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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Arockia Aswini, A. | Sivarani, T.S.
Article Type: Research Article
Abstract: Diabetic retinopathy becomes an increasingly popular cause of vision loss in diabetic patients. Deep learning has recently received attention as one of the most popular methods for boosting performance in a range of sectors, including medical image analysis and classification. The proposed system comprises three steps; they are image preprocessing, image segmentation, and classification. In preprocessing, the image will be resized, denoising the image and enhancing the contrast of the image which is used for further processing. The lesion region of diabetic retinopathy fundus image is segmented by using Feature Fusion-based U-Net architecture. A blood vessel of a retinal image …is extracted by using the spatial fuzzy c means clustering (SFCM) algorithm. Finally, the diabetic retinopathy images are classified using a modified capsule network. The convolution and primary capsule layers collect features from fundus images, while the class capsule and softmax layers decide whether the image belongs to a certain class. Using the Messidor dataset, the proposed system’s network efficiency is evaluated in terms of four performance indicators. The modified contrast limited adaptive histogram equalization technique enhanced the Peak Signal to Noise Ratio (PSNR), mean square error, and Structural Similarity Index Measure (SSIM) have average values of 36.18, 6.15, and 0.95, respectively. After enhancing the image, segmentation is performed to segment the vessel and lesion region. The segmentation accuracy is measured for the proposed segmentation algorithm by using two metrics namely intersection over union (IoU) and Dice similarity coefficient. Then modified capsule network is constructed for classifying the stages of diabetic retinopathy. The experimental result shows that the proposed modified capsule network got 98.57% of classification accuracy. Show more
Keywords: Diabetic retinopathy, Messidor dataset, Image preprocessing, segmentation, classification
DOI: 10.3233/JIFS-221112
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5521-5542, 2023
Authors: Tarakci, Fatih | Ozkan, Ilker Ali | Yilmaz, Sema | Tezcan, Dilek
Article Type: Research Article
Abstract: Rheumatoid Arthritis (RA) is a very common autoimmune disease that causes significant morbidity and mortality, and therefore early diagnosis and treatment are important. Early diagnosis of RA and knowing the severity of the disease are very important for the treatment to be applied. The diagnosis of RA usually requires a physical examination, laboratory tests, and a review of the patient’s medical history. In this study, the diagnosis of RA was made with two different methods using a fuzzy expert system (FES) and machine learning (ML) techniques, which were designed and implemented with the help of a specialist in the field, …and the results were compared. For this purpose, blood counts were taken from 286 people, including 91 men and 195 women from various age groups. In the first method, an FES structure that determines the severity of RA disease has been established from blood count using the laboratory test results of CRP, ESR, RF, and ANA. The FES result that determines RA disease severity, the Anti-CCP level that is used to distinguish RA disease, and the patient’s medical history were used to design the Decision Support System (DSS) that diagnoses RA disease. The DSS is web-based and publicly accessible. In the second method, RA disease was diagnosed using kNN, SVM, LR, DT, NB, and MLP algorithms, which are widely used in machine learning. To examine the effect of the patient’s history on RA disease diagnosis, two different models were used in machine learning techniques, one with and one without the patient’s history. The results of the fuzzy-based DSS were also compared with the diagnoses made by the specialist and the diagnoses made according to the 2010 ACR / EULAR RA classification criteria. The performed DSS has achieved a diagnostic success rate of 94.05% on 286 patients. In the study of machine learning techniques, the highest success rate was achieved with the LR model. While the success rate of the model was 91.25 % with only blood count data, the success rate was 97.90% with the addition of the patient’s history. In addition to the high success rate, the results show that the patient’s history is important in diagnosing RA disease. Show more
Keywords: Fuzzy expert system, rheumatoid arthritis, decision support system, machine learning, diagnosis of disease
DOI: 10.3233/JIFS-221582
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5543-5557, 2023
Authors: Dong, Yumin | Li, Ziyi | Chen, Zhengquan | Xu, Yuewen | Zhang, Yunan
Article Type: Research Article
Abstract: Early diagnosis of breast cancer plays an important role in improving survival rate. Physiological changes of breast tissue can be observed and measured through medical electrical impedance, and the results can be used as a preliminary diagnosis by doctors before treatment. In this paper, quantum genetic algorithm (QGA) and support vector machine (SVM) were combined to classify breast tissues to help clinicians in diagnosis. The algorithm uses QGA to optimize the parameters of SVM and improve the classification performance of SVM. In this experiment, the electrical impedance data measured from breast tissue provided by UCI [58 ] was used as …the data set. Objectively speaking, the data volume of the data set is small and the representativeness is not strong enough. However, the experimental results show that QGA-SVM shows better classification performance, and it is better than SVM. Show more
Keywords: Quantum genetic algorithm, Support Vector Machines, Breast cancer, Medical electrical impedance
DOI: 10.3233/JIFS-212957
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5559-5571, 2023
Authors: Wang, Yaqin | Xu, Jing | Luo, Chen
Article Type: Research Article
Abstract: The mechanical properties of the ultra-great workability concrete (UGWC ) are deeply related to the weights of components, curing period and condition, and occasionally property of admixtures. This study aimed to appraise the usefulness of the adaptive neuro-fuzzy inference system (ANFIS) technique for forecasting the compressive strength of UGWC and enhancing the accuracy of the literature. To outline the forecasting process, two improved ANFIS were suggested, in which determinative variables of them were determined by metaheuristic algorithms named imperialist competitive algorithm (ICA) and multi-verse optimizer (MVO) algorithms. For this purpose, 170 data samples were collected from published literature separated …accidentally for the train and test phase. The calculated performance criteria for proposed ANFIS models demonstrate that both ICA-ANFIS and MVO-ANFIS models can result in justifiable workability for f c of the UGWC prediction procedure. The MVO-ANFIS model could outperform ICA-ANFIS regarding all criteria. For instance, the value of R 2 and VAF for the ICA-ANFIS model are roughly smaller than the MVO-ANFIS model, at 0.9012 and 90% in the training dataset and 0.8973 and 89% in the testing stage, respectively. While the best values of criteria have belonged to the MVO-ANFIS model, with R 2 at 0.937 and 0.944 for the train and test phases, respectively. Overall, the hybrid MVO-ANFIS model can obtain higher workability than ICA-ANFIS and literature (R2 at 0.801), where causes are recognized as the proposed model. Show more
Keywords: Terms— Ultra great workability concrete, compressive strength prediction, adaptive neuro-fuzzy inference system, Hybrid ANFIS
DOI: 10.3233/JIFS-221409
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5573-5587, 2023
Authors: Guo, Xiaobin | Zhuo, Quanxiu
Article Type: Research Article
Abstract: This paper considers the perturbation analysis of a class of fully fuzzy linear systems in which the coefficient matrix is a positive fuzzy matrix. The original fuzzy linear systems is extended into a brand new and simple crisp matrix equation using an embedding method. By discussing the perturbation of the extended crisp linear equation, the paper completes the perturbation analysis of the original fuzzy linear system. There are three cases of perturbation are analysed and the respective relative error bounds for solutions of fuzzy linear system are derived. Some numerical examples are given to illustrated our obtained results.
