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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Shukla, Shilpi | Jain, Madhu
Article Type: Research Article
Abstract: Human emotion recognition with the evaluation of speech signals is an emerging topic in recent decades. Emotion recognition through speech signals is relatively confusing because of the speaking style, voice quality, cultural background of the speaker, environment, etc. Even though numerous signal processing methods and frameworks exists to detect and characterize the speech signal’s emotions, they do not attain the full speech emotion recognition (SER) accuracy and success rate. This paper proposes a novel algorithm, namely the deep ganitrus algorithm (DGA), to perceive the various categories of emotions from the input speech signal for better accuracy. DGA combines independent component …analysis with fisher criterion for feature extraction and deep belief network with wake sleep for emotion classification. This algorithm is inspired by the elaeocarpus ganitrus (rudraksha seed), which has 1 to 21 lines. The single line bead is rarest to find, analogously finding a single emotion from the speech signal is also complex. The proposed DGA is experimentally verified on the Berlin database. Finally, the evaluation results were compared with the existing framework, and the test result accomplishes better recognition accuracy when compared with all other current algorithms. Show more
Keywords: Speech signal, emotion recognition, deep analysis, deep ganitrus algorithm, recognition accuracy
DOI: 10.3233/JIFS-201491
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5353-5368, 2022
Authors: Deshwal, Deepti | Sangwan, Pardeep | Dahiya, Naveen | Nehra, Neelam | Dahiya, Aman
Article Type: Research Article
Abstract: Good feature representation is the chief requirement for improving Language Identification (LID) system recognition performance. In this work LID system for Indian languages is proposed based on unsupervised feature learning utilizing Deep Belief Network (DBN). The proposed methodology is implemented in two parts. The first phase of this work is based on extracting MFCC features combined with SDC hybrid features. The resultant hybrid features are further stacked to Deep Belief Network (DBN). The second phase of the proposed work is investigating the performance of various Feed forward back propagation neural network models for classification using different training algorithms. Effect of …combining different activation functions and varying the hidden neurons is also investigated The performance of the resultant models is evaluated on the basis of some performance metrics such as the epochs, training time, Mean Square Error, Regression and Mean Absolute Percentage Error. Results indicate that optimal performance is achieved in model trained with Levenberg Marquardt (LM) training algorithm. The activation functions used in the hidden and output layer are “tansig” and “purelin”. Similarly, the effect of varying the number of neurons in the hidden layer is not significant in improving the performance of the derived models. FFBPNN models trained with PL and TS activation functions gave best performance indices. A user defined language database in four different languages Hindi, English, Tamil and Malayalam is used for this work. Show more
Keywords: Language identification system, deep belief network, hybrid features, learning algorithm, activation function
DOI: 10.3233/JIFS-210186
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5369-5385, 2022
Authors: Balasubramanian, Suganya | Akila, I.S.
Article Type: Research Article
Abstract: Agricultural Food Supply chain management has arisen as an area of day-to-day importance for the agricultural food sector, since stakeholders involved in the execution of decision-making processes. Quality less agricultural products are added to the market in day-to-day life which leads to usage of chemicals in the production process. These leads to the major issues that gives the impact on agricultural product’s quality as well as overall well-being of the consumers. Devices are needed to identify the quality of the food products which are highly demanded due to the lack of transparency in the recent processes. Henceforth Blockchain technology is …evolving as a decentralized and secure infrastructure which could replace involvement of a third party to verify the transactions inside the system. The purpose of the proposed work is to implement a Blockchain based solution i.e. constructing a Decentralized Application (DApp) using Hyperledger Fabric framework to verify the food quality and the cause of the agricultural supply chain. A private permissioned Blockchain concept is chosen instead of a public Blockchain in the proposed work to ensure transparency and secure transaction by consenting any person to access the network. Smart contract chain code was instantiated for the deployment of Blockchain network. All the performers who are involved in the supply chain must be able to interact with the system to achieve the transparency. Transaction and queries related to a food product are validated by peers of the Blockchain network. A Barcode & QR code-based scanning mechanism is used to indicate the customer’s satisfaction with their products. Transactions without third party gives Farmer’s reputation for their products. A unique DApp mechanism is used to identify each product within the food supply chain. Thus, the proposed system has been implemented as a prototype and validated using smart contracts. Show more
Keywords: Blockchain, agri-food supply chain, traceability, hyperledger fabric, smart contracts
DOI: 10.3233/JIFS-211265
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5387-5398, 2022
Authors: Magdin, Martin | Sulka, Timotej | Fodor, Kristián
Article Type: Research Article
Abstract: The paper deals with the issue of classification of emotional state from speech. Due to the applied k-NN algorithm, the original solution achieved an overall classification success in the range of 20 to 35%, depending on the used audio sample input data database. In the original application, we have used the Praat program to extract the characteristics. In the current version of the application, the use of Praat has been eliminated and we have developed our solution based on neural networks. Therefore, 3 experiments with forward, 1 and 2D convolutional neural networks were performed to determine the overall success of …the classification. Their common feature is that the prediction success was always highest in tests with a test subset of the RAVDESS database, with the best result being obtained using a 1D convolutional network (78.93%). Tests with the EMO-DB database were successful at 35.76%, 31.75% and 25.49%. In all three experiments, the worst results were obtained in tests with the SAVEE database - 20.24%, 18.45% and 22.02%. Show more
Keywords: EmoRec2, real time classification, databases, speech, neural nets
DOI: 10.3233/JIFS-211402
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5399-5415, 2022
Authors: Chen, Lei | Han, Jun | Tian, Feng
Article Type: Research Article
Abstract: The registration of the infrared (IR) image and the low-light-level (LLL) image remains a challenging problem due to poor dispersion of feature points, low correlation of structure and texture information. In this paper, we propose a method based on neighbourhood difference chain code to address the challenge. First we extracted the feature points of the images with the binary eight or sixteen-neighborhood information. And then construct the descriptor of the feature point by neighborhood difference chain code. At last we use the Euclidean distance to match the feature points. We adopt TNO and INO data sets to verify our method, …and by comparing with four objective evaluation parameters obtained by other three methods. The result demonstrated that the proposed algorithm performs competitively, compared to the state-of-arts such as Harris, SIFT and SURF, in terms of accuracy of registration and speed. Show more
Keywords: IR and LLL images, feature points, chain code, neighborhood difference chain code
DOI: 10.3233/JIFS-211503
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5417-5430, 2022
Authors: Lu, Jun | Liu, Yangyang
Article Type: Research Article
Abstract: Since the implementation of the Private Education Promotion Law in China, reasonably evaluating the competence of private higher-learning institutions (PHLIs) has become an urgent issue. Based on an analysis of the advantages and disadvantages of the existing evaluation methods and index system, this paper proposes a comprehensive method for evaluating the competitiveness of private colleges and universities. The evaluation index system is constructed, and private colleges and universities are then evaluated by means of the best worst method (BWM) and vague set theory. Finally, S university in Zhejiang Province is evaluated as an example. The results show that the university …has strong competitiveness in operating its schools, but the quality of the schools and its ability to operate them need to be strengthened. Compared with other experimental approaches, this method can be used for effective and reasonable evaluation of the competitiveness of private universities. Show more
Keywords: Competitiveness evaluation, vague set theory, best worst method, private higher-learning institutions
DOI: 10.3233/JIFS-211612
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5431-5441, 2022
Article Type: Research Article
Abstract: By a fuzzy preorder, it means a (0, 1]-binary relation on a nonempty set which is self-reflective and transitive. For a fuzzy preorder on a universe, this paper constructs a kind of fuzzy rough set model based on the multiplication and division of real numbers. The definable sets and the related fuzzy topology are studied.
Keywords: Fuzzy preorder, rough set, approximation operator, definable set
DOI: 10.3233/JIFS-211709
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5443-5451, 2022
Authors: Dung, Ngo Q. | Viet, Le H.
Article Type: Research Article
Abstract: Nowadays, the number and types of IoT devices are increasing rapidly, which leads to an expansion in the attack surface of this kind of device. Besides, the number of Botnet malware on IoT devices also grows with a lot of new variants. This context leads to an urgent demand for an effective solution in detecting new variants of IoT Botnet malware. There have been many studies focusing on IoT Botnet malware detection using static and dynamic analysis. In particular, the combination of the dynamic method with machine learning has shown outstanding advantages to detect IoT Botnet variants. However, the preprocessing …of behavioral data originated from malware is still complicated, and the number of input vector dimensions of the machine learning model is still huge. In addition, these models also consume a lot of resources and have limited detection capabilities. Besides, dynamic analysis studies based on system calls mostly use call frequency characteristics and have not effectively exploited IoT Botnet malware’s life cycle characteristics. In this paper, we propose the Directed System Call Graph (DSCG) feature to sequentially structure the system calls. This DSCG graph will be vectorized and used as an input for building a malware analysis model based on popular machine learning classifiers such as KNN, SVM, Decision Tree, etc. Experiments on the datasets demonstrate that the features extracted from this graph have low complexity but still ensure high accuracy in detecting IoT Botnets, especially with newly emerged IoT Botnet families. The proposed model was evaluated with ACC = 98.01 % , TPR = 97.93 % , FPR = 1.5 % , AUC = 0.9961 on a dataset of 5023 IoT Botnets and 3888 benign samples. Show more
Keywords: IoT Botnet, features extraction, system calls, machine learning, malware detection
DOI: 10.3233/JIFS-211882
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5453-5470, 2022
Authors: Abbas, Qaisar
Article Type: Research Article
Abstract: Due to the wide range of diseases and imaging modalities, a retrieving system is a challenging task to access the corresponding clinical cases from a large medical repository on time. Several computer-aided systems (CADx) are developed to recognize medical imaging modalities (MIM) based on various standard machine learning (SML) and advanced deep learning (DL) algorithms. Pre-trained models like convolutional neural networks (CNN) are used in the past as a transfer learning (TL) architecture. However, it is a challenging task to use these pre-trained models for some unseen datasets with a different domain of features. To classify different medical images, the …relevant features with a robust classifier are needed and still, it is unsolved task due to MIM-based features. In this paper, a hybrid MIM-based classification system is developed by integrating the pre-trained VGG-19 and ResNet34 models into the original CNN model. Next, the MIM-DTL model is fine-tuned by updating the weights of new layers as well as weights of original CNN layers. The performance of MIM-DTL is compared with state-of-the-art systems based on cancer imaging archive (TCIA), Kvasir and lower extremity radiographs (LERA) datasets in terms of statistical measures such as accuracy (ACC), sensitivity (SE) and specificity (SP). On average, the MIM-DTL model achieved 99% of ACC, SE of 97.5% and SP of 98% along with smaller epochs compare to other TL. The experimental results show that the MIM-DTL model is outperformed to recognize medical imaging modalities and helps the healthcare experts to identify relevant diseases. Show more
Keywords: Medical images classification, deep learning, data augmentation, transfer learning, fine tuning, hybrid features
DOI: 10.3233/JIFS-212171
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5471-5486, 2022
Authors: Kumar, Prashant | Shakti, Shivam | Datta, Naireet | Sinha, Shashwat | Ghosh, Partha
Article Type: Research Article
Abstract: Cloud Computing is the distribution of computing resources on demand to the users over Internet. But with virtual existence of data and resources comes the problem of privacy and security. In such environments Intrusion Detection System (IDS) comes in handy. They read huge chunks of data to find out attack patterns. But learning through this huge amount of data is very time consuming. So, data reduction is necessary. Using feature selection methods, number of features can be reduced by eliminating redundant and irrelevant attributes from datasets. In this paper the authors have proposed a Penalty Reward based Ant Colony Optimization …(PRACO) method for feature selection. The penalty and reward terms used in this paper help in better exploration-exploitation trade-off by rewarding the useful features and penalizing the other ones. Along with that the concepts of max-relevance and min-redundancy are also used to indicate interactions between selected features. The proposed model is assessed on 10% KDD Cup 99, NSL-KDD and UNSW-NB15 datasets. It was observed that the PRACO method achieved 81.682% and 83.584% accuracy on average during train-test phase using NSL-KDD and UNSW-NB15 datasets. The results provide substantial evidence that the proposed model is effective in finding optimal results and thus provide IDS with increased efficiency. Show more
Keywords: Cloud computing, Intrusion Detection System (IDS), feature selection, Penalty Reward based Ant Colony Optimization (PRACO), NSL-KDD
DOI: 10.3233/JIFS-212196
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5487-5500, 2022
Authors: Batista Contarato, Rodrigo | Pereira, Rogério Passos do Amaral | Valadão, Carlos Torturella | Cuadros, Marco A.S.L. | Salles, José Leandro Felix | Almeida, Gustavo Maia de
Article Type: Research Article
Abstract: The generalized predictive controller (GPC) is an efficient strategy for controlling processes with time-varying parameters, as long as the GPC tuning parameters are chosen correctly. This study aims to present a new online tuning algorithm for the parameters of the GPC. The controllers are initially tuned by a model simulation (offline), via genetic algorithm, seeking quick answers and a small error. After variations in the setpoint, injection of disturbances in the output of the plant, and variations in the gains of the system operating in closed loop, the algorithm performs an online adjustment of these parameters using Fuzzy Logic. Based …on the error information between the setpoint and the controlled variable and the variation of this error, the algorithm readjusts the tuning parameters of the GPC, so the performance of the control system response is not degraded. The algorithm is validated via model simulations representing the main characteristics of industrial plants. In the simulations, tests are presented by applying disturbances in the output of the plant, changing the dynamics of the model, and changing the setpoint. It is shown that the performance indexes of each plant are presented as being at least similar to those presented in [1 ], because it is still widely used in recent applications, and in some cases of variation of the dynamics of the plant, the proposed algorithm remained with a satisfactory result, while the presented by [1 ] became unstable. Show more
Keywords: Predictive control, fuzzy logic, tuning algorithm, process control
DOI: 10.3233/JIFS-212322
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5501-5513, 2022
Authors: Qu, Shaojian | Jiang, Shan | Feng, Can
Article Type: Research Article
Abstract: The principle of maximum utility is generally adopted to design the optimal insurance contracts, which should consider the influence of different factors such as the probability of accident, premium, compensation, and so on. However, most literatures deal with these variables from a static perspective. This paper considers the accident probability and the value of insurance subject based on the time of accident, which is rarely involved in the previous studies and considers the utility function of the insurer and the policyholder from a dynamic perspective. Firstly, to make this model more universally applicable, we establish an insurance model that considers …the time of the accident and different premium payment forms for policy-holders and insurers respectively. Next, we derive a robust premium insurance model based on min-max regret, in which the time of the accident can be assumed to be certain and uncertain respectively. Then, we conduct numerical experiments and analyze the utility of the policy-holders, demonstrating guarantee period and value of insurance subject are significant when insuring and derive the optimal coverage rate. These results also show that the insurance model that takes into account the time of accident performs better. Show more
Keywords: Time of accident, premium payment, value of insurance subject, min-max regret
DOI: 10.3233/JIFS-212391
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5515-5534, 2022
Authors: Raja, K. | Patan, Muzeeb Khan | Ahmed, Md. Azahar | Ganeshan, P.
