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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.
Article Type: Other
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6893-6893, 2022
Authors: Batyrshin, Ildar | Gomide, Fernando | Kreinovich, Vladik | Shahbazova, Shahnaz
Article Type: Editorial
DOI: 10.3233/JIFS-219322
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6895-6896, 2022
Authors: Tavares, Emmanuel | Silva, Alisson Marques | Moita, Gray Farias
Article Type: Research Article
Abstract: Evolving models have shown great success in processing non-stationary data that change their characteristics over time. Motivated by elaborating a high-performance model for data classification, the present work proposes a new evolving fuzzy classifier. The proposed model, named evolving Fuzzy Mean Classifier (eFMC), has a low computational cost and is autonomous, i.e., no has user-defined parameters. The eFMC is based on fuzzy clustering structures, where the membership degree between the samples and the clusters is used to obtain the output. In the proposed approach, each class is represented by a cluster, and new clusters are created whenever a new class …is discovered. The centers of the clusters are updated through the sample’s means calculated incrementally. Computational experiments were carried out to evaluate and compare the performance of the eFMC in terms of accuracy and processing time. Experimental results and comparisons against alternative state-of-the-art evolving classifiers show that the eFMC is accurate and fast, characteristics essential for adaptive classifiers, especially in online and real-time environments. Show more
Keywords: Evolving systems, adaptive classifier, fuzzy systems, evolving fuzzy mean classifier
DOI: 10.3233/JIFS-212831
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6897-6908, 2022
Authors: Ekmekci, Dursun | Shahbazova, Shahnaz N.
Article Type: Research Article
Abstract: One of the most important issues for FLC systems is the problem of finding the right balance between interpretability and accuracy. For this delicate balance, several methods which can be integrated into fuzzy logic, and tune the fuzzy logic parameters adaptively, have been proposed. One of these popular approaches is the heuristic optimization method. However, in terms of optimization, designing fuzzy logic control is a complex optimization problem that is discrete in terms of rule optimization and numerical in terms of optimization of membership degrees parameters. In this context, heuristic-based adaptive fuzzy control systems focus on either fuzzy rule optimization, …weighting fuzzy rules, or parameter optimization. In this paper, unlike the others, an adaptive weighted fuzzy logic control (awFLC) method, which weights the inputs instead of the rules, is proposed. First, the membership degree of each input is calculated. Then, the resultant weight is determined by combining the weighted input membership degrees. For a crisp result, the average of the membership degrees of the resultant weight to the output membership functions is calculated. In awFLC, the interaction between membership functions is achieved by average membership degree, communication between inputs is achieved by the weighting of inputs, and mapping between inputs-outputs is achieved by the resultant weight value. Thus, the approach, which turns into a purely numerical optimization problem, provides convenience for heuristic search. In awFLC, optimal values for input weights and variable parameters are searched by the genetic algorithm. The performance of the method was tested on traction power control, and the results were compared with the ANFIS results. With awFLC, an 8.13% average error was obtained, while ANFIS produced solutions with an average error rate of 8.97%. Show more
Keywords: Adaptive fuzzy control, weighted fuzzy control, adaptive weighted fuzzy control
DOI: 10.3233/JIFS-220753
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6909-6916, 2022
Authors: Pokorádi, László | Kocak, Sinan | Tóth-Laufer, Edit
Article Type: Research Article
Abstract: Reliability and safety have always been the main focus while developing critical automotive systems such as brakes. Failure Modes and Effect Analysis (FMEA) is one of the primary systematic reliability analysis tools. Due to the arising uncertainties and subjectivity, in models such as this one the fuzzy approach is highly popular, i.e., the fuzzy rule-based risk assessment methods can be used to model and depict the subjective opinions of estimators mathematically. In this paper, the authors propose a methodological approach to the implementation of fuzzy rule-based Hierarchical FMEA (H-FMEA), where the membership functions are different depending on the layer. Moreover, …the information between each layer is transferred in the form of fuzzy numbers instead of crisp values, in order to further improve the reliability of the system. Show more
Keywords: Risk assessment, FMEA, hierarchical FMEA, fuzzy inference, wheel speed sensor
DOI: 10.3233/JIFS-212664
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6917-6923, 2022
Authors: Aguilar, Diego | Batyrshin, Ildar
Article Type: Research Article
Abstract: In recent years it has become popular to represent the foreign exchange market as a correlation network using the Pearson correlation coefficient as a measure of co-movement of exchange rates. We show that the Pearson correlation of financial time series could be misleading in analyzing their co-movements. We propose representing the co-movement of exchange rates as a non-directed graph using the measure of local trends associations (LTA). Each node in the graph represents a currency, and an edge between nodes represents an existing high association between currencies. We present several methods for network summary visualization showing the highest associations between …nodes. One method allows comparing graphs corresponding to different correlation and association measures. Another one is appropriate for comparing graphs using the same association measure. We present a dynamic analysis of association networks and the network of associations with a selected currency named a “node of interest.” We show that the currency networks based on LTA are better explainable than networks based on Pearson correlation. LTA based relationships between currencies better reflect geographical, economic or political relationships between corresponding countries. Show more
Keywords: Co-movement of financial time series, local trends association, FOREX network, time-series data mining
DOI: 10.3233/JIFS-220840
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6925-6932, 2022
Authors: Cortez, Solymar Ayala | Bokati, Laxman | Velasco, Aaron | Kreinovich, Vladik
Article Type: Research Article
Abstract: In many applications, including analysis of seismic signals, Daubechies wavelets perform much better than other families of wavelets. In this paper, we provide a possible theoretical explanation for the empirical success of Daubechies wavelets. Specifically, we show that these wavelets are optimal with respect to any optimality criterion that satisfies the natural properties of scale- and shift-invariance.
Keywords: Daubechais wavelets, seismology, invariance
DOI: 10.3233/JIFS-212021
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6933-6938, 2022
Authors: Almásy, Márton György | Hörömpő, András | Kiss, Dániel | Kertész, Gábor
Article Type: Research Article
Abstract: Revolutionary changes of deep reinforcement learning are leading to high-performing intelligent solutions in multiple fields, including healthcare. At the moment, chemotherapy and radiotherapy are common types of treatments for cancer, however, both therapies are usually radical procedures with undesirable side effects. There is an increasing number of evidence that patient-based optimal schedule has a significant impact in increasing efficiency and survival, and reducing side effects during both therapies. To apply artificial intelligence in therapy optimization, an adequate model of tumor growth incorporating the effect of the treatment is mandatory. A method on training a controller for dosage and scheduling, reinforcement …learning can be applied, where a well-chosen agent rewarding function is key to achieve optimal behavior. In this survey paper, some selected tumor growth models, reinforcement learning based solutions and especially agent reward functions are reviewed and compared, providing a summary on state of the art approaches. Show more
Keywords: Tumor growth models, reinforcement learning, reward functions, cancer therapy
DOI: 10.3233/JIFS-212351
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6939-6946, 2022
Authors: Contreras, Jonatan | Ceberio, Martine | Kosheleva, Olga | Kreinovich, Vladik
Article Type: Research Article
Abstract: Neural networks – specifically, deep neural networks – are, at present, the most effective machine learning techniques. There are reasonable explanations of why deep neural networks work better than traditional “shallow” ones, but the question remains: why neural networks in the first place? why not networks consisting of non-linear functions from some other family of functions? In this paper, we provide a possible theoretical answer to this question: namely, we show that of all families with the smallest possible number of parameters, families corresponding to neurons are indeed optimal – for all optimality criteria that satisfy some reasonable requirements: namely, …for all optimality criteria which are final and invariant with respect to coordinate changes, changes of measuring units, and similar linear transformations. Show more
Keywords: Neural networks, invariance, function approximation, theoretical explanation
DOI: 10.3233/JIFS-212009
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6947-6951, 2022
Authors: Hernández, Nayeli | Batyrshin, Ildar | Sidorov, Grigori
Article Type: Research Article
Abstract: Sentiment analysis is a task that belongs to natural language processing and it is highly used in texts extracted from social networks. This task consists of assigning the labels or classes: positive, negative or neutral to the text. However, analyzing a piece of text extracted from social networks to determine if it represents a positive or negative sentiment is a difficult task, because social media texts contain slangs, typographical errors and cultural context. The shortcomings of traditional frequency based feature extraction models such as bag of words or TF-IDF affect the accuracy of sentiment classification. To improve the precision in …the sentiment classification task, it is possible to use natural language modelling methods that are able to learn contextual information from words. In this work, word embedding such as Word2Vec, GloVe and Doc2VecC with different dimensions are used. The resulting word vectors will be used to train recurring neural networks such as LSTM, BiLSTM, GRU and BiGRU, to improve sentiment classification. Show more
Keywords: Sentiment analysis, natural language processing, deep learning, neural network, text classification
DOI: 10.3233/JIFS-211909
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6953-6963, 2022
Authors: Bochkarev, Vladimir V. | Maslennikova, Yulia S. | Shevlyakova, Anna V.
Article Type: Research Article
Abstract: In recent years, methods based on word embedding models have been widely used for solving problems of semantic change estimation. The models are trained on text corpora of various years. Semantic change is detected by analyzing changes in distance between words using vector space alignment or by analyzing changes in a set of words that are most similar in meaning to a target word. Testing for statistical significance of the detected effects has not been detailly discussed in previous studies. This paper focuses on the problem of testing statistical significance of semantic change. Besides, we consider the problem of finding …a confidence interval of estimates of semantic distance between words. We allow for the influence of two random factors. The first one is associated with the use of random initial conditions and stochastic optimization when training the model, the second one results from a random selection of texts for a training corpus. The proposed approach is based on the use of resampling of a training set of texts. The proposed method is tested on the COHA corpus. Show more
Keywords: Semantic change, word embedding, bootstrapping, Corpus of Historical American English
DOI: 10.3233/JIFS-212179
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6965-6977, 2022
Authors: Rico-Preciado, Erick | Laureano, Mayte H. | Calvo, Hiram
Article Type: Research Article
Abstract: Learning relationships between nodes in a directed graph is a task that has been widely studied and it has been applied to a large number of topics and research areas. We establish a definition of particular kind of relationship, called analogy in a directed multigraph. An analogy can be defined for a certain pair of concepts, and the paths connecting them are called explanation of this analogy. We experiment with a structure built from real oneiric stories obtained from psychoanalytic descriptions (e.g. mother is represented as a bull; book represents power). Analogies found by the analysts are automatically identified by …means of linguistically motivated patterns. Analogies have degrees of similarity based on the words used to describe them: represents, is a, is like a, can be a, refers to, etc. Once they are identified and graded, they are represented in the multidigraph, allowing us to provide different hypotheses in how these analogies can be explained. In order to enrich the concept graph, we added information from ConceptNet and WordNet. In addition, we propose a learning method for association rules that, given the degree of the analogy and a starting concept, allow reaching a destination concept. For example, starting from “dream”, we obtain the path <dream, psychic, neurosis, symptom>, being "dream is a symptom" a description previously given by a psychoanalyst, that was not included when training the algorithm. We evaluated 100 analogies on 171 concepts with 8,034 properties using Leave One Out cross validation, and found that the correct analogy was found within the all the possible paths for 94% of the analogies, restricted to 85% if only the top 20% possible paths are considered. This implies that, by using our method, it is possible to learn analogies between two concepts by reconstructing paths of different lengths based on local decisions considering concept, property and degree of analogy. Show more
Keywords: Directed graphs, analogy, concept representation, explainable artificial intelligence, psychoanalysis
DOI: 10.3233/JIFS-211895
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6979-6994, 2022
Authors: Balouchzahi, Fazlourrahman | Shashirekha, Hosahalli Lakshmaiah | Sidorov, Grigori | Gelbukh, Alexander
Article Type: Research Article
Abstract: Curfews and lockdowns around the world in the Covid-19 era have increased the usage of the internet drastically and accordingly the amount of data shared on social media. In addition to using social media for sharing useful information, some miscreants are using the power of social media to spread hate speech and offensive content. Filtering the offensive language content manually is a laborious task due to the huge volume of data. Further, rapid developments in hardware and software technology have provided opportunities for users to post their comments not only in English but also in their native language scripts. However, …based on the ease of Roman script usage, social media users specifically in multilingual countries like India, prefer to comment in code-mixed and multi-script texts. The typical systems that are employed to process and analyze monolingual texts are usually not appropriate for these kinds of texts. Further, as these texts do not adhere to the rules and regulations of any language to frame the words and sentences, the complexity of analyzing such texts increases. The novelty of the present study is to address the Offensive Language Identification (OLI) task in code-mixed and multi-script texts, this paper proposes to use relevant syllable and character n-grams features to train Machine Learning (ML) classifiers. The performance of the proposed models is evaluated on three Dravidian language pairs, namely: Malayalam-English, Tamil-English, and Kannada-English. The performances of ML classifiers prove the effectiveness of syllable and character n-grams features for code-mixed and multi-script texts analysis. Show more
Keywords: Code-mixed, multi-script, offensive language identification, syllable, character n-grams
DOI: 10.3233/JIFS-212872
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6995-7005, 2022
Authors: Zhang, Ming | Du, Qian | Yang, Jianxun | Liu, Song
Article Type: Research Article
Abstract: The Pile movement is one of the most crucial matters in designing piles and foundations that need to be estimated for any project failure. Over the variables used in forecasting Pile Settlement, many methods have been introduced to appraise it. However, existing a wide range of theoretical strategies to investigate the pile subsidence, the soil-pile interactions are still ambiguous for academic researchers. Most studies have tried to work out the subsidence rate in piles after loading passing time by artificial intelligence methods. Generally, the Artificial Neural Network (ANN) has drawn attention to show the actual views of pile settlement over …the loading phase vertically. This research aims to present the Hybrid Radial Basis Function neural network integrated with the Novel Arithmetic Optimization Algorithm and Biogeography-Based Optimization to calculate the optimal number of neurons embedded in hidden layers. The transportation network of Klang Valley, Mass Rapid Transit in Kuala Lumpur, Malaysia, was chosen to analyze the piles’ settlement and earth features using HRBF-AOA and HRBF-BBO scenarios. Over the prediction process, the R-values of HRBF-AOA and HRBF-BBO were obtained at 0.9825 and 0.9724, respectively. The MAE also shows a similar trend as 0.2837 and 0.323, respectively. Show more
Keywords: Pile in rock, settlement, prediction, radial basis function, biogeography-based optimization, arithmetic optimization algorithm, r-value correlation
DOI: 10.3233/JIFS-221021
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7009-7022, 2022
Authors: Dharaniya, R. | Indumathi, J. | Uma, G.V.
Article Type: Research Article
Abstract: Text generation is one of the complex tasks associated with natural language processing. For efficient text generation, syntax and semantics of the language have to be considered to assign context to key phrases. The main objective of the proposed work is to perform text generation specifically for movie scripts. The training data consist of a self-annotated corpus of movie scripts depicting scenes, specific to certain genre where the annotation mainly focuses on a specific director’s movie scripts. The scene generation is set forth by word embedding with sentiment classification where the emotionally analyzed words are vectorized using the EmoVec algorithm …performing sentiment analysis. Based on the sentiment and location associated with each scene, context for the phrases are identified and proceeded to build a well-defined script. Bidirectional Long Short-Term Memory BLSTM with multi-head Attention is used to capture the information processed in both forward and backward propagation in order to understand future context. The vocabulary is built using Stanford’s Internet Movie Database IMDB datasets to perform word based encoding for which requirement of an extensive vocabulary is imminent. Show more
Keywords: Intelligent system, semantic computing, long short-term memory, natural language processing, recurrent neural network
DOI: 10.3233/JIFS-212271
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7023-7039, 2022
Authors: Li, Yueen | Feng, Qi | Huang, Tao | Wang, Shennan | Cong, Weifeng | Knighton, Edwin
Article Type: Research Article
Abstract: The Artificial Neural Networks (ANN) are more widely used in the New Product Development (NPD) process in recent years. The product data generation process is a prerequisite for the application of the ANN algorithm. In the development of new products, the Kansei Engineering (KE) method is an effective emotion-based data generation method. The Semantic Difference (SD) method is usually used to obtain data to apply to design idea generation. Facing the data demand of product creativity, it is important to establish the relationship between consumer perception and product expression. Numerical relationships are not linear and several methods are required for …solving these problems. The method of the Back Propagation (BP) neural network is simple and effective to be used in this case. This paper proposes an innovative data modeling method using digital coding and KE. This model explores a rational design method of perceptual intention and builds an intelligent model. Compared with traditional method, the modified model can quickly and accurately reflect the users’ perceptual needs, make the design more scientific, improve the design efficiency, and reduce design costs. This method is used in the design of electric welding machines, and this process can effectively provide technical support for NPD process in small and medium-sized enterprises. Show more
Keywords: New product development, KE, semantic difference, ANN, BP
DOI: 10.3233/JIFS-212441
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7041-7055, 2022
Authors: Chen, Shuang | Ren, Tao | Qv, Ying | Shi, Yang
Article Type: Research Article
Abstract: Dealing with the explosive growth of web sources on the Internet requires the use of efficient systems. Automatic text summarization is capable of addressing this issue. Recent years have seen remarkable success in the use of graph theory on text extractive summarization. However, the understanding of why and how they perform so well is still not clear. In this paper, we intend to seek a better understanding of graph models, which can benefit from graph extractive summarization. Additionally, analysis has been performed qualitatively with the graph models in the design of recent graph extractive summarization. Based on the knowledge acquired …from the survey, our work could provide more clues for future research on extractive summarization. Show more
Keywords: Text summarization, extractive summarization, graph theory, extraction scheme, sentiment analysis
DOI: 10.3233/JIFS-220433
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7057-7065, 2022
Authors: Elanangai, V. | Vasanth, K.
