Journal of Intelligent & Fuzzy Systems - Volume 44, issue 3

Authors: Sharmila Devi, J. | Balasubramanian, P.

Article Type: Research Article

Abstract: Milling seems to be the most extensively utilized production technology in modern manufacturing industries, and it plays a significant role. Chatter is a type of disturbance in the form of vibration that has a negative impact on machining operation. Chatter recognition utilizing sensor outputs is a hot topic in academia. Although some progress has indeed been documented utilizing various featurization techniques and ml techniques, conventional approaches have a number of limitations, including manual preparation and a huge dataset need. Although, these are widely being used to evaluate milling operations in terms of production efficiency & work piece surface quality,.they are …not suited for real applications due to their computing duration and require large data for training process. Therefore, in this study, three well-performing deep learning approaches such as LSTM, DTW, and Bi-LSTM are used to provide an effective way for monitoring and managing chatter in the milling processes with the Duplex 2205 material. Here, some of the parameters like acceleration is measured while the milling operation is taking place, and the measured acceleration value is processed using selected three DL techniques for identifying the presence of chatter and are tested to see which one performs the best. The Bi-LSTM outperformed other approaches in detecting chatter present, according to the data. Show more

Keywords: Bi-directional long short term memory, long short term memory, dynamic time warping, deep learning, acceleration, milling chatter detection

DOI: 10.3233/JIFS-221091

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3647-3666, 2023

Authors: Wang, Yongguo | Bi, Xuewen | Zhang, Xinxin

Article Type: Research Article

Abstract: The high power generation growth by photovoltaic systems needs to forecast the power generation profile during a day. It is also required to evolve the high-efficient and optimal on-grid/off-grid photovoltaic power generation units. Furthermore, some advantages can be achieved by integrating photovoltaic systems with storage devices such as battery energy storage systems. Thus, optimizing the hybrid systems comprising photovoltaic and battery energy storage systems is needed to evaluate the best capacity. In the present work, a novel control and sizing scheme is proposed for the battery energy storage system in a photovoltaic power generation plant in one-hour ahead and one-day …ahead during the dispatching phase. Then, the proposed prediction strategy is recommended for solar irradiation and power utilization. The control approach comprises a predictive control method concerning a Radial Basis Function network optimized by Levenberg-Marquardt back-propagation learning algorithm. Using the RBF network for simulation leads to a WAPE % =1.68 %. Show more

Keywords: Photovoltaic systems, battery energy storage system, control method, prediction method, RBF neural network, experimental dataset

DOI: 10.3233/JIFS-221123

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3667-3680, 2023

Authors: Nuhoho, Raphael Elimeli | Wenyu, Chen | Baffour, Adu Asare

Article Type: Research Article

Abstract:  As digital image acquisition becomes ubiquitous in recent years, the need for indoor scene recognition becomes more pronounced. Existing methods leverage the features of composing objects in a scene and overlook the adverse impacts of the common objects reoccurring in other scenes. This drawback decreases the feature discrimination between scenes (e.g., living room, dining room, and bedroom) due to reoccurring objects (e.g., tables, chairs, and toys). We propose a method of training convolutional networks by punishing or discounting the local object representations’ predictive ability and encouraging the network to learn global scene layout representations. To retain more vital information for …the scene feature representation, we introduce an activation function (with unbounded above, bounded below, smooth, and non-monotonic properties) to allow more low-negative values to flow through the network, discarding high negative values. We evaluate the proposed methods on MIT Indoor 67 and Scene 15 datasets. The experiment findings show that the proposed methods capture global scene concepts and improve performance. Show more

Keywords: Indoor scene recognition, feature representation, activation functions, convolutional neural network

DOI: 10.3233/JIFS-221975

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3681-3693, 2023

Authors: Meziani, Ahlem | Bourouis, Abdelhabib | Chebout, Mohamed Sedik

Article Type: Research Article

Abstract: Effective risk management reaction improves the absorption of critical impacts on supply chains. Supply chain risk (SCR) sources, like control, process, demand, and supply, need to be identified, assessed, and mitigated to make rational decisions immediately. Late detection of a disruptive event can cause delays in handling risk. Since SCRs consist of complex, uncertain, and incomplete information, most of the provided risk assessment mechanisms cannot handle it in real-time. Hence, in this paper, we introduce NeutroMAS4SCRM, a framework incorporating real-time Multi-Agent Systems (MAS) with Neutrosophic Data Analytic Hierarchy Processes to best deal with the complexity, uncertainty, and vagueness of SCR …management-related issues and which can hence help decision-makers adopt less risky decisions. In addition, the MAS technology contribution to SCR management is outlined through a comparative study among the most recent studies. In contrast, the proposed MAS for the supply chain is implemented under the JADE agent platform, where the FIPA-ACL-based message content is specified using a dedicated ontology. A simulation-based decision support system is used to assess the cost risk and its harmful effects and determine how well the proposed framework can help companies manage risks efficiently. The simulation has proven to reduce risk costs by about 85%. Show more

Keywords: Supply chain risk management, single-valued neutrosophic set, neutrosophic DAHP, multi agent system, simulation

DOI: 10.3233/JIFS-222305

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3695-3716, 2023

Authors: Lakshmi Narayanan, S. | Ignatia, K. Majella Jenvi | Alfurhood, Badria Sulaiman | Bhat, Nagaraj

Article Type: Research Article

Abstract: A Gaussian Curvature-based Local Tetra Descriptor (GCLTrP) is proposed in this paper to incorporate geometric discriminative feature extraction using a hybrid combination of Gaussian Curvature (GC) and Local Terta Pattern (LTrP). The texture of an image is locally discriminant, capturing the equivalent binary response from the gaussian curvature. The extracted feature value is fed into the Enhanced Grey Wolf Optimization (EGWO), a lightweight metaheuristic searching algorithm that selects the best optimal textural features. The proposed GCLTrP with EGWO method’s effective performance is validated using the benchmarks dataset, and the results are tested using the performance evaluation metric. In comparison to …other cutting-edge methods, the proposed method achieves the highest overall classification accuracy of 100% on the Brodatz and RS datasets. In terms of computational redundancy and noise reduction, the proposed technique outperforms the other existing techniques. Show more

Keywords: Feature extraction, feature selection, classification, texture analysis

DOI: 10.3233/JIFS-222481

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3717-3731, 2023

Authors: Huong, Trieu Thu | Lan, Luong Thi Hong | Giang, Nguyen Long | Binh, NguyenThi My | Vo, Bay | Son, Le Hoang

Article Type: Research Article

Abstract:  Transfer learning (TL) is further investigated in computer intelligence and artificial intelligence. Many TL methodologies have been suggested and applied to figure out the problem of practical applications, such as in natural language processing, classification models for COVID-19 disease, Alzheimer’s disease detection, etc. FTL (fuzzy transfer learning) is an extension of TL that uses a fuzzy system to pertain to the vagueness and uncertainty parameters in TL, allowing the discovery of predicates and their evaluation of unclear data. Because of the system’s increasing complexity, FTL is often utilized to further infer proper results without constructing the knowledge base and environment …from scratch. Further, the uncertainty and vagueness in the daily data can arise and modify the process. It has been of great interest to design an FTL model that can handle the periodicity data with fast processing time and reasonable accuracy. This paper proposes a novel model to capture data related to periodical phenomena and enhance the quality of the existing inference process. The model performs knowledge transfer in the absence of reference or predictive information. An experimental stage on the UCI and real-life dataset compares our proposed model against the related methods regarding the number of rules, computing time, and accuracy. The experimental results validated the advantages and suitability of the proposed FTL model. Show more

Keywords: Complex fuzzy set, mamdani complex fuzzy inference system, transfer learning, fuzzy transfer learning, complex fuzzy transfer learning

DOI: 10.3233/JIFS-222582

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3733-3750, 2023

Authors: Wu, Guanghua | Li, Hongsheng | Li, Hongyu | Guo, Shiping | Ma, Wenjian | Dong, Jing

Article Type: Research Article

Abstract: The business expansion installation can only simply record the most basic business information, which leads to the problems of complex power supply procedures and low efficiency. Therefore, a study on the optimal power supply parameters of the business expansion installation based on grey correlation degree and fuzzy C-means clustering algorithm is proposed. Firstly, the grey correlation degree is used to process the optimal power supply parameter data of industrial expansion and installation, and the parameters of fuzzy C-means clustering algorithm are set. On this basis, an intelligent management system for the optimal power supply process of industrial expansion and installation …is constructed, and the system development conditions are set up; According to the four business links of project reserve, business acceptance, collaborative operation and performance evaluation, the customer business expansion and installation function module is constructed, so as to realize the calculation of the optimal power supply line of the business expansion and installation and complete the research on the optimal power supply parameters. The experimental results show that the output stability, output throughput performance and parameter optimization ability of this method for the line impedance characteristic control of the power supply of the industrial expansion device are good and are always on the rise. At 3 cm, the output throughput reaches 1.9%, and the parameter analysis ability can reach 350 pixels, which has certain application value. Show more

Keywords: Grey correlation degree, fuzzy C-means clustering algorithm, business expansion newspaper installation, Optimal power supply parameters

DOI: 10.3233/JIFS-222926

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3751-3762, 2023

Authors: Zhang, Shasha | Liu, Xiaodi | Garg, Harish | Zhang, Shitao

Article Type: Research Article

Abstract: With the implementation and steady progress of the Belt and Road (B&R) initiative, China’s investment in countries along the B&R has maintained a high growth trend. Generally speaking, investment problems are often accompanied by high risk and uncertainty, and how to make the suitable investment decision is a difficult issue. This paper investigates an investment decision approach under the probabilistic hesitant fuzzy environment. Firstly, a new probabilistic hesitant fuzzy distance and correlation coefficient are defined to overcome the defects of the existing probabilistic hesitant fuzzy information measures. Secondly, an attribute weight integrated model is constructed by combining the maximum deviation …method, the CRITIC method and the maximum entropy principle, which is able to take into account the correlation between attributes and make full use of the decision information. In addition, a disappointment theory-based probabilistic hesitant fuzzy multi-attribute decision making (PHFMADM) method is proposed to solve the investment decision problem, which can integrate the psychological behavior of decision makers into the decision making process and make the decision results more authentic and reliable. Finally, the rationality and validity of the method are verified by comparing with the existing methods. Show more

Keywords: Investment decision making, Distance, Correlation coefficient, Disappointment theory, Probabilistic hesitant fuzzy sets

DOI: 10.3233/JIFS-223059

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3763-3786, 2023

Authors: Zhou, Tong | Zhang, Shuai | Zhang, Dongping | Chan, Verner | Yang, Sihan | Chen, Mengjiao

Article Type: Research Article

Abstract: With the increasing demand for express delivery and enhancement of sustainable logistics, the collaborative multi-depot delivery based on electric vehicles has gradually attracted the attention of logistics industry. However, most of the existing studies assumed that the products required by different customers could be delivered from any homogeneous depot, ignoring the limitations in facilities and environment of depots in reality. Thus, this study proposed a novel collaborative multi-heterogeneous-depot electric vehicle routing problem with mixed time windows and battery swapping, which not only involves the multi-heterogeneous-depot to meet different customer demands, but also considers the constraints of mixed time windows to …ensure timely delivery. Furthermore, a customer-oriented multi-objective optimization model minimizing both travel costs and time window penalty costs is proposed to effectively improve both delivery efficiency and customer satisfaction. To solve this model, an extended non-dominated sorting genetic algorithm-II is proposed. This combines a new coding scheme, a new initial population generation method, three crossover operators, three mutation operators, and a particular local search strategy to improve the performance of the algorithm. Experiments were conducted to verify the effectiveness of the proposed algorithm in solving the proposed model. Show more

Keywords: Electric vehicle routing problem, multi-objective optimization, collaborative multi-heterogeneous-depot, mixed time windows, nondominated sorting genetic algorithm-II

DOI: 10.3233/JIFS-223298

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3787-3805, 2023

Authors: Li, Shiyong | Li, Wenzhe | Sun, Wei | Liu, Jia

Article Type: Research Article

Abstract: The advantages of cloud computing attract a large number of enterprises to deploy their applications into the cloud, thereby reducing their own operating costs. This paper considers deploying inelastic applications into the cloud and proposes an optimal resource allocation model. The deployment functions for inelastic applications are nonconvex (e.g., sigmoidal), then the resource allocation model becomes a hard nonconvex optimization problem. The traditional gradient-based resource allocation algorithm cannot effectively achieve the global optimum. Therefore, this paper applies particle swarm optimization (PSO) method to design a resource allocation scheme. This scheme can not only effectively solve the resource allocation problem of …deploying inelastic enterprise applications into the cloud, but also solve the hard problem of deploying multi-class applications into the cloud when the enterprise can support both elastic and inelastic applications. We also compare the performance of the proposed PSO-based resource allocation scheme with some other methods and illustrate some numerical examples to verify the effectiveness and superiority of the proposed resource allocation scheme. Show more

Keywords: Cloud deployment, inelastic applications, resource allocation, nonconvex optimization, PSO

DOI: 10.3233/JIFS-201239

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3807-3823, 2023

Authors: Khan, Indadul | Dutta, Prasanta | Maiti, Manas Kumar | Basuli, Krishnendu

Article Type: Research Article

Abstract: In this study, Bat algorithm (BA) is modified along with K -opt operation and one newly proposed perturbation approach to solve the well known covering salesman problem (CSP). Here, along with the restriction of the radial distances of the unvisited cities from the visited cities another restriction is imposed where a priority is given to some cities for the inclusion in the tour, i.e., some clusters to be created where the prioritised cities must be the visiting cities and the corresponding CSP is named as Prioritised CSP (PCSP). In the algorithm, 3-opt and 4-opt operations are used for two different …purposes. The 4-opt operation is applied for generating an initial solution set of CSP for the BA and the 3-opt operation generates some perturbed solutions of a solution. A new perturbation approach is proposed for generating neighbour solutions of a potential solution where the exchange of some cities in the tour is made and is named as K -bit exchange operation. The proposed solution approach for the CSP and PCSP is named as the modified BA embedded with K -bit exchange and K -opt operation (MBAKEKO). It is a two-stage algorithm where in the first stage of the algorithm the clustering of the cities is done with respect to a fixed visiting city of each cluster in such a manner that the distances of the other cities of the cluster must lie with in the fixed covering distance of the problem and in the second stage the BA is applied to find the minimum cost Hamiltonian circuit by passing through the visiting cities of the clusters. MBAKEKO is tested with a set of benchmark test problems with significantly large sizes from the TSPLIB. To measure the performance of MBAKEKO, its results are compared with the results of different well-known approaches for CSPs available in the literature. It is observed from the comparison studies that MBAKEKO searches the minimum cost tour for any of the considered instances compared to all other well-known algorithms in the literature. It can be concluded from the numerical studies that the performance of MBAKEKO is better with respect to the state-of-the-art algorithms available in the literature. Show more

Keywords: Combinatorial optimization, covering salesmen problem, priority based covering salesman problem, K-bit exchange operation, K-opt, Bat Algorithm

DOI: 10.3233/JIFS-220396

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3825-3849, 2023

Authors: Kanimozhi Selvi, C.S. | Jayaprakash, D. | Poonguzhali, S.