Keywords: Fuzzy linear system, fuzzy solutions, matrix norm, perturbation analysis
DOI: 10.3233/JIFS-222392
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5589-5599, 2023
Authors: Kamarudin, Nur Khairani | Firdaus, Ahmad | Zabidi, Azlee | Ernawan, Ferda | Hisham, Syifak Izhar | Ab Razak, Mohd Faizal
Article Type: Research Article
Abstract: Many smart mobile devices, including smartphones, smart televisions, smart watches, and smart vacuums, have been powered by Android devices. Therefore, mobile devices have become the prime target for malware attacks due to their rapid development and utilization. Many security practitioners have adopted different approaches to detect malware. However, its attacks continuously evolve and spread, and the number of attacks is still increasing. Hence, it is important to detect Android malware since it could expose a great threat to the users. However, in machine learning intelligence detection, too many insignificant features will decrease the percentage of the detection’s accuracy. Therefore, there …is a need to discover the significant features in a minimal amount to assist with machine learning detection. Consequently, this study proposes the Pearson correlation coefficient (PMCC), a coefficient that measures the linear relationship between all features. Afterwards, this study adopts the heatmap method to visualize the PMCC value in the color of the heat version. For machine learning classification algorithms, we used a type of fuzzy logic called lattice reasoning. This experiment used real 3799 Android samples with 217 features and achieved the best accuracy rate of detection of more than 98% by using Unordered Fuzzy Rule Induction (FURIA). Show more
Keywords: Fuzzy, feature selection, unordered fuzzy rule induction (FURIA), machine learning, android
DOI: 10.3233/JIFS-222612
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5601-5615, 2023
Authors: Qian, Jin | Han, Xing | Yu, Ying | Liu, Caihui
Article Type: Research Article
Abstract: Fuzzy rough sets and multi-granularity rough sets are essential extensions of Pawlak rough sets, which have become artificial intelligence research hotspots. Previous studies of the rough sets based on the fuzzy T-equivalence relation did not take the multi-granularity into account. The multi-granularity data is typically the multi-view cognition obtained by different granularity of the data, and its distinctive feature is that the data can be presented in different granularity spaces. In this paper, we integrate the idea of multi-granularity and propose four new models of “optimistic,” “pessimistic,” “optimistic-pessimistic,” and “pessimistic-optimistic” decision-theoretic rough sets based on the fuzzy T-equivalence relation for …the first time, followed by a preliminary analysis of the intrinsic relations and properties of these new decision-theoretic rough set models by a concrete example. At last, we use experiments to show the effectiveness of suggested models, proving that they are both rational and practical. Show more
Keywords: Three-way decision, fuzzy similarity relationship, multi-granularity, decision-theoretic rough set, rough set
DOI: 10.3233/IFS-222910
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5617-5631, 2023
Authors: Betshrine Rachel, R. | Nehemiah, Khanna H. | Marishanjunath, C.S. | Manoharan, Rebecca Mercy Victoria
Article Type: Research Article
Abstract: A Computer Aided Diagnosis (CAD) framework to diagnose Pulmonary Edema (PE) and covid-19 from the chest Computed Tomography (CT) slices were developed and implemented in this work. The lung tissues have been segmented using Otsu’s thresholding method. The Regions of Interest (ROI) considered in this work were edema lesions and covid-19 lesions. For each ROI, the edema lesions and covid-19 lesions were elucidated by an expert radiologist, followed by texture and shape extraction. The extracted features were stored as feature vectors. The feature vectors were split into train and test set in the ratio of 80 : 20. A wrapper based feature …selection approach using Squirrel Search Algorithm (SSA) with the Support Vector Machine (SVM) classifier’s accuracy as the fitness function was used to select the optimal features. The selected features were trained using the Back Propagation Neural Network (BPNN) classifier. This framework was tested on a real-time PE and covid-19 dataset. The BPNN classifier’s accuracy with SSA yielded 88.02%, whereas, without SSA it yielded 83.80%. Statistical analysis, namely Wilcoxon’s test, Kendall’s Rank Correlation Coefficient test and Mann Whitney U test were performed, which indicates that the proposed method has a significant impact on the accuracy, sensitivity and specificity of the novel dataset considered. Comparative experimentations of the proposed system with existing benchmark ML classifiers, namely Cat Boost, Ada Boost, XGBoost, RBF SVM, Poly SVM, Sigmoid SVM and Linear SVM classifiers demonstrate that the proposed system outperforms the benchmark classifiers’ results. Show more
Keywords: Pulmonary Edema, Covid-19, Squirrel Search Algorithm (SSA), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN) classifiers
DOI: 10.3233/JIFS-222564
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5633-5646, 2023
Authors: Li, Bin | Tian, Ying | Liu, Xiaopeng | Yan, Qinghua | Hu, Zhigang
Article Type: Research Article
Abstract: Person re-identification identify a specific person in surveillance network by similarity measurement between images of different camera views. However, existing metric learning based methods suffer from over-fitting problem. To solve this problem, a resampled linear discriminant analysis (LDA) method was proposed based on the statistical and topological characteristics of pedestrian images. This method utilized the k-nearest neighbours to form potential positive sample pairs. The potential positive pairs are used to improve the metric model and generalize the metric model to the test data. By minimizing the inter-class divergence of potential positive sample pairs, a semi-supervised re-sampling LDA person re-identification algorithm …was established. It was then tested on the VIPeR, CUHK01 and Market 1501datasets. The results show that the proposed method achieves the best performance compared to some available methods. Especially, the proposed method outplays the best comparison method by 0.6% and 5.76% at rank-1 identification rate on the VIPeR and CUHK01 datasets respectively. At the same time, the improved LDA algorithm has improved the rank-1 identification accuracy of traditional LDA method by 9.36% and 32.11% on these two datasets respectively. Besides, the proposed method is limited to Market-1501 dataset when the test data is of large size. Show more
Keywords: Person re-identification, measurement model, LDA, semi-supervised learning
DOI: 10.3233/JIFS-220924
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5647-5658, 2023
Authors: Parvathi, S. | Vaishnavi, P.
Article Type: Research Article
Abstract: Breast cancer is considered as a most dangerous type of cancer found in women among all the cancers. Around 2.3 million women in the world are affected by this cancer and there is no cure if it is left untreated at an earlier stage. Therefore, early diagnosis of this disease is an important consideration to save the life of millions of women. Many machine learning models have been evolved in the recent years for breast cancer detection. However, all the currently available works focused only on improving the prediction accuracy, they need more attention on providing reliable services. This work …presents an efficient breast cancer detection mechanism using deep learning strategies. The various assortments like breast image shapes, the intensity of images, regions of an image, illuminations, and contrast are the conceivable factors that define breast cancer identification. This study offers a strong image detection process for breast cancer mammography images by considering the whole slide image. Here, the input process for the preprocessing stage will remove the noise present in the image using Gaussian Filter (GF). The preprocessed image moves to the image segmentation and then forward to the feature extraction for extracting the features of the images using Cauchy distribution-based segmentation and Shearlet based feature extraction. Then the specialized features can be isolated using the Entropy PCA based feature selection. Finally, the breast cancer area is to be detected as benign or malignant accurately by using the Unified probability with LSTM neural network classification (UP-LSTM) for whole slide image (WSI). The attained outcomes and the detected outcomes were stored in cloud using a security mechanism for further monitoring purposes. To provide an efficient security, a Bio-inspired Iterative Honey Bee (BI-IHB) encryption is employed which is decrypted on user request. The reliability of the stored data is then found using FMEA (Failure mode and effective analysis) approach. From the experimental analysis, it is observed that UP-LSTM classifier model offers accuracy of 99.26% , sensitivity of 100% , and precision value of 98.59% which is better than the other state of the art techniques. Show more
Keywords: Breast cancer, Gaussian Filter, Cauchy distribution-based segmentation, Shearlet based feature extraction, Entropy PCA based feature selection, unified probability with LSTM neural network classification, FMEA (failure mode and effective analysis), benign, malignant, histopathology images, WSI, cloud security, bio-inspired iterative honey bee (BI-IHB).