Article Type: Research Article
Abstract: In today’s world, Steel plates play essential materials for various industries like the national defense industry, chemical industry, automobile industry, machinery manufacturing, etc. However, some defects may occur in a few plates during the manufacture of stainless-steel plates which directly impact the quality of the stainless-steel plate. If the faulted plate detection can be done manually, then it leads to errors and a time-consuming process. Hence, a computerized automated system is necessary to detect the abnormalities. In this paper, a novel Adaptive Faster Region Convolutional Neural Networks (AFRCNN) scheme has been proposed for automatic fault detection of stainless-steel plates. The …proposed AFRCNN scheme comprises three phases: identification, detection, and recognition. Primarily, the damaged plates are identified using Region Proposal Network and Fully Convolutional Neural Network functioning as a combined process under AFRCNN. In the next phase, the number corresponding to the particular plate is recognized through the standard Automated Plate Number Recognition approach with the support of the character recognition technique. The simulation results manifest that the proposed AFRCNN scheme obtains a superior classification accuracy of 99.36%, specificity of 99.24%, and F1-score of 98.18% as compared with the existing state-of-the-art schemes. Show more
Keywords: Fault detection, stainless steel plates, convolutional neural network, classification, region proposal network
DOI: 10.3233/JIFS-213031
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7067-7079, 2022
Authors: Alqhtani, Samar M.
Article Type: Research Article
Abstract: Disasters occur due to naturally stirring events like earthquake, floods, tsunamis, storms hurricanes, wildfire, and other geologic measures. Social media fake image posting influence is increasing day by day regarding the natural disasters. A natural disaster can result in the death or destruction of property, as well as economic damage, the severity of which is determined by the resilience of the affected population and the infrastructure available. Many researchers applied different machine learning approaches to detect and classification of natural disaster types, but these algorithms fail to identify fake labelling occurs on disaster events images. Furthermore, when many natural disaster …events occur at a time then these systems couldn’t handle the classification process and fake labelling of images. Therefore, to tackle this problem I have proposed a FLIDND-MCN: Fake Label Image Detection of Natural Disaster types with Multi Model Convolutional Neural Network for multi-phormic natural disastrous events. The main purpose of this model is to provide accurate information regarding the multi-phormic natural disastrous events for emergency response decision making for a particular disaster. The proposed approach consists of multi models’ convolutional neural network (MMCNN) architecture. The dataset used for this purpose is publicly available and consists of 4,428 images of different natural disaster events. The evaluation of proposed model is measured in the terms of different statistical values such as sensitivity, specificity, accuracy, precision, and f1-score. The proposed model shows the accuracy value of 0.93 percent for fake label disastrous images detection which is higher as compared to the already proposed state-of-the-art models. Show more
Keywords: Convolutional neural network, fake labeling, natural disaster, image classification
DOI: 10.3233/JIFS-213308
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7081-7095, 2022
Authors: Kavitha, P. | Latha, L. | Palaniswamy, Thangam
Article Type: Research Article
Abstract: Big Data is a popular research area where a vast amount of data is created, replicated, and consumed by society. The quality of the data used directly influences big data knowledge discovery. The existence of noise is the most prevalent problem influencing data quality. The following techniques were developed to reduce noise in data with a distributed setting: Homogenous Ensemble for Big Data (HME-BD) and Heterogeneous Ensemble for Big Data (HTE-BD). In this article, the performance of HTE-BD is improved further by developing Enhanced HTE-BD (EHTE-BD), which combines Logistic Regression based Support Vector Machine (LR-SVM) in conjunction with RF, LR, …and KNN to reduce noisy data. Furthermore, the Multi-Objective Evolutionary Fuzzy Method for Subgroup Discovery throughout Big Data (MEFASD-BD) was used to resolve the multi-objective optimization challenge, and the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) was utilized to handle the rising dimensionality issue through subgroup discovery. To address the NSGA-II’s slow convergence rate, an Improved Multi-Objective Meta-Heuristic Fuzzy approach for discovering subgroups in big data is described, that contains a meta-heuristic method for subgroup discovery known as the Multi-Objective Differential Search Algorithm (MODSA). It selects the most relevant subgroups from vast amounts of data, reducing the data’s dimensionality. The Fuzzy Deep Neural Network (FDNN) classifier assesses the main subgroups. By removing noisy data and selecting the most relevant subgroups, the performance of FDNN in classifying vast amounts of data is improved. Show more
Keywords: Big data analysis, logistic regression-based support vector machine, multi-objective differential search algorithm, fuzzy deep neural network, random forest, high dimensionality problem, subgroup discovery, slow convergence rate
DOI: 10.3233/JIFS-220171
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7097-7113, 2022
Authors: Ponmalar, S. Joshibha | Prasad, Valsalal | Kannadasan, Raju
Article Type: Research Article
Abstract: A novel technique is presented for Maximum Power Point Tracking (MPPT) based photovoltaic (PV) system in partial shadow conditions for harvesting maximum power. In this paper, a hybrid technique is developed, which combines Black Widow Optimization (BWO) with Recurrent Neural Network (RNN). To train the data set and provide a control signal for the converter, an RNN is used. After fitting the training data sets, the suggested method achieved maximum power by utilizing BWO based on the control parameters. This proposed method minimizes the difference between actual and average power. Using an optimization technique, the main goal of this proposed …strategy is to obtain peak power harvest under various conditions, including partial shading, while minimizing error function, With the help of MATLAB/Simulink software, the conclusions are revealed under various partial shading conditions. For each category, the observed results are evaluated at various time intervals. The proposed method is also compared to other techniques such as the Ant Colony Optimization (ACO)-RNN system, Particle Swarm Optimization (PSO)-RNN system, and Gravitational Search Algorithm (GSA)-RNN system. The proposed system is 36.11% faster than GSA with RNN, 39.47% faster than PSO, and 42.5% faster than ACO with RNN in terms of tracking speed. Significantly, the proposed work is 0.87% more efficient than the other models in terms of obtaining maximum power. In terms of obtaining maximum power, the proposed work BWOA-RNN is more effective than other methods. Show more
Keywords: Partial shading, maximum power point tracking (MPPT), photovoltaic (PV), black widow optimization (BWO), recurrent neural network (RNN), gravitational search algorithm (GSA), ant colony optimization (ACO), and particle swarm optimization (PSO)
DOI: 10.3233/JIFS-220892
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7115-7133, 2022
Authors: Li, Wenfeng | Deng, Xiaoping | Wang, Ruiqi | Meng, Songping
Article Type: Research Article
Abstract: Energy or load disaggregation, as one essential part of non-intrusive load monitoring (NILM), is an efficient way to separate the consumption information of target appliances from the whole consumption data, and can accordingly help to regulate people’s energy consumption behaviors. However, the consumptions of the target appliances are usually affected by the variance of the opening time, working condition and user interference, so it is a difficult task to realize precise disaggregation. To further improve the energy disaggregation accuracy, this paper proposes a new parallel disaggregation strategy with two subnets for the energy consumption disaggregation of the target appliances in …the residential buildings. In the proposed strategy, the parallel disaggregation network contains a long-term disaggregation network and a short-term disaggregation network, which can automatically and respectively learn the long-term trend features and short-term dynamic characteristics of the electrical appliances. This parallel structure can make full use of the advantages of different methods in feature extraction, so as to model the appliance features more comprehensively. To better extract the long-term and short-term features, in the long-term disaggregation subnet, we propose the double branch bi-directional temporal convolution network (DBB-TCN) which has a wider receptive field than the traditional temporal convolution networks (TCN), while in the short-term disaggregation subnet, we adopt the convolution auto-encoder to learn the short-term characteristics of the target appliances. Finally, detailed experiments and comparisons are made with two real-world datasets. Experimental results verified that the proposed parallel disaggregation method performs better than the existing methods under various evaluation criteria. Show more
Keywords: Non-intrusive load monitoring, energy disaggregation, deep learning, temporal convolution network, auto-encoder
DOI: 10.3233/JIFS-212679
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7135-7151, 2022
Authors: Ubale Kiru, Muhammad | Belaton, Bahari | Chew, Xinying | Almotairi, Khaled H. | Hussein, Ahmad MohdAziz | Aminu, Maryam
Article Type: Research Article
Abstract: One of the fastest-growing fields in today’s world is data analytics. Data analytics paved the way for a significant number of research and development in various fields including medicine and vaccine development, DNA analysis, artificial intelligence and many more. Data plays a very important role in providing the required results and helps in making critical decisions and predictions. However, ethical and legislative restrictions sometimes make it difficult for scientists to acquire data. For example, during the COVID-19 pandemic, data was very limited due to privacy and regulatory issues. To address data unavailability, data scientists usually leverage machine learning algorithms such …as Generative Adversarial Networks (GAN) to augment data from existing samples. Today, there are over 450 algorithms that are designed to re-generate or augment data in case of unavailability of the data. With many algorithms in the market, it is practically impossible to predict which algorithm best fits the problem in question, unless many algorithms are tested. In this study, we select the most common types of GAN algorithms available for image augmentation to generate samples capable of representing a whole data distribution. To test the selected models, we used two unique datasets, namely COVID-19 CT images and COVID-19 X-Ray images. Five different GAN algorithms, namely CGAN, DCGAN, f-GAN, WGAN, and CycleGAN, were selected and applied to the samples to see how each algorithm reacts to the samples. To evaluate their performances, Visual Turing Test (VTT) and Fréchet Inception Distance (FID) were used. The VTT result shows that a human expert can accurately distinguish between different samples that were produced. Hence, CycleGAN scored 80% in CT image dataset and 77% in X-Ray image dataset. In contrast, the FID result revealed that CycleGAN had a high convergence and therefore generated high quality and clearer images on both datasets compared to CGAN, DCGAN, f-GAN, and WGAN. This study concluded that the CycleGAN model is the best when it comes to image augmentation due to its friendliness and high convergence. Show more
Keywords: Generative adversarial networks, CGAN, DCGAN, f-GAN, WGAN, CycleGAN
DOI: 10.3233/JIFS-220017
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7153-7172, 2022
Authors: Li, Song | Wang, Jie-Sheng | Song, Hao-Ming | Zheng, Yue | Zhang, Xing-Yue
Article Type: Research Article
Abstract: Archimedes optimization algorithm (AOA) is a metaheuristic algorithm inspired by the Archimedes physical law. It simulates the principle of buoyancy applied upward to partially or completely submerged objects. The decay energy of buoyancy, Lévy flight and Tangent flight are introduced into AOA. The buoyancy energy is adopted as the judgment condition of global search and local search. Then the location updating methods based on Lévy flight and Tangent flight are proposed so as to enhance its ergodicity and unrepeatable, improve the convergence speed and accuracy. Finally, through a large number of simulation experiments on 25 benchmark functions in CEC-BC-2017, the …improved AOAs are compared to show their advantages and disadvantages. On the other hand, two engineering design problems (pressure vessel design and spring design problem) are optimized. The experimental results show that the AOA based on buoyancy energy mixed Lévy flight and Tangent flight can solve the function optimization and engineering optimization problems well. It has the strong balance between exploration and exploitation, fast convergence speed and high search accuracy. Show more
Keywords: Archimedes optimization algorithm, buoyancy energy, Lévy flight, tangent flight
DOI: 10.3233/JIFS-221039
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7173-7197, 2022
Authors: Monikandan, A.S. | Agees Kumar, C.
Article Type: Research Article
Abstract: In this research, UPQC (Unified Power Quality Conditioner) with optimized hybrid fuzzy controller based GBSSA (Gaussian Barebone Salp Swarm Algorithm) with EPLL (Enhanced Phase Locked Loop) have been proposed for power quality enhancement in power distribution networks. Using the proposed method, the difficulties in major of the power distribution system networks can be solved, related to power quality issues. GBSSA has been employed in this research, to improve solution accuracy and optimization efficiency. Given that, it is permissible to add some extra time cost to acquire a better solution, based on the Non-Free Lunch (NFL) theory, and that the time …consumption of function evaluation is rather large, when addressing actual optimization problems, the extra time consumption can be overlooked to some extent. The EPLL control method improves the standard PLL, by reducing its fundamental flaw, which is the occurrence of main frequency errors, as well as double frequency errors. It controls the DC-bus voltage of unified power quality conditioners, during supply voltage and load voltage turbulences. The proposed UPQC control technique has been found to be resilient, to a variety of source and load perturbations, including unbalanced, transient distorted supply, voltage sag, unbalanced load and voltage swell. The proposed optimized GBSSA hybrid fuzzy controller with EPLL has been proven to be more effective in reducing the THD (Total Harmonic Distortion) to 3.22%. Moreover, comparative analysis with a conventional TSF-PLL has been performed with that of Takagi-Sugeno fuzzy controller and implemented using MATLAB (MATrix Laboratory). Show more
Keywords: UPQC, enhanced PLL, GBSSA, hybrid fuzzy controller, power quality issues
DOI: 10.3233/JIFS-213263
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7199-7211, 2022
Authors: Hu, Wujin | Li, Bo | Chen, Likang
Article Type: Research Article
Abstract: Physical Health is an important part of health education and health promotion in my country, and the health literacy level of students majoring in physical education in colleges and universities is an important factor in the development of health education in primary and secondary schools, and also directly affects the implementation of school health education in the future. The physical health evaluation of College students is frequently viewed as the multiple attribute decision making (MADM) issue. In our article, we combine the geometric Heronian mean (GHM) operator, generalized weighted Heronian mean (GWHM) operator with 2-tuple linguistic neutrosophic numbers (2TLNNs) to …propose the generalized 2-tuple linguistic neutrosophic geometric HM (G2TLNGHM) operator and generalized 2-tuple linguistic neutrosophic weighted geometric HM (G2TLNWGHM) operator. Meanwhile, some ideal properties of built operator are studied. Then, the G2TLNWGHM operator is applied to deal with the MADM problems under 2TLNNs. Finally, an example for Physical health evaluation of College students is used to show the proposed methods. Show more
Keywords: Multiple attribute decision making (MADM), neutrosophic numbers, 2-tuple linguistic neutrosophic numbers set (2TLNNSs), G2TLNGHM operator, G2TLNWGHM operator, physical health evaluation of College students
DOI: 10.3233/JIFS-221684
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7213-7225, 2022
Authors: Mahfouz, Mohamed A.