Article Type: Research Article

Abstract: Autism spectrum disorder is a neuro-developmental disorder that affects communication and social skills in individuals. Screening and diagnosis of autism using conventional methods, such as interviews with parents or caregivers and observational assessments takes a long time. The accurate diagnosis of autism by physicians and healthcare professionals seems to be challenging. By analyzing data on autistic children, medical professionals can learn about autism screening assessment decision making. The present study aims to develop a parental autism screening tool termed the Indian Autism Grading Tool (IAGT) for early screening of autism. Data are collected using the Indian Autism Parental Questionnaire and …assigned with grades. This dataset is employed to test five supervised machine learning models, which compare classification performance based on accuracy, precision and recall. The most effective model should be used to implement the autism screening application. MLR is known to be more robust and to support fewer data sets, so it can be employed for the implementation of ML-powered mobile applications. MLR achieves the overall accuracy of 97.85%, which equates to 0.72%, 2.37%, 0.84% and 1.54% better than SVM, DT, KNN and GNB respectively. The proposed tool is developed in both Tamil and English. The pilot study is conducted with 30 children and the predictability of the tool is compared with the clinician. Therefore, the tool consistently achieves the same level of accuracy as clinicians. Show more

Keywords: Artificial intelligence, machine learning techniques, autism spectrum disorder, Indian autism parental questionnaire, mobile application

DOI: 10.3233/JIFS-221087

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3851-3865, 2023

Authors: Amanullah, M. | Thanga Ramya, S. | Sudha, M. | Gladis Pushparathi, V.P. | Haldorai, Anandakumar | Pant, Bhaskar

Article Type: Research Article

Abstract: On the basis of quality estimate, early prediction and identification of software flaws is crucial in the software area. Prediction of Software Defects SDP is defined as the process of exposing software to flaws through the use of prediction models and defect datasets. This study recommended a method for dealing with the class imbalance problem based on Improved Random Synthetic Minority Oversampling Technique (SMOTE), followed by Linear Pearson Correlation Technique to perform feature selection to predict software failure. On the basis of the SMOTE data sampling approach, a strategy for software defect prediction is given in this paper. To address …the class imbalance, the defect datasets were initially processed using the Improved Random-SMOTE Oversampling technique. Then, using the Linear Pearson Correlation approach, the features were chosen, and using the k-fold cross validation process, the samples were split into training and testing datasets. Finally, Heuristic Learning Vector Quantization is used to classify data in order to predict software problems. Based on measures like sensitivity, specificity, FPR, and accuracy rate for two separate datasets, the performance of the proposed strategy is contrasted with the approaches to classification that presently exist. Show more

Keywords: Index Terms: Software defect prediction, improved random-SMOTE oversampling technique, linear pearson correlation, heuristic learning vector quantization (LVQ), training and test datasets

DOI: 10.3233/JIFS-220480

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3867-3876, 2023

Authors: Suganthi, M. | Arun Prakash, R.

Article Type: Research Article

Abstract: Everything becomes smart in the modern era, for everything we need a better plan or arrangements. In the olden days, essential information was noted as a document with the help of paper and pen or printed texts. But the intelligent world needs a paperless environment by converting handwritten or printed text documents into software copies. This can be achieved by the electronic data conversion concept called Optical Character Recognition (OCR). OCR of some documents is complex because of different writing styles and quality of scanned image issues, which can be solved by adopting a deep learning technique for better accuracy. …We employed Long Short Term Memory (LSTM) for English Optical Character Recognition for paperless and effortless data storage and fast access in this work. Still, the records may contain the entities like names, contact details, drug details, diseases, educational qualifications, dates, etc. These entities cannot be separated by employing OCR alone; we need an entity recognition framework for deeper and faster data analysis. For efficient Named Entity Recognition, we utilize the Adaptive Fuzzy Inference System (ANFIS) powered by the algorithms CRF and BERT to automatically label each entity by training the vast amount of unlabeled text data. The ANFIS model is equipped with both linguistic and numerical knowledge. It is more accurate than the ANN when it comes to identifying patterns and classification data. Also, it is more transparent to the user. Our proposed framework aims to improve the performance of the character recognition system by using a feed-forward network. One of the main issues that have been identified in the development of this system is noise. Through this network, we can provide a single input and one output layer. The main components of the system are the training and recognition sections. These two sections are mainly focused on image acquisition and feature extraction. Besides these, they also include training and simulation of the classifier. The first step in the process of image recognition is to extract the features from the normalized image matrix. We then train the network using a proposed training algorithm. Experimentation on medical records attains a higher accuracy value of 0.9637, recall value of 0.9627, and f1 score of 0.9627, respectively. Show more

Keywords: Paperless environment, Optical Character Recognition, Named Entity Recognition, faster data analysis, accuracy

DOI: 10.3233/JIFS-221486

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3877-3890, 2023

Authors: Hovorushchenko, Tetiana | Medzatyi, Dmytro | Voichur, Yurii | Lebiga, Mykyta

Article Type: Research Article

Abstract: The paper develops the method for forecasting the level of software quality based on quality attributes. This method differs from the known ones in that it provides forecasting the quality level of future software based on the processing the software quality attributes’ values, which are available in the software requirements specification (SRS). So, the proposed method makes it possible to compare the SRSs, to immediately refuse the realization of a software based on unsuccessful SRS (saving money and time, reducing the probability of failed and challenged projects), and to make a reasonable choice of the specification for the further implementation …of a software with the highest quality (of course, if errors will not be introduced at subsequent stages of the software life cycle). During the experiments, 4 SRS were analyzed, which were fulfilled by different IT firms of Khmelnytskyi (Ukraine) for the solution of the same task. Taking into account the forecasted quality level of the future software, which will have developed according to each of the analyzed SRS, a comparison of the 4 analyzed SRS was made, and a reasoned choice of the specification was made for the further realization of the highest quality software. Show more

Keywords: Software quality, software quality attributes, software quality characteristics, software quality level, artificial neural network (ANN)

DOI: 10.3233/JIFS-222394

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3891-3905, 2023

Authors: Ma, Hongdong | Li, Gang | Liu, Rongyue | Shen, Mengdi | Liu, Xiaohui

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-212780

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3907-3925, 2023

Authors: Ferrari, Allan Christian Krainski | Leandro, Gideon Villar | Coelho, Leandro dos Santos | Delgado, Myriam Regattieri De Biase Silva

Article Type: Research Article

Abstract: The rat swarm optimizer is one of the most recent metaheuristics focused on global optimization. This work proposes a fuzzy mechanism that aims to improve the convergence of this algorithm, adjusting the amplitude of the parameter that directly affects the chasing mechanism of the behavior of rats. The proposed fuzzy model uses the normalized fitness of each individual and the population diversity as input information. For evaluation criteria, the fuzzy mechanism proposed, was implemented in the optimization of third-three single objective problems. For comparison criteria, the proposed fuzzy variant is compared with other algorithms, such as GWO (Grey Wolf Optimizer), …SSA (Salp Swarm Algorithm), WOA (Whale Optimization Algorithm), and also with two proposed alternative fuzzy variants. One of the simpler fuzzy variants uses only population diversity as input information, while the other uses only the normalized fitness value of each rat. The results show that the proposed fuzzy system improves the convergence of the conventional version of the rat algorithm and is also competitive with other metaheuristics. The Friedman test shows statistically the results obtained. Show more

Keywords: Rat swarm optimizer, metaheuristics, fuzzy system, optimization, friedman test

DOI: 10.3233/JIFS-222522

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3927-3942, 2023

Authors: Ranjeeth Kumar, C. | Kalaiarasu, M.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-222795

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3943-3958, 2023

Authors: Paramasivam Thuraipandi, Sivagurunathan | Nagarajan, Sathish Kumar

Article Type: Research Article

Abstract: The spectrum scarcity problem in today’s wireless communication network is addressed through the use of a cognitive radio network (CRN). Detection in the spectrum is made easier by cooperative spectrum sensing (CSS), which is a tool developed by the military. The fusion centre receives the sensing information from each secondary user and uses it to make a global conclusion about the presence of the principal user. Literature has offered several different methods for decision making that lack scalability and robustness. CSS censoring is inspected in the attendance of faded settings in the current study. Rayleigh fading, which affects reporting channels …(R), is examined in detail. Multiple antennae and an energy detector (ED) are used by each secondary user (SU). A selection combiner (SC) combines the ED outputs with signals from the primary user (PU), which are established by several antennas on SU, before the joint signal is utilised to make a local result. SUs are expurgated at the fusion centre (FC) using a hybrid Support Vector Machine (SVM) that significantly improves detection performance and reduces the number of false positives. With a minimum false alarm probability of 0.1, error rate of 0.04, spectrum utilization of 99%, throughput of 2.9kbps and accuracy of 99%, proposed model attains better performance than standard SVM and Artificial Neural Network (ANN) models. Show more

Keywords: Energy detector, primary user, cognitive radio network, secondary user, cooperative spectrum sensing

DOI: 10.3233/JIFS-222983

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3959-3971, 2023

Authors: Wang, Jing

Article Type: Research Article

Abstract: Before neural networks, image style transfer procedures had a common idea: analyze images with a certain style, build a mathematical or statistical model for the style, and then change the image to be transferred, so that it better fits the established model. In this paper, k-means and semantic closed natural matting algorithm is combined for image segmentation, the style and content in the image are extracted based on neural network, and the resulting image is synthesized by image reconstruction technology to realize the migration of national costume styles. Due to the serious artifacts of the output image, an improved image …style transfer algorithm is adopted to constrain the transformation from the input image to the output image in the local affine transformation of the color space, and this constraint is expressed as a completely differentiable parameter term, image distortion is suppressed effectively. In the process of real photo style transfer, there is also space inconsistency. Smoothing is done to ensure that the space style is consistent after style processing, it greatly speeds up the operation speed. On NVIDIA GTX1080TI graphics card, algorithm is tested with 256×256 resolution images. It includes three indicators of average running time, memory usage and the number of styles generated by a single model, which are 0.06 s, 136.06 MB and 1 respectively. These indicators can reflect the efficiency and flexibility of the algorithm. Show more

Keywords: Garment feature extraction, deep learning, closed form natural image matting algorithm, K-means, image segmentation, image style transfer

DOI: 10.3233/JIFS-220761

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3973-3986, 2023

Authors: Sreenivasulu, A. | Subramanian, S. | Sangameswara Raju, P.

Article Type: Research Article

Abstract: The world’s energy offer has been beneath an incredible pressure because of the speedy depletion of fossil resources, energy security, environmental issues and therefore the ever-increasing fashionable living sophistication. The problem of persistent hikes in oil costs, climate threats and soaring energy demand has pleased the worldwide interest to exploiting and investment in renewable sorts of energy (RE), alternative energy specially. A electrical phenomenon, PV system is simple to put in, has no moving components, is sort of freed from maintenance, reduced vulnerability to power loss and is expandable. Despite these benefits, PV energy prices significantly on top of fossil …fuels. This can be because of its lower effectiveness and better prices. In PV systems tracking MPPT in effective manner is still the problem. In this paper, the 1000 W grid connected PV system has been taken for analysis of various MPPT techniques. Grid connected PV system modeled, tested under totally different irradiation conditions and conjointly for partial shading conditions. additional it’s enforced under partial shading condition for early MPPT ways, improvement methodology,at finally adopted deep learning methodology for the system and therefore the obtained results were compared with different methods. Show more

Keywords: Maximum power point tracking, deep learning, partial shading conditions, efficiency, power

DOI: 10.3233/JIFS-221465

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3987-3998, 2023

Authors: Deepa, K. | Ranjeeth Kumar, C.

Article Type: Research Article

Abstract: The remarkable developments in biotechnology as well as the health sciences have resulted in the production of an enormous amount of data, including high-throughput screening genomics information and clinical information obtained through extensive electronic health records (EHRs). The application of data mining and machine learning techniques in the biosciences is today more vital than ever to achieving this objective as attempts are made to intelligently translate all readily available data into knowledge. Diabetes mellitus (DM), a group of metabolic disorders, is well known to have a serious detrimental effect on population lives all over the world. Large-scale research into all …aspects of diabetic has resulted in the production of enormous amounts of data (detection, etiopathophysiology, therapy, etc.). The goal of the current study is to conduct a thorough examination of the use of machine learning, data mining methods and tools in the field of diabetes research, with the first classification making an appearance to be the most popular. These applications relate to a Statistical model and Diagnosis, b) Diabetic Complications, c) Multiple genes Background and Environment, and e) Free Healthcare and Management. Numerous machine learning algorithms were applied. 85% of the methods used were supervised learning approaches, whereas 15% were uncontrolled ones, including association rules. Developed on improved support vector machines, the most successful and widely used algorithm (SVM). Medical datasets were predominantly used in terms of data kind. Show more

Keywords: Diabetes mellitus, data mining, machine learning techniques, medical datasets, screening genomics information and early diagnosis

DOI: 10.3233/JIFS-222574

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3999-4011, 2023

Authors: Yu, Jianping | Yuan, Laidi | Zhang, Tao | Fu, Jilin | Cao, Yuyang, | Li, Shaoxiong | Xu, Xueping

Article Type: Research Article

Abstract: The development of natural language processing promotes the progress of general linguistic studies. Based on the selected features and the extracted rules for word sense disambiguation (WSD), some valuable knowledge of the relations between linguistic features and word sense classes may be discovered, which may provide theoretical and practical evidence and references for lexical semantic study and natural language processing. However, many available approaches of feature selection for WSD are in the end to end operation, they can only select the optimal features for WSD, but not provide the rules for WSD, which makes knowledge discovery impossible. Therefore, a new …Filter-Attribute partial ordered structure diagram (Filter-APOSD) approach is proposed in this article to fulfill both feature selection and knowledge discovery. The new approach is a combination of a Filter approach and an Attribute Partial Ordered Structure Diagram (APOSD) approach. The Filter approach is designed and used for filtering the simplest rules for WSD, and the APOSD approach is used to provide the complementary rules for WSD and visualize the structure of the datasets for knowledge discovery. The features occurring in the final rule set are selected as the optimal features. The proposed approach is verified by the benchmark data set from the SemEval-2007 preposition sense disambiguation corpus with around as the target word for WSD. The test result shows that the accuracy of WSD of around is greatly improved comparing with the one by the state of the art, and 17 out of 22 features are finally selected and ranked according to their contribution to the WSD, and some knowledge on the relations between the word senses and the selected features is discovered. Show more

Keywords: Filter-APOSD approach, feature selection, word sense disambiguation, knowledge discovery, English preposition

DOI: 10.3233/JIFS-222715

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4013-4028, 2023

Authors: Huang, Bogang | Chen, Fu

Article Type: Research Article

Abstract: The physical education teaching quality evaluation is a very important part of the current physical education teaching reform in colleges and universities, and many experts and scholars have achieved fruitful results in this area, which has played a role in promoting the development of physical education teaching evaluation theory and practice. But at the same time, it should be soberly recognized that, with the deepening reform of physical education teaching in colleges and universities, the current teaching quality evaluation system can no longer meet the needs of the current education situation, and there are still many problems that need to …be further studied and improved. The teaching quality decision evaluation of college volleyball training is looked as the MAGDM. Thus, a useful MAGDM process is needed to cope with it. The information entropy is used for determination of target weight. Based on the grey relational analysis (GRA) and probabilistic double hierarchy linguistic term sets (PDHLTSs), this paper constructs the PDHLTS-GRA for MAGDM issues. Finally, an example for teaching quality evaluation of college volleyball training is used to illustrate the designed method. Show more

Keywords: Multiple attribute group decision making (MAGDM), probabilistic double hierarchy linguistic term sets (PDHLTSs), GRA method, teaching quality evaluation

DOI: 10.3233/JIFS-222945

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4029-4039, 2023

Authors: Pandey, Mamta | Litoriya, Ratnesh | Pandey, Prateek

Article Type: Research Article

Abstract: Massive open online courses (MOOCs) are a recent e-learning programme that has received widespread acceptance among several colleges. Student dropout from MOOCs is a big worry in higher education and policy-making circles, as it occurs frequently in colleges that offer these types of courses. The majority of student dropouts are caused by causes beyond the institution’s control. Using an IF-DEMATEL (Intuitive Fuzzy Decision-making Trial and Evaluation Laboratory) approach, the primary factors and potential causal relationships for the high dropout rate were identified. The most effective aspects of massive open online courses (MOOCs) are identified using IF-DEMATEL and CIFCS. Moreover, it …explains the interconnectedness of the various MOOC components. As an added measure, a number of DEMATEL techniques are used to conduct a side-by-side comparison of the results. Decisions made by the educational organisation could benefit from the findings. According to the research, there are a total of twelve indicators across four dimensions that are related to online course withdrawal amongst students. Then, experienced MOOC instructors from various higher education institutions were invited to assess the level of influence of these characteristics on each other. Academic skills and talents, prior experience, course design, feedback, social presence, and social support were identified as six primary characteristics that directly influenced student dropout in MOOCs. Interaction, course difficulty and length, dedication, motivation, and family/work circumstances have all been found to play a secondary part in student dropout in massive open online courses (MOOCs). The causal connections between the major and secondary factors were traced and discussed. The results of this study can help college professors and administrators come up with and implement effective ways to reduce the high number of students who drop out of massive open online courses (MOOCs). Show more

Keywords: Massive open online courses, lifelong learning, intuinistic fuzzy DEMATEL, online learning environments

DOI: 10.3233/JIFS-190357

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4041-4058, 2023

Authors: Peng, Lijuan | Xu, Dongsheng

Article Type: Research Article

Abstract: The MULTIMOORA (multiple multi-objective optimization by ratio analysis) method is useful for multiple criteria decision-making method. It is based on expected utility theory and assumes that decision makers are completely rational. However, some studies show that human beings are usually bounded rational, and their regret aversion behaviors play an important role in the decision-making process. Interval neutrosophic sets can more flexibly depict uncertain, incomplete and inconsistent information than single-valued neutrosophic sets. Therefore, this paper improves the traditional MULTIMOORA method by combining the regret theory under interval neutrosophic sets. Firstly, the regret theory is used to calculate the utility value and …regret-rejoice value of each alternatives. Secondly, the criteria weights optimization model based on the maximizing deviation is constructed to obtain the weight vector. Then, the MULTIMOORA method is used to determine the order of the alternatives. Finally, an illustrative example about school selection is provided to demonstrate the feasibility of the proposed method. Sensitivity analysis shows the validity of the regret theory in the proposed method, and the ranking order change with different regret avoidance parameter. Comparisons are made with existing approaches to illustrate the advantage of the proposed method in reflecting decision makers’ psychological preference. Show more

Keywords: Interval neutrosophic set, regret theory, multiple criteria decision making, MULTIMOORA

DOI: 10.3233/JIFS-212903

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4059-4077, 2023

Authors: Venkata Lakshmi, S. | Sujatha, K. | Janet, J.