Article Type: Research Article
Abstract: Integration of renewable energy sources into existing grid influence the stability of the power system. This article introduces the application of cascade controller in hybrid power system which enhance the frequency stability during power perturbations of the load and generation. For this study, a thermal power unit is considered with integration of a microgrid consist of regular diesel generator, renewable power generating units, energy storage and other power managing devices. Proportional-integral and proportional-integral-derivative (PI-PID) cascade controller is provided for this hybrid power system to reduce the frequency oscillations during system uncertainties. The optimal values of the PI-PID controller are achieved …by using water evaporation optimization (WEO) algorithm with fast convergence rate. Investigations are carried out in different scenarios of the IM and results are compared with the PID controller to showcase the advantages of the cascade controller for frequency regulation. Simulations are carried out in MATLAB-SIMULINK® software environment. Show more
Keywords: Frequency control, microgrid, PI-PID cascade controller, water evaporation algorithm
DOI: 10.3233/JIFS-212434
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5535-5549, 2022
Authors: Peng, Peng | Ni, Zhiwei | Wu, Zhangjun | Zhu, Xuhui | Xia, Pingfan
Article Type: Research Article
Abstract: In order to further improve the enthusiasm of spatial crowdsourcing workers, considering the service quality of workers, different incentive strategies are proposed and tasks are assigned. Firstly, the incentive model is constructed from the unit time revenue of task and online idle time, and the evaluation function of the evaluation model is constructed; Secondly, the task allocation is transformed into a combinatorial optimization problem by delay matching, and an improved glowworm swarm algorithm is proposed to solve the problem by discrete coding, introducing six kinds of mobile modes, adaptive probability matching and infeasible solution processing; Finally, the algorithm is used …to solve the task allocation. The experimental results show that compared with the travel cost minimization strategy and random allocation strategy, the positive incentive index of the proposed strategy is improved by 11.79% and 14.60% respectively, and the fair incentive index is improved by 0.83% and 0.22% respectively, which can effectively improve the positive incentive range and incentive fairness of workers. Show more
Keywords: Spatial crowdsourcing, service quality, task assignment, glowworm swarm algorithm
DOI: 10.3233/JIFS-212531
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5551-5566, 2022
Authors: Çoban, Sezer | Kiracı, Kasım | Akan, Ercan | Uzun, Metin
Article Type: Research Article
Abstract: Unmanned Aerial Vehicles (UAVs) are increasingly used in the military field. Especially in recent years, UAVs have been a very effective instrument in gaining airspace superiority and military success. Many countries compete with each other to develop better UAV technology or improve the technical features of UAVs. Therefore, it is critical to determine which UAV has the best performance, considering technical and operational characteristics, because the vehicles with more advanced performance can provide countries with strategic superiority. The purpose of this study is to investigate the technical, cost, and operational performance of Medium Altitude Long Endurance UAVs (MALE UAVs). In …the study, as a result of a wide literature review, we determined a performance criterion for this type of vehicle. The model presented here uses an Interval Type-2 Fuzzy Analytical Hierarch Process (IT2FAHP) and an Interval Type-2 Fuzzy Technique for Order of Preference by Similarity to an Ideal Solution (IT2FTOPSIS) hybrid method. The findings indicate that some MALE UAVs have superior technical and operational performance over others and demonstrate that range, max take-off weight, and payload are important criteria in determining the performance and superiority of these vehicles. Show more
Keywords: MALE UAV selection, AHP, TOPSIS, interval type-2 fuzzy sets
DOI: 10.3233/JIFS-212574
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5567-5594, 2022
Authors: Mishra, Atul | Shaikh, Soharab Hossain | Sanyal, Ratna
Article Type: Research Article
Abstract: In natural language processing, multiword expressions (MWEs) play a significant role in understanding the context and meaning of a sentence. A MWE comprises two or more words that are handled as if they were one. MWE has the property that the constituent words are consistent and are often used in related contexts. Hindi is used as a case study. We employed three properties: linguistic or syntactical pattern, a relationship between constituent words, and context similarity and proposed a three-phase hybrid approach to extract MWE from unstructured Hindi text. Experimental analysis and comparison of results on the TDIL dataset show the …superiority of the proposed hybrid method over the context-based method and association-based methods. Show more
Keywords: Association score, hybrid method, cross-lingual information retrieval, rule extraction
DOI: 10.3233/JIFS-212595
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5595-5605, 2022
Authors: Pavendan, K. | Nagarajan, V.
Article Type: Research Article
Abstract: Biological wastewater treatment with the use of algae-bacteria consortia for the uptake of nutrient and recovery of resource is considered as the ‘paradigm shift’ from the process of mainstream wastewater treatment plants (WWTPs) so as to mitigate the pollution and thus promoting the circular economy. In this regard, the application of machine learning algorithms (MLAs) was found to be effectual and beneficial for the prediction of uncertain performances in the process of treatment and it shows a satisfactory result for the effective optimization, monitoring, uncertainty prediction and so on in the environment systems. The proposed approach aims at modelling the …treatment of wastewater, growth of micro algae and flocculation harvesting at the photobioreactor (PBR) along with the utilization of machine learning techniques. Initially, the raw data from the PBR was taken and is pre-processed using z-score normalization technique followed by extraction and selection of features that are more appropriate. The Adaptive neuro-fuzzy inference system (ANFIS) model is built along with the modified Fuzzy C-Means algorithm (MFCM) so as to cluster the huge amount of data. ANFIS is employed for the estimation of controller output parameters and for controlling the temperature inside the reactor. The output controller parameter performance can be enhanced by the use of optimization approach. The discrete Multilayer perceptron (DMLP) with the hyper tuning parameters of Iterative Levi’s Flight Dependent Cuckoo search optimization algorithm (ILF-CSO) is employed for the prediction purpose of attained cultivation growth rate and the pH of treated wastewater. The optimization technique based on machine learning model in turn offers the best possible solution needed for the estimation of output parameters. Thus, the removal rate of effluent T-N concentrations from the wastewater treatment is predicted with some intervals of day. At last, the performance is estimated in terms of growth rate, temperature variations, biomass, nitrate and phosphate concentrations, and error rates (RMSE, APE), and determination coefficient (R2). The attained outcome shows that the presented model is effectual and has the potential to apply for controlling and predicting the biological wastewater treatment plants. Show more
Keywords: Wastewater treatment, micro algae growth, flocculation harvesting, photo-bioreactor, modified fuzzy C-Means, discrete multilayer perceptron, ILF-CSO, effluent T-N concentrations, biomass production
DOI: 10.3233/JIFS-212676
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5607-5620, 2022
Authors: Bharathi, P. Divya | Narayanan, V. Anantha | Sivakumar, P. Bagavathi
Article Type: Research Article
Abstract: With the rapid industrialization and urbanization worldwide, air quality levels are deteriorating at an unprecedented rate and posing a substantial threat to humans and the environment. This brings the concern to effectively monitor and forecast air quality levels in real-time. Conventional air quality monitoring stations are built based on centralized architectures involving high latency, communication technologies demanding high power, sensors involving high costs and decision making with moderate accuracy. To address the limitations of the existing systems, we propose a smart and distinct Air Quality Monitoring and Forecasting system embracing Fog Computing with IoT and Deep Learning (DL). The system …is a three-layered architecture with the Sensing layer first, Fog Computing layer in between, and Cloud Computing layer at the end. Fog Computing is a powerful new generation paradigm that brings storage, computation, and networking at the edge of the IoT network and reduce network latency. A DL based BiLSTM (Bidirectional Long Short-Term Memory) model is deployed in the Fog Computing layer. The proposed system aims at real-time monitoring and accurate air quality forecasting to support decision making and aid timely prevention and control of pollutant emissions by alerting the stakeholders when a dangerous Air Quality Index (AQI) is expected. Experimental results show that the BiLSTM model has a better predictive performance considering the meteorological parameters than the baseline models in terms of MAE and RMSE. A proof of concept realizing the proposed system is elaborated in the paper. Show more
Keywords: Air quality monitoring, air quality prediction, fog computing, deep learning, bi-directional long short-term memory
DOI: 10.3233/JIFS-212713
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5621-5642, 2022
Authors: Zhang, Yapeng | Guo, Yanling | Xiao, Yaning | Tang, Wenxiu | Zhang, Haoyu | Li, Jian
Article Type: Research Article
Abstract: The material constriction is one of the important factors that influence the forming accuracy of selective laser sintering (SLS). Currently, in order to reduce the shrinkage and improve the quality of products, the optimal combination of machining process parameters is mainly determined by numerous experiments. This often takes valuable time and costs a lot, but the results are mediocre. With the development of intelligent optimization algorithms, they are applied in various disciplines for solving complex problems. Hence, for reducing the shrinkage of parts and overcoming the limitation in the optimization of the process parameters, this paper proposes a novel hybrid …improved Hunger Games Search algorithm (HGS) with extreme learning machine (ELM) model for predicting the shrinkage of parts. Firstly, the orthogonal experiments were conducted based on the five key process parameters, the obtained parts datasets were divided into the training set and test set. Secondly, the Cube mapping and refracted opposition-based learning strategies are adopted to increase the convergence speed and solution accuracy of HGS. In addition, the regression prediction model was constructed with the improved HGS(IHGS) and ELM, and this model is trained using the training set. Finally, the test set is used to evaluate the trained model and find the optimal combination of process parameters with the lowest shrinkage of parts. The experimental results suggest that the IHGS-ELM model proposed in this study has high forecasting precision, with the R2 and RMSE are only 0.9124 and 0.2433, respectively. This model can guide the laser sintering process of polyether sulfone (PES) powder. Show more
Keywords: Selective laser sintering, shrinkage, hunger games search, cube mapping, refracted opposition-based learning
DOI: 10.3233/JIFS-212799
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5643-5659, 2022
Authors: Zhang, Dan | Ma, Yingcang | Zhu, Hengdong | Smarandache, Florentin
Article Type: Research Article
Abstract: The traditional neutrosophic clustering method only performs cluster analysis on the data itself, and often ignores the supervision information of data. In order to solve the above problems, a label-guided weighted semi-supervised neutrosophic clustering algorithm is proposed in the paper. On the one hand, the paired constraint information is used to construct the supervision weight coefficient and the distance measurement learning is combined to re-measure the degree of membership of the data and the cluster center; On the other hand, by minimizing the sum of squares of error between membership matrix and label matrix, the purpose of clustering results guided …by label information is realized. Experiments on various data sets and comparisons with other clustering algorithms show that the new clustering algorithm can make full use of supervisory information and improve the accuracy of clustering. Show more
Keywords: Semi-supervised clustering, label information, neutrosophic set, clustering
DOI: 10.3233/JIFS-212812
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5661-5672, 2022
Authors: Zhang, Yun | Zou, Xiangxiang | Yu, Shujuan | Huang, Liya | Wang, Weigang | Zhao, Shengmei | Wang, Xiumei
Article Type: Research Article
Abstract: Facial expression recognition is a current research hotspot and can be applied to computer vision fields such as human-computer interaction and affective computing. The lack of diversity and category recognition information in the neural network input may affect the performance of the network, resulting in insufficient extraction of facial expression features. In order to address the above problems, a lightweight deep convolution neural network with convolution block attention module is proposed in this paper. The implementation of the lightweight DNN relies on the use of deep separable convolution and residual blocks. The combination of the convolution block attention module and …the improved classification function can optimize the lightweight model. We use accuracy and confusion matrix to evaluate different models, ultimately achieving 71.5% and 99.5% accuracy on the Fer2013 and CK+ datasets respectively. The experimental results show that our model has good feature representation capabilities. Show more
Keywords: Facial expression recognition, deep neural network, attention mechanism, AM-Softmax
DOI: 10.3233/JIFS-212846
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5673-5683, 2022
Authors: Hussain, Azmat | Mahmood, Tahir | Ali, Muhammad Irfan | Iampan, Aiyared
Article Type: Research Article
Abstract: Recently, some improvement has been made in the dominant notion of fuzzy set that is Yager investigated the generalized concept of fuzzy set, Intuitionistic fuzzy set (IFS) and Pythagorean fuzzy set (PFS) and called it q-rung orthopair fuzzy (q-ROF) set (q-ROFS). The aim of this manuscript is to present the concept of q-ROF soft (q-ROFS t ) set (q-ROFS t S) based on the Dombi operations. Since Dombi operational parameter possess natural flexibility with the resilience of variability. Some new operational laws are defined based on hybrid study of soft sets and q-ROFS. The advantage of Dombi operational …parameter is very important to express the experts’ attitude in decision making. In this paper, we present q-ROFS t Dombi average (q-ROFS t DA) aggregation operators including q-ROFS t Dombi weighted average (q-ROFS t DWA), q-ROFS t Dombi ordered weighted average (q-ROFS t DOWA) and q-ROFS t Dombi hybrid average (q-ROFS t DHA) operators. Moreover, we investigate q-ROFS t Dombi geometric (q-ROFS t DG) aggregation operators including q-ROFS t Dombi weighted geometric (q-ROFS t DWG), q-ROFS t Dombi ordered weighted geometric (q-ROFS t DOWG), and q-ROFS t Dombi hybrid geometric (q-ROFS t DHG) operators. The basic properties of these operators are presented with detail such us Idempotency, Boundedness, Monotonicity, Shift invariance, and Homogeneity. Thus from the analysis and advantages of proposed model, it is clear that the investigated q-ROFS t DWA operator is the generalized form of IF S t DWA, PFS t DWA and q-ROFDWA operators. Similarly, the investigated q-ROFS t DWG operator is the generalized form of IF S t DWG, PFS t DWG and q-ROFDWG operators. By applying the develop approach, this manuscript contains the technique and algorithm for multicriteria decision making (MCDM). Further a numerical example is developed to illustrate the flexibility and applicability of the developed operators. Show more
Keywords: PFS, q-ROFS, Soft Sets, q-ROFStS, Dombi Operators, q-ROFSt DWA, q-ROFSt DOWA, q-ROFSt DHA, q-ROFSt DWG, q-ROFSt DOWG and q-ROFSt DHG Operator, MCDM
DOI: 10.3233/JIFS-212921
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5685-5702, 2022
Authors: Yao, Lingjuan | Feng, Zonghong | Wang, Yong
Article Type: Research Article
Abstract: In this paper, we introduce the notion of BF-contexts and show that the set of hyper-concepts of the BF-contexts is a bifinite domain. Conversely, given a bifinite domain we can obtain a BF-context such that all the hyper-concepts of it is isomorphic to the bifinite domain. Further, We obtain category equivalent to that of bifinite domains and BF-contexts.
Keywords: Rough approximable concept, BF-context, Bifinite domain, categorical equivalence
DOI: 10.3233/JIFS-212939
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5703-5708, 2022
Authors: Yue, Tan | He, Zihang | Li, Chang | Hu, Zonghai | Li, Yong
Article Type: Research Article
Abstract: The number of scientific papers has been increasing ever more rapidly. Researchers have to spend a lot of time classifying papers relevant to their study, especially into fine-grained subfields. However, almost all existing paper classification models are coarse-grained, which can not meet the needs of researchers. Observing this, we propose a lightweight fine-grained classification model for scientific paper. Dynamic weighting coefficients on feature words are incorporated into the model to improve the classification accuracy. The feature word weight is optimized by the Mean Decrease Accuracy (MDA) algorithm. Considering applicability, the lightweight processing is conducted through algorithm pruning and training sample …pruning. Comparison with mainstream models shows simultaneous improvement in accuracy and time efficiency by our model. Show more
Keywords: Artificial intelligence application, fine-grained classification, lightweight processing, machine learning, paper classification system
DOI: 10.3233/JIFS-213022
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5709-5719, 2022
Authors: Prabhu, T.N. | Karuppasamy, K.
