Journal of Intelligent & Fuzzy Systems - Volume 43, issue 5

Authors: Yu, Jianping | Fu, Jilin | Bai, Tana | Zhang, Tao | Li, Shaoxiong

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Abstract: This article has been retracted.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-220388

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6383-6393, 2022

Authors: Gopinath, P. | Shivakumar, R.

Article Type: Research Article

Abstract: Recognition of finger vein patterns is essential technique that analyses the finger vein patterns to enable accurate authentication of an individual. A proper, accurate and quick learning of patterns is essentially required for improving the classification pattern. It is essential in developing an intelligent algorithm to effectively study and classify the patterns. In this paper, we develop an improved deep learning hybrid model for feature extraction and classification. A dimensional reduction deep neural network (DR-DNN) model has included a dimensional reduction model for extracting the essential features by reducing the dimensionality of feature datasets. A convolutional neural network (CNN) helps …in classifying the benign vein patterns from the malignant vein patterns. The effectiveness is compared against existing deep learning classifiers to measure how effective the deep learning model is used for classifying finger vein patterns for biometric authentication. The results shows that the proposed method achieves an accuracy rate of 97.16% for the proposed method, where the other existing methods including CNN, Recurrent Neural Network (RNN) and Deep Neural Nets (DNN) has an accuracy rate of 86%, 80.66% and 88.31%, respectively. Show more

Keywords: Deep neural networks, Deep Convolutional Neural Network, feature extraction, classification, finger vein patterns

DOI: 10.3233/JIFS-220423

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6395-6403, 2022

Authors: Lin, Ting-Yu | Hung, Kuo-Chen | Lin, Kuo-Ping | Hon, Jau-Shin | Chiu, Anthony Shun Fung

Article Type: Research Article

Abstract: With the economic growth of the world, sustainable development is a popular issue in recent years. Sustainable assessment is an important part of sustainable development. There are many previous scholars have used multiple-criteria decision-making (MCDM) to develop different evaluation frameworks in different fields. Elimination et Choix Traduisant la Realite II (ELECTRE II) is one of the most commonly used methods for MCDM. ELECTRE II uses alternatives, criteria, and criteria weighting from decision-makers to calculate the concordance and discordance indices. These two indices are used to rank the alternatives. The concordance and discordance indices in ELECTRE II are important because they …are the key to make accurate decisions. Previous scholars have failed to make comprehensive calculations for these indices, nor make their units of measure comparable, which negatively affected their results. This study improved the approach in calculating these indices and illustrated it using three case studies: (1) university examination results, (2) a sustainability assessment of groundwater remediation and (3) an assessment of power generation technologies. This improved ELECTRE II method offers decision-makers an objective basis for decision-making. Show more

Keywords: Sustainability assessment, multiple-criteria decision-making, ELECTRE II, decision analysis

DOI: 10.3233/JIFS-220441

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6405-6418, 2022

Authors: Manjula, P. | Priya, S. Baghavathi

Article Type: Research Article

Abstract: In today’s world, a Network Intrusion Detection System (NIDS) plays a vital role in order to secure the Wireless Sensor Network (WSN). However, the traditional NIDS model faced critical constraints with network traffic data due to growth in the complexity of modern attacks. These constraints have a direct impact on the overall performance of the WSN. In this paper, a new robust network intrusion classification framework based on the enhanced Visual Geometry Group (VGG-19) pre-trained model has been proposed to prolong the performance of WSN. Primarily, the pre-trained weights from the ImageNet dataset are utilized to train the parameters of …the VGG-19. Afterward, a Hybrid Deep Neural Network based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) will be employed to extract the influential features from network traffic data to enlarge the intrusion detection accuracy. The proposed VGG-19 + Hybrid CNN-LSTM model exploits both binary classification and multi-classification to classify attacks as either normal or attacked. A network intrusion benchmark dataset is used to assess the performance of the suggested system. The results reveal that the proposed VGG-19 + Hybrid CNN-LSTM learning system surpasses other pre-trained models with a superior accuracy of 98.86% during the multi-classification test. Show more

Keywords: Intrusion detection, classification, deep neural network, convolutional neural network, machine learning

DOI: 10.3233/JIFS-220444

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6419-6432, 2022

Authors: Suresh Kumar, K. | Helen Sulochana, C. | Radhamani, A.S. | Ananth Kumar, T.

