Journal of Intelligent & Fuzzy Systems - Volume 43, issue 3

Authors: Mohankumar, B. | Karuppasamy, K.

Article Type: Research Article

Abstract: Congestion and security plays a most important key role in the wireless sensor network. In our previous work, energy balances routing is ensured by introducing the Energy Balancing and Optimal Routing Based Secured Data Transmission (EBORDT). But in this research work, congestion and security is not focused. This will lead to increased data loss rate along with entire network collision. These issues are focused and resolved in the proposed research work by introducing the method namely Honest aware Congestion concerned Secured Edge Disjoint Multipath Routing (HC-SEDMR) Scheme. In this work cluster head selection is done using hybridized simulated annealing with …fuzzy rule descriptors. Base on these selected cluster head, clustering is done using Normalized optimal clustering algorithm. After clustering Energy and Edge disjoint aware optimal routing is done using Congestion and Collision aware Edge Disjoint multipath routing. Here secured routing is ensured by choosing honest nodes for the forwarding process. This is done by using honest forwarding node selection method. The performance analysis of the research work is done in the NS2 simulation environment from which it is proved that the proposed method attains better performance than the existing methodology. Show more

Keywords: Honesty, congestion, edge disjoint routing, clustering, simulated annealing, multipath routing

DOI: 10.3233/JIFS-212841

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2219-2229, 2022

Authors: Kheirollahi, Hooshang | Rostamzadeh, Mahfouz | Marzang, Soran

Article Type: Research Article

Abstract: Classic data envelopment analysis (DEA) is a linear programming method for evaluating the relative efficiency of decision making units (DMUs) that uses multiple inputs to produce multiple outputs. In the classic DEA model inputs and outputs of DMUs are deterministic, while in the real world, are often fuzzy, random, or fuzzy-random. Many researchers have proposed different approaches to evaluate the relative efficiency with fuzzy and random data in DEA. In many studies, the most productive scale size (mpss) of decision making units has been estimated with fuzzy and random inputs and outputs. Also, the concept of fuzzy random variable …is used in the DEA literature to describe events or occurrences in which fuzzy and random changes occur simultaneously. This paper has proposed the fuzzy stochastic DEA model to assess the most productive scale size of DMUs that produce multiple fuzzy random outputs using multiple fuzzy random inputs with respect to the possibility-probability constraints. For solving the fuzzy stochastic DEA model, we obtained a nonlinear deterministic equivalent for the probability constraints using chance constrained programming approaches (CCP). Then, using the possibility theory the possibilities of fuzzy events transformed to the deterministic equivalents with definite data. In the final section, the fuzzy stochastic DEA model, proposed model, has been used to evaluate the most productive scale size of sixteen Iranian hospitals with four fuzzy random inputs and two fuzzy random outputs with symmetrical triangular membership functions. Show more

Keywords: Data envelopment analysis, fuzzy-random, most productive scale size, hospitals

DOI: 10.3233/JIFS-202456

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2231-2241, 2022

Authors: Vijayalakshmi, P. | Muthumanickam, K. | Karthik, G. | Sakthivel, S.

Article Type: Research Article

Abstract: Adenomyosis is an abnormality in the uterine wall of women that adversely affects their normal life style. If not treated properly, it may lead to severe health issues. The symptoms of adenomyosis are identified from MRI images. It is a gynaecological disease that may lead to infertility. The presence of red dots in the uterus is the major symptom of adenomyosis. The difference in the extent of these red dots extracted from MRI images shows how significant the deviation from normality is. Thus, we proposed an entroxon-based bio-inspired intelligent water drop back-propagation neural network (BIWDNN) model to discover the probability …of infertility being caused by adenomyosis and endometriosis. First, vital features from the images are extracted and segmented, and then they are classified using the fuzzy C-means clustering algorithm. The extracted features are then attributed and compared with a normal person’s extracted attributes. The proposed BIWDNN model is evaluated using training and testing datasets and the predictions are estimated using the testing dataset. The proposed model produces an improved diagnostic precision rate on infertility. Show more

Keywords: Medical image processing, adenomyosis, endometriosis, infertility, BIWDNN