Article Type: Research Article
Abstract: The required division and exponentiation operations needed per iteration for the possibilistic c-means (PCM) clustering algorithm complicate its implementation, especially on homomorphically-encrypted data. This paper presents a novel efficient soft clustering algorithm based on the possibilistic paradigm, termed SPCM. It aims at easing future applications of PCM to encrypted data. It reduces the required exponentiation and division operations at each iteration by restricting the membership values to an ordered set of discrete values in [0,1], resulting in a better performance in terms of runtime and several other performance indices. At each iteration, distances to the new clusters’ centers are determined, …then the distances are compared to the initially computed and dynamically updated range of values, that divide the entire range of distances associated with each cluster center into intervals (bins), to assign appropriate soft memberships to objects. The required number of comparisons is O(log the number of discretization levels). Thus, the computation of centers and memberships is greatly simplified during execution. Also, the use of discrete values for memberships allows soft modification (increment or decrement) of the soft memberships of identified outliers and core objects instead of rough modification (setting to zero or one) in related algorithms. Experimental results on synthetic and standard test data sets verified the efficiency and effectiveness of the proposed algorithm. The average percent of the achieved reduction in runtime is 35% and the average percent of the achieved increase in v-measure, adjusted mutual information, and adjusted rand index is 6% on five datasets compared to PCM. The larger the dataset, the higher the reduction in runtime. Also, SPCM achieved a comparable performance with less computational complexity compared to variants of related algorithms. Show more
Keywords: Clustering algorithms, fuzzy clustering, possibilistic c-means, hybrid soft clustering, homomorphic encryption
DOI: 10.3233/JIFS-213172
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7227-7241, 2022
Authors: Thessalonica, D. Juliet | Khanna Nehemiah, H. | Sreejith, S. | Kannan, A.
Article Type: Research Article
Abstract: Software developers find it difficult to select the specific detection rules for different smell types. A set of metrics, thresholds and labels constitutes a code smells detection rule. The generated rules must be optimized efficiently to ensure successful rule selection. The objective is to identify how rules are generated from the labeled data set and selected using bio-inspired algorithms. The goals are met by employing the C4.5 and RIPPER algorithms to generate rules then, optimized using two bio-inspired algorithms, the Salp Swarm Algorithm (SSA) and Cockroach Swarm Optimization (CSO). The optimized sets of rules are evaluated using the similarity metrics …which are computed with the help of expected and the detected code smells. The common rule subsets from SSA and CSO are merged to produce the optimal rule subset which can be used for code smell detection. The proposed work has been experimented on Xerces-J, Log4J, Gantt Project and JFreeChart dataset. The work detected code smells with an accuracy of 91.7% for Xerces-J, 96.7% for JFreeChart, 88.6% for Gantt Project and 98% for Log4J. The findings will be useful for both theory and research since the proposed framework allows focusing on rule selection. Show more
Keywords: Software metric, code smell, Salp Swarm Algorithm, Cockroach Swarm Optimization
DOI: 10.3233/JIFS-220474
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7243-7260, 2022
Authors: Wang, Zeyuan | Wei, Guiwu | Guo, Yanfeng
Article Type: Research Article
Abstract: The main research of this paper is decision making under the dual probabilistic linguistic term sets (DPLTSs). This paper introduces a method, which combined TODIM method and CRITIC method. In this research, the CRITIC method is used to determine the weight, and the distance formula of TODIM method has been modified in order to adapt to DPLTS situation. Then, the TODIM method is used for multi-attribute group decision making (MAGDM) problem. Finally, a case study concerning investment project selection is given to demonstrate the merits of the developed methods. This combined method can be used for the automatic areal feature …matching, medical quality assessment, and ranking of matching processes. There are very few papers about using TODIM method under DPLTS situation at present, so this is a new perspective on MAGDM. The DPLTS-TODIM-CRITIC method was compared with correlation coefficient method and closeness coefficient method, and it is easy to find the advantage of this new method over the other two existing methods. Show more
Keywords: Multi-attribute group decision making (MAGDM), dual probabilistic linguistic term set, TODIM, CRITIC, Generalized normalized distance measure; investment project selection
DOI: 10.3233/JIFS-220502
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7261-7276, 2022
Authors: Agyemang, Isaac Osei | Zhang, Xiaoling | Adjei-Mensah, Isaac | Agbley, Bless Lord Y. | Mawuli, Bernard Cobbinah | Fiasam, Linda Delali | Sey, Collins
Article Type: Research Article
Abstract: Waypoints have enhanced the prospect of fully autonomous drone applications. However, Geographical Position System (GPS) spoofing and signal interferences are key issues in waypoint-based drone applications. Also, conceptual waypoint-based drone applications require accurate awareness of waypoints based on environmental cues and integration of additional sensing modalities. Additional sensor modalities may overwhelm drones’ processing resources, reducing operational time. This study proposes W-MobileNet, a denoising model for autonomous trajectory trail navigation based on precision control of a path planner, denoising capabilities of Weiner filters, and perceptual knowledge of convolutional neural networks. Creatively integrating the modules of W-MobileNet results in an intuitive drone …navigation controller characterized by position, orientation, and speed estimation. Further, a generic loss function that significantly aids models to converge faster during training is proposed based on adaptive weights. An extensive evaluation of a simulated and real-world experiment shows that W-MobileNet is more favorable in precision and robustness than contemporary state-of-the-art models. W-MobileNet has the potential to become one of the standards for autonomous drone applications. Show more
Keywords: Navigation, waypoint, drone, unmanned aerial vehicle, autonomous, deep convolutional neural network
DOI: 10.3233/JIFS-220693
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7277-7295, 2022
Authors: Tian, Xianghua | Luan, Feng | Li, Xu | Wu, Yan | Chen, Nan
Article Type: Research Article
Abstract: In the hot strip rolling process, accurate prediction of bending force is beneficial to improve the accuracy of strip crown and flatness, and further improve the strip shape quality. Due to outliers and noise are commonly present in the data generated in the rolling process, not only the prediction accuracy should be considered, but also the uncertainty of prediction results should be described quantitatively. Therefore, for the first time, the authors establish an interval prediction model for bending force in hot strip rolling process. In this paper, we use Artificial Neural Network (ANN) and whale optimization algorithm (WOA) to produce …a prediction interval model (WOA-ANN) for bending force in hot strip rolling. Based on the point prediction by ANN, interval prediction is completed by using lower upper bound estimation (LUBE) and WOA, and three indexes are used to evaluate the performance of the model. This paper uses real world data from steel factory to determine the optimal network structure and parameters of the interval prediction model. Furthermore, the proposed WOA-ANN model is compared with other interval prediction models established by other three optimization algorithms. The experimental results show that the proposed WOA-ANN model has high reliability and narrow interval width, and can well complete the interval prediction of bending force in hot strip rolling. This study provides a more detailed and rigorous basis for setting bending force in hot strip rolling process. Show more
Keywords: Artificial neural network (ANN), whale optimization algorithm (WOA), bending force, lower upper bound estimation (LUBE), interval prediction
DOI: 10.3233/JIFS-221338
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7297-7315, 2022
Authors: Qi, Quan-Song
Article Type: Research Article
Abstract: The performance evaluation of public charging service quality is frequently viewed as the multiple attribute group decision-making (MAGDM) issue. In this paper, an extended TOPSIS model is established to provide a new means to solve the performance evaluation of public charging service quality. The TOPSIS method integrated with FUCOM method in probabilistic hesitant fuzzy circumstance is applied to rank the optional alternatives and a numerical example for performance evaluation of public charging service quality is used to test the newly proposed method’s practicability with the comparison with other methods. The results display that the approach is uncomplicated, valid and simple …to compute. The main results of this paper: (1) A novel PHF-TOPSIS method is proposed; (2) The extended TOPSIS method is developed in the probabilistic hesitant fuzzy environment; (3) The FUCOM method is used to obtain the attribute weight; (4) The normalization process of the original data has adapted the latest method to verify the precision; (5) The built models and methods are useful for other selection issues and evaluation issues. Show more
Keywords: Multiple attributes group decision making (MAGDM), probabilistic hesitant fuzzy sets (PHFS), FUCOM method, TOPSIS method, performance evaluation, public charging service quality
DOI: 10.3233/JIFS-220999
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7317-7328, 2022
Authors: Karthik, G.L. | Samson Ravindran, R.
Article Type: Research Article
Abstract: Fetal Electrocardiogram (FECG) analysis helps in diagnosis of fetal heart. Extracting FECG from composite abdominal signal that contains noises like maternal ECG (MECG), electrical interference etc is a topic of great research interest, and several approaches have been reported. The proposed method is Heuristic RNN-based Kalman Filter for Fetal Electrocardiogram Extraction (HRKFFEE) which is based on redundant noise and signal patterns in the residual signal of FECG and MECG. Two functional blocks are used in the proposed method. The first functional block is based on Heuristic RNN equipped with legacy Long Short-Term Memory (LSTM) for assembling a knowledgebase and the …second functional block is RNN-based Kalman filter. Upon testing, the proposed method delivers better average values of accuracy, F Score, Precision and Specificity as 93.118%, 93.106%, 92.9495 % and 92.98% respectively. Show more
Keywords: FECG Extraction, RNN-based Kalman filter, Legacy LSTM
DOI: 10.3233/JIFS-221549
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7329-7340, 2022
Authors: Zhang, Guidong | Sheng, Yuhong | Shi, Yuxin
Article Type: Research Article
Abstract: The multivariate uncertain regression model reveals the relationship between the explanatory and response variables to us very effectively. In this paper, firstly, the uncertain maximum likelihood estimation method for the parameters of the one-dimensional uncertain regression model is extended to the multivariate uncertain regression model to obtain estimates of the parameters. Secondly, in order to determine the reasonableness of the estimated values that are obtained by the various parameter estimation methods, uncertain hypothesis testing is applied to the multivariate uncertain regression model. Finally, some numerical examples are given to verify the feasibility of the method.
Keywords: Multivariate uncertain regression model, maximum likelihood estimation, uncertain hypothesis testing
DOI: 10.3233/JIFS-213322
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7341-7350, 2022
Authors: Bakhat, Khush | Kifayat, Kashif | Islam, M. Shujah | Islam, M. Mattah
Article Type: Research Article
Abstract: The method of marking video clips with action symbols is known as vision-based human activity recognition. Robust solutions to this problem have a variety of practical implementations. Due to differences in motion performance, recording environments, and inter-personal differences, the challenge is difficult. We specifically resolve these problems in this study work, and we solve imitations of state-of-the-art research. Projected human activity recognition is based on an amalgamation of CEV & SGM features. The proposed solution outperforms current models and produces state-of-the-art outcomes as compared to the best effectiveness of the control, according to experimental results on the datasets.
Keywords: Complex networks, entropy, human activity recognition, human action recognition, CEV, SGM
DOI: 10.3233/JIFS-213514
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7351-7362, 2022
Authors: Pavithra, P. | Hariharan, B.
Article Type: Research Article
Abstract: Cloud computing become increasingly more famous consistently, as numerous associations tend to outsource their information. With the outbreak of email message leakage, the protection and security of sensitive email data have become clients’ essential concerns. Encrypted email data is a superior method to ensure security, yet it will enormously restrict the searching. To take care of this difficulty keyword-based search over encrypted information is presented. The current search strategies permit the client to search utilizing just the specific keywords. There is no capacity to bear errors and format irregularities. In order to overcome those drawbacks, optimal secured fuzzy-based multi-keyword search …over encrypted email data is proposed here. The email sender encrypts the email data before outsourcing the data to a cloud server. For encryption, the proposed method utilizes the optimal secure XOR (OSXOR) encryption algorithm. Here the key value is optimally selected by the mayfly optimization algorithm (MOA). After the encryption, the encrypted email is outsourced to the cloud server. The data owner creates an encrypted searchable index using an input file to enable querying across encrypted emails and then assigns either the index or the gathering of encrypted messages to a cloud server. The receiver receives them from the cloud server and is fed back information, but it is unable to comprehend the signal. The recipient of the encrypted email can decode it and create a search trapdoor in the encrypted email database. For searching, fuzzy-based multi-keyword search is proposed. The effectiveness of the proposed methodology is analyzed in terms of different metrics namely, Memory Usage, Execution time, Encryption and decryption time and search time. The experimental result shows that the proposed method takes a minimum search time is 0.51 s and it achieves maximum searching accuracy of 98%. The implementation is done in JAVA with a Cloud simulator. Show more
Keywords: Cloud computing, encryption, decryption, mayfly optimization, fuzzy-based multi keyword search and search time
DOI: 10.3233/JIFS-213521
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7363-7375, 2022
Authors: Xu, Xinrui
Article Type: Research Article
Abstract: At present, with the continuous changes in the market situation and the continuous improvement of the supply chain network structure, the competition in all walks of life has become more and more intense, which has risen from simple enterprise competition to competition in the entire supply chain. In the construction industry, the structure of the construction supply chain is more complex and diverse, and it is more necessary to select high-quality suppliers for sincere cooperation. This requires construction companies to establish a complete supply chain management system, select high-quality suppliers to achieve win-win cooperation and improve their competitiveness. Therefore, construction …enterprises need to comprehensively consider various factors, build a reasonable and feasible evaluation index system according to their own demand for materials, and use appropriate evaluation methods to select material suppliers with specific advantages, so as to ensure the entire construction supply chain of the project. of smooth operation. In this paper, we introduced some calculating laws on interval-valued intuitionistic fuzzy sets (IVIFSs), Hamacher sum and Hamacher product and further propose the induced interval-valued intuitionistic fuzzy Hamacher ordered weighted average (I-IVIFHOWA) operator. Meanwhile, we also study some ideal properties of built operator. Then, we apply the I-IVIFHOWA operator to deal with the multiple attribute decision making (MADM) problems under IVIFSs. Finally, an example for selecting the building material suppliers is used to test this new approach. Show more
Keywords: Multiple attribute decision making (MADM), interval-valued intuitionistic fuzzy sets (IVIFSs), IOWA operator, I-IVIFHOWA operator, building material suppliers
DOI: 10.3233/JIFS-221001
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7377-7386, 2022
Authors: Baytürk, Engin | Küçükdeniz, Tarık | Esnaf, Şakir
Article Type: Research Article
Abstract: Location-routing problem (LRP) contains two Np-hard problems as, facility location (FL) and vehicle routing problem (VRP), in the same content. Since both problems directly affect the cost of distributions of the products and supply chain, the decision of location and routing is important for the success of companies. Therefore, many attempts are made to solve LRP problem in the literature. Researchers proposed exact and heuristic methods for LRP. However, exact methods cannot provide solutions for considerably large instances. In this paper, a new heuristic method is proposed for continuous or planar LRP. The proposed method contains fuzzy c-means for continuous …location problem and simulated annealing algorithm for vehicle routing problem, respectively. The proposed method is applied to both capacitated and uncapacitated LRP instances that are widely used in the literature. Results of the proposed method are compared with successful researches that are made on this problem in terms of the total cost. Show more
Keywords: Location-routing problem, simulated annealing algorithm, fuzzy c-means
DOI: 10.3233/JIFS-221168
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7387-7398, 2022
Authors: Jayasree, T. | Selvin Retna Raj, T.
Article Type: Research Article
Abstract: In this paper, the classification of power quality disturbances using combined ST/MST (S-Transform/Modified S-Transform) and Radial Basis Function Neural Network (RBFNN) is proposed. The extraction of significant features from the power quality disturbance signals is one of the challenging tasks in recognizing different disturbances. The Stockwell Transform/Modified Stockwell Transform (ST/MST) based features are distinct, understandable and more immune to noise. The important attributes present in the signals are retrieved from the ST/MST contours, MST 3D plots and MST based statistical curves. The relevant features are also extracted from the statistical curves. The extracted features are given as input to the …RBFNN for further classification. This method is evaluated under both noisy and noiseless conditions. The performance of the proposed approach is compared with other conventional approaches in the literature. The simulation results demonstrate that the proposed MST based RFNN technique is more effective for the detection and classification of power quality disturbances. Show more
DOI: 10.3233/JIFS-212399
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7399-7415, 2022
Authors: Du, Yuqin | Du, Xiangjun | Li, Yuanyuan | Hou, Fujun
Article Type: Research Article
Abstract: The aim of this paper is to introduce a Frank operator in the q-rung orthopair triangular fuzzy linguistic environment on the basis of the notion of the Frank operator and the q-rung orthopair fuzzy set. Firstly, the concept of a q-rung orthopair triangular fuzzy linguistic set (q-ROTrFLS) is proposed, then several basic operations, score, and accuracy functions to compare the q-ROTrFLS values are defined. Secondly, a series of q-rung orthopair triangular fuzzy linguistic Frank aggregation operators are developed, such as q-rung orthopair triangular fuzzy linguistic Frank weighted average (q-ROTrFLWA)operator,q-rung orthopair triangular fuzzy linguistic Frank weighted geometric (q-ROTrFLWG) operator, and we …introduce several relevant properties of these operators and prove their validity, and show the relevant relationship between some operators. Thirdly, two different decision-making approaches are constructed in the q-rung orthopair triangular fuzzy linguistic environment. Furthermore, a practical example is given to explain the developed methods. Finally, a comparative study is conducted, and the relevant sensitivity analysis is also discussed, and the outcome shows the prominence and the effectiveness of the developed methods compared to previous studies. Show more
Keywords: q-rung orthopair triangular fuzzy linguistic set, Frank operator, multi-attribute decision making (MADM), q-rung orthopair set
DOI: 10.3233/JIFS-220556
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7417-7445, 2022
Authors: Huang, Dan | Lin, Hai | Li, Zhaowen
Article Type: Research Article
Abstract: Information system (IS) is a significant model in the field of artificial intelligence. Information structure is not only a research direction in the field of granular computing (GrC), but also an important method to study an IS. A multiset-valued information system (MVIS) refers to an IS where information values are multisets. A MVIS can be seen as a model that is the result of information fusion of multiple categorical ISs. This model helps deal with missing values in the dataset. This paper studies information structures in a MVIS on the view of GrC and consider their application for uncertainty measurement …(UM). First of all, some notions of multisets and probability distribution sets (PDSs) are proposed. Naturally, relationships between multisets and PDSs are researched. Then, the concept of a MVIS based on the notion of multisets is given, and the internal structure of a MVIS is revealed by an incomplete information system (IIS). Furthermore, tolerance relations in a MVIS are defined by using Hellinger distance, and tolerance classes are obtained to construct the information structures of a MVIS. Considering the association of information structures, relationships between information structures are raised from the two aspects of dependence and separation. Moreover, some properties between information structures are provided by using information distance and inclusion degree. Finally, four UMs as the applications of information structures are investigated, and comprehensive experiments on several datasets demonstrate the feasibility and superiority of the proposed measures. These results will be helpful for establishing a framework of GrC in a MVIS and studying UM. Show more
Keywords: GrC, RST, Information fusion, PDS, MVIS, Information structure, UM
DOI: 10.3233/JIFS-220652
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7447-7469, 2022
Authors: Neelamegam, G. | Marikkannu, P.
