Journal of Intelligent & Fuzzy Systems - Volume 40, issue 6

Authors: Guo, Jiong | Lei, Deming | Li, Ming

Article Type: Research Article

Abstract: Energy-efficient flexible job shop scheduling problems (EFJSP) have been investigated fully; however, energy-related objectives often have lower importance than other ones in many real-life situations and this case is hardly considered in the previous works. In this study, EFJSP with sequence-dependent setup times (SDST) is considered, in which total tardiness and makespan are given higher importance than total energy consumption. A two-phase imperialist competitive algorithm (TPICA) is proposed. The importance difference among objectives is implemented by treating all objectives equally in the first phase and making energy consumption not to exceed a diminishing threshold in the second phase. A dynamical …differentiating assimilation and a novel imperialist competition with the enforced search are implemented. Extensive experiments are conducted and the computational results show that TPICA is very competitive for EFJSP with SDST. Show more

Keywords: Flexible job shop scheduling, energy-efficient scheduling, imperialist competitive algorithm, sequence-dependent setup times

DOI: 10.3233/JIFS-210198

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12125-12137, 2021

Authors: Cao, Xin-Zi | Luo, Sheng-Zhou | Li, Jing-Cong | Pan, Jia-Hui

Article Type: Research Article

Abstract: The grade and stage of bladder tumors is an essential key for diagnosing and treating bladder cancer. This study proposed an automated bladder tumor prediction system to automatically assess the bladder tumor grade and stage automatically on Magnetic Resonance Imaging (MRI) images. The system included three modules: tumor segmentation, feature extraction and prediction. We proposed a U-ResNet network that automatically extracts morphological and texture features for detecting tumor regions. These features were used in support vector machine (SVM) classifiers to predict the grade and stage. Our proposed method segmented the tumor area and predicted the grade and stage more accurately …compared to different methods in our experiments on MRI images. The accuracy of bladder tumor grade prediction was about 70%, and the accuracy of the data set was about 77.5%. The extensive experiments demonstrated the usefulness and effectiveness of our method. Show more

Keywords: Bladder tumor segmentation, U-ResNet network, grade and stage, feature extraction, support vector machine

DOI: 10.3233/JIFS-210263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12139-12150, 2021

Authors: Xu, Zhixuan | Chen, Caikou | Han, Guojiang | Gao, Jun

Article Type: Research Article

Abstract: As a successful improvement on Low Rank Representation (LRR), Latent Low Rank Representation (LatLRR) has been one of the state-of-the-art models for subspace clustering due to the capability of discovering the low dimensional subspace structures of data, especially when the data samples are insufficient and/or extremely corrupted. However, the LatLRR method does not consider the nonlinear geometric structures within data, which leads to the loss of the locality information among data in the learning phase. Moreover, the coefficients of the learnt representation matrix can be negative, which lack the interpretability. To solve the above drawbacks of LatLRR, this paper introduces …Laplacian, sparsity and non-negativity to LatLRR model and proposes a novel subspace clustering method, termed latent low rank representation with non-negative, sparse and laplacian constraints (NNSLLatLRR), in which we jointly take into account non-negativity, sparsity and laplacian properties of the learnt representation. As a result, the NNSLLatLRR can not only capture the global low dimensional structure and intrinsic non-linear geometric information of the data, but also enhance the interpretability of the learnt representation. Extensive experiments on two face benchmark datasets and a handwritten digit dataset show that our proposed method outperforms existing state-of-the-art subspace clustering methods. Show more

Keywords: Subspace clustering, low rank representation, latent low rank representation, non-negative sparse laplacian constraints

