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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Li, Zepeng | Huang, Rikui | Zhang, Yufeng | Zhu, Jianghong | Hu, Bin
Article Type: Research Article
Abstract: Knowledge Graph Embedding (KGE), which aims to embed the entities and relations of a knowledge gxraph into a low-dimensional continuous space, has been proven to be an effective method for completing a knowledge graph and improving the quality of the knowledge graph. The translation-based models represented by TransE, TransH, TransR and TransD have achieved great success in this regard. There is still potential for improvement in dealing with complex relations. In this paper, we find that the lack of flexibility in entity embedding limits the model’s ability to model complex relations. Therefore, we propose single-directional-flexible (sdf) models and multi-directional-flexible (mdf) …models to increase the flexibility and expressiveness of entity embeddings. These two methods can be applied to the TransD model and its variant models without increasing any time cost and space cost. We conduct experiments on benchmarks such as WN18 and FB15k. The experimental results show that the models significantly surpasses the classical translation models in both tasks of triplet classification and link prediction. In particular, for Hits@1 of link prediction of WN18, we get 71.7% after applying our method to TransD, which is much better than 24.1% of TransD. Show more
Keywords: Knowledge graph embedding, translation model, complex relation, single-directional-flexible model, multi-directional-flexible model
DOI: 10.3233/JIFS-211553
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3093-3105, 2023
Authors: Wan, Chenxia | Fang, Liqun | Cao, Shaodong | Luo, Jiaji | Jiang, Yijing | Wei, Yuanxiao | Lv, Cancan | Si, Weijian
Article Type: Research Article
Abstract: The investigation on brain magnetic resonance imaging (MRI) of cerebral small vessel disease (CSVD) classification algorithm based on deep learning is particularly important in medical image analyses and has not been reported. This paper proposes an MRI classification algorithm based on convolutional neural network (MRINet), for accurately classifying CSVD and improving the classification performance. The working method includes five main stages: fabricating dataset, designing network model, configuring the training options, training model and testing performance. The actual training and testing datasets of MRI of CSVD are fabricated, the MRINet model is designed for extracting more detailedly features, a smooth categorical-cross-entropy …loss function and Adam optimization algorithm are adopted, and the appropriate training parameters are set. The network model is trained and tested in the fabricated datasets, and the classification performance of CSVD is fully investigated. Experimental results show that the loss and accuracy curves demonstrate the better classification performance in the training process. The confusion matrices confirm that the designed network model demonstrates the better classification results, especially for luminal infarction. The average classification accuracy of MRINet is up to 80.95% when classifying MRI of CSVD, which demonstrates the superior classification performance over others. This work provides a sound experimental foundation for further improving the classification accuracy and enhancing the actual application in medical image analyses. Show more
Keywords: Cerebral small vessel disease, brain magnetic resonance imaging, convolutional neural network, feature extraction, classification accuracy
DOI: 10.3233/JIFS-213212
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3107-3114, 2023
Authors: Zhang, Xinyu | Yu, Long | Tian, Shengwei
Article Type: Research Article
Abstract: In today’s social media and various frequently used lifestyle applications, the phenomenon that people express their sentiment via comments or instant barrage is common. People not only show their joys and sorrows in the process of expression but also present their opinions to one thing in many aspects which include. Nowadays, aspect-based sentiment analysis has become a mature and wildly-used technology. There are many public datasets considered as a benchmark to test model performance, such as Laptop2014, Restaurant2014, Twitter, etc. In our work, we also use these public datasets as the test criteria. Current mainstream models generally use the methods …of stacking multi-RNNs layers or combining neural networks and BERT or other pre-trained models. On account of the importance displayed by the dependence between aspect words and sentiment words, we investigate a novel model (BGAT) blending bidirectional gated recurrent unit (BiGRU) and relational graph attention network (RGAT) to learn dependencies information. Extensive experiments have been conducted on five datasets, the results demonstrate the great capability of our model. Show more
Keywords: Aspect-based sentiment analysis, graph attention network, BiGRU, dependency information, natural language processing
DOI: 10.3233/JIFS-213020
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3115-3126, 2023
Authors: Mythrei, S. | Singaravelan, S.
