Journal of Intelligent & Fuzzy Systems - Volume 39, issue 5

Authors: Elavarasan, Dhivya | Vincent, Durai Raj

Article Type: Research Article

Abstract: The development in science and technical intelligence has incited to represent an extensive amount ofdata from various fields of agriculture. Therefore an objective rises up for the examination of the available data and integrating with processes like crop enhancement, yield prediction, examination of plant infections etc. Machine learning has up surged with tremendous processing techniques to perceive new contingencies in the multi-disciplinary agrarian advancements. In this pa- per a novel hybrid regression algorithm, reinforced extreme gradient boosting is proposed which displays essentially improved execution over traditional machine learning algorithms like artificial neural networks, deep Q-Network, gradient boosting, ran- dom forest …and decision tree. Extreme gradient boosting constructs new models, which are essentially, decision trees learning from the mistakes of their predecessors by optimizing the gradient descent loss function. The proposed hybrid model performs reinforcement learning at every node during the node splitting process of the decision tree construction. This leads to effective utilizationofthesamplesbyselectingtheappropriatesplitattributeforenhancedperformance. Model’sperformanceisevaluated by means of Mean Square Error, Root Mean Square Error, Mean Absolute Error, and Coefficient of Determination. To assure a fair assessment of the results, the model assessment is performed on both training and test dataset. The regression diagnostic plots from residuals and the results obtained evidently delineates the fact that proposed hybrid approach performs better with reduced error measure and improved accuracy of 94.15% over the other machine learning algorithms. Also the performance of probability density function for the proposed model delineates that, it can preserve the actual distributional characteristics of the original crop yield data more approximately when compared to the other experimented machine learning models. Show more

Keywords: Crop yield prediction, reinforcement learning, extreme gradient boosting, intelligent agrarian application

DOI: 10.3233/JIFS-200862

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7605-7620, 2020

Authors: Zhao, Tao | Li, Haodong | Dian, Songyi

Article Type: Research Article

Abstract: In this paper, we propose a method to assess the collision risk and a strategy to avoid the collision for solving the problem of dynamic real-time collision avoidance between robots when a multi-robot system is applied to perform a given task collaboratively and cooperatively. The collision risk assessment method is based on the moving direction and position of robots, and the collision avoidance strategy is based on the artificial potential field (APF) and the fuzzy inference system (FIS). The traditional artificial potential field (TAPF) has the problem of the local minimum, which will be optimized by improving the repulsive field …function. To adjust the speed of the robot adaptively and improve the security performance of the system, the FIS is used to plan the speed of robots. The hybridization of the improved artificial potential field (IAPF) and the FIS will make each robot safely and quickly find a collision-free path from the starting position to the target position in a completely unknown environment. The simulation results show that the strategy is effective and useful for collision avoidance in multi-robot systems. Show more

Keywords: Multi-robot, collision avoidance, path planning, improved artificial potential field, fuzzy inference system

DOI: 10.3233/JIFS-200869

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7621-7637, 2020

Authors: Wang, Hongyan | Huang, Zhi | Lu, Jinbo

Article Type: Research Article

Abstract: In this paper, by replacing the integral mass flow equation to fractional-order mass flow equation, the fractional-order mathematical model of 2DOF pneumatic-hydraulic upper limb rehabilitation training system is established. A new 2DOF fractional-order fuzzy PID (FOFPID) controller is designed, to provides a new reference for improving the control accuracy of the pneumatic system. In the design of the controller, the weight parameters of the input terms are transformed into the weight parameters of the error, and the input, which are analyzed to improve the accuracy of the controller design. The parameters of the control system are determined by multi-objective particle …swarm optimization. To prove the effectiveness of the proposed control method, the experimental research was carried out by building the experimental platform of pneumatic-hydraulic upper limb rehabilitation training system. The results show that the 2DOF FOFPID controller has better performance than other designed controllers under different working conditions. Show more

Keywords: Pneumatic-hydraulic drive, rehabilitation training system, fractional-order modeling, fractional-order fuzzy PID control

DOI: 10.3233/JIFS-200891

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7639-7651, 2020

Authors: Kumar, Ranjan | Edalatpanah, SA | Mohapatra, Hitesh

Article Type: Research Article

Abstract: There are different conditions where SPP play a vital role. However, there are various conditions, where we have to face with uncertain parameters such as variation of cost, time and so on. So to remove this uncertainty, Yang et al. [1 ] “[Journal of Intelligent & Fuzzy Systems, 32(1), 197-205”] have proposed the fuzzy reliable shortest path problem under mixed fuzzy environment and claimed that it is better to use their proposed method as compared to the existing method i.e., “[Hassanzadeh et al.; A genetic algorithm for solving fuzzy shortest path problems with mixed fuzzy arc lengths, Mathematical and Computer Modeling, …57(2013) 84-99”  [2 ]]. The aim of this note is, to highlight the shortcoming that is carried out in Yang et al. [1 ] article. They have used some mathematical incorrect assumptions under the mixed fuzzy domain, which is not true in a fuzzy environment. Show more