DOI: 10.3233/JIFS-210274

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12151-12165, 2021

Authors: Liu, Luping | Wang, Meiling | He, Xiaohai | Qing, Linbo | Zhang, Jin

Article Type: Research Article

Abstract: Joint extraction of entities and relations from unstructured text is an essential step in constructing a knowledge base. However, relational facts in these texts are often complicated, where most of them contain overlapping triplets, making the joint extraction task still challenging. This paper proposes a novel Sequence-to-Sequence (Seq2Seq) framework to handle the overlapping issue, which models the triplet extraction as a sequence generation task. Specifically, a unique cascade structure is proposed to connect transformer and pointer network to extract entities and relations jointly. By this means, sequences can be generated in triplet-level and it speeds up the decoding process. Besides, …a syntax-guided encoder is applied to integrate the sentence’s syntax structure into the transformer encoder explicitly, which helps the encoder pay more accurate attention to the syntax-related words. Extensive experiments were conducted on three public datasets, named NYT24, NYT29, and WebNLG, and the results show the validity of this model by comparing with various baselines. In addition, a pre-trained BERT model is also employed as the encoder. Then it comes up to excellent performance that the F1 scores on the three datasets surpass the strongest baseline by 5.7%, 5.6%, and 4.4%. Show more

Keywords: Information extraction, sequence to sequence, transformer network, pointer network, syntax-guided attention network

DOI: 10.3233/JIFS-210281

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12167-12183, 2021

Authors: Gao, Xiue | Jiang, Panling | Xie, Wenxue | Chen, Yufeng | Zhou, Shengbin | Chen, Bo

Article Type: Research Article

Abstract: Decision fusion is an effective way to resolve the conflict of diagnosis results. Aiming at the problem that Dempster-Shafer (DS) theory deals with the high conflict of evidence and produces wrong results, a decision fusion algorithm for fault diagnosis based on closeness and DS theory is proposed. Firstly, the relevant concepts of DS theory are introduced, and the normal distribution membership function is used as the evidence closeness. Secondly, the harmonic average is introduced, and the weight of each evidence is established according to the product of closeness of each evidence and its harmonic average. Thirdly, the weight of conflicting …evidence is regularized, and the final decision fusion result is obtained by using the Dempster’s rule. Lastly, the simulation and application examples are designed. Simulation and application results show that the method can effectively reduce the impact of diagnostic information conflicts and improve the accuracy of decision fusion. What’s more, the method considers the overall average distribution of evidence in the identification framework, it can reduce evidence conflicts while preserving important diagnostic information. Show more

Keywords: Fault diagnosis, decision fusion, DS theory, closeness, harmonic average

DOI: 10.3233/JIFS-210283

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12185-12194, 2021

Authors: Chen, Ting-Yu

Article Type: Research Article

Abstract: The purpose of this paper is to evolve a novel area-based Pythagorean fuzzy decision model via an approach-oriented measure and an avoidance-oriented measure in support of multiple criteria decision analysis involving intricate uncertainty of Pythagorean fuzziness. Pythagorean membership grades embedded in a Pythagorean fuzzy set is featured by tensible functions of membership, non-membership, indeterminacy, strength, and direction, which delivers flexibility and adaptability in manipulating higher-order uncertainties. However, a well-defined ordered structure is never popular in real-life issues, seldom seen in Pythagorean fuzzy circumstances. Consider that point operators can make a systematic allocation of the indeterminacy composition contained in Pythagorean fuzzy …information. This paper exploits the codomains of the point operations (i.e., the quantities that express the extents of point operators) to launch new measurements of approach orientation and avoidance orientation for performance ratings. This paper employs such measurements to develop an area-based performance index and an area-based comprehensive index for conducting a decision analysis. The applications and comparative analyses of the advanced area-based approach to some decision-making problems concerning sustainable recycling partner selection, company investment decisions, stock investment decisions, and working capital financing decisions give support to methodological advantages and practical effectiveness. Show more

Keywords: Area-based Pythagorean fuzzy decision model, approach-oriented measure, avoidance-oriented measure, multiple criteria decision analysis, Pythagorean fuzziness

DOI: 10.3233/JIFS-210290

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12195-12213, 2021

Authors: Ghafarokhi, Omid Izadi | Moattari, Mazda | Forouzantabar, Ahmad

Article Type: Research Article

Abstract: With the development of the wide-area monitoring system (WAMS), power system operators are capable of providing an accurate and fast estimation of time-varying load parameters. This study proposes a spatial-temporal deep network-based new attention concept to capture the dynamic and static patterns of electrical load consumption through modeling complicated and non-stationary interdependencies between time sequences. The designed deep attention-based network benefits from long short-term memory (LSTM) based component to learning temporal features in time and frequency-domains as encoder-decoder based recurrent neural network. Furthermore, to inherently learn spatial features, a convolutional neural network (CNN) based attention mechanism is developed. Besides, this …paper develops a loss function based on a pseudo-Huber concept to enhance the robustness of the proposed network in noisy conditions as well as improve the training performance. The simulation results on IEEE 68-bus demonstrates the effectiveness and superiority of the proposed network through comparison with several previously presented and state-of-the-art methods. Show more