Article Type: Research Article

Abstract: In recent years, speech processing resides a major application in the domain of signal processing. Due to the audibility loss of some speech signals, people with hearing impairment have difficulty in understanding speech, which reintroduces a crucial role in speech recognition. Automatic Speech Recognition (ASR) development is a major challenge in research in the case of noise, domain, vocabulary size, and language and speaker variability. Speech recognition system design needs careful attention to challenges or issues like performance and database evaluation, feature extraction methods, speech representations and speech classes. In this paper, HDF-DNN model has been proposed with the hybridization …of discriminant fuzzy function and deep neural network for speech recognition. Initially, the speech signals are pre-processed to eliminate the unwanted noise and the features are extracted using Mel Frequency Cepstral Coefficient (MFCC). A hybrid Deep Neural Network and Discriminant Fuzzy Logic is used for assisting hearing-impaired listeners with enhanced speech intelligibility. Both DNN and DF have some problems with parameters to address this problem, Enhanced Modularity function-based Bat Algorithm (EMBA) is used as a powerful optimization tool. The experimental results show that the proposed automatic speech recognition-based hybrid deep learning model is effectively-identifies speech recognition more than the MFCC-CNN, CSVM and Deep auto encoder techniques. The proposed method improves the overall accuracy of 8.31%, 9.71% and 10.25% better than, MFCC-CNN, CSVM and Deep auto encoder respectively. Show more

Keywords: Speech recognition, adaptive filter, feature extraction, deep learning, discriminant fuzzy function, deep neural networks, Mel-frequency

DOI: 10.3233/JIFS-212945

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4079-4091, 2023

Authors: Yang, Can | Li, Ping

Article Type: Research Article

Abstract: Mobile game providers benefit by selling virtual items in the game. Each event is described as an example in the player log data, and the player indicates the purchase status of the various game props as a plurality of tags, the game props recommendation question is abstractd into a multi-instance multi-label learning problem. On this basis, the fast multi-instance multi-label learning algorithm is designed for recommendation of mobile online game props, and semi-supervised learning is used to improve the recommendation performance. Off-line data sets and the online game experimental results of the actual online mobile phone show that the game …props based on multi-instance multi-tagging learning technology brings a significant increase in game revenue. Show more

Keywords: Machine learning, Multi-Instance Multi-Label Learning (MIML), semi-supervised learning, recommendation

DOI: 10.3233/JIFS-220703

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4093-4102, 2023

Authors: Yang, Feifei | Zhang, Pengfei

Article Type: Research Article

Abstract:  Multi-source information fusion is a sophisticated estimating technique that enables users to analyze more precisely complex situations by successfully merging key evidence in the vast, varied, and occasionally contradictory data obtained from various sources. Restricted by the data collection technology and incomplete data of information sources, it may lead to large uncertainty in the fusion process and affect the quality of fusion. Reducing uncertainty in the fusion process is one of the most important challenges for information fusion. In view of this, a multi-source information fusion method based on information sets (MSIF) is proposed in this paper. The information set …is a new method for the representation of granularized information source values using the entropy framework in the possibilistic domain. First, four types of common membership functions are used to construct the possibilistic domain as the information gain function (or agent). Then, Shannon agent entropy and Shannon inverse agent entropy are defined, and their summation is used to evaluate the total uncertainty of the attribute values and agents. Finally, an MSIF algorithm is designed by infimum-measure approach. The experimental results show that the performance of Gaussian kernel function is good, which provides an effective method for fusing multi-source numerical data. Show more

Keywords: Multi-source information fusion, information sets, Shannon entropy, uncertainty, fuzzy membership degree

DOI: 10.3233/JIFS-222210

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4103-4112, 2023

Authors: Gowthami, S. | Harikumar, R.

Article Type: Research Article

Abstract: Melanoma is one of the widespread skin cancers that has affected millions in past decades. Detection of skin cancer at preliminary stages may become a source of reducing mortality rates. Hence, it is required to develop an autonomous system of reliable type for the detection of melanoma via image processing. This paper develops an independent medical imaging technique using Self-Attention Adaptation Generative Adversarial Network (SAAGAN). The entire processing model involves the process of pre-processing, feature extraction using Scale Invariant Feature Transform (SIFT), and finally, classification using SAAGAN. The simulation is conducted on ISIC 2016/PH2 datasets, where 10-fold cross-validation is undertaken …on a high-end computing platform. The simulation is performed to test the model efficacy against various images on several performance metrics that include accuracy, precision, recall, f-measure, percentage error, Matthews Correlation Coefficient, and Jaccard Index. The simulation shows that the proposed SAAGAN is more effective in detecting the test images than the existing GAN protocols. Show more

Keywords: Autonomous, melanoma, generative adversarial network, scale invariant feature transform, synthetic datasets

DOI: 10.3233/JIFS-220015

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4113-4122, 2023

Authors: Bensoltane, Rajae | Zaki, Taher

Article Type: Research Article

Abstract: Aspect-based sentiment analysis (ABSA) is a challenging task of sentiment analysis that aims at extracting the discussed aspects and identifying the sentiment corresponding to each aspect. We can distinguish three main ABSA tasks: aspect term extraction, aspect category detection (ACD), and aspect sentiment classification. Most Arabic ABSA research has relied on rule-based or machine learning-based methods, with little attention to deep learning techniques. Moreover, most existing Arabic deep learning models are initialized using context-free word embedding models, which cannot handle polysemy. Therefore, this paper aims at overcoming the limitations mentioned above by exploiting the contextualized embeddings from pre-trained language models, …specifically the BERT model. Besides, we combine BERT with a temporal convolutional network and a bidirectional gated recurrent unit network in order to enhance the extracted semantic and contextual features. The evaluation results show that the proposed method has outperformed the baseline and other models by achieving an F1-score of 84.58% for the Arabic ACD task. Furthermore, a set of methods are examined to handle the class imbalance in the used dataset. Data augmentation based on back-translation has shown its effectiveness through enhancing the first results by an overall improvement of more than 3% in terms of F1-score. Show more

Keywords: Aspect-based sentiment analysis, aspect category detection, deep learning, BERT, data augmentation, arabic language

DOI: 10.3233/JIFS-221214

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4123-4136, 2023

Authors: Aruna, K. | Pradeep, G.

Article Type: Research Article

Abstract: Container technology is highly significant in Information and Communication Technology (ICT) systems. To maximize container effectiveness, scaling plays a significant part. Therefore, in the fog computing framework, containers are an ideal solution for hosting and scaling services. Fog networks help to increase the number of connected devices by connecting to external gateways through the Fog of Things (FoT). It is a new approach to designing and implementing fog computing systems for the IoT. The research article aims on a novel Container with a Fog-based Scalable Self-organizing Network (CFSSN) framework and use a Self-Organizing Network based Light Weight Container (SON-LWC) algorithm …for moving container services for scaling expansion. This work focuses on how to transfer service or data from container to fog and self-group network. It goes over the most recent container migration methodologies, covering both live and cold migration services. Using intelligent container improves high bandwidth efficiency and provides a solution for a scalable network. Show more

Keywords: Docker, container, ICT, CFSSN, FoT, DevOps, SON-LWC

DOI: 10.3233/JIFS-221524

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4137-4148, 2023

Authors: Weng, Ling | Lin, Jian | Lv, Shujie | Huang, Yan

Article Type: Research Article

Abstract: As the increasingly serious water pollution problem affects the sustainable development of the ecological environment, the research of water pollution treatment engineering cannot be delayed. Among them, the performance evaluation of water pollution treatment engineering is a major focus. After reading the existing studies, it is found that most of the existing performance evaluation indicators of water pollution treatment engineering have qualitative indicators and there is an unbalanced preference representation. Intuitionistic multiplicative linguistic sets can be a good representation of the qualitative preference and non-preference of decision-makers in the context of decision-making containing unbalanced phenomena. Therefore, to better solve the …problem of water pollution treatment engineering, this paper introduces intuitionistic multiplicative linguistic sets to the problem of water pollution treatment engineering and proposes an effective theory for it. First, considering the multiplicative nature of the intuitionistic multiplicative linguistic set, a new score function and accuracy function are defined, and on this basis, the priority rules of intuitionistic multiplicative linguistic set are given to prepare for the subsequent water pollution treatment engineering performance ranking. And the distance measure of intuitionistic multiplicative linguistic set is introduced and a CRITIC attribute weight determination model under intuitionistic multiplicative linguistic set is obtained on this basis. Secondly, the Choquet integral operator is applied to better represent the correlation between elements. However, the nature of membership degree and non-membership degree shows that it is not reasonable to aggregate the information of intuitionistic multiplicative linguistic sets with a single increasing and decreasing transformation. Therefore, in this paper, we propose the IMLS bi-direction exponent Choquet integral operator, which is inspired by the bi-direction Choquet integral. Lastly, we improve the original preference function of the classical PROMETHEE II method to obtain the bi-directional PROMETHEE II method in intuitionistic multiplicative linguistic information. Finally, a numerical case is also provided to illustrate the scientific and rational application of the bi-directional PROMETHEE II method in intuitionistic multiplicative linguistic information for the performance evaluation of water pollution treatment engineering. Show more

Keywords: Intuitionistic multiplicative linguistic sets, bi-direction Choquet integral, performance evaluation, water pollution treatment, PROMETHEE II method

DOI: 10.3233/JIFS-223373

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4149-4173, 2023

Authors: Rawshdeh, Amani A. | Al-jarrah, Heyam H. | Tiwari, Surabhi | Tallafha, Abdalla A.

Article Type: Research Article

Abstract: In this paper, we use the soft set theory and the concept of semi-linear uniform spaces to introduce the notion of soft semi-linear uniform spaces with its generalization, briefly soft-GSL US . We investigate some properties of soft topology that induced by soft-GSL US . Also, we use the members of soft-GSL US to define a soft proximity space and a soft filter then we establish the relationships between them. Finally, we give the perceptual application of soft semi-linear uniform structures by employing the natural transformation of a soft semi-linear uniform space to a soft proximity.

Keywords: Soft set, soft point, soft topology, soft semi-linear uniform spaces, soft proximity

DOI: 10.3233/JIFS-220587

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4175-4184, 2023

Authors: Lin, Jiaoqing | Yu, Rui | Xu, Xinrui

Article Type: Research Article

Abstract: The construction of real estate projects is a large and complex system project, and the completion of the construction goals on time and with quality is the key to the sustainable development of construction enterprises. In the process of real estate project construction, the management performance of building decoration material suppliers will directly affect the efficiency of real estate enterprises. How to correctly evaluate the building material suppliers (BMSs) of real estate enterprises and establish a good partnership affects the economic benefits of the enterprise and the possibility of subsequent cooperation between the two sides, which has become one of …the issues of importance to real estate enterprises. The selection and application of BMSs is the MAGDM. In this defined paper, the defined 2-tuple linguistic neutrosophic number (2TLNN) grey relational analysis (2TLNN-GRA) decision method is generated based on GRA and 2-tuple linguistic neutrosophic sets (2TLNSs). The 2TLNN-GRA method is generated for MAGDM. Finally, the decision example for BMSs selection is generated and some comparisons is generated. Show more

Keywords: Multiple attribute group decision making (MAGDM), 2TLNSs, GRA method, building material suppliers (BMSs)

DOI: 10.3233/JIFS-221410

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4185-4196, 2023

Authors: Du, Weidong

Article Type: Research Article

Abstract: Nowadays, the model compression method of knowledge distillation has drawn great attentions in Recommender systems (RS). The strategy of bidirectional distillation performs the bidirectional learning for both the teacher and the student models such that these two models can collaboratively improve with each other. However, this strategy cannot effectively exploit representation capabilities of each item and lack of the interpretability for the importance of items. Thus, how to develop an effective sampling scheme is still valuable for us to further study and explore. In this paper, we propose an improved rank discrepancy-aware item sampling strategy to enhance the performance of …bidirectional distillation learning. Specifically, by employing the distillation loss, we train the teacher and student models to reflect the fact that a user has partiality for the unobserved items. Then, we propose the improved rank discrepancy-aware sampling strategy based on feedback learning mechanism to transfer just the useful information which can effectively enhance each other. The key part of the multiple distillation training aims to select valuable items which can be re-distilled in the network for training. The proposed technique can effectively solve the problem of high ambiguity in nature for recommender system. Experimental results on several real-world recommender system datasets well demonstrate that the improved bidirectional distillation strategy shows better performance. Show more

Keywords: Bidirectional distillation, student-teacher learning, rank discrepancy aware items selection, recommender system

DOI: 10.3233/JIFS-222063

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4197-4206, 2023

Authors: Li, Qiyu | Langari, Reza

Article Type: Research Article

Abstract: Human-computer interaction(HCI) has broad range of applications. One particular application domain is rehabilitation devices. Several bioelectric signals can potentially be used in HCI systems in general and rehabilitation devices in particular. Surface ElectroMyoGraphic(sEMG) signal is one of the more important bioelectric signals in this context. The sEMG signal is formed by muscle activation although the details are rather complex. Applications of sEMG are referred is commonly referred to as myoelectric control since the dominant use of this signal is to activate a device even if (as the term control may imply) feedback is not always used in the process. With …the development of deep neural networks, various deep learning architectures are used for sEMG-based gesture recognition with many researchers having reported good performance. Nevertheless, challenges remain in accurately recognizing sEMG patterns generated by gestures produced by hand or the upper arm. For instance one of the difficulties in hand gesture recognition is the influence of limb positions. Several papers have shown that the accuracy of gesture classification decreases when the limb position changes even if the gesture remains the same. Prior work by our team has shown that dynamic gesture recognition is in principle more reliable in detecting human intent, which is often the underlying idea of gesture recognition. In this paper, a Convolutional Neural Network (CNN) with Long Short-Term Memory or LSTM (CNN-LSTM) is proposed to classify five common dynamic gestures. Each dynamic gesture would be performed in five different limb positions as well. The trained neural network model is then used to enable a human subject to control a 6 DoF (Degree of Freedom) robotic arm with 1 DoF gripper. The results show a high level of accurate performance achieved with the proposed approach. In particular, the overall accuracy of the dynamic gesture recognition is 84.2%. The accuracies vary across subjects but remain at approximately 90%for some subjects. Show more

