Journal of Intelligent & Fuzzy Systems - Volume 44, issue 1

Authors: Kumar, Arvind | Singh Sodhi, Sartaj

Article Type: Research Article

Abstract: A Neural Network is one of the techniques by which we classify data. In this paper, we have proposed an effectively stacked autoencoder with the help of a modified sigmoid activation function. We have made a two-layer stacked autoencoder with a modified sigmoid activation function. We have compared our autoencoder to the existing autoencoder technique. In the existing autoencoder technique, we generally use the logsigmoid activation function. But in multiple cases using this technique, we cannot achieve better results. In that case, we may use our technique for achieving better results. Our proposed autoencoder may achieve better results compared to …this existing autoencoder technique. The reason behind this is that our modified sigmoid activation function gives more variations for different input values. We have tested our proposed autoencoder on the iris, glass, wine, ovarian, and digit image datasets for comparison propose. The existing autoencoder technique has achieved 96% accuracy on the iris, 91% accuracy on wine, 95.4% accuracy on ovarian, 96.3% accuracy on glass, and 98.7% accuracy on digit (image) dataset. Our proposed autoencoder has achieved 100% accuracy on the iris, wine, ovarian, and glass, and 99.4% accuracy on digit (image) datasets. For more verification of the effeteness of our proposed autoencoder, we have taken three more datasets. They are abalone, thyroid, and chemical datasets. Our proposed autoencoder has achieved 100% accuracy on the abalone and chemical, and 96% accuracy on thyroid datasets. Show more

Keywords: Autoencoder, sigmoid activation function, logsigmoid, neural network, classification, stacked autoencoder

DOI: 10.3233/JIFS-212873

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1-18, 2023

Authors: Abbasi, Hamid | Yaghoobi, Mahdi | Sharifi, Arash | Teshnehlab, Mohammad

Article Type: Research Article

Abstract: This paper presents an innovative architecture called cascade chaotic fuzzy system (CCFS) for the function approximation and chaotic modeling. The proposed model can dominate complications in the type-2 fuzzy systems and increase the chaotic performance of a whole framework. The proposed cascade structure is based on combining two or more one-dimensional chaotic maps. The combination provides a new chaotic map with more high nonlinearity than its grain maps. The fusion of cascade chaotic structure into the neurons of the membership layer of a conventional fuzzy system makes the CCFS more capable of confronting nonlinear problems. Based on the General Function …Approximation and Stone-Weierstrass theorem, we show that the proposed model has the function approximation property. By analyzing the bifurcation diagram and applying the CCFS to the problem of chaotic modeling, the new model is investigated. Simulation results and analysis are demonstrated to illustrate the concept of general function approximation. Show more

Keywords: Chaotic fuzzy system, function approximation, chaotic neural network, oscillatory neuron

DOI: 10.3233/JIFS-213405

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 19-40, 2023

Authors: Bhatnagar, Manisha | Thankachan, Dolly

Article Type: Research Article

Abstract: Trust enabled wireless networks use temporal behaviour information of nodes in order to classify them into different trust categories. This information is utilized by the router for high performance communication that is optimized in terms of end-to-end delay, energy consumption, throughput, packet delivery ratio, and other quality of service (QoS) parameters. Establishing security in trust enabled wireless networks is a difficult task, because high trust nodes might be compromised by external or internal attacks, thereby disrupting normal communication. In order to perform this task, blockchain based security models are deployed. These models provide high transparency, comprehensive traceability, distributed processing, and …data immutability, which makes them highly deployable for trust enabled networks. Blockchain models enforce compulsive verification of data before communication, which makes them resilient to DDoS, MITM, denial of service, and other data-based attacks. In order to enforce these checks, each of the block is hashed, and the hash values are compared with every existing block in the chain. These checks include hash uniqueness, and hash pattern validations; the later of which is decided by the network designer(s). As the length of blockchain increases, computational complexity of adding a new block (a.k.a. blockchain mining) increases exponentially, which adds to the end-to-end delay, and energy consumption of wireless nodes, which is a drawback of these models. To avoid this, sidechains & blockchain sharding models are developed. These models work by dividing the existing blockchain into multiple parts (based on a certain pre-set criteria), and then use the parts for high speed and low power mining. But again, due to increase in number of sidechains, the computational complexity of managing these chains, and locating data blocks within them increases exponentially. Moreover, in any practical wireless network, there is a need to communicate modifiable data, which is not supported by current blockchain implementations. In order to resolve these issues, this text proposes a transformable blockchain sharding model, which is managed via a light weight meta heuristic method for high-speed data access. The proposed model aims at reducing computational complexity of sidechain maintenance with the help of directed acyclic graphs (DAGs) for storing of hash ranges. The model also incorporates a transformable blockchain solution, wherein the block structure is designed to incorporate selectively mutable as well as non-mutable information. Both the mutable and non-mutable information is encrypted using high performance elliptic curve cryptosystem, which makes it highly secure against network attacks. The proposed model showcases 15% improvement in network lifetime, 8% reduction in end-to-end delay, 22% reduction in computational complexity, and 18% improvement in network throughput when compared with various blockchain and sidechain based wireless networks, thereby assisting in development of a high QoS and highly secure wireless network. Show more

Keywords: Wireless, trust-enabled, sharding, blockchain, meta heuristic, DAG

DOI: 10.3233/JIFS-213482

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 41-58, 2023

Authors: Priyanka, S. | Shanthi, S.

Article Type: Research Article

Abstract: Drowsiness is the inactivated state of the brain and observed during the transition from awaken state to sleepy state. This inactive state diminishes an individual’s attention and leads to accidents during professional or personal activities. The prediction of this inactive (drowsiness) state using AI plays a substantial role in the avoidance of accidents. The advancements in the field of Artificial Intelligence and Neuroscience approaches are used for the prediction of this inactive drowsy state. In order to prevent these devastating accidents, the state of drowsiness of the driver has to be be monitored. Electroencephalogram (EEG) is a predominant tool adopted …to examine various states of the brain effectually. It is generally known as Brain-Computer Interface System. The EEG channels are used for predicting the inactive state while implementing the real-time applications. However, the researchers face various challenges during execution based on the classification and channel selection process. This research concentrates on modelling and efficient drowsiness prediction methods and intends to bridge the gap encountered in the existing approaches. A novel stacked Long Short-Term Memory(s  - LSTM ) with Deep Fully Connected- Convolutional Neural Network (DFC  - CNN ) is used to learn and memorize the long-term feature dependencies and attains essential information based on time-series prediction. Single and multi-channel EEG data is considered to measure the statistical characteristics of available EEG signals. The online available OpenBCI sleep analysis data is used for performing the experimentation, and run in GoogleColab environment. The proposed s  - LSTM model provides a better trade-off compared to existing approaches. The model generalization is improved with the validation of combined feature subjects. Here, metrics like prediction accuracy, RMSE, false positives, scaling coefficients related to false positives are measured to show the significance of the model. Show more

Keywords: Drowsiness, deep learning, stacked long-short term memory, accident risk, statistical measure, generalization

DOI: 10.3233/JIFS-220024

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 59-73, 2023

Authors: Tan, Guxia

Article Type: Research Article

Abstract: A heart attack is a common cause of death globally. It can be treated successfully through a simple and accurate diagnosis. Getting the right diagnosis at the right time is very important for the treatment of heart failure. Currently, the conventional method of diagnosing heart disease is not reliable. Machine learning is a type of artificial intelligence that can be used to analyze the data collected by sensors. Data mining is another type of technology that can be utilized in the healthcare industry. These techniques help predict heart disease based on various factors. We developed a prediction and recommendation model …aimed at predicting heart disease using the Optimized Deep Belief Network. It does so by taking into account the various features of the heart disease UCI and Stalog database. Finally, the proposed method classifies healthy people and people with heart illness with an accuracy of 97.91%. Show more

Keywords: MSVDIS, MV-data, FR-set, FRIC-model, Evaluation function, A-reduction

DOI: 10.3233/JIFS-220225

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 75-90, 2023

Authors: Perumal, Balamurugan | Ganeshan, Arulkumaran | Jayagopalan, Santhosh | Preetha, K.S. | Selamban, Ramasamy | Elangovan, Dinesh | Balasubramani, Sumathy

Article Type: Research Article

Abstract: The problem of smart agriculture has been well studied and the security in Wireless Sensor Networks (WSN) has been analyzed in detail. There are a number of approaches discussed in the literature to support the growth of agriculture by considering different factors. But still the performance of plant management is not up to the expected level in terms of plant management and security concern. To handle these issues, an efficient multi view image based plant management technique which consider color and contrast features to obtain the features of fluid, plant, climate to compute different supportive measures like Fluid Specific Growth …Support (FSGS), Plant Specific Growth Support (PSGS) and Climate Specific Growth Support (CSGS) measures to compute the value of Plant Growth Measure (PGM) and Crop Yield Measure (CYM). Also, using the same support measures, the presence of diseased plants is identified and fertilizers are regulated accordingly. Similarly, the wireless sensor network has been used as monitoring environment which has several routes to monitor different locations of agriculture lands. The presences of different routes are monitored for the transmission of different agriculture data. To handle the security issues, a low rate attack detection scheme is presented which finds the routes and for each route the method computes Service centric Legitimate Support (SCLS) to find low rate attacks. Similarly, the data security by controlling different smart devices in agriculture lands is enforced by using service centric data encryption (SCDE) scheme which uses different encryption scheme and keys to encrypt the data being used for controlling the devices of agricultural lands. The proposed method improves the performance of smart agriculture and improves the data security with higher low rate detection accuracy. Show more

Keywords: WSN, smart agriculture, data security, low rate attack, plant management, crop yield

DOI: 10.3233/JIFS-220594

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 91-100, 2023

Authors: Chen, Peng | Zhu, Dongge

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-220675

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 101-112, 2023

Authors: Masood, Faraz | Faridi, Arman Rasool

Article Type: Research Article

Abstract: Blockchain technology is getting famous, and use cases of blockchain range from financial services to the Metaverse. It is considered a platform for web 3.0. As a result, many industries are planning to adopt blockchain. A simple public blockchain is not suitable for most business scenarios, so hybrid and private blockchains came into existence, but it is important to decide which type of blockchain should be adopted during the project planning phase. Various models can be found in the literature to determine if blockchain should be adopted and, if so, which type of blockchain should be adopted. However, these models …are already becoming obsolete as they determine the usage of blockchain using simple yes or no. In order to overcome these problems, all these models are converted from binary-based selection to fuzzy-based selection, and decision matrices are created. Various multi-criteria decision analysis methods are applied, and final results are obtained. In addition, a novel model is presented, and a MATLAB application is developed to let the user determine if blockchain can be integrated with any technology or not. This application can be used as a standard in the project’s planning phase and helps avoid losses to the industry. Show more

Keywords: Blockchain, decision making, distributed ledger, SAW, TOPSIS

DOI: 10.3233/JIFS-220830

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 113-124, 2023

Authors: Sundaramurthy, Shanmugam | Sugumaran, Vijayan | Thangavelu, Arunkumar | Sekaran, Karthik

