Journal of Intelligent & Fuzzy Systems - Volume 41, issue 2

Authors: Liu, Peide | Zhang, Pei

Article Type: Research Article

Abstract: A normal wiggly hesitant fuzzy set is a very useful tool to mine the potential uncertain information given by decision makers, which is considered as an extension of hesitant fuzzy set and can improve the effectiveness of decision making. Power average operator can relieve the impact on decision result of unreasonable data, and the generalized Maclaurin symmetric mean operator (GMSM) is an extension of Maclaurin symmetric mean operator with wider range of applications, which can consider the relationship among decision attributes. By integrating the advantages of them, in this paper, we develop the normal wiggly hesitant fuzzy power GMSM (NWHFPGMSM) …operator and its weighted form based on the distance measure of two normal wiggly hesitant fuzzy elements, then we further discuss their properties and some special cases. Thus, a new multi-attribute decision making method based on the NWHFPGMSM operator under normal wiggly hesitant fuzzy environment is proposed. Finally, we select some examples to illustrate the effectiveness and superiority of the proposed method in this paper through comparison and analysis with other methods. Show more

Keywords: Normal wiggly hesitant fuzzy set, power average operator, generalized maclaurin symmetric mean operator, multi-attribute decision making

DOI: 10.3233/JIFS-202112

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3895-3920, 2021

Authors: Mohammady Talvar, Houshyar | Haj Seyyed Javadi, Hamid | Navidi, Hamidreza | Rezakhani, Afshin

Article Type: Research Article

Abstract: IoT-based network systems use a modern architecture called fog computing, In which data providing data services is economical with low latency. This paper tends to solve the challenge of resource allocation in fog computing. Solving the resource allocation challenge leads to increased profits, economic savings, and optimal computing systems use. Here resource allocation is improved by making use of the combined algorithm Nash equilibrium and auction. In the proposed method, each player is assigned a matrix. Each player matrix includes fog nodes (FNs), data service subscribers (DSSs), and data service operators (DSOs). Each player generates the best strategy based on …the other players strategy in all stages of the algorithm. The simulation results show that FNs profit in the combined Nash and Auction equilibrium algorithms is superior to the Stackelberg game algorithm. Show more

Keywords: Fog computing, resource allocation, IoT, nash equilibrium, auction algorithm

DOI: 10.3233/JIFS-202122

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3921-3932, 2021

Authors: Adu, Kwabena | Yu, Yongbin | Cai, Jingye | Mensah, Patrick Kwabena | Owusu-Agyemang, Kwabena

Article Type: Research Article

Abstract: Convolutional neural networks (CNNs) for automatic classification and medical image diagnosis have recently displayed a remarkable performance. However, the CNNs fail to recognize original images rotated and oriented differently, limiting their performance. This paper presents a new capsule network (CapsNet) based framework known as the multi-lane atrous feature fusion capsule network (MLAF-CapsNet) for brain tumor type classification. The MLAF-CapsNet consists of atrous and CLAHE, where the atrous increases receptive fields and maintains spatial representation, whereas the CLAHE is used as a base layer that uses an improved adaptive histogram equalization (AHE) to enhance the input images. The proposed method is …evaluated using whole-brain tumor and segmented tumor datasets. The efficiency performance of the two datasets is explored and compared. The experimental results of the MLAF-CapsNet show better accuracies (93.40% and 96.60%) and precisions (94.21% and 96.55%) in feature extraction based on the original images from the two datasets than the traditional CapsNet (78.93% and 97.30%). Based on the two datasets’ augmentation, the proposed method achieved the best accuracy (98.48% and 98.82%) and precisions (98.88% and 98.58%) in extracting features compared to the traditional CapsNet. Our results indicate that the proposed method can successfully improve brain tumor classification problems and support radiologists in medical diagnostics. Show more

Keywords: Brain tumor classification, capsule networks, deep neural network, atrous convolution, dynamic routing