Article Type: Research Article
Abstract: The Cloud-based storage is able to store more information in gigabyte size in all formats such as text, image or video and it can access at any time with their login credentials. In such a system, reducing the duplication of data and increasing security is an important factor for efficient storage. In this work, the file level de-duplication process is applied on the Magnetic Resonance Imaging (MRI) brain image by reducing the shares of the image to retrieve an original image from the cloud. To reduce the storage problem in this an optimization-based RSSS is used. The objective of this …investigation is to decrease the storage blow-up problem in Cloud storage and reduce the duplicate files in the Cloud storage of the health care centre. The proposed model comprises of two subsets: In the first set, the input image is divided into a number of shares using RSSS scheme. In the second set, the minimum share is determined by using the optimization process and it is encrypted and it is stored in the Cloud. Initially, the image is divided into number of shares for reconstructing using the ramp secret sharing scheme.Without these shares, the original image cannot be recovered. But storing all the shares result in high storage capacity. It is overcome with the help of Ant Lion optimization (ALO) to determine the minimum number of shares required for recovering the image. The ALO works to minimizing the Mean Square Error (MSE) of the image reconstruction to find the minimum shares. Then, the minimum shares are encrypted and converted into hash keys. Those hash keys are stored in the Cloud storage. The proposed ALO-RSSS is achieved its objective by reducing the shares to 2 as compared to the traditional method as well as the PSNR is 27% improved. Show more
Keywords: Cloud security, data de-duplication, ramp secret sharing scheme, ant lion optimization, shares, storage blow up
DOI: 10.3233/JIFS-212898
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7471-7484, 2022
Authors: Gnanaprakasam, C.N. | Brindha, G. | Gnanasoundharam, J. | Ahila Devi, E.
Article Type: Research Article
Abstract: In this paper proposes an efficient hybrid approach for resolve the issues based on unit commitment model integrated with electric vehicles considering the responsive load. The proposed hybrid approach is the combined performance of both the Multi-fidelity meta-optimization and Turbulent Flow of water based optimization (TFWO) and later it is known as MFM-TFWO method. The major objective of proposed approach is reduction of operational costs, reduction of real power losses, and reduction of emissions and improves the voltage stability index. The proposed system is incorporated with wind turbine and photovoltaic, electrical and thermal energy storage systems. The MFM approach is …performed for the optimization of the best combination of thermal unit depend on uncertainty; cost minimization, constraints of the system. For capturing the uncertainty and ensuring the demand satisfaction is performed by the TFWO approach. The proposed approach evaluates the impact of the stochastic behavior of electric vehicles and responsive load of the demand side management. The proposed method considers the uncertainty of PV, wind, thermal, electrical demands, and electric vehicles. At last, the proposed model is actualized in MATLAB/Simulink platform and the performance is compared with other techniques. The simulation results depicted that electric vehicles and responsive loads on energy management is decreasing the operation cost and emissions. Show more
Keywords: Operational costs, active power losses, emissions, voltage stability index, combined cooling heating and power, electric vehicle, responsive loads, energy storage
DOI: 10.3233/JIFS-220810
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7485-7510, 2022
Authors: Bai, Yuhang | Wang, Chunbo | Zhang, Lizhong
Article Type: Research Article
Abstract: With the continuous opening up of China’s dairy market to foreign countries, dairy products import volume continues to grow rapidly. The structural vector autoregressive model (SVAR) was used in this article to analyze the impact of dairy product imports on China’s raw milk production from 1996 to 2017. It is found that, dairy product import volume has a positive impact on China’s raw milk production, and negative impact on the liquid dairy product; and mainly negative impacts on the cost control variables in the short term. The price of corn has a stronger impact on the raw milk production compared …with that of the soybean meal prices and crude oil price; the impact of Domestic raw milk demand on raw milk production fluctuates frequently in the short term, and has a positive impact on the diary export. Based on this, this article believes that adjusting the milk industry policy, optimizing the dairy products import structure and the dairy cows’ source structure, and advocating scientific feeding can effectively alleviate the impact caused by dairy products import. Show more
Keywords: Dairy import, raw milk production, shock effect, Structure vector autoregressive model (SVAR)
DOI: 10.3233/JIFS-221220
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7511-7524, 2022
Authors: Anitha, R. | Bapu, B.R. Tapas
Article Type: Research Article
Abstract: In wireless sensor network (WSN), routing is one of the substantial maneuvers for distributing data packets to the base station. But malevolent node outbreaks will happen during routing process, which exaggerate the wireless sensor network operations. Therefore, a secure routing protocol is required, which safeguards the routing fortification and the wireless sensor network effectiveness. The existing routing protocol is dynamically volatile during real time instances, and it is very hard to recognize the unsecured routing node performances. In this manuscript, a Deep Dropout extreme Machine learning optimized Improved Alpha-Guided Grey Wolf based Crypto Hash Signature Token fostered Blockchain Technology is …proposed for secure dynamic optimal routing in Wireless Sensor Networks (SDOR-DEML-IAgGWO-CHS-BWSN). In this, Crypto Hash signature (CHS) token are generated for flow accesses with a secret key owned by each routing sensor node and it also offers an optimal path for data transmission. Then the secured dynamic optimal routing information is delivered through the proposed Blockchain based wireless sensor network platform with the help of Deep Dropout Extreme Machine learning optimized Improved Alpha-Guided Grey Wolf routing algorithm. Then the proposed method is simulated using the NS-2 (Network Simulator) tool. The simulation performance of the proposed SDOR-DEML-IAgGWO-CHS-BWSN method provide 76.26%, 65.57%, 60.85%, 48.99% and 42.9% lower delay during 30% malicious routing environment, 73.06%, 63.82%, 59.25%, 44.79% and 38.84% lower delay during 60% malicious routing environment is compared with the existing methods. Show more
Keywords: Wireless sensor network, secured routing protocol, malicious node attacks, Deep Dropout extreme machine learning, Improved Alpha-Guided Grey Wolf, Crypto Hash Signature token, blockchain technology
DOI: 10.3233/JIFS-212455
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7525-7543, 2022
Authors: Fang, Min | Liu, Lu | Ye, Yuxin | Zhu, Beibei | Han, Jiayu | Peng, Tao
Article Type: Research Article
Abstract: Knowledge graphs have been introduced into recommender systems due to the rich connectivity information. Many knowledge-aware recommendation methods use graph neural networks (GNNs) to capture the high-order structural and semantic information of knowledge graphs. However, previous GNN-based methods have the following limitations: (1) they fail to make full use of the neighborhood information of entities and (2) they ignore the importance of user interaction sequences on reflecting user preferences. As such, these models are insufficient for generating accurate representations of users and items. In this study, we propose a K nowledge-aware H ierarchical A ttention N etwork (KHAN) to provide …better recommendation. Specifically, the proposed model mainly consists of an item encoder and a user encoder. The item encoder is equipped with a hierarchical attention network, which is used to generate entity (item) representations by carefully aggregating neighborhood information of entities. The user encoder is also designed to learn more informative user representations from user interaction sequences using multi-head self-attention. The learned user representations are then combined with user representations introduced in the item encoder through a gating mechanism to generate the final user representations. Extensive experiments on two real-world datasets about movie and restaurant recommendation demonstrate the effectiveness of our model. Show more
Keywords: Recommender system, knowledge graph, graph neural network, hierarchical attention network
DOI: 10.3233/JIFS-212918
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7545-7557, 2022
Authors: Cai, Jinya | Zhang, Haiping | Yu, Xinping
Article Type: Research Article
Abstract: The modified bee colony algorithm is one of the excellent methods that has been proposed in recent years for data clustering. This MBCO algorithm randomly values the primary centers of the cluster by selecting a number of data from the data set, which makes the algorithm sensitive to the presence of noise and outgoing data in the data set and reduces its performance. Therefore, to solve this problem, the proposed method used three approaches to quantify the initial centers of the clusters. In the proposed method, first the initial centers of the clusters are generated by chaos methods, KMeans++algorithm and …KHM algorithm to determine the optimal position for the centers. Then the MBCO algorithm starts working with these centers. The performance of the proposed method compared to a number of other clustering methods was evaluated on 7 UCI datasets based on 6 clustering evaluation criteria. For example, in the iris data set, the proposed method with chaos approaches, KHM and KMeans++with accuracy of 0.8725, 0.8737 and 0.8725, respectively, and the MBCO method with accuracy of 0.8678, and in terms of CH criteria, the proposed method with chaotic approaches, KHM and KMeans++reached values of 0.3901, 0.54848, 0.5147 and MBCO method of 0.3620, respectively. Better achieved. In general, the results of the experiments according to the 6 evaluation criteria showed better performance of the proposed method compared to other methods in most data sets according to the 6 evaluation criteria. Show more
Keywords: Modified bee colony optimization, KMeans++algorithm, KHM algorithm, clustering
DOI: 10.3233/JIFS-220739
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7559-7575, 2022
Authors: Balasubramanian, C. | Lal Raja Singh, R.
Article Type: Research Article
Abstract: This paper proposes an efficient energy management approach for managing the demand response and energy forecasting in a smart grid using Internet of Things (IoT). The proposed energy management approach is the hybrid technique that is the joint execution of adaptive neuro fuzzy inference system (ANFIS) and balancing composite motion optimization (BCMO), thus it is called ANFIS-BCMO technique. An energy management approach is developed using price-based demand response (DR) program for IoT-enabled residential buildings. Then, we devised a approach depends on ANFIS-BCMO technique to systematically manage the energy use of smart devices in IoT-enabled residential buildings by programming to relieve …peak-to-average ratio (PAR), diminish electricity cost, and increase user comfort (UC). This maximizes effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings on smart cities. The ANFIS-BCMO technique automatically responds to price-based DR programs to combat the main problem of DR programs that is the limitation of the consumer’s knowledge to respond when receiving DR signals. For consumers, the proposed ANFIS-BCMO based strategy programs appliances to exploit benefit based on reduced electricity bill. By then, the proposed method increases the stability of the electrical system by smoothing the demand curve. At last, the proposed model is executed on MATLAB/Simulink platform and the proposed method is compared with existing systems. Show more
Keywords: Energy management, demand response, energy forecast, smart grid, internet of things
DOI: 10.3233/JIFS-221040
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7577-7593, 2022
Authors: Yen, Chih-Ping
Article Type: Research Article
Abstract: We examine correlation coefficients for single-valued neutrosophic hesitant fuzzy sets (SVNHFSs) to point out their questionable results for the ideal alternative. Then, we propose three similarity measure methods to solve multi-criteria decision-making (MCDM) problems. Three applications, namely, ranking of alternatives, dysfunctional comments of turbine engine generators, and disease diagnoses for patients, illustrate the stability and effectivity of our new similarity. Our findings will help researchers deal with similarity measures in the future.
Keywords: Multiple criteria decision-making, correlation coefficient, single-valued neutrosophic hesitant fuzzy sets
DOI: 10.3233/JIFS-221142
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7595-7604, 2022
Authors: Sindhusaranya, B. | Geetha, M.R. | Rajesh, T. | Kavitha, M.R.
Article Type: Research Article
Abstract: Blood vessel segmentation of the retina has become a necessary step in automatic disease identification and planning treatment in the field of Ophthalmology. To identify the disease properly, both thick and thin blood vessels should be distinguished clearly. Diagnosis of disease would be simple and easier only when the blood vessels are segmented accurately. Existing blood vessel segmentation methods are not supporting well to overcome the poor accuracy and low generalization problems because of the complex blood vessel structure of the retina. In this study, a hybrid algorithm is proposed using binarization, exclusively for segmenting the vessels from a retina …image to enhance the exactness and specificity of segmentation of an image. The proposed algorithm extracts the advantages of pattern recognition techniques, such as Matched Filter (MF), Matched Filter with First-order Derivation of Gaussian (MF-FDOG), Multi-Scale Line Detector (MSLD) algorithms and developed as a hybrid algorithm. This algorithm is authenticated with the openly accessible dataset DRIVE. Using Python with OpenCV, the algorithm simulation results had attained an accurateness of 0.9602, a sensitivity of 0.6246, and a specificity of 0.9815 for the dataset. Simulation outcomes proved that the proposed hybrid algorithm accurately segments the blood vessels of the retina compared to the existing methodologies. Show more
Keywords: Hybrid algorithm, blood vessel segmentation, first-order derivation of Gaussian, matched filter, multi-scale line detector
DOI: 10.3233/JIFS-221137
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7605-7615, 2022
Authors: Panjavongroj, Saruntorn | Phruksaphanrat, Busaba
Article Type: Research Article
Abstract: Enterprise Resource Planning (ERP) gives an organization a competitive edge by centralizing organization data and collaborating among supply chain networks. There are many types of software packages available, so most of the research proposed methods for prioritizing the best system. However, they did not suggest how to implement it, or which practices should be employed. So, this paper aims to propose a framework and a method for the selection of an ERP system and the best practice for implementation at the same time. A hybrid method of Logarithmic Fuzzy Preference Programming (LFPP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) is …presented for solving the problem. LFPP is reliable in obtaining weights, while DEMATEL can capture interrelationships between practices. It was applied to a case study supply chain network of a Thai automotive parts manufacturer to demonstrate the applicability; it could establish the priorities of criteria, rank alternatives, and select the appropriate practices at the same time. There is no study about software and practice selection by use of this proposed method before. The comparison of LFPP and the Fuzzy Full Consistency Method (FUCOM-F) was also performed. It shows that FUCOM-F uses a smaller number of pairwise comparisons than LFPP, but the obtained weights of LFPP are more consistent with the evaluation matrix than using FUCOM-F. Show more
Keywords: ERP system selection, logarithmic fuzzy preference programming (LFPP), decision-making trial and evaluation laboratory (DEMATEL), multiple attribute decision making, supply chain network
DOI: 10.3233/JIFS-221476
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7617-7631, 2022
Authors: Kiruba, D. Giji | Benitha, J.