Article Type: Research Article

Abstract: Rheumatoid Arthritis (RA) is a chronic autoimmune disease whose symptoms are hard to determine due to the overlapping indications of the condition with other illnesses such as dengue, malaria, etc. As the symptoms of RA disease are similar to inflammatory diseases, general physicians (GPs) find it difficult to detect the disease earlier. A computer aided framework is proposed in this study to assist and support the GPs to diagnose RA better. In this work Improved Harmony Search Optimization (IHSO) approach is proposed to select the significant feature subset of RA and Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as a …classification model. The performance of the proposed IHSO-ANFIS model is examined with metrics such as Balanced Accuracy (Bacc), Area under Curve (AUC), Sensitivity (Sen), Specificity (Spec), and Matthew’s Correlation Coefficient (MCC) using 10-Fold cross-validation. Additionally, the results of the IHSO-ANFIS are compared with HSO-ANFIS, ANFIS without any feature selection and standard bench mark datasets. IHSO-ANFIS attained 87.05% Bacc, 89.95% AUC and 0.6586 MCC on the RA dataset. From the results it is clear that IHSO-ANFIS could assist general physicians to diagnose RA earlier and pave the way for timely treatment. Show more

Keywords: Rheumatoid arthritis, hybrid harmony search, particle swarm optimization, disease diagnosis, ANFIS

DOI: 10.3233/JIFS-221252

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 125-137, 2023

Authors: Suresh, M. | Venkata Satya Vivek, Tammineedi | Venkat, Yalla | Chokkalingam, Mohan

Article Type: Research Article

Abstract: The lack of awareness of blind spots in vehicle transport results in more deaths nowadays. To address this issue, the multi-obstacle detection and measurement of the depth of the nearing vehicle, height, and width is necessary. In recent years, Fuzzy logic is being used to access smart decision-making for control actions. To handle the specific task efficiently, ambiguous and imprecise linguistic data is required. In this context, a non-linear intelligent fuzzy decision-making system has been proposed to estimate blind spots. An inference engine, a defuzzification interface to identify the blind spot both day and night, and a fuzzy rule-base are …included. Shadows and edges can be used as linguistic parameters to identify vehicles in the daytime. The lamps are elevated higher than the air dams to avoid casting a shadow under the car at night. One in-sourcing vehicle and three out-sourcing vehicles are tested to determine the driver’s blind spot and a more comfortable driver’s seat and a rear-view mirror using the proposed system. A fuzzy matrix with a triangular number obtained from the crisp matrix is used to alert the driver of the likelihood of a collision using LEDs or buzzers. Show more

Keywords: Non-linearity, Fuzzy decision-making system, blind spot estimation, vehicle detection system

DOI: 10.3233/JIFS-213426

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 139-148, 2023

Authors: Kaladevi, P. | Janakiraman, Sengathir | Ramalingam, Praveen | Muthusankar, D.

Article Type: Research Article

Abstract: The advent of machine learning in the recent decade has excelled in determining new potential features and non-linear relationships existing between the data derived from the Electronic Health Records (EHR). Machine learning also enhances the process of handling data with maximum predictor variables compared to observations during the data mining process of prediction. The EHR data is often confronted with quality issues that are related to misclassification, missingness and measurement errors. In this context, ensemble classification schemes are determined to be essential for preventing the quality issues of EHR data. Moreover, the data sources like EHR include sensitive information that …needs to be protected from disclosure before it is forwarded to the mining process. Further, the sensitive data of EHR must be hidden without modifying the dataset such that it does not influence the prediction accuracy of the incorporated ensemble classification mechanism. In this paper, the process of hiding EHR data is facilitated through Improved Sensitivity Drift based k-Anonymized Data Perturbation Scheme (ISD-k-ADP) that randomly perturbs the data in the dataset by including restricted amount of noise. This controlled amount of included noise is derived carefully from the Sensitivity Drift based depending on the expected privacy level before it is sent to the process of classification. This ISD-k-ADP scheme is reliable such that, it prevents the impact induced by the hidden data during the process of Two Stage Bagging Pruning based Ensemble Classification (TSBP-EC). Furthermore, the TSBP-EC uses the methods of distance and accuracy based pruning that aids in minimizing the size of the ensemble for ensuring effective and efficient classification using machine learning. The simulation results of the proposed ISD-k-ADP-TSBP-EC scheme is determined to be predominant based on Classification Accuracy, Precision, Recall and Kappa Statistic in contrast to the standard schemes. Show more

Keywords: Ensemble classification, two stage bagging pruning, sensitivity drift, heuristic-based data perturbation, electronic health records, machine learning

DOI: 10.3233/JIFS-221615

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 149-166, 2023

Authors: Uma Maheswari, K. | Valarmathi, A.

Article Type: Research Article

Abstract: A heart attack is a common cause of death globally. It can be treated successfully through a simple and accurate diagnosis. Getting the right diagnosis at the right time is very important for the treatment of heart failure. Currently, the conventional method of diagnosing heart disease is not reliable. Machine learning is a type of artificial intelligence that can be used to analyze the data collected by sensors. Data mining is another type of technology that can be utilized in the healthcare industry. These techniques help predict heart disease based on various factors. We developed a prediction and recommendation model …aimed at predicting heart disease using the Optimized Deep Belief Network. It does so by taking into account the various features of the heart disease UCI and Stalog database. Finally, the proposed method classifies healthy people and people with heart illness with an accuracy of 97.91%. Show more

Keywords: Heart disease, diagnosis, machine learning, deep learning

DOI: 10.3233/JIFS-221272

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 167-184, 2023

Authors: Zhenlin, Wei | Chuantao, Wang | Xuexin, Yang

Article Type: Research Article

Abstract: Sentiment classification aims to complete the automatic judgment task of text sentiment tendency. In the sentiment classification task of online reviews, traditional deep learning models require a large number of manually annotated samples of sentiment tendency for supervised training. Faced with massive online review data, the feasibility of manual tagging is worrisome. In addition, the traditional deep learning model ignores the imbalanced distribution of the number of classification samples, which will lead to a decline in classification performance in the practical application of the model. Considering that the online review data contains weak tagging information such as scores and labels, …and the distribution is imbalanced, a weak tagging and imbalanced networks for online review sentiment classification is constructed. The experimental results show that the model significantly outperforms the traditional deep learning model in the sentiment classification task of hotel review data. Show more

Keywords: Sentiment classification, imbalanced classification, weak tagging, deep learning

DOI: 10.3233/JIFS-221565

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 185-194, 2023

Authors: Balasubramanian, Kishore | Prabu, A.V. | Shaik, Mahammad Firose | Naik, R. Anjaneyulu | Suguna, S. Kanimozhi

Article Type: Research Article

Abstract: Today’s healthcare sectors are driven and work to rescue patients as soon as possible by giving them the right care and treatment. A healthcare monitoring system works in two ways: by keeping track of the patient’s activities and overall health. For prompt treatment, such as giving the right and suitable medication, administering an injection, and providing additional medical help, nursing supervision is required. Wearable sensors are fixed or connected to the patient’s body and can follow their health. These IoT medical gadgets let clinicians diagnose patients and comprehend the processes from remote. However, the amount of data produced by IoT …devices is so large that it cannot be handled manually. A model for automated analysis is required. Convolution Neural Network with Long-Short Term Memory (CNN-LSTM) was therefore suggested in this study as a Hybrid Deep Learning Framework (HDLF) for a Patient Activity Monitoring System (PAMS) that brings all healthcare activities with its classes. To incorporate medical specialists from all over the world and enhance treatment outcomes, the framework offers an advanced model where patient activities, health conditions, medications, and other activities are distributed in the cloud. An effective architecture for Wearable Sensor Network-based Human Action Recognition that combines neural network Simple Recurrent Units (SRUs) and Gated Recurrent Units (GRUs). For assessing the multimodal data input sequence, deep SRUs and a variety of internal memory states is utilized in this research. Furthermore, for addressing the concerns about accuracy oscillations or instability with decreasing gradients, a deep GRUs to store and learn the knowledge is conveyed to the future state. The analysis suggests that CNN-LSTM is then contrasted with some of the currently used algorithms, and it is found that the new system has a 99.53% accuracy rate. The difference between this accuracy result and the current value is at least 4.73%. Show more

Keywords: Sensor network, Body Wearable Sensors, surveillance monitoring, Healthcare Monitoring System (HMS), Physiological Parameter Analyzation

DOI: 10.3233/JIFS-212958

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 195-211, 2023

Authors: Zhao, Shuping | Wang, Dong | Lei, Ting | Wang, Yifan

Article Type: Research Article

Abstract: The selection of a waste-to-energy (WTE) plant site is the core issue that determines whether the WTE project can effectively treat municipal solid waste, reduce environmental pollution, and promote the development of a circular economy, and is often determined through group decision-making. The complexity of this group decision problem makes the opinions of decision makers often with uncertainty. The single-valued neutrosophic set (SVNS) can reduce the loss of information that contains uncertainty by quantitatively describing the information through three functions. In addition, existing studies on group decision-making for WTE plant siting suffer from the problem that decision maker weights do …not change in concert with those decision makers’ decision information. Therefore, this study proposes a group decision-making method based on SVNSs. First, a group consensus strategy is proposed to improve group consensus by removing the evaluation value of the corresponding solution for decision makers who do not reach consensus and are unwilling to modify their preferences. Second, a decision maker weight determination and adjustment method is proposed to readjust the decision maker weights from the solution level according to their respective consensus degree when the decision makers’ preference information changes. This method enables the decision makers’ preferences and weights to be changed jointly. An illustrative example and a comparative analysis of WTE plant siting decisions demonstrate the feasibility and superiority of the method. The experimental results show that the method is effective in helping decision makers to select the optimal WTE plant site more accurately. Show more

Keywords: Waste-to-energy, site selection, single-valued neutrosophic sets, group consensus

DOI: 10.3233/JIFS-220124

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 213-224, 2023

Authors: Abu Talha, Muhammad | Zafar, Adeel

Article Type: Research Article

Abstract: False information is becoming more frequent in distributing disinformation by distorting people’s awareness and decision-making by altering their views or knowledge. The propagation of disinformation has been aided by the proliferation of social media and online forums. Allowing it to readily blend in with true information. Parody news and rumors are the most common types of misleading and unverified information, and they should be caught as soon as possible to avoid their disastrous consequences. As a result, in recent years, there has been a surge in interest in effective detection approaches. For this study, a customized dataset was built that …included both real and parody tweets from Pakistan and India. This study proposes a two-step strategy for detecting parody tweets. In the first stage of the approach the unstructured data is converted into structured data set. In the second step, multiple supervised artificial intelligence algorithms were employed. An experimental assessment of the different classification methods inside a customized dataset was undertaken in this study, and these classification models were compared using evaluation metrics. Our results showed accuracy of 92%. Show more

Keywords: Social media, parody tweets, binary classification, machine learning, deep learning, word embedding