Article Type: Research Article
Abstract: Intrusion attack is considered as the major concerns to be focussed in wireless sensor network which should be seriously viewed for identification of secure and trustworthy information processing. The various characteristics involved in Intrusion attacks should be adapted precisely since it impacts on result of the intrusion detection in terms of accuracy. PCA-based centralized approach (PCACID) and Knowledge based Intrusion Detection Strategy (KBIDS) is suggested in this research for achieving the accurateintrusion detection. Though KBIDS is involved in achieving accurate detection, the demerit is that time complexity and computational overhead are progressively more which in turn influences on the entire …network performance. Traffic Variation based Intrusion Detection System (TV-IDS) plays a major role in mitigating these issues. In addition to it, Fuzzy based mean shift clustering is also suggested for incorporating clustering feature process which influences precise clustering result with the advantage of less time complexity. The decision classifier takes its role after the assessment of data points bias variations. This variation factor helps in recognizing smaller traffic variation and not determined as irregular data. The classification is achieved by hybrid genetic neuro fuzzy classifier. The updating of ANFIS weight values is accomplished concurrently with optimal selection by means of genetic algorithm. The optimal route path is chosen by greatly utilizing the artificial bee colony algorithm. The various fitness parameters involved in this research are energy level of nodes, bandwidth, etc., for efficient data transmission successfully. MATLAB simulation platform is greatly utilized for assessment of overall results for validating that proposed TV-IDS achieves improved outcomes comparatively. Show more
Keywords: Intrusion detection, feature extraction, feature grouping, traffic variation, optimal route path selection
DOI: 10.3233/JIFS-213027
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5721-5731, 2022
Authors: Li, Fanshu | Yao, Dengfeng | Jiang, Minghu | Kang, Xinchen
Article Type: Research Article
Abstract: A new smoking behavior recognition algorithm based on a weak supervision fine-grained structure and the EficientDet network is proposed in this study to solve the poor recognition effect and lack of data samples of smoking behavior in complex situations. The proposed algorithm uses the framework of a fine-grained two-level attention model with weak supervision. First, the feature edge of the image block is detected by a structured method, and the edge is screened by non-maximum suppression to form a candidate region block. Smoking behavior can then be recognized effectively by combining the results of the object-level filter for specific objects …and the local-level filter for locating discriminant parts. Second, the object-level filter uses an improved EfficientDet network to classify prospective objects and candidate regions with strong features. The present smoking behavior recognition algorithm and coarse- and fine-grained algorithms are compared to verify the effectiveness of the algorithm. Experimental results show that the accuracy of the proposed algorithm is 93.10%, which is higher than that of the optimal smoking behavior detection algorithm by 1.7%, and the error detection rate is 3.6%. Show more
Keywords: Smoking, EfficienDet network, weakly supervised fine-grained target detection, attention mechanism, behavior recognition
DOI: 10.3233/JIFS-213042
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5733-5747, 2022
Authors: Ye, Xiang | He, Zihang | Li, Bohan | Li, Yong
Article Type: Research Article
Abstract: Geometric invariant feature representation plays an indispensable role in the field of image processing and computer vision. Recently, convolution neural networks (CNNs) have witnessed a great research progress, however CNNs do not excel at dealing with geometrically transformed images. Existing methods enhancing the ability of CNNs learning invariant feature representation rely partly on data augmentation or have a relatively weak generalization ability. This paper proposes orientation adaptive kernels (OA kernels) and orientation adaptive max pooling (OA max pooling) that comprise a new topological structure, orientation adaptive neural networks (OACNNs). OA kernels output the orientation feature maps which encode the orientation …information of images. OA max pooling max-pools the orientation feature maps by automatically rotating the pooling windows according to their orientation. OA kernels and OA max pooling together allow for the eight orientation response of images to be computed, and then the max orientation response is obtained, which is proved to be a robust rotation invariant feature representation. OACNNs are compared with state-of-the-art methods and consistently outperform them in various experiments. OACNNs demonstrate a better generalization ability, yielding a test error rate 3.14 on the rotated images but only trained on “up-right” images, which outperforms all state-of-the-art methods by a large margin. Show more
Keywords: Orientation adaptive kernel, rotation invariance, image transformation, feature extraction
DOI: 10.3233/JIFS-213051
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5749-5758, 2022
Authors: Krishnakumar, K. | Gandhi, S. Indira | Sivaranjani, C.K.
Article Type: Research Article
Abstract: Video stitching has become popular due to recent advancements in technology to provide broad views and high-resolution displays. Comprehensive view or panoramic videos and high-resolution displays are created by stitching videos captured by multiple cameras or by a single camera at different points of time. This paper proposes a video stitching technique with stabilization for moving multi-camera videos adopting the wavelet decomposition technique. This method uses only those feature points that reduce the mismatching and increase the precision in estimating the transformation from among the feature points identified by the Speed-Up Robust Features detector. This work differs from the similar …work of others in two directions. Instead of using all selected feature points for the matching purpose, only significant among them are used. Unlike others, the frames are stabilized before they are stitched. Show more
Keywords: Stitching, Stabilization, Wavelet Transform, SURF, Threshold
DOI: 10.3233/JIFS-213069
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5759-5770, 2022
Authors: Zhang, Hao | Hua, Haiyang | Liu, Tianci
Article Type: Research Article
Abstract: Most of the deep learning object detection methods based on multi-modal information fusion cannot directly control the quality of the fused images at present, because the fusion only depends on the detection results. The indirectness of control is not conducive to the target detection of the network in principle. For the sake of the problem, we propose a multimodal information cross-fusion detection method based on a generative adversarial network (CrossGAN-Detection), which is composed of GAN and a target detection network. And the target detection network acts as the second discriminator of GAN during training. Through the content loss function and …dual discriminator, directly controllable guidance is provided for the generator, which is designed to learn the relationship between different modes adaptively through cross fusion. We conduct abundant experiments on the KITTI dataset, which is the prevalent dataset in the fusion-detection field. The experimental results show that the AP of the novel method for vehicle detection achieves 96.66%, 87.15%, and 78.46% in easy, moderate, and hard categories respectively, which is improved about 7% compared to the state-of-art methods. Show more
Keywords: Target detection, multimodal data, GAN, controllable fusion
DOI: 10.3233/JIFS-213074
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5771-5782, 2022
Authors: Tian, Yu | Zong, Zhaojun | Hu, Feng
Article Type: Research Article
Abstract: Complex uncertain variables are measurable functions from uncertainty spaces to the set of complex numbers and are used to model complex uncertain quantities. In this paper, we investigate Egoroff’s theorem and Lusin’s theorem for complex uncertain sequences. For studying these theorems, we introduce two concepts: strongly order continuous and regular. And as far as we know, our results are new.
Keywords: Complex uncertain variables, Egoroff’s theorem, Lusin’s theorem
DOI: 10.3233/JIFS-213151
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5783-5792, 2022
Authors: Wang, Haolun | Zhang, Faming
Article Type: Research Article
Abstract: Frank operations are more robust and flexible than other algebraic operations, and interaction operational laws consider interrelationship between membership functions in Pythagorean fuzzy number. Combining the strengths of both, we define some Frank interaction operational laws of Pythagorean fuzzy numbers for the first time in this article. Based on this, the Pythagorean fuzzy Frank interaction weighted averaging and geometric operators are developed. Meanwhile, we discuss their basic properties and related special cases. Furthermore, a novel multiple attribute decision-making framework is established based on the modified WASPAS method in Pythagorean fuzzy environment. The proposed method is implemented in a real-case study …of cloud computing product selection to test the proposed methodology’s plausibility. A sensitivity analysis is conducted to verify our method’s reliability, and the effectiveness and superiority are illustrated by comparative study. Show more
Keywords: Frank interaction operational laws, Pythagorean fuzzy Frank interaction aggregation operators, PyF-ITARA, WASPAS, cloud computing product
DOI: 10.3233/JIFS-213152
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5793-5816, 2022
Authors: Yao, Xuan | Wang, Hai | Xu, Zeshui
Article Type: Research Article
Abstract: Preference relations are often used to derive the priority of attributes and/or alternatives. Linguistic term with weakened hedges (LTWH) as a type of complex linguistic expressions can more straightforwardly describe the linguistic information provided by DMs when evaluating under uncertainties. The preference relations represented by LTWHs are an effective tool to model linguistic information. The concept and properties of additive consistency have been proposed before. This paper aims to study the multiplicative consistency of preference relations expressed by LWTHs. This paper constructs the principle of inspection for multiplicative consistency. Especially, the theories and algorithms for consistency checking and improving are …proposed. We develop an automatic approach to improve a LWHPR that is not multiplicatively consistent. Finally, we demonstrate the practicality of the proposed method through a case study of evaluating the attributes in the prevention of haze pollution in China. Show more
Keywords: Preference relations, multiplicative consistency, linguistic term with weakened hedges
DOI: 10.3233/JIFS-213170
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5817-5832, 2022
Authors: Zhang, Qinghui | Tian, Xinxin | Chen, Weidong | Yang, Hongwei | Lv, Pengtao | Wu, Yong
Article Type: Research Article
Abstract: Unsound wheat kernel recognition is an important part of wheat quality inspection, and it is also a key indicator to measure wheat quality. Research on unsound wheat kernel recognition is of great significance to the correct evaluation of wheat quality. The existing researches on unsound wheat kernel recognition are mainly to directly optimize the classical classification networks, and the recognition effect is often unsatisfactory due to insufficient training data. Aiming at the problem that the recognition rate of unsound wheat kernels is not ideal due to the lack of training data, we propose a Transfer Learning Feature Fusion (TLFF) model. …The model uses transfer learning and feature fusion to identify unsound wheat kernels. First, feature extraction is performed by deep Convolutional Neural Networks (CNNs) VGG-16 and VGG-19 pre-trained on the large public dataset ImageNet. Then, the features extracted by the pre-trained neural networks are fused and classified through the flattening layer, fully connected layer, Dropout layer, and Softmax layer. We conduct experiments on single model, two-model fusion, three-model fusion, and four-model fusion, and select the three-model fusion scheme to perform this task. Finally, we vote on the output results of the three best fusion models to further improve the recognition rate. The pre-trained models we use are trained on a large public dataset ImageNet. Since the scale of the dataset is very large, these pre-trained models also have good generalization performance for images other than ImageNet dataset. Therefore, although our dataset is small, we can still achieve good recognition results. Experimental results show that the recognition performance of the TLFF model is significantly better than the existing unsound wheat kernel recognition models. Show more
Keywords: Transfer learning, feature fusion, unsound wheat kernel recognition, convolutional neural network, voting
DOI: 10.3233/JIFS-213195
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5833-5858, 2022
Authors: Zhao, Zhen-Yu | Ma, Xu
Article Type: Research Article
Abstract: The power industry has significantly contributed to the prosperity of the national economy, and accurate prediction can reflect the development trend of the power system and power market. The short-term electricity consumption of a country exhibits both annual growth certainty and random change uncertainty, which can be suitably considered with the grey forecasting model. Regarding the short-term trends of electricity consumption in China, this study established an optimized multivariate grey forecasting model with variable background values (OGM(1, N) model) to forecast the electricity consumption level in China. The established model could be converted into the GM(1, N) model and different …variant models by adjusting the model parameters. With Beijing, Tianjin and Shanghai as examples, the OGM(1, N) model is compared to the GM(1, N) model and its variant model. The excellent prediction results confirm the feasibility of the proposed model. Then, the proposed model is applied to study China’s electricity consumption. The research results indicated that the OGM(1, N) model attains an extraordinarily high precision in the prediction of electricity consumption and can provide a practical reference for accurate electricity consumption prediction. Show more
Keywords: Electricity consumption, multivariate grey forecasting model, variable background values, China
DOI: 10.3233/JIFS-213210
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5859-5875, 2022
Authors: Babypriya, B. | Renoald, A. Johny | Shyamalagowri, M. | Kannan, R.
Article Type: Research Article
Abstract: In the context of this paper a three phase grid connected Photo-Voltaic (PV) system that is used to design with MPPT and developed Grey Wolf optimization (GWO) algorithm for analyzing the power quality issues in the grid system. The proposed Grey Wolf optimization (GWO) algorithm is incorporated in the prototype model and compared with other related optimization algorithms namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The various loading conditions as well as solar irradiances are modeled by using MATLAB simulation and experimentally validated by a DSPIC (DS 1104) based prototype model. A three phase PV grid connected non-linear …load is observed in different operating environmental conditions. The optimization control algorithms was developed and implemented in Super-Lift Inverter (SLI) grid connected system. The findings of this work are, grid reactive power demand is compensated using DSTATCOM, and also from the real power of renewable energy system. But, majority of the active power is provided or absorbed by DSTATCOM component. The objective of this proposed work is that the three optimization control algorithms are examined, and the PV integrated grid tied system maintains a compensation power at Unity Power Factor (UPF). The proposed optimization methods produce load output power factor values such as 0.89 (GWO), 0.88, (PSO) and 0.86 (GA). Show more
Keywords: PV system, particle swarm optimization, genetic algorithm, Grey Wolf optimization, Grid
DOI: 10.3233/JIFS-213259
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5877-5896, 2022
Authors: Jiang, Yadan | Qiu, Dong
Article Type: Research Article
Abstract: The difference operation for fuzzy number is an essential concept for the fuzzy set theory. There are several differences proposed: generalized difference, generalized Hukuhara difference and granule difference. Based on these differences, generalized differentiability, generalized Hukuhara differentiability and granule differentiability are also proposed, respectively. In this paper, the relations among these three kinds of differences and that of related three kinds of differentiability are clarified.
Keywords: Generalized differences, generalized differentiability, granule differentiability, fuzzy-number-valued function
DOI: 10.3233/JIFS-213270
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5897-5911, 2022
Authors: Tran, Van Quan | Nguyen, Linh Quy
Article Type: Research Article
Abstract: The use of recycled glass in the concrete mix instead of natural coarse aggregates and supplemental cementitious material has several advantages, including the conservation of natural resources, the reduction of CO2 emissions, and cost savings. However, due to their qualities, the mechanical properties of concrete containing Ground Glass Particles (GGP) differ from those of natural aggregates concrete. As a result, assessing the compressive strength (CS) of concrete with GGP is crucial. Therefore, this paper proposes the hybrid Machine Learning (ML) model including the Gradient Boosting (GB) and Bayesian optimization (BO) algorithms for predicting the compressive strength of concrete containing …GGP. The hybrid ML model is developed and validated based on the training dataset (70% of the data) and the test dataset (30% of the remaining data), respectively. The performance of hybrid ML model is evaluated by three criteria, such as the Pearson correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The K-Fold Cross-Validation technique is also used to verify the reliability of the hybrid ML model). The best performance of the hybrid ML model is determined with the R = 0.9843, RMSE = 1.7256 (MPa), and MAE = 1.3154 (MPa) for training dataset and R = 0.9784, RMSE = 2.4338 (MPa) and MAE = 1.9618 (MPa) for testing dataset. Based on the best hybrid ML model, the sensitivity analysis including SHapley Additive exPlanation (SHAP) and Partial Dependence Plots (PDP) 2D are investigated to obtain an in-depth examination of each individual input variable on the predicted compressive strength of concrete contaning GGP. The sensitivity analysis shows that four factors, such as curing age, surface area, TiO2 , and temperature have the most effect on the compressive strength of concrete containing GGP. Show more
Keywords: Gradient boosting, bayesian optimization, compressive strength, concrete, machine learning, ground glass particles
DOI: 10.3233/JIFS-213298
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5913-5927, 2022
Authors: Zhang, Lijun | Duan, Lixiang
Article Type: Research Article
Abstract: To address data distribution discrepancy across scenarios, deep transfer learning is used to help the target scenario complete the recognition task using similar scenario data. However, fault misrecognition or low diagnostic accuracy occurs due to the weak expression of the deep transfer model in cross-scenario application. The Convolutional Block Attention Module (CBAM) can independently learn the importance of each channel and space features, recalibrate the channel and space features, and improve image classification performance. This study introduces the CBAM module using the Residual Network (ResNet), and proposes a transfer learning model that combines the CBAM module with an improved ResNet, …denoted as TL_CBAM_ResNet17. A miniature ResNet17 deep model is constructed based on the ResNet50 model. The location of the CBAM module embedded in the ResNet17 model is determined to strengthen model expression. For effective cross-scenario transfer and reduced data distribution discrepancy between source and target domains, a multi-kernel Maximum Mean Discrepancy (MK–MMD) layer is added in front of the classifier layer in the ResNet17 model to select data with common domain features. Considering a reciprocating compressor as the research object, cross-scenario datasets are produced by the vibration signals from the simulation test bench and simulation signals from the dynamic simulation model. Mutual transfer experiments are conducted using these datasets. The proposed method (TL_CBAM_ResNet17) demonstrates better classification performance than TCA, JDA, the TL_ResNet50 model, the TL_ResNet17 model, and the TL_ResNet17 model integrated with other attention mechanism module, and greatly improves the accuracy of fault diagnosis and generalization of the model in cross-scenario applications. Show more
Keywords: Cross-scenario, transfer learning, reciprocating compressor, ResNet, CBAM, dynamic simulation
DOI: 10.3233/JIFS-213340
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5929-5943, 2022
Authors: Manoharan, G. | Sivakumar, K.