Keywords: Composite load modeling, deep attention neural network, encoder-decoder, long short-term memory, convolutional neural network, wide-area monitoring system

DOI: 10.3233/JIFS-210296

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12215-12226, 2021

Authors: Gasmi, Ibtissem | Azizi, Mohamed Walid | Seridi-Bouchelaghem, Hassina | Azizi, Nabiha | Belhaouari, Samir Brahim

Article Type: Research Article

Abstract: Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from …the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques. Show more

Keywords: Collaborative filtering, context, topic modeling, PSO, LDA, sparsity problem

DOI: 10.3233/JIFS-210331

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12227-12242, 2021

Authors: Xu, Kaijie | E, Hanyu | Quan, Yinghui | Cui, Ye | Nie, Weike

Article Type: Research Article

Abstract: In this study, we develop a novel clustering with double fuzzy factors to enhance the performance of the granulation-degranulation mechanism, with which a fuzzy rule-based model is designed and demonstrated to be an enhanced one. The essence of the developed scheme is to optimize the construction of the information granules so as to eventually improve the performance of the fuzzy rule-based models. In the design process, a prototype matrix is defined to express the Fuzzy C-Means based granulation-degranulation mechanism in a clear manner. We assume that the dataset degranulated from the formed information granules is equal to the original numerical …dataset. Then, a clustering method with double fuzzy factors is derived. We also present a detailed mathematical proof for the proposed approach. Subsequently, on the basis of the enhanced version of the granulation-degranulation mechanism, we design a granular fuzzy model. The whole design is mainly focused on an efficient application of the fuzzy clustering to build information granules used in fuzzy rule-based models. Comprehensive experimental studies demonstrate the performance of the proposed scheme. Show more

Keywords: Partition matrix, granulation-degranulation mechanism, information granules, fuzzy clustering, rule-based models, prototype matrix

DOI: 10.3233/JIFS-210336

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12243-12252, 2021

Authors: Ahkouk, Karam | Machkour, Mustapha | Majhadi, Khadija | Mama, Rachid

Article Type: Research Article

Abstract: In the last decade, many intelligent interfaces and layers have been suggested to allow the use of relational databases and extraction of the content using only the natural language. However most of them struggle when exposed to new databases. In this article, we present SQLSketch, a sketch-based network for generating SQL queries to address the problem of automatically translate Natural Languages questions to SQL using the related databases schemas. We argue that the previous models that use full or partial sequence-to-sequence structure in the decoding phase can, in fact, have counter-effect on the generation operation and came up with more …loss of the context or the meaning of the user question. In this regard, we use a full sketch-based structure that decouples the generation process into many small prediction modules. The SQLSketch is evaluated against GreatSQL, a new cross-domain, large-scale and balanced dataset for the Natural Language to SQL translation task. For a long-term aim of making better models and contributing in adding more improvements to the semantic parsing tasks, we propose the GreatSQL dataset as the first balanced cross-domain corpus that includes 45,969 pairs of natural language questions and their corresponding SQL queries in addition to simplified and well structured ground-truth annotations. We establish results for SQLSketch using GreatSQL dataset and compare the performance against two popular types of models that represent the sequential and partial-sketch based approaches. Experimental result shows that SQLSketch outperforms the baseline models by 13% in exact matching accuracy and achieve a score of 23.9% to be the new state-of-the-art model on GreatSQL. Show more

Keywords: Natural language processing, text to SQL translation, database interfaces, natural language translation, machine translation