Keywords: Human-computer interaction, sEMG signal, neural network, gesture recognition

DOI: 10.3233/JIFS-222985

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4207-4221, 2023

Authors: Wang, Jing

Article Type: Research Article

Abstract: The clothing images on the Internet is growing rapidly, and there is an increasing demand for the clothing images’ intelligent classification. In this paper, Region-Based Fully Convolutional Networks (R-FCN) is introduced into the clothing image recognition. In the clothing image classification, because the network training time is long and the recognition rate of deformed clothing images is low, an improved framework HSR-FCN is proposed. The regional suggestion network and HyperNet network in R-FCN are integrated in the new framework, the learning approach of image features is changed in HSR-FCN, the higher accuracy can be achieved in a shorter training time. …A spatial transformation network is introduced into the model, the input clothing image and feature map are spatially transformed and aligned, the feature learning is strengthened for multi-angle clothing and deformed clothing. The experimental results show that the improved HSR-FCN model is used to strengthen effectively the learning of deformed clothing images, and with a shorter training time, the average accuracy rate of the original network model R-FCN is increased by about 3%, it reachs 96.69%. Show more

Keywords: Garment images, deep learning, image classification, region-based fully convolutional networks (R-FCN), HyperNet, region proposal networks, spatial transformation networks

DOI: 10.3233/JIFS-220109

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4223-4232, 2023

Authors: Song, Tao

Article Type: Research Article

Abstract: The quality of physical education (PE) teaching in colleges and universities is the basis for the development of PE disciplines in colleges and universities, so currently thinking about how to effectively improve the quality of PE teaching in colleges and universities has become the first and foremost problem for many college and university PE departments to solve. In order to solve this problem, it is necessary to build a reasonable and scientific evaluation and monitoring system of PE teaching quality, because only by establishing an effective evaluation and monitoring system of teaching quality can we evaluate and supervise all the …PE operation properly and scientifically, and then give feedback in the process of evaluation and supervision, such evaluation and monitoring system can greatly promote the continuous improvement of PE teaching quality in colleges and universities. This is also one of the most effective means to improve the quality of PE and achieve the goal of PE in colleges and universities. The PE teaching quality evaluation in Colleges and Universities is frequently viewed as the multiple attribute group decision making (MAGDM) issue. In this paper, the 2-tuple linguistic neutrosophic number grey relational analysis (2TLNN-GRA) method is built based on the traditional grey relational analysis (GRA) and 2-tuple linguistic neutrosophic sets (2TLNNSs). Then, a numerical example for PE teaching quality evaluation in Colleges and Universities has been given and some comparisons is used to illustrate advantages of 2TLNN-GRA method. Show more

Keywords: Multiple attribute group decision making (MAGDM) problems, 2-tuple linguistic neutrosophic sets (2TLNSs), GRA method, teaching quality evaluation

DOI: 10.3233/JIFS-221857

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4233-4244, 2023

Authors: Reji, M. | Joseph, Christeena | Nancy, P. | Lourdes Mary, A.

Article Type: Research Article

Abstract: Intrusion detection systems (IDS) can be used to detect irregularities in network traffic to improve network security and protect data and systems. From 2.4 times in 2018 to three times in 2023, the number of devices linked to IP networks is predicted to outnumber the total population of the world. In 2020, approximately 1.5 billion cyber-attacks on Internet of Things (IoT) devices have been reported. Classification of these attacks in the IoT network is the major objective of this research. This research proposes a hybrid machine learning model using Seagull Optimization Algorithm (SOA) and Extreme Learning Machine (ELM) classifier to …classify and detect attacks in IoT networks. The CIC-IDS-2018 dataset is used in this work to evaluate the proposed model. The SOA is implemented for feature selection from the dataset, and the ELM is used to classify attacks from the selected features. The dataset has 80 features, in the proposed model used only 22 features with higher scores than the original dataset. The dataset is divided into 80% for training and 20% for testing. The proposed SOA-ELM model obtained 94.22% accuracy, 92.95% precision, 93.45% detection rate, and 91.26% f1-score. Show more

Keywords: Intrusion detection, IoT, SOA, ELM, feature selection, attack classification, machine learning

DOI: 10.3233/JIFS-222427

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4245-4255, 2023

Authors: Karthiga, S. | Abirami, A.M.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-220408

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4257-4272, 2023

Authors: Tang, Chao | Tang, Yong | Zeng, Zhuolin | Zhang, Linghao | Xiang, Siyu

Article Type: Research Article

Abstract: Because the traditional methods do not select the best feature collection in feature analysis, the accuracy and effectiveness of user feature clustering are not high, and the accuracy of user feature classification is not high. Therefore, this paper proposes a customer feature analysis method based on power consumption feature selection and behavior portrait of different people. The optimal feature set is obtained according to the maximum correlation and minimum redundancy criterion, and the user portrait task is described. The spatial feature domain classification method is used to classify the user portrait information, and the user label database is constructed according …to the classification results. The AP clustering algorithm is used to cluster the power user portrait information and complete the customer feature analysis. Experimental results show that this method effectively improves the accuracy and effectiveness of user feature clustering, and the accuracy of user feature classification is high, indicating that the application effect is good. Show more

Keywords: Power consumption characteristics, behavioral portraits, customer characteristics, AP clustering algorithm, information classification

DOI: 10.3233/JIFS-220615

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4273-4283, 2023

Authors: Yin, Ming | Zhou, Pan | Xu, Taige | Jiang, Jijiao

Article Type: Research Article

Abstract: Requirements are important in software development. Ambiguous requirements cause inconsistent understanding by developers, which leads to rework, delayed delivery, and other problems, and may even have devastating effects on the project. A large number of requirements text written in natural language are not concise, intuitive, and accurate. This condition increases the workload of designers and the difficulty of their tasks. An effective solution for the aforementioned problems is to extract actors and use cases from the requirement texts. This study proposes a model for extracting actors and using cases automatically, which combines bi-directional long short-term memory (BiLSTM) and conditional random …fields. BiLSTM is used to capture the contextual information of the texts, and CRF is used to calculate the tag transfer score and determine the most accurate tag sequence, which aims to extract actors and use cases. Results show that the accuracy of extraction is significantly improved compared with the baseline method, which verifies the effectiveness of the proposed method in extracting actors and use cases. Show more

Keywords: BiLSTM-CRF, software requirements, actors and use cases, context

DOI: 10.3233/JIFS-221094

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4285-4299, 2023

Authors: Park, Choonkil | Rehman, Noor | Ali, Abbas

Article Type: Research Article

Abstract: The q -rung orthopair fuzzy sets accommodate more uncertainties than the Pythagorean fuzzy sets and hence their applications are much extensive. Under the q -rung orthopair fuzzy set, the objective of this paper is to develop new types of q -rung orthopair fuzzy lower and upper approximations by applying the tolerance degree on the similarity between two objects. After employing tolerance degree based q -rung orthopair fuzzy rough set approach to it any times, we can get only the six different sets at most. That is to say, every rough set in a universe can be approximated by only six …sets, where the lower and upper approximations of each set in the six sets are still lying among these six sets. The relationships among these six sets are established. Furthermore, we propose tolerance degree based multi granulation optimistic/pessimistic q -rung orthopair fuzzy rough sets and investigate some of their properties. Another main contribution of this paper is to disclose the ideas of different kinds of approximations called approximate precision, rough degree, approximate quality and their mutual relationship. Finally a technique is devloped to rank the alternatives in a q -rung orthopair fuzzy information system based on similarity relation. We find that the proposed method/technique is more efficient when compared with other existing techniques. Show more

Keywords: q-rung orthopair fuzzy set, fuzzy rough set, similarity relation, tolerance classes, multigranulation q-rung orthopair fuzzy rough sets

DOI: 10.3233/JIFS-221249

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4301-4321, 2023

Authors: Apinaya Prethi, K.N. | Sangeetha, M. | Nithya, S.

Article Type: Research Article

Abstract: Due to decentralized infrastructure in modern Internet-of-Things (IoT), the tasks should be shared around the edge devices via network resources and traffic prioritizations, which weaken the information interoperability. To solve this issue, a Minimized upgrading batch Virtual Machine (VM) Scheduling and Bandwidth Planning (MSBP) was adopted to reduce the amount of batches needed to complete the system-scale upgrade and allocate the bandwidth for VM migration matrices. But, the suboptimal use of VM and possible loss of tasks may provide inadequate Resource Allocation (RA). Hence, this article proposes an MSBP with the Priority-based Task Scheduling (MSBP-PTS) algorithm to allocate the tasks …in a prioritized way and maximize the profit by deciding which request must handle by the edge itself or send to the cloud server. At first, every incoming request in its nearest fog server is allocated and processed by the priority scheduling unit. Few requests are reallocated to other fog servers when there is an inadequate resource accessible for providing the request within its time limit. Then, the request is sent to the cloud if the fog node doesn’t have adequate resources, which reduces the response time. However, the MSBP is the heuristics solution which is complex and does not ensure the global best solutions. Therefore, the MSBP-PTS is improved by adopting an Optimization of RA (MSBP-PTS-ORA) algorithm, which utilizes the Krill Herd (KH) optimization instead of heuristic solutions for RA. The simulation outcomes also demonstrate that the MSBP-PTS-ORA achieve a sustainable network more effectively than other traditional algorithms. Show more

Keywords: Internet-of-things, edge devices, resource allocation, priority levels, task scheduling, krill herd optimization

DOI: 10.3233/JIFS-221430

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4323-4334, 2023

Authors: Wu, Nengkai | Jia, Dongyao | Zhang, Chuanwang | Li, Ziqi

Article Type: Research Article

Abstract: Cervical cancer is one of the most common causes of death in women in the world, and early screening is an effective means of diagnosis and treatment, which can greatly improve the survival rate. Cervical cell classification model is an effective means to assist screening. However, the existing single model, including CNNs and machine learning methods, still has shortcomings such as unclear feature meaning, low accuracy and insufficient supervision. To solve the shortcomings of a single model, a novel framework based on strong feature Convolutional Neural Networks (CNN)-Lagrangian Support Vector Machine (LSVM) model is proposed for the accurate classification of …cervical cells. Strong features extracted by hybrid methods are fused with the abstract ones from hidden layers of LeNet-5, then the fused features are processed with dimension reduction and fed into the LSVM classifier optimized by Adaboost for classification. Proposed model is evaluated using the augmented Herlev and private dataset with the metrics including accuracy (Acc) , sensitivity (Sn) , and specificity (Sp) , which outperformed the baselines and state-of-the-art approaches with the Acc of 99.5% and 94.2% in 2&7-class classification, respectively. Show more

Keywords: Cervical cancer, strong feature, convolutional neural networks (CNN), lagrangian support vector machine (LSVM), cancer cell classification

DOI: 10.3233/JIFS-221604

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4335-4355, 2023

Authors: Liu, Zhiyong | Jin, Ying | Bao, Hong | Zhao, Yong

Article Type: Research Article

Abstract: A novel for an integrated fault and states estimator was proposed for the generalized linear discrete-time system with disturbances. The proposed scheme was based on a Self-Organizing fuzzy Luenberger system to estimate the states and approximate the fault information simultaneously for the generalized linear discrete-time system. For this purpose, a generalized linear discrete-time system with disturbances was transformed into an equivalent standard state-space system with disturbances and faults. Then, the faults and disturbances of generalized linear discrete-time system can be separated with the coordinate transform, meanwhile the Self-Organizing fuzzy Luenberger estimator was designed to obtain the accurate fault information. Based …on the obtained fault information, the fault detection experiments were performed, and the fault feature parameters required for fault isolation were determined. Finally, the proposed strategy was applied for a direct current motor to demonstrate the effectiveness of the proposed approach. Show more

Keywords: Self-organizing fuzzy Luenberger method, coordinate transform, faults and disturbances separation, fault detection, generalized linear discrete time system

DOI: 10.3233/JIFS-222890

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4357-4370, 2023

Authors: Wu, Xiu-Yun | Niu, Yu-Jie | Zhang, Hui-Min

Article Type: Research Article

Abstract: In this paper, the notion of M -fuzzifying convex remotehood systems is introduced and characterizations of M -fuzzifying convex spaces are obtained. Further, notions of remote mappings and M -fuzzifying convex quasi-uniformities are introduced. It is proved that M -fuzzifying convex quasi-uniform space and M -fuzzifying convex space are mutually induced. In order to discuss categorical relationships among M -fuzzifying convex quasi-uniform spaces, M -fuzzifying convex remotehood spaces and M -fuzzifying convex spaces, notions of M -fuzzifying quasi-uniformizable convex structures and M -fuzzifying quasi-uniformizable convex remotehood spaces are presented. It is proved that the category of M -fuzzifying quasi-uniformizable convex …spaces and the category of M -fuzzifying quasi-uniformizable convex remotehood spaces can be embedded into the category of M -fuzzifying convex quasi-uniform spaces as subcategories. Show more

Keywords: M-fuzzifying convex space, M-fuzzifying convex remotehood space, M-fuzzifying quasi-uniformizable convex space, M-fuzzifying convex quasi-uniform space, M-fuzzifying quasi-uniform preserving mapping

DOI: 10.3233/JIFS-223036

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4371-4382, 2023

Authors: Liu, Shengyao | Lin, Jiaoqing | Xu, Xinrui

Article Type: Research Article

Abstract: The construction industry is the basic industry of the country. With the rapid development of the economy, the construction industry has grown rapidly and the competition in the construction market has become more intense. The competition in the construction market is not only between individual enterprises, but also between the whole supply chain that provides products. Therefore, it is imperative to introduce the idea of supply chain management, strengthen the cooperation with suppliers and improve competitiveness. Supplier evaluation and selection is one of the first issues to be solved for the development of supply chain management. The selection and application …of building material suppliers is a classic multiple attribute decision making (MADM). In this paper, the intuitionistic fuzzy sets (IFSs) and Hamacher operations is introduced and the induced intuitionistic fuzzy Hamacher power ordered weighted average (I-IFHPOWA) operator is built. Meanwhile, the properties of built operator are analyzed. Then, the I-IFHPOWA operator is applied to solve the MADM under IFSs. Finally, an example for building material supplier selection is utilized to proof this built model. Show more

Keywords: Multiple attribute decision making (MADM), intuitionistic fuzzy sets (IFSs), I-OWA operator, I-IFHPOWA operator, building material suppliers

DOI: 10.3233/JIFS-221437

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4383-4395, 2023

Authors: Jagadish Kumar, N. | Balasubramanian, C.

Article Type: Research Article

Abstract: In a cloud computing system, resources can be accessed at a minimal cost whenever users raise request needs. The primary goal of cloud computing is to provide cost-efficiency of service scheduling to clients fast while using the least number of resources. Cloud Service Provisioning (CSP) can match consumer needs with minimal use of resources. There are several metaheuristic optimization algorithms have been developed in the field of CSP resource minimization and adequate computing resources are required to ensure client satisfaction. However, it performs poorly under a variety of practical constraints, including a vast amount of user data, smart filtering to …boost user search, and slow service delivery. In this regard, propose a Black Widow Optimization (BWO) algorithm that reduces cloud service costs while ensuring that all resources are devoted only to end-user needs. It is a nature-inspired metaheuristic algorithm that involved a multi-criterion correlation that is used to identify the relationship between user requirements and available services and thereby, it is defined as an MS-BWO algorithm. Thus finds the most efficient virtual space allocation in a cloud environment. It uses a service provisioning dataset with metrics like energy usage, bandwidth utilization rate, computational cost, and memory consumption. In terms of data performance, the proposed MS-BWO outperforms exceed than other existing state-of-art-algorithms including Work-load aware Autonomic Resource Management Scheme(WARMS), Fuzzy Clustering Load balancer(FCL), Agent-based Automated Service Composition (A2SC) and Load Balancing Resource Clustering (LBRC), and an autonomic approach for resource provisioning (AARP ) Show more

Keywords: Cloud service provisioning, Resource utilization, Virtual machine, Metaheuristic black widow optimization

DOI: 10.3233/JIFS-222048

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4397-4417, 2023

Authors: Ali, Jawad | Ali, Jawad | Naeem, Muhammad | Mahmood, Waqas

Article Type: Research Article

Abstract: The q-rung picture linguistic set (q-RPLS) is an effective tool for managing complex and unpredictable information by changing the parameter ‘q’ regarding hesitancy degree. In this article, we devise some generalized operational laws of q-RPLS in terms of the Archimedean t-norm and t-conorm. Based on the proposed generalized operations, we define two types of generalized aggregation operators, namely the q-rung picture linguistic averaging operator and the q-rung picture linguistic geometric operator, and study their relevant characteristics in-depth. With a view toward applications, we discuss certain specific cases of the proposed generalized aggregation operators with a range of parameter values. Furthermore, …we explore q-rung picture linguistic distance measure and its required axioms. Then we put forward a technique for q-RPLSs based on the proposed aggregation operators and distance measure to solve multi-attribute decision-making (MADM) challenges with unknown weight information. At last, a practical example is presented to demonstrate the suggested approaches’ viability and to perform the sensitivity and comparison analysis. Show more

Keywords: q-rung-Picture linguistic fuzzy set, generalized operations, generalized aggregation operators, entropy, decision-making

DOI: 10.3233/JIFS-222292

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4419-4443, 2023

Authors: Umamaheswari, K.M. | Muthu kumaran, A.M.J.