DOI: 10.3233/JIFS-212866

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2243-2251, 2022

Authors: Xu, Meiling | Tian, Boping | Fu, Yongqiang

Article Type: Research Article

Abstract: Credit scoring is widely used by financial institutions for default prediction, however, a significant portion of online credit loan customers have inadequate or unverifiable credit histories, making it difficult for financial institutions to make effective credit decisions. Since the widespread use of smartphones and the popularity of mobile applications, it is worth investigating whether mobile application usage behaviors (App behaviors) of customers can effectively predict online loan defaults. This paper proposes a combined algorithm of CNN and LightGBM, and establishes credit scoring models with App behaviors to evaluate the default risk of online credit loans based on logistic regression, LightGBM, …CNN and the combined algorithm, respectively. The experimental results suggest that App behaviors have an obvious effect on the default prediction of customers applying for online credit loans, and the combined model outperforms the other models in terms of the area under the curve (AUC). Furthermore, integrated credit scoring models are developed by combining App behaviors with traditional scoring features. A comparison of the integrated models and the traditional scoring model indicates that the integrated models have achieved a significant improvement in classification performance and App behaviors can be a powerful complement to the traditional credit scoring model. Show more

Keywords: Credit scoring, online credit loans, mobile application usage behaviors, logistic regression, combined model

DOI: 10.3233/JIFS-211825

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2253-2264, 2022

Authors: Chen, Yan | Yu, Ying | Wang, Ya-Meng | Lou, Jun-He

Article Type: Research Article

Abstract: Probabilistic Uncertain Linguistic Term Set (PULTS), as an emerging and effective linguistic expression tool, can appropriately describe the complex evaluation information of decision makers. The cloud model is powerful in handling complex cognitive linguistic information, based on which, this paper proposes two new Multicriteria Decision-Making (MCDM) Methods with PULTSs. Firstly, to avoid the problem of information loss in traditional linguistic conversion methods, Probabilistic Uncertainty Trapezium Cloud (PUTC) is proposed to quantify linguistic evaluation information. Secondly, the Probabilistic Uncertainty Trapezium Cloud Weighted Bonferroni mean (PUTCWBM) operator is defined, while presenting a new cloud score function and similarity measures. Additionally, two ranking …methods are proposed, one on the basis of the similarity measures of PUTCs and ideal solutions, the other on the basis of the PUTCWBM operator and the cloud score function. Finally, the two methods are verified with an example of evaluation on masks, and the effectiveness and superiority of the methods are further illustrated through sensitivity analysis and method comparison. Show more

Keywords: Multicriteria decision-making, probabilistic uncertain linguistic term set, probabilistic uncertain Trapezium cloud, similarity measure, cloud score function

DOI: 10.3233/JIFS-213001

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2265-2282, 2022

Authors: Pirzad Mashak, Neda | Akbarizadeh, Gholamreza | Farshidi, Ebrahim

Article Type: Research Article

Abstract: Prostate cancer is one of the most common cancers in men, which takes many victims every year due to its latent symptoms. Thus, early diagnosis of the extent of the lesion can help the physician and the patient in the treatment process. Nowadays, detection and labeling of objects in medical images has become especially important. In this article, the prostate gland is first detected in T2 W MRI images by the Faster R-CNN network based on the AlexNet architecture and separated from the rest of the image. Using the Faster R-CNN network in the separation phase, the accuracy will increase as …this network is a model of CNN-based target detection networks and is functionally coordinated with the subsequent CNN network. Meanwhile, the problem of insufficient data with the data augmentation method was corrected in the preprocessing stage, for which different filters were used. Use of different filters to increase the data instead of the usual augmentation methods would eliminate the preprocessing stage. Also, with the presence of raw images in the next steps, it was proven that there was no need for a preprocessing step and the main images could also be the input data. By eliminating the preprocessing step, the response speed increased. Then, in order to classify benign and malignant cancer images, two deep learning architectures were used under the supervision of ResNet18 and GoogleNet. Then, by calculating the Confusion Matrix parameters and drawing the ROC diagram, the capability of this process was measured. By obtaining Accuracy = 95.7%, DSC = 96.77% and AUC = 99.17%, The results revealed that this method could outperform other well-known methods in this field (DSC = 95%) and (AUC = 91%). Show more

Keywords: Prostate cancer, data augmentation, filtering, feature extraction, localization, deep learning

DOI: 10.3233/JIFS-212990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2283-2298, 2022

Authors: Venkatasamy, B. | Kalaivani, L.

