Journal of Intelligent & Fuzzy Systems - Volume 40, issue 1

Authors: Oner, Tahsin | Katican, Tugce | Saeid, Arsham Borumand

Article Type: Research Article

Abstract: The aim of this study is to introduce fuzzy filters of Sheffer stroke Hilbert algebra. After defining fuzzy filters of Sheffer stroke Hilbert algebra, it is shown that a quotient structure of this algebra is described by its fuzzy filter. In addition to this, the level filter of a Sheffer stroke Hilbert algebra is determined by its fuzzy filter. Some fuzzy filters of a Sheffer stroke Hilbert algebra are defined by a homomorphism. Normal and maximal fuzzy filters of a Sheffer stroke Hilbert algebra and the relation between them are presented. By giving the Cartesian product of fuzzy filters of …a Sheffer stroke Hilbert algebra, various properties are examined. Show more

Keywords: (Sheffer stroke) Hilbert algebra, (normal) fuzzy filter

DOI: 10.3233/JIFS-200760

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 759-772, 2021

Authors: Liu, Shuai | Xu, Ying | Guo, Lingming | Shao, Meng | Yue, Guodong | An, Dong

Article Type: Research Article

Abstract: Tens of thousands of work-related injuries and deaths are reported in the construction industry each year, and a high percentage of them are due to construction workers not wearing safety equipment. In order to address this safety issue, it is particularly necessary to automatically identify people and detect the safety characteristics of personnel at the same time in the prefabricated building. Therefore, this paper proposes a depth feature detection algorithm based on the Extended-YOLOv3 model. On the basis of the YOLOv3 network, a security feature recognition network and a feature transmission network are added to achieve the purpose of detecting …security features while identifying personnel. Firstly, a security feature recognition network is added side by side on the basis of the YOLOv3 network to analyze the wearing characteristics of construction workers. Secondly, the S-SPP module is added to the object detection and feature recognition network to broaden the features of the deep network and help the network extract more useful features from the high-resolution input image. Finally, a special feature transmission network is designed to transfer features between the construction worker detection network and the security feature recognition network, so that the two networks can obtain feature information from the other network respectively. Compared with YOLOv3 algorithm, Extended-YOLOv3 in this paper adds security feature recognition and feature transmission functions, and adds S-SPP module to the object detection and feature recognition network. The experimental results show that the Extended-YOLOv3 algorithm is 1.3% better than the YOLOV3 algorithm in AP index. Show more

Keywords: YOLOv3, target detection, depth feature extraction, S-SPP module, deep learning

DOI: 10.3233/JIFS-200778

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 773-786, 2021

Authors: Saravanan, G. | Yuvaraj, N.

Article Type: Research Article

Abstract: Mobile Cloud Computing (MCC) addresses the drawbacks of Mobile Users (MU) where the in-depth evaluation of mobile applications is transferred to a centralized cloud via a wireless medium to reduce load, therefore optimizing resources. In this paper, we consider the resource (i.e., bandwidth and memory) allocation problem to support mobile applications in a MCC environment. In such an environment, Mobile Cloud Service Providers (MCSPs) form a coalition to create a resource pool to share their resources with the Mobile Cloud Users. To enhance the welfare of the MCSPs, a method for optimal resource allocation to the mobile users called, Poisson …Linear Deep Resource Allocation (PL-DRA) is designed. For resource allocation between mobile users, we formulate and solve optimization models to acquire an optimal number of application instances while meeting the requirements of mobile users. For optimal application instances, the Poisson Distributed Queuing model is designed. The distributed resource management is designed as a multithreaded model where parallel computation is provided. Next, a Linear Gradient Deep Resource Allocation (LG-DRA) model is designed based on the constraints, bandwidth, and memory to allocate mobile user instances. This model combines the advantage of both decision making (i.e. Linear Programming) and perception ability (i.e. Deep Resource Allocation). Besides, a Stochastic Gradient Learning is utilized to address mobile user scalability. The simulation results show that the Poisson queuing strategy based on the improved Deep Learning algorithm has better performance in response time, response overhead, and energy consumption than other algorithms. Show more

Keywords: Mobile cloud computing, mobile cloud service providers, mobile cloud users, poisson linear, stochastic, deep neural resource allocation

DOI: 10.3233/JIFS-200799

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 787-797, 2021

Authors: Xu, Xiaoyun | Wu, Jingzheng | Yang, Mutian | Luo, Tianyue | Meng, Qianru | Li, Weiheng | Wu, Yanjun