DOI: 10.3233/JIFS-202261

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3933-3950, 2021

Authors: Geng, Kaifeng | Ye, Chunming

Article Type: Research Article

Abstract: Facing the worsening environmental problems, green manufacturing and sustainable development have attracted much attention. Aiming at the energy-efficient distributed re-entrant hybrid flow shop scheduling problem considering the customer order constraints (EDORHFSP) under Time-of-Use (TOU) electricity price, a mathematical model is established to minimize the maximum completion time and total consumption energy cost. In the study, some customer orders require production in multiple factories and jobs belonging to the same customer order must be processed in one factory. Firstly, a memetic algorithm (MA) was proposed to solve the problem. To improve the performance of the algorithm, encoding and decoding methods, energy …cost saving procedure, three heuristic rules about the population initialization and some neighborhood search methods are designed. Then, Taguchi method is adopted to research the influence of parameters setting. Lastly, numerical experiments demonstrate the effectiveness and superiority of MA for the EDORHFSP. Show more

Keywords: Energy-efficient, memetic algorithm, Time-of-Use electricity price, distributed re-entrant hybrid flow shop scheduling, customer order constraints

DOI: 10.3233/JIFS-202963

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3951-3971, 2021

Authors: Chen, Lifang | Wei, Mengru

Article Type: Research Article

Abstract: With the popularity of depth sensors and 3D scanners, 3D point cloud has developed rapidly. 3D scene understanding based on deep learning has become a research hotspot. However, many existing networks failed to fully consider the local structures of point clouds, limiting their abilities to exploit the complicated relationships between points. In this paper, we propose Enriching Local Features Network (ELF-Net), which enriches local features of point clouds. We propose Local Points Encoding Module (LPEM) and Feature Concatenate Module (FCM) in our network. Specifically, LPEM is designed to encode the information of eight orientations and 3D coordinate information of local …points. We stack the encoding units to achieve multi-scale representation, which is conducive to obtaining robustness and capturing details of the network. In Set Abstraction (SA) module, we apply farthest point sampling (FPS) method to sample the initial points and ball query method is used to group the neighboring points within a radius. FCM is designed to update the representations of local points by applying graph attention mechanism in local regions, which aims to enrich neighboring point feature representations. Finally, our network also proposes a new multivariate loss function, which combines the Center Loss function and Cross Entropy loss function to act on the classification branch. Experimental results show the effectiveness of our proposed network on ModelNet40 (achieves 92.35% accuracy), ScanNet (achieves 85.46% accuracy) and S3DIS (achieves 86.4% accuracy) datasets. Show more

Keywords: Point cloud classification and segmentation, local points encoding module, feature concatenate module, multivariate loss function

DOI: 10.3233/JIFS-210065

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3973-3983, 2021

Authors: Wan, Quan | Wu, Lin | Yu, Zhengtao

Article Type: Research Article

Abstract: Initial results of neural architecture search (NAS) in natural language processing (NLP) have been achieved, but the search space of most NAS methods is based on the simplest recurrent cell and thus does not consider the modeling of long sequences. The remote information tends to disappear gradually when the input sequence is long, resulting in poor model performance. In this paper, we present an approach based on dual cells to search for a better-performing network architecture. We construct a search space that is more compatible with language modeling tasks by adding an information storage cell inside the search cell, so …that we can make better use of the remote information of the sequence and improve the performance of the model. The language model searched by our method achieves better results than those of the baseline method on the Penn Treebank data set and WikiText-2 data set. Show more

Keywords: Neural architecture search, natural language processing, recurrent neural network