Keywords: normal fuzzy number, Shortest path problem (SPP), fuzzy shortest path problem (FSPP), mixed fuzzy environment

DOI: 10.3233/JIFS-200923

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7653-7656, 2020

Authors: Zhou, Linyong | You, Shanping | Ren, Bimo | Yu, Xuhong | Xie, Xiaoyao

Article Type: Research Article

Abstract: Pulsars are highly magnetized, rotating neutron stars with small volume and high density. The discovery of pulsars is of great significance in the fields of physics and astronomy. With the development of artificial intelligent, image recognition models based on deep learning are increasingly utilized for pulsar candidate identification. However, pulsar candidate datasets are characterized by unbalance and lack of positive samples, which has contributed the traditional methods to fall into poor performance and model bias. To this end, a general image recognition model based on adversarial training is proposed. A generator, a classifier, and two discriminators are included in the …model. Theoretical analysis demonstrates that the model has a unique optimal solution, and the classifier happens to be the inference network of the generator. Therefore, the samples produced by the generator significantly augment the diversity of training data. When the model reaches equilibrium, it can not only predict labels for unseen data, but also generate controllable samples. In experiments, we split part of data from MNIST for training. The results reveal that the model not only behaves better classification performance than CNN, but also has better controllability than CGAN and ACGAN. Then, the model is applied to pulsar candidate dataset HTRU and FAST. The results exhibit that, compared with CNN model, the F-score has increased by 1.99% and 3.67%, and the Recall has also increased by 6.28% and 8.59% respectively. Show more

Keywords: Generative adversarial nets, convolutional neural network, unbalanced dataset, pulsar candidate identification

DOI: 10.3233/JIFS-200925

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7657-7669, 2020

Authors: Liu, Xuning | Zhang, Guoying | Zhang, Zixian

Article Type: Research Article

Abstract: The feature selection of influencing factors of coal and gas outbursts is of great significance for presenting the most discriminative features and improving prediction performance of a classifier, the paper presents an effective hybrid feature selection and modified outbursts classifier framework which aims at solving exiting coal and gas outbursts prediction problems. First, a measurement standard based on maximum information coefficient(MIC) is employed to identify the wide correlations between two variables; Second, based on a ranking procedure using non-dominated sorting genetic algorithm(NSGAII), maximum relevance minimum redundancy(MRMR) algorithm is subsequently performed to find out candidate feature set highly related to the …class label and uncorrelated with each other; Third, random forest(RF) is employed to search the optimal feature subset from the candidate feature set, then the optimal feature subset that influences the classification performance of coal and gas outbursts is obtained; Finally, an improved classifier model has been proposed that combines gradient boosting decision tree(GBDT) and k-nearest neighbor(KNN) for outbursts prediction. In the modified classifier model, the GBDT is utilized to assign different weights to features, then the weighted features are input into the KNN to verify the effectiveness of proposed method on coal and gas outbursts dataset. The experimental results conclude that our proposed scheme is effective in the number of feature and prediction accuracy when compared with other related state-of-the-art prediction models based on feature selection for coal and gas outbursts. Show more

Keywords: Coal and gas outbursts, Maximum information coefficient, Non-dominated sorting genetic algorithm, Maximum relevance minimum redundancy, Random forest, Gradient boosting decision tree, K-nearest neighbor

DOI: 10.3233/JIFS-200937

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7671-7691, 2020

Authors: Guo, Jingni | Xu, Junxiang | Liao, Wei

Article Type: Research Article

Abstract: The multimodal transport network in the region with complex environment and being easily affected by disturbance factors is used as the research object in our work. The characteristics of the cascading failure of such multimodal transport network were analyzed. From the perspective of network load redistribution, the risk control methods for the cascading failure of the multimodal transport network were investigated. This research aims to solve the problem that traditional load redistribution methods usually ignore the original-destination (OD) constraint and uncertain risks. The conditional value-at-risk (CVaR) was improved based on the Bureau of Public Roads (BPR) road impedance function to …quantify the uncertainty of the disturbance factors. A nonlinear programming model was established with the generalized travel time as the objective function. A parallelly-running cellular ant colony algorithm was designed to solve the model. Empirical analysis was conducted on the multimodal transport network in Sichuan-Tibet region of China. The results of the empirical analysis verified the applicability of the proposed load redistribution method to such kind of regions and the effectiveness of the algorithm. This research provides theoretical basis and practical reference for the risk control of the cascading failure of multimodal transport networks in some regions. Show more

Keywords: Uncertain disturbance, multimodal transport network, risk control, load redistribution, cellular ant colony algorithm