DOI: 10.3233/JIFS-221973
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5659-5674, 2023
Authors: Dehghani, Alireza | Bagherifard, Karamolah | Nejatian, Samad | Parvin, Hamid
Article Type: Research Article
Abstract: Data pre-processing is one of the crucial phases of data mining that enhances the efficiency of data mining techniques. One of the most important operations performed on data pre-processing is missing values imputation in incomplete datasets. This research presents a new imputation technique using K-means and samples weighting mechanism based on Grey relation (KWGI). The Grey-based K-means algorithm applicable to all samples of incomplete datasets clusters the similar samples, then an appropriate kernel function generates appropriate weights based on the Grey relation. The missing values estimation of the incomplete samples is done based on the weighted mean to reduce the …impact of outlier and vague samples. In both clustering and imputation steps, a penalty mechanism has been considered to reduce the similarity of ambiguous samples with a high number of missing values, and consequently, increase the accuracy of clustering and imputation. The KWGI method has been applied on nine natural datasets with eight state-of-the-art and commonly used methods, namely CMIWD, KNNI, HotDeck, MeanI, KmeanI, RKmeanI, ICKmeanI, and FKMI. The imputation results are evaluated by the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) criteria. In this study, the missing values are generated at two levels, namely sample and value, and the results are discussed in a wide range of missingness from low rate to high rate. Experimental results of the t -test show that the proposed method performs significantly better than all the other compared methods. Show more
Keywords: K-means imputation, missing values imputation, kernel-based weighting, grey relation analysis, data pre-processing
DOI: 10.3233/JIFS-200774
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5675-5697, 2023
Authors: Zhao, Peichen | Yue, Qi | Deng, Zhibin
Article Type: Research Article
Abstract: Probabilistic hesitant fuzzy set (PHFS), as a complex ambiguous information representation tool, has been widely used in decision making problem, but is rarely applied in a two-sided matching (TSM). Therefore, it is important and necessary to investigate the TSM problem with PHFS. This paper proposes a decision method for TSM with probabilistic hesitant fuzzy Numbers (PHFNs) and applies it to the person-job fit problem. Firstly, a novel TSM decision model on the basis of PHFNs is constructed. In order to solve this model, the TSM model is transformed into a two-goal TSM model by using linear weighted method. And then, …the two-goal TSM model with PHFN can be changed into a single-goal TSM model with scores through score function. The perfect alternative of TSM could be obtained through model solution. Finally, an example is given to illustrate the feasibility and effectiveness of the proposed method. Show more
Keywords: Two-sided matching decision making (TSMDM), Probabilistic hesitant fuzzy number (PHFN), Optimal model
DOI: 10.3233/JIFS-213010
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5699-5709, 2023
Authors: Pathik, Babita | Pathik, Nikhlesh | Sharma, Meena
Article Type: Research Article
Abstract: The software development and maintenance phase succeeded with significant regression testing activity. The software must be re-tested every time it upgrades to preserve its quality. Software testing as a whole is an expensive and tedious task due to resource constraints. Using the prioritization technique implies regression testing to re-test software after it has been modified. In this situation, the prioritization technique can use information acquired about earlier test case executions to generate test case orderings. The approaches for test case prioritization arrange them all in such a sequence that maximizes their efficacy in accomplishing specific goals. This paper presents a …hybrid technique for change-testing or regression testing through test case prioritization. The suggested method first generates the test cases, then clustered in untested and unimportant groups using kernel-based fuzzy c-means clustering technique. The appropriate test cases are then considered for prioritization using the grey wolf optimizer. The results compared with the approaches such as ant colony, particle swarm, and genetic algorithm optimization method, and it is observed that the proposed approach efficiency increased by 91% of fault detection rate. Show more
Keywords: Clustering, fuzzy c-means, grey wolf optimizer, test case prioritization, test suite augmentation
DOI: 10.3233/JIFS-222433
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5711-5718, 2023
Authors: Bilal, Ahmad | Munir, Muhammad Mobeen
Article Type: Research Article
Abstract: The largest absolute eigenvalue of a matrix A associated to the graph G is called the A -Spectral Radius of the graph G , and A -energy of the graph G is defined as the absolute sum of all its eigenvalues. In the present article, we compute Randic energies, Reciprocal Randic energies, Randic spectral radii and Reciprocal Randic radii of s -shadow and s -splitting graph of G . We actually relate these energies and Spectral Radii of new graphs with the energies and Spectral Radii of original graphs.
Keywords: Shadow graph, splitting graph, randic energy, randic spectral radius, reciprocal randic energy, reciprocal randic spectral radius, eigenvalues
DOI: 10.3233/JIFS-221938
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5719-5729, 2023
Authors: Ashok Kumar, L. | Karthika Renuka, D. | Saravana Kumar, S.
Article Type: Research Article
Abstract: Human-wildlife conflicts in the habitats along the forest fringes are a substantial issue. An automated monitoring system that can find animal breaches and deter them from foraging fields is essential to solve this conflict. However, automatically forefending the intruding animals is a challenging task. In this paper, we propose a deep learning model for elephant identification using YOLO lite with knowledge distillation which could be easily deployed in edge devices. We also propose an elephant re-identification system using Siamese network which is helpful in tracking the number of times the elephant tries to forage the field. This re-encounter information about …the same elephant can be used to decide the averting sound for the particular elephant. The proposed system is found to show an accuracy of 89%, which is provides good performance improvement when compared to the state of art models proposed for animal identification. Thus the proposed lite weight knowledge distillation based animal identification model and deep learning based animal re-identification model can be employed in edge devices for real time monitoring and animal deterring to safe guard the farm fields. Show more
Keywords: Neural networks, knowledge distillation, siamese neural network, classification, re-identification, computer vision
DOI: 10.3233/JIFS-222672
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5731-5743, 2023
Authors: Zhang, Yangjingyu | Cai, Qiang | Wei, Guiwu | Chen, Xudong
Article Type: Research Article
Abstract: Based on the traditional TOPSIS method and 2-tuple linguistic Pythagorean fuzzy numbers (2TLPFNs), this paper builds a novel 2TLPF-TOPSIS method that combines cumulative prospect theory (CPT) to cope with the multiple attribute group decision-making (MAGDM). This new method takes into account the decision-makers’ mind and the uncertainty of decision-making, and is more in line with the real decision-making environment. First, this paper briefly reviews some necessary theories related to PFS, as well as the calculation rules and comparison methods of 2TLPFNs. Then, since there is often subjective randomness when determining the weight, the entropy method is utilized to objectively determine …the weight. After that, give the specific calculation steps of the new method. In order to show the effectiveness of the new method, apply it into a specific numerical example about evaluating airline business operations capability, and compare it with the other four different methods. The ranking results depict that the new method designed is effective and reasonable, and has good application value of MAGDM problems. Show more
Keywords: Multiple attribute group decision making (MAGDM), 2-tuple linguistic Pythagorean fuzzy numbers (2TLPFNs), TOPSIS method, cumulative prospect theory (CPT), airline business operations capability
DOI: 10.3233/JIFS-220776
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5745-5758, 2023
Article Type: Research Article
Abstract: A unique approach for assessing the compressive strength (CS ) of high-performance concrete (HPC ) incorporating blast furnace slag (BFS ) and fly ash (FA ) has been created using support vector regression (SVR ) analytics. In order to identify crucial SVR methodology variables that could be adjusted for improved performance, the Henry gas solubility optimization (HGSO ) and Cuckoo search optimization (CSO ) algorithms were both employed in this study. The recommended methods were developed utilizing 1030 experiments and eight inputs, including the CS as the forecasting objective, admixtures, aggregates, and curing age as the main mix …design component. The results were then contrasted with those from related literature. The estimate results suggest that combined HGSO-SVR and CSO-SVR analysis might perform extraordinarily well in estimating. The Root mean square error value for the HGSO - SVR decreased remarkably when compared to the CSO - SVR . As can be seen from the comparisons, the HGSO - SVR that was built beats anything previously published. In conclusion, the suggested HGSO - SVR analysis might be determined as the proposed system for forecasting the CS of HPC improved with FA and BFS . Show more
Keywords: High-performance concrete, Compressive strength, fly ash, blast furnace slag, estimation, SVR, HGSO, COA
DOI: 10.3233/JIFS-222348
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5759-5772, 2023
Authors: Zhang, Xiaolu | Wan, Jun | Luo, Ji
Article Type: Research Article
Abstract: Interval-valued q-rung orthopair fuzzy number (IVq-ROFN) is a popular tool for modeling complex uncertain information and has gained successful applications in the field of comprehensive evaluation. However, most of the existing studies are based on the absolute values of evaluation data but fail to take incentive effects into account. Reasonable and appropriate incentive can guide the evaluated objects to better achieve the decision goals. Therefore, this study develops an incentive mechanism-based interval-valued q-rung orthopair fuzzy dynamic comprehensive evaluation method. Firstly, new interval-valued q-rung orthopair fuzzy measures including deviation measure and correlation coefficient are proposed for managing IVq-ROFNs data. To overcome …the limitations of the existing aggregating operators that are not suitable for scenarios with need of many times of data aggregation, we introduce two new interval-valued q-rung orthopair fuzzy aggregating operators. Furthermore, a new interval-valued orthopair fuzzy CRITIC method is developed to objectively determine the importance of the evaluated criteria. More importantly, the horizontal incentive effects within a single period and the vertical incentive effects during multiple periods under IVq-ROFNs environments are proposed to reward (or punish) the evaluated objects in the evaluation process. The evaluated results are determined based on the full compensatory model and the multiplicative form model. The main advantage of the developed method is that the expectations of decision-makers and the dynamic characteristics during multiple periods are taken fully into account, which can make the evaluation results more reasonable and reliable. Finally, this developed comprehensive evaluation method is applied to evaluate the green development level of Jiangxi province within eleven cities from 2016 to 2020. We observe that the cities x 2 , x 3 , x 4 , x 5 , x 7 , x 8 are rewarded within positive incentive values and the cities x 1 , x 6 , x 9 , x 10 , x 11 are punished within negative incentive values. Especially, the positive incentive value for the city x 3 is the biggest and the negative incentive value for the city x 9 is the biggest. The best city in term of GDL is x 3 . The evaluated results with consideration of incentive effects are in line with the expectation of the decision-maker. Show more
Keywords: Interval-valued q-rung orthopair fuzzy number, Comprehensive evaluation, CRITIC, Incentive effect, Green development level
DOI: 10.3233/JIFS-222505
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5773-5787, 2023
Authors: Pavithra, S. | Manimaran, A.
