Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Nath, Sudarshan | Das Gupta, Suparna | Saha, Soumyabrata

Article Type: Research Article

Abstract: Skin disease is currently considered to be one of the most common diseases in the globe. Most of the human population has experienced it at some point but not all skin illnesses are as severe as others. There are some diseases that are symptomless or show fewer symptoms. Skin cancer is a potentially fatal outcome of serious skin illnesses that might develop if they are not detected in time. Due to the fact that medical professionals aren’t always quick or reliable enough to make a proper diagnosis. There is a hefty price tag attached to employing sophisticated equipment. Therefore, we …propose a system capable of classifying skin diseases using deep learning approaches, such as CNN architecture and six preset models including MobileNet, VGG19, ResNet, EfficientNet, Inception, and DenseNet. Acne, blisters, cold sores, psoriasis, and vitiligo are some of the most often seen skin conditions, thus we scoured the web resources for relevant photographs of these conditions. We have applied data augmentation methods to extend the size of the dataset and include more image variations. In the validation dataset, we achieved an accuracy rate of approx 99 percent, while in the test dataset; we achieved an accuracy rate of approx 90 percent. Our proposed method would help to diagnose skin diseases in a faster and more cost-effective way. Show more

Keywords: Skin disease, deep learning, CNN, MobileNet, VGG19, ResNet, EfficientNet, Inception, DenseNet, Acne, blisters, cold sore, psoriasis, vitiligo

DOI: 10.3233/JIFS-222773

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2023

Authors: Mariyam, Farhana | Mehfuz, Shabana | Sadiq, Mohd.

Article Type: Research Article

Abstract: Goal oriented software requirements analysis method is used for the analysis of elicited functional goals (FGs) and non-functional goals (NFGs) of a system in which goals are decomposed and refined into sub-goals until requirements from the sub-goals are identified. Based on the critical analysis, we found that most of the attention of goal-oriented methods is on the crisp and fuzzy logic during the analysis of the software goals or requirements. In these methods’ prior information about the type of membership function is required; and the selection of membership function depends on the subjective justification. As a result, it lacks objectivity …and may affect the ranking values of the goals or requirements during the analysis. Therefore, this paper presents a rough attributed goal-oriented software requirements analysis (RAGOSRA) method in which rough preference matrix has been used to capture the opinions of different stakeholders. The result of the RAGOSRA method is compared by considering the following criteria, i.e., goal types, goal links, types of data used in the analysis, stakeholder perceptions and time complexity with some fuzzy based methods. Based on the time complexity analysis, it is found that RAGOSRA method requires only 4 operations for the selection of goals for the dataset having NFGs and FGs of an institute examination system. On the other hand, FAGOSRA method, fuzzy TOPSIS method, and fuzzy AHP method requires 36, 200, and 240 operations respectively. Show more

Keywords: Requirements engineering, goal-oriented requirements engineering, software requirements analysis, multi-criteria decision-making method, rough-set theory, institute examination system

DOI: 10.3233/JIFS-221300

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Yousif, Majeed A. | Hamasalh, Faraidun K.

Article Type: Research Article

Abstract: In this paper, a novel numerical scheme is developed using a new construct by non-polynomial spline for solving the time fractional Generalize Fisher equation. The proposed models represent bacteria, epidemics, Brownian motion, kinetics of chemicals and fuzzy systems. The basic concept of the new approach is constructing a non-polynomial spline with different non-polynomial trigonometric and exponential functions to solve fractional differential equations. The investigated method is demonstrated theoretically to be unconditionally stable. Furthermore, the truncation error is analyzed to determine the or-der of convergence of the proposed technique. The presented method was tested in some examples and compared graphically with …analytical solutions for showing the applicability and effectiveness of the developed numerical scheme. In addition, the present method is compared by norm error with the cubic B-spline method to validate the efficiency and accuracy of the presented algorithm. The outcome of the study reveals that the developed construct is suitable and reliable for solving nonlinear fractional differential equations. Show more

Keywords: Non-polynomial spline, generalize fisher equation, truncation error, stability analysis

DOI: 10.3233/JIFS-222445

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Li, Xiaoning | Yu, Qiancheng | Yang, Yufan | Tang, Chen | Wang, Jinyun