Article Type: Research Article
Abstract: In this web era, entity linking plays a major role. In the web the information’s are associated with different kinds of data and objects. Heterogeneous information networks (HIN) involved multi composed interlinked interconnected objects with various types of connections which is more prominent in this real world. Most of the research work focused towards processing homogeneous networks as well as linking entities with Wikipedia as knowledge base. In this paper we proposed a probabilistic based domain specific entity linking system that will link named entity mentions detected from unstructured web text corpus with corresponding entity in the existing domain specific …Heterogeneous information networks as knowledge base. This work is most challenging due to entity name ambiguity as well as knowledge in the network that are limited one. The proposed model framework presents a model that will link named entity from unstructured web text with domain specific Heterogeneous information network mainly focuses on to learn the weight of meta path. The experiments are done over real world dataset such as DBLP and IMDB dataset. Show more
Keywords: DBLP, IMDB dataset, homogeneous networks
DOI: 10.3233/JIFS-220331
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3127-3135, 2023
Authors: Xu, Huiyan
Article Type: Research Article
Abstract: The diagnosis cycle of schizophrenia is long, there is no objective diagnostic basis. The over-energy entropy product of the speech fluency rectangular parameter is designed in the paper, the fuzzy clustering is used to double locate speech pause areas and to assist in the diagnosis of schizophrenia. The pause area of speech is located based on the low speech fluency and flat energy in schizophrenia patients, an extraction algorithm is given for speech fluency quantification parameters, support vector machine (SVM) classifier is used in the approach. The fluency acoustic features of speech are taken from 28 schizophrenia patients and 28 …normal controls, these are used to verify the effect of the method in schizophrenia recognition, there is a correct rate of over 85%. The automatic schizophrenia identification based on energy entropy product and fuzzy clustering can provide objective, effective and non-invasive auxiliary for clinical diagnosis of schizophrenia. Show more
Keywords: Schizophrenia, speech fluency rectangle parameter, fuzzy clustering, hyperenergy entropy product, speech pauses in schizophrenia
DOI: 10.3233/JIFS-220248
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3137-3151, 2023
Authors: Purohit, Amit | Patheja, Pushpinder Singh
Article Type: Research Article
Abstract: Sentiment analysis is a natural language processing (NLP) technique for determining emotional tone in a body of text. Using product reviews in sentiment analysis and opinion mining various methods have been developed previously. Although, existing product review analyzing techniques could not accurately detect the product aspect and non-aspect. Hence a novel Detach Frequency Assort is proposed to detect the product aspect term using TF-ISF (Term frequency-inverse sentence frequency) with Part of Speech (POS) tags for sentence segmentation and additionally using Feedback Neural Network to combine product aspect feedback loop. Furthermore, decision-making problem occurs during classification of sentiments. Hence, to solve …this problem a novel technique named, Systemize Polarity Shift is proposed in which flow search based Support Vector Machine (SVM) with Bag of Words model classifies pre-trained review comments as positive, negative, and neutral sentiments. Moreover, the identification of specific products is not focused in sentiment analysis. Hence, a novel Revival Extraction is proposed in which a specific product is extracted based on thematic analysis method to obtain accurate data. Thus, the proposed Product Review Opinion framework gives effective optimized results in sentiment analysis with high accuracy, specificity, recall, sensitivity, F1-Score, and precision. Show more
Keywords: Sentiment analysis, opinion mining, support vector machine, thematic analysis
DOI: 10.3233/JIFS-213296
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3153-3169, 2023
Authors: Hernández, Sergio | López, Juan Luis | López-Cortés, Xaviera | Urrutia, Angelica
Article Type: Research Article