DOI: 10.3233/JIFS-210359

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12253-12263, 2021

Authors: Zhong, Xianyou | Gao, Xiang | Mei, Quan | Huang, Tianwei | Zhao, Xiao

Article Type: Research Article

Abstract: Gear fault vibration signals are commonly non-stationary, and useful fault information is often buried in heavy noise, which makes it difficult to extract gear fault features. How to select the suitable fault frequency bands is the key to gear fault diagnosis. To address the above problems, a method combining the improved minimum entropy deconvolution (MED) and accugram, named IMEDA, is proposed for extracting gear fault features. Firstly, a selection index based on permutation entropy (PE) and correlation coefficient is defined. Then, the optimal filter length can be effectively selected by the step-length searching method using the proposed index as objective …function, and the improved MED is employed to preprocess the gear vibration signals. Finally, the accugram analysis is performed for the preprocessed signals to obtain the optimal frequency band, and the fault characteristic frequencies are extracted from the square envelope spectrum of the signals in the optimal band. The method is validated by gear experimental data with gear wear-out failure. The analysis results demonstrate that the proposed method owns superior effect by comparing with the fast kurtogram (FK), MED combined with FK (MED-FK), accugram and infogram. Show more

Keywords: Minimum entropy deconvolution, accugram, frequency band selection, fault feature extraction

DOI: 10.3233/JIFS-210405

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12265-12282, 2021

Authors: An, Qing | Tang, Ruoli | Su, Hongfeng | Zhang, Jun | Li, Xin

Article Type: Research Article

Abstract: Due to the promising performance on energy-saving, the building integrated photovoltaic system (BIPV) has found an increasingly wide utilization in modern cities. For a large-scale PV array installed on the facades of a super high-rise building, the environmental conditions (e.g., the irradiance, temperature, sunlight angle etc.) are always complex and dynamic. As a result, the PV configuration and maximum power point tracking (MPPT) methodology are of great importance for both the operational safety and efficiency. In this study, some famous PV configurations are comprehensively tested under complex shading conditions in BIPV application, and a robust configuration for large-scale BIPV system …based on the total-cross-tied (TCT) circuit connection is developed. Then, by analyzing and extracting the feature variables of environment parameters, a novel fast MPPT methodology based on extreme learning machine (ELM) is proposed. Finally, the proposed configuration and its MPPT methodology are verified by simulation experiments. Experimental results show that the proposed configuration performs efficient on most of the complex shading conditions, and the ELM-based intelligent MPPT methodology can also obtain promising performance on response speed and tracking accuracy. Show more

Keywords: Building integrated photovoltaic system, maximum power point tracking, PV configuration, intelligent control, extreme learning machine

DOI: 10.3233/JIFS-210424

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12283-12300, 2021

Authors: Li, Huanhuan | Ji, Ying | Qu, Shaojian

Article Type: Research Article

Abstract: Decision-makers usually have a variety of unsure situations in the environment of group decision-making. In this paper, we resolve this difficulty by constructing two-stage stochastic integrated adjustment deviations and consensus models (iADCMs). By introducing the minimum cost consensus models (MCCMs) with costs direction constraints and stochastic programming, we develop three types of iADCMs with an uncertainty of asymmetric costs and initial opinions. The factors of directional constraints, compromise limits and free adjustment thresholds previously thought to affect consensus separately are considered in the proposed models. Different from the previous consensus models, the resulting iADCMs are solved by designing an appropriate …L-shaped algorithm. On the application in the negotiations on Grains to Green Programs (GTGP) in China, the proposed models are demonstrated to be more robust. The proposed iADCMs are compared to the MCCMs in an asymmetric costs context. The contrasting outcomes show that the two-stage stochastic iADCMs with no-cost threshold have the smallest total costs. Moreover, based on the case study, we give a sensitivity analysis of the uncertainty of asymmetric adjustment cost. Finally, conclusion and future research prospects are provided. Show more

Keywords: Two-stage stochastic integrated adjustment deviations and consensus model, directional constraints, uncertain adjustment costs, uncertainty initial opinions, L-shaped algorithm

DOI: 10.3233/JIFS-210443

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12301-12319, 2021

Authors: Al-Tarawneh, Ahmed | Al-Saraireh, Ja’afer

Article Type: Research Article

Abstract: Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This …work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks. Show more