DOI: 10.3233/JIFS-221200

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 225-236, 2023

Authors: Liu, Xuning | Zhang, Zixian | Zhang, Guoying

Article Type: Research Article

Abstract: Accurate and rapid prediction of the coal and gas outburst is very significant for preventing accident and protecting environment, the paper presents a novel feature selection and outburst classifier framework which can identify effective candidate features and improve the classification accuracy. First, Apriori is applied for preliminarily extracting the association rules from sample data and attribute features in coal and outburst, and it can present the effective sample data and features for outburst prediction. Second, in order to reduce the redundancy of the strong association rules obtained from Apriori, Boruta is applied for selecting all highly relevant optimal features based …on the obtained strong association rules. Third, Random Forest(RF) is used to assign different weights to different features in optimal candidate features considering the importance of different features to outburst, based on the above obtained high-quality sample data and optimal features, the parameters of KNN model optimized by Bayesian Optimization(BO) is used to predict the coal and gas outburst. The experimental results show that the proposed feature selection model Apriori-Boruta can obtain significant sample data, and the proposed RF- KNN optimized classifier model can achieve higher performance in terms of the number of optimal features and prediction accuracy compared with traditional prediction models. Show more

Keywords: Coal and gas outburst, Apriori, Boruta, RF, KNN

DOI: 10.3233/JIFS-213457

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 237-250, 2023

Authors: Arslan, Serdar | Yazici, Adnan

Article Type: Research Article

Abstract: The semantic query problem is commonly called the semantic gap and is one of the significant problems in multimedia data retrieval. In this study, we focus on multimedia data retrieval by combining semantic information with data content to solve the semantic gap problem effectively. The main idea behind the combination of low-level content descriptors and the concept of multimedia data is to represent the content information with the semantic information by adding a low-level content descriptor as a new dimension to the index structure. This new dimension is represented by constructing an array index structure that uses a fuzzy clustering …algorithm. Thus, a new high-dimensional index structure, named MM-FOOD, supporting querying of multimedia data, including fuzzy querying, is presented in this paper. This proposed index structure’s construction and query algorithms are explained throughout this paper. Our experiments show that our indexing mechanism is considerably efficient compared to the basic indexing approach, which stores low-level content and semantic concept descriptors in separate structures when the data size is large. Show more

Keywords: High-dimensional indexing, multimedia data retrieval, fuzzy querying, multidimensional scaling

DOI: 10.3233/JIFS-220673

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 251-282, 2023

Authors: Wang, Jinyan | Wu, Fangjing

Article Type: Research Article

Abstract: Most of the published literature on concrete containing fly ash was limited to predicting the hardened properties of concrete. It is understood that exist so restricted studies focusing on forecasting both hardened and fresh features of self-compacting concrete (SCC). Hence, it is goaled for developing models for predicting the fresh and hardened properties of SCC by the support vector regression method (SVR). This study aims to specify SVR method key parameters using Ant lion optimization (ALO) and Biogeography-based optimization (BBO) algorithms. The considered properties of SCC in the fresh phase are the L-box test, V-funnel test, slump flow, and in …the hardened phase is CS. Results demonstrate powerful potential in the learning section for all considered properties as well as approximating in the testing phase. It can be seen that the proposed models have R2 incredible value in the learning and testing phase. It means that the correlation between observed and predicted properties of SCC from hybrid models is acceptable so that it represents high accuracy in the training and approximating process. All in all, in most of the cases, the SVR model developed by ALO outperforms BBO-SVR, which depicts the capability of the ALO algorithm for determining the optimal parameters of the considered method. Show more

Keywords: Fly ash, self-compacting concrete, rheological properties, support vector regression, ALO, BBO, compressive strength

DOI: 10.3233/JIFS-220744

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 283-297, 2023

Authors: Merbah, Amal | Makrizi, Abdelilah | Essoufi, El Hassan

Article Type: Research Article

Abstract: One of the pertinent concerns in traffic management is to optimize the waiting time at the traffic light junctions. We have has already developed an integrated nonlinear model which heavily relies on the genetic algorithm (GA). Indeed, GA proves efficient in terms of the computational time given the environmental constraints and the various variables inherent to the types of users and the degree of priority allotted to each of them. However, it was revealed that some issues having to do with instability require further adjustments. In the present article the aforementioned model is revisited with the aim of addressing …the high standard deviations attributed to the objective function. More specifically, the present work considers the side effects of GA in sweeping the entire space of eligible solutions. In this respect, fuzzy Logic (FL) is integrated as a major component in order to orient the GA research. At the computational level, GA places the solution found by FL at the center of the solution space around which the initial population can be built. The implementation of this hybrid method reduces both the waiting time at traffic lights and the standard deviation of the results, showing a significant improvement in the management system. Show more

Keywords: Traffic control, nonlinear model, fuzzy logic, genetic algorithms

DOI: 10.3233/JIFS-221535

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 299-307, 2023

Authors: Akram, Muhammad | Umer Shah, Syed Muhammad | Allahviranloo, Tofigh

Article Type: Research Article

Abstract:  Transportation Problems (TP) have multiple applications in supply chain management to reduce costs. Efficient methods have been developed to address TP when all factors, including supply, demand, and unit transportation costs, are precisely known. However, due to uncertainty in practical applications, it is necessary to study TP in an uncertain environment. In this paper, we define the Trapezoidal Fermatean Fuzzy Number (TrFFN) and its arithmetic operations. Then we introduce a new approach to solve TP, where transportation cost, supply, and demand are treated as TrFFN, and we call it Fermatean Fuzzy TP (FFTP). We illustrate the feasibility and superiority of …this method with two application examples, and compare the performance of this method with existing methods. Furthermore, the advantages of the proposed method over existing methods are described to address TP in uncertain environments. Show more

Keywords: Trapezoidal Fermatean fuzzy sets, linear programming problem, transportation problem, supply and demand

DOI: 10.3233/JIFS-221959

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 309-328, 2023

Authors: Rakesh, V. | Balamurugan, R.

Article Type: Research Article

Abstract: Recently, Induction motor (IM) become the most prevalent machine type and finds an applications in many fields such as industries, electric cars etc., A typical IMD system includes IM, power controller, converter and measurement sensors. The effective performance of the IM indirectly depends upon the sensors connected with IMD. Recently, sensor fault diagnosis plays a vital role in IMD control. Thus, this work formulated a unique methodology using current vector determined from the stator currents of IM to identify sensor failures. ANN topology is incorporated to detect the Sensor failure. MATLAB software is utilized to verify the efficacy of the …suggested topology. To demonstrate the practicality of this technology, experimental verification is carried out. The efficiency of the proposed approach for IM drives is demonstrated by both simulation and experimental findings. From the obtained results, it is proven that this technique detects the failure of the sensors within less time duration (about 0.25 ms). Hence, it can be effectively utilized in automobile industry. Show more

Keywords: Induction motor, ANN, fault detection, current sensor, speed sensor, sensor failure

DOI: 10.3233/JIFS-221998

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 329-339, 2023

Authors: Yan, Zhang | Miyuan, Zhang | Yajun, Wang | Xibiao, Cai | Yanjun, Li

Article Type: Research Article

Abstract: Since the BP neural network has poor performance and unstable learning rate in the maximum power point tracking (MPPT) algorithm of photovoltaic (PV) system, an adaptive particle swarm optimization BP neural network-fuzzy control PV MPPT algorithm (APSO-BP-FLC) is proposed in this paper. First, the inertia weight, learning factor and acceleration factor of particle swarm optimization (PSO) are self-updating, and the mutation operator is adopted to initialize the position of each particle. Second, the APSO algorithm is used to update the optimal weight threshold of BP neural network, where the input layer is irradiation and temperature, and the output layer is …the maximum power point (MPP) voltage. Third, the fuzzy logical control (FLC) is employed to adjust the duty cycle of Boost converter. The inputs of FLC are voltage difference and duty ratio D(n-1) at the previous time, and the output is duty ratio D(n). Moreover, D(n-1) is optimized by |dP/dU| to improve the search range of FLC. The irradiation, temperature and MPP voltage of PV cell are adopted as the datasets for simulation in a city in Shaanxi province, China. Simulation results show that the proposed MPPT algorithm is superior to the APSO-BP, FLC and perturbation and observation (P&O) algorithm with tracking performance, steady state oscillation rate and efficiency. In addition, the efficiency of proposed MPPT algorithm is improved by 0.37%, 6.2%, and 6.8% as compared to APSO-BP, FLC and P&O algorithm. Show more

Keywords: Adaptive particle swarm optimization algorithm (APSO), BP neural network, fuzzy control, PV power generation, MPPT

DOI: 10.3233/JIFS-213387

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 341-351, 2023

Authors: Vajravelu, Ashok | Selvan, K.S. Tamil | Jamil, Muhammad Mahadi Bin Abdul | Jude, Anitha | Diez, Isabel de la Torre

Article Type: Research Article

Abstract: Wireless Capsule Endoscopy (WCE) allows direct visual inspecting of the full digestive system of the patient without invasion and pain, at the price of a long examination by physicians of a large number of photographs. This research presents a new approach to color extraction to differentiate bleeding frames from normal ones and locate more bleeding areas. We have a dual-system suggestion. We use entire color information on the WCE pictures and the pixel-represented clustering approach to get the clustered centers that characterize WCE pictures as words. Then we evaluate the status of a WCE framework using the nearby SVM and …K methods (KNN). The classification performance is 95.75% accurate for the AUC 0.9771% and validates the exciting performance for bleeding classification provided by the suggested approach. Second, we present a two-step approach for extracting saliency maps to emphasize bleeding locations with a distinct color channel mixer to build a first-stage salience map. The second stage salience map was taken with optical contrast.We locate bleeding spots following a suitable fusion approach and threshold. Quantitative and qualitative studies demonstrate that our approaches can correctly distinguish bleeding sites from neighborhoods. Show more

Keywords: Bleeding classification and region detection, words-based color histograms, wireless capsule endoscopy

DOI: 10.3233/JIFS-213099

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 353-364, 2023

Authors: Zhao, Xiaohan | Zhu, Liangkuan | Wu, Bowen

Article Type: Research Article

Abstract: Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, an improved mayfly algorithm (IMA)-based color image segmentation method is proposed. Tent mapping initializes the female mayfly population to increase population diversity. Lévy flight is introduced in the wedding dance iterative formulation to make IMA jump from the local optimal solution quickly. Two nonlinear coefficients were designed to speed up the convergence of the algorithm. To better verify the effectiveness, eight benchmark functions are used to test the performance of …IMA. The average fitness value, standard deviation, and Wilcoxon rank sum test are used as evaluation metrics. The results show that IMA outperforms the comparison algorithm in terms of search accuracy. Furthermore, Kapur entropy is used as the fitness function of IMA to determine the segmentation threshold. 10 Berkeley images are segmented. The best fitness value, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and other indexes are used to evaluate the effect of segmented images. The results show that the IMA segmentation method improves the segmentation accuracy of color images and obtains higher quality segmented images. Show more

Keywords: Non-linear attraction coefficients, Tent chaotic mapping, Lévy flight, color image segmentation, mayfly algorithm

DOI: 10.3233/JIFS-221161

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 365-380, 2023

Authors: Hemam, Sofiane Mounine | Hioual, Ouided | Hioual, Ouassila

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-221989

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 381-393, 2023

Authors: Wang, Xindi | Xu, Zeshui | Qin, Yong

Article Type: Research Article

Abstract: In this paper, we establish a chance constrained model for the priority of hesitant fuzzy preference relation based on the idea of statistical distribution for preference information as stochastic variables with unknown distribution. Inspired by the idea of conditional value-at-risk (CVaR) robust optimization, a deterministic convex reformulation is proposed for tackling the chance constrained problem. The existing state-of-the-art methods usually assume that the probability density function of preference information is known a priori, such as Gaussian distribution. However, it is generally over-conservatism. On the contrary, our proposed method provides a tractable second-order cone (SOC) reformulation for the chance constrained problem …with the first and second moments, which is easy to handle and calculate. We also analyze the weight acquisition problem of hesitant fuzzy preference relation with unknown distribution preference using the SOC programming method, and obtain the priority weight with its approximately equivalent computationally tractable conic optimization model. A case study is conducted which shows that the proposed method achieves a good general conclusion by comparing it with the optimization method under Gaussian distribution. In addition, this method can also get better decision support for incomplete preference information. Show more