DOI: 10.3233/JIFS-200968

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7693-7704, 2020

Authors: Kachouei, Mohammad | Ebrahimnejad, Ali | Bagherzadeh-Valami, Hadi

Article Type: Research Article

Abstract: Data Envelopment Analysis (DEA) is a non-parametric approach based on linear programming for evaluating the performance of decision making units (DMUs) with multiple inputs and multiple outputs. The lack of the ability to generate the actual weights, not considering the impact of undesirable outputs in the evaluation process and the measuring of efficiencies of DMUs based upon precise observations are three main drawbacks of the conventional DEA models. This paper proposes a novel approach for finding the common set of weights (CSW) to compute efficiencies in DEA model with undesirable outputs when the data are represented by fuzzy numbers. The …proposed approach is based on fuzzy arithmetic which formulates the fuzzy additive DEA model as a linear programing problem and gives fuzzy efficiencies of all DMUs based on resulting CSW. We demonstrate the applicability of the proposed model with a simple numerical example. Finally, in the context of performance management, an application of banking industry in Iran is presented for analyzing the influence of fuzzy data and depicting the impact of undesirable outputs over the efficiency results. Show more

Keywords: Data envelopment analysis, undesirable outputs, fuzzy numbers, common set of weights

DOI: 10.3233/JIFS-201022

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7705-7722, 2020

Authors: Ali, Mohamed R. | Hadhoud, Adel R. | Ma, Wen-Xiu

Article Type: Research Article

Abstract:  In this approximation study, a nonlinear singular periodic model in nuclear physics is solved by using the Hermite wavelets (HW) technique coupled with a numerical iteration technique such as the Newton Raphson (NR) one for solving the resulting nonlinear system. The stimulation of offering this numerical work comes from the aim of introducing a consistent framework that has as effective structures as Hermite wavelets. Two numerical examples of the singular periodic model in nuclear physics have been investigated to observe the robustness, proficiency, and stability of the designed scheme. The proposed outcomes of the HW technique are compared with available …numerical solutions that established fitness of the designed procedure through performance evaluated on a multiple execution. Show more

Keywords: Singular periodic systems in nuclear physics, Hermite wavelets, hybrid approach, Gaussian formula of integration, collocation technique

DOI: 10.3233/JIFS-201045

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7723-7731, 2020

Authors: Cao, Jing | Xu, Xuan-hua | Dai, Fei | Pan, Bin

Article Type: Research Article

Abstract: This study uses opinion dynamics to explore the influence of extremists in the consensus process of large group decision-making. When moderates are exposed to extremists, their risk preference will be affected. By using the opinion leader theory for reference, the influence model of extremists is constructed. To better study the influence of extremists, the similarity of risk preference between extremists and moderates is modeled to measure their similarity degree. From this model, for every moderate, the extremists are divided into two groups: homogeneous group and heterogeneous group. Finally, the risk preference evolution model is structured by considering that moderates change …their risk preference dynamically according to their initial preference, their attitude towards the homogeneous groups, and the heterogeneous groups. Finding from data analysis shows that moderates with high acceptance toward the influence of extremists are more likely to reach group consensus. It is also found that the preference trend of moderates with a certain degree of acceptance toward heterogeneous groups fluctuates with a ‘W’ shape. This study bridges the gap between opinion dynamics and group decision making. Meanwhile, the model inspires new explanations and new perspectives for the group consensus process. Show more

Keywords: Extremists, opinion dynamics, group emergency decision-making, group consensus, risk preference evolution

DOI: 10.3233/JIFS-201106

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7733-7746, 2020

Authors: Van Hoa, Ngo | Allahviranloo, Tofigh | Vu, Ho

Article Type: Research Article

Abstract: In this paper, we present the Hyers–Ulam stability and Hyers–Ulam-Rassias stability (HU-stability and HUR-stability for short) for fuzzy initial value problem (FIVP) by using fixed point theorem. We improve and extend some known results on the stability for FDEs by dropping some assumptions. Some examples illustrate the theoretical results.

Keywords: HU stability, HUR stability, fuzzy differential equation, uncertainly, fixed point theory, 34A12, 45J05

DOI: 10.3233/JIFS-201109

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7747-7755, 2020

Authors: Gembalczyk, Grzegorz | Duda, Slawomir | Switonski, Eugeniusz | Mezyk, Arkadiusz

Article Type: Research Article

Abstract: Training with use of mechatronic devices is an innovative rehabilitation method for patients with various locomotor dysfunction. High efficiency of training is noted in systems that combine a treadmill or orthosis with a body weight support system. Speed control is a limitation of such rehabilitation systems. In commercially available devices, the treadmill speed is constant or set by the therapist. Even better training results should be obtained for devices in which the speed of the treadmill will be automatically adjusted to the patient walking pace. This study presents a mechatronic device for locomotor training that uses an algorithm to adjust …the speed of the treadmill. This speed is controlled with use of a sensor that measures the rope inclination. The end of rope is fastened to the orthopaedic harness. Speed control is realized in such a way that ensures the smallest possible swing angle of the rope. A fuzzy controller was applied to adjust the treadmill speed. The drive system of the treadmill is equipped in a servodrive with PMSM motor and energy recovery module, which allows smooth speed control, limiting acceleration and minimizing electricity consumption. The presented solution was implemented in a real object and subjected to experimental tests. Show more