Article Type: Research Article

Abstract:  Many websites are attempting to offer a platform for users or customers to leave their reviews and comments about the products or services in their native languages. The cross-domain adaptation (CDA) analyses sentiment across domains. The sentiment lexicon falls short resulting in issues like feature mismatch, sparsity, polarity mismatch and polysemy. In this research, an augmented sentiment dictionary is developed in our native regional language (Tamil) that intends to construct the contextual links between terms in multi-domain datasets to reduce problems like polarity mismatch, feature mismatch, and polysemy. Data from the source domain and target domain both labeled and unlabeled …are used in the proposed dictionary. To be more specific, the initial dictionary uses normalised pointwise mutual information (nPMI) to derive contextual weight, whereas the final dictionary uses the value of terms across all reviews to compute the accurate rank score. Here, a deep learning model called BERT is used for sentiment classification. For cross-domain adaptation, a modified multi-layer fuzzy-based convolutional neural network (M-FCNN) is deployed. This work aims to build a single dictionary using large number of vocabularies for classifying the reviews in Tamil for several target domains. This extendible dictionary enhances the accuracy of CDA greatly when compared to existing baseline techniques and easily handles a large number of terms in different domains. Show more

Keywords: Cross-domain adaptation (CDA), BERT classification, modified multi-layer fuzzy convolutional neural networks (M-FCNN)

DOI: 10.3233/JIFS-220448

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6433-6450, 2022

Authors: Titus, P. | Ajitha Fancy, J. | Joshi, Gyanendra Prasad | Amutha, S.

Article Type: Research Article

Abstract: A set S  ⊆ V in a graph G is a MED -set if every vertex in V  - S has a monophonic eccentric vertex in S . The MED -number γme  (G ) is the cardinality of a minimum MED -set of G . A set S  ⊆ V in a graph G is a CMED -set if S is a MED -set and the induced subgraph is connected. The CMED -number γcme  (G ) is the cardinality of a minimum CMED -set of G . We investigate some properties of the CMED …-sets. Also, we determine the bounds of the CMED -number and find the same for some standard graphs. The CMED -number has applications in security based communication networks in real life situations. This motivated us to introduce and investigate CMED -set in a graph. Show more

Keywords: Monophonic eccentric vertex, MED-set, MED-number, CMED-set, CMED-number

DOI: 10.3233/JIFS-220463

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6451-6460, 2022

Authors: Menekse, Akin | Akdag, Hatice Camgoz

Article Type: Research Article

Abstract: Combinative distance-based assessment (CODAS) is a multi-criteria decision-making (MCDM) method that is based on the Euclidean and Hamming distances of alternatives from the average scores of attributes. Spherical fuzzy sets, as the recent extensions of ordinary fuzzy sets, were developed based on Pythagorean and neutrosophic sets and enable decision-makers to express their membership, non-membership, and hesitancy degrees independently and in a larger domain than most other fuzzy extensions. This paper proposes a new interval-valued spherical fuzzy CODAS method and provides extra space for catching the vagueness in the nature of the problem. The feasibility and practicality of the proposed model …are illustrated with an application for evaluating the reopening readiness of academic units for campus education in the era of COVID-19. Three decision-makers from a higher education institution evaluate four academic units with respect to five strategic criteria and prioritize them according to their readiness levels for the campus type of education. Sensitivity and comparative analyses, theoretical and practical contributions, limitations, and future research avenues are also presented in the study. Show more

Keywords: CODAS, interval-valued spherical fuzzy, COVID-19, higher education institution, reopening readiness