Article Type: Research Article
Abstract: Soft graphs are an interesting way to represent specific information. In this paper, a new form of graphs called Z-soft covering based rough graphs using soft adhesion is defined. Some important properties are explored for the newly constructed graphs. The aim of this study is to investigate the uncertainty in Z-soft covering based rough graphs. Uncertainty measures such as information entropy, rough entropy and granularity measures related to Z-soft covering-based rough graphs are discussed. In addition, we develop a novel Multiple Attribute Group Decision-Making (MAGDM) model using Z-soft covering based rough graphs in medical diagnosis to identify the patients at …high risk of chronic kidney disease using the collected data from the UCI Machine Learning Repository. Show more
Keywords: Soft graphs, soft covering rough set, uncertainty measures, decision making
DOI: 10.3233/JIFS-223678
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5789-5802, 2023
Authors: Noor Mohamed, Sheerin Sitara | Srinivasan, Kavitha
Article Type: Research Article
Abstract: Recent technological developments and improvement in the medical domain demands advancement, to address the issue of early disease detection. Also, the current pandemic has resulted in considerable progress of improvement in the medical domain, through online consultation by physicians for different diseases using clinical reports and medical images. A similar process is adopted in developing a Visual Question Answering (VQA) system in the medical field. In this paper, existing medical VQA datasets, appropriate techniques, suitable quantitative metrics, real time challenges and, the implementation of one VQA approach with algorithms and performance evaluation are discussed. The medical VQA datasets collected from …multiple sources are represented in different perspectives (organwise, planewise, modality-type and abnormality-type) for a better understanding and visualization. Then the techniques used in VQA are subsequently grouped and explained, based on evolution, complexity in the dataset and the need for semantics in understanding the questions. In addition, the implementation of a VQA approach using VGGNet and LSTM is carried out for existing and improved datasets, and analyzed with accuracy and BLEU score metrics. The improved datasets, created through dataset reduction and augmentation approaches, resulted in better performance than the existing datasets. Finally, the challenges of the medical VQA domain are examined in terms of datasets, combining techniques, and modifying the parameters of existing performance metrics for future research. Show more
Keywords: Visual question answering, medical VQA, ImageCLEF, VQA-MED dataset, VQA-RAD dataset, VGGNet, LSTM, challenges of VQA
DOI: 10.3233/JIFS-222569
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5803-5819, 2023
Authors: Chen, Bo | Cai, Qiang | Wei, Guiwu | Mo, Zhiwen
Article Type: Research Article
Abstract: This paper intends to treat the green supplier selection (GSS) problem as a multi-attribute group decision making (MAGDM) problem, adopt the linguistic Z-number that can more flexibly and accurately express the evaluation information, and expand the traditional multi-attribute boundary approximate area comparison (MABAC) method, combine the CRITIC method of standard importance and consider the risk vector to finally determine the optimal solution. More specifically, the linguistic Z-number is used to describe the fuzzy evaluation information of experts on alternatives under attributes, then the expanded CRITIC method is used to obtain the weight of each given attribute, and finally the MABAC …method with added risk vector and expanded is used to obtain the ranking of alternatives and obtain the best solution. Finally, taking green supplier selection as an example, and comparing with other methods, the reliability and effectiveness of the constructed method are verified. The results show that this method can express the evaluation information of experts flexibly and completely, and obtain the ranking results of given schemes through fewer steps, which is reliable and effective. Show more
Keywords: Multi-attribute group decision-making (MAGDM), linguistic Z-number (LZN), CRITIC method, MABAC method, green supplier selection, risk vector
DOI: 10.3233/JIFS-223447
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5821-5836, 2023
Authors: Chalasani, Rama Devi | Radhika, Y.
Article Type: Research Article
Abstract: ITK inhibitor is used for the treatment of asthma and activity of inhibitor prediction helps to provide better treatment. Few researches were carried out for the analysis and prediction of kinases activity. Existing methods applied for the inhibitor prediction have limitations of imbalance dataset and lower performance. In this research, the Posterior Probabilistic Weighted Average Based Ensemble voting (PPWAE)ensemble method is proposed with various classifier for effective prediction of kinases activity. The PPWAE model selects the most probable class from the classification method for prediction. The co-train model has two advantages: Features are trained together to increases the learning rate …of model and probability is measured for each model to select the efficient classifier. Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Classification and Regression Tree (CART), and Nave Bayes were among the classifiers employed. The results suggest that the Probabilistic Co-train ensemble technique performs well in kinase activity prediction. In the prediction of ITK inhibitor activity, the suggested ensemble method has a 74.27 percent accuracy, while the conventional SVM method has a 60% accuracy. Show more
Keywords: Decision tree, ITK inhibitor, posterior probabilistic weighted average based ensemble voting, random forest, support vector machine
DOI: 10.3233/JIFS-221412
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5837-5846, 2023
Authors: Qiao, Jian-min | Li, Wo-yuan
Article Type: Research Article
Abstract: The research work on two-sided matching decision problem considering interval triangular fuzzy information is very scarce, and it needs to be further studied. Based on this, this paper proposes a two-sided matching model based on interval triangular fuzzy sets, with the background of the two-sided matching problem in the interval triangular fuzzy set environment. Firstly, the theory of two-sided matching and interval triangular fuzzy sets is given; Secondly, the comprehensive mean value formula is defined, and the interval triangular fuzzy evaluation matrix is transformed into the comprehensive mean value matrix by using the comprehensive mean value formula; Thirdly, a two-sided …matching model is built with the goal of maximizing the satisfaction of each subject; Finally, the feasibility and effectiveness of the proposed method are verified by examples of investment fund institutions and financing enterprises. Show more
Keywords: Interval triangular fuzzy set, Integrated mean, Two-sided matching
DOI: 10.3233/JIFS-222108
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5847-5857, 2023
Authors: Zhang, Sumin | Ye, Jun
Article Type: Research Article
Abstract: Group decision-making is that individuals collectively make a choice from a set of alternatives. Then, in complex decision-making problems, the decision-making process is no longer subject to a single individual, but group decision-making. Hence, the decision reliability and credibility of the collective evaluation information become more critical. However, current decision-making methods lack the confidence level and credibility measure of group evaluation information. To ensure the confidence level and credibility measure of small-scale group decision-making problems, the aim of this paper is to propose a Multi-Attribute Group Decision-Making (MAGDM) approach using a hyperbolic sine similarity measure between Confidence Neutrosophic Number Credibility …Sets (CNNCSs) in the circumstance of Fuzzy Credibility Multi-Valued Sets (FCMVSs). To achieve this aim, this paper contains the following works. First, we present FCMVS to represent the mixed information of fuzzy sequences and credibility degree sequences with different and/or identical fuzzy values. Second, according to the normal distribution and confidence level of fuzzy values and credibility degrees in FCMVS, FCMVS is transformed into CNNCS to avoid the operational issue between different fuzzy sequence lengths in FCMVSs and to ensure the confidence neutrosophic numbers/confidence intervals of fuzzy values and credibility degrees. Third, a hyperbolic sine similarity measure of CNNCSs is established in the circumstance of FCMVSs. Fourth, a MAGDM approach is developed based on the weighted hyperbolic sine similarity measure in the circumstance of FCMVSs. Fifth, the proposed MAGDM approach is applied to an actual example of the equipment supplier choice problem to illustrate the efficiency and rationality of the proposed MAGDM approach in a FCMVS circumstance. In general, this study reveals new contributions in the representation, transformation method, and similarity measure of small-scale group assessment information, as well as the proposed MAGDM method subject to the normal distribution and confidence levels in small-scale MAGDM scenarios. Show more