Abstract: Recommendations analysis of road safety requires decision-making tools that accommodate weather uncertainties. Operation and maintenance of transport infrastructure have been one of the sub-areas that require attention due to its importance in the quality of the road. Several investigations have proposed artificial neural networks and Bayesian networks to assess the risk of the road. These methods make use of historic accident records to generate useful road safety metrics; however, there is less information on how climatic factors and road surface conditions affect the models that generate recommendations for safe traffic. In this research, Bayesian Network, as a Hidden Markov Models, …and Apriori method are proposed to evaluate the open and closed state of the road. The weather and road surface conditions are explicitly written as a sequence of latent variables from observed data. Different weather variables were studied in order to evaluate both road states (open or close) and the results showed that the Hidden Markov Model provides explicit insight into the sequential nature of the road safety conditions but does not provide a directly interpretable result for human decision making. In this way, we complement the study with the Apriori algorithm using categorical variables. The experimental results show that combining the Hidden Markov Model and the Apriori algorithm provides an interpretable rule for decision making in recommendations of road safety to decide an opening or closing of the road in extreme weather conditions with a confidence higher than 90%. Show more
Keywords: Road safety analysis, hidden markov models, apriori methods
DOI: 10.3233/JIFS-211746
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3171-3187, 2023
Authors: Kannan, Sridharan
Article Type: Research Article
Abstract: In today’s world, mining and learning applications play an essential role in healthcare sectors and intend to transform all the data into an understandable form. However, the healthcare sectors require an automated disease prediction system for better medical analysis and emphasize better prediction accuracy for evaluation purposes. In this paper, a new automated prediction model based on Linearly Support Vector Regression and Stacked Linear Swarm Optimization (LSVR-SLSO) has been proposed to predict heart disease accurately. Primarily, the features are analyzed in a linear and non-linear manner using LSVR feature learning approaches. The extracted features are then fed into the SLSO …model in order to extract the global optimal solutions. These global solutions will reduce the data dimensionality and computational complexity during the evaluation phase. Moreover, the optimal solution facilitates the proposed model to predict heart disease appropriately. The simulation can be carried out through the MATLAB environment by utilizing a publicly available benchmark heart disease dataset. The performance results evident that the proposed LSVR-SLSO model can efficiently predict heart disease with superior accuracy of 98%, precision of 98.76%, and recall of 99.7% when compared with conventional approaches. The better performance of the proposed model will pave the way to act as an effective clinical decision support tool for physicians during an emergency. Show more
Keywords: Heart disease prediction, feature selection, optimization, automated system, mining and learning
DOI: 10.3233/JIFS-212772
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3189-3202, 2023
Authors: Wang, Kaixiang | Yang, Ming | Yang, Wanqi | Wang, Lei
Article Type: Research Article
Abstract: Deep neural networks have been adopted in multi-label classification for their excellent performance, however, existing methods fail to comprehensively utilize the high-order correlations between instances and the high-order correlations between labels, and these methods are difficult to deal with label noise effectively. We propose a novel end-to-end deep framework named Robust Fused Hypergraph Neural Networks for Multi-Label Classification (RFHNN), which can effectively utilize the two kinds of high-order correlations and adopt them to mitigate the impact of label noise. In RFHNN, Hypergraph Neural Networks (HNNs) are adopted to mine and utilize the high-order correlations of the instances in the feature …space and the label space respectively. The high-order correlations of the instances can not only improve the accuracy of the classification and the discrimination of the proposed model, but also lay the foundation for the subsequent noise correction module. Meanwhile, a hypergraph construction method based on the Apriori algorithm is proposed to realize Hypergraph Neural Networks (HNNs), which can mine robust second-order and high-order label correlations effectively. Effective classifiers are learned based on the correlations between the labels, which will not only improve the accuracy of the model, but can also enhance the subsequent noise correction module. In addition, we have designed a noise correction module in the networks. With the help of the high-order correlations among the instances and the effective classifier, the framework can effectively correct the noise and improve the robustness of the model. Extensive experimental results on datasets demonstrate that our proposed approach is better than the state-of-the-art multi-label classification algorithms. When dealing with the multi-label training datasets with noise in the label space, our proposed method also has great performance. Show more