Keywords: Hesitant fuzzy preference relation, unknown distribution, CVaR, SOC, incomplete preference information

DOI: 10.3233/JIFS-220472

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 395-408, 2023

Authors: Shally, | Kumar, Sunil | Gupta, Punit

Article Type: Research Article

Abstract: The proliferation of cloud computing infrastructure has increased the energy demand remarkably. Energy-efficient resource management is essential for running a cost effective and environment friendly data center. Virtual Machine (VM) consolidation is a well-accepted method for reducing the energy consumption of the cloud data center. Quality of service is an equally important aspect of cloud services. VM migrations caused by consolidation often cause degradation in QoS. These two parameters have been dealt with individually in most research and very few addressed both energy efficiency and QoS simultaneously. We have proposed a new E nergy and Q oS E fficient (EQSE) …VM selection and placement method for improving the energy efficiency along with quality of service (QoS). VM selection and placement are two critical steps of VM consolidation. EQSE uses Resource Gap Minimization (RGM) algorithm for VM selection and Utilization-Aware Best-Fit Decreasing (UABFD) algorithm for placement of these VMs. EQSE along with dynamic thresholds reduces energy consumption and improves the quality of service by reducing the number of VM migrations. CloudSim simulation performed on PlanetLab data establishes the superiority of the proposed method compared to the existing state of the art methods of VM consolidation. Show more

Keywords: Energy efficient method, resource gap minimization, EQSE, energy efficient cloud data center, SLA aware resource management

DOI: 10.3233/JIFS-220535

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 409-419, 2023

Authors: Rahman, K. | Iqbal, Q.

Article Type: Research Article

Abstract: The aim of the paper is to introduce some complex Einstein aggregation operators for aggregating the different complex Pythagorean fuzzy sets (CPFSs) by considering the dependency between the pairs of its membership degrees. In the existing studies of fuzzy and its extensions, the uncertainties present in the data are handled with the help of degrees of membership that are the subset of real numbers, which may also loss some valuable data and hence consequently affect the decision results. A modification to these, complex Pythagorean fuzzy set handles the uncertainties with the degree whose ranges are extended from real subset to …the complex subset with unit disc and hence handle the two dimensional information in a single set. Thus motivated by this and this paper we present some novel Einstein aggregation operators, namely complex Pythagorean fuzzy Einstein weighted averaging (CPFEWA) operator, complex Pythagorean fuzzy Einstein ordered weighted averaging (CPFEOWA) operator, complex Pythagorean fuzzy Einstein hybrid averaging (CPFEHA) operator, induced complex Pythagorean fuzzy Einstein ordered weighted averaging (I-CPFEOWA) operator, and induced complex Pythagorean fuzzy Einstein hybrid averaging (I-CPFEHA) operator. Also develop some of their desirable properties. Furthermore, based on these operators a multi-attribute group decision making problems developed. An illustrative example related to the selection of the best alternative is considered to show the effectiveness, of the novel developed methods. Show more

Keywords: Einstein operational laws, CPFEWA operator, CPFEOWA operator, CPFEHA operator, I-CPFEOWA operator, I-CPFEHA operator, decision-making problem

DOI: 10.3233/JIFS-221538

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 421-453, 2023

Authors: Yu, Song | Tan, Weimin | Zhang, Chengming | Tang, Chao | Cai, Lihong | Hu, Dong

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-211862

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 455-466, 2023

Authors: Anand, R.

Article Type: Research Article

Abstract: The COVID-19 outbreak has impacted huge number of individuals all around the world and has caused a great economic loss all over the world. Vaccination is most effective solution to prevent this disease. It helps in protecting the whole community. It improves the human immune system and fights against corona virus reducing the death rate. This paper deals with the different types of COVID-19 vaccine and their related distribution, it includes measures to ensure safe and secured distribution of the vaccine through block chain technology with the help of supply chain. Any malfunction in the chain is identified by the …trust value of the function point method and the value of the Markov Chain. Show more

Keywords: COVID-19, vaccination, corona, pandemic, blockchain, markov chain

DOI: 10.3233/JIFS-220614

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 467-475, 2023

Authors: Qiyas, Muhammad | Abdullah, Saleem | Naeem, Muhammad | Khan, Neelam

Article Type: Research Article

Abstract: In daily life, the decision making problem is a complicated work related to uncertainties and vagueness. To overcome this vagueness and uncertainties, many fuzzy sets and theories have been presented by different scholars and researchers. EDA𝒮 (Evaluation based on distance from average solution) method plays a major role in decision-making problems. Especially, when multi-attribute group decision-making (MAGDM) problems have more conflicting attribute. In this paper, a new approach known as Spherical fuzzy rough-EDA𝒮 (SFR-EDA𝒮) method is used to handle these uncertainties in the MAGDM problem. The aggregation operators have the ability to combine different sources of information, which plays an …essential role in decision making (DM) problem. Keeping in view the increasing complexity of the DM problem, it will be useful to combine the aggregation operators with the fuzzy sets in solving DM problem. Therefore, an aggregation operator known as SFR-EDA𝒮 method is utilized. For this propounded some new averaging and geometric aggregation is investigated. Moreover, the essential and desirable properties with some particular cases are deliberated and discussed detail. To evaluate the emergency program, a MAGDM approach is used based on the new introduced operators. Later on, the viability and applicability the proposed method is certified by a detailed analysis with the other existing approaches. Show more

Keywords: Spherical fuzz sets, rough sets, EDA𝒮 method, aggregation operators

DOI: 10.3233/JIFS-211056

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 477-498, 2023

Authors: Gopikarani, N. | Gayathri, B. | Praja, S.S. | Sridharan, Sneha

Article Type: Research Article

Abstract: Counterfeit drugs are without a doubt becoming a greater hazard to consumers and the pharmaceutical sector. As a result, real-time visibility of drug manufacturing and management is required. The proposed system uses Ethereum blockchain as the main technology. The primary advantage of blockchain technology is that the transactions are maintained in immutable digital ledger format and it may be read easily without jeopardizing the users’ security and privacy. In our proposed system, the admin validates and adds the manufacturers. The manufacturer after registering and logging in can perform tasks like adding the drug and seller list. The seller can place …order to the manufacturer which the manufacturer can accept or reject. The seller can update status of order of accepted orders to delivered. The customer can view the order details by entering the serial number on the drug package. Any transaction or exchange that occurs in the network is recorded in the chain. It functions similarly to other networks, but blockchain technology is distinguished by the fact that no data can be removed or altered by anyone in the network. No changes to the network can be made unless it has been validated by all of the network’s authorized users. All the information stored can be read by anybody so to incorporate more security, AES has been used to store data in the blockchain. The use of AES encryption technique distinguishes this system from all the existing implementations. Thus, this makes it easy to trace to the exact point in the supply chain and detect any counterfeit drugs in movement. As an extension to the drug counterfeit prevention system a Drug Recommendation System is also performed using the ensemble model with a combination of Random Forest and Logistic Regression for sentiment analysis training. Furthermore, when compared to the existing Linear SVM model, which has an accuracy of 90.39%, the suggested model has the best accuracy of 93.31%. Using the obtained sentiment for each drug, the drug is predicted accurately for the specified medical condition. Show more

Keywords: Blockchain (BC), Ethereum, smart contract, health- care, ensemble model, logistic regression, random forest

DOI: 10.3233/JIFS-220636

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 499-517, 2023

Authors: Lu, Shuya | Cao, Minglei

Article Type: Research Article

Abstract: Through scientific theoretical methods, we take the internal control optimization of the Financial Sharing Center of H company as the research object. Firstly, we introduce the Financial Sharing Center and the development background and research significance of internal control under this mode, sort out the existing international research and related concepts, analyze the problems existing in the internal control stage of the Financial Sharing Center, and analyze the problems one by one from the five elements of internal control. What is more innovative is that we use the quality function deployment theory in the field of system science, combined with …the intuitionistic fuzzy set theory, G1 method and entropy method in fuzzy mathematics to evaluate the five elements affecting the internal control optimization of the Financial Sharing Center of H company, and give the priority of optimization in theory. Finally, according to the implementation conditions of the Financial Sharing Center, this paper puts forward relevant countermeasures and suggestions to optimize the internal control of the financial sharing mode of H company, which can also provide experience for other enterprises that are building the Financial Sharing Center. Show more

Keywords: Financial sharing mode, internal control, Financial Sharing Center, quality function deployment theory, G1 method, entropy method

DOI: 10.3233/JIFS-221540

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 519-541, 2023

Authors: Zhou, Chunguo | Qiao, Ning | Mao, Jin | Zeng, Zhicheng | Zhou, Yongjun

Article Type: Research Article

Abstract: In order to improve the comprehensive performance of adaptive cruise control system in the car-following process and take the safety into account, an improved model predictive control algorithm considering multi-performance objective optimization is designed. In the prediction model part, the grey Verhulst model with saturation state is introduced to predict the acceleration disturbance of the preceding vehicle, and the particle swarm optimization algorithm is used to estimate the parameters, which is then applied to the car following model. The control problem is transformed into a quadratic programming problem with multiple constraints through multi-objective quadratic performance index, and the vector constraint …management method is introduced to solve the problem of no feasible solution caused by hard constraints. The emergency acceleration, deceleration and stable following are simulated. Finally, the Worldwide Harmonized Light Vehicles Test Cycle is co-simulated. The results show that the improved model predictive control algorithm can improve the tracking capability, fuel economy and comfort of adaptive cruise system. Show more

Keywords: Adaptive cruise, multi-performance objective optimization, model predictive control, grey prediction

DOI: 10.3233/JIFS-221690

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 543-553, 2023

Authors: Sun, Gang | Wang, Mingxin | Li, Xiaoping | Huang, Wei

Article Type: Research Article

Abstract: In real life, people often need to aggregate some multi criteria fuzzy information and then make reasonable and effective decisions. The distance measure in intuitionistic fuzzy set (IFS) space is an important tool to deal with multi criteria information fuzzy decision making problems. Motivated by these reasons, an intuitionistic fuzzy TOPSIS multi criteria decision-making method is proposed based on distance measure represented by centroid coordinates. Firstly, some existing distance measures in IFS space are summarized, and some of existing shortcomings are discussed. Secondly, the concept of hesitation factor is proposed by using the centroid coordinate representation of hesitation region, and …then a new distance measure between two intuitionistic fuzzy numbers is defined. It is proved that the distance measure satisfies the traditional distance axioms. Then, an intuitionistic fuzzy TOPSIS method based on the proposed distance measure is developed. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method. Also, the superiority and advantages of the method are shown via comparative analysis and discussion. Show more

Keywords: Intuitionistic fuzzy set (IFS), centroid coordinate representation, hesitation factor, distance measure, TOPSIS method

DOI: 10.3233/JIFS-221732

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 555-571, 2023

Authors: Thilagavathi, S. | GeethaPriya, C.