DOI: 10.3233/JIFS-220468

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6461-6476, 2022

Authors: Rajnish, Kumar | Bhattacharjee, Vandana

Article Type: Research Article

Abstract: Software defect prediction is used to assist developers in finding potential defects and allocating their testing efforts as the scale of software grows. Traditional software defect prediction methods primarily concentrate on creating static code metrics that are fed into machine learning classifiers to predict defects in the code. To achieve the desired classifier performance, appropriate design decisions are required for deep neural network (DNN) and convolutional neural network (CNN) models. This is especially important when predicting software module fault proneness. When correctly identified, this could help to reduce testing costs by concentrating efforts on the modules that have been identified …as fault prone. This paper proposes a CONVSDP and DNNSDP (cognitive and neural network) approach for predicting software defects. Python Programming Language with Keras and TensorFlow was used as the framework. From three NASA system datasets (CM1, KC3, and PC1) selected from PROMISE repository, a comparative analysis with machine learning algorithms (such as Random Forest (RF), Decision Trees (DT), Nave Bayes (NF), and Support Vector Machine (SVM) in terms of F-Measure (known as F1-score), Recall, Precision, Accuracy, Receiver Operating Characteristics (ROC) and Area Under Curve (AUC) has been presented. We extract four dataset attributes from the original datasets and use them to estimate the development effort, development time, and number of errors. The number of operands, operators, branch count, and executable LOCs are among these attributes. Furthermore, a new parameter called cognitive weight (Wc) of Basic Control Structure (BCS) is proposed to make the proposed cognitive technique more effective, and a cognitive data set of 8 features for NASA system datasets (CM1, KC3, and PC1) selected from the PROMISE repository to predict software defects is created. The experimental results showed that the CONVSDP and DNNSDP models was comparable to existing classifiers in both original datasets and cognitive data sets, and that it outperformed them in most of the experiments. Show more

Keywords: Machine learning, software defect prediction, CNN model, cognitive weight, basic control structures, neural network

DOI: 10.3233/JIFS-220497

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6477-6503, 2022

Authors: Singh, Upendra | Gupta, Puja | Shukla, Mukul

Article Type: Research Article

Abstract: Image Incorporation concerns, including background confusion, uneven population distribution, and variations in scale and familiarity, can make group counting difficult. Pre-existing information and multi-level contextual representations are required to handle these problems effectively with deep neural networks and Mask-RCNN. Numerous studies on crowd counting use density maps without segmentation, which treat a group of individuals as a single entity. This article offers a hybrid method for crowd counting that combines Mask-RCNN (MRCNN) and a bidirectional convolutional long-term memory network (ConvLSTM), dubbed (CC: MRCNN-biCLSTM). The CC: MRCNN-biCLSTM is based on the Mask-RCN; it first segments instances and generates density maps, which …are passed into adversarial learning during the training phase. Finally, the bidirectional convolutional LSTM is being used to return metrics and counts for individuals within a group of individuals. Following that, the suggested activity detection technique based on the Bayesian non-linear filter AD-BNF is used to identify a person’s activity. Additionally, the suggested approach resolves human grouping and enhances metric performance. Extensive studies demonstrate that the suggested method outperforms more sophisticated techniques on four frequently used difficult criteria for density map precision and quality. Show more

Keywords: Mask-RCNN, bidirectional ConvLSTM, cluster counting, adversarial learning, activity detection

DOI: 10.3233/JIFS-220503

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6505-6520, 2022

Authors: Khan, Madad | Anis, Saima | Zuev, Sergei | Ullah, Hikmat | Zeeshan, Muhammad

Article Type: Research Article

Abstract:  In this paper, we have discussed some new operations and results of set theory for complex fuzzy sets (CFSs). Moreover, we developed the basic results of CFSs under the basic operations such as complex fuzzy simple difference, bounded sum, bounded difference, dot product, bounded product, union, intersection, and Cartesian product. We explored the CFSs and discussed the related properties with examples such as complex fuzzy bounded sum over the intersection, complex fuzzy dot product over the union, etc. Identifying the reference signals under the environment of CFSs have always been a challenging. Many algorithms based on set theoretic operations and …distance measures have been proposed for identifying a reference signal using any common system. But linear time invariant (LTI) system is considered easy to analyze the linear and time-varying signals. We used CFSs in signals and systems. We developed an algorithm based on convolution product and LTI system under the complex fuzzy environment. We identified a high degree of resemblance (reference signal) of the received signals to the reference signal in a linear time-invariant (LTI) system that receives an input signal and produces an output signal. Show more

Keywords: Complex fuzzy sets, inverse discrete Fourier transform, signals and systems, linear-time invariant (LTI) system

DOI: 10.3233/JIFS-220517

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6521-6548, 2022