Keywords: Fuzzy credibility multi-valued set, confidence neutrosophic number credibility set, hyperbolic sine similarity measure, group decision making
DOI: 10.3233/JIFS-223065
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5859-5869, 2023
Authors: Hu, Danhui | Huang, Zeqi | Yin, Kan | Li, Feng
Article Type: Research Article
Abstract: Considering that the operation of power transmission and transformation equipment is not timely discovered due to the untimely data integration, a multi-dimensional heterogeneous data clustering algorithm for power transmission and transformation equipment based on multimodal deep learning is proposed. The multi-modal deep learning method is used to mine relevant data and measure the similarity between the data, which can improve the accuracy of subsequent multi-bit heterogeneous data clustering of power transmission and transformation equipment. Set up a clustering center and process data clustering to complete multi-dimensional heterogeneous data clustering of power transmission and transformation equipment. The experimental results show that …the method has high clustering accuracy in the clustering of voltage deviation overrun times, voltage harmonic total distortion rate overrun times, and voltage flicker overrun times. Show more
Keywords: Multimodal deep learning, power transmission and transformation equipment, heterogeneous data, clustering, mining, similarity
DOI: 10.3233/JIFS-222924
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5871-5878, 2023
Authors: Hou, Yongchao | Fei, Jingtai | Xia, Xiaofang | Cui, Jiangtao
Article Type: Research Article
Abstract: As data collection increases, more and more sensitive data is being used to publish query results. This creates a significant risk of privacy disclosure. As a mathematically provable privacy theory, differential privacy (DP) provides a tool to resist background knowledge attacks. Fuzzy differential privacy (FDP) generalizes differential privacy by employing smaller sensitivity and supporting multiple similarity measures. Thus the output error can be reduced under FDP. Existing FDP mechanisms employ sliding window strategy, which perturb the true query value to an interval with this value as the midpoint to maintain similarity of outputs from neighboring datasets. It is still possible …for an attacker to infer some sensitive information based on the difference between the left and right endpoints of the output range. To address this issue, this article present two solutions: fixed interval perturbation and infinite interval perturbation. These strategies perturb the true query values of two neighboring datasets to the same interval and provide fuzzy differential privacy protection for the dataset. We apply the proposed method to the privacy-preserving problem of bipartite graph subgraph counting and verify the effectiveness by experiments. Show more
Keywords: Fuzzy differential privacy, privacy protection, subgraph counting, bicliques
DOI: 10.3233/JIFS-221505
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5879-5892, 2023
Authors: Fan, Jiongjiong
Article Type: Research Article
Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219329 .
DOI: 10.3233/JIFS-220931
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5893-5919, 2023
Authors: Luo, Hongyun | Lin, Xiangyi | Yu, Yan
Article Type: Research Article
Abstract: This paper aims to analyze the coupling coordination degree of technology, economy, social responsibility, and ethic of technological innovation in high-tech enterprises, and provide basis for the optimization of technological innovation system structure in high-tech enterprises. Using data of high-tech enterprises in China Statistical Yearbook and China Statistical Yearbook of Science and Technology in 2018, the authors applied Cloud model to index transformation, consistent fuzzy preference relations to determine index weights, coupling degree model to measure the coupling degree of responsible innovation system of high-tech enterprises in China. Research results show that the responsible innovation system of China’s high-tech enterprises …in 2018 is in a low degree of coordination and coupling stage, and the high-tech enterprises in China invest relatively little in technical level, social development, and ethical innovation. This research contributes to the literature on responsible innovation, ethical responsibility in the high-tech enterprises, which is conducive to improving the quality of innovation activities. However, this research collected data from a single country at a single point in time. This paper studies from the perspective of responsible innovation and measures the coupling degree between innovation and ethical responsibility of high-tech enterprises. The establishment of coupling analysis model can not only effectively calculate the coupling degree of technological innovation system, but also deeply analyze the shortcomings of each subsystem of technological innovation system, and provide a basis for the formulation of promotion strategy. Show more
Keywords: Responsible innovation, high-tech enterprises, coupling synergy, ethical responsibility, cloud model
DOI: 10.3233/JIFS-221269
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5921-5936, 2023
Authors: Chen, Yun | Ma, Chongsen | Ou, Liang
Article Type: Research Article
Abstract: Collusion between governments and enterprises has occurred in many economies around the world in the context of government investment projects and tenders. Not only is collusion an illegal act, but it may also lead to learning and imitation by non-colluding parties. Therefore, to control collusion and ensure the quality of government investment projects, investigating the spread of collusion in the bidding process of such projects is important. This study presents a simulation of the diffusion process of collusion among multiple entities through NetLogo, drawing on a contagious disease model. The effectiveness of the hypothesised control tools is validated through the …changing trend of collusion in bidding in China. The findings provide a new approach to controlling collusion based on the perspective of the proliferation of bidding behaviour and have some reference value for the government to formulate policies. Show more
Keywords: Infectious disease model, collusive behaviour, diffusion mechanism, solicitation, simulation analysis
DOI: 10.3233/JIFS-222490
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5937-5952, 2023
Authors: He, Qiang | Wang, Guanqun | Wang, Hengyou | Chen, Linlin
Article Type: Research Article
Abstract: Multivariate time series anomaly detection has been investigated extensively in recent years. Capturing long-term time series information is one of the challenges in this field. We propose a novel multivariate time series anomaly detection framework MTAD-TCGA comprising several modules that efficiently and accurately capture dependencies in long-term multivariate time series. The proposed model contains a temporal convolutional module and uses two parallel graph attention layers to learn the complex dependencies of time series in both the temporal and feature dimensions. A Gated Recurrent Unit layer, based on an improved attention mechanism, and an auto-regressive model is used for prediction, and …the prediction model and reconstruction model are jointly optimized. Finally, the threshold is selected by extreme value theory, and then anomalies are identified. The experimental results on three public datasets show our framework is superior to other state-of-the-art models, achieving F1 scores uniformly at levels above 0.9, verifying the effectiveness and feasibility of the MTAD-TCGA method. Show more
Keywords: Long-term time series, anomaly detection, time convolution network, graph attention network, gated recurrent unit
DOI: 10.3233/JIFS-222554
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5953-5962, 2023
Authors: Liu, Jiacheng | Chau, Gavin | Su, Pianpain
Article Type: Research Article
Abstract: Improving college students’ satisfaction with the teaching quality of ideological and political theory courses in colleges and universities is the need to promote college students to consciously fulfill their ideological and political quality, and it is also the need to further form a strong driving force for the teaching reform of ideological and political theory courses in colleges and universities. The requirements of teaching level are also the requirements to further enhance the competitiveness of colleges and universities. The ideological and political education quality (IPEQ) evaluation of college students is looked as multiple attribute group decision-making (MAGDM) problem. In this …paper, the 2-tuple linguistic neutrosophic TOPSIS (2TLN-TOPSIS) model is built based on the traditional TOPSIS and 2-tuple linguistic neutrosophic sets (2TLNSs). Firstly, the 2TLNSs is introduced. Then, combine the TOPSIS model with 2TLNSs, the 2TLN-TOPSIS model is established for MAGDM. Finally, a numerical example for IPEQ evaluation of College students have been given and some comparisons are also conducted to further illustrate advantages of the 2TLN-TOPSIS method. Show more