Article Type: Research Article

Abstract: As the scale of software systems continues expanding, software architecture is receiving more and more attention as the blueprint for the complex software system. An outstanding architecture requires a lot of professional experience and expertise. In current practice, architects try to find solutions manually, which is time-consuming and error-prone because of the knowledge barrier between newcomers and experienced architects. The problem can be solved by easing the process of apply experience from prominent architects. To this end, this paper proposes a novel graph-embedding-based method, AI-CTO, to automatically suggest software stack solutions according to the knowledge and experience of prominent architects. …Firstly, AI-CTO converts existing industry experience to knowledge, i.e., knowledge graph. Secondly, the knowledge graph is embedded in a low-dimensional vector space. Then, the entity vectors are used to predict valuable software stack solutions by an SVM model. We evaluate AI-CTO with two case studies and compare its solutions with the software stacks of large companies. The experiment results show that AI-CTO can find effective and correct stack solutions and it outperforms other baseline methods. Show more

Keywords: Knowledge graph, graph embedding, software architecture

DOI: 10.3233/JIFS-200899

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 799-812, 2021

Authors: Kazemi, Sajad | Mavi, Reza Kiani | Emrouznejad, Ali | Kiani Mavi, Neda

Article Type: Research Article

Abstract: Data Envelopment Analysis (DEA) is the most popular mathematical approach to assess efficiency of decision-making units (DMUs). In complex organizations, DMUs face a heterogeneous condition regarding environmental factors which affect their efficiencies. When there are a large number of objects, non-homogeneity of DMUs significantly influences their efficiency scores that leads to unfair ranking of DMUs. The aim of this study is to deal with non-homogeneous DMUs by implementing a clustering technique for further efficiency analysis. This paper proposes a common set of weights (CSW) model with ideal point method to develop an identical weight vector for all DMUs. This study …proposes a framework to measuring efficiency of complex organizations, such as banks, that have several operational styles or various objectives. The proposed framework helps managers and decision makers (1) to identify environmental components influencing the efficiency of DMUs, (2) to use a fuzzy equivalence relation approach proposed here to cluster the DMUs to homogenized groups, (3) to produce a common set of weights (CSWs) for all DMUs with the model developed here that considers fuzzy data within each cluster, and finally (4) to calculate the efficiency score and overall ranking of DMUs within each cluster. Show more

Keywords: Data envelopment analysis, fuzzy DEA, non-homogeneous, clustering, common set of weights (CSW)

DOI: 10.3233/JIFS-200962

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 813-832, 2021

Authors: Khan, Y. A. | Chu, Y. M. | Abbas, S. Z.

Article Type: Research Article

Abstract: This paper investigates governments’ performance in the country. We achieved this objective differently. We employed an inverse method of assessment, with the utilization of factor copula modeling technique, to study the dependence relationship of exchange rates returns as auxiliary variables, the performance of political and army government tenures in the country in the last two decades are evaluated. Through factor analysis, common factors for the exchange rate are obtained. The analysis shows that conditioned on the common factors, the dependence amongst the elected currencies are strongly asymmetric in most of the tenures except the term of Pakistan Muslim League-Nawaz, and …condition on common factor Clayton copula demonstrating hypothesis is more suitable. However, we perceive high left tail reliance among foreign currency returns during Pakistan Muslim League-Nawaz tenure, and the condition on common factor Gumbel copula molding assumption is more appropriate. We are signifying the foulest government performance in the country among all occupancies under consideration. Show more

Keywords: Factor analysis, asymmetric, clayton copula, exchange rate, gumbel copula

DOI: 10.3233/JIFS-200996

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 833-847, 2021

Authors: Saleem, Nasir | Khattak, Muhammad Irfan | Al-Hasan, Mu’ath | Jan, Atif

Article Type: Research Article

Abstract: Speech enhancement is a very important problem in various speech processing applications. Recently, supervised speech enhancement using deep learning approaches to estimate a time-frequency mask have proved remarkable performance gain. In this paper, we have proposed time-frequency masking-based supervised speech enhancement method for improving intelligibility and quality of the noisy speech. We believe that a large performance gain can be achieved if deep neural networks (DNNs) are layer-wise pre-trained by stacking Gaussian-Bernoulli Restricted Boltzmann Machine (GB-RBM). The proposed DNN is called as Gaussian-Bernoulli Deep Belief Network (GB-DBN) and are optimized by minimizing errors between the estimated and pre-defined masks. Non-linear …Mel-Scale weighted mean square error (LMW-MSE ) loss function is used as training criterion. We have examined the performance of the proposed pre-training scheme using different DNNs which are established on three time-frequency masks comprised of the ideal amplitude mask (IAM), ideal ratio mask (IRM), and phase sensitive mask (PSM). The results in different noisy conditions demonstrated that when DNNs are pre-trained by the proposed scheme provided a persistent performance gain in terms of the perceived speech intelligibility and quality. Also, the proposed pre-training scheme is effective and robust in noisy training data. Show more

