulation experiment results show that the MDO algorithm has certain advantages for solving optimization problems with higher dimensions. Show more

Keywords: Swarm intelligence optimization algorithm, microbial culture kinetics, microbial population, microbial dynamics optimization (MDO)

DOI: 10.3233/JIFS-201828

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5697-5713, 2021

Authors: Sun, Hongchang | wang, Yadong | Niu, Lanqiang | Zhou, Fengyu | Li, Heng

Article Type: Research Article

Abstract: Building energy consumption (BEC) prediction is very important for energy management and conservation. This paper presents a short-term energy consumption prediction method that integrates the Fuzzy Rough Set (FRS) theory and the Long Short-Term Memory (LSTM) model, and is thus named FRS-LSTM. This method can find the most directly related factors from the complex and diverse factors influencing the energy consumption, which improves the prediction accuracy and efficiency. First, the FRS is used to reduce the redundancy of the input features by the attribute reduction of the factors affecting the energy consumption forecasting, and solves the data loss problem caused …by the data discretization of a classical rough set. Then, the final attribute set after reduction is taken as the input of the LSTM networks to obtain the final prediction results. To validate the effectiveness of the proposed model, this study used the actual data of a public building to predict the building’s energy consumption, and compared the proposed model with the LSTM, Levenberg-Marquardt Back Propagation (LM-BP), and Support Vector Regression (SVR) models. The experimental results reveal that the presented FRS-LSTM model achieves higher prediction accuracy compared with other comparative models. Show more

Keywords: Short-term energy consumption prediction, fuzzy rough set, long short-term memory, QuickReduct, public buildings

DOI: 10.3233/JIFS-201857

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5715-5729, 2021

Authors: Tong, Mingyu | Duan, Huiming | Luo, Xilin

Article Type: Research Article

Abstract: In view of the uncertainties in short-time traffic flows and the multimode correlation of traffic flow data, a grey prediction model for short-time traffic flows based on tensor decomposition is proposed. First, traffic flow data are expressed as tensors based on the multimode characteristics of traffic flow data, and the principle of the tensor decomposition algorithm is introduced. Second, the Verhulst model is a classic grey prediction model that can effectively predict saturated S-type data, but traffic flow data do not have saturated S-type data. Therefore, the tensor decomposition algorithm is applied to the Verhulst model, and then, the Verhulst …model of the tensor decomposition algorithm is established. Finally, the new model is applied to short-term traffic flow prediction, and an instance analysis shows that the model can deeply excavate the multimode correlation of traffic flow data. At the same time, the effect of the new model is superior to five other grey prediction models. The predicted results can provide intelligent transportation system planning, control and optimization with reliable real-time dynamic information in a timely manner. Show more

Keywords: Intelligent transportation, short-term traffic flow forecasting, grey model, tensor decomposition

DOI: 10.3233/JIFS-201873

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5731-5741, 2021

Authors: Guo, Yiming | Zhang, Hui | Xia, Zhijie | Dong, Chang | Zhang, Zhisheng | Zhou, Yifan | Sun, Han

Article Type: Research Article

Abstract: The rolling bearing is the crucial component in the rotating machinery. The degradation process monitoring and remaining useful life prediction of the bearing are necessary for the condition-based maintenance. The commonly used deep learning methods use the raw or processed time domain data as the input. However, the feature extracted by these approaches is insufficient and incomprehensive. To tackle this problem, this paper proposed an improved Deep Convolution Neural Network with the dual-channel input from the time and frequency domain in parallel. The proposed methodology consists of two stages: the incipient failure identification and the degradation process fitting. To verify …the effectiveness of the method, the IEEE PHM 2012 dataset is adopted to compare the proposed method and other commonly used approaches. The results show that the improved Deep Convolution Neural Network can effectively describe the degradation process for the rolling bearing. Show more

Keywords: Rolling bearing, Deep Convolution Neural Network, remaining useful life prediction, dual-channel input

DOI: 10.3233/JIFS-201965

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5743-5751, 2021

Authors: Kreinovich, Vladik

Article Type: Book Review

DOI: 10.3233/JIFS-189730

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5753-5755, 2021

Authors: Kreinovich, Vladik

Article Type: Book Review

DOI: 10.3233/JIFS-189731

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5757-5758, 2021