Keywords: Multiple attribute group decision making (MAGDM), 2-tuple linguistic neutrosophic sets (2TLNSs), TOPSIS method, ideological and political education quality (IPEQ)
DOI: 10.3233/JIFS-223387
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5963-5975, 2023
Authors: Zhou, Ling | Zhang, Qian | Li, Haili | Zhao, Xuehan
Article Type: Research Article
Abstract: How to make good use of new network technology and design the classroom teaching of a course needs to be based on the teaching object, teaching content, and the teacher’s mastery of the technology and teaching platform. In teaching design, scholars also put forward different teaching links based on their own teaching experience. The cooperative learning links should be designed in college teaching. To build a positive and interdependent organizational structure and an equal and democratic learning atmosphere will help students to stimulate their learning motivation and sense of responsibility. The fuzzy evaluation of the teaching effect of the “micro-ideological …and political” model in medical colleges and universities is viewed as the multiple attribute group decision making (MAGDM) issue. In such paper, Taxonomy method is designed for solving the MAGDM under interval-valued neutrosophic sets (IVNSs). First, the score function of IVNSs and Criteria Importance Though Intercrieria Correlation (CRITIC) method is used to derive the attribute weights. Second, then, the interval-valued neutrosophic numbers Taxonomy (IVNN-Taxonomy) method is built to deal with MAGDM problem. Finally, a numerical example for teaching effect evaluation of the “micro-ideological and political” model in medical colleges and universities is given to illustrate the built method. Show more
Keywords: Multiple attribute group decision making (MAGDM), interval-valued neutrosophic sets (IVNSs), Taxonomy method, CRITIC method, teaching effect evaluation
DOI: 10.3233/JIFS-224186
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5977-5989, 2023
Authors: Das, Subhranil | Mishra, Sudhansu Kumar
Article Type: Research Article
Abstract: Planning a collision-free path while preserving processing time and minimizing cost function has been considered a significant challenge in developing an Autonomous Mobile Robot (AMR). Various optimization techniques for avoiding obstacles and path planning problems have been proposed recently. But, the computation time for executing these techniques is comparatively higher and has lesser accuracy. In this paper, the State Estimation Obstacle Avoidance (SEOA) algorithm has been proposed for estimating the position and velocity of both of the wheels of the AMR. Moreover, this algorithm has been also applied in path planning for reaching the destination point in minimum computational time. …Five different positions of static obstacle are demonstrated in a real time static environment where the proposed SEOA algorithm has been compared with state-of-the-art path planning algorithms such as A* and VFH. The simulation results demonstrate that the proposed algorithm takes lesser computational time to generate the collision free path when compared to other mentioned algorithms. Show more
Keywords: Autonomous mobile robot, static obstacle, optimization, state estimation, path planning
DOI: 10.3233/JIFS-221426
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5991-6002, 2023
Authors: Goel, Sachin | Agrawal, Rajeev | Bharti, R.K.
Article Type: Research Article
Abstract: Epilepsy is the most common neurological disorder by which over 65 million people are affected across the world. Recent research has shown a very large interest to predict and diagnose epilepsy well before time. The continuous monitoring of EEG signals for seizure detection in electroencephalogram (EEG) is a very tedious and time taking process and therefore requires a qualified and trained clinical specialist. This paper presents a novel approach to detect and predict the epileptic signal in the recorded electroencephalogram (EEG). There is always a requirement for a nonlinear technique to examine the EEG signals due to the random nature …of EEG signals. Therefore, we are providing an alternate method that extracts various entropy measures such Sample Entropy, Spectral Entropy, Permutation Entropy, and Shannon Entropy as statistical features from EEG signal. Based on these extracted features LSTM Fully connected Neural Network is used to classify the EEG signals as Focal and Non-focal. The proposed method gives a new insight into EEG signals by providing sensitivity as an added measure using deep learning along with accuracy and precision. Show more
Keywords: Epilepsy, focal & non-focal classification, LSTM, entropy measures
DOI: 10.3233/JIFS-222745
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6003-6020, 2023
Authors: Yi, Lingzhi | Long, Jiao | Huang, Jianxiong | Xu, Xunjian | Feng, Wenqing | She, Haixiang
Article Type: Research Article
Abstract: In order to improve the accuracy and reliability of fault diagnosis of oil-immersed power transformers, a fault diagnosis method based on the Modified Artificial Gorilla Troops Optimizer (MGTO) and the Stochastic Configuration Networks with Block Increments (BSCN) is proposed. First, the original artificial gorilla troop optimization algorithm is improved, which effectively improves the convergence speed and optimization accuracy of the algorithm. Secondly, the conventional Stochastic Configuration Networks (SCN) learning methodology is modified when the fault diagnosis model is constructed. The original SCN adopts point incremental approach to gradually add hidden nodes, while BSCN adopts block increment approach to learn features. …It significantly accelerates training. MGTO algorithm is used to jointly optimize regularization parameter and scale factor in BSCN model, and the fault diagnosis model with the highest accuracy is constructed. The experimental results show that the accuracy of MGTO-BSCN for transformer fault diagnosis reaches 95.9%, which is 3.5%, 9.9% and 11.7% higher than BSCN fault diagnosis models optimized by GTO, Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) respectively, reflecting the superiority of MGTO algorithm. Meanwhile, the comparison with the traditional model shows that the proposed method has obvious advantages in diagnostic effect. Show more
Keywords: Transformer, Stochastic configuration networks, GTO optimization algorithm, Fault diagnosis
DOI: 10.3233/JIFS-223443
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6021-6034, 2023
Authors: Jose, Merin | Mathew, Sunil C.
Article Type: Research Article
Abstract: This paper presents the idea of Alexandrov L -quasi-G-filter space and examines its relationship with L -fuzzy relations. It is proved that every Alexandrov L -quasi-G-filter induces an L -fuzzy relation and vice-versa. By identifying certain functors, the study has brought out the connections between the categories of Alexandrov L -quasi-G-filter spaces and Alexandrov L -fuzzy pre-uniform spaces. Further, the study has explored and thereby establishes the scope of Alexandrov L -quasi-G-filter spaces in mathematical modeling and decision-making processes.
Keywords: Lattice, category, functor, fuzzy relation
DOI: 10.3233/JIFS-223832
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6035-6046, 2023
Authors: Gao, Lu | Yao, Bingxue | Li, Lingqiang
Article Type: Research Article
Abstract: Approximate accuracy is an important concept in rough set theory, which is defined by upper and lower approximations. Generally speaking, the higher precision means the better application performance. The approximation accuracy can be improved by minimizing the upper approximation and maximizing the lower approximation. Recently, Zhou [52 ] introduced two types of fuzzy-covering based rough set models by using inclusion relation between fuzzy sets. In this paper, by replacing inclusion relation with implication degree, we investigate two new fuzzy covering-based rough set models. Compared with inclusion relationship, the inclusion degree can describe the contained relation between fuzzy sets in more …detail. This makes our upper approximation smaller than Zhou’s upper approximation, while the lower approximation is larger than Zhou’s. Therefore, the approximate accuracy of our model is higher than that of Zhou. Furthermore, we apply the new model to the study of multi-attribute decision-making (MADM). Combined with the car buying problem, we demonstrate the effectiveness of our model and compare it with other methods. The results show that we can get the same optimal choice as other methods. However, according to Zhou’s model, we cannot get the optimal choice. Show more
Keywords: Rough set, fuzzy set, approximation operator, covering, inclusion degree
DOI: 10.3233/JIFS-221097
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6047-6063, 2023
Authors: Balan, Elakkiya | Saraniya, O.