Keywords: Supervised speech enhancement, deep learning, deep belief networks, restricted boltzmann machine, intelligibility, quality

DOI: 10.3233/JIFS-201014

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 849-864, 2021

Authors: Gong, Zengtai | Wang, Junhu

Article Type: Research Article

Abstract: Up to now, there have been a lot of research results about multi-attribute decision making problems by fuzzy graph theory. However, there are few investigations about multi-attribute decision making problems under the background of indecisiveness. The main reason is that the difference of cognition and the complexity of thinking by decision makers, for the same question have different opinions. In this paper, we proposed a hesitant fuzzy hypergraph model based on hesitant fuzzy sets and fuzzy hypergraphs. At the same time, some basic graph operations of hesitant fuzzy hypergraphs are investigated and several equivalence relationship between hesitant fuzzy hypergraphs, hesitant …fuzzy formal concept analysis and hesitant fuzzy information systems are discussed. Since granular computing can deal with multi-attribute decision-making problems well, we considered the hesitant fuzzy hypergraph model of granular computing, and established an algorithm of multi-attribute decision-making problem based on hesitant fuzzy hypergraph model. Finally an example is given to illustrate the effectiveness of the algorithm. Show more

Keywords: Hesitant fuzzy sets, hesitant fuzzy graph, hesitant fuzzy hypergraph, granular computing, graph decision

DOI: 10.3233/JIFS-201016

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 865-875, 2021

Authors: Hourali, Samira | Zahedi, Morteza | Fateh, Mansour

Article Type: Research Article

Abstract: Coreference resolution is critical for improving the performance of all text-based systems including information extraction, document summarization, machine translation, and question-answering. Most of coreference resolution solutions rely on using knowledge resources like lexical knowledge, syntactic knowledge, world knowledge and semantic knowledge. This paper presents a new knowledge-based coreference resolution model using neural network architecture. It uses XLNet embeddings as input and does not rely on any syntactic or dependency parsers. For more efficient span representation and mention detection, we used entity-level information. Mentions were extracted from the text with an unhand engineered mention detector, and the features were extracted from …a deep neural network. We also propose a nonlinear multi-criteria ranking model to rank the candidate antecedents. This model simultaneously determines the total score of alternatives and the weight of the features in order to speed up the process of ranking alternatives. Compared to the state-of-the-art models, the simulation results showed significant improvements on the English CoNLL-2012 shared task (+6.4 F1). Moreover, we achieved 96.1% F1 score on the n2c2 medical dataset. Show more

Keywords: Natural language processing, coreference resolution, knowledge management, entity level information, neural network, multi-criteria ranking model

DOI: 10.3233/JIFS-201050

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 877-892, 2021

Authors: Mannar Mannan, J. | Sindhanai Selvan, K. | Mohemmed Yousuf, R.

Article Type: Research Article

Abstract: Massive digital documents on Internet leading to use e-learning, and it becomes an emerging field of research due to the massive growth of internet users. E-learning requires suitable document ranking method to avoid navigating to the next Search Engine Result Page (SERP) frequently. The existing document ranking methods are lacking to rank the documents independently based on the conceptual contents. This paper proposes a novel method for ranking the documents independently based on the different classification of term it contains. In this approach, the terms are classified into five categories such as (1) direct query term, (2) expanded terms, (3) …semantically related term, (4) supporting terms and (5) stop words. The query has been expanded using domain ontology to acquire more semantic terms for better understanding of user query. The semantic weight has been applied independently over different categories of terms in a document for ranking. The document with the highest augmented value in each category of terms has been ranked first. Remaining documents are ranked in the same way and are arranged in the descending order. The WordNet tool is utilized as a knowledge base and Wu and Palmer semantic distance method have applied for measuring semantic distance between the query and document terms for ranking the terms. The experiments show that the performance of the proposed document ranking method for e-learning retrieved better document compared with existing document ranking methods. Show more

Keywords: Ontology, semantic ranking, classification, information retrieval

DOI: 10.3233/JIFS-201070

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 893-905, 2021