Article Type: Research Article

Abstract: Cloud technology has raised significant prominence providing a unique market economic approach for resolving large-scale challenges in heterogeneous distributed systems. Through the use of the network, it delivers secure, quick, and profitable information storage with computational capability. Cloud applications are available on-demand to meet a variety of user QoS standards. Due to a large number of users and tasks, it is important to achieve efficient scheduling of tasks submitted by users. One of the most important and difficult non-deterministic polynomial-hard challenges in cloud technology is task scheduling. Therefore, in this paper, an efficient task scheduling approach is developed. To achieve …this objective, a hybrid genetic algorithm with particle swarm optimization (HGPSO) algorithm is presented. The scheduling is performed based on the multi-objective function; the function is designed based on three parameters such as makespan, cost, and resource utilization. The proper scheduling system should minimize the makespan and cost while maximizing resource utilization. The proposed algorithm is implemented using WorkflowSim and tested with arbitrary task graphs in a simulated setting. The results obtained reveal that the proposed HGPSO algorithm outperformed all available scheduling algorithms that are compared across a range of experimental setups. Show more

Keywords: Cloud computing, HGPSO, workflow, task scheduling, makespan, resource utilization, multi-objective function, and fitness

DOI: 10.3233/JIFS-222842

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4445-4458, 2023

Authors: Garg, Harish | Kahraman, Cengiz | Ali, Zeeshan | Mahmood, Tahir

Article Type: Research Article

Abstract: Complex Pythagorean fuzzy set (CPFS) is a massive influential principle for managing ambiguity and inconsistent information in genuine life dilemmas. To determine the relationship among any number of attributes, the Hamy mean (HM) operators based on interaction operational laws are very dominant and massive flexible to manage awkward and problematic information. This study aims to combine the complex Pythagorean fuzzy (CPF) information with interaction HM operators to initiate the CPF interaction HM (CPFIHM) operator, CPF interaction weighted HM (CPFIWHM) operator, CPF interaction dual HM (CPFIDHM) operator, CPF interaction weighted dual HM (CPFIWDHM) operator and their powerful properties. Additionally, a decision-making …strategy for determining the security threats in the computer is elaborated under the interaction of HM operators based on the CPF setting. Numerous examples are illustrated with the help of presented operators to determine the consistency and flexibility of the investigated operators. Finally, with the help of sensitivity analysis, advantages, and geometrical representation, the supremacy, and efficiency of the presented works are also elaborated. Show more

Keywords: Complex pythagorean fuzzy sets, interaction hamy mean operators, interaction dual hamy mean operators, security threats in computers

DOI: 10.3233/JIFS-220947

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4459-4479, 2023

Authors: Neena Raj, N.R. | Shreelekshmi, R.

Article Type: Research Article

Abstract: This paper presents a secure image authentication scheme for tamper localization and recovery at pixel level. The proposed scheme encrypts the watermark comprising tamper localization code and self-recovery code using chaotic sequence to ensure security. This scheme uses pixel to block conversion technique for ensuring lossless recovery of the original image from an untampered watermarked image. For enhancing the localization accuracy, a multilevel tamper localization strategy is used. The experimental results show that the proposed scheme generates watermarked images with minimal information loss and can withstand copy-move, image splicing, content removal, vector quantization, collage and content only attacks. This scheme …has better security, better tamper localization accuracy and better recovered image quality under extensive tampering and takes less computation time in comparison to the state-of-the-art schemes. Show more

Keywords: Chaotic sequence, fragile watermarking, image authentication, image recovery, tamper localization

DOI: 10.3233/JIFS-221245

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4481-4493, 2023

Authors: Shi, Jianzhong

Article Type: Research Article

Abstract: Fuzzy clustering has been widely applied in T-S fuzzy model identification for nonlinear systems, however, tradition type-1 fuzzy clustering algorithms can’t deal with uncertainties in real world, an improved interval type-2 fuzzy c-regression model (IT2-FCRM) clustering is proposed for T-S fuzzy model identification in this paper. The improved IT2-FCRM adapts a new objective function, which makes the boundary of clustering more clearly and reduces the influence of outliers or noisy data on clustering results. The premise parameters of T-S fuzzy model are upper and lower hyperplanes obtained by improved IT2-FCRM, and the upper and lower hyperplanes are used to build …hyper-plane-shaped type-2 Gaussian membership function. Compared with the hyper-sphere-shaped membership function of tradition IT2-FCRM, the hyper-plane-shaped membership function is more coincided with point to plane sample distance described by FCRM clustering. The simulation results of several benchmark problems and a real bed temperature in circulating fluidized bed plant show that the identification algorithm has higher accuracy. Show more

Keywords: Fuzzy identification, interval type-2 fuzzy c-regression model, fuzzy clustering, T-S fuzzy model, orthogonal least squares

DOI: 10.3233/JIFS-221434

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4495-4507, 2023

Authors: Bakhshi, M. | Ahn, S. S. | Jun, Y. B. | Xin, X. L. | Borzooei, R. A.

Article Type: Research Article

Abstract: We study the lattice structure of fuzzy A-ideals in an mv-module M (fai (M), symbolically) and show that it is a complete Heyting lattice and so the set of its pseudocomplements forms a Boolean algebra. In the sequel, the properties of fuzzy congruences in an mv-module are investigated and using them some structural theorems are stated and proved. Finally, it is proved that fai (M) can be embedded into the lattice of fuzzy congruences.

Keywords: mv-module, fuzzy A-ideal, fuzzy congruence, distributive lattice

DOI: 10.3233/JIFS-221552

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4509-4519, 2023

Authors: Li, Yufei | Hu, Nanyan | Ye, Yicheng | Wu, Menglong

Article Type: Research Article

Abstract: In order to solve the problem of underground goafs, particularly in light of the importance ranking of evaluation indices being more subjective and catastrophe progression values being large and too concentrated in the catastrophe progression method, the importance of multiple indices is ranked by the maximizing deviation method. An S-shaped curve is used to establish a regression function to improve the value of catastrophe progression method. First, three first-level evaluation indices and eight second-level evaluation indices are selected to establish an index system for risk evaluation of the underground goaf. Next, based on the principle of catastrophe progression method, an …improved catastrophe model for its risk evaluation is established. Finally, sample training and verification are performed based on the improved evaluation model. The evaluation results show that the improved catastrophe progression method objectively ranks the importance of the evaluation indices of each layer, which improves the credibility of the evaluation results. The evaluation results are consistent with the actual geological data and detection results, which verifies the validity and accuracy of the evaluation model. However, only 87.5% of the risk levels obtained by the fuzzy comprehensive evaluation method are completely consistent with the improved catastrophe progression method, and the ranking error of risk value within one rank also accounted for 87.5%. Therefore, the results calculated by the improved catastrophe progression method are more accurate. The numerical gap of the improved catastrophe progression values becomes larger, from [0.796, 0.969] to [0.275, 0.691], which is 2.405 times of the interval difference of the catastrophe progression values before the improvement, which makes the numerical distribution of the catastrophe progression values more scientific and reasonable, with a higher resolution level. Therefore, it is reasonable and feasible to use the improved catastrophe progression method for the risk evaluation of the underground goaf, which can provide a certain theoretical basis and engineering guidance for underground goaf disaster control and management. Show more

Keywords: Catastrophe progression method, maximizing deviation method, regression model, underground goaf, risk evaluation, catastrophe model

DOI: 10.3233/JIFS-222094

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4521-4536, 2023

Authors: Bai, Wenhui | Zhang, Chao | Zhai, Yanhui | Sangaiah, Arun Kumar

Article Type: Research Article

Abstract: Water quality inspection (WQI) is one of the primary ways to ensure the safe utilization of water resources, and complicated data modeling, fusion and analysis play a significant role in seeking the resource with the best water quality. Nevertheless, the challenges of missing data, relatively large differences in decision results and bounded rationality owned by decision-makers (DMs) in terms of WQI still exist nowadays. Thus, from the aspect of stable and behavioral decision-making in multi-granularity incomplete intuitionistic fuzzy information systems (MG-IIFISs), the paper investigates a comprehensive multi-attribute group decision-making (MAGDM) approach for the application of WQI. First, the concept of …MG-IIFISs is built by modeling MAGDM problems with intuitionistic fuzzy numbers (IFNs), then a new transformation scheme is constructed for transforming MG-IIFISs into multi-granularity intuitionistic fuzzy information systems (MG-IFISs) based on the similarity principle. Second, three types of multigranulation intuitionistic fuzzy probabilistic rough sets (MG IF PRSs) are developed by referring to the MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form) method. Afterwards, attribute weights are objectively calculated based on the best-worst method (BWM), and a new stable and behavioral MAGDM approach is constructed by means of the TODIM (an acronym in Portuguese for interactive multi-criteria decision-making) method. At last, a case study in the setting of WQI is conducted with the support of a UCI data set, and sensitivity analysis, comparative analysis and experimental analysis are investigated to display the validity of the proposed approach. In general, the proposed approach improves the stability of decision results via MULTIMOORA and BWM, and also fully considers the bounded rationality of DMs’ psychological behaviors from the aspect of the TODIM method, which has certain advantages in the community of MAGDM studies. Show more

Keywords: Granular computing, rough set, MULTIMOORA, incomplete intuitionistic fuzzy information system, water quality inspection

DOI: 10.3233/JIFS-222385

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4537-4556, 2023

Authors: Kaur, Kamalpreet | Gupta, Asha

Article Type: Research Article

Abstract: The present paper proposes a novel version of inducing nano topology by using new kinds of approximation operators via two ideals with respect to a general binary relation. This approach improves the accuracy of the approximation quite significantly. These newly defined approximations constitute the generalized version of rough sets defined by Pawlak in 1982. A comparison is drawn between the suggested technique and the already existing ones to demonstrate the significance of the proposed ideology. In addition, the standard notion of nano topology, based on an equivalence relation is generalized to the binary relation, which can have a broader scope …when applied to intelligent systems. Also, the significance of this approach is demonstrated by an example where an algorithm is given to find the key factors responsible for the profit of a company along with the comparison to the previous notions. Likewise, the proposed algorithm can be used in all fields of science to simplify complex information systems in extracting useful data by finding the core. Show more

Keywords: Nano topology, rough sets, ideals, bi-ideal approximation, core

DOI: 10.3233/JIFS-222958

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4557-4567, 2023

Authors: Arulaalan, M. | Aparna, K. | Nair, Vicky | Banala, Rajesh

Article Type: Research Article

Abstract: It is difficult for underwater archaeologists to recover the fine details of a captured image on the seabed when the image quality worsens due to the presence of more noisy artefacts, a mismatched device colour map, and a blurry image. To resolve this problem, we present a machine learning-based image restoration model (ML-IRM) for improving the visual quality of underwater images that have been deteriorated. Using this model, a home-made bowl set-up is created in which a different liquid concentration is used to replicate seabed water variation, and an object is dipped, or a video is played behind the bowl …to recognise the object texture captured image in high-resolution for training the image restoration model is proposed. Gaussian and bidirectional pre-processing filters are used to both the high and low frequency components of the training image, respectively. To improve the clarity of the high-frequency channel background, soft-thresholding decreases the presence of distracting artefacts. On the other hand, the ML-IRM model can effectively keep the object textures on a low frequency channel. Experiment findings show that the proposed ML-IRM model improves the quality of seabed images, eliminates colour mismatches, and allows for more detailed information extraction. Blue shadow, green shadow, hazy, and low light test samples are randomly selected from all five datasets including U45 [1 ], EUVP [2 ], DUIE [3 ], UIEB [4 ], UM-ImageNet [5 ], and the proposed model. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are computed for each condition separately. We list the values of PSNR (at 16.99 dB, 15.96 dB, 18.09 dB, 15.67 dB, 9.39 dB, 17.98 dB, 19.32 dB, 14.27 dB, 12.07 dB, and 25.47 dB) and SSIM (at 0.52, 0.57, 0.33, 0.47, 0.44, and 0.23, respectively. Similarly, it demonstrates that the proposed ML-IRM achieves a satisfactory result in terms of colour correction and contrast adjustment when applied to the problem of improving underwater images captured in low light. To do so, high-resolution images were captured in two low-light conditions (after 6 p.m. and again at 6 a.m.) for the training image datasets, and the results of their observations were compared to those of other existing state-of-the-art-methods. Show more

Keywords: ML-IRM, image denoising, different low-lighting conditions, Gaussian and bidirectional filters, high and low frequency channel

DOI: 10.3233/JIFS-223310

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4569-4591, 2023

Authors: He, Xiaoxu

Article Type: Research Article

Abstract: In clinical practice, segmenting polyps from colonoscopy images plays an important role in the diagnosis and treatment of colorectal cancer since it provides valuable information. However, accurate polyp segmentation is full of changes due to the following reasons: (1) the small training datasets with a limited number of samples and the lack of data variability; (2) the same type of polyps with a variation in texture, size, and color; (3) the weak boundary between a polyp and its surrounding mucosa. To address these challenges, we propose a novel robust deep neural network based on data augmentation, called Robust Multi-center Multi-resolution …Unet (RMMSUNet), for the polyp segmentation task. Data augmentation and Multi-center training are both utilized to increase the amount and diversity of training dataset. The new multi-resolution blocks make up for the lack of fine-grained information in U-Net, and ensures the generation of more accurate pixel-level segmentation prediction graphs. Region-based refinement is added as the post-processing for the network output, to correct some wrongly predicted pixels and further refine the segmentation results. Quantitative and qualitative evaluations on the challenging polyp dataset show that our RMMSUNet improves the segmentation accuracy significantly, when comparing to other SOTA algorithms. Show more

Keywords: Image segmentation, colon cancer, U-Net, polyp segmentation, data augmentation

DOI: 10.3233/JIFS-223340

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4593-4604, 2023

Authors: Ramamurthy, Priyadarshini | Nandagopal, Malarvizhi

Article Type: Research Article

Abstract: Fog computing enables the data analysis done nearer to the place of data generated, which makes a very short response time. Trust is essential for the effective performance of the fog nodes to overcome uncertainty, vulnerability, and risk. To enhance the trusted connection in fog computing, blockchain technology is adopted as a solution. Inclusion of blockchain in fog environment ensures consistency and security among the fog nodes. Each fog node mines every transaction and stores them as block creating a chain of blocks. In this proposed work, the adaption of blockchain technology is designed as a suitable solution for establishing …trusted security between fog nodes and for which a qualitative assessment is done. Show more

Keywords: Fog computing, fog nodes, blockchain, trust, data security, ethereum blockchain, smart contract