Article Type: Research Article
Abstract: IoT-Mobile Wireless Sensor Networks (IMWSNs) are being employed in a variety of simulators to visually demonstrate the exposure, energy usage situation, and expected life duration of Internet of Things (IoT) mobile sensors. The majority of academics have projected and expanded routing procedures in order to extend the network’s life cycle. In IMWSNs, clustering is the most important process for improving energy efficiency. In cluster approaches, each IoT sensor node provides the acquired data to the cluster-head of their own cluster. The cluster-head embraces the conscientiousness of gathering prepared information and directing it to the arranged network’s basestation. A fuzzy based …energy proficient secure clustered routing (FEPSRC) is proposed in this research effort, which takes the residue energy, remoteness from the basestation, and compactness of IoT sensor nodes in its locality as input to the Fuzzy-Inference-System. For cluster-head selection, an eligibility ratio is calculated for each IoT sensor node. This protocol guarantees energy harmonizing by electing the preeminent IoT sensor node for the position of cluster-head, velocity of IoT sensor nodes are estimated and also provides best path for routing. The simulation consequence illustrates that projected fuzzy based energy proficient secure clustered routing condensed entire power expenditure, diminishes E-to-E delay, amplifies packet deliverance percentage and accomplishes maximal network life span. Show more
Keywords: Clustering, CH, energy effectiveness, fuzzy-logic, network life span
DOI: 10.3233/JIFS-212014
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7633-7645, 2022
Authors: Yousuff, Mohamed | Babu, Rajasekhara
Article Type: Research Article
Abstract: Melanoma, a kind of fatal skin cancer, originates in melanin secreting cells of the dermis. Disease identification in the early stages assures a high survival rate for the patient. Most of the existing techniques retard the cancer detection phase. Surface-Enhanced Raman Spectroscopy (SERS) can capture fine details from the specimens that machine learning models can utilize to discriminate between healthy and diseased individuals rapidly. Our research work proposes a deep autoencoder based hybrid dimensionality reduction approach with a machine learning model on SERS spectrums of human skin fibroblast for melanoma cancer diagnostics. SERS measurements of 307 samples in total, belonging …to two different classes, such as normal (157 samples) and malignant melanoma (150 samples), are used in this study. The SERS spectra measurements for both the samples lie between 100cm-1 and 4278cm-1 . The variations in the intensity of Raman bands between both classes are intrinsically subtle. Neighborhood Component Analysis (NCA) technique has been exerted to transform 2090 dimensional spectral features into 2090 dimensional vectors and then the Deep Autoencoder (DAE) model is used to handle the nonlinearity in the data and produce the latent space, while Linear Discriminant Analysis (LDA) classifier have been employed for discriminating the normal and cancer cells. The k-fold cross-validation technique with a k value of 10 is implemented to assess the metrics of the model. The stated hybrid (NCA and DAE) model with 10-dimension latent space achieves an accuracy of 98%, the sensitivity of 99% and specificity of 97%, respectively. Due to the high-intensity nature of the SERS spectrum, the existing linear dimensionality reduction based discriminating model fails if the class label (Normal or Cancer) gets distributed on the low variance side. The proposed methodology captures both linear and nonlinear underlying structures present in the spectrums, resulting in better classification compared to the standard dimensionality reduction techniques. Show more
Keywords: Dimensionality reduction, neighborhood component analysis, deep autoencoder, linear discriminant analysis, surface enhanced raman spectroscopy, melanoma
DOI: 10.3233/JIFS-212777
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7647-7661, 2022
Authors: Thilagavathy, A. | Mohanaselvi, S.
Article Type: Research Article
Abstract: In recent years, the extensions of fuzzy sets are much more familiar in almost all fields as they are reliable in defining the imprecise information of every decision-making situation. In this sequence of extensions, the cubical fuzzy sets are very efficient in dealing with imprecise information as it extends picture and spherical fuzzy sets. This article is interested in developing a new improved cubical fuzzy possibility degree measure. The desirable properties of the developed measure are also discussed. The advantage of the proposed measure is that it is capable of comparing the cubical fuzzy numbers in fuzzy nature itself and …provides the degrees of preference relations between them. A comparison study is made with the existing ranking measures to exhibit the feasibility and validity of the proposed approach. Based on the improved measure, a method for ranking cubical fuzzy numbers is constructed. A solution approach to a cubical fuzzy multiple attribute decision-making problem is presented. To exhibit the potency and the practical applicability of the proposal, two real-life instances of selecting the best-cutting fluid for cutting gears have been illustrated. The results are compared with the literature. Show more
Keywords: Cubical fuzzy set, cubical fuzzy number (CFN), possibility degree measure (PDM), improved possibility degree measure (IPDM), ranking
DOI: 10.3233/JIFS-220686
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7663-7678, 2022
Authors: Mahmood, Tahir | Ali, Zeeshan | Aslam, Muhammad | Chinram, Ronnason
Article Type: Research Article
Abstract: The major influence of this manuscript is to diagnose a valuable and considerable technique of Complex Picture Fuzzy Uncertain Linguistic setting and described its useful and valuable operational laws. The theory of Complex Picture Fuzzy Uncertain Linguistic information is massive modified and generalized than the Complex intuitionistic Fuzzy Uncertain Linguistic, Complex Fuzzy Uncertain Linguistic, Fuzzy Uncertain Linguistic and Uncertain Linguistic information. Keeping the supremacy and dominancy of the Complex Picture Fuzzy Uncertain Linguistic information, we investigated the Complex Picture Fuzzy Uncertain Linguistic Arithmetic Heronian Mean, Complex Picture Fuzzy Uncertain Linguistic Weighted Arithmetic Heronian Mean, Complex Picture Fuzzy Uncertain Linguistic Geometric …Heronian Mean and Complex Picture Fuzzy Uncertain Linguistic Weighted Geometric Heronian Mean operators. The property of idempotency, boundedness, monotonicity, and various well-known results with certain specific cases of the invented work are also deliberated. Furthermore, in the availability of the above-proposed analysis, we constructed a multi-attribute decision-making technique by considering the diagnosed operators for complex picture fuzzy uncertain linguistic information to enhance the worth and rationality of the invented theory. Finally, we illustrated the merits and restrictions of the novel operators by comparing them with certain prevailing operators based on fuzzy generalization. Finally, in the presence of evaluated examples, we compared the pioneered operators with various existing operators to enhance the feasibility and worth of the invented operators. Show more
Keywords: Complex picture fuzzy uncertain linguistic sets, arithmetic/geometric Heronian mean operators, decision-making methods
DOI: 10.3233/JIFS-221768
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7679-7716, 2022
Authors: Srinivas, Kachibhotla | Phani Kumar, Ch. Raghavendra
Article Type: Research Article
Abstract: The segmentation of images is a technique used to extract information from a digital picture. One of the main applications in image segmentation is especially in medical image processing detection of an abnormal aspect to diagnose diseases. Ovarian cysts are formed in women who are unbalanced in estrogen and progesterone hormones. The polycystic ovarian syndrome is known as this condition. Women have a fluid collection in their ovaries called follicles. The image captures the follicles by ultrasound scanning. The detection of follicles from the echo sound image requires an optimized segmentation algorithm. Quantification of the ovary and follicle volumes and …follicle counts for diagnosis and management in assisted replication is performed in clinical practice. Now for a few days, most women face infertility problems in the age group between 22 and 35. To analyze and classify the problems, the decision can start with the use of advanced technology to structurally compare the normal ovary to the affected ovary. Ovarian imagery is an effective instrument for the treatment of infertility. In human reproduction, follicle monitoring is particularly important. The primary method of doctors’ assessment is a periodic measurement of the size and form of follicles over several days. The field of medical imaging is one of the most popular applications of image processing techniques. The widespread popularity of image analysis technology in the field of diagnostic devices is due to the advancement of advanced imaging instruments combined with developments in algorithms unique to medical image processing, both for diagnostic tests and therapeutic preparation. Ultrasound imaging is a technique that uses high-frequency sound waves to capture images from within the human body. The echoes of reflected sound waves are captured and shown in real-time. It’s a good way to look at the nucleus, liver, kidneys, gall bladder, and ovaries, among other internal organs. The main contribution lies in identifying dominant follicles, that is growing and capable of producing an egg after the follicular phase, which is our primary goal, and this is where our suggested study comes in. Follicular ovulation doesn’t occur in all women, and not all of the dominant follicular levels are strong enough just to result in a pregnancy. Today, the follicles monitor human interaction using non-automatic means. Our proposed approach for the detection of follicle polycystic ovarian using AKF is not only helpful for generating highly efficient results but also proves to be best when compared with the state of art results from the existing methods. Show more
Keywords: Advanced Kalman Filter (AKF), Adaptive Particle Swarm Optimization (APSO), Dice similarity coefficient (DSC), Kalman Filter (KF), Pigeon Inspired Optimization (PIO), Machine Learning (ML), True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN)
DOI: 10.3233/JIFS-212857
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7717-7732, 2022
Authors: Shukla, Diksha | Chandra, Ganesh | Pandey, Babita | Dwivedi, Sanjay K.
Article Type: Research Article
Abstract: With the rise of social networks, people now express their sentiments more frequently and comfortably through their social media activities on different events, person, and every little thing surrounding them. This generates a lot of unstructured data; billions of users post tweets every day as a daily regime on Twitter itself. This has given rise to many texts classification and analysis tasks, Sentiment Analysis (SA) being one of them. Through SA, it is conferred whether the users have negative or positive orientations in their opinions; the results of this task are significantly useful for decision-makers in various fields. This paper …presents various facets of SA, like the process followed in SA, levels, approaches, and sentences considered in SA. Aspects such as growth, techniques, the share of various platforms, and SA pipeline are also covered in this paper. At last, we have highlighted some major challenges in order to define future directions. Show more
Keywords: Sentiment analysis, machine learning, lexicon based approach, hybrid approach
DOI: 10.3233/JIFS-213372
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7733-7763, 2022
Authors: Liu, Fan | Luo, Muchen | Zhang, Yingyan | Zhou, Shuling | Wu, Xue | Lin, Aiyong | Guo, Yuxia | Liu, Conghu
Article Type: Research Article
Abstract: This study aims to improve regional agricultural production efficiency and promote sustainable agricultural development by presenting a data-driven evaluation method for regional agricultural production efficiency. Based on data collection and processing of regional agricultural input-output factors in Anhui Province, China, from 2014 to 2019, a data envelopment analysis Malmquist model is constructed for data modeling. Static analysis of regional agricultural production efficiency and production redundancy is conducted, and the dynamic change of regional agricultural production efficiency is measured. The results show that technical efficiency is the core driving factor for improving regional agricultural production efficiency. The findings indicate significant policy …implications for improving agricultural production efficiency from the perspective of regional agricultural high-quality development. This study provides theoretical and methodological support for the sustainable development of regional agriculture. Show more
Keywords: Data-driven, agricultural production efficiency, sustainable development, agricultural technical efficiency, high-quality development
DOI: 10.3233/JIFS-220052
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7765-7778, 2022
Authors: Jarernsuk, Saran | Phruksaphanrat, Busaba
Article Type: Research Article
Abstract: Fuzzy multiple objective linear programming (FMOLP) approaches have been used to be solved many applications of multi-objective decision-making (MODM) problems. Several methods have been proposed including max-min approaches, preemptive approaches, and weighted approaches. However, they have some limitations in use; some may not be able to obtain efficient solutions or can give only a few solutions. Some methods need to be solved in two steps or specify the target level, which may be difficult for decision-makers (DMs). In this research, a new single-phase interactive fuzzy programming approach with priority control that can find several efficient solutions is proposed. It is …not necessary to specify the target value for each objective and it can solve with only one step for each solution. The DM can easily select the appropriate solutions from a set of efficient solutions. This method is different from existing single-phase approaches by controlling the satisfaction level of the last priority instead of using weight additive. Simple examples and a practical example of a perishable product supply chain network were tested to show the effectiveness of the proposed model. The performance of the proposed method was also compared with existing methods to verify and validate the model. Show more
Keywords: Fuzzy programming, multiple objective decision making, fuzzy-efficient solution, priority, perishable products supply chain network
DOI: 10.3233/JIFS-220367
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7779-7792, 2022
Authors: Zhang, Shanshan | Wei, Guiwu | Lin, Rui | Chen, Xudong
Article Type: Research Article
Abstract: The purpose of this paper is to provide an efficient multiple attribute group decision making (MAGDM) method to better serve the decision-makers(DMs), considering the real psychological state and preferences of the DMs when facing different risks. In real life, MAGDM is a very complex and uncertain problem, which needs to be considered from many aspects. In view of this, the intuitionistic fuzzy TOPSIS method on the basis of cumulative prospect theory (IF-CPT-TOPSIS) is proposed in this paper, which can effectively solve the above problems, cater to the expectations of DMs, and make the decision results more objective and more reliable. …The originality of this paper comes from three aspects. First, the new score function is combined with CRITIC method to calculate the weight of attributes, which eliminates the influence of Subjective preference of DMs and makes the primary information more reasonable. Furthermore, the use of the new score function can effectively avoid the exception conditions in the IFS problems. Secondly, applying the distance measurement formula of IFSs to determine the degree of approaching the ideal solution, so that the decision model can reflect the environmental changes more quickly. Thirdly, calculate the relative profit loss ratio of each scheme. Hence, each scheme is ranked and the optimal one is selected. Finally, in order to demonstrate the effectiveness of the model, a specific example is given and comparative analysis with the existing methods. The results show that the improved IF-CPT-TOPSIS method is useful and can be widely adapted for complex MAGDM problems. Show more
Keywords: Multiple attribute group decision making (MAGDM), intuitionistic fuzzy sets (IFSs), New score function, CRITIC method, IF-CPT-TOPSIS method
DOI: 10.3233/JIFS-220638
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7793-7806, 2022
Authors: Lin, Liangcheng | Xu, Yonggang | Zhang, Yue | Kang, Chaoqun | Sun, Jian
Article Type: Research Article
Abstract: In order to ensure the safe transmission of the information of the secondary distribution system across the regional network, this paper studies a security monitoring method of the secondary distribution system across the regional network based on the Internet of things technology and the improved fuzzy clustering algorithm. The Internet of things technology is used to collect the information transmission in cross region network of the secondary power distribution system and store it in the database; Combined with the shadow set to improve the basic fuzzy C-means clustering algorithm, the improved fuzzy C-means clustering algorithm is obtained. The cross region …information transmission in the clustering database is divided into two categories: security and risk, and the risk information obtained by clustering is divided into four risk types, so as to realize the security monitoring of information transmission in cross region network of secondary power distribution system. The results show that the average monitoring rate of this method can reach 93.93%, the information collection is efficient and accurate, the number of packet losses is low, and the clustering results are stable and reliable, which can ensure the safe information transmission of cross region network of the secondary power distribution system. Show more
Keywords: Internet of things technology, fuzzy clustering algorithm, secondary power distribution system, network cross region transmission, security monitoring, cluster analysis
DOI: 10.3233/JIFS-221154
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7807-7819, 2022
Authors: Guo, Zhendong | Li, Xiaohong | Zhang, Kai | Guo, Xiaoyong
Article Type: Research Article
Abstract: In this paper, it is proposed that the redundancy in convolutional neural networks of object detection can be effectively removed via an adaptive pruning threshold method (APTCNN) which is associated with scaling factors in batch normalization layers. In this way, the channel pruning can be done iteratively with varying pruning threshold until the satisfactory performance is obtained. The method is also useful for identifying the unimportant convolutional layers. Therefore it can be applied for layer pruning. The experiments are conducted on three benchmark object detection datasets. APTCNN is verified for pruning the backbone network of object detectors YOLOv3 and YOLOv3-spp. …It is shown that the importance of channels and layers are accurately ranked by the proposed adaptive threshold. For the channel pruning, our method reduces the size of YOLOv3 and YOLOv3-spp by 32× and 48× respectively, and accelerates 1.7× and 1.9× respectively. However, the accuracy suffers only 0.77% and 1.32% loss, respectively. As a result, the redundancy in the network architecture can be efficiently removed yielding a slimmed model that has lower computing operations, reduced size, and without compromising accuracy. Show more
Keywords: Object detection, adaptive pruning threshold, channel pruning, layer pruning
DOI: 10.3233/JIFS-213002
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7821-7831, 2022
Authors: Jia, Qilong | Fan, Song
Article Type: Research Article
Abstract: This paper studies the robot-written character identification problem under an end-to-end semi-supervised deep learning framework consisting of semi-supervised learning and deep learning modules. The learning framework allows a deep neural network to be trained on labeled and pseudo-labeled samples where pseudo-labeled samples refer to the samples with labels predicted by the semi-supervised learning module. Moreover, to guarantee the feasibility of the learning framework, a two-stage strategy is proposed for training the deep neural network. Specifically, the two-stage training strategy adopts pseudo-labeled samples firstly to train a deep neural network, then the deep neural network is refined using labeled samples one …more time. As a result, more samples can be used for training a deep neural network, which is significant to the performance improvement of a deep neural network in the case of inadequate labeled samples. More importantly, the deep neural networks trained under the proposed learning framework perform better than the famous deep neural networks in a robot-written character identification experiment. Show more