Keywords: Multi-label classification, fused hypergraph neural network, high-order label correlations, noise correction, robust classification framework
DOI: 10.3233/JIFS-212844
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3203-3218, 2023
Authors: Jie, Zheng | Daijun, Wei | Liming, Tang
Article Type: Research Article
Abstract: For D numbers theory, there are some drawbacks in the D numbers’ integration rule. For example, the missing information is ignored in the final decision judgment for multi-attribute decision (MADM). For this problem, some researchers have improved the D numbers’ integration rules based on optimistic criterion for overcoming the shortcoming of D numbers’ integration rule. However, optimistic and pessimistic criterion are two sides of the coin for fuzzy environment. Therefore, in this article, a new D numbers’ integration rules based on pessimistic criterion is proposed. We improve the D numbers’ integration rules to redefine the missing information distribution rules based …on pessimistic criterion. The missing information is distributed in inverse proportion to each D number according to the size of the original evidence credibility. Two examples of MADM is applied by the proposed method, the results show that the proposed method can be applied to MADM. Show more
Keywords: Uncertainty, multiple attributes decision making, D numbers, integration representation, pessimistic criterion
DOI: 10.3233/JIFS-211533
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3219-3231, 2023
Authors: Yuan, Yinlong | Hua, Liang | Cheng, Yun | Li, Junhong | Sang, Xiaohu | Zhang, Lei | Wei, Wu
Article Type: Research Article
Abstract: Reward signal reinforcement learning algorithms can be used to solve sequential learning problems. However, in practice, they still suffer from the problem of reward imbalance, which limits their use in many contexts. To solve this unbalanced reward problem, in this paper, we propose a novel model-based reinforcement learning algorithm called the expected n-step value iteration (EnVI). Unlike traditional model-based reinforcement learning algorithms, the proposed method uses a new return function that changes the discount of future rewards while reducing the influence of the current reward. We evaluated the performance of the proposed algorithm on a Treasure-Hunting game and a …Hill-Walking game. The results demonstrate that the proposed algorithm can reduce the negative impact of unbalanced rewards and greatly improve the performance of traditional reinforcement learning algorithms. Show more
Keywords: Reinforcement learning, Model-based learning, Unbalanced reward, Multi-step methods
DOI: 10.3233/JIFS-210956
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3233-3243, 2023
Authors: Song, Xudong | Chen, Yilin | Liang, Pan | Wan, Xiaohui | Cui, Yunxian
Article Type: Research Article
Abstract: In recent years, imbalanced data learning has attracted a lot of attention from academia and industry as a new challenge. In order to solve the problems such as imbalances between and within classes, this paper proposes an adaptive boundary weighted synthetic minority oversampling algorithm (ABWSMO) for unbalanced datasets. ABWSMO calculates the sample space clustering density based on the distribution of the underlying data and the K-Means clustering algorithm, incorporates local weighting strategies and global weighting strategies to improve the SMOTE algorithm to generate data mechanisms that enhance the learning of important samples at the boundary of unbalanced data sets and …avoid the traditional oversampling algorithm generate unnecessary noise. The effectiveness of this sampling algorithm in improving data imbalance is verified by experimentally comparing five traditional oversampling algorithms on 16 unbalanced ratio datasets and 3 classifiers in the UCI database. Show more
Keywords: Imbalanced data, oversampling, classifier, boundary weighted, within and between class imbalance
DOI: 10.3233/JIFS-220937
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3245-3259, 2023
Authors: Nallasivan, G. | Akshaya, V.S. | Padmavathy, C.
Article Type: Research Article
Abstract: This Paper Deals With Image Retrieval Process Of Liver Computer Tomography (Ct) Scan Images Using Orthogonal Moment Features And Content Based Image Retrieval. Medical Images Are Useful Diagnostic Evidence As It Can Provide Vital Information In Anatomical Pathology. The Objective Is To Efficiently Retrieve Medical Images From The Database Using Orthogonal Moments And Content Based Image Retrieval Methods. The Orthogonal Moment Viz Discrete Racah Polynomial, Continuous Legendre Moments And Zernike Moments Are Computed For The Study. The Region Of Interest Based Segmentation And Watershed Segmentation Is Applied To The Preprocessed Input Images And Features Are Extracted Using Orthogonal Moments And …Shape And Texture Features Are Extracted Using Content Based Image Retrieval (Cbir). The Performances Of Each Moment In Terms Of Accuracy And Error Rate Are Compared With Cbir. Show more