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-210858

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 573-593, 2023

Authors: Ramya, R. | Padmapriya, K.

Article Type: Research Article

Abstract: The clustering approach can improve wireless sensor network parameters such as lifetime enhancement, load balancing, reliable communication, and fault tolerance. The Cluster head in the cluster is responsible for reliable data transmission between node and sink or base station. Selecting suitable cluster heads and establishing an optimal path for data transmission is the main objective of this research work. Fuzzy-based clustering based on cluster head selection, optimized routing using particle swarm optimization (PSO), adaptive whale optimization algorithm (AWOA) are presented in this research work. Fuzzy logic considers the parameters like the distance between base station to node, node centrality, node …degree, and residual energy for cluster head selection. The optimization model obtains an optimized node for routing from the selected cluster heads. In terms of network lifetime, delay, energy consumption, packet delivery ratio, and energy efficiency, simulation analysis of the proposed model is compared to conventional routing algorithms such as bacteria foraging optimization (BFO), Tree-based data gathering (TBDG) algorithm, Immune inspired routing (IIR), Low-Energy Adaptive Clustering Hierarchy (LEACH), and Hybrid Energy-Efficient Distributed (HEED) protocol. The results demonstrate that the proposed approach outperforms existing approaches in terms of network lifetime and energy efficiency. Show more

Keywords: Wireless sensor network, cluster head selection, Fuzzy logic, whale optimization, routing

DOI: 10.3233/JIFS-220963

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 595-610, 2023

Authors: Hu, Wujin | Li, Bo | Li, Changyue | Zhang, Tong

Article Type: Research Article

Abstract: Physical Health is an important part of health education and health promotion in our country. Strengthen the research on the comprehensive evaluation of college students’ physical health, establish a representative, scientific, practical and operable index system, provide simple evaluation methods, scientifically evaluate the physical and health status of college students, and promote the scientific development of college students. Effective physical exercise, the development of good physical exercise habits and the promotion of school physical education teaching reform are of great significance. The physical health evaluation of College students is frequently viewed as the multiple attribute decision making (MADM) issue. In …this paper, the generalized Heronian mean (GHM) operator and generalized weighted Heronian mean (GWHM) operator with fuzzy number intuitionistic fuzzy numbers (FNIFNs) is extended to build fuzzy number intuitionistic fuzzy GHM (FNIFGHM) operator and fuzzy number intuitionistic fuzzy GWHM (FNIFGWHM) operator. Then we depicted the FNIFWHM operator on the strength of this technique. In the rear, a case in point for Physical health evaluation of College students is described to prove the built methods. Show more

Keywords: Multiple attribute decision making (MADM), fuzzy number intuitionistic fuzzy numbers (FNIFNs), fuzzy number intuitionistic fuzzy GHM (FNIFGHM) operator, fuzzy number intuitionistic fuzzy GWHM (FNIFGWHM) operator, physical health evaluation

DOI: 10.3233/JIFS-221248

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 611-624, 2023

Authors: Karthika, J. | Rajkumar, M. | Vishnupriyan, J.

Article Type: Research Article

Abstract: Distributed generators (DG) with inverter based on renewable sources are generally utilized in microgrids. Most of these sources work in droop control mode to effectively share the load. Higher droop is chosen on these systems to recover dynamic power sharing. This paper proposes a Hybrid Control Technique for Small Signal Stability Analysis for Microgrids under Uncertainty. The proposed topology is to recover the capacity of power system is used to restore the normal operating condition. The proposed hybrid technique is the combination of chaotic Henry gas solubility optimization (CHGSO) and recalling-enhanced recurrent neural network (RENNN) and therefore called the CHGSO-RENNN …technique. The proposed technique is used to optimally predict the internal and external current loop control parameters in light and the variety of power and current parameters. The small stability is revealed through the working conditions of the whole machine. The overall stability of the small signal is investigated in a linear model so that both source and load are used to characterize the state matrix of the frame that is used for eigenvalue examination. The PI controller gain parameters are optimally tuned and the controller offers reliable frame operation. The proposed technique is performed on MATLAB/Simulink work platform. Show more

Keywords: Fuel cell, battery storage system, ultra capacitor, diesel generator, flywheel storage system, chaotic henry gas solubility optimization and recalling-enhanced recurrent neural network

DOI: 10.3233/JIFS-221425

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 625-645, 2023

Authors: Kang, Xinhui | Nagasawa, Shin’ya

Article Type: Research Article

Abstract: To show the unique charm of Jiangxi’s traditional culture, it is of great importance to apply Jiangxi’s unique red culture to products’ creative designs. This paper aims to apply Kansei Engineering (KE) and interactive genetic algorithm (IGA) to extract the apparent symbol elements of Jiangxi red culture and then transform them into the creative watch design with modern culture. First of all, KE is used to extract customers’ emotional resonance to red culture, and 16 pairs of Kansei image vocabulary pairs are preliminarily collected. The theory of semiotics is used to extract symbols such as shapes, colors, and patterns from …the perspective of Jiangxi’s red architecture. Secondly, through the designers’ subjective aesthetic thinking, these cultural symbols are broken up and reconstructed, thus forming the morphological deconstruction table combined with the case of the watch. Finally, IGA is implemented to code and decode the cultural symbols, thus building a product form’s evolutionary design system. Through biological genetic manipulation, cultural symbols of refinement, particularity, and regionality are retained. Then these superior cultural genes are integrated into the innovation of the watch to get creative products with the characteristics of Jiangxi red culture. The model proposed in this paper optimizes the decision-making process of cultural creative product design, and also explores a sustainable development path of culture. Show more

Keywords: Kansei engineering, interactive genetic algorithm, cultural and creative product design, jiangxi red culture

DOI: 10.3233/JIFS-221737

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 647-660, 2023

Authors: Liang, Meishe | Mi, Jusheng | Zhang, Shaopu | Jin, Chenxia

Article Type: Research Article

Abstract: Ranking intuitionistic fuzzy numbers is an important issue in the practical application of intuitionistic fuzzy sets. Many scholars rank intuitionistic fuzzy numbers by defining different measures. These measures do not comprehensively consider the fuzzy semantics expressed by membership degree, nonmembership degree, and hesitancy degree. As a result, the ranking results are often counterintuitive, such as the indifference problems, the non-robustness problems, etc. In this paper, according to geometrical representation, a novel measure for intuitionistic fuzzy numbers is defined, which is called the ideal measure. After that, a new ranking approach is proposed. It’s proved that the ideal measure satisfies the …properties of weak admissibility, membership degree robustness, nonmembership degree robustness, and determinism. A numerical example is applied to illustrate the effectiveness and feasibility of this method. Finally, using the presented approach, the optimal alternative can be acquired in multi-attribute decision-making problems. Comparison analysis shows that the ideal measure is more effective and simple than other existing methods. Show more

Keywords: Intuitionistic fuzzy number, intuitionistic fuzzy set, ideal measure, multi-attribute decision making

DOI: 10.3233/JIFS-221041

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 661-672, 2023

Authors: Yu, Yang | He, Kun | Yan, Gang | Cen, Shixin | Li, Yang | Yu, Ming

Article Type: Research Article

Abstract: Vehicle Re-Identification (Re-ID) aims to discover and match target vehicles in different cameras of road surveillance. The high similarity between vehicle appearances and the dramatic variations in viewpoints and illumination cause great challenges for vehicle Re-ID. Meanwhile, in safety supervision and intelligent traffic systems, one needs a quick efficient method of identifying target vehicles. In this paper, we propose a Multi-Attention Guided Feature Enhancement Network (MAFEN) to extract robust vehicle appearance features. Specifically, the Fusing Spatial-Channel information multi-receptive fields Feature Enhancement module (FSCFE) is first proposed to aggregate richer and more representative multi-receptive fields features at different receptive fields sizes. …It also learned the spatial structure information and channel dependencies of the multi-receptive fields features and embedded them to enhance the feature. Then, we construct the Spatial Attention-Guided Adaptive Feature Erasure (SAAFE) module, which uses spatial attention to erase the most distinguishing features. The network’s attention is shifted to potentially salient features to strengthen the ability of the network to extract salient features. In addition, a multi-loss knowledge distillation (MLKD) method using MAFEN as a teacher network is designed to improve computational efficiency. It uses multiple loss functions to jointly supervise the student network. Experimental results on three public datasets demonstrate the merits of the proposed method over the state-of-the-art methods. Show more

Keywords: Vehicle re-identification, deep learning, multi-receptive fields, feature erasure, knowledge distillation

DOI: 10.3233/JIFS-221468

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 673-690, 2023

Authors: Karthika, J. | Rajkumar, M. | Vishnupriyan, J.

Article Type: Research Article

Abstract: The increased use of DC microgrid for complex application leads to the need for advanced control design for stable operation of the system. Loads connected to a DC microgrid are controlled by power electronic devices and exhibit constant power load (CPL) behavior, which is a serious challenge for stability as it enhances nonlinearity and reduces effective damping. This manuscript proposes an effective hybrid approach based on DC micro grid (MG) connected constant power loads. The proposed control approach is the consolidation of Marine Predators Algorithm (MPA) and mayfly optimization algorithm (MOA), hence it is named as hybrid MPA-MOA approach. The …DC microgrid system contains the sources, like two photovoltaic (PV), two wind turbine (WT), grid, battery. The major objective of the proposed approach is “to find the problems while interfacing the sources of the microgrid and increase the security of the system”. The proposed approach contains two controllers, they are primary and secondary. The primary controller is based on droop controller that shares the current and limits the oscillations because of the constant power loads (CPL). The secondary controller is used to regulate the voltage of the system from a single area. The secondary control is executed using the proposed MPA-MOA method. The proposed method is executed on MATLAB/Simulink platform; its performance is analyzed with the existing methods. The THD (%), efficiency (%) and Eigen value of the proposed technique achieves 1.4%, 92% and -9.3541±j2.4209. Show more

Keywords: Microgrid, primary controller, secondary controller, stability, marine predators algorithm, mayfly optimization algorithm

DOI: 10.3233/JIFS-221632

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 691-712, 2023

Authors: Xu, Zhiyun | Hu, Zhaoyan | Zheng, Xiaoyao | Zhang, Haiyan | Luo, Yonglong

Article Type: Research Article

Abstract: Adding noise to user history data helps to protect user privacy in recommendation systems but affects the recommendation performance. To solve this problem, a matrix factorization tourism point of interest recommendation model based on interest offset and differential privacy is proposed in this paper. The recommendation performance of the model is improved by analyzing user interest preferences. Specifically, user interest offsets are extracted from user tags and user ratings under time-series factors to calculate user interest drift. Then, similar neighbors are found to train user feature preferences which are integrated into the matrix model in the form of regular terms. …Meanwhile, based on the differential privacy theory, a privacy neighbor selection algorithm combining the K-Medoides clustering algorithm and index mechanism is designed to effectively protect the identity of neighbors and prevent KNN attacks. Besides, the Laplace mechanism is used to implement differential privacy protection for the model’s gradient descent process. Finally, the feasibility of the proposed recommendation model is verified through experiments, and the experimental results indicate that this model has advantages in recommendation accuracy and privacy protection. Show more