Keywords: Tweets, hacking, prediction, twitter, social networks

DOI: 10.3233/JIFS-210458

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12321-12337, 2021

Authors: Ling, Chunyan | Lu, Zhenzhou

Article Type: Research Article

Abstract: To measure the effects of the fuzzy inputs on structural safety degree, this paper establishes the failure credibility-based global sensitivity by the fuzzy expected value of the absolute difference between the unconditional failure credibility and conditional one. To establish the failure credibility-based global sensitivity, the conditional failure credibility is firstly defined according to the original definition of conditional event and the relationship among the possibility, necessity and credibility, in which no extra assumption is introduced. After that, the equivalent expression of the failure credibility is deduced, on which the Bayesian transformation of the conditional failure credibility is obtained in this …paper. Then, a single-loop method based on the sequential quadratic programming is applied to efficiently estimate the defined failure credibility-based global sensitivity. According to the result of the constructed failure credibility-based global sensitivity, designers can pay more attentions to the more important fuzzy inputs to have a better control of the structural safety degree. The presented examples demonstrate the feasibility of the constructed failure credibility-based global sensitivity and the efficiency of the proposed solution. Show more

Keywords: Fuzzy input, failure credibility, global sensitivity, fuzzy expected value, conditional failure credibility, sequential quadratic programming

DOI: 10.3233/JIFS-210461

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12339-12359, 2021

Authors: Song, Xudong | Zhu, Dajie | Liang, Pan | An, Lu

Article Type: Research Article

Abstract: Although the existing transfer learning method based on deep learning can realize bearing fault diagnosis under variable load working conditions, it is difficult to obtain bearing fault data and the training data of fault diagnosis model is insufficient£¬which leads to the low accuracy and generalization ability of fault diagnosis model, A fault diagnosis method based on improved elastic net transfer learning under variable load working conditions is proposed. The improved elastic net transfer learning is used to suppress the over fitting and improve the training efficiency of the model, and the long short-term memory network is introduced to train the …fault diagnosis model, then a small amount of target domain data is used to fine tune the model parameters. Finally, the fault diagnosis model under variable load working conditions based on improved elastic net transfer learning is constructed. Finally, through model experiments and comparison with conventional deep learning fault diagnosis models such as long short-term memory network (LSTM), gated recurrent unit (GRU) and Bi-LSTM, it shows that the proposed method has higher accuracy and better generalization ability, which verifies the effectiveness of the method. Show more

Keywords: Elastic net, fault diagnosis, LSTM, transfer learning

DOI: 10.3233/JIFS-210503

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12361-12369, 2021

Authors: Zou, Yuan | Yang, Daoli | Pan, Yuchen

Article Type: Research Article

Abstract: Gross domestic product (GDP) is the most widely-used tool for measuring the overall situation of a country’s economic activity within a specified period of time. A more accurate forecasting of GDP based on standardized procedures with known samples available is conducive to guide decision making of government, enterprises and individuals. This study devotes to enhance the accuracy regarding GDP forecasting with given sample of historical data. To achieve this purpose, the study incorporates artificial neural network (ANN) into grey Markov chain model to modify the residual error, thus develops a novel hybrid model called grey Markov chain with ANN error …correction (abbreviated as GMCM_ANN), which assembles the advantages of three components to fit nonlinear forecasting with limited sample sizes. The new model has been tested by adopting the historical data, which includes the original GDP data of the United States, Japan, China and India from 2000 to 2019, and also provides predications on four countries’ GDP up to 2022. Four models including autoregressive integrated moving average model, back-propagation neural network, the traditional GM(1,1) and grey Markov chain model are as benchmarks for comparison of the predicted accuracy and application scope. The obtained results are satisfactory and indicate superior forecasting performance of the proposed approach in terms of accuracy and universality. Show more

Keywords: Gross domestic product, grey Markov chain, artificial neural network, residual correction, forecasting

DOI: 10.3233/JIFS-210509

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12371-12381, 2021

Authors: Noon, Serosh Karim | Amjad, Muhammad | Ali Qureshi, Muhammad | Mannan, Abdul