Keywords: Orthogonal moment, Cbir, accuracy, Mse, Psnr
DOI: 10.3233/JIFS-221667
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3261-3269, 2023
Authors: Liu, Hui
Article Type: Research Article
Abstract: Since 2010, China’s traditional industry has entered a critical stage of development and enterprise reform and development is imminent. Product homogenization is serious in this market, so that the competition among enterprises is fierce. At the same time, international brands continue to enter the Chinese consumption market, which intensifies the competition and seriously squeezes the market share of Chinese local brands. However, the popularization and development of the Internet and the change of people’s consumption concept and level make the market put forward higher requirements for the development of business operation and many traditional family enterprises have embarked on the …road of transformation. It is of great significance and value to clarify the influence of internal factors of family enterprises on strategic transformation. The performance evaluation of family business strategic transition is really a multiple attribute group decision making (MAGDM) problems. In this paper, the 2-tuple linguistic neutrosophic number grey relational analysis (2TLNN-GRA) method is proposed along with on the traditional grey relational analysis (GRA) and 2-tuple linguistic neutrosophic sets (2TLNNSs). Firstly, the 2TLNNSs is introduced. Then, combine the traditional fuzzy GRA model with 2TLNNSs information, the 2TLNN-GRA method is established and the computing steps for MAGDM are built. Finally, a numerical example for performance evaluation of family business strategic transition has been given and some comparisons is used to illustrate advantages of 2TLNN-GRA method. Show more
Keywords: Multiple attribute group decision making (MAGDM) problems, 2-tuple linguistic neutrosophic sets (2TLNSs), GRA method, family business strategic transition
DOI: 10.3233/JIFS-221514
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3271-3283, 2023
Authors: Ni, Ting | Wang, Bo | Jiang, Jiaxin | Wang, Meng | Lei, Qing | Deng, Xinman | Feng, Cuiying
Article Type: Research Article
Abstract: The issue of how to fully utilize natural daylighting of public buildings is one of the greatest practical objectives for lighting savings. The rapid and accurate prediction of the daylighting coefficient at the early design stage can provide a quantitative basis for energy-saving optimization. However, it is not comprehensive to determine the design parameters according to experience. The key problem that is still facing designers is the interoperability between building modeling and energy simulation tools. In this paper, an integrated approach using a dataset created by building information modeling and artificial neural network technology is developed for the fast optimal …daylight factor prediction of large public spaces at the early design stage. According to this approach, the value of daylight factors is calculated for different windowsill heights, window heights and widths by Autodesk® Revit and Ecotect Analysis to form a dataset. With this dataset, an artificial neural network model is established using the backpropagation algorithm to predict the relevant design parameters. With their large interior spaces, the reading areas of the aboveground five floors in Chengdu University of Technology Library are selected to carry out the daylight factor experiment and rapid prediction. A total of 495 groups of experimental data are randomly divided into training and testing sets. The root mean squared errors are below 0.1, which indicates a high regression model fitting. A total of 225,369 groups of prepared data are used in the prediction model to obtain the optimal windowsill height (1.0 m), window height (2.4 m) and window width (2.1 m) for five floors in the case of the maximum daylighting coefficient. Finally, a smartphone app is designed to facilitate daylight factor prediction without any experience in modeling and simulation tools, which is simple and available to realize prediction visualization and historical result analysis. Show more
Keywords: Daylight factor, rapid prediction, building information modelling, artificial neural network, library, app
DOI: 10.3233/JIFS-220930
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3285-3297, 2023
Authors: Namala, Vasu | Karuppusamy, S. Anbu
Article Type: Research Article
