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

Authors: Yousaf, Waqas | Umar, Arif | Shirazi, Syed Hamad | Khan, Zakir | Razzak, Imran | Zaka, Mubina

Article Type: Research Article

Abstract: Automatic logo detection and recognition is significantly growing due to the increasing requirements of intelligent documents analysis and retrieval. The main problem to logo detection is intra-class variation, which is generated by the variation in image quality and degradation. The problem of misclassification also occurs while having tiny logo in large image with other objects. To address this problem, Patch-CNN is proposed for logo recognition which uses small patches of logos for training to solve the problem of misclassification. The classification is accomplished by dividing the logo images into small patches and threshold is applied to drop no logo area …according to ground truth. The architectures of AlexNet and ResNet are also used for logo detection. We propose a segmentation free architecture for the logo detection and recognition. In literature, the concept of region proposal generation is used to solve logo detection, but these techniques suffer in case of tiny logos. Proposed CNN is especially designed for extracting the detailed features from logo patches. So far, the technique has attained accuracy equals to 0.9901 with acceptable training and testing loss on the dataset used in this work. Show more

Keywords: Logo detection, logo recognition, deep learning, AlexNet, ResNet, CNN

DOI: 10.3233/JIFS-190660

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Lee, Bor-Hon | Yang, Albert Jing-Fuh | Chen, Yenming J.

Article Type: Research Article

Abstract: A large categories of time series fluctuate dramatically, for example, prices of agriculture produce. Traditional methods in time series and stochastic prediction may not capture such dynamics. This paper tries to use machine learning to tune the model for a real situation by establishing a price determination mechanism on the model of stochastic automata (SA) and evolutionary game (EG). Time series volatility attributed to the chaotic process can be obtained through the learning algorithm of Markov Chain Monte Carlo (MCMC). Using machine learning through the chaotic analysis of stochastic automata and evolutionary games, we find that a more spatially aggregated …distribution (smaller entropy) leads to larger time series fluctuations, regardless of the initial distribution of crops. By integrating the factors discovered in this study, we can develop a better learning algorithm in a highly volatile time series in agriculture prices. Show more

Keywords: Distribution entropy, spatial diffusion, stochastic automata (SA), evolutionary game (EG), machine learning

DOI: 10.3233/JIFS-189609

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2021

Authors: Hei, Hongzhong | Jian, Xianzhong | Xiao, Erliang

Article Type: Research Article

Abstract: The widespread application of infrared human action recognition in intelligent surveillance has attracted significant attention. However, the infrared action recognition dataset is limited, which limits the development of infrared action recognition. Existing methods for infrared action recognition are based on features in the same sample, without paying attention to within-class differences. Motivated by the idea of weighting video information, this paper proposes a novel infrared action recognition framework to rew eight the s amples of training sets named REWS to solve the problems of limited infrared action data and the large within-class differences in the infrared action recognition dataset. In …the proposed framework, we first map infrared action video data to a low-dimensional feature space, and use the cosine similarity between the feature data of the training set and the testing set to determine the weight of the training set samples. Each training set sample has an independent weight. Then, a support vector machine (SVM) is trained by the training sets with weights to recognize the infrared actions. Experimental results demonstrate that our approach can achieve state-of-the-art performance compared with hand-crafted features based methods on the benchmark InfAR dataset. Show more

Keywords: Infrared, action recognition, within-class differences, samples reweighting, cosine similarity

DOI: 10.3233/JIFS-192068

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Bai, Haoyue | Zhang, Haofeng | Wang, Qiong

Article Type: Research Article

Abstract: Zero Shot learning (ZSL) aims to use the information of seen classes to recognize unseen classes, which is achieved by transferring knowledge of the seen classes from the semantic embeddings. Since the domains of the seen and unseen classes do not overlap, most ZSL algorithms often suffer from domain shift problem. In this paper, we propose a Dual Discriminative Auto-encoder Network (DDANet), in which visual features and semantic attributes are self-encoded by using the high dimensional latent space instead of the feature space or the low dimensional semantic space. In the embedded latent space, the features are projected to both …preserve their original semantic meanings and have discriminative characteristics, which are realized by applying dual semantic auto-encoder and discriminative feature embedding strategy. Moreover, the cross modal reconstruction is applied to obtain interactive information. Extensive experiments are conducted on four popular datasets and the results demonstrate the superiority of this method. Show more

Keywords: Zero shot learning, domain shift, dual auto-encoder, discriminative projection

DOI: 10.3233/JIFS-201920

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Lin, Rongde | Li, Jinjin | Chen, Dongxiao | Huang, Jianxin | Chen, Yingsheng

Article Type: Research Article

Abstract: Fuzzy covering rough set model is a popular and important theoretical tool for computation of uncertainty, and provides an effective approach for attribute reduction. However, attribute reductions derived directly from fuzzy lower or upper approximations actually still occupy large of redundant information, which leads to a lower ratio of attribute-reduced. This paper introduces a kind of parametric observation sets on the approximations, and further proposes so called parametric observational-consistency, which is applied to attribute reduction in fuzzy multi-covering decision systems. Then the related discernibility matrix is developed to provide a way of attribute reduction. In addition, for multiple observational parameters, …this article also introduces a recursive method to gradually construct the multiple discernibility matrix by composing the refined discernibility matrix and incremental discernibility matrix based on previous ones. In such case, an attribute reduction algorithm is proposed. Finally, experiments are used to demonstrate the feasibility and effectiveness of our proposed method. Show more

Keywords: Attribute reduction, fuzzy discernibility matrix, fuzzy multi-covering systems, incremental discernibility matrix, observational consistency, refined discernibility matrix