Article Type: Research Article

Abstract: Cotton is an important commodity because of its use in various industries across the globe. It is grown in many countries and is imported/exported as a cash crop due to its large utility. However, cotton yield is adversely affected by the existence of pests, viruses and pathogenic bacteria, etc. For the last one decade or so, several image processing/deep learning-based automatic plant leaf disease recognition methods have been developed but, unfortunately, they rarely address the cotton leaf diseases. The proposed work presents a simple yet efficient deep learning-based framework to recognize cotton leaf diseases. The proposed model is capable of …achieving the near ideal accuracy with early convergence to save computational cost of training. Further, due to the unavailability of publicly available datasets for this crop, a dataset is also collected comprising of three diseases namely curl virus, bacterial blight and fusarium wilt in addition to the healthy leaf Images. These images were collected from the Internet and fields of Southern Punjab region in Pakistan where the cotton crop is grown on thousands of acres every year and is exported to the Europe and the US either as a raw material or in the form of knitted industrial/domestic products. Experimental results have shown that almost all variants of our proposed deep learning framework have shown remarkably good recognition accuracy and precision. However, proposed EfficientNet-B0 model achieves 99.95% accuracy in only 152 seconds with best generalization and fast inference. Show more

Keywords: Cotton leaf disease, efficientnet, mobilenet, deep leaning, agriculture

DOI: 10.3233/JIFS-210516

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12383-12398, 2021

Authors: Abughazalah, Nabilah | Khan, Majid | Munir, Noor | Zafar, Amna

Article Type: Research Article

Abstract: In this article, we have designed a new scheme for the construction of the nonlinear confusion component. Our mechanism uses the notion of a semigroup, Inverse LA-semigroup, and various other loops. With the help of these mathematical structures, we can easily build our confusion component namely substitution boxes (S-boxes) without having specialized structures. We authenticate our proposed methodology by incorporating the available cryptographic benchmarks. Moreover, we have utilized the technique for order of preference by similarity to ideal solution (TOPSIS) to select the best nonlinear confusion component. With the aid of this multi-criteria decision-making (MCDM), one can easily select the …best possible confusion component while selecting among various available nonlinear confusion components. Show more

Keywords: Nonlinear confusion component, semigroup, loop, TOPSIS, MCDM

DOI: 10.3233/JIFS-210524

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12399-12410, 2021

Authors: Wang, H.Y. | Wang, J.S. | Zhu, L.F.

Article Type: Research Article

Abstract: Fuzzy C-means (FCM) clustering algorithm is a widely used method in data mining. However, there is a big limitation that the predefined number of clustering must be given. So it is very important to find an optimal number of clusters. Therefore, a new validity function of FCM clustering algorithm is proposed to verify the validity of the clustering results. This function is defined based on the intra-class compactness and inter-class separation from the fuzzy membership matrix, the data similarity between classes and the geometric structure of the data set, whose minimum value represents the optimal clustering partition result. The proposed …clustering validity function and seven traditional clustering validity functions are experimentally verified on four artificial data sets and six UCI data sets. The simulation results show that the proposed validity function can obtain the optimal clustering number of the data set more accurately, and can still find the more accurate clustering number under the condition of changing the fuzzy weighted index, which has strong adaptability and robustness. Show more

Keywords: Fuzzy C-means clustering algorithm, clustering validity function, membership matrix, intra-class compactness, inter-class separation

DOI: 10.3233/JIFS-210555

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12411-12432, 2021

Authors: Yavuz, Enes

Article Type: Research Article

Abstract: We define statistical Cesàro and statistical logarithmic summability methods of sequences in intuitionistic fuzzy normed spaces(IFNS ) and give slowly oscillating type and Hardy type Tauberian conditions under which statistical Cesàro summability and statistical logarithmic summability imply convergence in IFNS . Besides, we obtain analogous results for the higher order summability methods as corollaries. Also, two theorems concerning the convergence of statistically convergent sequences in IFNS are proved in the paper.