Keywords: Fuzzy controller, rehabilitation treadmill, speed adaptation, gait reeducation

DOI: 10.3233/JIFS-201111

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7757-7767, 2020

Authors: Basiri, Mohammad-Ali | Alinezhad, Esmaeil | Tavakkoli-Moghaddam, Reza | Shahsavari-Poure, Nasser

Article Type: Research Article

Abstract: This paper presents a multi-objective mathematical model for a flexible job shop scheduling problem (FJSSP) with fuzzy processing times, which is solved by a hybrid intelligent algorithm (HIA). This problem contains a combination of a classical job shop problem with parallel machines (JSPM) to provide flexibility in the production route. Despite the previous studies, the number of parallel machines is not pre-specified in this paper. This constraint with other ones (e.g., sequence-dependent setup times, reentrant workflows, and fuzzy variables) makes the given problem more complex. To solve such a multi-objective JSPM, Pareto-based optimization algorithms based on multi-objective meta-heuristics and multi-criteria …decision making (MCDM) methods are utilized. Then, different comparison metrics (e.g., quality, mean ideal distance, and rate of achievement simultaneously) are used. Also, this paper includes two major phases to provide a new model of the FJSSP and introduce a new proposed HIA for solving the presented model, respectively. This algorithm is a hybrid genetic algorithm with the SAW/TOPSIS method, namely HGASAW/HGATOPSIS. The comparative results indicate that HGASAW and HGATOPSIS outperform the non-dominated sorting genetic algorithm (NSGA-II) to tackle the fuzzy multi-objective JSPM. Show more

Keywords: Flexible job shop scheduling, multi-objective optimization, sequence-dependent setup times, multi-criteria decision making, hybrid intelligent algorithms.

DOI: 10.3233/JIFS-201120

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7769-7785, 2020

Authors: Imran, Muhammad | Akhter, Shehnaz | Shaker, Hani

Article Type: Research Article

Abstract: Inequalities are a useful method to investigate and compare topological indices of graphs relatively. A large collection of graph associated numerical descriptors have been used to examine the whole structure of networks. In these analysis, degree related topological indices have a significant position in theoretical chemistry and nanotechnology. Thus, the computation of degree related indices is one of the successful topic of research. Given a molecular graph H , the general Randić connectivity index is interpreted as R α ( H ) = ∑ ℛ ∈ E ( H ) ( …deg H ( a ) deg H ( b ) ) α , with α is a real quantity. Also a graph transformation of H provides a comparatively simpler isomorphic structure with an ease to work on different chemical properties. In this article, we determine the sharp bounds of general Randić index of numerous graph transformations, such that semi-total-point, semi-total-line, total and eight individual transformations H fgh , where f , g , h  ∈ {+ , -} of graphs by using combinatorial inequalities. Show more

Keywords: General Randić index, transformation graph, semi-total-point graph, semi-total-line graph

DOI: 10.3233/JIFS-201139

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7787-7794, 2020

Authors: Reyad, Omar | Hamed, Kadry | Karar, Mohamed Esmail

Article Type: Research Article

Abstract: Bit-string generator (BSG) is based on the hardness of known number theoretical problems, such as the discrete logarithm problem with the elliptic curve (ECDLP). Such type of generators will have good randomness and unpredictability properties as it is challenged to find a solution regarding this mathematical dilemma. Hash functions in turn play a remarkable role in many cryptographic tasks to accomplish different security levels. Hash-enhanced elliptic curve bit-string generator (HEECBSG) mechanism is proposed in this study based on the ECDLP and secure hash function. The cryptographic hash function is used to achieve integrity and security of the obtained bit-strings for …highly sensitive plain data. The main contribution of the proposed HEECBSG is transforming the x -coordinate of the elliptic curve points using a hash function H to generate bit-strings of any desirable length. The obtained pseudo-random bits are tested by the NIST test suite to analyze and verify its statistical and randomness properties. The resulted bit-string is utilized here for encrypting various medical images of the vital organs, i.e. the brain, bone, fetuses, and lungs. Then, extensive evaluation metrics have been applied to analyze the successful performance of the cipherimage, including key-space analysis, histogram analysis, correlation analysis, entropy analysis and sensitivity analysis. The results demonstrated that our proposed HEECBSG mechanism is feasible for achieving security and privacy purposes of the medical image transmission over unsecure communication networks. Show more

Keywords: Elliptic curve cryptography, hash function, bit-string generator, medical image encryption, security analysis

DOI: 10.3233/JIFS-201146

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7795-7806, 2020

Authors: Habib, Shaista | us Salam, Wardat | Butt, M. Arif | Akram, M. | Smarandache, F.