Article Type: Research Article
Abstract: Outlier detection in multivariate data is one of the critical challenges in preprocessing phase. Many outlier detection methods have been emerged for the past few years to perform outlier detection efficiently in multivariate datasets. The prediction accuracy cannot be improved without proper outlier analysis and the prediction model might not confirm the expected behavior. The generation of huge data in real time applications makes the outlier detection process more crucial and challenging. Most of the currently available detection methods are based on mean and covariance that are not suitable for handling large volume of datasets, they are suitable for handlind …static data and simple data to detect outliers. They cannot cope up with large scale data. So, there is a need for an efficient outlier detection model to detect the outliers in multivariate datasets. The primary objective of this research work is to develop a robust model for outlier detection in multivariate data. To achieve this, the work proposed an enhanced Hidden Semi-Markov Model (HSMM) which allows arbitrary time distribution in its states to detect outliers. The proposed work utilized six benchmark datasets and the performance is compared with several outlier detection algorithms such as HMM, iForest, FastABOD, and Expose. The work achieves 98.2 % of accuracy which is significantly better for detecting outliers in multivariate dataset. The proposed work improvised the percentage of acheivements between 2% to 25% than the currently available models.. The experimental analysis shows that the proposed model performs well than the currently available models in terms of accuracy, and receiver operation curve (ROC). Show more
Keywords: Outlier detection, multivariate data, Hidden Markov Model, iForest, expose
DOI: 10.3233/JIFS-213374
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5945-5951, 2022
Authors: Liu, Sijia | Guo, Zixue
Article Type: Research Article
Abstract: In order to solve the problem of multi-attribute decision-making with unknown weights under probabilistic hesitant fuzzy information, considering the shortcomings of the existing probabilistic hesitant fuzzy distance measure, such as weak distinguishing ability, a probabilistic hesitant fuzzy multi-attribute decision-making method based on improved distance measures is proposed. Firstly, the hesitancy degree of probabilistic hesitant fuzzy element and the improved difference measure of probabilistic hesitant fuzzy element are defined, and an improved probabilistic hesitant fuzzy distance measure based on hesitancy degree, incompleteness degree and improved difference measure is proposed. Secondly, based on the improved distance measure, a mathematical programming model with …the goal of minimizing the relative approach degree is con-structed to determine the attribute weights of evaluation indexes in multi-attribute decision making problems. Using it as a base, a new probabilistic hesitant fuzzy multi-attribute decision-making method is proposed by combining the improved probabilistic hesitant fuzzy distance measure with the compromise ratio method. Finally, the proposed method is applied to the problem of green supplier selection, and the feasibility and effectiveness of the proposed method are verified by case analysis and comparison with other methods. Show more
Keywords: Probabilistic hesitant fuzzy set, multi-attribute decision-making, distance measure
DOI: 10.3233/JIFS-213427
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5953-5964, 2022
Authors: Song, Xudong | Wang, Hao | Liu, Yifan | Wang, Zi | Cui, Yunxian
Article Type: Research Article
Abstract: Aiming at the inherent defects of BP neural network in the field of rolling bearing fault diagnosis, based on the optimization of particle swarm optimization algorithm, this paper uses a variety of optimization strategies to optimize the particle swarm optimization algorithm, and then uses the optimized particle swarm optimization algorithm to optimize the BP neural network. Therefore, a new fault diagnosis method (Dual Strategy Particle Swarm Optimization BP neural network, DSPSOBP) is proposed. DSPSOBP fault diagnosis method is mainly divided into two steps. The first step is EMD decomposition of vibration signal, and the second step is to classify rolling …bearing faults by using BP neural network optimized by Double Strategy Particle Swarm Optimization algorithm. Experiments show that DSPSOBP has stronger advantages than BP neural network basic fault diagnosis model. Show more
Keywords: Bearing fault diagnosis, BP neural network, optimization algorithm, particle swarm optimization
DOI: 10.3233/JIFS-213485
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5965-5971, 2022
Authors: Bennajeh, Anouer | Said, Lamjed Ben
Article Type: Research Article
Abstract: Studying driver behaviors has become a major concern for the transportation community, businesses, and the public. Thus, based on the simulation, we proposed an adaptive driving model in the car-following driving behavior and based on the normative behavior of the driver during decision-making and anticipation, whose intention is to ensure the objectives of imitation of ordinary human behavior and road safety. The presented model is based on a software agent paradigm to model a human driver and the Fuzzy Logic Theory to reflect the driver agent’s reasoning. To validate our model, we used the dataset from the program of the …US Federal Highway Administration. In this context, we notice an excellent homogeneity in the deviation of the adopted trajectory of the autonomous driver agent from the adopted trajectories by the human drivers. Moreover, the advantage of our model is that it works with different velocities. Show more
Keywords: Fuzzy logic, decision-making, anticipation, adaptive control, autonomous agent
DOI: 10.3233/JIFS-213498
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5973-5983, 2022
Authors: Apinaya Prethi, K.N. | Sangeetha, M.
Article Type: Research Article
Abstract: Network resources and traffic priorities can be utilized to distribute requested tasks across edge nodes at the edge layer. However, due to the variety of tasks, the edge nodes have an impact on data accessibility. Resource management approaches based on Virtual Machine (VM) migration, job prioritization, and other methods were used to overcome this problem. A Minimized Upgrading Batch VM Scheduling (MSBP) has recently been developed, which reduces the number of batches required to complete a system-scale upgrade and assigns bandwidth to VM migration matrices. However, due to poor resource sharing caused by suboptimal VM utilization, the MSBP was unable …to effectively ensure the global best solutions. In order to distribute resources and schedule tasks optimally during VM migration, this paper proposes the MSBP with Multi-objective Optimization of Resource Allocation (MORA) method. The major goal of this proposed methodology is to take into account different objectives and solve the Pareto-front problem to enhance lifetime of the fog-edge network. First, it formulates an NP-hard challenge for MSBP by taking into account a variety of factors such as network sustainability, path contention, network delay, and cost-efficiency. The Multi-objective Krill Herd optimization (MoKH) algorithm is then used to address the NP-hard issue using the Pareto optimality rule and produce the best solution. First, it introduces an NP-hard challenge for MSBP by accounting in network sustainability, path contention, network latency, and cost-efficiency. The Pareto optimality rule is then implemented to overcome the NP-hard problem and provide the optimum solution employing the Multi-objective Krill Herd optimization (MoKH) algorithm. This increases network lifetime and improves resource allocation cost efficiency. Finally, the simulation results show that the MSBP-MORA distributes resources more efficiently and hence increases network lifetime when compared to other traditional algorithms. Show more
Keywords: VM migration, MSBP, resource allocation, pareto-front issue, multi-objective Krill herd optimization algorithm, NP-hard challenge
DOI: 10.3233/JIFS-213520
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5985-5995, 2022
Authors: Chen, Liuxin | Wang, Yutai | Yang, Dongmei
Article Type: Research Article
Abstract: Picture fuzzy linguistic set is a vital solution to express complex and uncertain information, which has been applied in multi-attribute group decision-making (MAGDM). However, the credibility of decision-making information is unconsidered, which may give rise to the inaccuracy of final result. To solve this problem, the picture fuzzy Z-linguistic set (PFZLS) composed of linguistic term, picture fuzzy number, and credibility is proposed, which could express more complete decision-making information. Subsequently, operation rules, comparison methods, and distance measures of PFZLS are introduced. In addition, the weighted geometric average operator and the classical VIKOR method are extended and combined to solve the …MAGDM problem under the picture fuzzy Z-linguistic environment. Finally, an illustrative example about the emergency decision-making (EDM) problem of forest fire accident is proposed, and a series of comparative analyses are presented to verify the rationality and superiority of the PFZLS. Show more
Keywords: Multi-attribute group decision-making, picture fuzzy Z-linguistic set, weighted geometric average operator, VIKOR method
DOI: 10.3233/JIFS-213531
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5997-6011, 2022
Authors: Wang, Fan | Tian, Shengwei | Yu, Long | Long, Jun | Zhou, Tiejun | Wang, Bo | Wang, Junwen | Wang, Yongtao
Article Type: Research Article
Abstract: Human multi-modal emotions analysis includes time series data with different modalities, such as verbal, visual, and auditory. Due to different sampling rates from each modality, the collected data streams are unaligned. The asynchrony cross-modality increases the difficulty of multi-modal fusion. Therefore, we propose a new Cross-Modality Reinforcement model (CMR) based on recent advances in a cross-modality transformer, which performs multi-modal fusion in unaligned multi-modal sequences for emotion prediction. To deal with the long-time dependencies of unaligned sequences, we introduce a time domain aggregation to model the single modal, by aggregating the information in the time dimension, and enhance contextual dependencies. …Moreover, a CMR strategy is introduced in our approach.With the main and secondary modalities as inputs to the module, main modal features are strengthened through cross-modality attention and cross-modality gate, and the secondary modality information flows to the main modality potentially, while retaining main modality-specific features and complementing the missing cues. This process gradually learns the common contributing features between the main and secondary modalities and reduces the noise caused by the variability of the modal features. Finally, the enhanced features are used to make predictions about human emotions. We evaluate CMR on two multi-modal sentiment analysis benchmark datasets, and we report the accuracy of 82.7% on the CMU-MOSI and 82.5% and CMU-MOSEI, respectively, which demonstrates our method outperforms current state-of-the-art methods. Show more
Keywords: Cross-modality processing, multi-modal fusion, multi-modal unaligned sequences, multi-modal sentiment analysis
DOI: 10.3233/JIFS-213536
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6013-6025, 2022
Authors: Kong, Yuting | Qian, Yurong | Tan, Fuxiang | Bai, Lu | Shao, Jinxin | Ma, Tinghuai | Tereshchenko, Sergei Nikolayevich
Article Type: Research Article
Abstract: Data clustering has been applied and developed in all walks of life, which can provide convenience for enterprise service optimization. However, when the original data to be analyzed contains users’ personal privacy information, the clustering analysis process of the data holder may expose users’ privacy. Differential privacy k-means algorithm is a clustering method based on differential privacy protection technology, which can solve the privacy disclosure problem in the process of data clustering. In the differential privacy k-means algorithm, Laplacian noise controlled by privacy parameter ɛ is added to the center point of clustering to protect user sensitive information and clustering …results in the original data, but the addition of noise will affect the utility of clustering. In order to balance the availability and privacy of the differential privacy k-means clustering algorithm, the research on the improvement of the algorithm pays more attention to the selection of the initial clustering center or the optimization of the outlier processing, but does not consider the different contribution degree of each dimension data to the clustering. Therefore, this paper proposes a differential privacy CVDP k-means clustering algorithm based on coefficient of variation. The CVDP scheme first eliminates outliers in the original data through data density, and then designs weighted data point similarity calculation method and initial centroid selection method using variation coefficient. Experimental results show that CVDP k-means algorithm has some improvements in availability, performance and privacy. Show more
Keywords: Differential privacy, differential privacy k-means clustering, coefficient of variation, CVDP k-means
DOI: 10.3233/JIFS-213564
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6027-6045, 2022
Authors: Zhang, Shu | Wang, Yuhong
Article Type: Research Article
Abstract: This paper aims to improve the accuracy of software defect prediction by using a prediction model based on grey incidence analysis and Naive Bayes algorithm. The model employs the Naïve Bayes as the basic classifier of the software defect prediction model. The grey incidence analysis is used to analyze the relation between software modules and ideal modules. Then, the grey correlation degree is embedded into the Naive Bayes classification model as a feature attribute. According to the comparison and analysis of NASA’s public dataset, the prediction model in this paper improves the prediction accuracy.
Keywords: Naive Bayes, grey incidence analysis, software defect prediction
DOI: 10.3233/JIFS-213570
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6047-6060, 2022
Authors: Shi, Wen | Huang, Yongming | Zhang, Guobao | Yang, Wankou
Article Type: Research Article
Abstract: Degradation prognostic plays a crucial role in increasing the efficiency of health management for rolling element bearings (REBs). In this paper, a novel four-step data-driven degradation prognostics approach is proposed for REBs. In the first step, a series of degradation features are extracted by analyzing the vibration signals of REBs in time domain, frequency domain and time-frequency domain. In the second step, three indicators are utilized to select the sensitive features. In the third step, different health state labels are automatically assigned for health state estimation, where the influence of uncertain initial condition is eliminated. In the last step, a …multivariate health state estimation model and a multivariate multistep degradation trend prediction model are combined to estimate the residence time in different health status and remaining useful life (RUL) of REBs. Verification results using the XJTU-SY datasets validate the effectiveness of the proposed method and show a more accurate prognostics results compared with the existing major approaches. Show more
Keywords: Degradation prognostic, rolling element bearings (REBs), health state estimation, remaining useful life (RUL)
DOI: 10.3233/JIFS-213586
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6061-6076, 2022
Authors: Elmenshawy, Maha A. | Hamza, Taher | El-Deeb, Reem
Article Type: Research Article
Abstract: Due to the obvious significant expansion in the number of online Arabic textual information, Arabic Text Summarization has become a focus of intense research. Manual text summarization necessitates a large investment of time, effort, and money. Hence, Automatic Arabic Text Summarization (AATS) is currently necessary to create accurate and relevant summaries from the huge amount of accessible content. The developed techniques and methodologies for AATS are still in their immaturity because of the intrinsic complexity of the structure and morphology of the Arabic language. AATS methods could be categorized as extractive, abstractive, hybrid extractive to abstractive. The extractive method selects …and combines the most important sentences from the input document(s) to produce the summary. While the abstractive method needs deep understanding of the input document(s) for creating the summary with sentences that differ from the original ones. The extractive to abstractive method is a hybrid strategy for creating an informative and cohesive summary using the extractive summary as a first step. This paper provides a detailed explanation of the fundamental issues related to Arabic text summarization. It describes and analyzes the various methods and systems currently in use, traces their history and monitors their performance. The challenges and trends are explored. Show more
Keywords: Automatic text summarization, arabic text summarization, summarization approaches, extractive approaches, abstractive approaches, summary evaluation
DOI: 10.3233/JIFS-213589
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6077-6092, 2022
Authors: Rani, R.Jhansi | Vasanth, K.