Keywords: Intuitionistic fuzzy normed space, tauberian theorem, cesàro and logarithmic summability methods, statistical convergence, slow oscillation, 03E72, 40A05, 40G05, 40G15, 40E05

DOI: 10.3233/JIFS-210596

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12433-12442, 2021

Authors: Wang, Fang | Li, Hai-Mei | Li, Yan-Lai | Wu, Ai-Ping

Article Type: Research Article

Abstract: Quality function deployment (QFD) is a customer-oriented tool for developing products. Based on the idea of the best-worst method (BWM), a novel model is developed to determine the relative importance ratings (RIRs) of customer requirements (CRs) with interval grey linguistic (IGL) information, which plays a significant role in QFD. CRs are rated with IGL variables, and the degree of greyness degree function that can be used to handle the IGL variables is defined based on the power utility function. Then, considering customer heterogeneity, a model is constructed to derive the RIRs of CRs by following the logic of the BWM. …Finally, a case study of 5 G smartphone development is provided to verify the validity and the feasibility of the proposed method. Show more

Keywords: Customer requirements, QFD, interval grey linguistic, best-worst method, utility function

DOI: 10.3233/JIFS-210799

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12443-12458, 2021

Authors: Lu, Hanchuan | Khalil, Ahmed Mostafa | Alharbi, W. | El-Gayar, M. A.

Article Type: Research Article

Abstract:  In this article, we propose a novel concept of the generalized picture fuzzy soft set by combining the picture fuzzy soft set and the fuzzy parameter set. For possible applications, we explain five kinds of operations (e.g., subset, equal, union, intersection, and complement) based on generalized picture fuzzy soft sets. Then, we establish several theoretical operations of generalized picture fuzzy soft sets. In addition, we present the new type by using the AND operation of the generalized picture fuzzy soft set for fuzzy decision-making and clarify its applicability with a numerical example. Finally, we give a comparison between the picture …fuzzy soft set theory and the generalized picture fuzzy soft set theory. It is shown that our proposed (i.e., generalized picture fuzzy soft set theory) is viable and provide decision makers a more mathematical insight before making decisions on their options. Show more

Keywords: Picture fuzzy set, soft set, generalized picture fuzzy soft set, Algorithm 1, Algorithm 2, decision-making

DOI: 10.3233/JIFS-201706

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12459-12475, 2021

Authors: Hamdoun, Hala | Sagheer, Alaa | Youness, Hassan

Article Type: Research Article

Abstract: Machine learning methods have been adopted in the literature as contenders to conventional methods to solve the energy time series forecasting (TSF) problems. Recently, deep learning methods have been emerged in the artificial intelligence field attaining astonishing performance in a wide range of applications. Yet, the evidence about their performance in to solve the energy TSF problems, in terms of accuracy and computational requirements, is scanty. Most of the review articles that handle the energy TSF problem are systematic reviews, however, a qualitative and quantitative study for the energy TSF problem is not yet available in the literature. The purpose …of this paper is twofold, first it provides a comprehensive analytical assessment for conventional, machine learning, and deep learning methods that can be utilized to solve various energy TSF problems. Second, the paper carries out an empirical assessment for many selected methods through three real-world datasets. These datasets related to electrical energy consumption problem, natural gas problem, and electric power consumption of an individual household problem. The first two problems are univariate TSF and the third problem is a multivariate TSF. Compared to both conventional and machine learning contenders, the deep learning methods attain a significant improvement in terms of accuracy and forecasting horizons examined. In the meantime, their computational requirements are notably greater than other contenders. Eventually, the paper identifies a number of challenges, potential research directions, and recommendations to the research community may serve as a basis for further research in the energy forecasting domain. Show more

Keywords: Energy time series forecasting, conventional forecasting methods, machine learning, deep learning, energy management systems

DOI: 10.3233/JIFS-201717

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12477-12502, 2021

Authors: Zhang, Na | Yan, Shuli | Fang, Zhigeng | Yang, Baohua

Article Type: Research Article

Abstract: In view of the situation that tasks or activities in the GERT model may have multiple realizations, this paper explores the time dependence of each repeated execution node under the condition of fuzzy information, and studies the characteristics of the z-tag fuzzy GERT model and its analytic algorithm. Firstly, the F-GERT model related to the number of executions of activities is defined, and the simplified rules, related properties and theorems of the network model are examined. Secondly, solving algorithm, conditional moment generating function and process arrival time of the F-GERT model for repeated execution time are studied. Finally, the application …of F-GERT queuing system based on element execution time in weapon equipment management is discussed. The feasibility and effectiveness of the model and algorithm are verified by the practical application of the project. Show more

Keywords: Project management, GERT model, fuzzy information, z-tag, moment generating function, network structure

DOI: 10.3233/JIFS-201731

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12503-12519, 2021