Article Type: Research Article

Abstract: Cardiovascular diseases are the leading cause of death worldwide. Early diagnosis of heart disease can reduce this large number of deaths so that treatment can be carried out. Many decision-making systems have been developed, but they are too complex for medical professionals. To target these objectives, we develop an explainable neutrosophic clinical decision-making system for the timely diagnose of cardiovascular disease risk. We make our system transparent and easy to understand with the help of explainable artificial intelligence techniques so that medical professionals can easily adopt this system. Our system is taking thirty-five symptoms as input parameters, which are, gender, …age, genetic disposition, smoking, blood pressure, cholesterol, diabetes, body mass index, depression, unhealthy diet, metabolic disorder, physical inactivity, pre-eclampsia, rheumatoid arthritis, coffee consumption, pregnancy, rubella, drugs, tobacco, alcohol, heart defect, previous surgery/injury, thyroid, sleep apnea, atrial fibrillation, heart history, infection, homocysteine level, pericardial cysts, marfan syndrome, syphilis, inflammation, clots, cancer, and electrolyte imbalance and finds out the risk of coronary artery disease, cardiomyopathy, congenital heart disease, heart attack, heart arrhythmia, peripheral artery disease, aortic disease, pericardial disease, deep vein thrombosis, heart valve disease, and heart failure. There are five main modules of the system, which are neutrosophication, knowledge base, inference engine, de-neutrosophication, and explainability. To demonstrate the complete working of our system, we design an algorithm and calculates its time complexity. We also present a new de-neutrosophication formula, and give comparison of our the results with existing methods. Show more

Keywords: Single-valued neutrosophic number, explainable artificial intelligence, cardiovascular diseases, decision making, de-neutrosophication, algorithm

DOI: 10.3233/JIFS-201163

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7807-7829, 2020

Authors: Konwar, Nabanita

Article Type: Research Article

Abstract: The aim of this paper is to define the notion of intuitionistic fuzzy b metric space (in short, IFb MS) along with some useful results. We establish some important Lemmas in order to study the Cauchy sequence in IFb MS. To further develop the work, we establish some fixed point theorems and study the existence of unique fixed point of some self mappings in IFb MS. We also develop the concept of Ćirić quasi-Contraction theorem in IFb MS. Examples are provided to validate the non-triviality of the results.

Keywords: Intuitionistic fuzzy b-metric space, Cauchy sequence, fixed point theorem, unique fixed point, Ćirić quasi-contraction theorem

DOI: 10.3233/JIFS-201233

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7831-7841, 2020

Authors: Wen, Haili | Xia, Fei | Tang, Hongxiang

Article Type: Research Article

Abstract: An information system (IS) is a database that expresses relationships between objects and attributes. An IS with decision attributes is said to be a decision information system (DIS). An incomplete real-valued decision information system (IRVDIS) is a DIS based on incomplete real-valued data. This paper studies three-way decision (3WD) for incomplete real-valued data and its application. In the first place, the distance between two objects on the basis of the conditional attribute set in an IRVDIS is constructed. In the next place, the fuzzy T cos -equivalence relation on the object set of an IRVDIS is received by means …of Gaussian kernel. After that, the decision-theoretic rough set model for an IRVDIS is presented. Furthermore, the 3WD method is proposed based on this model. Lastly, to illustrate the feasibility of the proposed method, an application of the proposed method is given. It is worth mentioning that levels of risk may be determined by thresholds that can be directly acquired according to risk preference of different decision-makers, as well as the decision rule for each decision class under different levels of risk is showed in tabular forms. Show more

Keywords: 3WD, IRVDIS, Method, Decision-theoretic rough set, Gaussian kernel, Inclusion degree, Auto diagnostic

DOI: 10.3233/JIFS-201272

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7843-7862, 2020

Authors: Dong, Yuanxiang | Cheng, Xiaoting | Chen, Weijie | Shi, Hongbo | Gong, Ke

Article Type: Research Article

Abstract: In actual life, uncertain and inconsistent information exists widely. How to deal with the information so that it can be better applied is a problem that has to be solved. Neutrosophic soft sets can process uncertain and inconsistent information. Also, Dempster-Shafer evidence theory has the advantage of dealing with uncertain information, and it can synthesize uncertain information and deal with subjective judgments effectively. Therefore, this paper creatively combines the Dempster-Shafer evidence theory with the neutrosophic soft sets, and proposes a cosine similarity measure for multi-criteria group decision making. Different from the previous studies, the proposed similarity measure is utilized to …measure the similarity between two objects in the structure of neutrosophic soft set, rather than two neutrosophic soft sets. We also propose the objective degree and credibility degree which reflect the decision makers’ subjective preference based on the similarity measure. Then parameter weights are calculated by the objective degree. Additionally, based on credibility degree and parameter weights, we propose the modified score function, modified accuracy function, and modified certainty function, which can be employed to obtain partial order relation and make decisions. Later, we construct an aggregation algorithm for multi-criteria group decision making based on Dempster’s rule of combination and apply the algorithm to a case of medical diagnosis. Finally, by testing and comparing the algorithm, the results demonstrate that the proposed algorithm can solve the multi-criteria group decision making problems effectively. Show more