DOI: 10.3233/JIFS-210207

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3985-3992, 2021

Authors: Mu, Tianshi | Lin, Kequan | Zhang, Huabing | Wang, Jian

Article Type: Research Article

Abstract: Deep learning is gaining significant traction in a wide range of areas. Whereas, recent studies have demonstrated that deep learning exhibits the fatal weakness on adversarial examples. Due to the black-box nature and un-transparency problem of deep learning, it is difficult to explain the reason for the existence of adversarial examples and also hard to defend against them. This study focuses on improving the adversarial robustness of convolutional neural networks. We first explore how adversarial examples behave inside the network through visualization. We find that adversarial examples produce perturbations in hidden activations, which forms an amplification effect to fool the …network. Motivated by this observation, we propose an approach, termed as sanitizing hidden activations, to help the network correctly recognize adversarial examples by eliminating or reducing the perturbations in hidden activations. To demonstrate the effectiveness of our approach, we conduct experiments on three widely used datasets: MNIST, CIFAR-10 and ImageNet, and also compare with state-of-the-art defense techniques. The experimental results show that our sanitizing approach is more generalized to defend against different kinds of attacks and can effectively improve the adversarial robustness of convolutional neural networks. Show more

Keywords: Adversarial examples, sanitizing hidden activations, adversarial robustness, convolutional neural networks

DOI: 10.3233/JIFS-210371

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3993-4003, 2021

Authors: Kazemi, Mohsen | Niknam, Taher | Bahmani-Firouzi, Bahman | Nafar, Mehdi

Article Type: Research Article

Abstract: This paper uses the coordinated energy management strategy for different sources and storages in the framework of flexible grid-connected energy hubs that participate in the day-ahead (DA) energy and reserve markets. In the base problem, this method maximizes the difference between the expected revenue of hubs gained by selling energy and reserve power in the proposed markets and the expected cost of lost flexibility (COLF). Also, it is subject to linearized optimal power flow (LOPF) equations in the electricity, gas and district heating systems, as well as hub constraints including different sources, storages and reserve models. This problem contains uncertainties …of load, market price, reserve requirement, renewable power and hub mobile storages parameters. Therefore, the hybrid stochastic/robust optimization (HSRO) is suitable to model these uncertain parameters and obtain robust capability for the hub to improve the system flexibility. Accordingly, the bounded uncertainty-based robust optimization (BURO) is used in this paper to model the uncertainty of hub mobile storages to achieve the hub robust potential in improving the system flexibility, and other uncertain parameters are modeled according to scenario-based stochastic programming (SBSP). Finally, the proposed strategy is implemented on a standard test system. The obtained numerical results confirm the capability of the suggested scheme in improving the economic status of sources and storages using the coordinated energy management strategy in the form of an energy hub, as well as enhancing economic conditions, operation, and flexibility of energy networks thanks to hubs for having access to optimal scheduling. Show more

Keywords: Coordinated energy management, cost of lost flexibility, energy and reserve market, flexible grid-connected energy hub, hybrid stochastic/robust optimization

DOI: 10.3233/JIFS-201284

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4005-4020, 2021

Authors: Faragallah, Osama S. | Muhammed, Abdullah N. | Taha, Taha S. | Geweid, Gamal G.N.

Article Type: Research Article

Abstract: This paper presents a new approach to the multi-modal medical image fusion based on Principal Component Analysis (PCA) and Singular value decomposition (SVD).The main objective of the proposed approach is to facilitate its implementation on a hardware unit, so it works effectively at run time. To evaluate the presented approach, it was tested in fusing four different cases of a registered CT and MRI images. Eleven quality metrics (including Mutual Information and Universal Image Quality Index) were used in evaluating the fused image obtained by the proposed approach, and compare it with the images obtained by the other fusion approaches. …In experiments, the quality metrics shows that the fused image obtained by the presented approach has better quality result and it proved effective in medical image fusion especially in MRI and CT images. It also indicates that the paper approach had reduced the processing time and the memory required during the fusion process, and leads to very cheap and fast hardware implementation of the presented approach. Show more

Keywords: Image fusion, PCA, SVD, medical images, fusion

DOI: 10.3233/JIFS-202884

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4021-4033, 2021

Authors: Gao, Jinding

Article Type: Research Article

Abstract: In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion …of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions. Show more

Keywords: Swarm intelligence optimization algorithm, population dynamics, environmental pollution, microbial control

DOI: 10.3233/JIFS-210127

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4035-4049, 2021

Authors: Rampriya, R.S. | Sabarinathan, | Suganya, R.