Keywords: Matrix factorization, recommendation system, differential privacy, interest shift, clustering

DOI: 10.3233/JIFS-211542

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 713-727, 2023

Authors: Hussain, Zahid | Abbas, Sahar | Rahman, Shams ur | Hussain, Rashid | Sharif, Razia

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-212098

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 729-743, 2023

Authors: Bansal, Kanishk | Singh, Amar

Article Type: Research Article

Abstract: Computer Vision (CV) is constantly inundated with massive volumes of data. One of the most challenging types of data for an Artificial Intelligence (AI) system is imagery data. Convolutional neural networks (CNNs) are utilized to cope with Big Data of such type, but progress is gradual. The 3 Parent Genetic Algorithm (3PGA), an evolutionary computation method, is employed to evolve a default CNN in this study. 3PGA is an extension of GA which has been developed further for better optimization. We observed from the literature that 3PGA is giving excellent results on standard benchmark functions as compared to other recent …soft-computing-based approaches. The accuracy of the evolved CNN increased from 53% to 75%, resulting in a net improvement of more than 40%. Furthermore, it was noted that the hyperparametric combinations or features of a CNN, which are very distinct from those commonly utilized, appear to perform better. A geographical landmarks dataset from Google was used for testing purposes. Landmark recognition is one of the most time-consuming jobs for an AI system, and the optimization of a network on a landmarks dataset shows that evolutionary computation can be substantially used in the future for the evolution of Artificial Neural Networks (ANNs). Show more

Keywords: Convolutional neural network, 3 parent genetic algorithm, optimization, geographical landmark recognition, hyperparametric features

DOI: 10.3233/JIFS-221473

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 745-756, 2023

Authors: Souidi, Mohammed El Habib | Haouassi, Hichem | Ledmi, Makhlouf | Maarouk, Toufik Messaoud | Ledmi, Abdeldjalil

Article Type: Research Article

Abstract: Multi-Pursuers Multi-Evader Game (MPMEG) is considered as a multi-agent complex problem in which the pursuers must perform the capture of the detected evaders according to the temporal constraints. In this paper, we propose a metaheuristic approach based on a Discrete Particle Swarm Optimization in order to allow a dynamic coalition formation of the pursuers during the pursuit game. A pursuit coalition can be considered as the role definition of each pursuer during the game. In this work, each possible coalition is represented by a feasible particle’s position, which changes the concerned coalition according to its velocity during the pursuit game. …With the aim of showcasing the performance of the new approach, we propose a comparison study in relation to recent approaches processing the MPMEG in term of capturing time and payoff acquisition. Moreover, we have studied the pursuit capturing time according to the number of used particles as well as the dynamism of the pursuit coalitions formed during the game. The obtained results note that the proposed approach outperforms the compared approaches in relation to the capturing time by only using eight particles. Moreover, this approach improves the pursuers’ payoff acquisition, which represents the pursuers’ learning rate during the task execution. Show more

Keywords: Multi-agent systems, coalition formation algorithm, discrete particle swarm optimization, pursuit-evasion game

DOI: 10.3233/JIFS-221767

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 757-773, 2023

Authors: Peng, Weishi | Fang, Yangwang

Article Type: Research Article

Abstract: In performance evaluation, the widely used root-mean-square error is easily affected by large error terms and is also an incomprehensive measure. Therefore, the error spectrum as a comprehensive measure was proposed for parameter estimation. However, error spectrum (ES) is a three-dimension plot (among ES, r axis and time t axis) in the whole time horizon in dynamic evaluation system, which is not intuitive and easy to be analyzed. To smooth this, a new dynamic error spectrum (NDES) is proposed in dynamic evaluation system in this paper. Firstly, the NDES is defined for EPE in dynamic systems. Secondly, the …computation method is proposed to calculate the NDES. Thirdly, several nice properties of NDES are presented for dynamic system performance evaluation. Finally, the effectiveness of the proposed new dynamic error spectrum is verified by a numerical example. Show more

Keywords: Performance evaluation, decision support systems, parameter estimation, new dynamic error spectrum

DOI: 10.3233/JIFS-221958

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 775-782, 2023

Authors: Luo, Wei | Feng, Tao | Liang, Hong

Article Type: Research Article

Abstract: Change detection in synthetic aperture radar (SAR) images is an important part of remote sensing (RS) image analysis. Contemporary researchers have concentrated on the spatial and deep-layer semantic information while giving little attention to the extraction of multidimensional and shallow-layer feature representations. Furthermore, change detection relies on patch-wise training and pixel-to-pixel prediction while the accuracy of change detection is sensitive to the introduction of edge noise and the availability of original position information. To address these challenges, we propose a new neural network structure that enables spatial-frequency-temporal feature extraction through end-to-end training for change detection between SAR images from two …different points in time. Our method uses image patches fed into three parallel network structures: a densely connected convolutional neural network (CNN), a frequency domain processing network based on a discrete cosine transform (DCT), and a recurrent neural network (RNN). Multi-dimensional feature representations alleviate speckle noise and provide comprehensive consideration of semantic information. We also propose an ensemble multi-region-channel module (MRCM) to emphasize the central region of each feature map, with the most critical information in each channel employed for binary classification. We validate our proposed method on four benchmark SAR datasets. Experimental results demonstrate the competitive performance of our method. Show more

Keywords: Change detection, SFTNet, feature extraction, synthetic aperture radar (SAR) images, deep learning, neural network

DOI: 10.3233/JIFS-220689

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 783-800, 2023

Authors: Kuppulakshmi, V. | Sugapriya, C. | Nagarajan, D.

Article Type: Research Article

Abstract: Inventory plays an important role in the production process. One of the primary reasons why inventory management modeling is essential for the industry is because it will suffer immensely if there are insufficient food products to stock during the shutdown period. By determining the combined optimal cost of the retailers and wholesalers, this research significantly improves the service of the supply chain from wholesaler to retailer. The stochastic number for the imperfect perishable items is provided in this inventory study. By altering the parameter values, the uniform distribution is used to calculate these damaged items. This approach identifies the backordering …quantity for both regular and uncertain fish band circumstances. The cost of maintaining the inventory will rise significantly of increased wastage due to a rise in deteriorating, which will result in the loss of perishable food items. The primary goal of this research paper is to transport them without being destroyed until they reach their desired consumers. By determining the back ordering quantity during a shutdown, one can decrease the overall expenses incurred by the retailers. These computational complexity measures are proven in a fuzzy uncertain environment. The main goal of this paper is to analyze the variation of demand during the unanticipated period and find the optimum total cost of the perishable products. The growth of production in a particular area at a particular time, interconnect with another large number of products in the same area and is calculated by Verhulst’s demand with time depended on proficiency rate. Concerning the existing Verhulst’s demand pattern for the production process, this paper introduced that for perishable items in a fuzzy unanticipated situation. A bountiful system analysis is performed to find the cost function under fuzzy environment and the sensitivity analysis is carried out to perform the key representation constant. Show more

Keywords: Perishable items, unanticipated period, verhulst’s demand, hexagonal fuzzy number, backorders, geographical market, fish ban period

DOI: 10.3233/JIFS-220832

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 801-814, 2023

Authors: Yang, Yulin | Sheng, Yuhong

Article Type: Research Article

Abstract: The contradiction between logistics and distribution capacity and people’s increasing demand has attracted more and more attention from all walks of life. At present, there are many problems and deficiencies in logistics in China. This paper establishes the location model of the national supply chain logistics center, taking the vegetable logistics distribution as an example. Based on the principle of maximum satisfaction and satisfying demand, a vegetable material flow location model with the ability to predict the annual yield of vegetables is proposed by using the advantages of Holt’s linear trend method and immune algorithm in solving multi-objective optimization problems. …Finally, the algorithm is applied to select the optimal logistics center in 31 provinces in China to maximize customer satisfaction. Thus, Chengdu, Guangzhou, Nanchang, Nanjing, Shijiazhuang, and Changchun re used as vegetable logistics centers. Show more

Keywords: Uncertainty theory, location model, immune algorithm, uncertain programming, expected value model

DOI: 10.3233/JIFS-220885

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 815-825, 2023

Authors: Wu, Mengmeng

Article Type: Research Article

Abstract: Using Ultra-High Performance Concrete (UHPC) as the highly resistant material is widely advised in constructing sensitive structures to enhance safety. The utilization of eco-friendly contents such as fly-ash and silica-fume replacing cement can decrease the pollution rate in the production process of concrete and improve the compressive strength (CS) factor. There are many ways to appraise the CS of concretes as empirically and mathematically Artificial Neural Networks (ANN) as the high-accurate model is used in the present study. In this regard, Radial Basis Function (RBF) coupling with Biogeography-Based Optimization (BBO) and Flow Direction Algorithm (FDA) created the two high-accurate frameworks: …BBO-RBF and FDA-RBF. Enhancing the accuracy of RBF to predict the CS and decreasing the ANN net complexity leads to having better results evaluated by various metrics. Therefore, using the proposed frameworks, the correlation index of R2 to model the CS in the training phase for FDA-RBF was calculated at 0.9, although BBO-RBF could get 0.85, with a 0.5% difference. However, the RMSE of FDA-RBF was 9 MPa, and for BBO-RBF, this index was calculated at 10 MPa the former model has about three percent more accuracy than the latter. Show more

Keywords: Ultra-high-performance concrete, radial basis function, flow direction algorithm, biogeography-based optimization, compressive strength

DOI: 10.3233/JIFS-221092

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 827-837, 2023

Authors: Xiong, Qiang | Lian, Shuai | Zeng, Zhangying | He, Runxin | Zhu, Binxin | Yang, Xinqi

Article Type: Research Article

Abstract: The vulnerability patch R&D has become an important part of information security governance. An effective collaboration with software vendors in patch R&D is of great significance to reduce the existence time of information security risks. This works aims to explore the relationship between vulnerability information disclosure and patch R&D of software vendors. The data regarding the vulnerability and software vendors is gathered from third-party vulnerability sharing platforms, including (China’s national information security vulnerability database, CNNVD) and Tianyacha.com. Based on the theory of organizational information processing, linear regression model and Cox proportional risk regression model are built for appropriately addressing the …research questions. The results show that the vulnerability disclosure of the third-party sharing platform can improve the patch R&D probability of software vendors. The information processing requirements, such as vulnerability information attention, vulnerability score and whether vulnerabilities are disclosed in advance accelerate the vulnerability patch R&D. The enterprise information processing capability indicators, including the industry dependence of software product customers and the staff size of software vendors accelerate the patch R&D. The number of products affected by the vulnerabilities and the number of software copyrights of software vendors have no significant impact on patch R&D. Show more

Keywords: Patch R&D, vulnerability information disclosure, information processing theory, third-party vulnerability sharing platforms

DOI: 10.3233/JIFS-221316

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 839-853, 2023

Authors: Ibrahim, Hariwan Z.