Article Type: Research Article
Abstract: COVID-19 is a rapidly proliferating transmissible virus that substantially impacts the world population. Consequently, there is an increasing demand for fast testing, diagnosis, and treatment. However, there is a growing need for quick testing, diagnosis, and treatment. In order to treat infected individuals, stop the spread of the disease, and cure severe pneumonia, early covid-19 detection is crucial. Along with covid-19, various pneumonia etiologies, including tuberculosis, provide additional difficulties for the medical system. In this study, covid-19, pneumonia, tuberculosis, and other specific diseases are categorized using Sharpened Cosine Similarity Network (SCS-Net) rather than dot products in neural networks. In order …to benchmark the SCS-Net, the model’s performance is evaluated on binary class (covid-19 and normal), and four-class (tuberculosis, covid-19, pneumonia, and normal) based X-ray images. The proposed SCS-Net for distinguishing various lung disorders has been successfully validated. In multiclass classification, the proposed SCS-Net succeeded with an accuracy of 94.05% and a Cohen’s kappa score of 90.70%; in binary class, it achieved an accuracy of 96.67% and its Cohen’s kappa score of 93.70%. According to our investigation, SCS in deep neural networks significantly lowers the test error with lower divergence. SCS significantly increases classification accuracy in neural networks and speeds up training. Show more
Keywords: COVID-19, chest X-ray, cosine normalization, tuberculosis, pneumonia
DOI: 10.3233/JIFS-222840
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6065-6078, 2023
Authors: Zhang, Jinhua | Zhang, Qishan | Zhang, Jinxin
Article Type: Research Article
Abstract: This paper discusses how to deal with the greyness problem in the system from the perspective of “result”. Aiming at the greyness problem of the traditional grey relational analysis result, an information fusion grey relational analysis method based on D-S evidence theory and multi solution information fusion is proposed, which mends the traditional grey relational analysis method. The results show that the method proposed in the study has better effect than the traditional grey relational analysis method, and has higher accuracy in the wear particle identification, which indicates that it can further expand the application scope of the grey relational …analysis method. Show more
Keywords: Grey relational analysis, greyness of grey relational analysis, D-S evidence theory, information fusion
DOI: 10.3233/JIFS-223323
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6079-6088, 2023
Authors: Wang, Hongli | Fei, Liguo | Feng, Yuqiang
Article Type: Research Article
Abstract: In order to overcome the weakness of subjectivity of variable and subjectivity of membership function in fuzzy probability the cloud probability model and its algorithm are proposed. Firstly, the representation model of cloud probability is given based on the fusion of cloud model and fuzzy probability. Then the cloud probability algorithm of continuous random variable based on slice method is proposed. Then the relationship between slice number and cloud probability is discussed. And the cloud probability algorithm of discrete random variable is given. Finally, through the application case of e-commerce intelligent decision-making based on cloud probability the effectiveness of the …proposed cloud probability algorithm is verified. The research in this paper has good reference significance for dealing with the events represented by uncertain variables. Show more
Keywords: Cloud probability, fuzzy probability, cloud model, double cloud model, slicing method, fuzziness-randomness
DOI: 10.3233/JIFS-222518
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6089-6102, 2023
Authors: Dou, Fei | Wei, Yun | Huang, Yakun | Ning, Yao | Wang, Li
Article Type: Research Article
Abstract: In the condition of large passenger flow, subway station managers take measures of passenger flow control organization for reducing high safety operation risks at subway stations. The volume of passenger flow in urban railway network operation continues to increase and the Congestion of passenger flow is very high. Passenger flow control measures can greatly give birth to the pressure of transportation and ensure an urban rail transit system’s safe operation. In this paper, we develop a cloud model-based method for passenger flow control, which extends the four-level risk-control grade of a large passenger flow at facilities by considering its fuzzy …and stochastic characteristics. Then, an efficient passenger flow control strategy for subway stations is made, where the control time and locations are simultaneously determined. Finally, a station in the Beijing subway is studied to test the validity of the proposed approach. The results show that the time of maximum queuing length is much shorter and the density of passenger flow is lower than existing methods in practice. With the in-depth study of complex network controllability, many studies have applied to control judgment and real network optimization. This paper analyzes the cloud-model-based method for passenger flow control at subway stations and therefore a new method can be incorporated for developing and optimizing control strategies. A few researchers have attempted to find the solution to the problem of crowding risk classification and the passenger flow control strategy. The focus of some studies simultaneously solves the passenger flow control with multiple stations. Show more
Keywords: Subway stations, passenger flow control, risk-control grade identification, cloud model, crowding, control problem, normal cloud
DOI: 10.3233/JIFS-223110
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6103-6115, 2023
Authors: Yang, Can
Article Type: Research Article
Abstract: In the context of sharing economy, logistics companies have begun to adopt a new collection and distribution model based on external vehicles, and external vehicles are used to provide customers with collection/distribution services. A kind of Multi-Vehicle Split Pickup and Delivery Problem with Time Windows (MVSPDPTW) is studied in the paper. The minimum total length of the vehicle’s travel path is the objective function, and a mixed-integer linear programming model is established. A high-efficiency Tabu Simulated Annealing (TSA) algorithm is proposed. Two new neighborhood search operators are designed in the algorithm, they are used to repair the violation of capacity …constraints and the operation of car replacement. In the method, the neighborhood search range is expanded and the algorithm is avoided from falling into a local optimum. In addition, a taboo mechanism and a penalty mechanism for violation of constraints are added to the algorithm, the effective tailoring of the search space is realized and the algorithm’s global optimization ability is improved. Finally, a large number of simulation experiments were performed on the algorithm based on the Solomon data set and the constructed simulation data set, and the effectiveness of the algorithm is verified in the experiments. Show more
Keywords: Vehicle routing, simulated annealing, intelligent optimization, split demand, pickup and delivery
DOI: 10.3233/JIFS-223708
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6117-6129, 2023
Authors: Liang, Jun | Huang, Keyi | Qiu, Shaojian | Lin, Hai | Lian, Keng
Article Type: Research Article
Abstract: Trend following strategies have a wide-ranging role in quantitative trading fields, which can capture important unilateral market trends for large gains, while this is vulnerable to losses in the period of consolidation. In this paper, we explored the trend trading system in the Chinese futures market based on machine learning techniques and statistical methods. This research utilized the Long-Short-Term Memory network to extract features of time series then predicted the price movements by Machine Learning classifiers. Moreover, based on rebar futures data, the results reveal that the annualized return improved from 6.39% to 15.68% after the trading signals generated in …the trading strategy were filtered using the XGBoost model. Also, futures on gold and soybean were used to further test the integrated strategy and the results of the experiment show the effectiveness of the model in filtering false trading signals. Show more
Keywords: Machine learning, LSTM, time series forecasting, trend following strategies, deep learning
DOI: 10.3233/JIFS-223873
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6131-6149, 2023
Authors: Li, Li
Article Type: Research Article
Abstract: Eliminating poverty, improving people’s livelihood, building a well-off society in all aspects and achieving common prosperity are the essential requirements of socialism. The 19th Party Congress formally put forward the strategy of rural revitalization and made it the focus of the direction of rural reform and development in the new period. In this context, how to make the basic strategy of precise poverty alleviation implemented and put into practice, and how to realize the work of precise poverty alleviation to better contribute to rural revitalization are both practical and academic questions that need to be further explored. The efficiency evaluation …of integrated development of agriculture and tourism to promote rural revitalization under the policy of precise poverty alleviation is viewed as the multi-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic number cross-entropy (TFNN-CE) method is built based on the traditional cross-entropy and triangular fuzzy neutrosophic sets (TFNSs). Furthermore, Then, TFNN-CE method is established for MADM. Finally, a numerical example for efficiency evaluation of integrated development of agriculture and tourism to promote rural revitalization under the policy of precise poverty alleviation has been given to further illustrate advantages of the built method. Show more
Keywords: Multiple attribute decision making (MADM) problems, triangular fuzzy neutrosophic sets (TFNSs), cross-entropy method, 2TLNN-CE method, efficiency evaluation
DOI: 10.3233/JIFS-224126
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6151-6161, 2023
Authors: Zeng, Guoqiang | Zhou, Huan | Tang, Jianrong
Article Type: Research Article
Abstract: COVID-19, as a public health emergency, poses a major challenge to the economic and social development of numerous countries, impacting not only the psychology and behavior of residents at the individual level, but also the stability of society at the societal level, typified by the increased difficulty of smooth economic operation due to residents’ irrational consumption behavior. In the present study, we have drawn on the psychological stimuli-organism-response (SOR) and mediating effect theories to explore the causes of irrational consumption behavior. Finally, based on the actual situation in the questionnaire survey, we propose the system for assessing the effectiveness of …the government’s response to the epidemic and measures under the influence of irrational consumption. In the results of our study, we found that the positive and negative irrational consumption adjustment coefficients of the government under the influence of its inherent image were 6.72% and 17.64%, respectively; the government intervention indices under the positive and negative perceptions output according to QFD theory were 0.14 and 0.02, respectively; and the positive and negative measure effectiveness indices of the government response programs were 2.28 and 2.10, respectively. Thus, through our study, we concluded that residents’ positive perception of government image would reduce the occurrence of irrational consumption behavior, while the improvement of irrational consumption behavior by perfect psychological services under residents’ negative perception of government image is more obvious. On the basis of summarizing the experience of this COVID-19, this study can serve the prediction and regulation of residents’ irrational consumption behavior under the government response to public health emergencies, and also enrich the research literature on irrational consumption behavior in related consumption behavior studies, and more importantly, provide theoretical and empirical support for similar academic cases in the international community. Show more
Keywords: Consumer behavior research, irrational consumer behavior, QFD theory, SOR model, mediating effects
DOI: 10.3233/JIFS-223505
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6163-6182, 2023
Authors: Navin, K.S. | Nehemiah, H. Khanna | Nancy Jane, Y. | Veena Saroji, H.