Article Type: Research Article

Abstract: In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad …semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net. Show more

Keywords: Railroad aerial images, efficient net, modified residual net, attention layer, semantic segmentation

DOI: 10.3233/JIFS-210349

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4051-4068, 2021

Authors: Pan, Hongguang | Wen, Fan | Huang, Xiangdong | Lei, Xinyu | Yang, Xiaoling

Article Type: Research Article

Abstract: In the field of super-resolution image reconstruction, as a learning-based method, deep plug-and-play super-resolution (DPSR) algorithm can be used to find the blur kernel by using the existing blind deblurring methods. However, DPSR is not flexible enough in processing images with high- and low-frequency information. Considering a channel attention mechanism can distinguish low-frequency information and features in low-resolution images, in this paper, we firstly introduce this mechanism and design a new residual channel attention networks (RCAN); then the RCAN is adopted to replace deep feature extraction part in DPSR to achieve the adaptive adjustment of channel characteristics. Through four test …experiments based on Set5, Set14, Urban100 and BSD100 datasets, we find that, under different blur kernels and different scale factors, the average peak signal to noise ratio (PSNR) and structural similarity (SSIM) values of our proposed method increase by 0.31dB and 0.55%, respectively; under different noise levels, the average PSNR and SSIM values increase by 0.26dB and 0.51%, respectively. Show more

Keywords: image reconstruction, channel attention mechanism, residual channel attention networks, blur kernel

DOI: 10.3233/JIFS-202696

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4069-4078, 2021

Authors: Ma, Lina | Xu, Fengju | Wang, Lihua | Taslima, Akther

Article Type: Research Article

Abstract: Capital enrichment (CE) results from capital flows, which reflect the capital distribution among different regions and industries. This paper constructs the evaluation model of resource allocation efficiency from the perspective of capital and innovation resources. It expounds on CE’s theoretical mechanism by using the panel data from 2011 to 2018 for system GMM estimation. It finds that the manufacturing capital allocation efficiency (CAE) and innovation resource allocation efficiency (IRAE) show a volatile development trend. Both static and dynamic panel models show that there is a significant U-shaped curvilinear relationship between CE and CAE, CE and IRAE. CE’s inhibitory effect on …CAE and IRAE decreases with the improvement of CE until it exceeds the critical value of 8.27 and 8.93. After that, its impact on CAE and IRAE changes from negative to positive. Show more

Keywords: China’s manufacturing industry, capital enrichment, capital allocation efficiency, innovation resource allocation efficiency

DOI: 10.3233/JIFS-202856

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4079-4095, 2021

Authors: Rodríguez, Antonio

Article Type: Research Article

Abstract: Taking concepts from supply management, we developed a specification-assessment-compliance approach to obtain a transparent multi-criteria decision-making method. We designed an artificial-neuron-like node that allows the implementation of networks to represent hierarchies of evaluation criteria. A new graphical model based on functions in the unit segment uses the concept of Pythagorean fuzzy set (PFS). The specification PFSs’ entropies modulate the widths of one-sided triangular fuzzy numbers (TFNs) with positive slopes that become the evaluation nodes’ activation functions. All the specifications refer to the same point to facilitate the evaluation and ensure coherence. One-sided TFNs with negative slopes biunivocally represent the assessment …PFSs at the input layer of the network. A risk case study on the options for the outsourcing of an information technology development project shows the proposed method’s implementation. We compare the results with those of the application of two other previous methods. Show more

Keywords: Multi-criteria decision-making, Pythagorean fuzzy set, triangular fuzzy number, artificial neuron, information entropy

DOI: 10.3233/JIFS-210029

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4097-4108, 2021

Authors: Riaz, Muhammad | Garg, Harish | Farid, Hafiz Muhammad Athar | Aslam, Muhammad