Keywords: Deep learning, semi-supervised learning, robot-written character, neural networks
DOI: 10.3233/JIFS-221389
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7833-7846, 2022
Authors: Jiang, Rui | Liu, Shulin
Article Type: Research Article
Abstract: In recent years, with the steady development of the national economy and the continuous improvement of people’s living standards, the desire for material pursuits has gradually transformed into the pursuit of spiritual food, and the attention to health and body is highly valued. It gave birth to and promoted the development of the sports industry. High-standard college stadiums provide many conveniences for students and faculty, and the construction and management of college stadiums are also an important part of the development of my country’s sports industry. However, there are still some drawbacks in the management mode and utilization efficiency of …college stadiums. The utilization efficiency evaluation of college stadiums is frequently looked as the multiple attribute group decision-making (MAGDM) problem. Depending on the VIKOR process and fuzzy number intuitionistic fuzzy sets (FNIFSs), this paper designs a novel FNIF-VIKOR process to assess the resource utilization efficiency of college stadiums. First of all, some basic theories related to FNIFSs are briefly introduced. In addition, the weights of attributes are obtained objectively by utilizing CRITIC weight method. Afterwards, the conventional VIKOR process is extended to FNIFSs to obtain the final order of the alternative. Eventually, an application case for utilization efficiency evaluation of college stadiums and some comparative analysis are fully given. The results show that the built algorithms method is useful for assessing the resource utilization efficiency of college stadiums. Show more
Keywords: Multiple attribute group decision making (MAGDM), fuzzy number intuitionistic fuzzy sets (FNIFSs), VIKOR method, CRITIC method, utilization efficiency evaluation
DOI: 10.3233/JIFS-221452
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7847-7861, 2022
Authors: Fan, Xin | Tian, Shengwei | Yu, Long | Han, Min | Liu, Lu | Cheng, Junlong | Wu, Weidong | Kang, Xiaojing | Zhang, Dezhi
Article Type: Research Article
Abstract: Automatic segmentation of aortic true lumen based on deep learning can save the time for diagnosis of aortic dissection. However, fuzzy boundary, small true lumen region, and high similarity usually leads to inaccurate prediction. To make better use of the details supplemented by the encoder to restore boundaries, we decompose the recovery of detail features in the decoder into two sub-processes: calibration and distraction mining. And we propose a novel calibration and distraction mining (CDM) module. It utilizes deep features to calibrate shallow features so that features are concentrated in the main region. Then, it leverages the distraction mining procedure …to extract false-negative features as a supplement to calibrated features and recover details of the segmentation object. We construct CDM-Net and verify its performance on the Aorta-CT dataset (private dataset), it achieves the Dice similarity coefficient of 96.94% and the Jaccard index coefficient of 94.08%, which is the best compared with 10 latest methods. Similarly, we explore its robustness on three more public datasets, including ISIC 2018 dataset (skin lesion segmentation), the 2018 data science bowl dataset (nucleus segmentation), LUNA dataset (lung segmentation). Experimental results prove that our method produces competitive results on all three data sets. Through quantitative and qualitative research, the proposed CDM-Net has good performance and can process aortic slices with complex semantic features, additional experiments show that it has good robustness, and it has the potential to be applied and expanded conveniently. Show more
Keywords: Aortic true lumen, semantic segmentation, calibration, distraction mining
DOI: 10.3233/JIFS-220242
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7863-7875, 2022
Authors: Geng, Kaifeng | Wu, Shaoxing | Liu, Li
Article Type: Research Article
Abstract: Although re-entrant hybrid flow shop scheduling is widely used in industry, its processing and delivery times are typically determined using precise values that frequently ignore the influence of machine failure, human factors, the surrounding environment, and other uncertain factors, resulting in a significant gap between theoretical research and practical application. For fuzzy re-entrant hybrid flow shop scheduling problem (FRHFSP), an integrated scheduling model is established to minimize the maximum completion time and maximize the average agreement index. According to the characteristics of the problem, a hybrid NSGA-II (HNSGA-II) algorithm is designed. Firstly, a two-layer encoding strategy based on operation and …machine is designed; Then, a hybrid population initialization method is designed to improve the quality of the initial population; At the same time, crossover and mutation operators and five neighborhood search operators are designed to enhance the global and local search ability of the algorithm; Finally, a large number of simulation experiments verify the effectiveness and superiority of the algorithm. Show more
Keywords: Re-entrant hybrid flow shop, multi objective optimization, fuzzy scheduling, average agreement index
DOI: 10.3233/JIFS-221089
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7877-7890, 2022
Authors: Zhang, Xianyong | Fan, Yunrui | Yao, Yuesong | Yang, Jilin
Article Type: Research Article
Abstract: Attribute reduction based on rough sets is an effective approach of data learning in intelligent systems, and it has two basic types. Traditional classification-based attribute reducts mainly complete the classification task, while recent class-specific reducts directly realize the class-pattern recognition. Neighborhood rough sets have the covering-structure extension and data-diversity applicability, but their attribute reducts concern only the neighborhood classification-based reducts. This paper proposes class-specific attribute reducts based on neighborhood rough sets, so as to promote the optimal identification and robust processing of specific classes. At first, neighborhood class-specific reducts are defined, and their basic properties and heuristic algorithms are acquired …by granulation monotonicity. Then, hierarchical relationships between the neighborhood classification-based and class-specific reducts are analyzed, and mutual derivation algorithms are designed. Finally, the theoretical constructions and mutual relationships are effectively verified by both decision table examples and data set experiments. The neighborhood class-specific reducts robustly extend the existing class-specific reducts, and they also provide a hierarchical mechanism for the neighborhood classification-based reducts, thus facilitating wide applications of class-pattern processing. Show more
Keywords: Rough sets, neighborhood rough sets, attribute reduction, class-specific attribute reducts, classification-based attribute reducts
DOI: 10.3233/JIFS-213418
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7891-7910, 2022
Authors: Chen, Fu | Huang, Bogang
Article Type: Research Article
Abstract: Health literacy is an important part of health education and health promotion in my country, and the health literacy level of students majoring in physical education in colleges and universities is an important factor in the development of health education in primary and secondary schools, and also directly affects the implementation of school health education in the future. The physical health literacy evaluation of College students is frequently viewed as the multiple attribute group decision making (MAGDM) issue. In such paper, Taxonmy method is designed for solving the MAGDM under probabilistic double hierarchy linguistic term sets (PDHLTSs). First, the expected …function of PDHLTSs and Criteria Importance Though Intercrieria Correlation (CRITIC) method is used to derive the attribute weights. Second, then, the optimal choice is obtained through calculating the smallest probabilistic double hierarchy linguistic development attribute values from the probabilistic double hierarchy linguistic positive ideal solution (PDHLPIS). Finally, a numerical example for physical health literacy evaluation of College students is given to illustrate the built method. Show more
Keywords: Multiple attribute group decision making (MAGDM), PDHLTSs, Taxonmy method, CRITIC method, physical health literacy evaluation
DOI: 10.3233/JIFS-221164
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7911-7922, 2022
Authors: Sukheja, Deepak | Shah, Javaid Ahmad | Madhu, G. | Nagini, S. | Kiranmayee, B.V. | Kautish, Sandeep
Article Type: Research Article
Abstract: Making the correct decision in a real-time situation is extremely difficult. In today’s technological age, computational methods are available, and they may assist the company’s top leaders in making sound decisions and strengthening the organization. There are several techniques for dealing with decision-making problems, one of which is the use of Hendecagonal fuzzy numbers. These fuzzy numbers are used to represent the ambiguity or ambiguity of eleven linguistic variables. To address these shortcomings, we use the ranking method, relativity function, and comparison matrix to aid in decision-making because we have eleven constraints (linguistic variables) that can be expressed in a …hendecagonal fuzzy number matrix (HdcgFNM). The raking technique, relativity function, and comparison matrix were evaluated using a case study. Show more
Keywords: Fuzzy logic, decision making, Hendecagonal fuzzy numbers (HdcgFNM), relativity function
DOI: 10.3233/JIFS-212416
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7923-7936, 2022
Authors: Lakshmana Kumar, R. | Subramanian, R. | Karthik, S.
Article Type: Research Article
Abstract: Mobile Adhoc Networks (MANET) in modern research have many optimal energy conservation mechanisms that can be deployed easily and in a faster manner. The routing approaches associated with energy consumption play a dominant role in routing the data packets between the mobile sensor nodes within the range of optimization. However, major challenges associated with energy consumption in MANETs include reduced lifetime of sensor nodes, poor coverage, and throughput. Most methods tend to reduce the interference of data while traversing between the sensor nodes and increase the capacity of the network. This results in delays while transmitting the packets across the …network, and this may result in failure of packets being transmitted. To resolve this issue, in this paper, we propose an ant colony optimization combined with a flower pollination algorithm for minimal energy consumption and throughput maximisation in MANETs. This hybrid meta-heuristic model resolves the issues, including delays, poor coverage, and reduced network lifetime. This hybrid model uses the estimation of neighbourhood distance among the nodes for optimal placement of nodes for effective location. The estimation of location is found using a flower pollination algorithm with a levy flight mechanism. The estimation is carried out in a hyper sphere model that helps in finding the coverage area of the sensor nodes. Depending upon the estimation of neighbourhood distance among the sensor nodes, the consumption of energy among the sensor nodes in MANETs is reduced. The simulation was conducted between the proposed hybrid approach and conventional soft computing heuristics, where the results show that the proposed model achieves a higher rate of energy conservation and reduces delay than other methods. Show more
Keywords: Mobile adhoc network, flow pollination, neighbourhood distance, ant colony optimization
DOI: 10.3233/JIFS-212450
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7937-7948, 2022
Authors: Wang, Jing | Wang, Ting
Article Type: Research Article
Abstract: Microgrids (MGs) are defined as a set of loads, generation sources and energy storage devices that act as a controllable load or generator, and can supply power and heat to local areas. Management of generated power in MGs is among the main topics that should be addressed for MG design and operation. The existence of distributed generation (DG) resources has caused MG management to face new issues. Depending on the level of exchange between the MG and main grid, MG operation can be classified into two modes: off-grid (islanded) and grid-connected. Optimal energy management in the systems with multiple MGs …has created new challenges in power systems. Therefore, it is important to develop energy management systems (EMSs) focusing on the optimal performance of MG resources and controlling power exchange between the grid and MGs. The present study aims to present a structure with two control layers, called primary and secondary control, for energy management in the systems with multiple MGs and different ownership. Moreover, a flexible distributed EMS is proposed to coordinate the operation of interconnected MGs. Each MG is regarded as an independent unit with local objectives to optimize its operating costs and exchanged power. It is assumed that interconnected MGs are connected to each other by a common bus, through which they can exchange power. MG planning is simulated considering load flow equations and voltage constraints in a system consisting of multiple MGs over a 24-h period. The simulation results indicate using the proposed EMS can improve MG efficiency and reliability. The simulation is performed in MATLAB software by grasshopper optimization algorithm (GOA). Uncertainties and scenario generation and reduction are considered in modeling. Show more
Keywords: Distributed energy management system, Microgrid (MG), distributed generation resource, power exchange
DOI: 10.3233/JIFS-220568
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7949-7961, 2022
Authors: Wang, Huifang | Zhang, Shili
Article Type: Research Article
Abstract: The compressive strength of high-performance concrete encounters difficulties in prediction due to supplementary cementitious materials in its mix designs. There are non-linear relationships between the input materials and the compressive strength. Distinguishing these relationships is intensified through innovative mix designs of high-performance concrete. Artificial neural networks based model incorporated in the present study to narrow down the intensified difficulties of compressive strength prediction. Moreover, to improve the robustness and flexibility of the model and reduce its complexity, Grey Wolf and Ant Colony Optimization algorithms optimize the ANN model. Different statistical metrics are employed to appraise the assessment of models. Considering …RMSE values, the values of ”GWANN-I ” and ”ACANN-I” are 1.6674 and 1.8653, respectively, delivering an acceptable performance in compressive strength prediction of HPC concrete. The OBJ values demonstrated that the ACANN-I with the value of 1.4499 outperforms best compared to other developed hybrid models and can be introduced as the best model for HPC compressive strength prediction. Show more
Keywords: Compressive strength, high-performance concrete, Grey Wolf Optimization, Ant Colony Optimization, artificial neural networks
DOI: 10.3233/JIFS-220736
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7963-7974, 2022
Authors: Yuvaraja, M.
Article Type: Research Article
Abstract: The use of wireless sensor networks (WSNs) for data collection is widespread. The resource constraint is an important factor in WSN communications design. The issue arises naturally in WSNs as a result of uneven energy consumption caused by multi-hop routing and dynamic network models, which substantially affects network lifetime. The nodes are dispersed over distant sensing areas and are powered by finite or limited energy batteries that are difficult to replace. The energy of nodes is reduced as a result of changes in network topology or the network’s lifespan and the main intention of this research is to figure out …how to make sensor networks last longer. The suggested study work focuses on a specific routing strategy for WSNs that employs the AO-star algorithm with a Fuzzy approach and link stability for extending the network lifetime. The technique chooses the optimum routing path by the sensing point to the receiving node based on how much energy is consumed, the smallest number of nodes with the shortest latency, and lower transmission loads with higher throughput. To compare the proposed strategy’s efficiency in energy consumption balancing and network lifespan enhancement, the proposed technique may achieve a 30% longer average network lifetime than the A-star algorithm. Show more
Keywords: Energy, fuzzy, loads, simulation data and WSN
DOI: 10.3233/JIFS-212977
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7975-7982, 2022
Authors: Wang, Liqin | Chu, Hang | Dong, Yongfeng | Liu, Enhai | Li, Linhao
Article Type: Research Article
Abstract: Many real-world knowledge graphs are complex and keep evolving over time. Inferring missing facts in temporal knowledge graphs is a fundamental and challenging task. Previous studies focus on link prediction in static knowledge graphs which hardly extracts the temporal features effectively. In this paper, we propose a novel deep learning model, namely KBGAT-BiLSTM, which is capable of solving long-term predict problems and is suitable for temporal knowledge graph with complex structures. First, we adapt the Graph Attention Network (GAT) to learn the structural features of knowledge graph. Then we utilize the Bidirectional Long Short-Term Memory Networks (BiLSTM) to learn the …temporal features and obtain the low-dimensional embeddings of entities and relations. Finally, we employ a scoring function for link prediction in temporal knowledge graphs. Through extensive experiments on YAGO, WIKI, and ICEWS18 datasets, we demonstrate the effectiveness of our model, compare the performance of our model with several different state-of-the-art methods and further analyze the properties of the proposed method. Show more
Keywords: Knowledge graph, link prediction, graph attention network, temporal
DOI: 10.3233/JIFS-210943
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7983-7994, 2022
Authors: Janardhan, G. | Surendra Babu, N. N. V. | Srinivas, G. N.
Article Type: Research Article
Abstract: A hybrid method for transformer-less grid-tie hybrid Renewable Energy Source (HRES), such as photovoltaic (PV) and wind energy system (WES) with minimization of common mode leakage current is proposed in this manuscript. The proposed system is the combined execution of Vascular Invasive Tumor Growth (VSTG) Optimization Algorithm and extreme gradient boosting (XGBOOST) named VSTG-XGBOOST control topology. The main intention of transformerless grid-connected HRES system is “to lessen the leakage current, maximum power point (MPP) extraction and maximal power point tracking (MPPT), the active and reactive power controller, and having the unity power factor. To attain the above-mentioned aims, the following …actions have been performed in this proposed work. Two turn-off snapper circuits are inserted parallel to the switches to share the input DC voltage among snubber capacitors. By then, VSTG is used to estimate the optimal gain parameters under various source currents as normal value is used to generate the optimal control signal database offline. Based on the attained dataset, the XGBOOST forecasts the optimal control signals of the grid-connected HRES inverter in the online way. This control technique allows two sources to supply the load separately depending on the availability of the energy sources and keeps common DC voltage constant. Show more
Keywords: Transformer-less grid-tie inverter, common mode leakage current, photovoltaic, Hybrid Renewable Energy Source, snubber capacitors
DOI: 10.3233/JIFS-213362
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7995-8019, 2022
Authors: Rashid, M.H.M. | Altaweel, Nifeen Hussain
Article Type: Research Article
Abstract: In this paper, we introduce a new fuzzy contraction mapping and prove that such mappings have fixed point in τ -complete fuzzy metric spaces. As an application, we shall utilize the results obtained to show the existence and uniqueness of random solution for the following random linear random operator equation. Moreover, we shall show the existence and uniqueness of the solutions for nonlinear Volterra integral equations on a kind of particular fuzzy metric space.