Article Type: Research Article
Abstract: Latent fingerprint recognition plays an essential role for law enforcement agencies to detect criminals and security purposes. One of the key stages utilized in the latent fingerprint recognition model is to automatically learn consistent minutiae from fingerprint images. However, the existing state-of-the-art recognition approaches are not adequate since live-scan fingerprint images and enhancements are necessary for each step of the recognition process. Hence, an automated recognition system along with appropriate minutiae learning algorithm is required for matching the latent fingerprint exactly. In this paper, an efficient recognition system using dictionary learning and Local Context-Perception deep neural network (LCPnet) has been …proposed to enhance the accuracy of latent fingerprint recognition. Primarily, the Total Variation decomposition model is utilized to remove the smooth background noise and dictionary learning contributes to the extraction of multiple patches. Afterward, the LCPnet is trained for 12 patch types to develop a salient minutiae descriptor where every descriptor is trained using LCPnet with a particular patch size at a location surrounding the minutiae. The proposed detection system has been tested through two latent public datasets. Here, three different types of templates (LCPnet minutiae, LCPnet texture, and LCPnet minutiae+texture) are analyzed for evaluating the proposed fingerprint detection system. The performance results manifest that the proposed system acquires a superior recognition accuracy of 99.44% and 99.58% under two different datasets. Show more
Keywords: Latent fingerprint, dictionary learning, ridge enhancement, minutiae extraction, deep neural network
DOI: 10.3233/JIFS-220056
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6093-6108, 2022
Authors: Jiang, Hailiang | Chen, Yumin | Kong, Liru | Cai, Guoqiang | Jiang, Hongbo
Article Type: Research Article
Abstract: Learning Vector Quantization (LVQ) is a clustering method with supervised information, simple structures, and powerful functions. LVQ assumes that the data samples are labeled, and the learning process uses labels to assist clustering. However, the LVQ is sensitive to initial values, resulting in a poor clustering effect. To overcome these shortcomings, a granular LVQ clustering algorithm is proposed by adopting the neighborhood granulation technology and the LVQ. Firstly, the neighborhood granulation is carried out on some features of a sample of the data set, then a neighborhood granular vector is formed. Furthermore, the size and operations of neighborhood granular vectors …are defined, and the relative and absolute granular distances between granular vectors are proposed. Finally, these granular distances are proved to be metrics, and a granular LVQ clustering algorithm is designed. Some experiments are tested on several UCI data sets, and the results show that the granular LVQ clustering is better than the traditional LVQ clustering under suitable neighborhood parameters and distance measurement. Show more
Keywords: Supervised learning, granular computing, LVQ clustering, neighborhood granules
DOI: 10.3233/JIFS-220092
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6109-6122, 2022
Authors: Sood, Mansi | Gera, Jaya | Kaur, Harmeet
Article Type: Research Article
Abstract: This work creates, evaluates, and optimizes a domain-based dictionary using labeled domain documents as the input. The dictionary is created using selected unigrams and bigrams from the labeled text documents. Dictionary is evaluated using the Naïve Bayes classification model. Classification Accuracy obtained is used as a metric to evaluate the effectiveness of the dictionary. The paper also studies the impact of applying the Stochastic Gradient Descent (SGD) technique, with Lasso and Ridge Regularization, on the effectiveness of a domain-based dictionary. Both, Lasso and Ridge regularization, with Ridge faring better than Lasso, help to optimize the dictionary size, without any significant …reduction in the accuracy. The created dictionaries are evaluated on the dataset used for their creation and subsequently on an unseen dataset as well. The applicability of a created dictionary to classify the documents belonging to a different dataset gives an idea about the generality of that dictionary. The paper establishes that the dictionaries created using the above methodology are generic enough to classify documents of other unseen datasets. Show more
Keywords: Domain-based dictionary, unigram, bigram, Naïve Bayes classification, Stochastic Gradient Descent
DOI: 10.3233/JIFS-220110
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6123-6136, 2022
Authors: Zhu, Linkai | Wang, Wennan | Huang, Maoyi | Chen, Maomao | Wang, Yiyun | Cai, Zhiming
Article Type: Research Article
Abstract: A lot of manual work goes into identifying a topic for an article. With a large volume of articles, the manual process can be exhausting. Our approach aims to address this issue by automatically extracting topics from the text of large numbers of articles. This approach takes into account the efficiency of the process. Based on existing N-gram analysis, our research examines how often certain words appear in documents in order to support automatic topic extraction. In order to improve efficiency, we apply custom filtering standards to our research. Additionally, delete as many noncritical or irrelevant phrases as possible. In …this way, we can ensure we are selecting unique keyphrases for each article, which capture its core idea1 . For our research, we chose to center on the autonomous vehicle domain, since the research is relevant to our daily lives. We have to convert the PDF versions of most of the research papers into editable types of files such as TXT. This is because most of the research papers are only in PDF format. To test our proposed idea of automating, numerous articles on robotics have been selected. Next, we evaluate our approach by comparing the result with other models. Show more
Keywords: Automatic topic extraction, frequency statistic, keyphrase, N-gram
DOI: 10.3233/JIFS-220115
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6137-6146, 2022
Authors: Shi, Lukui | Zu, Haoran | Tai, Jikai | Niu, Weifei
Article Type: Research Article
Abstract: Because X-ray welding images have complex backgrounds and welding defects have different sizes and shapes, effectively detecting welding defects in X-ray images is still a challenge. To solve these problems, a shape-aware network (SA-NET) was proposed, whose core was the shape-aware module (SAM). SAM includes a free-shape region proposal network (FS-RPN) and a two-level regression head (TR-Head). FS-RPN predicts the shape of the anchor boxes corresponding to each position on the feature maps, and aligns the feature maps according to the predicted anchor box shape. Then, the offset and the foreground classification score of the anchor boxes are predicted according …to the aligned feature maps. Thus, FS-RPN generates the proposal regions with a higher quality. TR-Head uses the first-level detection head, which only contains one regression branch, to further improve the quality of the proposal regions by fine-tuning the proposal regions. It employs the second-level detection head, which consists of one classification branch and one regression branch, to predict the categories and the boxes of defects. The experimental results showed that SA-NET effectively improved the quality of the proposal regions and greatly improved the detection effect of welding defects, especially defects with special shapes. Show more
Keywords: Welding defects, defect detection, shape awareness, FS-RPN, TR-head
DOI: 10.3233/JIFS-220132
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6147-6162, 2022
Authors: Martinez-Gil, Jorge | Chaves-Gonzalez, Jose Manuel
Article Type: Research Article
Abstract: The automatic semantic similarity assessment field has attracted much attention due to its impact on multiple areas of study. In addition, it is also relevant that recent advances in neural computation have taken the solutions to a higher stage. However, some inherent problems persist. For example, large amounts of data are still needed to train solutions, the interpretability of the trained models is not the most suitable one, and the energy consumption required to create the models seems out of control. Therefore, we propose a novel method to achieve significant results for a sustainable semantic similarity assessment, where accuracy, interpretability, …and energy efficiency are equally important. We rely on a method based on multi-objective symbolic regression to generate a Pareto front of compromise solutions. After analyzing the output generated and comparing other relevant works published, our approach’s results seem to be promising. Show more
Keywords: Knowledge engineering, sustainable computing, semantic similarity assessment
DOI: 10.3233/JIFS-220137
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6163-6174, 2022
Authors: Du, Ye | Yao, Bingxue
Article Type: Research Article
Abstract: Zhan and Jiang defined covering-based compact and loose variable precision fuzzy rough set models. Soon after, they proposed a reflexive fuzzy β-neighborhood operator and defined a covering-based generalized variable precision fuzzy rough set. Based on them, in this paper, we use the reflexive fuzzy β-neighborhood operator to establish two covering-based generalized variable precision fuzzy rough set models, which are called covering-based generalized compact and loose variable precision fuzzy rough set models, respectively. Then, we investigate the important properties of the two rough set models and their relationship to the original models. Finally, we apply the covering-based generalized compact variable precision …fuzzy rough set model to decision-making problems. A simple example is given to verify the validity of the model and compare the results with other models. Show more
Keywords: Fuzzy rough set , compact, loose, fuzzy logical operator
DOI: 10.3233/JIFS-220152
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6175-6187, 2022
Authors: Angammal, S. | Hannah Grace, G.
Article Type: Research Article
Abstract: In agriculture crop planning plays an important role. There are many uncertainties and indeterminacy factors that occur in agriculture cultivation process. Hence, many parameters are determined by using intuitionistic fuzzy number. The present study discussed the new Interactive Neutrosophic Programming Approach (INPA) based on Neutrosophic Set (NS) to increase the production and profit of Ariyalur district medium farm holder with minimum expenditure taking into account the land, labour, water & food requirement constraints. To verify and validate the proposed method, the results obtained by INPA are compared with some existing optimization approaches such as FOT, IFOT and Torabi interactive fuzzy …optimization approach and it is clear that the proposed approach is superior than the existing model. Show more
Keywords: Crop planning, multi objective optimization, intuitionistic fuzzy parameter, membership function, neutrosophic programming
DOI: 10.3233/JIFS-220156
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6189-6201, 2022
Authors: Jiang, Man
Article Type: Research Article
Abstract: In this paper, the notions of hesitant fuzzy dot subalgebras, hesitant fuzzy normal dot subalgebras, and hesitant fuzzy dot ideals of B-algebras are presented, and some of their features are examined, in this study. The homomorphic image and inverse image of hesitant fuzzy dot subalgebras, as well as the hesitant fuzzy dot ideal, are investigated. We also explore some related characteristics of hesitant fuzzy relations on the family of hesitant fuzzy dot subalgebras and hesitant fuzzy dot ideal of B-algebras.
Keywords: B-algebra, hesitant fuzzy dot subalgebra, hesitant fuzzy normal dot subalgebra, hesitant fuzzy dot ideal, hesitant fuzzy ρ- product relation
DOI: 10.3233/JIFS-220158
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6203-6212, 2022
Authors: Yu, Shun-Chi
Article Type: Research Article
Abstract: In the recent decades, genetic algorithms (GAs) have often been applied as heuristic techniques at various settings entailing production scheduling. However, early convergence is one of the problems associated with this approach. This study develops an efficient local search rule for the target-oriented rule in traditional GAs. It also addresses the problem of two-stage multiprocessor flow-shop scheduling (FSP) by viewing the due window and sequence-dependent setup times as constraints faced by common flow shops with multiprocessor scheduling suites in the actual production scenario. Using the simulated data, this study verifies the effectiveness and robustness of the proposed algorithm. The results …of data testing demonstrate that the proposed method may outperform other algorithms, including a significant hybrid algorithm, in addressing the problems considered. Show more
Keywords: Target-oriented, genetic algorithm, two-stage multiprocessor flow shop scheduling, due window, setup time
DOI: 10.3233/JIFS-220174
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6213-6228, 2022
Authors: Xu, Zhiwei | Li, Peng | Wei, Cuiping
Article Type: Research Article
Abstract: In recent years, to address the continued aging of China’s population, the Chinese government has focused on the issue of pensions through a series of pension policies. The traditional system of institutional pensions is facing serious challenges, with a variety of novel pension modes placing them under enormous pressure. Furthermore, the development of institutional pensions has been restricted by many factors, such as long construction cycles and high fees, meaning that this traditional system no longer meets the pension needs of the elderly. Improving the service quality of institutional pensions is inevitable for future progress. Thus, identifying the key factors …that influence the service quality of institutional pensions, and understanding the relationships between these factors, is hugely significant. Furthermore, traditional decision-making trial and evaluation laboratory (DEMATEL) method can not solve this problem because the number of factors is too large. To address these issues, we establish an evaluation system for Chinese pension institutions, and propose a hierarchical DEMATEL model based on probabilistic linguistic term sets (PLTSs), which can help decision makers to find the key factors influencing service quality in institutional pensions and deal with the evaluation problem with a large number of criteria. The proposed hierarchical DEMATEL model based on PLTSs fully reflects experts’ preferences and evaluation information, and is able to identify the directions in which China’s pension institutions should improve their quality of service. In addition, we use the best-worst method (BWM) to calculate the importance values of each subsystem, which makes the cause-effect relationship between subsystems more reasonable than the traditional DEMATEL method. Finally, we apply our method to evaluate nursing homes in Zhenjiang, Jiangsu province and propose some managerial implications. Show more
Keywords: Evaluation, hierarchical DEMATEL, PLTS, institutional pension, aging
DOI: 10.3233/JIFS-220181
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6229-6251, 2022
Authors: Prabhu, S. | Deepa, S. | Arulperumjothi, M. | Susilowati, Liliek | Liu, Jia-Bao
Article Type: Research Article
Abstract: Power utilities must track their power networks to respond to changing demand and availability conditions to ensure effective and efficient operation. As a result, several power companies continuously employ phase measuring units (PMUs) to continuously check their power networks. Supervising an electric power system with the fewest possible measurement equipment is precisely the vertex covering graph-theoretic problems otherwise a variation of the dominating set problem, in which a set D is defined as a power dominating set (PDS) of a graph if it supervises every vertex and edge in the system with a couple of rules. If the distance …vector eccentrically characterizes each node in G with respect to the nodes in R , then the subset R of V (G ) is a resolving set of G . The problem of finding power dominating set and resolving set problems are proven to be NP-complete in general. The finite subset R of V (G ) is said to be resolving-power dominating set (RPDS) if it is both resolving and power dominating set, which is another NP-complete problem. The η p (G ) is the minimal cardinality of an RPDS of a graph G . A neural network is a collection of algorithms that tries to figure out the underlying correlations in a set of data by employing a method that replicates how the human brain functions. Various neural networks have seen rapid progress in multiple fields of study during the last few decades, including neurochemistry, artificial intelligence, automatic control, and informational sciences. Probabilistic neural networks (PNNs) offer a scalable alternative to traditional back-propagation neural networks in classification and pattern recognition applications. They do not necessitate the massive forward and backward calculations that ordinary neural networks entail. This paper investigates the resolving-power domination number of probabilistic neural networks. Show more
Keywords: Metric dimension, basis, phasor measurement unit, power domination, probabilistic neural network, resolving set
DOI: 10.3233/JIFS-220218
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6253-6263, 2022
Authors: Kumar, B. Praveen | Hariharan, K. | Shanmugam, R. | Shriram, S. | Sridhar, J.
Article Type: Research Article
Abstract: Integration of the latest technological advancements such as Internet of Things (IoT) and Computational Intelligence (CI) techniques is an active research area for various industrial applications. The rapid urbanization and exponential growth of vehicles has led to crowded traffic in cities. The deployment of IoT infrastructures for building smart and intelligent traffic management system greatly improves the quality and comfort of city dwellers. This work aims at building a cost effective IoT enabled traffic forecasting system using deep learning techniques. The case study experimentation is done in a real time traffic environment. The main contributions of this work include: (i) …deploying road side sensor station built with ultrasonic sensor and Arduino Uno controller for obtaining traffic flow data (ii) building an IoT cloud system based on open source Thingspeak cloud platform for monitoring real time traffic (iii) performing short term traffic forecast using Recurrent Neural Network (RNN) models such as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The performance of the prediction model is compared with the traditional statistical methods such as Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA) and Convolutional Neural Network (CNN). The results show good performance metrics with RMSE of 5.8, 7.9, 10.2 for LSTM model and 6.7, 8.6, 10.9 for GRU model for three different scenarios such as whole day, morning congested hour and evening congested hour datasets. Show more
Keywords: IoT, cloud, vehicle detector, traffic flow forecast, time series prediction, RNN, LSTM, GRU
DOI: 10.3233/JIFS-220230
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6265-6276, 2022
Authors: Vinothkumar, V. | Kanimozhi, R.