DOI: 10.3233/JIFS-222014

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4605-4612, 2023

Authors: Eti, Serkan | Dinçer, Hasan | Yüksel, Serhat | Gökalp, Yaşar

Article Type: Research Article

Abstract: In this study, a new fuzzy decision-making model is created to evaluate whether the solar panels are efficient to minimize energy costs of the hospitals. The weights of the criteria are calculated by considering T-Spherical fuzzy decision-making trial and evaluation laboratory (DEMATEL) method. Moreover, for the purpose of measuring the coherency of the findings, analysis results are also calculated for different t values. Additionally, by making improvements to some criticisms to the classical DEMATEL method, a new technique is created by the name of TOP-DEMATEL while integrating some steps of technique for order preference by similarity to ideal solution (TOPSIS) …to the DEMATEL technique. The main novelty of this study is that it is analyzed whether the solar panels are effective in reducing the costs of hospitals with an original decision-making model. It is concluded that generating own energy in the long run is the most crucial item according to both T-Spherical fuzzy DEMATEL and TOP-DEMATEL methods. The analysis results are quite similar for different t values. This situation gives information about the coherency and reliability of the findings. This situation gives information that the solar panels should be taken into consideration for the hospitals because they will minimize energy dependency of the hospitals. On the other side, the results of T-Spherical fuzzy TOP-DEMATEL indicate that the high initial investment cost is the second most critical factor in this respect. This finding is quite different by comparing with the results of T-Spherical fuzzy TOP-DEMATEL. Hence, it is seen that cost effectiveness should also be taken into consideration for the decision of generating the solar panels in the hospitals. Show more

Keywords: T-Spherical fuzzy sets, TOP-DEMATEL, solar energy, health industry

DOI: 10.3233/JIFS-222968

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4613-4625, 2023

Authors: Mathina Kani, Mohamed Ali Jinna | Parvathy, Meenakshi Sundaram | Maajitha Banu, Samsammal | Abdul Kareem, Mohamed Saleem

Article Type: Research Article

Abstract: In this article, a methodological approach to classifying malignant melanoma in dermoscopy images is presented. Early treatment of skin cancer increases the patient’s survival rate. The classification of melanoma skin cancer in the early stages is decided by dermatologists to treat the patient appropriately. Dermatologists need more time to diagnose affected skin lesions due to high resemblance between melanoma and benign. In this paper, a deep learning based Computer-Aided Diagnosis (CAD) system is developed to accurately classify skin lesions with a high classification rate. A new architecture has been framed to classify the skin lesion diseases using the Inception v3 …model as a baseline architecture. The extracted features from the Inception Net are then flattened and are given to the DenseNet block to extracts more fine grained features of the lesion disease. The International Skin Imaging Collaboration (ISIC) archive datasets contains 3307 dermoscopy images which includes both benign and malignant skin images. The dataset images are trained using the proposed architecture with the learning rate of 0.0001, batch size 64 using various optimizer. The performance of the proposed model has also been evaluated using confusion matrix and ROC-AUC curves. The experimental results show that the proposed model attains a highest accuracy rate of 91.29 % compared to other state-of-the-art methods like ResNet, VGG-16, DenseNet, MobileNet. A confusion matrix and ROC curve are used to evaluate the performance analysis of skin images. The classification accuracy, sensitivity, specificity, testing accuracy, and AUC values were obtained at 90.33%, 82.87%, 91.29%, 87.12%, and 87.40%. Show more

Keywords: Image processing, deep learning, feature extraction, image classification, Inception v3 model, computer aided diagnosis

DOI: 10.3233/JIFS-221386

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4627-4641, 2023

Authors: Liang, Shaohui | Wei, Botao

Article Type: Research Article

Abstract: Teaching-learning-based optimization algorithm (TLBO) is a swarm intelligence optimization algorithm that simulates classroom teaching phenomenon. In order to solve the problem that TLBO algorithm is easy to fall into local optimum and has poor stability, an improved teaching-learning-based optimization algorithm based on fusion difference mutation (IDMTLBO) is proposed. Firstly, adaptive teaching factors are introduced. Secondly, in the teaching stage, each student studies according to the gap between himself and the teacher, which improves the convergence speed and convergence accuracy of the algorithm. Finally, in the learning stage, students are divided into two levels according to their learning level, and two …students are randomly selected to improve the iterative equation in the learning stage with the difference mutation strategy, It improves the disadvantage that the algorithm is easy to fall into local optimum. Numerical experiments show that the convergence speed and convergence accuracy of the algorithm are obviously better than TLBO algorithm, DMTLBO algorithm, DSTLBO algorithm. Show more

Keywords: Teaching-learning-based optimization, adaptive teaching factors, the improved teaching stage, learning stages, differential mutation

DOI: 10.3233/JIFS-221019

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4643-4651, 2023

Authors: Fathima Perveen, P. A. | John, Sunil Jacob | Kamacı, Hüseyin | Baiju, T.

Article Type: Research Article

Abstract: Picture fuzzy sets are a direct extension of fuzzy sets and intuitionistic fuzzy sets, recently developed as a mathematical tool for solving uncertainty-related problems. In this paper, a novel similarity measure and corresponding weighted similarity measure between two picture fuzzy sets are proposed after indicating some disadvantages of the current similarity measures of picture fuzzy sets through some exemplary numerical examples. Also, some of their basic properties are discussed. Further, a picture fuzzy decision making algorithm based on the similarity aggregation method is constructed and then applied to the decision making problem. It is also used to deal with a …medical diagnosis problem to detect which disease a patient may be suffering from. Finally, the effectiveness of the proposed similarity measure is demonstrated by making comparison with the present picture fuzzy similarity measures. Show more

Keywords: Fuzzy sets, picture fuzzy sets, similarity measure, medical diagnosis, decision making

DOI: 10.3233/JIFS-222334

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4653-4665, 2023

Authors: Wang, Chaofeng

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-212862

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4667-4679, 2023

Authors: Elavarasan, B. | Muhiuddin, G. | Porselvi, K. | Jun, Y. B.

Article Type: Research Article

Abstract: Many uncertainties arise in real-world problems, making them impossible to solve using conventional approaches. Researchers all over the world have developed new mathematical theories like fuzzy set theory and rough set theory to better understand the uncertainties that occur in various fields. Soft set theory, which was recently introduced, offers a novel approach to real-world problem solving by removing the need to set the membership function. This is helpful in resolving a variety of issues, and much progress is being made these days. Recently, Jun introduced the concept of a hybrid structure, which blends the concepts of a fuzzy set …as well as a soft set. In this paper, we define the hybrid k -sum and hybrid k -product of k -ideals of semiring and investigate their properties. We illustrate with an example that the hybrid sum and hybrid product of two k-ideals are not always hybrid ideals. We also describe semiring regularity constraints in terms of hybrid k -ideal structures. Show more

Keywords: Semiring, hybrid structure, ideal, hybrid product, k-ideal, hybrid k-product, hybrid ideals, hybrid k-ideals

DOI: 10.3233/JIFS-222335

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4681-4691, 2023

Authors: Fu, Xue | Zhu, Liangkuan | Wu, Bowen | Wang, Jingyu | Zhao, Xiaohan | Ryspayev, Arystan

Article Type: Research Article

Abstract: To improve the traditional image segmentation, an efficient multilevel thresholding segmentation method based on improved Chimp Optimization Algorithm (IChOA) is developed in this paper. Kapur entropy is utilized as the objective function. The best threshold values for RGB images’ three channels are found using IChOA. Meanwhile, several strategies are introduced including population initialization strategy combining with Gaussian chaos and opposition-based learning, the position update mechanism of particle swarm algorithm (PSO), the Gaussian-Cauchy mutation and the adaptive nonlinear strategy. These methods enable the IChOA to raise the diversity of the population and enhance both the exploration and exploitation. Additionally, the search …ability, accuracy and stability of IChOA have been significantly enhanced. To prove the superiority of the IChOA based multilevel thresholding segmentation method, a comparison experiment is conducted between IChOA and 5 six meta-heuristic algorithms using 12 test functions, which fully demonstrate that IChOA can obtain high-quality solutions and almost does not suffer from premature convergence. Furthermore, by using 10 standard test images the IChOA-based multilevel thresholding image segmentation method is compared with other peers and evaluated the segmentation results using 5 evaluation indicators with the average fitness value, PSNR, SSIM, FSIM and computational time. The experimental results reveal that the presented IChOA-based multilevel thresholding image segmentation method has tremendous potential to be utilized as an image segmentation method for color images because it can be an effective swarm intelligence optimization method that can maintain a delicate balance during the segmentation process of color images. Show more

Keywords: Multi-threshold color image segmentation, chimp optimization algorithm, particle swarm algorithm, self-adaptive strategy, Kapur’s entropy

DOI: 10.3233/JIFS-223224

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4693-4715, 2023

Authors: Perumal, T. Sudarson Rama | Jegatheesan, A. | Jayachandran, A.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-220308

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4717-4732, 2023

Authors: Aarthi, S. | Shanmugasundari, M.

Article Type: Research Article

Abstract: This paper presents boundless capacity, one server’s fuzzy and intuitionistic fuzzy queuing models. This study’s primary objective is to demonstrate and compare the performance of a single server queuing model with infinite capacity using fuzzy queuing theory and intuitionistic fuzzy queuing theory. This article demonstrates that intuitionistic fuzzy theory performs better when solving queuing problems. Furthermore, by integrating fuzzy queuing models into an intuitionistic fuzzy framework, their relevance in authentic situations is augmented. The fuzzy queuing theory model’s performance measurements are delivered as a range of values, but the intuitionistic fuzzy queuing theory model offers a broad array of values. …In this context, the arrival and the service rates are both triangular (TFN) and intuitionistic triangular fuzzy numbers (TIFN). An assessment is performed to determine the evaluation criteria, employing a design protocol in which the fuzzy values are taken as-is without being turned into crisp values. As a result, in an ambiguous environment, we can use the proposed approach to pick scientific findings. In this study, we are using the TFN in an intuitionistic fuzzy environment, compensating for the degree of stability and denial so that the sum of both virtues is never higher than one. We proffered many non-normal arithmetic techniques for this sort of fuzzified integer. The envisaged compositions are intuitive and concise, as they evolved by utilising canonical algebraic mathematics. In real-world situations, this tactic is simple and straightforward to enact. The nearest interval number is then used to round a TIFN. The key advantage of this strategy is that it allows us to quickly solve a constrained unrestrained optimization model with TIFN coefficients using a multi-section heuristic. The prevailing methodologies and initiatives are destined to be relevant to different types of updated decision-making obstacles in focusing on economic equity, funding, presidency, and environmental sciences, which will be the focus of our future research. And two numerical problems are solved to showcase the sustainability of the suggested technique. In this queuing model, we predict a variety of components, including prospective queue length, expected system length, and sojourn time in both the queue and the system. The statistical analysis reveals that the quantified performance indicators of the intuitionistic fuzzy queuing model agree well with the performance measurements of the fuzzy queuing model. Even though the average correlation between the two concepts is nearly equivalent, TIFN provides a more extensive range of possibilities than TFN. Despite the fact that fuzzy set theory is used to contend with unpredictability in decision-making circumstances, it only relates to the extent of membership and lacks a model for reluctance. The fact that each asset’s affirmation and deprivation levels are comprehended is the special feature of intuitionistic fuzzy sets. As a consequence, it becomes more meticulous, suitable, and generalizable. Show more

Keywords: Intuitionistic fuzzy queue, intuitionistic fuzzy arithmetic, sojourn time, triangular intuitionistic fuzzy numbers

DOI: 10.3233/JIFS-221367

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4733-4746, 2023

Authors: Lavanya, G. | Velammal, B.L. | Kulothungan, K.

Article Type: Research Article

Abstract: A network of real time devices that can sense and transmit the information from the deployed environment by using multi hop communication is called as Wireless Sensor Network (WSNs). Despite the rapid advancement of WSN, where an increasing number of physical devices so called as sensors nodes are connected with each other, providing the improved security with optimized energy consumption during data transmission, communication and computation remains huge challenge. In wireless sensor networks, numerous sensor nodes are deployed in the physical environment to sense and collect the required information from the given environment. The sensed information is needed to be …transmitted from the nodes to the control station in an energy efficient manner. Data aggregation is one kind of techniques which will optimize the energy usage in wireless sensor networks during the data transmission. In data aggregation, the unnecessary data is removed which will significantly reduce energy of the nodes during data transmission. However, collected data during the data aggregation should be completely protected and there are various threats that can be launched by the intruders to carry out unauthorised data access and can cause threat to the integrity of the network. Therefore, ensuring data security during the data aggregation process is very important and essential for the security of the network. In this paper, a Secure Cluster based Data Aggregation Protocol (SCDAP) have been proposed to provide better security through secure authentication and verification process, and to reduce overall energy consumption of the network by implementing secure clustering process to eliminate the redundant data in the network. Moreover, the proposed system is more efficient in generating public and private keys for effective and secure data transmission and verification process. The proposed system is experimentally tested in NS-3 tool and proves that the proposed system reduces high energy consumption, computational and communicational cost, end-to-end delay and improves the packet delivery ratio. Moreover, the proposed system provides better security in the network when compared to other existing systems during the data aggregation. Show more

Keywords: Wireless networks, data aggregation, energy optimization, efficient authentication, key generation

DOI: 10.3233/JIFS-223256

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4747-4757, 2023

Authors: Alabdaly, Ammar A. | El-Sayed, Wagdy G. | Hassan, Yasser F.

Article Type: Research Article

Abstract: The task of cell segmentation in microscope images is difficult and popular. In recent years, deep learning-based techniques have made incredible progress in medical and microscopy image segmentation applications. In this paper, we propose a novel deep learning approach called Residual-Atrous MultiResUnet with Channel Attention Mechanism (RAMRU-CAM) for cell segmentation, which combines MultiResUnet architecture with Channel Attention Mechanism (CAM) and Residual-Atrous connections. The Residual-Atrous path mitigates the semantic gap between the encoder and decoder stages and manages the spatial dimension of feature maps. Furthermore, the Channel Attention Mechanism (CAM) blocks are used in the decoder stages to better maintain the …spatial details before concatenating the feature maps from the encoder phases to the decoder phases. We evaluated our proposed model on the PhC-C2DH-U373 and Fluo-N2DH-GOWT1 datasets. The experimental results show that our proposed model outperforms recent variants of the U-Net model and the state-of-the-art approaches. We have demonstrated how our model can segment cells precisely while using fewer parameters and low computational complexity. Show more

Keywords: Cell segmentation, convolutional neural network, deep neural networks, medical image segmentation, u-net

DOI: 10.3233/JIFS-222631

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4759-4777, 2023

Authors: Sun, Jiaqi | Wang, Pingxin | Yu, Hualong | Yang, Xibei

Article Type: Research Article

Abstract: Essentially, the problem solving of attribute reduction can be regarded as a process of reduct searching which will be terminated if a pre-defined restriction is achieved. Presently, among a variety of searching strategies, meta-heuristic searching has been widely accepted. Nevertheless, it should be emphasized that the iterative procedures in most meta-heuristic algorithms rely heavily on the random generation of initial population, such a type of generation is naturally associated with the limitations of inferior stability and performance. Therefore, a constraint score guidance is proposed before carrying out meta-heuristic searching and then a novel framework to seek out reduct is developed. …Firstly, for each attribute and each label in data, the index called local constraint score is calculated. Secondly, the qualified attributes are identified by those constraint scores, which consist of the foundation of initial population. Finally, the meta-heuristic searching can be further employed to achieve the required restriction in attribute reduction. Note that most existing meta-heuristic searchings and popular measures (evaluate the significance of attributes) can be embedded into our framework. Comprehensive experiments over 20 public datasets clearly validated the effectiveness of our framework: it is beneficial to reduct with superior stabilities, and the derived reduct may further contribute to the improvement of classification performance. Show more

Keywords: Attribute reduction, constraint score, meta-heuristic searching, rough set