Abstract: The amount of audio visual content kept in networked repositories has increased dramatically in recent years. Many video hosting websites exist, such as YouTube, Metacafe, and Google Video. Currently, indexing and categorising these videos is a time-consuming task. The system either asks the user to provide tags for the videos they submit, or manual labelling is used. The aim of this research is to develop a classifier that can accurately identify videos. Every video has content that is either visual, audio, or text. Researchers categorised the videos based on any of these three variables. With the Pattern Change with Size …Invariance (PCSI) algorithm, this study provides a hybrid model that takes into account all three components of the video: audio, visual, and textual content. This study tries to classify videos into broad categories such as education, sports, movies, and amateur videos. Key feature extraction and pattern matching would be used to accomplish this. A fuzzy logic and ranking system would be used to assign the tag to the video. The proposed system is tested only on a virtual device in addition a legitimate distributed cluster for the aim of reviewing real-time performance, especially once the amount and duration of films are considerable. The efficiency of video retrieval is measured with metrics like accuracy, precision, and recall is over 99% success. Show more
Keywords: Video indexing, video retrieval, key feature extraction, pattern change with size invariance (PCSI) algorithm
DOI: 10.3233/JIFS-221905
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3299-3313, 2023
Authors: Ghasemi, Mohsen | Bagherifard, Karamollah | Parvin, Hamid | Nejatian, Samad
Article Type: Research Article
Abstract: Software developers want to meet the requirements of customers in next versions. Choosing which set of requirements can be done according to cost and time is an NP-hard problem known as Next Release Problem (NRP). In this article, a multi objective evolutionary algorithm (MOEA) framework is proposed to solve NRP. The framework applies the non-repetitive population, integrates solutions and external repository. Furthermore, a novel approach is implemented to satisfy the constraints of the problem. In this framework, six evolutionary algorithms are implemented and using seven quality indicators, the achieved results of that algorithms are compared with the original versions of …same algorithms. Through using HV (the ratio of the region covered by Pareto Front) and NDS (the number of solutions in the Pareto Front) metrics, the effects of the proposed algorithms are compared with other works’ results. The efficacy of the proposed MOEA framework is measured using three real world datasets. The gained results represent that the implemented algorithms perform better than other related algorithms previously published. Show more
Keywords: Next release problem, multi-objective evolutionary algorithm, search-based software engineering, teaching-learning based optimization, non-repetitive population
DOI: 10.3233/JIFS-200223
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3315-3339, 2023
Authors: Kazi, Samreen | Rahim, Maria | Khoja, Shakeel
Article Type: Research Article
Abstract: The study examines various studies on Named Entity Recognition (NER) and Part of Speech (POS) tagging for the Urdu language conducted by academics and researchers. POS and NER tagging for Urdu still faces obstacles in terms of increasing accuracy while lowering false-positive rates and labelling unknown terms, despite the efforts of numerous researchers. In addition, ambiguity exists when tagging terms with different contextual meanings within a sentence. Due to the fact that Urdu is an inflectional, derivational, morphologically rich, and context-sensitive language, the existing models, such as Linguistic rule application, N-gram Markov model, Tree Tagger, random forest (RF) tagger, etc., …were unable to produce accurate experimental results on Urdu language data. The significance of this study is that it fills a gap in the literature concerning the lack of POS and NER tagging for the Urdu language. For Urdu POS and NER tagging, we propose a deep learning model with a well-balanced set of language-independent features as well as a survey of important Urdu POS/NER techniques. In addition, this is the first study to use residual biDirectional residual Long short-term memory (residual biLSTM) architecture trained on the Urmono dataset in conjunction with the randomly initialised word2vec, fastText and mBERT embeddings are utilised to generate word or character vectors.For each experiment, the paper also employs the evaluation methods of Macro-F1, precision, precision, and recall. The proposed method with mbert embedding as word vectors provides best results of F1 score for POS and NER at 91.11% and 99.11% respectively. Also, the accuracy, precision and recall for POS are reported at 94.85%, 91.79% and 90.77%. Similarly, the accuracy, precision and recall for NER of the proposed model are reported at 99.77%, 98.78% and 99.45% respectively, which are higher than baseline models. Show more
Keywords: POS, NER, Urdu language, tagger, natural language, linguistic, deep learning, machine learning
DOI: 10.3233/JIFS-211275
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3341-3351, 2023
Authors: Lawrance, N.A. | Shiny Angel, T.S.