Article Type: Research Article
Abstract: To increase the life and efficiency of power electronics equipment in a utility distribution system, the power quality improvement is essential part. In this work, to improve power quality by using Robust Resilient Back Propagation Neural Network (RBPNN) scheme for a Photovoltaic (PV)-Integrated Unified Power Quality Conditioner (UPQC) with cascaded multi-level inverter configurations are described. Among the proposed methods, there is no need to use a transformer and filter when multilevel UPQC is applied, and it is one of the great advantages. The proposed UQPC offers a PV array composition with a power converter connected to a DC-link capacitor that …can compensate for voltage sag, swell, voltage interruption, harmonics and reactive power. The Robust Resilient Back Propagation Neural Network controller is generate gating pulses to the UPQC. The reference currents and voltages for the controller are estimated using Synchronous Reference Frame (SRF) theory. The proposed cascaded multi-level inverter-based UPQC is designed using Matlab/Simulink Software. The simulation results confirm that the proposed method gives good results compared with existing Adaptive neural Fuzzy Inference System (ANFIS) and fuzzy logic methods. A real-time hardware system is also established to validate the simulation results. The effectiveness of the proposed system RBPN-UPQC approach is compared for both simulation and experimental results gives better low THD level 1.22%. Show more
Keywords: Unified power quality conditioner, photovoltaic, resilient back propagation neural network, harmonics, power quality
DOI: 10.3233/JIFS-220231
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6277-6294, 2022
Authors: Kırmacı, Volkan | Guler, Evrim | Kaya, Hüseyin
Article Type: Research Article
Abstract: This study consists of modeling studies for thermal separation of a Ranque Hilsch Vortex Tube (RHVT) by using four different machine learning methods. Compressed air used RHVT, the data obtained as a result of experiments with different nozzles were modeled with linear, k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Machine (SVM) regression methods to compare each other. Nozzle properties and inlet pressure were used as input parameters, and the total temperature gradient ΔT was examined as the output. Experiment results were handled in two groups as training and test groups at different percentages. ΔT calculated by modeling hot …and cold output test data, and ΔT calculated directly with the experiments were modeled and compared. According to the obtained results, the highest percentage of accuracy value of 97.58% was obtained with the SVM method, and this value was obtained with the set in which 90%–10% of the experimental results were used as the training and test data, respectively. The accuracy ratios calculated with RF, kNN, and linear regression models under the same conditions are 93.99, 88.49, and 78.97, respectively. Show more
Keywords: Vortex tube, thermal performance, prediction, regression models
DOI: 10.3233/JIFS-220274
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6295-6306, 2022
Authors: Jain, Vipin | Kashyap, Kanchan Lata
Article Type: Research Article
Abstract: This work presents the analysis of significant sentiments and attitudes of people towards the COVID-19 vaccination. The tweeter messages related to the COVID-19 vaccine is used for sentiment evaluation in this work. The proposed work consists of two steps: (i) natural processing language (NLP) and (ii) classification. The NLP is utilized for text pre-processing, tokenization, data labelling, and feature extraction. Further, a stack-based ensemble machine learning model is used to classify sentiments as positive, negative, or neutral. The stack ensemble machine learning model includes seven heterogeneous machine learning techniques namely, Naive Bayes, Logistic regression, Decision Tree, Random Forest, AdaBoost Classifier, …Gradient Boosting, and extreme Gradient Boosting (XGB). The highest classification accuracy of 97.2%, 88.34%, 88.22%, 85.23%, 86.30%, 87.54%, 86.63%, and 88.78% is achieved by ensemble machine learning model, Logistic regression, AdaBoost, Decision Tree, Naive Bayes, Random Forest, Gradient Boosting, and XGB Classifier, respectively. Show more
Keywords: COVID-19 vaccinations, sentiments, social-media, machine learning, ensemble machine learning
DOI: 10.3233/JIFS-220279
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6307-6319, 2022
Authors: Saad, Muhammad | Rafiq, Ayesha
Article Type: Research Article
Abstract: T-spherical fuzzy sets, the direct extension of fuzzy sets, intuitionistic fuzzy sets and picture fuzzy sets are examined in this composition, and a mathematical examination among them is set up. A T-spherical fuzzy set can demonstrate phenomenon like choice utilizing four trademark capacities indicating the level of choice of inclusion, restraint, resistance, and exclusion, another example of such situation is that human opinion cannot be restricted to yes or no but it can be yes, abstain, no and refusal. T-spherical fuzzy set can deal the said situation with a boundless space. With the assistance of some mathematical outcomes, it is …talked about that current similarity measures have a few drawbacks and could not be implemented where the data is in T-spherical fuzzy mode. Thus, some new similarity measures in T-spherical fuzzy environment are proposed, with the assistance of certain outcomes, it is demonstrated that the suggested similarity measures are generalization of current ones. Further the proposed similarity measures are applied in pattern recognition with numerical supportive examples. The maximum spanning tree clustering algorithm has been extended into T-spherical fuzzy context and supports our theory with numerical examples. A parallel investigation of fresh and existing similarity measures have been made and some of the benefits of designated work have been discussed. Show more
Keywords: T-spherical fuzzy sets, T-spherical fuzzy similarity measures, pattern recognition, maximum spanning tree, clustering
DOI: 10.3233/JIFS-220289
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6321-6331, 2022
Authors: Suphalakshmi, A. | Ahilan, A. | Jeyam, A. | Subramanian, Malliga
Article Type: Research Article
Abstract: Cervical cancer is the most common and deadly malignancy affecting women worldwide. The prediction and treatment of this malignancy are necessary in order to avoid serious complications. In recent days, deep learning has enhanced the accuracy of cervical cancer prediction in its early stages. In this study, a deep learning based EN-FELM approach is proposed to detect and classify the cervical cells. Initially, the pap smear images are pre-processed to eliminate the background distortions. The EfficientNet is a reversed bottleneck MBConv used for feature extraction. Consequently, fuzzy extreme learning machine (FELM) is used to classify the healthy, benign, low squamous …intraepithelial lesions (LSIL) and high squamous intraepithelial lesions (HSIL). The proposed model acquires the best classification accuracy on Herlev and SIPaKMeD datasets range of 99.6% and 98.5% respectively. As a result, the classification using FELM produces more efficient and accurate result which is significantly high compared to the traditional classifiers. The proposed EN-FELM improves the overall accuracy of 0.2%, 0.13% and 14.6% better than Autoencoder, LSTM and KNN with CNN respectively. Show more
Keywords: Cervical cancer, fuzzy extreme learning machine (FELM), efficientnet, pap smear images, classification
DOI: 10.3233/JIFS-220296
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6333-6342, 2022
Authors: Ulu, Cenk
Article Type: Research Article
Abstract: Almost all exact inversion methods provide inverse solutions for only one input variable of fuzzy systems. These methods have certain limitations on the fuzzy system structure such as monotonic rule bases, singleton rule consequents, and invertibility check. These requirements limit the modeling capabilities of the fuzzy systems and also may result in poor application performances. In this study, an exact analytical inversion method is presented for multi-input-single-output decomposable TS fuzzy systems with either singleton or linear consequents. In the proposed method, fuzzy system structures do not need to have monotonic rule bases, singleton rule consequents, or any invertibility conditions. Thus, …more flexible fuzzy systems can be used in inverse model based applications. The proposed method provides a simple and systematic way to obtain unique inverse solutions of all input variables simultaneously with respect to any desired system output value. For this purpose, an inversion trajectory approach that guarantees the existence and uniqueness of the inverse solutions is introduced. The inversion trajectory consists of a set of paths defined on the specific edges of universe of discourses of the decomposed fuzzy subsystems. Using this approach, the inverse definition of the overall fuzzy system can easily be derived only by inverting the related decomposed fuzzy subsystems on this inversion trajectory and then combining their inverse definitions. In this way, the inverse definition of the overall fuzzy system is obtained as consisting of analytical solutions of linear and quadratic equations for singleton and linear consequent cases, respectively. Simulation studies are given for the inversion of two and three-input-single-output fuzzy systems, and the exactness and effectiveness of the proposed method are demonstrated. Show more
Keywords: Fuzzy systems, decomposability, inversion, multivariable systems, Takagi-Sugeno fuzzy systems
DOI: 10.3233/JIFS-220329
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6343-6356, 2022
Authors: Karabacak, Yusuf | Yaşar, Ali | Saritaş, İsmail
Article Type: Research Article
Abstract: In this paper, a simulation study enhanced to model that the speed control of brushless direct current (BLDC) motors used in electric vehicles with intelligent control methods. The simulation study was prepared in Matlab/Simulink environment. The first control method is Type-1 fuzzy logic control (T1FLC), and the second control method is the Intermittent Type-2 fuzzy logic control (IT2FLC) model. Membership functions for different membership numbers have been created for both types of FLC models. These are 3×3, 5×5, 7×7. Control methods are prepared in Matlab/M-file environment. The model is defined as the input variable of the error, which is the …difference between the reference speed and the motor speed, and the output variable of the Pulse Width Modulation (PWM) signal applied to the motor. The simulation study maintains the speed of the BLDC motor up to the reference speed with T1FLC and IT2FLC controllers, depending on the reference speed and applied load values. Depending on the number of different memberships, the effects of controller performances on the control of motor speed have been observed. The graphs and findings of the experiment are shown in the results and discussion section. Show more
Keywords: Speed control, fuzzy logic system, BLDC motor
DOI: 10.3233/JIFS-220344
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6357-6370, 2022
Authors: Al Ghour, Samer
Article Type: Research Article
Abstract: Let (Y , σ , B ) be a soft topological space. We introduce two new classes of soft subsets of (Y , σ , B ): soft connectedness relative to (Y , σ , B ) and soft θ -connectedness relative to (Y , σ , B ). We show that the class of soft connected subsets relative to (Y , σ , B ) includes the class of soft θ -connected subsets relative to (Y , σ , B ), but that these two classes do not always coincide. However, they coincide when (Y , σ , B …) is soft regular. We have provided several properties for each of these classes of soft sets. As two main results, we prove that for a given soft function f pu : (Y , σ , B ) ⟶ (Y , σ , B ) and a soft subset H of (Y , σ , B ), the soft set f pu (H ) is θ -connected relative to (Y , σ , B ) if (f pu is soft weakly continuous and H is connected relative to (Y , σ , B )) or (f pu is soft θ -continuous and H is θ -connected relative to (Y , σ , B )). Also, we investigate the correspondence between our new concepts in a soft topological space and their corresponding topological spaces properties. Moreover, we provide some examples to illustrate the obtained results and relationships. Show more
Keywords: θ-closure, soft separation, soft connected, soft θ-continuous, soft generated soft topological space, soft induced topological spaces
DOI: 10.3233/JIFS-220371
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6371-6381, 2022
Authors: Yu, Jianping | Fu, Jilin | Bai, Tana | Zhang, Tao | Li, Shaoxiong
Article Type: Research Article
Abstract: Semantic merger, which is a phenomenon of semantic convergence of two meanings of a word in a certain context, is a kind of semantic indeterminacy in natural language, however, it brings trouble for natural language processing. Discovery of the features causing semantic mergers has been a significant but tough issue in natural language processing. Until now this issue has remained untouched. Therefore, in this article, this issue is studied. Based on a 1.8 million word English multi-genre corpus, taking English modal verb may as the target word, the contextual features causing semantic mergers between may (root possibility) and may …(epistemic possibility) are investigated by an approach of attribute partial order diagram (APOD). First, the objects of may is categorized into 3 classes based on the idea of the three-way decision: may 1 (root possibility), may 2 (epistemic possibility) and may 3 (merger), then the rules for word sense disambiguation (WSD) of the three classes are extracted, respectively, and a comparison is made among the rules for different classes, and finally the features causing semantic merger of may are discovered. The discovered knowledge provides valuable evidence for finding the semantic merger, the cause of the semantic merger and the solution of semantic mergers of may , and the proposed approach can also be use for other modal verbs, which may benefit the natural language processing of English modal verbs. Show more
Keywords: Semantic merger, features causing semantic merger, English modal verb, attribute partial order diagram
DOI: 10.3233/JIFS-220388
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6383-6393, 2022
Authors: Gopinath, P. | Shivakumar, R.
Article Type: Research Article
Abstract: Recognition of finger vein patterns is essential technique that analyses the finger vein patterns to enable accurate authentication of an individual. A proper, accurate and quick learning of patterns is essentially required for improving the classification pattern. It is essential in developing an intelligent algorithm to effectively study and classify the patterns. In this paper, we develop an improved deep learning hybrid model for feature extraction and classification. A dimensional reduction deep neural network (DR-DNN) model has included a dimensional reduction model for extracting the essential features by reducing the dimensionality of feature datasets. A convolutional neural network (CNN) helps …in classifying the benign vein patterns from the malignant vein patterns. The effectiveness is compared against existing deep learning classifiers to measure how effective the deep learning model is used for classifying finger vein patterns for biometric authentication. The results shows that the proposed method achieves an accuracy rate of 97.16% for the proposed method, where the other existing methods including CNN, Recurrent Neural Network (RNN) and Deep Neural Nets (DNN) has an accuracy rate of 86%, 80.66% and 88.31%, respectively. Show more
Keywords: Deep neural networks, Deep Convolutional Neural Network, feature extraction, classification, finger vein patterns
DOI: 10.3233/JIFS-220423
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6395-6403, 2022
Authors: Lin, Ting-Yu | Hung, Kuo-Chen | Lin, Kuo-Ping | Hon, Jau-Shin | Chiu, Anthony Shun Fung
Article Type: Research Article
Abstract: With the economic growth of the world, sustainable development is a popular issue in recent years. Sustainable assessment is an important part of sustainable development. There are many previous scholars have used multiple-criteria decision-making (MCDM) to develop different evaluation frameworks in different fields. Elimination et Choix Traduisant la Realite II (ELECTRE II) is one of the most commonly used methods for MCDM. ELECTRE II uses alternatives, criteria, and criteria weighting from decision-makers to calculate the concordance and discordance indices. These two indices are used to rank the alternatives. The concordance and discordance indices in ELECTRE II are important because they …are the key to make accurate decisions. Previous scholars have failed to make comprehensive calculations for these indices, nor make their units of measure comparable, which negatively affected their results. This study improved the approach in calculating these indices and illustrated it using three case studies: (1) university examination results, (2) a sustainability assessment of groundwater remediation and (3) an assessment of power generation technologies. This improved ELECTRE II method offers decision-makers an objective basis for decision-making. Show more
Keywords: Sustainability assessment, multiple-criteria decision-making, ELECTRE II, decision analysis
DOI: 10.3233/JIFS-220441
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6405-6418, 2022
Authors: Manjula, P. | Priya, S. Baghavathi
Article Type: Research Article
Abstract: In today’s world, a Network Intrusion Detection System (NIDS) plays a vital role in order to secure the Wireless Sensor Network (WSN). However, the traditional NIDS model faced critical constraints with network traffic data due to growth in the complexity of modern attacks. These constraints have a direct impact on the overall performance of the WSN. In this paper, a new robust network intrusion classification framework based on the enhanced Visual Geometry Group (VGG-19) pre-trained model has been proposed to prolong the performance of WSN. Primarily, the pre-trained weights from the ImageNet dataset are utilized to train the parameters of …the VGG-19. Afterward, a Hybrid Deep Neural Network based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) will be employed to extract the influential features from network traffic data to enlarge the intrusion detection accuracy. The proposed VGG-19 + Hybrid CNN-LSTM model exploits both binary classification and multi-classification to classify attacks as either normal or attacked. A network intrusion benchmark dataset is used to assess the performance of the suggested system. The results reveal that the proposed VGG-19 + Hybrid CNN-LSTM learning system surpasses other pre-trained models with a superior accuracy of 98.86% during the multi-classification test. Show more
Keywords: Intrusion detection, classification, deep neural network, convolutional neural network, machine learning
DOI: 10.3233/JIFS-220444
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6419-6432, 2022
Authors: Suresh Kumar, K. | Helen Sulochana, C. | Radhamani, A.S. | Ananth Kumar, T.
Article Type: Research Article
Abstract: Many websites are attempting to offer a platform for users or customers to leave their reviews and comments about the products or services in their native languages. The cross-domain adaptation (CDA) analyses sentiment across domains. The sentiment lexicon falls short resulting in issues like feature mismatch, sparsity, polarity mismatch and polysemy. In this research, an augmented sentiment dictionary is developed in our native regional language (Tamil) that intends to construct the contextual links between terms in multi-domain datasets to reduce problems like polarity mismatch, feature mismatch, and polysemy. Data from the source domain and target domain both labeled and unlabeled …are used in the proposed dictionary. To be more specific, the initial dictionary uses normalised pointwise mutual information (nPMI) to derive contextual weight, whereas the final dictionary uses the value of terms across all reviews to compute the accurate rank score. Here, a deep learning model called BERT is used for sentiment classification. For cross-domain adaptation, a modified multi-layer fuzzy-based convolutional neural network (M-FCNN) is deployed. This work aims to build a single dictionary using large number of vocabularies for classifying the reviews in Tamil for several target domains. This extendible dictionary enhances the accuracy of CDA greatly when compared to existing baseline techniques and easily handles a large number of terms in different domains. Show more
Keywords: Cross-domain adaptation (CDA), BERT classification, modified multi-layer fuzzy convolutional neural networks (M-FCNN)
DOI: 10.3233/JIFS-220448
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6433-6450, 2022
Authors: Titus, P. | Ajitha Fancy, J. | Joshi, Gyanendra Prasad | Amutha, S.