Article Type: Research Article

Abstract: This paper proposes an evolutionary ensemble model based on a Genetic Algorithm (GAEEM) to predict the transmission trend of infectious diseases based on ensemble again and prediction again. The model utilizes the strong global optimization capability of GA for tuning the ensemble structure. Compared with the traditional ensemble learning model, GAEEM has three main advantages: 1) It is set to address the problems of information leakage in the traditional Stacking strategy and overfitting in the Blending strategy. 2) It uses a GA to optimize the combination of base learners and determine the sub. 3) The feature dimension of the data …used in this layer is extended based on the optimal base learner combination prediction information data, which can reduce the risk of underfitting and increase prediction accuracy. The experimental results show that the R2 performance of the model in the six cities data set is higher than all the comparison models by 0.18 on average. The MAE and MSE are lower than 42.98 and 42,689.72 on average. The fitting performance is more stable in each data set and shows good generalization, which can predict the epidemic spread trend of each city more accurately. Show more

Keywords: Evolutionary ensemble, genetic algorithm, ensemble strategy, epidemics transmission prediction

DOI: 10.3233/JIFS-222683

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Zhang, Yu | Liu, Fan | Hu, Yupeng | Li, Xiaoli | Dong, Xiangjun | Cheng, Zhiyong

Article Type: Research Article

Abstract: Cross-domain recommendation aims to alleviate the target domain’s data sparsity problem by leveraging source domain knowledge. Existing GCN-based approaches perform graph convolution operations in each domain separately. However, the direct effect of item feature and topological structure information in the source domain are neglected for user preference modeling in the target domain. In this paper, we propose a novel Dual Attentive Graph Convolutional Network for Cross-Domain Recommendation (DAG4CDR). Specifically, we integrate the source and target domain’s interaction data to construct a unified user-item bipartite graph and then perform GCN propagation on the graph to learn user and item embeddings. Over …the unified graph, the interaction data from both domains can be leveraged to learn user and item embeddings via information propagation. In the embedding aggregation phase, the messages passed from different items of two domains to users are weighted by a designed dual attention mechanism, which considers the contributions of different items from both node- and domain-level. We conducted extensive experiments to validate the effectiveness of our method on several publicly available datasets, and the results demonstrate the superiority of our model on preference modeling for both common and non-common users. Show more

Keywords: Cross-domain recommendation, graph convolutional network, attention mechanism

DOI: 10.3233/JIFS-222411

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Jain, Manish | Kumar, Sanjay | Park, Choonkil

Article Type: Research Article

Abstract: The question of relaxing the compatible hypothesis of the pair of mappings in fixed point theory has always been remained an open problem. We address such an open problem raised by Choudhury et al. [4 ] and also explicitly settles the issue of monotone and continuity hypotheses of the involved mappings in coupled coincidence point results. Moreover, we state a gap in an example given in [3 ] and repair it. Application to the dynamic programming problem shows the usability of present work. Finally, we also propose an open problem for further investigation.

Keywords: GV-fuzzy metric space, φ-contractions, Hadɘić type t-norm, mixed monotone property, coupled coincidence point 2010 Mathematics Subject Classification. 47H10, 54H25.

DOI: 10.3233/JIFS-222637

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Guo, Xiaobin | Chen, Ying | Zhuo, Quanxiu

Article Type: Research Article

Abstract:  In paper the generalized real eigenvalue and fuzzy eigenvector of a crisp real symmetric matrix with respect to another real symmetric matrix is studied. The original generalized fuzzy eigen problem is extended into a crisp generalized eigen problem of a real symmetric matrix with high orders using the arithmetic operation of LR fuzzy matrix and vector. Two cases are analysed: (a) the unknown eigenvalue λ is a non negative real number; (b) the unknown eigenvalue λ is a negative real number. Two computing models are established and an algorithm for finding the generalized fuzzy eigenvector of a real symmetric matrix …is derived. Moreover, a sufficient condition for the existence of a strong generalized fuzzy eigenvector is given. Some numerical examples are shown to illustrated our proposed method. Show more

Keywords: Fuzzy numbers, fuzzy eigenvectors, matrix computation, fuzzy linear systems