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-221528

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 855-869, 2023

Authors: Hu, Shan | Han, Jialin | Rong, Lingda | Zong, Qiwei | Dai, Mingxiao

Article Type: Research Article

Abstract: After COVID-19, some initiatives such as Healthy China, and Smart Living have been widely mentioned. This study explored the factors influencing user satisfaction in sports and healthcare integration services to help system builders and interaction designers better seek opportunities and directions for systems construction. Based on grounded theory method, conducted semi-structured interviews with people who have home exercise needs, and then summarised the influencing factors after coding the raw information level by level. It applied the user experience honeycomb to classify potential variables, used principal component analysis (PCA) to extract representative evaluation indicators as observed variables, and followed the construction …of a theoretical model of the satisfaction factors. The structural equation model (SEM) was validated and analyzed to prove its scientific validity and reasonableness. Research showed that the core factors affecting the user experience of sports and healthcare integration system include usefulness, interactivity, usability, credibility, and findability, all of which have a positive and significant impact on user satisfaction. According to the results of empirical analysis, A multidimensional design strategy for sports and healthcare integration system is proposed to provide a reference for improving user satisfaction. Show more

Keywords: Post-Pandemic Era, user satisfaction, grounded theory, Principal Component Analysis (PCA), Structural Equation Model (SEM)

DOI: 10.3233/JIFS-221533

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 871-887, 2023

Authors: Wang, Jiangrui | Zhu, Jiwei | Zhao, Xin | Li, Liang | Wang, Bing

Article Type: Research Article

Abstract: Expert group decision-making in the process of engineering consulting is an important part of the smooth development of engineering projects. Whether the conceptual design scheme of the project is reasonable or not will directly affect the construction quality of the project. After the preliminary selection of a river ecological governance project, four conceptual design schemes were obtained. The owner invited 20 experts in relevant fields to make decisions on the four schemes collected in the early stage. The experts gave preference information for each scheme after reading the relevant materials of the project and clarifying the actual needs of the …project. Based on this background, this paper uses a combination of quantitative and qualitative methods to construct a model for group decision-making and conflict resolution in the engineering consulting process. We use the preference relationship to reflect the degree of experts’ preference for the scheme, cluster them through similarity calculation, calculate the conflict degree of group preference and personal preference respectively, and comprehensively use the sequence difference method and personal preference correction method to resolve the conflict, so that their opinions can be quickly agreed within the specified time. The results calculated by model are consistent with the actual situation of the project, which verifies the effectiveness of the model proposed in this paper and can provide a reference for similar project decision-making and conflict resolution process. Show more

Keywords: Group decisions and negotiations, engineering consulting, conflict resolution, preference correction

DOI: 10.3233/JIFS-222099

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 889-904, 2023

Authors: Sureshkumar, T. | Sivaraj, R. | Vijayakumar, M.

Article Type: Research Article

Abstract: The Internet of Things (IoT) has altered the world in the last few years due to its capacity to impact almost every part of life. However, IoT raises concerns about data security and privacy because it collects data from devices via the cloud, increasing its vulnerability to hacking. IoT security is a serious issue that has delayed its widespread adoption. Several security and privacy solutions have been proposed for IoT contexts that meet prevalent security criteria such as authentication, integrity, and secrecy. However, due to resource restrictions and heterogeneous IoT devices, present solutions are unable to address the security requirements …of the approaching large-scale IoT paradigm. Blockchain, well known for bitcoin and Ethereum, provides an intriguing approach for IoT security. The IoT and blockchain technologies may be combined and significant improvements in distributed systems have been made as a result of the widespread use of IoT technology. A novel framework with a unique design was proposed to improve security in bitcoin transaction by combining blockchain and SHA-256 hash algorithm. Additionally, the performance of proposed framework is compared with the state-of-the-art algorithms like MD5 and SHA1 in term of encryption time, power consumption, latency, speed and security. It is observed that the proposed framework takes 12 ms lesser latency than MD5 and consumes 2.7Wh lesser power consumption than SHA1 and provides better security than both the techniques. Show more

Keywords: Blockchain, IoT, security, bitcoin, privacy

DOI: 10.3233/JIFS-220366

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 905-918, 2023

Authors: Hu, Chunjiao | Huang, Hengjie

Article Type: Research Article

Abstract: Feature selection is one basic technology for data mining. This paper investigates feature selection for interval-valued data via fuzzy rough iterative computation model (FRIC -model). To depict the similarity between samples in an interval-valued decision information system (IVDIS), the fuzzy symmetry relation in an IVDIS is first introduced from the perspective of “The similarity between information values is fed back to the feature set”. After that, several attribute evaluation functions, such as fuzzy positive regions, dependency functions and attribute importance functions are defined. Subsequently, FRIC -model for interval-valued data is established by using the iterations of these functions. Next, An …feature selection algorithm in an IVDIS based on this model is presented. Lastly, numerical experiments and statistics tests are carried out to estimate the performance of the presented algorithm. The experimental results illustrate that the presented algorithm maintains high classification accuracy, and does not occupy too much memory. These findings will provide new perspective for feature selection in an IVDIS. Show more

Keywords: IVDIS, Feature selection, FRS, Attribute evaluation function, FRIC-model

DOI: 10.3233/JIFS-221621

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 919-938, 2023

Authors: Rajesh, D. | Rajanna, G.S.

Article Type: Research Article

Abstract: Smart Dust environment face additional challenges as a result of the use of movable Smart Dust basestation(BS), despite its benefits. The main point of contention is the BS positioning updates to the smart dust nodes. Each smart object ought to be aware of the BS location so that it can send its data to the BS. According to the prevailing Flooding approach, the moveable BS must continuously distribute its location throughout the network in order to inform smart dust nodes about the BS location. In every case, visit positioning upgrades from the BS can result in maximal power usage as …well as enhanced network breakdowns. Different sorts of routing architectures can be used to reduce BS position updating. A routing strategy based on the movable BS is successful if it preserves the network network’s power consumption and latencies to a minimum. The study’s main goal is to develop an energy-efficient routing mechanism focused on adaptive movable BS modification. In the Smart Dust Head (SDH) establishing the inferred surroundings, the most latest movable BS location will be preserved. As a result, rather than soliciting SDH in the environment, the location of the BS is propagated to the smart dust nodes located at the sectors in integrated networking. By transmitting request information to the nearest sector, the remaining SDH can find the most current BS location. The message’s recipient is determined based on the information gathered. The best fuzzy related clustering algorithm will be used to accomplish this. The Enhanced Oppositional grey wolf optimization (EOGWO) methodology can be used to perform the improvement. Optimum network throughput, low latency, and other metrics are used to assess performance. To enhance productivity, the findings will be analyzed and compared to previous routing methodologies. Show more

Keywords: Data collection, smart dust, lifetime, energy utilization, and movable BS

DOI: 10.3233/JIFS-221719

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 939-949, 2023

Authors: Sridhar, M. | Pankajavalli, P.B.

Article Type: Research Article

Abstract: In the resource-constrained wireless sensor network (WSN) geographic routing has been considered as an attractive method where it exploits the location data instead of global topology to transmit the data. The geographic routing protocol faces the routing issues when it is used by a heterogeneous device and utilizes high energy during the propagation of data. The lifespan of the sensor network depends on the efficiency of energy and capacity of the battery. Hence, successful data transmission, enrichment of battery capacity and energy utilization is necessary for WSN. To attain this requirements an effective change is made in the data transmission …environment and network topology. In this paper proposed a dynamic cluster based duty cycle scheduling is initiated for the data transmission. The cluster-based scheduling and routing in geographic routing protocol (CSRGR) utilize the clustering mechanism which in turn reduces the consumption of energy and maximizes the throughput. The objective function of the proposed approach provides a scheduling and routing strategy. The demonstration of simulation results shows the effective cluster size balancing with data transmission range dynamically. The proposed algorithm is compared with the existing approach and from the results, the energy consumption is minimum for diverse scenarios. Show more

Keywords: Duty cycle schedule, throughput, energy efficiency, routing, scheduling cluster, and geographic routing

DOI: 10.3233/JIFS-220966

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 951-961, 2023

Authors: Saravana Kumar, K. | Ramasubramanian, S.

Article Type: Research Article

Abstract: Cardiovascular disease (CVD) is a severe public health concern globally. Early and accurate CVD diagnosis is a difficult task but a necessary endeavour required to prevent further damage and protect patients’ lives. Machine Learning (ML)-based Clinical Decision Support Systems (CDSS) have the potential to assist healthcare providers in making accurate CVD diagnoses and treatments. Clinical data usually contains missing values (MVs); hence, the incorporated imputation techniques for ML have become a critical consideration when working with real-world medical datasets. Furthermore, removing instances with MVs will lead to essential data loss and produce incorrect results. To overcome these issues, this paper …proposes an efficient and reliable CDSS with Ensemble Two-Fold Classification (ETC) framework for classifying heart diseases. The effectiveness of the proposed ETC framework using different supervised ML algorithms is evaluated with four distinct imputation methods for handling MVs over the standard benchmark dataset, viz., the University of California, Irwin (UCI). Experimental results show that our proposed ETC framework with the k-Nearest Neighbors(k-NN) imputation method achieves better classification accuracy of 0.9999 and a lesser error rate of 0.0989 compared to other imputation methods and classifiers with similar execution times. Show more

Keywords: Clinical dataset, classification, data pre-processing, decision support system, heart disease prediction, imputation, machine learning algorithms, missing values

DOI: 10.3233/JIFS-221165

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 963-980, 2023

Authors: Sahoo, Santosh Kumar

Article Type: Research Article

Abstract: Social distance is considered one of the most effective prevention techniques to prevent the spread of Covid19 disease. To date, there is no proper system available to monitor whether social distancing protocol is being followed by individuals or not in public places. This research has proposed a hybrid deep learning-based model for predicting whether individuals maintain social distancing in public places through video object detection. This research has implemented a customized deep learning model using Detectron2 and IOU for monitoring the process. The base model adapted is RCNN and the optimization algorithm used is Stochastic Gradient Descent algorithm. The model …has been tested on real time images of people gathered in textile shops to demonstrate the real time application of the developed model. The performance evaluation of the proposed model reveals that the precision is 97.9% and the mAP value is 84.46, which makes it clear that the model developed is good in monitoring the adherence of social distancing by individuals. Show more

Keywords: Covid19, social-distancing, deep learning, Detectron 2, Intersection over Union, video object detection

DOI: 10.3233/JIFS-221174

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 981-999, 2023

Authors: Song, Hui-Hui | Wang, Ying-Ming | Jia, Xiang | Meng, Meng-Jun

Article Type: Research Article

Abstract: In order to avoid the hesitation of choosing between aggressive and benevolent strategies, we propose two cross-efficiency models to get interval cross-efficiency (ICE) from the relatively neutral angle in fuzzy environment, and then propose a novel aggregation method for ICE to solve the full ranking of Decision-Making Units (DMUs). Firstly, regard the expected value of fuzzy data as the input and output of Data Envelopment Analysis (DEA) method based on fuzzy set theory. Secondly, construct the cross-efficiency models based on the fuzzy expected values from the relatively neutral angle, and generate the lower and upper bounds of ICE for all …DMUs, which determines the interval cross-efficiency matrix (ICEM). Thirdly, project all ICE onto the plane as points, then seek the optimal rally point for each DMU based on ICEM as the comprehensive ICE. Fourthly, rank the comprehensive ICE to obtain the complete ranking of DMUs by using the optimal number sorting method. Finally, the proposed model is applied to the evaluation of manufacturing enterprises, and the results are compared with different models to prove its effectiveness. Show more

Keywords: Interval cross-efficiency DEA, fuzzy sets, fuzzy numbers, the optimal rally point, aggregation method

DOI: 10.3233/JIFS-221482

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1001-1015, 2023

Authors: Alqahtani, Yahya | Jamil, Muhammad Kamran | Alshehri, Hamdan | Ahmad, Ali | Azeem, Muhammad

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-220964

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1017-1028, 2023

Authors: Shobana Nageswari, C. | Vimal Kumar, M.N. | Vini Antony Grace, N. | Thiyagarajan, J.