Keywords: Neutrosophic soft sets, Dempster-Shafer evidence theory, cosine similarity measure, multi-criteria group decision making

DOI: 10.3233/JIFS-201328

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7863-7880, 2020

Authors: Siddique, Saba | Ahmad, Uzma | us Salam, Wardat | Akram, Muhammad | Smarandache, Florentin

Article Type: Research Article

Abstract: The concept of generalized complex neutrosophic graph of type 1 is an extended approach of generalized neutrosophic graph of type 1. It is an effective model to handle inconsistent information of periodic nature. In this research article, we discuss certain notions, including isomorphism, competition graph, minimal graph and competition number corresponding to generalized complex neutrosophic graphs. Further, we describe these concepts by several examples and present some of their properties. Moreover, we analyze that a competition graph corresponding to a generalized complex neutrosophic graph can be represented by an adjacency matrix with suitable real life examples. Also, we enumerate the …utility of generalized complex neutrosophic competition graphs for computing the strength of competition between the objects. Finally, we highlight the significance of our proposed model by comparative analysis with the already existing models. Show more

Keywords: Isomorphism, competition graphs of type 1, minimal graphs, competition matrix, comparative analysis

DOI: 10.3233/JIFS-201338

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7881-7897, 2020

Authors: Akbari-Bengar, Davood | Ebrahimnejad, Ali | Motameni, Homayun | Golsorkhtabaramiri, Mehdi

Article Type: Research Article

Abstract: Internet is one of the most influential new communication technologies has influenced all aspects of human life. Extensive use of the Internet and the rapid growth of network services have increased network traffic and ultimately a slowdown in internet speeds around the world. Such traffic causes reduced network bandwidth, server response latency, and increased access time to web documents. Cache memory is used to improve CPU performance and reduce response time. Due to the cost and limited size of cache compared to other devices that store information, an alternative policy is used to select and extract a page to make …space for new pages when the cache is filled. Many algorithms have been introduced which performance depends on a high-speed web cache, but it is not well optimized. The general feature of most of them is that they are developed from the famous LRU and LFU designs and take advantage of both designs. In this research, a page replacement algorithm called FCPRA (Fuzzy Clustering based Page Replacement Algorithm) is presented, which is based on four features. When the cache space can’t respond to a request for a new page, it selects a page of the lowest priority cluster and the largest login order; then, removes it from the cache memory. The results show that FCPRA has a better hit rate with different data sets and can improve the cache memory performance compared to other algorithms. Show more

Keywords: Web cache, performance, response time, page replacement algorithm, hit rate

DOI: 10.3233/JIFS-201360

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7899-7908, 2020

Authors: Wang, Chuantao | Yang, Xuexin | Ding, Linkai

Article Type: Research Article

Abstract: The purpose of sentiment classification is to solve the problem of automatic judgment of sentiment tendency. In the sentiment classification task of text data (such as online reviews), the traditional deep learning model focuses on algorithm optimization, but ignores the characteristics of the imbalanced distribution of the number of samples in each classification, which will cause the classification performance of the model to decrease in practical applications. In this paper, the experiment is divided into two stages. In the first stage, samples of minority class in the sample distribution are used to train a sequence generative adversarial nets, so that …the sequence generative adversarial nets can learn the features of the samples of minority class in depth. In the second stage, the trained generator of sequence generative adversarial nets is used to generate false samples of minority class and mix them with the original samples to balance the sample distribution. After that, the mixed samples are input into the sentiment classification deep model to complete the model training. Experimental results show that the model has excellent classification performance in comparing a variety of deep learning models based on classic imbalanced learning methods in the sentiment classification task of hotel reviews. Show more

Keywords: Sentiment classification, imbalanced classification, deep learning, generative adversarial network

DOI: 10.3233/JIFS-201370

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7909-7919, 2020

Authors: Fan, Liu | Yager, Ronald R. | Mesiar, Radko | Jin, LeSheng

Article Type: Research Article

Abstract: The evaluation for online shopping platform is the basis for further decision and policy taking. The collected individual opinion and evaluation information are often represented by some linguistic/preference vectors. Further aggregating those vector needs to simultaneously consider two contradictory factors: the original weights assigned and the inconsistencies involved which requires some new weights assigned. Around those weights allocation factors, to mitigate the negative effect of inconsistency in the collected information, we propose an integrated evaluation model. The model uses the scatter degree as a main indicator, and extends some weights allocation methods such as regular increasing monotone (RIM) quantifier based …weights allocation in a new environment, and applies the three sets expression based paradigm and formulation. The proposed model is able to simultaneously give emphasis on those input data with high consistency and to consider the preferences of decision makers. Some detailed evaluation processes and numerical examples are also provided for practitioners to refer to. Show more

Keywords: Aggregation operators, evaluation for online shopping platform, information fusion, multi-criteria decision making, weights adjustment and allocation