Article Type: Research Article
Abstract: Premature mortality from cardiovascular disease can be reduced with early detection of heart failure by analysing the patients’ risk factors and assuring accurate diagnosis. This work proposes a clinical decision support system for the diagnosis of congenital heart failure by utilizing a data pre-processing approach for dealing missing values and a filter-wrapper based method for selecting the most relevant features. Missing values are imputed using a missForest method in four out of eight heart disease datasets collected from the Machine Learning Repository maintained by University of California, Irvine. The Fast Correlation Based Filter is used as the filter approach, while …the union of the Atom Search Optimization Algorithm and the Henry Gas Solubility Optimization represent the wrapper-based algorithms, with the fitness function as the combination of accuracy, G-mean, and Matthew’s correlation coefficient measured by the Support Vector Machine. A total of four boosted classifiers namely, XGBoost, AdaBoost, CatBoost, and LightGBM are trained using the selected features. The proposed work achieves an accuracy of 89%, 84%, 83%, 80% for Heart Failure Clinical Records, 81%, 80%, 83%, 82% for Single Proton Emission Computed Tomography, 90%, 82%, 93%, 80% for Single Proton Emission Computed Tomography F, 80%, 80%, 81%, 80% for Statlog Heart Disease, 80%, 85%, 83%, 86% for Cleveland Heart Disease, 82%, 85%, 85%, 82% for Hungarian Heart Disease, 80%, 81%, 79%, 82% for VA Long Beach, 97%, 89%, 98%, 97%, for Switzerland Heart Disease for four classifiers respectively. The suggested technique outperformed the other classifiers when evaluated against Random Forest, Classification and Regression Trees, Support Vector Machine, and K-Nearest Neighbor. Show more
Keywords: Henry gas solubility optimization, atom search optimization algorithm, XGBoost, adaboost, catboost, LightGBM
DOI: 10.3233/JIFS-221348
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6183-6218, 2023
Authors: Jansi Rani, J. | Dhanasekar, S. | Micheal, David Raj | Manivannan, A.
Article Type: Research Article
Abstract: In dealing with real world transportation problems, several issues are frequently encountered as a result of uncontrollable factors. To deal this uncertainties, many authors have suggested transportation problems with intuitionistic fuzzy parameters. In this article, fully intuitionistic fuzzy transportation problem (InFTP) is considered in which the parameters are triangular intuitionistic fuzzy numbers. To solve this, initially intuitionistic fuzzy branch and bound technique are applied to get the initial basic feasible solution and then intuitionistic fuzzy modified distribution (InFMODI) method is applied to acquire the optimal solution of the fully InFTP. Also, a new ordering is developed here in which some …properties of compensation, linearity, additive and partial order relations are satisfied. The optimal solution obtained from the proposed method satisfies the condition of optimality and feasibility. Finally, two numerical examples are provided to show the effectiveness of the proposed method. Show more
Keywords: Intuitionistic fuzzy number, intuitionistic fuzzy transportation problem, intuitionistic fuzzy initial basic feasible solution, intuitionistic fuzzy optimal solution
DOI: 10.3233/JIFS-221345
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6219-6229, 2023
Authors: Dong, Lihui | Yuan, Weijin | Deng, Yunfeng
Article Type: Research Article
Abstract: This paper proposes a new model for characterizing the emergency evacuation process of people during a disaster. This model considers the change of visual field based on a cellular automata model combined with a behavioral heuristic model. Using the behavioral heuristic model, the dynamic field parameters related to the change of visual field are first established. Then, new judgment rules are developed for personnel encountering obstacles by combining the characteristics of the new field of view. Finally, an analytical comparison is made between the proposed model and the traditional evacuation model in terms of the changes in the fields of …view and the number of evacuees. The results show that the level of path service determines the efficiency of evacuation. It is also seen that herd mentality acts as a hindrance in cases where the personnel are dependent while otherwise acting as a facilitator. It is also shown that the evacuation time increases by the number of evacuees up to a certain threshold. Beyond that threshold the evacuation time fluctuates within a certain range by increasing the number of evacuees is not affected by changes in the field of view. The new model is also faster than the social force model, easier to calculate on a large scale, and more realistic than the traditional cellular model. Show more
Keywords: Cellular automata, Pedestrian dynamics, view, pedestrian evacuation
DOI: 10.3233/JIFS-222587
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6231-6247, 2023
Authors: Yavuz, Akif | Sen, Osman Taha
Article Type: Research Article
Abstract: This study aims to investigate the predictability of a friction-induced nonlinear dynamic behavior on a simplified yet controlled laboratory experiment through the fuzzy logic approach. First, a mass-sliding belt experiment is built to observe the effects of several operating parameters on the occurrence of nonlinear dynamic behavior. Second, experiments are performed at various levels of these operating parameter, and the data are recorded. Third, fuzzy logic model architectures with different membership functions are built, where these operating parameters are assumed as the input parameters. The output of the fuzzy logic model architecture is defined as a new parameter called squeal …index. Finally, a fuzzy logic model with a 96.97% prediction accuracy is obtained. Hence, it is shown that the proposed model can provide insight about the dynamic behavior of the system of interest without solving the nonlinear governing equations. Furthermore, the proposed model allows the prediction of the system state at operating conditions where experimentation is not possible, and it can be used for the determination of the critical operating parameters at which the system behavior switches from one state to another. Show more
Keywords: Fuzzy logic modelling, dynamic instability, friction induced vibration, mass-sliding belt experiment, disc brake squeal
DOI: 10.3233/JIFS-223177
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6249-6264, 2023
Authors: Hasan, Mohammad Kamrul | Ali, Md. Yasin | Sultana, Abeda | Mitra, Nirmal Kanti
Article Type: Research Article
Abstract: Picture fuzzy set (PFS), is a newly developed apparatus to treaty with uncertainties in problems where the opinions are yes, no, neutral, and refusal types. Extension principle is one of the key tools for describing uncertainties. It provides a general method for existing classical mathematical concepts to address fuzzy quantities. It has numerous applications in various arena of our real life. However, there are less works on extension principle for picture fuzzy sets. In this article, new extension principles namely minimal extension principle and average extension principle are proposed for picture fuzzy sets. Various properties of the minimal extension principle …and the average extension principle for PFSs are also established. We also prove some properties of Zadeh’s extension principle for PFSs. Finally, arithmetic operations for PFSs based on the average extension principle are developed with numerical illustrations. Show more
Keywords: Picture fuzzy set, Zadeh’s extension principle, minimal extension principle, average extension principle, arithmetic operations
DOI: 10.3233/JIFS-220616
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6265-6275, 2023
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