DOI: 10.3233/JIFS-222832

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4779-4800, 2023

Authors: Zhao, Xiaofang | Li, Faming | Chen, Biao | Li, Xiaofei | Lu, Shasha

Article Type: Research Article

Abstract: Examining the properties of High-Performance Concrete (HPC) has been a big challenge due to the highly heterogeneous relationships and coherence among several constituents. The employment of silica fume and fly ash as eco-friendly components in mixtures benefits the concrete to improve its physical features. Although machine learning approaches are utilized broadly in many studies solitarily to estimate the mechanical features of concrete, causing to reduce accuracy and lift the cost and complexities of computational networks. Consequently, current research aims to develop a Radial Basis Function Neural Network (RBFNN) integrating with optimization algorithms in order to precisely model the mechanical characteristics …of HPC mixtures including compressive strength (CS) and slump (SL). Feeding the dataset of HPC samples to hybrid models will result to reproduce the given CS and SL factors simultaneously. The results of the models showed that the maximum rate of correlation between estimated values and measured ones was obtained at 98.3% while the minimum rate of RMSE was calculated at 3.684 mm (and MPa) in the testing phase. Employing such soft-oriented approaches has been benefiting us to reduce costs and increase the result accuracy. Show more

Keywords: High-performance concrete, radial basis function, neural network, compressive strength, slump, whale optimization algorithm

DOI: 10.3233/JIFS-222805

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4801-4815, 2023

Authors: Li, Junxia

Article Type: Research Article

Abstract: In order to improve the estimation accuracy of structural change points of multi-dimensional stochastic model, the accurate estimation algorithm of structural change points of multi-dimensional stochastic model is studied. A multi-dimensional stochastic Graphical Modeling model based on multivariate normal hypothesis is constructed, and the relationship between the Graphical Gaussian model and the linear regression model is determined. The parameters of the multi-dimensional stochastic model are estimated by using the parameter estimation algorithm of the multi-dimensional stochastic model containing intermediate variables. According to the parameter estimation results of the multi-dimensional stochastic model, the structural change point estimation results of the multi-dimensional …stochastic model are obtained by using the accurate estimation algorithm of the structural change point based on the MLE identification local drift time. The experimental results show that the proposed algorithm has higher estimation accuracy of structural change points than the control algorithms, which shows that it can effectively estimate the structural change points of multi-dimensional random models and has higher practicability. Show more

Keywords: Multidimensional, stochastic model, structure, change point, accurate, estimation algorithm

DOI: 10.3233/JIFS-222821

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4817-4829, 2023

Authors: Nayana, P.G. | Iyer, Radha Rajamani

Article Type: Research Article

Abstract: Generalized Mycielskians are triangle-free networks with a large chromatic number, having a number of desirable characteristics like fast multi-path communication, high fault tolerance, reliable resource sharing, etc. Graph invariants like domination number and secure domination number can be used to protect the network by monitoring each node or moving to a failing node. A dominating set of a graph G is a subset of its vertex set, which can monitor every other vertex of the graph, and γ (G ), the domination number of G , is the least cardinality among all dominating sets of G . A secure dominating …set S of G is a dominating set with an additional property that, for each vertex u not in S there is a vertex v in S adjacent to u such that, the swapped set (S  - {v }) ∪ {u } is dominating. γs  (G ) is the secure domination number of G , which is the minimum cardinality among all secure dominating sets of G . In this paper, we analyzed γ and γs of the generalized Mycielskian (μm ) for path graphs P n and cycle graphs C n , varying both n and m , and used these results to obtain bounds for general graphs G . Show more

Keywords: Generalized Mycielskian, domination number, secure domination number

DOI: 10.3233/JIFS-223326

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4831-4841, 2023

Authors: Lizhu, Yue | Liwei, Yao

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-213294

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4843-4852, 2023

Authors: Swapna, S. | Premila, T.R. | Janaki, N. | Kirubakaran, D.

Article Type: Research Article

Abstract: This paper proposes a hybrid optimization system depending on optimal location for electric vehicles parking lot (PL) and capacitors on distribution system to maintain voltage profile with electricity loss. The proposed system is the consolidation of Seagull optimization algorithm (SGO) and salp swarm algorithm (SSA). The migration and attacking behaviors of seagull is empowered through SSA method. By this manner, the proposed hybrid optimization scheme is known as SGOS2 A method. Here, parking zone allocation with capacitor is considered to congestion management in conjunction through the compensation of reactive energy. So, one can optimally decide the size of automobile parking …space, SGOS2 A method is followed. Moreover, parking lot with capacitor allocation is considered to congestion control at the side of reactive power compensation. By this proper manipulate, the capacitors exact location, automobile parking space of electric vehicles on the grid, lessening of active with reactive power loss, voltage profile conversion is selected optimally. Besides, the proposed SGOS2 A scheme is activated on MATLAB/Simulink site, then the efficiency is examined with different techniques. The mean, median and standard deviation of the proposed approach achieves 1.0593, 1.0915 and 0.1050. Show more

Keywords: Electric vehicle, voltage and power loss, parking lot, seagull optimization algorithm, salp swarm algorithm

DOI: 10.3233/JIFS-220651

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4853-4868, 2023

Authors: Khatun, Mahafuja | Islam, Ridwan Arefin | Islam, Salekul

Article Type: Research Article

Abstract: In recent years, blockchain technology has been successfully used in many distributed environments where different stakeholders, who do not have any trust between them, interact with each other through a secured and transparent platform. The use of blockchain in healthcare insurance industry has not been studied methodically. In this study, we develop a blockchain based Secured and Automated Health Insurance Claim (B-SAHIC) processing system. First, we design an Entity-Relationship (ER) diagram identifying all actors with their respective data and relations between them. We also develop the business model and algorithms for all necessary steps. We implement the system in Hyperledger …Fabric and deploy a smart contract to implement these algorithms. We introduce CouchDB to store the OffChain data where we store World-State Database. B-SAHIC provides a web-based portal for all actors who interact with the blockchain. Privacy of clients’ claim-related data is ensured by encrypting treatment-related data with a new key that is derived uniquely for each new submission done of clients’ personal documents. We have also deployed Hyperledger Explorer, a user-friendly web application tool for monitoring the health state of each node participating on the blockchain network. We have studied the performance of B-SAHIC for two to six peer nodes. Moreover, our performance study shows that B-SAHIC is fast and scalable. In our study, the average query latency is decreased from 0.07 second (for two peer nodes) to 0.02 second (for six peer nodes) in case of 1300 queries per second while the average transaction latency remains unchanged (around 3.5 seconds) for 300 transactions per second. Moreover, B-SAHIC consumes minimum resources, around 350MB only for two peer nodes. We believe that the development process of this blockchain based platform can be applicable for the automation of other insurance industry too. Show more

Keywords: Health insurance, insurance claim processing, blockchain, hyperledger fabric, smart contract

DOI: 10.3233/JIFS-220690

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4869-4890, 2023

Authors: Zhao, Dazhi | Hao, Yunquan | Li, Weibin | Tu, Zhe

Article Type: Research Article

Abstract:  Whether the exact amount of training data is enough for a specific task is an important question in machine learning, since it is always very expensive to label many data while insufficient data lead to underfitting. In this paper, the topic that what is the least amount of training data for a model is discussed from the perspective of sampling theorem. If the target function of supervised learning is taken as a multi-dimensional signal and the labeled data as samples, the training process can be regarded as the process of signal recovery. The main result is that the least amount …of training data for a bandlimited task signal corresponds to a sampling rate which is larger than the Nyquist rate. Some numerical experiments are carried out to show the comparison between the learning process and the signal recovery, which demonstrates our result. Based on the equivalence between supervised learning and signal recovery, some spectral methods can be used to reveal underlying mechanisms of various supervised learning models, especially those “black-box” neural networks. Show more

Keywords: Machine learning, sampling theorem, frequency principle, signal recovery, neural network, Gaussian process regression

DOI: 10.3233/JIFS-211024

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4891-4906, 2023

Authors: Sathishkumar, B.R.

Article Type: Research Article

Abstract: Power dissipation at the network level to improve lifespan without degrading the bandwidth and collaboration is a fundamental impediment to effective spectral efficiency in wireless sensor networks (WSNs). This issue is made much more difficult. Wireless energy transfer (WET) for energizing remote sensor nodes gained interest. This research explores an FDD-based on-demand scenario with many relays where a transmitter is powered by direct and relayed links. A threshold is set for transmission energy & channel quality to decide whether the broadcasting can be efficient (for spectrum utilization) or the packet would not arrive at its destination. The network model offers …an energy-efficient scheduling strategy to decide whether to transmit information or not depending on the stored higher energy and network status. An energy-aware polling-based medium access control (MAC) mechanism, composite energy, and information first (CEDF) has also been developed to fine-tune packet delivery ratio by utilizing datagrams and energy packages to set polling prioritization. Computational simulations indicate that energy relayed and the recommended energy-efficient scheduled technique decrease the system’s active power losses supporting all theoretical predictions. Show more

Keywords: Polling, multi-relay, spectral efficiency, sensor network, MAC, data speed and power constraints

DOI: 10.3233/JIFS-223001

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4907-4930, 2023

Authors: Sharma, Preeti | Gangadharappa, M.

Article Type: Research Article

Abstract: Anomalous event recognition has a complicated definition in the complex background due to the sparse occurrence of anomalies. In this paper, we form a framework for classifying multiple anomalies present in video frames that happen in a context such as the sudden moment of people in various directions and anomalous vehicles in the pedestrian park. An attention U-net model on video frames is utilized to create a binary segmented anomalous image that classifies each anomalous object in the video. White pixels indicate the anomaly, and black pixels serve as the background image. For better segmentation, we have assigned a border …to every anomalous object in a binary image. Further to distinguish each anomaly a watershed algorithm is utilized that develops multi-level gray image masks for every anomalous class. This forms a multi-class problem, where each anomalous instance is represented by a different gray color level. We use pixel values, Optical Intensity, entropy values, and Gaussian filter with sigma 5, and 7 to form a feature extraction module for training video images along with their multi-instance gray-level masks. Pixel-level localization and identification of unusual items are done using the feature vectors acquired from the feature extraction module and multi-class stack classifier model. The proposed methodology is evaluated on UCSD Ped1, Ped2 and UMN datasets that obtain pixel-level average accuracy results of 81.15%,87.26% and 82.67% respectively. Show more

Keywords: Anomaly detection, video surveillance, feature extraction, multi-class classification, classifier

DOI: 10.3233/JIFS-221925

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4931-4947, 2023

Authors: Zheng, Tingting | Chen, Hao | Yang, Xiyang

Article Type: Research Article

Abstract: The traditional Ordered Weighting Average (OWA) operator is suitable for aggregating numerical attributes. However, this method fails when the attribute values are given in a linguistic form. In this paper, a novel aggregating method named Entropy and Probability based Fuzzy Induced Ordered Weighted Averaging (EPFIOWA) is proposed for Gaussian-fuzzy-number-based linguistic attributes. A method is first designed to obtain a reasonable weighting vector based on probability distribution and maximal entropy. Such optimal weighting vectors can be obtained under any given level of optimism, and the symmetric properties of the proposed model are proven. The linguistic attributes of EPFIOWA are represented by …Gaussian fuzzy numbers because of their concise form and good operational properties. In particular, the arithmetic operations and distance measures of Gaussian fuzzy numbers required by EPFIOWA are given systematically. A novel method to obtain the order-inducing variables of linguistic attribute values is proposed in the EPFIOWA operators by calculating the distances between any Gaussian fuzzy number and a set of ordered grades. Finally, two numerical examples are used to illustrate the proposed approach, with evaluation results consistent with the observed situation. Show more

Keywords: Gaussian fuzzy numbers, induced ordered weighted averaging operators, order-inducing variables, probability distribution, maximal entropy

DOI: 10.3233/JIFS-222241

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4949-4962, 2023

Authors: Abdulrahim, Basiya K. | Sulaiman, Nejmaddin A. | Sadiq, Gulnar W.

Article Type: Research Article

Abstract: This paper presents an efficient and straightforward methodology with less computational complexities to title the bi-level objective linear fractional programming problem with fuzzy interval coefficients (BILOLFPP with FIC). To construct the methodology, the concept of mean technique is utilized to tackle the fuzzy numbers in addition to adding to α = [mean  (a i ) , mean  (b i )] , i  = 1, …, n , then. Accordingly, the fuzzy programming issue is converted into a single objective linear fractional programming problem (SOLFPP with FIC) by the utilize of weight function. The fuzzy technique has significant structural transform metamorphosis during the …recent decades. Numerous to mention introduced have been undertaken to explanation fuzzy methodology for linear, non-linear programming issues. While, the previous finding that introduced have been conflicting, recent studies of competitive situations indicate that LFPP with fuzzy interval coefficients (LFPP with FIC) has an advantageous effect mostly on comparison situation. One of the suggestions which we found is interval approximations, closed interval approximation of sequential fuzzy number for resolving fuzzy number LFPP without changing it to a crisp issue. A new variant of modified simplex methodology is studied here just for resolving fuzzy number LFPP utilizing fuzzy arithmetic. Consequently, fuzzy representation of some important theories of fuzzy LFPP has been reproved. A fuzzy LFPP with FIC is worked out as numerical examples illustrate to the suggested methodology. On iterative processes, it decreases the overall processing time to explain, the modified simplex methodology for solving BILLFPP with FIC with out to crisp by taking numerical examples and compare with Nasseri, Verdegay and Mahmoudi methodology changing it to a crisp issue [9 ]. Show more

Keywords: Fuzzy number, FFLFPP, FFLFPP with fuzzy interval coefficients, FFBILLFPP with fuzzy interval coefficients, closed interval approximation, modified simplex methodology

DOI: 10.3233/JIFS-222519

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4963-4973, 2023

Authors: Nasr, Asmaa M. | ElGhawalby, Hewayda | Mareay, R.

Article Type: Research Article

Abstract: In several empirical situations, a decision is needed to be made based on data that is captured in some information system. The problem occurs when the information system holds complex data or even too much data attributes. This leads to the need for reducing the number of attributes required to obtain a decision. In this paper, a novel attributes’ reduction method is presented; the proposed method is based on constructing a weighted pre-topology that represents the information system under consideration. In addition, some essential operations for the weighted pre-topological space are presented; as well as, a brief study of their …properties. Show more

Keywords: Fuzzy pretopological space, closure set, interior set, information system

DOI: 10.3233/JIFS-223077

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4975-4985, 2023

Authors: Chen, Chuen-Jyh | Huang, Chieh-Ni | Yang, Shih-Ming

Article Type: Research Article

Abstract: Weather forecasts are essential to aviation safety. Unreliable forecasts not only cause problems to pilots and air traffic controllers, but also lead to aviation accidents and incidents. This study develops a long short-term memory (LSTM) integrating both multiple linear regression and the Pearson’s correlation coefficients to improve forecasting. A numerical dataset of 10 weather features (sea pressure, temperature, dew point temperature, relative humidity, wind speed, wind direction, sunshine rate, global solar radiation, visible mean, and cloud amount) is applied on every calendar day in a year to train and validate the LSTM for temperature forecasting. It is shown that data …standardization is necessary to rescale the data to improve training convergence and reduce training time. In addition, feature selection by multiple linear regression and by Pearson’s correlation coefficients are shown effective to the forecast accuracy of the LSTM. By selecting only the sensitive features (sea pressure, dew point temperature, relative humidity and relative humidity), the temperature forecasting errors can be reduced from RMSE 4.0274 to 2.2215 and MAPE 23.0538% to 5.0069%. LSTM deep learning with data standardization and feature selection is effective in forecasting for aviation safety. Show more

Keywords: Deep learning, aviation weather, long short-term memory, weather forecasting