Keywords: Random fixed point, random operator, random operator equation, contractive mapping, fixed point, t-norm, fuzzy metric space, non-archimedean fuzzy metric space
DOI: 10.3233/JIFS-220258
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8021-8040, 2022
Authors: Thai, Pon L.T. | Merry Geisa, J.
Article Type: Research Article
Abstract: Cervical cancer is the most frequent and fatal malignancy among women worldwide. If this tumor is detected and treated early enough, the complications it causes can be minimized. Deep learning demonstrated significant promise when imposed on biomedical difficulties such as medical image processing and disease prognostication. Therefore, in this paper, an automatic cervical cell classification approach named IR-PapNet is developed based on Inception-ResNet which is an optimized version of Inception. The learning model’s conventional ReLu activation is replaced with the parametric-rectified linear unit (PReLu) to overcome the nullification of negative values and dying ReLu. Finally, the model loss function is …minimized with the SGD optimization model by modifying the attributes of the neural network. Furthermore, we present a simple but efficient noise removal technique called 2D-Discrete Wavelet Transform (2D-DWT) algorithm for enhancing image quality. Experimental results show that this model can achieve a top-1 average identification accuracy of 99.8% on the pap smear cervical Herlev datasets, which verifies its satisfactory performance. The restructured Inception-ResNet network model can obtain significant improvements over most of the state-of-the-art models in 2-class classification, and it achieves a high learning rate without experiencing dead nodes. Show more
Keywords: Cervical cancer, medical image processing, deep learning, 2D-DWT, ResNet model
DOI: 10.3233/JIFS-220511
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8041-8056, 2022
Authors: Chen, Zhihua | Kosari, Saeed | Kaarmukilan, S.P. | Yuvapriya, C. | Atanassov, Krassimir T. | Rangasamy, Parvathi | Rashmanlou, Hossein
Article Type: Research Article
Abstract: Video Processing has found enormous applications in recent times from security systems to interplanetary missions. In real-life situations, most of the videos are fuzzy/vague/uncertain. Intuitionistic fuzzy set (IFS) is one of the effective tools for handling uncertainty. Among many extensions of IFSs, temporal intuitionistic fuzzy sets (TIFSs) are very interesting as they are time-dependent. Hence, TIFSs are suitable to define a video, which is dynamic and hence depends on time-moment. In this way, this work introduces a novel VIdeo PROCessing (VIPROC) algorithm, using temporal intuitionistic fuzzy sets to enhance videos, which is first of its kind in existence. The comparison …is made with fuzzy contrast intensification operation. VIPROC algorithm is designed using contrast intensification operation for video enhancement. The results are encouraging in comparison with the original test videos. The results are discussed taking into account the several frames of the test video. Further, the proposed algorithm can be applied/extended to engineering applications like motion tracking, traffic detection systems, real time videos captured through mobile (hand-held) devices, and so on. As no such algorithms are existing which use TIFSs to process a video, the authors got motivated to design and develop VIPROC algorithm. Show more
Keywords: Temporal intuitionistic fuzzy sets, contrast intensification, VIdeoPROCessing (VIPROC) algorithm
DOI: 10.3233/JIFS-220928
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8057-8072, 2022
Authors: Annamalai, Tamizhselvi | Liju Anton, J. | Yoganathan, P.
Article Type: Research Article
Abstract: Intelligent transport system is a greatly emerging technology in recent years. The stability and reliability of these systems is very important. In vehicular ad-hoc networks (VANET), the data transmission process can be improved by employing clustering process. The nodes can be clustered in order to utilize the maximum bandwidth of the network and improving network stability. In VANETs it is to introduce road safety and driver safety. In addition, security is a major concern and the malicious nodes need to be accurately detected. Several kinds of attacks can present in the VANETs. Hence an efficient authentication method and trust aware …method is essentially required. In this work, stability assured CNN based trust aware clustering and authenticated transmission is introduced. For data authentication quantum cryptography technique is employed. In clustering process, trust degree of nodes is computed, vehicle speed is observed, direction of vehicle and distance among nodes are taken. In addition, for ensuring more safety, the critical data transmission is given higher priority. Therefore in clustering, data criticality parameter is also considered. For cluster formation, convolution neural network is employed. After the clustering process, the quantum cryptography based authentication is implemented for vehicle units and road side units. Data among these units are transmitted with quantum channel encryption key. Then simulation results are observed for validating the proposed protocol. Show more
Keywords: Vehicular ad-hoc networks, wireless communication, routing protocols, cryptography, convolution neural network
DOI: 10.3233/JIFS-220460
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8073-8087, 2022
Authors: Liu, Lu | Sun, Qiming | Jiang, Tianhua | Deng, Guanlong | Gong, Qingtao | Li, Yaping
Article Type: Research Article
Abstract: Recently, energy-saving scheduling issues have attracted more and more attention in the manufacturing field. Meanwhile, in practical production, maintenance planning is viewed as a vital task in the workshop. However, the existing literature about energy-saving scheduling problems rarely consider the effect of preventive maintenance. Therefore, this paper investigates an energy-saving flexible job shop scheduling problem with preventive maintenance. A mathematical model is proposed considering the minimization of total energy consumption. To solve the problem, a novel discrete elephant herding optimization algorithm (NDEHO) is proposed according to the problem’s characteristics. To test the NDEHO’s performance, the Taguchi design of experiment approach …is adopted to get the best combination of parameters in the algorithm. Numerical experiments are conducted based on twenty-four instances, including four benchmark instances and twenty randomly generated instances. Computational data indicate that NDEHO outperforms other compared algorithms for solving the considered problem. Show more
Keywords: Energy-saving scheduling, flexible job shop, preventive maintenance, total energy consumption, elephant herding optimization
DOI: 10.3233/JIFS-220494
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8089-8107, 2022
Authors: Cao, Wei Hang | Jiang, Jian
Article Type: Research Article
Abstract: In this paper, considering the heterogeneity of travelers’ decision-making behavior caused by travel environment factors, thus affecting the choice of travel path, the theories and methods of travel path choice based on improved cumulative prospect theory (ICPT) in complex environment were proposed. On the basis of cumulative prospect theory (CPT), the value function was improved, and the parameter value range was enlarged. The nonlinear curve of value function and weight function of cumulative prospect theory was fitted through thousands of data tests and experiments. Then according to the decision preference, the decision makers were divided into different categories and the …reference point value relationship of heterogeneous decision makers was found. In this paper, fuzzy travel time reference point and periodic dynamic risk degree reference point were set up, and a dynamic path selection model based on heterogeneous double reference point was established to improve the cumulative prospect value. Taking the highway network in Sichuan-Tibet region for example, the optimal path selection scheme of heterogeneous travel groups under the complex environmental factors such as debris flow and landslide in each time stage was studied, and the influence of preference parameter of travel time and risk degree on path choice was analyzed, and then the parameter sensitivity in the cumulative prospect theory (ICPT) was analyzed. The research results verified the rationality of the improved theory and method proposed in this study, which not only provide a new way of thinking for the study of travel path choice in complex environment but also provide theoretical guidance value for supporting regional traffic planning and construction in complex environment. Show more
Keywords: Path choice, complex environment, improved cumulative prospect theory, heterogeneous preference points, Sichuan-Tibet region
DOI: 10.3233/JIFS-220597
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8109-8126, 2022
Authors: Surya, R. | Mullai, M.
Article Type: Research Article
Abstract: Inventory managers are expected to handle a large number of items in their inventory while adhering to budgetary and space limits, as well as the number of items bought from vendors. Multi-item inventory models with one or more resource constraints, such as budget, space, or number of orders. This paper talks about an EOQ model in neutrosophic multi-item inventory control models with constraints. The ordering costs, the holding costs, demands, storage area, investment amount, and the maximum average number of units are considered as triangular neutrosophic numbers, as opposed to crisp values, to make the inventory model more realistic. This …idea is used to decide the neutrosophic optimal order quantities with the assistance of the Lagrange multiplier. Eventually, the proposed method is delineated with a numerical instance and the results are analysed briefly. Show more
Keywords: Inventory, space constraint, investment constraint, neutrosophic sets, triangular neutrosophic numbers
DOI: 10.3233/JIFS-221143
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8127-8136, 2022
Authors: Ni, Chenmin | Fam, Pei Shan | Marsani, Muhammad Fadhil
Article Type: Research Article
Abstract: GPS monitoring systems and the development of driverless vehicles are almost inseparable from camera images. The images taken by traffic cameras often contain certain sky areas and noise, the traditional dark channel prior (DCP) algorithm easily produces color distortion and halo effect, when processing the hazy traffic images with sky and high brightness areas. An optimized Retinex model and dark channel prior algorithm (ORDCP) is proposed in this paper. Firstly by adjusting the calculation method of dark channel image, the proportion of dark channel is improved; Then, the transmittance image is corrected and smoothed by guided filtering and mean filtering. …Finally, the Retinex model is fused to save the details.ORDCP corrects the inaccurate calculation of scene transmittance value in DCP algorithm,and modifies some dehazing problems, such as the loss of details, halo effect, contrast and color distortion,etc. Using information entropy (IE) as the objective evaluation index, combined with the subjective evaluation, it is concluded that the algorithm proposed in this paper can effectively retain the detailed information of the image, and eliminate the halo effect. Meanwhile, it meets the visual characteristics of human eyes better, and has some practicality and applicability in traffic control and intelligent detection. Show more
Keywords: Haze removal, traffic image, Retinex model, dark channel prior
DOI: 10.3233/JIFS-221240
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8137-8149, 2022
Authors: Jiang, Jian
Article Type: Research Article
Abstract: This research proposes a Pythagorean fuzzy multi-attribute decision-making evaluation method based on the improved cumulative prospect theory. The method ranks the decision-making results by calculating the comprehensive cumulative prospect value. Firstly, the research improves the cumulative prospect theory based on the utility curve, and describes the psychological and behavioral characteristics of various decision-making groups with different risk preferences. Then, a distance measure method based on the geometric center of the Pythagorean fuzzy right triangle is designed. The main core of the distance measure method is that it converts the Pythagorean fuzzy number into a Pythagorean fuzzy right triangle. In terms …of attribute weighting, this research proposes a subjective and objective weighting method based on the combination of value function and deviation method of improved cumulative prospect theory. Finally, the Pythagorean fuzzy multi-attribute decision-making method based on the improved cumulative prospect theory is realized through the selection of reference object, the calculation of value function value, weight function value and cumulative prospect value. The results analysis and the comparison with other methods verify the effectiveness and advancement of the proposed decision-making method, especially that the proposed method has good applicability for the decision-making cases where the attribute value is Pythagorean fuzzy number, the attribute weight is unknown, and the psychological behavior of decision makers cannot be reflected. Show more
Keywords: Ecological sustainable development, location selection of emergency rescue center, improved cumulative prospect theory, Pythagorean fuzzy number, subjective and objective weighting method, Sichuan-Tibet Railway
DOI: 10.3233/JIFS-221301
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8151-8175, 2022
Authors: Xu, Xinrui | Deng, Dexue
Article Type: Research Article
Abstract: The selection of suppliers is an important part of the construction of engineering projects in supply chain management. If the partners in the supply chain are reliable enough, they can promote the continuous progress of the supply and demand sides in the cooperation, thereby achieving a win-win situation, which is conducive to the realization of a virtuous cycle process. Material suppliers provide the required products and raw materials for the production and construction of enterprises. They are an important source of construction projects and occupy a very important position in the development of enterprises. The supply of high-quality products can …lay a good foundation for the subsequent production and construction of the project, thereby promoting the smooth completion of the entire project. Therefore, rational evaluation and selection of suppliers has very important practical significance. The selection and application of building material suppliers is a classic multiple attribute decision making (MADM). In this paper, we introduced some calculating laws on intuitionistic fuzzy sets (IFSs), Hamacher sum and Hamacher product and further propose the induced intuitionistic fuzzy Hamacher power ordered weighted geometric (I-IFHPOWG) operator. Meanwhile, we also study some ideal properties of built operator. Then, we apply the I-IFHPOWG operator to deal with the multiple attribute decision making (MADM) problems under IFSs. Finally, an example for physical health literacy evaluation of College students is used to test this new approach. Show more
Keywords: Multiple attribute decision making (MADM), intuitionistic fuzzy sets (IFSs), I-OWG operator, I-IFHPOWG operator, building material suppliers
DOI: 10.3233/JIFS-221869
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8177-8186, 2022
Authors: Shi, Haosu | Han, Lina | Fang, Linbo | Dong, Huan
Article Type: Research Article
Abstract: An improved algorithm of image defogging was proposed based on dark channel prior in order to solve the low efficiency and color distortion in the bright area using original algorithm. If the image contains large areas of bright areas such as sky, white clouds or partial white objects and water surface, we can know that the dark channel prior theory does not apply to these areas. Firstly, it is necessary to clear the bright area of the image. According to principle that he adjacent pixel attributes have similarity, the image transmittance of the local region also has similarity, Block function …is Consruted. Applied the dark channel prior, judging whether each block includes a bright area by the absolute value of difference of atmospheric intensity and dark channel, the dark and bright areas of the image are obtained. So the estimation value of the adaptive space transmittance are also obtained. Secondly, the transmittance of bright region is small and it causes deviation, so the enhancement formula is used to modify it dynamically. In order to preserve the edge details after image restoration, for bright areas, using texture function to optimize transmittance independently, for others, using gradient and texture function together. Finally, it restored the fog-free image applying the atmospheric scattering model. The experimental results showed that the restored image had obvious details and rich color and fast processing speed through the proposed algorithm. The algorithm can also be applied to outdoor visual systems, such as video surveillance, intelligent traffic and so on. Show more
Keywords: Dark channel prior (DCP), image defogging, gradient information, texture information, transmittance, atmospheric scattering model
DOI: 10.3233/JIFS-221521
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8187-8193, 2022
Authors: Zhang, Lei | Bai, Wei | Guo, Shize | Xu, Youwei | Jiang, Kaolin | Pan, Yu | Zheng, Qibin | Chen, Jun | Pan, Zhisong
Article Type: Research Article
Abstract: Because multiple domain cyberspace joint attacks are becoming more widespread, establishing a multiple domain cyberspace defensive paradigm is becoming more vital. However, although some physical domain and social domain information is incorporated in present approaches, total modeling of cyberspace is absent, therefore thorough modeling of cyberspace is becoming increasingly necessary. This paper proposed a knowledge graph based multiple domain cyberspace modeling approach. A knowledge graph of multiple domain cyberspace is produced by extracting multiple domain entity information and entity relations such as physical domain, social domain, network domain, and information domain, so that semantic information of multiple domain cyberspace may …be described consistently. At the same time, this paper proposed a user’s permissions reasoning method based on multiple domain cyberspace knowledge graph to address the user’s permissions reasoning that relies on artificial reasoning principles. Through the model learning knowledge graph triples characteristics and rules, and implementing automatic reasoning of user’s permissions, this proposed method can abandon the artificial model of writing reasoning rules, allowing the machine to learn the reasoning rules using machine learning and other methods. Experimental results showed that the proposed method can learn relevant reasoning rules and accomplish automated reasoning of user’s permissions, and that the method’s accuracy and recall rates are higher than those of path ranking and translating embeddings. Show more
Keywords: Multi-domain cyberspace, knowledge graph, unified semantic description, user’s permissions reasoning, intelligent
DOI: 10.3233/JIFS-211696
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8195-8206, 2022
Authors: Rikhtechi, Leila | Rafeh, Vahid | Rezakhani, Afshin
Article Type: Research Article
Abstract: One of the most significant issues in information security today is monitoring users’ behavior while accessing software resources. This paper proposes a new access control model based principally on user behavior as a sequence of events regarding the processes within the software. The proposed model consists of three main components. The first component analyses system logs for events triggered by each user’s access to the system. The second component provides a policy engine to determine the risk of permitting the subsequent access requested by the user. According to the access history, the third component, which reflects the user’s behavior and …the existing policies, determines the level of risk of any subsequent access of the user and acts accordingly. To generate the policies in the detection engine, a behavior-based risk management cycle is presented by applying the Ordered Weighted Averaging method to determine and rank the behavior-based risks. For modeling the behaviors, the BIZAGI Studio tool is utilized, and also for investigating all possible conditions. Kaggle and two random datasets are used to evaluate the accuracy of the proposed method. The results show an increase in the accuracy of the proposed method compared to recent research. Applying the proposed method creates more precise access control and enhances information confidentiality. Show more
Keywords: Users’ behavior, access control, ordered weighted averaging, behavior-based risk management, software
DOI: 10.3233/JIFS-212377
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8207-8220, 2022
Authors: Anand, R.