DOI: 10.3233/JIFS-201376

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7921-7930, 2020

Authors: Tripathi, Gaurav | Singh, Kuldeep | Vishwakarma, Dinesh Kumar

Article Type: Research Article

Abstract: Violence detection is a challenging task in the computer vision domain. Violence detection framework depends upon the detection of crowd behaviour changes. Violence erupts due to disagreement of an idea, injustice or severe disagreement. The aim of any country is to maintain law and order and peace in the area. Violence detection thus becomes an important task for authorities to maintain peace. Traditional methods have existed for violence detection which are heavily dependent upon hand crafted features. The world is now transitioning in to Artificial Intelligence based techniques. Automatic feature extraction and its classification from images and videos is the …new norm in surveillance domain. Deep learning platform has provided us the platter on which non-linear features can be extracted, self-learnt and classified as per the appropriate tool. One such tool is the Convolutional Neural Networks, also known as ConvNets, which has the ability to automatically extract features and classify them in to their respective domain. Till date there is no survey of deciphering violence behaviour techniques using ConvNets. We hope that this survey becomes an exclusive baseline for future violence detection and analysis in the deep learning domain. Show more

Keywords: Violence detection, crowd behaviour, ConvNets, convolutional neural networks, deep learning, survey

DOI: 10.3233/JIFS-201400

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7931-7952, 2020

Authors: Sun, Yuan | Lin, Chih-Min

Article Type: Research Article

Abstract: This study presents a fuzzy brain emotional learning classifier (FBELC), combined with a modified particle swarm optimization (PSO) algorithm, that allows a network to automatically determine the optimum values for a reward signal and a classification threshold. The designed FBELC model imitates the brain decision process including the emotion information. To verify the predictive performance, a novel fitness function based on the accuracy of the training and cross-validation datasets is used for a PSO algorithm. This PSO-FBELC model is used to diagnose breast tumors and heart diseases. A comparison of simulations using the proposed PSO-FBELC with other processes shows that …the proposed model performs better in terms of recognition accuracy. Show more

Keywords: Fuzzy brain emotional learning classifier (FBELC), particle swarm optimization (PSO), disease diagnosis

DOI: 10.3233/JIFS-201418

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7953-7960, 2020

Authors: Hu, Junhua | Liu, Jie | Liang, Pei | Li, Bo

Article Type: Research Article

Abstract: Malaria is one of the three major diseases with the highest mortality worldwide and can turn fatal if not taken seriously. The key to surviving this disease is its early diagnosis. However, manual diagnosis is time consuming and tedious due to the large amount of image data. Generally, computer-aided diagnosis can effectively improve doctors’ perception and accuracy. This paper presents a medical diagnosis method powered by convolutional neural network (CNN) to extract features from images and improve early detection of malaria. The image sharpening and histogram equalization method are used aiming at enlarging the difference between parasitized regions and other …area. Dropout technology is employed in every convolutional layer to reduce overfitting in the network, which is proved to be effective. The proposed CNN model achieves a significant performance with the best classification accuracy of 99.98%. Moreover, this paper compares the proposed model with the pretrained CNNs and other traditional algorithms. The results indicate the proposed model can achieve state-of-the-art performance from multiple metrics. In general, the novelty of this work is the reduction of the CNN structure to only five layers, thereby greatly reducing the running time and the number of parameters, which is demonstrated in the experiments. Furthermore, the proposed model can assist clinicians to accurately diagnose the malaria disease. Show more

Keywords: Medical diagnosis, computer-aided diagnosis, deep learning, convolutional neural network, malaria

DOI: 10.3233/JIFS-201427

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7961-7976, 2020

Authors: Wu, Yixiang

Article Type: Research Article

Abstract: The product form evolutionary design based on multi-objective optimization can satisfy the complex emotional needs of consumers for product form, but most relevant literatures mainly focus on single-objective optimization or convert multiple-objective optimization into the single objective by weighting method. In order to explore the optimal product form design, we propose a hybrid product form design method based on back propagation neural networks (BP-NN) and non-dominated sorting genetic algorithm-II (NSGA-II) algorithms from the perspective of multi-objective optimization. First, the product form is deconstructed and encoded by morphological analysis method, and then the semantic difference method is used to enable consumers …to evaluate product samples under a series of perceptual image vocabularies. Then, the nonlinear complex functional relation between the consumers’ perceptual image and the morphological elements is fitted with the BP-NN. Finally, the trained BP-NN is embedded into the NSGA-II multi-objective evolutionary algorithm to derive the Pareto optimal solution. Based on the hybrid BP-NN and NSGA-II algorithms, a multi-objective optimization based product form evolutionary design system is developed with the electric motorcycle as a case. The system is proved to be feasible and effective, providing theoretical reference and method guidance for the multi-image product form design. Show more

Keywords: Morphological analysis method, kansei engineering, back propagation neural networks, multi-objective evolutionary algorithm