Article Type: Research Article
Abstract: A set S ⊆ V in a graph G is a MED -set if every vertex in V - S has a monophonic eccentric vertex in S . The MED -number γme (G ) is the cardinality of a minimum MED -set of G . A set S ⊆ V in a graph G is a CMED -set if S is a MED -set and the induced subgraph is connected. The CMED -number γcme (G ) is the cardinality of a minimum CMED -set of G . We investigate some properties of the CMED …-sets. Also, we determine the bounds of the CMED -number and find the same for some standard graphs. The CMED -number has applications in security based communication networks in real life situations. This motivated us to introduce and investigate CMED -set in a graph. Show more
Keywords: Monophonic eccentric vertex, MED-set, MED-number, CMED-set, CMED-number
DOI: 10.3233/JIFS-220463
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6451-6460, 2022
Authors: Menekse, Akin | Akdag, Hatice Camgoz
Article Type: Research Article
Abstract: Combinative distance-based assessment (CODAS) is a multi-criteria decision-making (MCDM) method that is based on the Euclidean and Hamming distances of alternatives from the average scores of attributes. Spherical fuzzy sets, as the recent extensions of ordinary fuzzy sets, were developed based on Pythagorean and neutrosophic sets and enable decision-makers to express their membership, non-membership, and hesitancy degrees independently and in a larger domain than most other fuzzy extensions. This paper proposes a new interval-valued spherical fuzzy CODAS method and provides extra space for catching the vagueness in the nature of the problem. The feasibility and practicality of the proposed model …are illustrated with an application for evaluating the reopening readiness of academic units for campus education in the era of COVID-19. Three decision-makers from a higher education institution evaluate four academic units with respect to five strategic criteria and prioritize them according to their readiness levels for the campus type of education. Sensitivity and comparative analyses, theoretical and practical contributions, limitations, and future research avenues are also presented in the study. Show more
Keywords: CODAS, interval-valued spherical fuzzy, COVID-19, higher education institution, reopening readiness
DOI: 10.3233/JIFS-220468
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6461-6476, 2022
Authors: Rajnish, Kumar | Bhattacharjee, Vandana
Article Type: Research Article
Abstract: Software defect prediction is used to assist developers in finding potential defects and allocating their testing efforts as the scale of software grows. Traditional software defect prediction methods primarily concentrate on creating static code metrics that are fed into machine learning classifiers to predict defects in the code. To achieve the desired classifier performance, appropriate design decisions are required for deep neural network (DNN) and convolutional neural network (CNN) models. This is especially important when predicting software module fault proneness. When correctly identified, this could help to reduce testing costs by concentrating efforts on the modules that have been identified …as fault prone. This paper proposes a CONVSDP and DNNSDP (cognitive and neural network) approach for predicting software defects. Python Programming Language with Keras and TensorFlow was used as the framework. From three NASA system datasets (CM1, KC3, and PC1) selected from PROMISE repository, a comparative analysis with machine learning algorithms (such as Random Forest (RF), Decision Trees (DT), Nave Bayes (NF), and Support Vector Machine (SVM) in terms of F-Measure (known as F1-score), Recall, Precision, Accuracy, Receiver Operating Characteristics (ROC) and Area Under Curve (AUC) has been presented. We extract four dataset attributes from the original datasets and use them to estimate the development effort, development time, and number of errors. The number of operands, operators, branch count, and executable LOCs are among these attributes. Furthermore, a new parameter called cognitive weight (Wc) of Basic Control Structure (BCS) is proposed to make the proposed cognitive technique more effective, and a cognitive data set of 8 features for NASA system datasets (CM1, KC3, and PC1) selected from the PROMISE repository to predict software defects is created. The experimental results showed that the CONVSDP and DNNSDP models was comparable to existing classifiers in both original datasets and cognitive data sets, and that it outperformed them in most of the experiments. Show more
Keywords: Machine learning, software defect prediction, CNN model, cognitive weight, basic control structures, neural network
DOI: 10.3233/JIFS-220497
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6477-6503, 2022
Authors: Singh, Upendra | Gupta, Puja | Shukla, Mukul
Article Type: Research Article
Abstract: Image Incorporation concerns, including background confusion, uneven population distribution, and variations in scale and familiarity, can make group counting difficult. Pre-existing information and multi-level contextual representations are required to handle these problems effectively with deep neural networks and Mask-RCNN. Numerous studies on crowd counting use density maps without segmentation, which treat a group of individuals as a single entity. This article offers a hybrid method for crowd counting that combines Mask-RCNN (MRCNN) and a bidirectional convolutional long-term memory network (ConvLSTM), dubbed (CC: MRCNN-biCLSTM). The CC: MRCNN-biCLSTM is based on the Mask-RCN; it first segments instances and generates density maps, which …are passed into adversarial learning during the training phase. Finally, the bidirectional convolutional LSTM is being used to return metrics and counts for individuals within a group of individuals. Following that, the suggested activity detection technique based on the Bayesian non-linear filter AD-BNF is used to identify a person’s activity. Additionally, the suggested approach resolves human grouping and enhances metric performance. Extensive studies demonstrate that the suggested method outperforms more sophisticated techniques on four frequently used difficult criteria for density map precision and quality. Show more
Keywords: Mask-RCNN, bidirectional ConvLSTM, cluster counting, adversarial learning, activity detection
DOI: 10.3233/JIFS-220503
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6505-6520, 2022
Authors: Khan, Madad | Anis, Saima | Zuev, Sergei | Ullah, Hikmat | Zeeshan, Muhammad
Article Type: Research Article
Abstract: In this paper, we have discussed some new operations and results of set theory for complex fuzzy sets (CFSs). Moreover, we developed the basic results of CFSs under the basic operations such as complex fuzzy simple difference, bounded sum, bounded difference, dot product, bounded product, union, intersection, and Cartesian product. We explored the CFSs and discussed the related properties with examples such as complex fuzzy bounded sum over the intersection, complex fuzzy dot product over the union, etc. Identifying the reference signals under the environment of CFSs have always been a challenging. Many algorithms based on set theoretic operations and …distance measures have been proposed for identifying a reference signal using any common system. But linear time invariant (LTI) system is considered easy to analyze the linear and time-varying signals. We used CFSs in signals and systems. We developed an algorithm based on convolution product and LTI system under the complex fuzzy environment. We identified a high degree of resemblance (reference signal) of the received signals to the reference signal in a linear time-invariant (LTI) system that receives an input signal and produces an output signal. Show more
Keywords: Complex fuzzy sets, inverse discrete Fourier transform, signals and systems, linear-time invariant (LTI) system
DOI: 10.3233/JIFS-220517
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6521-6548, 2022
Authors: Yang, Jing | Su, Wei
Article Type: Research Article
Abstract: Interval-valued neutrosophic set (IVNS) plays an important role in dealing with imprecise judgment information. For a multi-attribute decision making problem, the information of alternatives under different attributes is given in the form of interval valued neutrosophic number(IVNN). The objective of the presented paper is to develop a multiple-attribute decision making (MADM) method under interval-valued neutrosophic sets(IVNSs) using the new similarity measurement. The similarity measurement of IVNSs has always been a research hotspot. A new similarity measurement of IVNSs is first proposed in this paper based on Chebyshev distance. The proposed method enriches the existing similarity measurement methods. It can be …applied to not only IVNSs, but also single-valued neutrosophic sets(SVNSs). The influence of each attribute on the decision-making result can be described by the weight. How to formulate the weight scientifically is vital as well. In this paper, the objective weight is calculated by normalizing the grey correlation coefficient obtained by a score function which can be applied to IVNSs. The objective weight is then combined with the subjective one by considering an adjustment factor with the weighted summation method. The adjustment factor is determined by the importance of subjective weight. Finally, an example is used to illustrate the comparison results of the proposed algorithm and other three ones. The comparison shows that the proposed algorithm is effective and can identify the optimal scheme quickly. Show more
Keywords: Fuzzy multi-attribute decision making, similarity measure, chebyshev distance, interval-valued neutrosophic sets
DOI: 10.3233/JIFS-220534
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6549-6559, 2022
Authors: Tan, Guimei | Yu, Xichang
Article Type: Research Article
Abstract: As an important tool to measure the degree of difficulty of predicting the realization of an uncertain set, entropy theory of uncertain set has been investigated by many scholars. In order to measure the uncertainty associated with some uncertain sets, this paper first proposes the arc entropy for an uncertain set. Then a computational arc entropy formula via inverse membership function is introduced to calculate the arc entropy more quickly, and some properties of arc entropy are studied. Furthermore, some applications are also provided to illustrate the superiority of the arc entropy.
Keywords: Uncertainty theory, uncertain set, arc entropy, portfolio selection
DOI: 10.3233/JIFS-220564
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6561-6574, 2022
Authors: Tang, Jianfei | Zhao, Hui
Article Type: Research Article
Abstract: The focus of a large amount of research on malware detection is currently working on proposing and improving neural network structures, but with the constant updates of Android, the proposed detection methods are more like a race against time. Through the analysis of these methods, we found that the basic processes of these detection methods are roughly the same, and these methods rely on professional reverse engineering tools for malware analysis and feature extraction. These tools generally have problems such as high time-space cost consumption, difficulty in achieving concurrent analysis of a large number of Apk, and the output results …are not convenient for feature extraction. Is it possible to propose a general malware detection process implementation platform that optimizes each process of existing malware detection methods while being able to efficiently extract various features on malware datasets with a large number of APK? To solve this problem, we propose an automated platform, AmandaSystem, that highly integrates the various processes of deep learning-based malware detection methods. At the same time, the problem of over privilege due to the openness of Android system and thus the problem of excessive privileges has always required the accurate construction of mapping relationships between privileges and API calls, while the current methods based on function call graphs suffer from inefficiency and low accuracy. To solve this problem, we propose a new bottom-up static analysis method based on AmandaSystem to achieve an efficient and complete tool for mapping relationships between Android permissions and API calls, PerApTool. Finally, we conducted tests on three publicly available malware datasets, CICMalAnal2017, CIC-AAGM2017, and CIC-InvesAndMal2019, to evaluate the performance of AmandaSystem in terms of time efficiency of APK parsing, space occupancy, and comprehensiveness of extracted features, respectively, compared with existing methods were compared. Show more
Keywords: Cybersecurity, android malware analysis, static analysis, dynamic analysis, least privilege
DOI: 10.3233/JIFS-220567
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6575-6589, 2022
Authors: Cypto, J. | Karthikeyan, P.
Article Type: Research Article
Abstract: With the growth in vehicular traffic, there is a greater risk of road accidents. Over speeding, intoxicated driving, driver distractions, red-light runners, ignoring safety equipment such as seat belts and helmets, non-adherence to lane driving, and improper overtaking are the leading causes of accidents. Speed violation, in particular, has a significant influence on today’s transportation. Also, detecting this speed violation and punishing this violator are more time-consuming tasks. For that reason, a novel automatic speed violation detection in traffic based on Deep learning is proposed in this paper. This proposed method is separated into two working modules: object detection and …license plate recognition. The object detection module uses the most efficient PP YOLO neural networks. It utilizes open ALPR (Automatic License Plate Recognition) for the vehicle’s number plate identification, which passes the traffic above maximum speed. With the number plate details, the authorities can take action against the rule violator with less time and effort. The simulation results show that the proposed automatic speed violation detection system also has an accuracy rate of 98.8% for speed violation detection and 99.3% for license plate number identification, demonstrating that the approach described in this work has a higher performance in terms of accuracy. Furthermore, the proposed technique was compared to recent existing results. Show more
Keywords: Speed violation, intoxicated driving, deep learning, PP YOLO, object detection, license plate recognition
DOI: 10.3233/JIFS-220577
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6591-6606, 2022
Authors: Shi, Xuecheng | Lin, Zhichao | Zhou, Ligang | Bao, Hengjia
Article Type: Research Article
Abstract: Linguistic q-rung orthopair fuzzy numbers (Lq-ROFNs) are an effective tool for representing fuzzy linguistic information, and they can obtain a wider expression scope than linguistic intuitionistic fuzzy numbers and linguistic Pythagorean fuzzy numbers by increasing the value of parameter q . In this paper, we propose a new similarity measure called the grey similarity degree between any two Lq-ROFNs based on the concept of the grey correlation degree. Considering the significance of determining unknown weights, we also propose a grey correlation method to determine each expert’s weight under different alternatives and attributes, and we construct an optimization model to determine …incompletely known attribute weights. Furthermore, an approach to linguistic q-rung orthopair fuzzy multiple-attribute group decision making is proposed that combines the grey similarity degree with the PROMETHEE II method. Finally, a numerical example is given to illustrate the effectiveness of the proposed method, and a sensitivity analysis and comparison analysis are also performed. Show more
Keywords: Linguistic q-rung orthopair fuzzy numbers, grey correlation degree, grey similarity degree, PROMETHEE, group decision making
DOI: 10.3233/JIFS-220579
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6607-6625, 2022
Authors: An, Qing | Tang, Ruoli | Li, Xueyan | Zhang, Xiaodi | Li, Xin
Article Type: Research Article
Abstract: In order to optimally control the marine hybrid power system (HPS) under increasingly complex regulation constraints or hardware constraints, an efficient power-flow scheduling model and optimization algorithm are of great importance. This work focuses on the optimal power-flow scheduling of marine HPS, especially on the efficiency improvement of the penalty functions for satisfying complex constraints. To be specific, an optimal operation model of marine HPS is discussed, and the complex model constraints are described as various penalty functions. Secondly, a novel optimization algorithm, namely adaptive multi-context cooperatively coevolving differential evolution algorithm with random topology and mutated context vector (AMCCDE - rt - mcv ) …is developed to optimize the aforementioned model. In order to ensure the satisfaction of the complex model constraints, the detailed forms for penalty functions are researched and the optimal parameters for penalty functions are comprehensively compared, analysed and tested by a set of numerical experiments. Finally, the developed methodologies are tested by simulation experiments. Experimental results show that the damping factor, exponent parameter and punish strength constant effect the efficiency of penalty functions a lot, and the developed penalty functions can effectively satisfy all the model constraints with fast response speed. With the integration of penalty functions, the developed methodology can obtain promising performance on the optimal scheduling of the evaluated marine HPS. Show more
Keywords: Hybrid power system, optimal energy management, penalty function, optimization algorithm, differential evolution
DOI: 10.3233/JIFS-220645
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6627-6649, 2022
Authors: Suresh, K. | Jagatheeswari, P.
Article Type: Research Article
Abstract: Renewable energy has seen a substantial increase in deployment as an alternative to traditional power sources. However, two fundamental constraints exist that preclude widespread adoption: the availability of the generated power and the expense of the equipment. One of the most critical difficulties with this sort of hybrid system is to appropriately design the Hybrid Renewable Energy System (HRES) elements so that they fulfill all load requirements while requiring the least amount of investment and running expenditures. This research proposes a novel technique for evaluating the optimal smart grid linking Hybrid Renewable Energy (Solar photovoltaic and wind) with battery, to …increase profitability, dependability, and feasibility. A multiobjective function is suggested and constructed to be optimized utilizing two optimization algorithms: Enhanced Particle Swarm Optimization (EPSO) and Harris Hawks Optimization (HHO) algorithm with Fuzzy-Extreme Learning Machine (ELM). The primary goal for the HRES is to operate optimally to reduce the cost of energy generat ion through hourly day-ahead. Here, the Fuzzy-ELM is utilized to predict the required load of the smart grid-connected system and hybrid EPSO-HHO, which are introduced to solve the problem of HRES economic analysis. Finally, the suggested EPSO-EHO method is implemented in the MATLAB software, and its performance comparison is made with other existing methods such as PSO, WOA, and HHO. The simulation result shows that the cost of the newly suggested EPSO-HHO technique-based Hybrid Renewable Energy System is less than PSO, WOA, and HHO by 4.89 %, 4.51 %, and 4.05 %, respectively. Show more
Keywords: Harris Hawks’ Optimization, economic analysis, renewable energy sources, Extreme Learning Machine, smart grid
DOI: 10.3233/JIFS-220726
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6651-6662, 2022
Authors: Durmaz, Nida | Budak, Ayşenur
Article Type: Research Article
Abstract: This study aims to define the adoption barriers to Industry 4.0 for sustainable supply chain and define their causalities and, dependencies, hierarchical levels of these barriers. Firstly, a framework for critical barriers to Industry 4.0 for sustainable supply chain management is created with literature review and experts for the first time. Then an integrated approach of Grey DEMATEL – ANP is proposed to analyze the adoption barriers to Industry 4.0 in sustainable supply chain management. The proposed method determines the cause-effect relationship among barriers, the strength of interactions, and the relative weights of critical barriers to Industry 4.0 in a …sustainable supply chain. The results show that uncertainty about economic benefits, resistance to change, and lack of infrastructure and tools for Industry 4.0 in the Sustainable supply chain are crucial barriers to implementing Industry 4.0 technologies in SSC. This study can help decision-makers and managers define the barriers and provide the theoretical guideline to implement Industry 4.0 technologies across the sustainable supply chain successfully. Show more
Keywords: Sustainable supply chain management, Grey DEMATEL, ANP, Industry 4.0 adoption barriers
DOI: 10.3233/JIFS-220732
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6663-6682, 2022
Authors: Jiang, Ruiyang
Article Type: Research Article
Abstract: The Pile motion seems to be one of the most critical in pile failure that requires appraisal before installing piles. The variables to estimate the Pile Settlement parameter, there are several methods. Among existing theoretical ways to investigate the pile movement mathematically, most studies have tried to model the piles’ settlement overloading period using artificial intelligence. Thus, this research has used the Artificial Neural Network to have the actual status of pile motion vertically over the loading periods dynamically and statically. Therefore, the present research has utilized the Radial Basis Function Neural Network joint with Equilibrium Optimizer Algorithm and Grasshopper …Optimization Algorithm to figure out the optimum number of neurons within the hidden layer. Kuala Lumpur’s Klang Valley Mass Rapid Transit transportation network, Malaysia, opted to model the piles’ settlement and earth properties via the proposed hybrid RBF-GOA and RBF-EOA frameworks. By modeling both frameworks, the error index of RMSE for RBF-GOA and HRBF-EOA were gained to 0.6312 and 0.5947, respectively. However, the VAF indicator showed identical results of the rates 96.98 and 97.33, respectively. Overly, the RBF-EOA represented better than RBF-GOA by little efficiency. Show more
Keywords: Pile in rock, settlement, prediction, radial basis function, equilibrium optimizer algorithm, grasshopper optimization, R-value correlation
DOI: 10.3233/JIFS-220741
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6683-6695, 2022
Authors: Kalaichelvi, V. | Vimala Devi, P. | Meenakshi, P. | Swaminathan, S. | Suganya, S.