DOI: 10.3233/JIFS-223183

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4987-4997, 2023

Authors: Chen, Xinzhang | Tian, Xiaoyan | Ye, Hongtu

Article Type: Research Article

Abstract: As the most popular art category in the contemporary art field, visual art is no longer limited to traditional art categories such as painting, sculpture and photography, but develops into more diverse forms of expression with the continuous iteration of science and technology. As the most cutting-edge and popular concept in the world today, the research, development and application of science and technology have attracted close attention from all walks of life, including management, economy, transportation, education and teaching. However, there is no in-depth and clear research between the concept of metaverse and the concept of metaverse in the art …field, especially in the visual art field. We believe that visual art creation under the background of the metaverse will be an important direction of art development in the future, and will also greatly promote the improvement of the visual presentation quality of the metaverse. Therefore, we focus our research in this study on the issue of visual art quality assessment and propose a theory and method for assessing the quality of visual art in a future-oriented metaverse. This method is based on the G1 entropy method in fuzzy mathematics. In our research, we have built a visual art field architecture based on the metaverse. Considering the difference between the traditional visual art evaluation index system and the index system after the introduction of the concept of the future metaverse, we have built a brand-new visual art quality evaluation index system facing the future metaverse. This indicator is composed of four first-class indicators and twelve second-class indicators. We combine the subjective and objective weighting G1 entropy method as the method basis for the quantitative calculation results of the indicator weight. On the basis of quantitative analysis, we propose three-point countermeasures for improving the visual art quality of the future metaverse. Our research makes up for the gap in the theory of visual art quality evaluation after the introduction of the concept of the future metaverse, innovates the analysis of new concepts and the improvement of old methods, builds a new scene of organic combination of new technology and traditional visual art, and provides a new idea for the improvement of visual art quality in the future at home and abroad, It can also provide experience and theoretical support for the academic topic of similar art quality evaluation research at home and abroad. Show more

Keywords: Visual arts, metaverse, field architecture, G1-entropy method, AHP method

DOI: 10.3233/JIFS-223351

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4999-5019, 2023

Authors: Yang, Run

Article Type: Research Article

Abstract: In the past, different useful extensions of fuzzy sets were established by the researchers to manage the vagueness and uncertainty in various practical problems. Usually, the real numbers are utilized to express the decision information, but it is noted that the description of attributes using picture fuzzy sets (PFSs) proves to be more appropriate. As a powerful decision tool, PFSs provides more decision information that requires the application of some specific situations more types of response of human ideas: yes, contain, no, reject. QUALIFLEX (qualitative flexible multiple criteria method), is one of the well-known outranking methods to solve the multiple …attribute group decision making (MAGDM) problems with crisp numbers. The QUALIFLEX method can perfectly address the complex MAGDM problems where a lot of attributes are utilized to assess a limited number of alternatives. The electronic music acoustic quality evaluation is a classical MAGDM. This paper proposes and utilizes the QUALIFLEX to develop the picture fuzzy QUALIFLEX(PF-QUALIFLEX) method for MAGDM. The current study is mainly devoted to explore and extend the measurement of alternatives and ranking according to the QUALIFLEX under the background of PFSs. Furthermore, an example to evaluate the electronic music acoustic quality is handled through the proposed method to substantiate the extended approach. Show more

Keywords: Multiple attribute group decision making (MAGDM), picture fuzzy sets (PFSs), the extended QUALIFLEX method, electronic music acoustic quality evaluation

DOI: 10.3233/JIFS-223377

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5021-5032, 2023

Authors: Fu, Chengcai | Lu, Fengli | Wu, Fan | Zhang, Guoying

Article Type: Research Article

Abstract: The estimation of gangue content is the main basis for intelligent top coal caving mining by computer vision, and the automatic segmentation of gangue is crucial to computer vision analysis. However, it is still a great challenge due to the degradation of images and the limitation of computing resources. In this paper, a hybrid connected attentional lightweight network (HALNet) with high speed, few parameters and high accuracy is proposed for gangue intelligent segmentation on the conveyor in the top-coal caving face. Firstly, we propose a deep separable dilation convolution block (DSDC) combining deep separable convolution and dilation convolution, which can …provide a larger receptive field to learn more information and reduce the size and computational cost of the model. Secondly, a bridging residual learning framework is designed as the basic unit of encoder and decoder to minimize the loss of semantic information in the process of feature extraction. An attention fusion block (AFB) with skip pathway is introduced to capture more representative and distinctive features through the fusion of high-level and low-level features. Finally, the proposed network is trained through the expanded dataset, and the gangue image segmentation results are obtained by pixel-by-pixel classification method. The experimental results show that the proposed HALNet reduces about 57 percentage parameters compared with U-Net, and achieves state-of-the art performance on dataset. Show more

Keywords: Gangue intelligent segmentation, the top-coal caving face, depthwise separable dilation convolution, attention mechanism

DOI: 10.3233/JIFS-213506

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5033-5044, 2023

Authors: Annie Nancy, G. | Ramakrishnan, Kalpana | Senthil Nathan, J.

Article Type: Research Article

Abstract: Pressure injury usually develop in the bony prominence of immobile bedridden subjects. Predicting pressure injuries based on the subjects’ physiological information will reduce the burden of the caretakers in adjusting the frequency of repositioning such subjects. Visual assessment, diagnostic, and prognostic approaches only provide pressure injury information after onset. Therefore, the objective of this unique modeling technique is to predict the internal alterations that take place in human tissues before the onset of pressure injuries. In this approach the bio-mechanical and bio-thermal properties was integrated to predict the internal changes of skin, fat, and muscle layers when subjects were self-loaded …continuously for one hour in the sacrum region. A change in temperature of all the layers, as well as the distribution of Von-Mises stress in these layers, was observed. The inflammation caused by the changes in the temperature and the stress was measured from the simulation model. Ultrasound measurements was also taken for the same subjects in the supine position in the sacral region, before and after one hour by applying a self-load. An identical change in the thickness of the above-mentioned layers due to thermal expansion was noticed. Hence this computational model is hypothesized to give identical thermal expansion in comparison with the ultrasound measurements. There was an agreement between the thermal expansion using the simulation technique and the ultrasound technique which was assessed through Bland-Altman analysis, with a 96% confidence interval. Show more

Keywords: Bio-thermal model, bio-mechanical model, sacrum, pressure injury, multi-physics coupling

DOI: 10.3233/JIFS-222485

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5045-5057, 2023

Authors: Linhares, Luís Fernando | da Silva, Alisson Marques | Meireles, Magali Resende Gouvêa

Article Type: Research Article

Abstract: Private transport has become a viable and increasingly popular alternative to urban transportation. However, with this growth, an old and recurring problem becomes more latent: the relationship between passenger demands and taxi supply. This problem suggests the creation and use of techniques which make it possible to reduce the gap between the demand for taxi passengers and the effective contingent of vehicles needed to meet this demand. This work introduces a new approach to forecasting and classifying taxi passengers’ demands. The proposed approach uses historical data from taxi rides and meteorological data. The Kruskal-Wallis method identifies the most relevant variables, …and an evolving fuzzy system performs demand forecasting/classification. Five evolving systems are evaluated with our approach: Autonomous Learning Multi-Model (ALMMo), evolving Multivariable Gaussian Fuzzy System (eMG), evolving Fuzzy with Multivariable Gaussian Participatory Learning and Recursive Maximum Correntropy (eFCE), evolving Fuzzy with Multivariable Gaussian Participatory Learning and Multi-Innovations Recursive Weighted Least Squares (eFMI), and evolving Neo-Fuzzy Neuron (eNFN). In addition, computational experiments using real-world data were conducted to evaluate and compare the performance of the proposed approach. The results revealed that it obtained performance superior or comparable to state-of-the-art ones. Therefore, the experimental results suggest that the proposed approach is promising as an alternative for forecasting and classifying taxi passenger demand. Show more

Keywords: Taxi demand, forecasting, classification, evolving systems, fuzzy systems

DOI: 10.3233/JIFS-222115

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5059-5084, 2023

Authors: Naqvi, Deeba R. | Sachdev, Geeta | Ahmad, Izhar

Article Type: Research Article

Abstract: Game theory has been successfully applied in a variety of domains to deal with competitive environments between individuals or groups. The matrix games involving fuzzy, interval fuzzy, and intuitionistic fuzzy numbers exclusively examine the numeric components of an issue. However, several researchers have also examined various extensions of conventional game theory, considering the ambiguous situations for payoffs and goals. In many real-life scenarios, qualitative information is often critical in expressing the payoffs of a matrix game. Thus, the present work contributes to the field of matrix games where the payoffs have been quantified via qualitative variables, termed interval-valued hesitant fuzzy …linguistic sets. The mathematical formulation and solution concept for matrix games involving interval-valued hesitant fuzzy linguistic numbers is designed by utilizing an aggregation operator supported by linguistic scale function and solving them by employing score function. Finally, the proposed approach is validated by applying it to electric vehicle sales. Show more

Keywords: Interval-valued, linguistic set, hesitant fuzzy set, matrix games, average aggregation operator, linguistic scale function, electric vehicles

DOI: 10.3233/JIFS-222466

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5085-5105, 2023

Authors: Gullapelly, Aparna | Banik, Barnali Gupta

Article Type: Research Article

Abstract: Multi-object tracking (MOT) is essential for solving the majority of computer vision issues related to crowd analytics. In an MOT system designing object detection and association are the two main steps. Every frame of the video stream is examined to find the desired objects in the first step. Their trajectories are determined in the second step by comparing the detected objects in the current frame to those in the previous frame. Less missing detections are made possible by an object detection system with high accuracy, which results in fewer segmented tracks. We propose a new deep learning-based model for improving …the performance of object detection and object tracking in this research. First, object detection is performed by using the adaptive Mask-RCNN model. After that, the ResNet-50 model is used to extract more reliable and significant features of the objects. Then the effective adaptive feature channel selection method is employed for selecting feature channels to determine the final response map. Finally, an adaptive combination kernel correlation filter is used for multiple object tracking. Extensive experiments were conducted on large object-tracking databases like MOT-20 and KITTI-MOTS. According to the experimental results, the proposed tracker performs better than other cutting-edge trackers when faced with various problems. The experimental simulation is carried out in python. The overall success rate and precision of the proposed algorithm are 95.36% and 93.27%. Show more

Keywords: Computer vision, surveillance, tracking, correlation filters, holistic samples

DOI: 10.3233/JIFS-223516

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5107-5121, 2023

Authors: Nalini Joseph, L. | Anand, R.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219330 .

DOI: 10.3233/JIFS-223018

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5123-5135, 2023

Authors: Sudhagar, D. | ArokiaRenjit, J.

Article Type: Research Article

Abstract: Many real-time applications, including some emerging ones, rely on high-dimensional feature datasets. For simplifying the high-dimensional data, the various models are available by using the different feature optimization techniques, clustering and classification techniques. Even though the high-dimensional data is not handled effectively due to the increase in the number of features and the huge volume of data availability. In particular, the high-dimensional medical data needs to be handled effectively to predict diseases quickly. For this purpose, we propose a new Internet of Things and Fuzzy-aware e-healthcare system for predicting various diseases such as heart, diabetes, and cancer diseases effectively. The …proposed system uses a newly proposed Intelligent Mahalanobis distance aware Fuzzy Weighted K-Means Clustering Algorithm (IMFWKCA) for grouping the high dimensional data and also applies a newly proposed Moth-Flame Optimization Tuned Temporal Convolutional Neural Network (MFO-TCNN) for predicting the diseases effectively. The experiments have been done by using the UCI Repository Machine Learning datasets and live streaming patient records for evaluating the proposed e-healthcare system and have proved as better than others by achieving better performance in terms of precision, recall, f-measure, and prediction accuracy. Show more

Keywords: Feature optimization, clustering, e-healthcare system, high dimensional data, internet of things

DOI: 10.3233/JIFS-220629

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5137-5150, 2023

Authors: Saini, Munish | Adebayo, Sulaimon Oyeniyi | Singh, Harnoor | Singh, Harpreet | Sharma, Suchita

Article Type: Research Article

Abstract: The United Nations prescribed the Sustainable Development Goals (SDGs) to various nations to provide enduring answers to widespread problems and to give long-lasting solutions to common issues being faced across the globe. SDG 5 in particular was aimed at minimizing gender inequality by employing 9 targets and 14 indicators. The indicators serve as a yardstick to measure the progress of each of the 9 targets. This research takes an in-depth look at the perspectives of SDG 5 –Gender Inequalities, its targets, and indicators. Furthermore, explanatory data analysis and numerical association rule mining alongside QuantMiner are applied to the generated Indian …datasets on SDG 5 to extract patterns and associations among the fourteen indicators of SDG 5. The association rule mining carried out on the indicators reveals the pattern of association among these indicators. Legal provision for women and the rate of crimes against women have a perfect association of 100% while the association between legal provision for women and women who have experienced physical violence stands at 80%. The full relationships of all the 14 indicators are discussed extensively in the result and discussion section. Overall, it is established that these indicators are interdependent. This will make it easier for academics, the general public, and governmental and non-governmental organizations to understand the trends and form informed opinions on issues relating to gender inequality and SDG 5. Show more

Keywords: Sustainable development goals (SDG), gender equality, indicators, numerical association rule mining, knowledge extraction

DOI: 10.3233/JIFS-222384

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5151-5162, 2023

Authors: Karthikeyan, N. | Gugan, I. | Kavitha, M.S. | Karthik, S.

Article Type: Research Article

Abstract: The drastic advancements in the field of Information Technology make it possible to analyze, manage and handle large-scale environment data and spatial information acquired from diverse sources. Nevertheless, this process is a more challenging task where the data accessibility has been performed in an unstructured, varied, and incomplete manner. The appropriate extraction of information from diverse data sources is crucial for evaluating natural disaster management. Therefore, an effective framework is required to acquire essential information in a structured and accessible manner. This research concentrates on modeling an efficient ontology-based evaluation framework to facilitate the queries based on the flood disaster …location. It offers a reasoning framework with spatial and feature patterns to respond to the generated query. To be specific, the data is acquired from the urban flood disaster environmental condition to perform data analysis hierarchically and semantically. Finally, data evaluation can be accomplished by data visualization and correlation patterns to respond to higher-level queries. The proposed ontology-based evaluation framework has been simulated using the MATLAB environment. The result exposes that the proposed framework obtains superior significance over the existing frameworks with a lesser average query response time of 7 seconds. Show more

Keywords: Flood disaster management, ontology framework, spatial information, data pre-processing

DOI: 10.3233/JIFS-223000

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5163-5178, 2023

Authors: Yang, Hai-Long | Ren, Huan-Huan

Article Type: Research Article

Abstract: In this paper, we focus on the three-way decision model on incomplete single-valued neutrosophic information tables. Firstly, we define the minimum and maximum similarity measures between single-valued neutrosophic numbers (SVNNs) which may contain unknown values. On this basis, the notion of θ-weak similarity measure is given. Then, we introduce the conception of an incomplete single-valued neutrosophic information table (ISVNIT). For an incomplete single-valued neutrosophic information table, a new similarity relation is proposed based on the θ-weak similarity measure. Some properties are also studied. By using Bayesian decision theory and this similarity relation, we construct a three-way decision model on an …ISVNIT. Finally, an example of choosing product service providers is explored to illustrate the rationality and feasibility of the proposed model. We also discuss the influence of parameters in the model on decision results. Show more

Keywords: Three-way decision, single-valued neutrosophic number, incomplete single-valued neutrosophic information table, similarity measure

DOI: 10.3233/JIFS-221942

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5179-5193, 2023

Authors: Lei, Fan | Cai, Qiang | Wei, Guiwu | Mo, Zhiwen | Guo, Yanfeng

Article Type: Research Article

Abstract: The emergence of new energy electric vehicles (NEEV) can effectively reduce vehicle fuel consumption and alleviate the contradiction between fuel supply and demand. It has made great contributions to improving the atmospheric environment and promoting the development of environmental protection. However, the insufficient number of new energy electric vehicle charging stations (NEEVCSs) and unreasonable coverage areas have become obstacles to the large-scale promotion of new energy electric vehicles. Therefore, we build a multi-attribute decision making (MADM) model based on probabilistic double hierarchy linguistic weight Maclaurin symmetric mean (PDHLWMSM) operator and a MADM model based on probabilistic double hierarchy linguistic weight …power Maclaurin symmetric mean (PDHLWPMSM) operator to select the best charging station construction point from multiple alternative sites. In addition, the model constructed in this paper is compared with the existing MADM models to verify the scientificity of the model proposed in this paper. Show more

Keywords: Multiple attribute decision making (MADM), probabilistic double hierarchy linguistic term set (PDHLTS), PDHLWMSM operator, PDHLWPMSM operator, new energy electric vehicle charging station (NEEVS)

DOI: 10.3233/JIFS-221979

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5195-5216, 2023