Article Type: Research Article
Abstract: This paper is to improve the privacy and security in the distributed virtual environment using blockchain technology. One of the feature it provides is greater security in the decentralized virtual environment. A key aspect of this technology is used for various fields like healthcare, finance, business and cloud environment. Key issue of the virtual environment is to protect the data privacy and security which is difficult to handle. To overcome this issue, a new security model to protect the virtual environment is created and will focus on different types of attacks in blockchain technology.
Keywords: Blockchain security, virtualization, virtual security, privacy
DOI: 10.3233/JIFS-212619
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8221-8231, 2022
Authors: Erdebilli, Babek | Aslan Özşahin, Selcen Gülsüm
Article Type: Research Article
Abstract: Facility location models have been studied in the literature for decades as an outstanding branch of supply chain planning. Set-covering facility location models are among the most commonly used approaches to establishing and running a distribution network. However, real-life brings uncertain and imprecise parameters that need to be reflected in the model systematically and computably to achieve more efficient and precise solutions. That’s why fuzzy set covering models have been introduced in the literature from various perspectives. This work aimed to handle real-life uncertainties in an unbiased and autonomous way and provide more precise solutions to fuzzy set-covering facility location …models in real-life contexts. Therefore, we propose a novel approach, adopting the autonomous fuzzy methodology consisting of fuzzy trapezoidal set coverage to minimize the cost of establishing new facilities. This work’s main innovative achievements are that i) the set-covering facility location models were equipped with autonomous uncertainty management ability, ii) the trapezoidal fuzzy set coverage constituted a perfect fit for the management of uncertainties in a realistic way in the model, and iii) the relevant fuzzification was executed without any human/expert intervention/supervision. The well-known Turkish Network Data demonstrated the proposed model’s efficacy. Furthermore, the results show that the developed model contributed to the overall theoretical framework of fuzzy approach employment in optimization models and outperformed classical version in numerical experiments. Show more
Keywords: Autonomous fuzzy optimization, data-driven set covering, autonomous fuzzy set covering, trapezoidal fuzzy set covering, trapezoidal fuzzy coverage
DOI: 10.3233/JIFS-213220
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8233-8246, 2022
Authors: Anu Shalini, T. | Sri Revathi, B.
Article Type: Research Article
Abstract: This paper presents the design of a grid connected hybrid system using modified Z source converter, bidirectional converter and battery storage system. The input sources for the proposed system are fed from solar and wind power systems. A modified high gain switched Z source converter is designed for supplying constant DC power to the DC-link of the inverter. A hybrid deep learning (HDL) algorithm (CNN-BiLSTM) is proposed for predicting the output power from the hybrid systems. The HDL method and the PI controller generates pulses to the proposed system. The superiority of the proposed hybrid DL method is compared with …the conventional DL methods like CNN, LSTM, BiLSTM methods and the performance of the hybrid system is validated. A closed loop control framework is implemented for the proposed grid integrated hybrid system and its performance is observed by implementing the PI, Fuzzy and ANN controllers. A 1.5Kw hybrid system is designed in MATLAB/SIMULINK software and the results are validated. A prototype of the proposed system is developed in the laboratory and experimental results are obtained from it. From the simulation and experimental results, it is observed that the ANN controller with SVPWM (Space vector Pulse width Modulation) gives a THD (Total harmonic distortion) of 2.2% which is within the IEEE 519 standard. Therefore, from the results it is identified that the ANN-SVPWM method injects less harmonic currents into the grid than the other two controllers. Show more
Keywords: Power forecasting, timeseries forecasting, bidirectional long short-term memory, convolution neural network, renewable power generation
DOI: 10.3233/JIFS-220307
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8247-8262, 2022
Authors: Radhakrishnan, C. | Asokan, R.
Article Type: Research Article
Abstract: To safeguard private information, image steganography is extensively used. Research is focused on ways to enhance steganographic technologies so that they may increase compression ratio while maintaining steganography image integrity. Because of its essential qualities such as security, scalability, and robustness, Steganography is a preferred way of communicating protected secret information to prevent hacking and misuse. This proposed research offers a steganography approach based on Enhanced Chaotic Particle Swarm Optimization (ECPSO), which uses chaos theory to determine the optimal pixel positions in the cover picture to hide confidential information when keeping the steganography quality in the images. Both the cover …and secret pictures are separated into blocks to increase hiding capacity, with each component storing a sufficient quantity of secret data by mapping the pixels. The suggested ECPSO-Stegano system has better results with the criteria of Mean Square Error (MSE) of 0.00018%, Peak-Signal-to-Noise-Ratio (PSNR) of 79.66%, Bit Error Rate (BER) of 0.45% in average, and Structural Similarity Index (SSI) of 0.98 in average for various input size. It’s also robust to statistical threats. Show more
Keywords: Chaos map, BET, Stego-image, blocks, optimal pixel, confidentiality
DOI: 10.3233/JIFS-221093
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8263-8273, 2022
Authors: Sammeta, Naresh | Parthiban, Latha
Article Type: Research Article
Abstract: In recent times, a number of Internet of Things (IoT) related healthcare applications have been deployed for automating healthcare services and offering easy accessibility to patients. Several issues like security, fault-tolerant, and reliability have restricted the utilization of IoT services in real-time healthcare environments. To achieve security, blockchain technology can be utilized which offers effective interoperability of healthcare databases, ease of medical data access, device tracking, prescription database, hospital assets, etc. Therefore, this paper presents an optimal Elliptic curve cryptography-based encryption algorithm for a blockchain-enabled medical image transmission model, named OECC-BMIT. The presented OECC-BMIT model involves different stages of operations …such as encryption, optimal key generation, blockchain-enabled data transmission, and decryption. Firstly, the OECC-BMIT model performs Elliptic curve cryptography (ECC) based encryption technique to securely transmit the medical images. In order to generate the optimal set of keys for the ECC technique, modified bat optimization (MBO) algorithm is applied. Then, the encrypted images undergo secure transmission via blockchain technology. The encrypted images are decrypted on the recipient side and the original medical image is reconstructed effectively. Extensive sets of experimentations were performed to highlight the goodness of the OECC-BMIT algorithm and the obtained results pointed out the improved outcome over the state of art methods in terms of different measures. Show more
Keywords: Blockchain, encryption, healthcare, medical images, optimal key generation
DOI: 10.3233/JIFS-211216
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8275-8287, 2022
Authors: Ren, Yaxue | Wen, Yintang | Liu, Fucai | Zhang, Yuyan
Article Type: Research Article
Abstract: Chaotic systems are dynamic systems with aperiodic and pseudo-random properties, and systems in many fields exhibit chaotic time-series properties. Aiming at the fuzzy modeling problem of chaotic time series, this paper proposes a new fuzzy identification method considering the selection of important input variables. The purpose is to achieve higher model modeling and prediction accuracy by constructing a model with a simple structure. The relevant input variable was swiftly chosen in accordance with the input variable index after the Two Stage Fuzzy Curves method was used to determine the weight of the correlation between each input variable and the output …from a large number of selectable input variables. The center and width of the irregular Gaussian membership function were then optimized using the fuzzy C-means clustering algorithm and the particle swarm optimization technique, which led to the determination of the fuzzy model’s underlying premise parameters. Finally, the fuzzy model’s conclusion parameters were determined using the recursive least squares method. This model is used to simulate three chaotic time series, and the outcomes of the simulation are contrasted and examined. The outcomes demonstrate that the fuzzy identification system has higher prediction accuracy based on a simpler structure, demonstrating its validity. Show more
Keywords: Fuzzy identification, input variable selection, chaotic time series, fuzzy c-means algorithm, irregular gaussian function
DOI: 10.3233/JIFS-212527
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8289-8301, 2022
Authors: Alshareef, Esam Alsadiq | Ebrahim, Fawzi Omar | Lamami, Yosra | Milad, Mohamed Burid | Eswani, Mohamed S.A. | Bashir, Sedigh Abdalla | Bshina, Salah A.M. | Jakdoum, Anas | Abourqeeqah, Asharaf | Elbasir, Mohamed O | Elbahrit, Ellafi.A.
Article Type: Research Article
Abstract: Knee osteoarthritis severity grading from plain radiographs is of great significance in the diagnosis of osteoarthritis (OA). Recently, deep learning had a great impact on improving the Kellgren and Lawrence (KL) grading scheme of Knee osteoarthritis KOA using models that acquire the contextual features spontaneously without the need for any conventional high computational spatial configuration modeling. In this study, we apply the state-of-art Vision Transformer (ViT) for the KL grading of Knee Osteoarthritis and show that a simple transfer learning approach of such model can lead to better results than those achieved by other complex architectures over less number of …training data. The study concludes that such a pre-trained ViT, fine-tuned on OAI dataset yield to promising results in KL grading KOA, in which these results are in line with the state-of-art studies. Show more
Keywords: Knee, severity, radiographs, grading, models, feature
DOI: 10.3233/JIFS-220516
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8303-8313, 2022
Authors: Harikumar, Sandhya | Sathyajit, Rohit | Karumudi, Gnana Venkata Naga Sai Kalyan
Article Type: Research Article
Abstract: News feeds generate colossal amount of data consisting of important information hidden in the intricacies. State of the art methods are still at infancy in providing a very generic and publicly available solution to skim through the important information in the news from various sources and an ability to search using specific keywords in different languages. This paper focuses on designing a tool to extract semantic details from news articles published through various internet sources in various languages. The semantic information is stored within DBMS for ease of organizing and retrieving the data. Further, a querying facility to search through …entire articles based on the keyword or date-based search is also proposed to view the crisp content. The news articles in English, and two Indian languages - Hindi and Malayalam are considered for experimentation. The proposed strategy consists of two main components namely, Generative model creation and Query engine. Generative model aims to extract important entities and keywords along with their relevance to the article and other similar articles using Latent Dirichlet Allocation(LDA) and Named Entity Recognition(NER). Query engine is to facilitate on the fly retrieval of semantic content from the database, based on user keyword. The search engine, along with database indexing, reduces the access time to the database thereby retrieving the information in less time. Experimental results show that the proposed method is effective in terms of quality of information and time consumed for information retrieval. Show more
Keywords: News analytics, multilingual, natural language processing(NLP), Latent dirichlet allocation(LDA), semantic information retrieval
DOI: 10.3233/JIFS-221184
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8315-8327, 2022
Authors: Senthamizh Selvi, S. | Anitha, R.
Article Type: Research Article
Abstract: In India, most of the Science and Technology resources available are in English. Developing an Automatic Language Translation Engine from English (source language) to Tamil (target language) is very essential for the people who need to get technical resources in their native language. The challenges in designing such engines using Natural Language Processing (NLP) tools include Lexical, Structural, and Syntax level ambiguity. To solve these challenges, the development of a Part-Of-Speech (POS) tagger is essential. The Verb-Framed languages like Tamil, Japanese, and many languages in Romance, Semitic, and Mayan languages families have high morphological richness but lack either a large …volume of annotated corpora or manually constructed linguistic resources for building POS tagger. Moreover, the Tamil Language has a low resource, high word sense ambiguity, and word-free order form giving rise to challenges in designing Tamil POS taggers. In this paper, we postulate a Hybrid POS tagger algorithm for Tamil Language using Cross-Lingual Transformation Learning Techniques. It is a novel Mining-based algorithm (MT), which finds equivalent words of Tamil in English on less volume of English-Tamil bilingual unannotated parallel corpus. To enhance the performance of MT, we developed Tamil language-specific auxiliary algorithms such as Keyword-based tagging algorithm (KT) and Verb pattern-based tagging algorithm (VT). We also developed a Unique pair occurrence-tagging algorithm (UT) to find the one-time occurrence of Tamil-English pair words. Our experiments show that by improving Context-based Bilingual Corpus to Bilingual parallel corpus and after leaving one-time occurrence words, the proposed Hybrid POS tagger can predict 81.15% words, with 73.51% accuracy and 90.50% precision. Evaluations prove our algorithms can generate language resources, which can improve the performance of NLP tasks in Tamil. Show more
Keywords: Natural language processing, part-of-speech tagger, sandhi, bilingual parallel corpus, cross-lingual transformation learning
DOI: 10.3233/JIFS-221278
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8329-8348, 2022
Authors: Rajesh, D. | Kiruba, D. Giji
Article Type: Research Article
Abstract: A basic need in smart dust network is to accomplish energy proficiency during routing as sensor nodes have rare energy asset. Node’s mobility in smart dust represents a challenge to intend energy proficient routing algorithm. Clustering accomplishes energy effectiveness by diminishing association complication aloft of network is comparative to quantity of moveable smart dust nodes in network. This research methodology proposes novel Energy Efficient Secured CH Clustered Routing (E2 SCR) in Smart Dust tactic. A smart dust node is chosen as cluster head in event that it has high superfluous energy, better communication range and low mobility. Energy responsive (ER) …selection method and Maximal Nodal Superfluous Energy assessment method combined with this method to enhance energy conception during routing. Simulation results demonstrate that proposed clustering and routing algorithm is unique and energy efficient smart dust network. Show more
Keywords: Smart dust, clustering, cluster-head, superfluous energy, energy responsive
DOI: 10.3233/JIFS-212012
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8349-8357, 2022
Authors: Gopinath, N. | Prayla Shyry, S.
Article Type: Research Article
Abstract: As technology advances, it becomes easier to share large amounts of data over the internet. Cloud computing is one of the technologies that allows for easy data sharing over the internet. It is critical to provide security for this data when they are being shared across the internet. The security of data saved in cloud storage, as well as data transport and transmitting a key required to encrypt data between two parties, has been a source of concern for the industry, as a result of the growing use of cloud services in recent years. Collective attacks are significantly more powerful …than individual strikes, according to our research. Despite the fact that additional research works were studied in the previous literature review, there are some study concerns for not correcting third-party data hacking. Therefore, this paper focuses on the design of Secured Quantum Key Distribution (SQKD) with Fuzzy logic to improve the security of the shared key. Quantum Key Distribution, Post Quantum Key Distribution, and the EPR Proto-col are technologies that increase the security of data sharing. We have incorporated the Secured Quantum Key Distribution (SQKD) with Fuzzy logic in our proposed work to improve the security of the shared key. The proposed systems include some additional characteristics in addition to the existing approaches. The proposed model uses shifting algorithms and the fuzzification procedure to assure the security of the secret key in the Fuzzification of Quantum Key approach. The experimental results states that the mean value of security losses in SFQ is 1.8306051, and the mean value of QKD is 14.6448416, with standard deviations of 1.7329 and 13.863 for SFQ and QKD, respectively. Show more
Keywords: Quantum key distribution, fuzzy logic, SQKD, Q-bits and quantum cryptography
DOI: 10.3233/JIFS-220398
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8359-8369, 2022
Authors: Wei-Jie, Lucas Chong | Chong, Siew-Chin | Ong, Thian-Song
Article Type: Research Article
Abstract: Masked face recognition embarks the interest among the researchers to find a better algorithm to improve the performance of face recognition applications, especially in the Covid-19 pandemic lately. This paper introduces a proposed masked face recognition method known as Principal Random Forest Convolutional Neural Network (PRFCNN). This method utilizes the strengths of Principal Component Analysis (PCA) with the combination of Random Forest algorithm in Convolution Neural Network to pre-train the masked face features. PRFCNN is designed to assist in extracting more salient features and prevent overfitting problems. Experiments are conducted on two benchmarked datasets, RMFD (Real-World Masked Face Dataset) and …LFW Simulated Masked Face Dataset using various parameter settings. The experimental result with a minimum recognition rate of 90% accuracy promises the effectiveness of the proposed PRFCNN over the other state-of-the-art methods. Show more
Keywords: Covid-19, PRFCNN, random forest, principal component analysis, convolutional neural network
DOI: 10.3233/JIFS-220667
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8371-8383, 2022
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