DOI: 10.3233/JIFS-201998

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Zhou, Zaohong | Zou, Yongwen

Article Type: Research Article

Abstract: Problems involving decision-making and management of engineering projects call for attention from different quarters and the issue of decision-making for projects under the state planning in particular should be the major concern of project management. This study takes a traditional village protection project —- the preservation of Zaoshi Village, Xingan County, Jiangxi Province, China—- as a case in point. Treating the decision-making process as a system, the study employs ISM model to examine the system-level relationship between engineering projects. Artificial Intelligence is utilized for analyzing the planning of project structure and Fuzzy TOPSIS model is useful in estimating the weights …of the scenario and find the ranking of structure finally. Then, using the analytical data thus derived, the research focuses on identifying the optimum option for decision-making. By this process, the study intends to gain and share some insight into the issue and establish precedents for similar engineering projects. Show more

Keywords: Planning engineering, Artificial Intelligence (AI), ISM, Grey situation decision-making, Fuzzy TOPSIS

DOI: 10.3233/JIFS-189641

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Phawinee, Suphawimon | Cai, Jing-Fang | Guo, Zhe-Yu | Zheng, Hao-Ze | Chen, Guan-Chen

Article Type: Research Article

Abstract: Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then …the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app. Show more

Keywords: Face recognition, intelligent lock, ResNet, deep learning

DOI: 10.3233/JIFS-189624

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Leea, Chien-Cheng | Gao, Zhongjian | Huanga, Xiu-Chi

Article Type: Research Article

Abstract: This paper proposes a Wi-Fi-based indoor human detection system using a deep convolutional neural network. The system detects different human states in various situations, including different environments and propagation paths. The main improvements proposed by the system is that there is no cameras overhead and no sensors are mounted. This system captures useful amplitude information from the channel state information and converts this information into an image-like two-dimensional matrix. Next, the two-dimensional matrix is used as an input to a deep convolutional neural network (CNN) to distinguish human states. In this work, a deep residual network (ResNet) architecture is used …to perform human state classification with hierarchical topological feature extraction. Several combinations of datasets for different environments and propagation paths are used in this study. ResNet’s powerful inference simplifies feature extraction and improves the accuracy of human state classification. The experimental results show that the fine-tuned ResNet-18 model has good performance in indoor human detection, including people not present, people still, and people moving. Compared with traditional machine learning using handcrafted features, this method is simple and effective. Show more

Keywords: Human movement detection, Wi-Fi, CNN, ResNet, channel state information

DOI: 10.3233/JIFS-189629

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Konstantakopoulos, Grigorios D. | Gayialis, Sotiris P. | Kechagias, Evripidis P. | Papadopoulos, Georgios A. | Tatsiopoulos, Ilias P.

Article Type: Research Article

Abstract: Routing of vehicles and scheduling of deliveries play a crucial role in logistics operations as they affect both the distribution cost and customer satisfaction. That is why researchers have intensively studied this problem in conjunction with the multiple variables and constraints involved in the logistics operations. In this paper, the cases of time windows and simultaneous pickups and deliveries, where goods are simultaneously delivered and collected from customers within a predetermined time slot, are studied. The objective of our research is to create efficient routes that minimize both the number of vehicles and the total distance travelled, as both of …them affect the total distribution cost. Considering various plans of routes that are differentiated by the number of routes and the sequence of visitations, can be beneficial for decision-makers, since they have the opportunity to select the plan that better fits their needs. Therefore, in this paper we develop a multiobjective evolutionary algorithm (MOEA) that integrates an improved construction algorithm and a new crossover operator for efficient distribution services. Through the proposed MOEA a set of solutions (route plans), known as Pareto-optimal, is obtained, while single biased solutions are avoided. The proposed algorithm is tested in two well-known datasets in order to evaluate the algorithm’s efficiency. The results indicate that the algorithm’s solutions have small deviation from the best-published and some non-dominated solutions are also obtained. Show more

Keywords: Vehicle routing problem, time windows, simultaneous pickups and deliveries, multiobjective optimization, evolutionary algorithm, logistics

DOI: 10.3233/JIFS-202129

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Yu, Guo | Li, Weijian | Zhou, Xiaobo

Article Type: Research Article

Abstract: The Belt and Road (abbreviated as B&R) creates opportunities for the economy and trade development and the improvement of national relations of the Belt and Road countries. Development of service trade plays an important role in the Belt and Road. Henan Province takes the opportunity of B&R to develop the international service industry and makes some achievements. In the new era of development, analyzing the trade competitiveness of international service in Henan Province under the background of the Belt and Road is of great significance to help Henan Province recognize its own strengths and weaknesses in service trade development and …make targeted improvements. Studies have shown that the share of international market, revealed, advantages of comparative competitiveness trade and service trade openness of service trade in Henan Province under the background of the Belt and Road are constantly improving, but the overall development level is still low and slow development occurs. We utilize the concept of fuzzy embedded along with Analytic Network Process (ANP) which makes it suitable for managing vagueness of the linguistics information of assessment system. Therefore, in order to further improve the international competitiveness of service trade in Henan Province, it is necessary to optimize the international market share, competitiveness, and degree of openness. Specifically, it is necessary to transform and upgrade the traditional service trade industry, continue to expand the breadth and depth of service trade openness, strengthen the talent team building to improve the quality of employees, and promote the brand construction of service industry to enhance the industry’s development potential. Show more

Keywords: The belt and road, henan province, service trade, international competitiveness, fuzzy, analytic network process (ANP)

DOI: 10.3233/JIFS-189642

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021