Article Type: Research Article
Abstract: The technique of integrating images from two or more sensors that were taken from the same place or the same object is known as image fusion. The goal is to get more spectral and spatial information from the combined image as a whole than from the individual images. It is required to fuse the images in order to improve the spatial and spectral quality of both panchromatic and multispectral images. This study introduces a novel method for fusing remote sensing images that combines L0 smoothing, NSCT (Non-subsampled Contourlet Transform), SR (Sparse Representation), and MAR (Max absolute rule). The multispectral and …panchromatic images are initially divided into lower and higher frequency components using the L0 smoothing filter as the method of fusion. The fusion process is then carried out, utilising a technique that combines NSCT and SR to fuse low-frequency components. Similar to this, the Max-absolute fusion rule is used to fuse high-frequency components. In conclusion, the disintegration of fused low-frequency and high-frequency data yields the final image. Our method yields an enhanced outcome in terms of the correlation coefficient, Entropy, spatial frequency, and fusion of mutual information for both the term of picture quality enhancement and visual evaluation. This suggested approach produces superior outcomes after execution. This study makes use of the Landsat-7ETM+, IKONOS, and Quick Bird datasets. Different satellites are used to take each image. There have been two examples of each image used. In comparison to previous Traditional Methods, the proposed image fusion techniques’ output has a quality that is more than 20% higher. Show more
Keywords: Remote sensing, multispectral image, pan chromatic image, L0 smoothening filter, NSCT, SR
DOI: 10.3233/JIFS-213573
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3353-3367, 2023
Authors: Hu, Kekun | Zhu, Zheng | Xu, Yukun | Jiang, Chao | Dai, Chen
Article Type: Research Article
Abstract: Maintaining accurate topology of the low-voltage distribution grid (LVDG) are critical to the operations and maintenance of power distribution systems. However, this goal is hard to achieve due to the fast-changing LVDG topology. To this end, we focus on the abnormal customer-transformer relationships identification in the LVDG and propose an identification method based on an A daptive D ual-channel G raph W avelet Neural N etwork (ADGWN) consisting of two identical GWNs connected with the attention mechanism. In the proposed ADGWN, two GWNs learn customer embedding simultaneously from the LVDG topology graph and the feature graph that is …constructed from customer electricity consumption data with the k -Nearest Neighbor algorithm. The topology identification results of these two GNNs are then adaptively fused to form the ultimate identification result with the attention mechanism by dynamically balancing the aforementioned two types of information. To validate the performance of our proposed method, we further build a real benchmarking dataset from customer electricity consumption data collected from a certain substation in Shanghai, China. Experimental results show that the proposed ADGWN achieves 100.0% LVDG topology identification accuracy and significantly outperforms the state-of-the-art. Our proposed method can help operators of power distribution systems maintain the accurate topology in a timely and economic manner. Show more
Keywords: Low-voltage distribution grid, topology identification, dual-channel, graph wavelet transform, attention
DOI: 10.3233/JIFS-220653
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3369-3380, 2023
Authors: Jagtap, Vinayak | Kulkarni, Parag | Joshi, Pallavi
Article Type: Research Article
Abstract: A dynamic world has different uncertainties. These uncertainties always impact adversely while making decisions. Existing systems sometimes fail as they are trained without considering uncertainty inclusion due to the dynamic nature of the problem. This is quite observed in gaming, which is most dynamic and contributes adversely while deciding for the next move. Strategic games have fewer uncertainties rather than ground sports. Many types of factors add uncertainty to the system. There is a need of handling the required uncertainty which will help in making the decision. Also while finding similarities between games or matches, player and playing style results …don’t depict exact similarities between them. There is a need to measure uncertainty-based similarities as it helps in deciding the situation of the game or player. Here Uncertainty based decision support system is proposed which takes uncertainty as input rather than only considering patterns of input. Patterns always help if the system is more static while considering a dynamic system where we need to consider patterns and uncertainties in the scenarios. Results are shown on limited types of moves in game data and how uncertainty-based similarity and next move selection are improved. Show more
Keywords: Uncertainty based decision support, decision support, uncertainty, gaming
DOI: 10.3233/JIFS-221611
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3381-3397, 2023
Authors: Liu, Shengyan | Zhou, Yao | Wang, Xiao
Article Type: Research Article