Article Type: Research Article

Abstract: Solar photovoltaic industry is continuously thriving to improve the performance of power management systems with technology advancement. In the power system, balancing active and reactive power injection and absorption plays a pivotal role in maintaining Grid regulation and power factor and thus improving the efficiency and power handling capability. The Grid-tied solar PV inverters can be the cost-effective solution for reactive power compensation. Solar photovoltaic (SPV) system with the Grid-tied inverter is to produce active and reactive power in various ecological conditions. The Grid-tied PV inverter injects active and reactive power into the grid according to the desired value by …the Grid requirements. The Proposed system uses decoupled P-Q theory with Interval type-2 Fuzzy Logic Controller (IT2-FLC). Also, the proposed controller minimizes the Total Harmonic Distortion (THD) and produces regulated output power compared to the other control techniques. The proposed IT2-FLC is compared with a Type-1 Fuzzy Logic Controller (T1-FLC) and conventional mathematical PI controller for performance analysis using simulation. A 75 kW Grid-tied inverter is designed in simulation to prove the effectiveness of the system through Matlab/Simulink. A smaller rating prototype model is developed to verify the simulation results. Show more

Keywords: Solar Photovoltaic, grid-tied inverter, interval type-2 fuzzy logic controller, reactive power

DOI: 10.3233/JIFS-212721

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2299-2314, 2022

Authors: Haouassi, Hichem | Mahdaoui, Rafik | Chouhal, Ouahiba | Bekhouche, Abdelaali

Article Type: Research Article

Abstract: Many machine learning-based methods have been widely applied to Coronary Artery Disease (CAD) and are achieving high accuracy. However, they are black-box methods that are unable to explain the reasons behind the diagnosis. The trade-off between accuracy and interpretability of diagnosis models is important, especially for human disease. This work aims to propose an approach for generating rule-based models for CAD diagnosis. The classification rule generation is modeled as combinatorial optimization problem and it can be solved by means of metaheuristic algorithms. Swarm intelligence algorithms like Equilibrium Optimizer Algorithm (EOA) have demonstrated great performance in solving different optimization problems. Our …present study comes up with a Novel Discrete Equilibrium Optimizer Algorithm (NDEOA) for the classification rule generation from training CAD dataset. The proposed NDEOA is a discrete version of EOA, which use a discrete encoding of a particle for representing a classification rule; new discrete operators are also defined for the particle’s position update equation to adapt real operators to discrete space. To evaluate the proposed approach, the real world Z-Alizadeh Sani dataset has been employed. The proposed approach generate a diagnosis model composed of 17 rules, among them, five rules for the class “Normal” and 12 rules for the class “CAD”. In comparison to nine black-box and eight white-box state-of-the-art approaches, the results show that the generated diagnosis model by the proposed approach is more accurate and more interpretable than all white-box models and are competitive to the black-box models. It achieved an overall accuracy, sensitivity and specificity of 93.54%, 80% and 100% respectively; which show that, the proposed approach can be successfully utilized to generate efficient rule-based CAD diagnosis models. Show more

Keywords: Coronary artery disease, medical diagnosis, machine learning, rule-based diagnosis, rule discovery, population-based optimization, discrete equilibrium optimization algorithm

DOI: 10.3233/JIFS-213257

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2315-2331, 2022

Authors: Indira, K. | Karki, Maya V. | Mallika, H.

Article Type: Research Article

Abstract: Recognition of Kannada Characters is a complex task as the number of classes in Kannada language by considering all combinations of vowels and consonants is 623,893. In this paper, the complexity is reduced from 623,893 to just having 313 classes as Main aksharas (Vowel, Consonants,Vowel modifiers and Consonant modifiers) and 30 classes as vattu aksharas(conjuncts) by using two line segmentation. A novel CNN model for recognition of printed and handwritten Kannada characters is proposed. CNN model with two, three and four layers are designed for Main akshara and Vattu aksharas with different filter size. The database consists of total of …31,300 samples and 3000 samples of printed and handwritten characters of Main akshara and Vattu aksharas respectively. Simulation result revealed that CNN model with four layer architecture is the best model for recognition of Kannada characters. This model achieved a recognition accuracy of 98.83% and 99.29% for printed Main akshara and Vattu aksharas and 82.50% and 80.92% for handwritten main and vattu akshara respectively. Show more

Keywords: Deep learning, convolution neural network, SVM classifier, horizontal projection profile, vertical projection profile

DOI: 10.3233/JIFS-212680

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2333-2346, 2022

Authors: Dhamija, Ashutosh | Dubey, R.B.

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-212789

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2347-2362, 2022