DOI: 10.3233/JIFS-222641

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2023

Authors: Singh, Varsha | Agrawal, Prakhar | Tiwary, Uma Shanker

Article Type: Research Article

Abstract: Generating natural language description for visual content is a technique for describing the content available in the image(s). It requires knowledge of both the domains of computer vision and natural language processing. For this, various models with different approaches are suggested. One of them is encoder-decoder-based description generation. Existing papers used only objects for descriptions, but the relationship between them is equally essential, requiring context information. Which required techniques like Long Short-Term Memory (LSTM). This paper proposes an encoder-decoder-based methodology to generate human-like textual descriptions. Dense-LSTM is presented for better description as a decoder with a modified VGG19 encoder to …capture information to describe the scene. Standard datasets Flickr8K and Flickr30k are used for testing and training purposes. BLEU (Bilingual Evaluation Understudy) score is used to evaluate the generated text. For the proposed model, a GUI (Graphical User Interface) is developed, which produces the audio description of the output received and provides an interface for searching the related visual content and query-based search. Show more

Keywords: Convolutional neural network (CNN), dense-long short-term memory (Dense-LSTM), bilingual evaluation understudy score (BLEU), textual description generation

DOI: 10.3233/JIFS-222358

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Hu, Limei

Article Type: Research Article

Abstract: The traditional failure mode and effect analysis (FMEA), as an effective risk analysis technique, has several limitations in the uncertainty modeling and the weights determination of the risk indicators. This paper aims to propose a hybrid risk prioritization method simultaneously considering the characteristics of the reliability associated with the FMEA team members’ evaluation information and their psychological behavior to enhance the performance of the traditional FMEA model. The hybrid risk prioritization method is developed based on the generalized TODIM method and the weighted entropy measure with the linguistic Z-numbers (LZNs). First, the LZNs are adopted to depict the FMEA team …members’ cognition information and the reliability of these information. Second, a weighted entropy measure based on the fuzzy entropy and the LZNs is developed to obtain the risk indicators’ weights. Finally, the generalized TODIM method with the LZNs is constructed to obtain the risk priority orders of failure modes, which can effectively simulate the FMEA team members’ psychological character. The applicability and effectiveness of the proposed risk prioritization method is validated through an illustrative example of an integrated steel plant. The results of sensitivity analysis and comparative analysis indicate that the proposed hybrid risk prioritization method is effective and valid, and can get more accurate and practical risk ranking results to help enterprises formulate accurate risk prevention and control plans. Show more

Keywords: INDEX TERMS: Failure mode and effect analysis, risk prioritization, Linguistic Z-numbers, fuzzy entropy, the generalized TODIM method

DOI: 10.3233/JIFS-223132

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2023

Authors: Punarselvam, E.

Article Type: Research Article

Abstract: Parkinson’s disease is neurological degenerative disorder cause by deficient dopamine production which in turn harms the motor functionality and speech. With latest IoT advancement in the health care era, we propose intelligent and smart Parkinson’s disease detection system based on voice signal analysis. Addition to PDs detection, we propose remote health monitoring feature that keep on monitoring and diagnosing PD person activity. To perform all tasks efficiently we divide our propose model in three phases: monitoring, diagnosing and analysis. During monitoring phase, PDs person voice signal is monitored and captured via IoT sensor enabled Smartphone device. This voices signal is …further processed for PD detection over MEC server during diagnosing phase. We use Tunable Q factor wavelet transform (TQWT) for extracting feature from voice sample, these extracted feature are reduced FRS methods. For feature reduction PCA and LDA are used. Theses processed feature are then applied to hybrid case-based reasoning neuro-fuzzy (ANFIS) classification system to detect Parkinson’s disease. On the detection of PDs abnormality, the proposed healthcare monitoring system immediately generates notification to the patient simultaneously send detection report to centralized healthcare cloud system. This PDs detection report is further analyzed and stored at cloud server during analysis phase where report is analyzed by professional health expert and send the appropriate treatment and medication to PD infected person or care taker. For experimentation and performance evaluation benchmark baseline UCI dataset of PDs are used. We analyzed our proposed hybrid ANFIS-CBR classifier with existing classifiers over the accuracy, sensitivity and specificity parameter. Based on the result analysis, it is observed that proposed hybrid classifier maximum accuracy, sensitivity, and specificity of 98.23%, 99.1%, and 95.3% in comparison to other classifier. Show more

Keywords: Parkinson’s Disease (PDs), Internet of things (IoT), Tunable Q-factor wavelet transform, Feature reduction and selection (FRS), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Forward feature selection (FFS), Backward feature selection (BFS), Adaptive Neuro-fuzzy interference System (ANFIS), Case-Based Reasoning (CBR), MEC (Mobile edge computing), Cloud computing

DOI: 10.3233/JIFS-220941

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2023