Abstract: With the steady development of China’s economy, under the new economic normal, the creative cultural industry has been continuously optimized and developed in terms of structure, scale and quality, and the connotation of the creative cultural industry has been continuously enriched, forming a three-dimensional and diversified pattern. With the help of high-tech, culture, multimedia and other means, the current creative cultural industry is continuously absorbing and integrating it on a large scale, promoting the optimization, upgrading and innovative development of the industry. The consumer competitiveness evaluation in creative and cultural industries is a classical MAGDM problems. In this paper, WDBA …method is designed for solving the probabilistic linguistic MAGDM(PL-MAGDM) with the completely unknown weights. In the end, an empirical application for consumer competitiveness evaluation in creative and cultural industries is used to demonstrate the use of the developed method. The proposed method can also contribute to the selection of suitable alternative successfully in other selection problems. Show more
Keywords: Multiple attribute group decision making (MAGDM), probabilistic linguistic term sets (PLTSs), information entropy, WDBA method, consumer competitiveness evaluation in creative and cultural industries
DOI: 10.3233/JIFS-221799
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3399-3409, 2023
Authors: Andavan, Mohanaprakash Thottipalayam | Vairaperumal, Nirmalrani
Article Type: Research Article
Abstract: Background: Data redundancy (DR) and data privacy (DP) is a critical issue that increases storage and security problems in cloud environments. Data de-duplication (DD) is one of the efficient backup storage techniques to reduce DR. The main problem with using cloud computing (CC) is more storage, the cost of deployment and maintenance. Objective: To minimize this problem, High-performance Grade Byte Check and Fuzzy search Techniques (HP-GBC-FST) based DD is proposed in this paper. Methods: The HP-GBC-FST is based on the pre-process of data by comparing their first byte and categorizing the byte based on the first …byte. After DD, encryption has been processed on data to improve the data security in the cloud environment and then encrypted data is stored in the cloud. This HP-GBC-FST recognizes DR at the block level, reducing the redundancy of data more effectively. Then, HP-GBC-FST is created to detect and eliminate duplicates, improve security and storage efficiency (SE), reduce DD time and computation cost (CPC) in the DD verification and auditing phase. Result: The experiment has been conducted in an Intel I5 system and 500GB, 1Tb memory space and implemented in the Java programming environment. The results of the experiment reveal that the HP-GBC-FST improved the DD ratio and security by 3.7 and 97%, respectively, and reduced the DD time and CPC by 87% and 84.4%, respectively, over the existing technique. Conclusion: It concluded that the HP-GBC-FST has greater improvement over DD data in the cloud. Finally, the performance analysis of the HP-GBC-FST achieves higher storage, both privacy and security attributes, and incurs minimal CPC, DD time compared with the state he art research. Show more
Keywords: Fuzzy search (FS), cloud computing (CC), data deduplication (DD), encryption, grade byte check (GBC)
DOI: 10.3233/JIFS-220206
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3411-3425, 2023
Authors: Subha Darathy, C. | Agees Kumar, C.
Article Type: Research Article
Abstract: Tumor is the second major cause of death in women worldwide. Breast cancer diagnosis and treatment can be difficult for radiologists. As a result, primary care helps to avoid disease and mortality. The study’s main goal is to improve treatment choices and to save lives by detecting breast cancer earlier. For classification problems, we propose a DNN-ASCC architecture in this study. The Fast Non-Local Means Filter completes the initial preprocessing stage. The binary grasshopper optimization algorithm (BGOA) and the grey-level run length matrix are utilized to choose the best features for the feature extraction operation. The suggested hybrid classifier (DNN-ASCCS) …is critical for identifying normal and malignant tumors. Breast cancer is accurately detected by the suggested hybrid classifier. The recommended (DNN-ASCCS) was developed using MATLAB and datasets from the BIDCIDRI. The results of the simulation showed that the proposed technique has an accurate results in classification (99.17 percent) and robustness analysis is also done. When compared to alternative approaches, experimental results show that the suggested method is efficient. Show more
Keywords: Breast cancer, DNN-ASCCS, content based medical image retrieval
DOI: 10.3233/JIFS-222872
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3427-3440, 2023
Authors: Joseph Robinson, M. | Veeramani, C. | Vasanthi, S.
Article Type: Research Article
Abstract: Neutrosophic Set (NS) allows us to handle uncertainty and indeterminacy of the data. Several researchers have investigated the Transportation Problems (TP) with various forms of input data. This paper emphasizes a dynamic optimal solution framework for TPs in a neutrosophic setting. This paper investigates a Neutrosophic Transportation Problem (NTP) in which supply, demand, and transportation cost are considered as Single-Valued Neutrosophic Trapezoidal Numbers (SVNTrNs). The weighted possibilistic mean value of their truth, indeterminacy, and facility membership function are calculated. Then, NTP is modelled as a parametric Linear Programming Problem (LPP) and solved. Further, the drawbacks of the existing approaches and …advantages of the developed method are discussed. Finally, the real-time problem and numerical illustrations are presented and compared to existing solutions. This study helps the Decision-Makers (DMs) in budgeting their transportation expenses through strategic distribution. Show more
Keywords: Single valued neutrosophic trapezoidal number, transportation problem, linear programming problem, weighted possibilistic mean
DOI: 10.3233/JIFS-221802
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3441-3458, 2023
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