Article Type: Research Article
Abstract: The billions of bits of information are transferred each second through the internet every day. The information may be text, image, audio or video etc, accordingly, we need some protection mechanism while sharing confidential data. Generally, RSA algorithm is used for encrypting the Secret images. However, the security provided by Elliptic Curve Cryptography (ECC) is higher with lower sized key than the RSA algorithm. So, this article proposes an extended Elliptic Curve encryption approach for encrypting the secret images. In this system, the secret image is partitioned into three color image planes such as Red, Green and Blue. By applying …Radix-64 encoding and Mapping table, these planes are converted into elliptic curve points and then these points are encrypted using ECC algorithm. Again, these points are applied to the Radix-64 decoding and the mapping table to get ciphered-image. At last, the key parameters such as a, b, p and Generator point (G) are embedded in the last four pixel positions of the ciphered-image. In order to get the original secret image, the recipient must extract these key parameters from the encrypted image and then apply the remaining processes to the encrypted image in the opposite order. Experimental results tested using MATLAB R2021b and it shows that the NPCR and UACI values are 99.54% and 28.73 % and better quality feature is attained since the entropy value is almost closer to eight. So, the proposed image encryption has robust capacity to fight against the differential attack. Show more
Keywords: ECC, Radix-64 conversion, image encryption, image decryption, security
DOI: 10.3233/JIFS-220767
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6697-6708, 2022
Authors: Mishra, Anju | Singh, Laxman | Pandey, Mrinal | Lakra, Sachin
Article Type: Research Article
Abstract: Diabetic Retinopathy (DR) is a disease that damages the retina of the human eye due to diabetic complications, resulting in a loss of vision. Blindness may be avoided If the DR disease is detected at an early stage. Unfortunately, DR is irreversible process, however, early detection and treatment of DR can significantly reduce the risk of vision loss. The manual diagnosis done by ophthalmologists on DR retina fundus images is time consuming, and error prone process. Nowadays, machine learning and deep learning have become one of the most effective approaches, which have even surpassed the human performance as well as …performance of traditional image processing-based algorithms and other computer aided diagnosis systems in the analysis and classification of medical images. This paper addressed and evaluated the various recent state-of-the-art methodologies that have been used for detection and classification of Diabetic Retinopathy disease using machine learning and deep learning approaches in the past decade. Furthermore, this study also provides the authors observation and performance evaluation of available research using several parameters, such as accuracy, disease status, and sensitivity. Finally, we conclude with limitations, remedies, and future directions in DR detection. In addition, various challenging issues that need further study are also discussed. Show more
Keywords: Retinal fundus images, machine learning, deep learning, classification, Diabetic retinopathy
DOI: 10.3233/JIFS-220772
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6709-6741, 2022
Authors: Niu, Guo | Ma, Zhengming | Liu, Xi
Article Type: Research Article
Abstract: Non-negative Tucker decomposition of tensor data has a wide range of applications in machine learning. With the non-negative constraints, tensor data can be decomposed into the mode product of a core tensor and a series of projection matrices. The core tensor usually is be regarded as the low-dimensional representation of the original tensor. The process of dimensionality reduction preserves the global properties of tensor data. But many applications in machine learning expect the continuous dependencies of data local feature to remain unchanged during dimensionality reduction. To this end, this paper proposes a local homeomorphism regularized non-negative Tucker decomposition algorithm for …tensor data. The proposed method introduce a local homeomorphism regularized term to tensor non-negative Tucker decomposition constrain for effectively preserving the global and local characters of tensor. Experiments on four commonly used real data sets and six compared algorithms have demonstrate the well performance of the proposed algorithms. Show more
Keywords: Tensor dimensionality reduction, tensors, nonnegative Tucker decomposition, local homeomorphism
DOI: 10.3233/JIFS-220785
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6743-6754, 2022
Authors: Aras, Cigdem G. | Al-shami, Tareq M. | Mhemdi, Abdelwaheb | Bayramov, Sadi
Article Type: Research Article
Abstract: A bipolar soft set is given by helping not only a chosen set of “parameters” but also a set of oppositely meaning parameters called “not set of parameters”. It is known that a structure of bipolar soft set is consisted of two mappings such that F : E → P (X ) and G :⌉ E → P (X ), where F explains positive information and G explains opposite approximation. In this study, we first introduce a new definition of bipolar soft points to overcome the drawbacks of the previous definition of bipolar soft points given in [34]. Then, we explore …the structures of bipolar soft locally compact and bipolar soft paracompact spaces. We investigate their main properties and illuminate the relationships between them. Also, we define the concept of a bipolar soft compactification and investigate under what condition a bipolar soft topology forms a bipolar soft compactification for another bipolar soft topology. To elucidate the presented concepts and obtained results, we provide some illustrative examples. Show more
Keywords: bipolar soft set, bipolar soft topology, bipolar soft locally compactness, bipolar soft paracompactness
DOI: 10.3233/JIFS-220834
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6755-6763, 2022
Authors: Xu, Qin | Xu, Shumeng | Wang, Dongyue | Yang, Chao | Liu, Jinpei | Luo, Bin
Article Type: Research Article
Abstract: Representing features at multiple scales is of great significance for hyperspectral image classification. However, the most existing methods improve the feature representation ability by extracting features with different resolutions. Moreover, the existing attention methods have not taken full advantage of the HSI data, and their receptive field sizes of artificial neurons in each layer are identical, while in neuroscience, the receptive field sizes of visual cortical neurons adapt to the neural stimulation. Therefore, in this paper, we propose a Res2Net with spectral-spatial and channel attention (SSCAR2N) for hyperspectral image classification. To effectively extract multi-scale features of HSI image at a …more granular level while ensuring a small amount of calculation and low parameter redundancy, the Res2Net block is adopted. To further recalibrate the features from spectral, spatial and channel dimensions simultaneously, we propose a visual threefold (spectral, spatial and channel) attention mechanism, where a dynamic neuron selection mechanism that allows each neuron to adaptively adjust the size of its receptive fields based on the multiple scales of the input information is designed. The comparison experiments on three benchmark hyperspectral image data sets demonstrate that the proposed SSCAR2N outperforms several state-of-the-art deep learning based HSI classification methods. Show more
Keywords: Hyperspectral image classification, deep learning, convolutional neural networks (CNNs), Res2Net, visual attention mechanism
DOI: 10.3233/JIFS-220863
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6765-6781, 2022
Authors: Sanjana, R. | Ramesh, G.
Article Type: Research Article
Abstract: This paper is concerned with the solution mechanism to solve the transportation problem under unpredictability by using interval valued intuitionistic fuzzy parameters. The parameters are chosen as intervals in which costs are modeled by intuitionistic fuzzy numbers, whereas source and destination are taken as crisp values. Various methods of transportation problem like VAM, Monalisha’s Approximation method, Zero point method are used to illustrate the cost in interval numbers by using the interval arithmetic operations. For each method, a solution is derived without converting into crisp expression followed by a graphical representation.
Keywords: Interval valued intuitionistic fuzzy numbers, inteval valued intuitionistic fuzzy transportation problem, interval arithmetic, interval VAM, interval ZPM
DOI: 10.3233/JIFS-220946
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6783-6792, 2022
Authors: Lakkshmanan, Ajanthaa | Anbu Ananth, C. | Tiroumalmouroughane, S.
Article Type: Research Article
Abstract: Pancreatic tumor is the deadliest disease which needs earlier identification to reduce the mortality rate. With this motivation, this study introduces a Multi-Objective Metaheuristics with Intelligent Deep Learning Model for Pancreatic Tumor Diagnosis (MOM-IDL) model. The proposed MOM-IDL technique encompasses an adaptive Weiner filter based pre-processing technique to enhance the image quality and get rid of the noise. In addition, multi-level thresholding based segmentation using Kapur’s entropy is employed where the threshold values are optimally chosen by the barnacles mating optimizer (BMO). Besides, densely connected network (DenseNet-169) is employed as a feature extractor and fuzzy support vector machine (FSVM) is …utilized as a classifier. For improving the classification performance, the BMO technique was implemented for fine-tuning the parameters of the FSVM model. The design of MOBMO algorithm for threshold selection and parameter optimization processes shows the novelty of the work. A wide range of simulations take place on the benchmark dataset and the experimental results highlighted the enhanced performance of the MOM-IDL technique over the recent state of art techniques. Show more
Keywords: Pancreatic tumor, computer aided diagnosis, deep learning, image classification, parameter optimization
DOI: 10.3233/JIFS-221171
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6793-6804, 2022
Authors: Qiu, Chenye | Fang, Huixing | Liu, Ning
Article Type: Research Article
Abstract: Microgrid (MG) systems are growing at a rapid pace since they can accommodate the high amount of renewable energy. Since the MG consists of small distributed generators (DG) with volatile characteristics, an efficient energy management system is the main requisite in MG. In this paper, a chaotic sine cosine algorithm with crossover operator (CSCAC) is proposed for the day-ahead MG optimal energy scheduling problem. CSCAC includes a novel non-linear transition parameter based on the chaos system which can help the algorithm escape from local optima. A chaotic search operator is proposed to enhance the local search ability. Furthermore, a crossover …operator is devised to combine the advantages of different search strategies and achieve a comparatively better balance of exploration and exploitation. First, the effectiveness of CSCAC is validated on several benchmark functions. Then, it is applied to the day-ahead energy scheduling in a MG with three wind power plants, two photovoltaic power plants and a combined heat and power plant (CHP). Furthermore, it is implemented in two more cases considering the uncertainty and stochastic nature of the renewable power sources. Experimental results demonstrate the superiority of CSCAC over other comparative algorithms in the optimal MG energy management problem. Show more
Keywords: Sine cosine algorithm, microgrid, chaotic system, energy scheduling, uncertainty
DOI: 10.3233/JIFS-221178
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6805-6819, 2022
Authors: Zhang, Taoyun | Zhang, Yugang | Zhang, Guangdong | Xue, Ling | Wang, Jin
Article Type: Research Article
Abstract: In order to ensure the safe and efficient application of smart grid data, this paper studies the de privacy encryption and extraction of smart grid data based on Spark Streaming, and accurately completes the de privacy encryption and extraction of smart grid data. The construction of the model mainly includes two parts, which are de privacy decryption processing and data extraction. After data privacy is processed by using collaborative cognitive model, the data is processed by Spark Streaming, including data cleaning, data reduction, data standardization, etc. Then the data clustering center is extracted by using genetic neural algorithm. Finally, the …similarity between the data set and the clustering center is calculated, and the data with the greatest similarity is selected to realize data extraction. The test results show that: the model can quickly complete data cleaning, effectively identify the abnormal data information in the data, the recognition rate is 99.72%, and complete data clustering in a few iterations, so as to realize the de privacy encryption and extraction of smart grid data. Show more
Keywords: Smart grid, data de privacy, encryption and extraction model, collaborative cognitive model, data cleaning, maximum similarit
DOI: 10.3233/JIFS-221185
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6821-6830, 2022
Authors: Li, Xiang | Yu, Junqi | Wang, Qian | Dong, Fangnan | Cheng, Renyin | Feng, Chunyong
Article Type: Research Article
Abstract: Short-term energy consumption prediction of buildings is crucial for developing model-based predictive control, fault detection, and diagnosis methods. This study takes a university library in Xi’an as the research object. First, a time-by-time energy consumption prediction model is established under the supervised learning approach, which uses a long short-term memory (LSTM) network and a Multi-Input Multi-Output (MIMO) strategy. The experimental results validate the model’s validity, which is close enough to physical reality for engineering purposes. Second, the potential of the people flows factor in energy consumption prediction models is explored. The results show that people flow has great potential in …predicting building energy consumption and can effectively improve the prediction model performance. Third, a diagnostic method, which can recognize abnormal energy consumption data is used to diagnose the unreasonable use of the building during each hour of operation. The method is based on differences between actual and predicted energy consumption data derived from a short-term energy consumption prediction model. Based on actual building operation data, this work is enlightening and can serve as a reference for building energy efficiency management and operation. Show more
Keywords: Deep learning, energy consumption prediction, energy consumption diagnosis, people flows
DOI: 10.3233/JIFS-221188
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6831-6848, 2022
Authors: Senthil, P. | Selvakumar, S.
Article Type: Research Article
Abstract: Digital evidence is an integral part of any trial. Data is critical facts, encrypted information that requires explanation in order to gain meaning and knowledge. The current process of digital forensic research cannot effectively address the various aspects of a complex infrastructure. Therefore, digital forensics requires the optimal processing of a complex infrastructure that differs from the current process and structure. For a long time, digital forensic research has been utilized to discuss these issues. In this research, we offer a forensic investigation hybrid deep learning approach based on integrated multi-model data fusion (HDL-DFI). First, we concentrate on digital evidence …collection and management systems, which can be achieved by an integrated data fusion model with the help of an improved brain storm optimization (IBSO) algorithm. Here, we consider several multimedia data’s for evidence purposes, i.e. text, image, speech, physiological signals, and video. Then, we introduce a recurrent multiplicative neuron with a deep neural network (RM-DNN) for data de-duplication in evidence collection, which avoids repeated and redundant data. After that, we design a multistage dynamic neural network (MDNN) for sentimental analysis to decide what type of crime has transpired and classify the action on it. Finally, the accuracy, precision, recall, F1-score, G-mean, and area under the curve of our proposed HDL-DFI model implemented with the standard benchmark database and its fallouts are compared to current state-of-the-art replicas (AUC). The results of our experiments show that the computation time of the proposed model HDL-DFI is 20% and 25% lower than the previous model’s for uploading familiar and unfamiliar files, 22% and 29% lower for authentication generation, 23% and 31% lower for the index service test scenario, and 24.097% and 32.02% lower for familiarity checking . Show more
Keywords: Digital forensics, evidence collection, evidence protection, deep learning, multi model fusion
DOI: 10.3233/JIFS-221307
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6849-6862, 2022
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