Article Type: Research Article

Abstract: The low-carbon supply chain management is big a challenge for the researchers due to the rapid increase in global warming and environmental concerns. With the advancement of the environmental concerns and social economy, it is an unavoidable choice for a business to achieve sustainable growth for low-carbon supply chain management. Since the root of the chain depends upon the supplier selection and choosing an excellent low-carbon supply. Green supplier selection is one of the most crucial activities in low-carbon supply chain management, it is critical to develop rigorous requirements and a system for selection in low-carbon green supply chain management …(LCGSCM). A q-rung orthopair fuzzy number (q-ROFN) is pair of membership degree (MD) and non-membership degrees (NMD) which is reliable to address uncertainties in the various real-life problems. This article sets out a decision analysis approach for interactions between MDs and NMDs with the help of q-ROFNs. For this objective, we develop new aggregation operators (AOs) named as, q-rung orthopair fuzzy interaction weighted averaging (q-ROFIWA) operator, q-rung orthopair fuzzy interaction ordered weighted averaging (q-ROFIOWA) operator, q-rung orthopair fuzzy interaction hybrid averaging (q-ROFIHA) operator, q-rung orthopair fuzzy interaction weighted geometric (q-ROFIWG) operator, q-rung orthopair fuzzy interaction ordered weighted geometric (q-ROFIOWG) operator and q-rung orthopair fuzzy interaction hybrid geometric (q-ROFIHG) operator. These AOs define an advanced approach for information fusion and modeling uncertainties in multi-criteria decision-making (MCDM). At the end, a robust MCDM approach based on newly developed AOs is developed. Some significant properties of these AOS are analyzed and the efficiency of the developed approach is assessed with a practical application towards sustainable low-carbon green supply chain management. Show more

Keywords: MCDM, Aggregation operators, interaction relation, low-carbon green supply chain management

DOI: 10.3233/JIFS-210506

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4109-4126, 2021

Authors: Balamurugan, S. | Nageswari, S.

Article Type: Research Article

Abstract: Modular Multilevel Converter (MMC) plays a vital role in high voltage industries because of its high rating power conversion. Due to its usage in high voltage rating power conversion and switched capacitor usage in MMC structure, there arises a problem of unbalanced capacitor voltage, which causes circulating current and disturbance in output current regulation. To manage these problematic parameters, a FOPID (Fractional Order Proportional Integral Derivative) controller has been utilized, due to its dynamic tracking and fast response. Secondly, the gain values of FOPID are not efficient, and they are optimized for each control group at all times of MMC …working conditions. To provide a dynamic gain value by considering the dynamic change of error tracking parameters, Wild Spider Foraging Optimization (WSFO) algorithm has been developed based on the foraging behaviour of wild spider searching food (gain values) in view of changing the error of tracking parameters. The proposed algorithm has been evaluated in MATLAB Simulink by modeling the MMC structure with FOPID controller. The parameters of FOPID are optimized by bio-inspired algorithms like WSFO, Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). The outcomes of the proposed WSFO-FOPID provide minimum circulating current and effectively balance the capacitor voltage in MMC. When the effectiveness of the results has been verified with the existing ABC and PSO optimization approaches, the proposed algorithm outperforms. Show more

Keywords: Modular multilevel converter, wild spider foraging optimization, circulating current, total harmonic distortion

DOI: 10.3233/JIFS-210528

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4127-4147, 2021

Authors: Li, JX | Zhao, H | Zhu, S.P | Huang, H. | Miao, YJ | Jiang, ZY

Article Type: Research Article

Abstract: The classification of fresh tobacco leaves during the picking process plays an important role in the subsequent roasting. In this paper, a lightweight convolutional neural network is used to detect the maturity of tobacco leaves quickly. Fresh tobacco leaves in the datasets are divided into 3 categories by the picking position, and each category is divided into 4 maturity levels and finally gets 12 types of tobacco leaves with different maturity. To ensure the lightweight of the model, the new network is based on the MobileNetV2 to establish. By utilizing shortcut operation, the shallow network information is preserved, and network …degradation is suppressed. In the tobacco leaf datasets we obtained, the improved network has superior performance and compared with other classic networks, the model size and the number of operations have been reduced. Show more

