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

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: This article has been retracted.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

DOI: 10.3233/JIFS-221973

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5659-5674, 2023