Journal of Intelligent & Fuzzy Systems - Volume 44, issue 4

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Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

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

Authors: Wu, Chun | Yang, Liu

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