Keywords: Tobacco classification, lightweight network, MobileNetV2, shortcut

DOI: 10.3233/JIFS-210640

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4149-4158, 2021

Authors: Lei, Deming | Xi, Bingjie

Article Type: Research Article

Abstract: Distributed scheduling has attracted much attention in recent years; however, distributed scheduling problem with uncertainty is seldom considered. In this study, fuzzy distributed two-stage hybrid flow shop scheduling problem (FDTHFSP) with sequence-dependent setup time is addressed and a diversified teaching-learning-based optimization (DTLBO) algorithm is applied to optimize fuzzy makespan and total agreement index. In DTLBO, multiple classes are constructed and categorized into two types according to class quality. Different combinations of global search and neighborhood search are used in two kind of classes. A temporary class with multiple teachers is built based on Pareto rank and difference index and evolved …in a new way. Computational experiments are conducted and results demonstrate that the main strategies of DTLBO are effective and DTLBO has promising advantages on solving the considered problem. Show more

Keywords: Two-stage hybrid flow shop scheduling, distributed scheduling, fuzzy scheduling, teaching-learning-based optimization

DOI: 10.3233/JIFS-210764

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4159-4173, 2021

Authors: Yan, Zhiyu | Lv, Shuang

Article Type: Research Article

Abstract: Accurate prediction of traffic flow is of great significance for alleviating urban traffic congestions. Most previous studies used historical traffic data, in which only one model or algorithm was adopted by the whole prediction space and the differences in various regions were ignored. In this context, based on time and space heterogeneity, a Classification and Regression Trees-K-Nearest Neighbor (CART-KNN) Hybrid Prediction model was proposed to predict short-term taxi demand. Firstly, a concentric partitioning method was applied to divide the test area into discrete small areas according to its boarding density level. Then the CART model was used to divide the …dataset of each area according to its temporal characteristics, and KNN was established for each subset by using the corresponding boarding density data to estimate the parameters of the KNN model. Finally, the proposed method was tested on the New York City Taxi and Limousine Commission (TLC) data, and the traditional KNN model, backpropagation (BP) neural network, long-short term memory model (LSTM) were used to compare with the proposed CART-KNN model. The selected models were used to predict the demand for taxis in New York City, and the Kriging Interpolation was used to obtain all the regional predictions. From the results, it can be suggested that the proposed CART-KNN model performed better than other general models by showing smaller mean absolute percentage error (MAPE) and root mean square error (RMSE) value. The improvement of prediction accuracy of CART-KNN model is helpful to understand the regional demand pattern to partition the boarding density data from the time and space dimensions. The partition method can be extended into many models using traffic data. Show more

Keywords: Short-term taxi demand forecast, CART-KNN hybrid prediction model, spatial and temporal heterogeneity, concentric partitioning, time series

DOI: 10.3233/JIFS-210872

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4175-4186, 2021

Authors: Ge, Yang | Xin, Ding

Article Type: Research Article

Abstract: In the task of Person re-identification (reID), the range of motion of pedestrians often spans multiple camera areas, and their motion direction and behavior cannot be constrained, and irrelevant people or objects in different scenes will also obtain target pedestrian information for us Cause interference. At the same time, the surveillance system also has many characteristics such as a fixed shooting angle of a single camera, different angles between different cameras, and low image resolution. These characteristics make the task of Person re-identification difficult. This paper proposes a Multi-level Feature Extraction Network (MFEN) based on SEResNet-50. Extracting richer and more …diverse pedestrian features from poor-quality images will effectively improve the re-identification ability of the network, and MFEN can obtain Multistage key features in the image through the Feature Re-extraction Method (FRM) proposed in this paper. Experiments show that compared with AANet-50, MFEN has 3.85% /0.71% improvements of mAP/ Rank-1 on the Market1501 dataset, and 2.74% /1.28% improvements of mAP/ Rank-1 on the DukeMTMC-reID dataset. Show more

Keywords: Person re-identification, full supervision, attention mechanism, multi-level feature extraction network, feature re-extraction method

DOI: 10.3233/JIFS-211456

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4187-4201, 2021