DOI: 10.3233/JIFS-201439

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7977-7991, 2020

Authors: Ferrari, Allan Christian Krainski | Coelho, Leandro dos Santos | Leandro, Gideon Villar | Osinski, Cristiano | da Silva, Carlos Alexandre Gouvea

Article Type: Research Article

Abstract: The Whale Optimization Algorithm (WOA) is a recent meta-heuristic that can be explored in global optimization problems. This paper proposes a new parameter adjustment mechanism that influences the probability of the food recognition process in the whale algorithm. The adjustment is performed using a fuzzy inference system that uses the current iteration number as input information. Our simulation results are compared with other meta-heuristics such as the conventional version of WOA, Particle Swarm Optimization (PSO) and Differential Evolution (DE). All algorithms are used to optimize ten test functions (Sphere, Schwefel 2.22, Quartic, Rosenbrock, Ackley, Rastrigin, Penalty 1, Schwefel 2.21, Six …hump camel back and Shekel 1) in order to obtain their respective optimal values for be used as criteria for analysis and comparison. The results of the simulations show that the proposed fuzzy inference system improves the convergence of WOA and also is competitive in relation to the other algorithms, i.e., classical WOA, PSO and DE. Show more

Keywords: Benchmark functions, fuzzy system, global optimization, meta-heuristics optimization, whale optimization algorithm

DOI: 10.3233/JIFS-201459

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7993-8000, 2020

Authors: Lin, Yidong | Li, Jinjin | Liao, Shujiao | Zhang, Jia | Liu, Jinghua

Article Type: Research Article

Abstract: Knowledge reduction is one of critical problems in data mining and information processing. It can simplify the structure of the lattice during the construction of fuzzy-crisp concept lattice. In terms of fuzzy-crisp concept, we develop an order-class matrix to represent extents and intents of concepts, respectively. In order to improve the computing efficiency, it is necessary to reduce the size of lattices as much as possible. Therefore the judgement theorem of meet-irreducible elements is proposed. To deal with attribute reductions, we develop a discernibility Boolean matrix in formal fuzzy contexts by preserving extents of meet-irreducible elements via order-class matrix. A …heuristic attribute-reduction algorithm is proposed. Then we extend the proposed model to consistent formal fuzzy decision contexts. Our methods present a new framework for knowledge reduction in formal fuzzy contexts. Show more

Keywords: Attribute reduction, discernibility matrix, fuzzy-crisp concept, meet-irreducible elements

DOI: 10.3233/JIFS-201485

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 8001-8013, 2020

Authors: Lei, Fei | Dong, Xueying | Ma, Xiaohe

Article Type: Research Article

Abstract: With the development of the urban industry in recent years, air pollution in areas such as factories and streets has become more and more serious. Air quality problems directly affect the normal lives of residents. Effectively predicting the future air condition in the area through relevant historical data has high application value for early warning of this area. Through the study of the previous monitoring data, it is found that the pollutant data of adjacent monitoring stations are correlated in more periods. Therefore, this paper proposes a hybrid model based on CNN and Bi-LSTM, using CNN to synthesize multiple adjacent …stations with strong correlations to extract spatial features between data, and using Bi-LSTM to extract features in the time dimension to finally achieve pollutant concentration prediction. Using the historical data of 40 monitoring stations in different locations of Fushun city to conduct research. By comparing with the traditional prediction model, the results prove that the model proposed in this paper has higher accuracy and stronger robustness. Show more

Keywords: CNN, Bi-LSTM, temporal and spatial features, correlation analysis, PM2.5 prediction

DOI: 10.3233/JIFS-201515

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 8015-8025, 2020

Authors: Kong, Weiyi | Yang, Menglong | Huang, Qinzhen

Article Type: Research Article

Abstract: This paper proposes a Hilbert stereo reconstruction algorithm based on depth feature and stereo matching to solve the problem of occlusive region matching errors, namely, the Hilbert stereo network. The traditional stereo network pays more attention to disparity itself, leading to the inaccuracy of disparity estimation. Our design network studies the effective disparity matching and refinement through reconstruction representation of Hilbert’s disparity coefficient. Since the Hilbert coefficient is not affected by the occlusion and texture in the image, stereo disparity matching can conducted effectively. Our network includes three sub-modules, namely, depth feature representation, Hilbert cost volume fusion, and Hilbert refinement …reconstruction. Separately, texture features of different depth levels of the image were extracted through Hilbert filtering operation. Next, stereoscopic disparity fusion was performed, and then Hilbert designed to refine the difference regression stereo matching solution was used. Based on the end-to-end design, the structure is refined by combining the depth feature extraction module and Hilbert coefficient disparity. Finally, the Hilbert stereo matching algorithm achieves excellent performance on standard big data set and is compared with other advanced stereo networks. Experiments show that our network has high accuracy and high performance. Show more

Keywords: Stereo matching, hilbert analysis, image processing, multiple resolutions

DOI: 10.3233/JIFS-201749

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 8027-8038, 2020