Article Type: Research Article

Abstract: Ultrasound image quality management and assessment are an important stage in clinical diagnosis. This operation is often carried out manually, which has several issues, including reliance on the operator’s experience, lengthy labor, and considerable intra-observer variance. As a result, automatic quality evaluation of Ultrasound images is particularly desirable in medical applications. This research work plans to perform the fetal heart chamber segmentation and classification using the novel intelligent technology named as hybrid optimization algorithm Tunicate Swarm-based Grey Wolf Algorithm (TS-GWA). Initially, the US fetal images data is collected and data undergoes the preprocessing using the total variation technique. From the …preprocessed images, the optimal features are extracted using the TF-IDF approach. Then, Segmentation is processed on optimally selected features using Spatially Regularized Discriminative Correlation Filters (SRDCF) method. In the final step, the classification of fetal images is done using the Modified Long Short-Term Memory (MLSTM) Network. The fitness function behind the optimal feature selection as well as the hidden neuron optimization of MLSTM is the maximization of PSNR and minimization of MSE. The PSNR value is improved from 3.1 to 9.8 in the proposed method and accuracy of the proposed classification algorithm is improved from 1.9 to 12.13 compared to other existing techniques. The generalization ability and the adaptability of proposed TS-GWA method are described by conducting the various performance analysis. Extensive performance result shows that proposed intelligent techniques performs better than the existing segmentation methods. Show more

Keywords: Fetal heart chamber segmentation, optimal feature selection, modified long short term memory tunicate swarm-based grey wolf algorithm, fetal heart chamber classification

DOI: 10.3233/JIFS-221654

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1029-1041, 2023

Authors: Zhao, Guiping | Wang, Hongmei | Li, Zhanfa

Article Type: Research Article

Abstract: The absorption of capillary water is one of the most crucial factors in the flow of groundwater in rocks (CWA ). Although meticulous experimental studies are needed to determine a rock’s CWA , predictive techniques might cut down on the expense and effort. There are various data mining methods for this purpose, but the considered algorithms in this study were not proposed so far for predicting the CWA. Different rock samples were taken for this purpose from various locations, yielding diverse rocks. For the prediction procedures, four support vector regression (SVR ) models were created: a traditional SVR , two …ensembled models, and a hybrid SVR model using the whale optimization technique (WOA  - SVR ). Results show that all models have acceptable performance in predicting the CWA with R 2 larger than 0.797 and 0.806 for the training and testing data, respectively, representing the acceptable correlation between observed and predicted values. Regarding developed models, the conventional SVR model has the worst performance of all models. All statistical evaluation criteria were improved by assembling models, which present the ability of additive regression and bagging predictions in improving prediction processes. The hybrid WOA  - SVR model has the best performance considering all indices. This hybrid model could also gain the lowest values of error indices between all SVR models, which leads to outperforming the WOA  - SVR model compared to other methods. Show more

Keywords: Capillary water absorption, building stones, prediction, support vector regression, ensembled SVR, hybrid SVR

DOI: 10.3233/JIFS-221207

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1043-1055, 2023

Authors: Nithinsha, S. | Anusuya, S.

Article Type: Research Article

Abstract: The objective of the research work is to propose an intrusion detection system in a cloud environment using K-Means clustering-based outlier detection. In the open access and dispersed cloud architecture, the main problem is security and confidentiality because these are easily susceptible to intruders. Intrusion Detection System (IDS) is a commonly used method to identify the various attacks on the cloud which is easy to access from a remote area. The existing process can’t provide the data to transmit securely. This work describes and notifies the modernly established IDS and alarm management methods by giving probable responses to notice and …inhibit the intrusions in the cloud computing environment and to overcome the security and privacy issue. Proposed K-means Clustering based Outlier Detection (KmCOD) is used to detect the intruders and efficiently secure the data from malicious activity, where it is formulated respectively to increase the trustworthiness of the system by using applying intrusion detection techniques to virtual machines thus keeping the system safe and free from intrusion also provides system reliability. The parametric measures such as the detection rate, trace preprocessing, and correctly identified and incorrectly identified malicious activity are chosen. The performance analysis shows the accuracy of outlier detection as 81%, detection rate achieves 76%, packet arrival rate reaches 79%, pre-processing trace achieves 74%, and malicious activity rate of 21%. Show more

Keywords: Cloud, intrusion detection, data security, clustering algorithm, outlier detection, data privacy, anomaly

DOI: 10.3233/JIFS-220574

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1057-1068, 2023

Authors: Senthilkumar, D. | Reshmy, A.K. | Paulraj, S.

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-220412

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1069-1083, 2023

Authors: George, Remya | Jose, Reshma | Meenakshy, K. | Jarin, T. | Senthil Kumar, S.

Article Type: Research Article

Abstract: Law enforcement teams across the globe experience the highest occupational stress and stress-related diseases. Physical exercise and an active lifestyle are recommended as part of their profession to equip them to fight stress and related health adversities. The research is carried out using objective measures of Heart Rate Variability (HRV), Electro Dermal Activity (EDA), Heart Rate Recovery (HRR), and subjective questionnaires. HRV was generated with an electrocardiogram (ECG) signal acquired using NI myRIO 1900 interfaced with the Vernier EKG sensor. HRR was acquired with the help of a Polar chest strap exercise heart rate monitor and EDA acquisition was carried …out with Mindfield E-Sense electrodes. Then statistical features are extracted from the collected data, and feed to the AQCNN (Aquila convolution neural network) classifier to predict the stress. Signal analyses were done in Kubios 4.0, Ledalab V3.x in a MATLAB environment. The results pointed out that exercise training is effective in increasing the vagal tone of the Autonomic Nervous System (ANS) and hence improves the recovery potential of the cardiovascular system from stress. The proposed AQCNN method improves the accuracy by 95.12% which is better than 93.13%, 85.36% and 80.13% from Statistical technique, CNN and ML-SVM respectively. The findings have the potential to influence decision-making in the selection and training of recruits in high-stress positions, hence optimizing the cost and time of training by identifying maladaptive recruits early. Show more

Keywords: Exercise training, ANS adaptation, machine learning, stress-recovery, heart rate variability, heart rate recovery, electrodermal activity

DOI: 10.3233/JIFS-221588

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1085-1097, 2023

Authors: Saeed, Maha Mohammed | Al-Ghour, Samer | Mehmood, Arif | Al-Shomrani, Mohammed M. | Park, Choonkil | Lee, Jung Rye

Article Type: Research Article

Abstract: This work investigates the new notion of operators, including the interior operator, exterior operator and closure operator in bipolar vague soft topological spaces. On the basis of these notions few results are addressed in bipolar vague soft topological spaces. Lastly, the intriguing concept is that of a sequence’s limit and on the basis of this concept few more results are addressed in bipolar vague soft topological spaces.

Keywords: Bipolar vague soft set, bipolar vague soft operations, bipolar vague soft topological space, bipolar vague soft α-open sets, bipolar vague soft α-close sets, bipolar vague soft operators, bipolar vague soft equence

DOI: 10.3233/JIFS-220498

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1099-1116, 2023

Authors: Miao, Yong | Liu, Zedong | Zhuang, Zijing | Yan, Xiaofeng

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-220640

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1117-1127, 2023

Authors: Mohammed, Awsan | Ghaithan, Ahmed | Al-Yami, Fahad

Article Type: Research Article

Abstract: The oil and gas industry is one of the harshest environments on reinforced concrete structures. Enhancing the reliability of these industries has been identified as a critical goal to meet anticipated production targets and maintain competitiveness. The purpose of this paper is to rank and prioritize risk factors on reinforced concrete structural systems in the oil and gas industry to reduce failures and improve system reliability. The risk factors influencing reinforced concrete systems are identified based on the experts interviewed who specialized in risk analysis. In this paper, a risk assessment approach based on a hybrid fuzzy failure mode and …effect analysis is developed in order to rank the factors and improve the process of reinforced concrete maintenance prioritization. The developed approach is also compared with the other two methods; namely, conventional failure mode and effect analysis (FMEA) and grey rational analysis (GRA) integrated with FMEA. The three developed approaches are designed to acquire the highest risk priority number (RPN) values; conventional RPN, GRA-FMEA RPN, and Fuzzy-FMEA RPN. These values will be utilized as the focus of improvements to reduce the possibility of some kind of failure occurring a second time and improve the deteriorated reinforced concrete structure to minimize the likelihood of failures. The results revealed that high-risk systems include the compression train, steam turbine, and combustion gas turbine generator, while the majority require maintenance of the supporting concrete foundation as soon as second-degree deterioration occurs. Furthermore, the results indicated that the Fuzzy FMEA approach was appropriate for assessing deteriorated reinforced concrete structures.. This work represents a step forward in the development of a tool that can be used to assess the risk of degraded concrete structures and improve their integrity through proper monitoring and maintenance. Show more

Keywords: Risk assessment, concrete structures, oil and gas industry, fuzzy FMEA, grey rational analysis

DOI: 10.3233/JIFS-221328

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1129-1151, 2023

Authors: Shabbir, Wasif | Aijun, Li | Taimoor, Muhammad | Yuwei, Cui

Article Type: Research Article

Abstract: Flight performance of unmanned aerial vehicles (UAVs) strongly depends on implemented attitude tracking control. For designing better controllers, nonlinear control design techniques are often opted instead of control design based on linearized models. Uncertainty in nonlinear dynamics estimation may arise due to inaccuracies in aerodynamic derivatives and simplifications/assumptions made during the derivation of nonlinear models. This paper considers attitude tracking control of fixed-wing UAVs having uncertain dynamics and corrupted gyro sensor outputs. An integral chain differentiator (ICD) is used to provide the analytical redundancy to the gyros used to measure the angular rates. Two control design schemes are proposed, a …neuro-fuzzy adaptive sliding mode control (NFASMC) and an ICD approximation-based fuzzy adaptive sliding mode control (ICD-FASMC). In NFASMC, the uncertain part of the dynamics is estimated using an adaptive radial basis function neural network. Gyro sensor output errors are estimated in real-time, using ICD based error estimation scheme and used in the control law along with the sensor’s corrupted outputs. In ICD-FASMC, the uncertain dynamics and angular rates of UAV are estimated using the ICD such that the requirement of the gyro sensor outputs for control design is bypassed. The switching gain of the designed controllers is made adaptive using fuzzy logic to mitigate the chattering effect. The stability of the proposed controllers is proved using the Lyapunov approach. The proposed schemes are implemented using a nonlinear simulation of a fixed-wing UAV. Simulation results are presented to show the effectiveness of the proposed techniques. Show more

Keywords: Neural network, tracking control, sliding mode control, fuzzy logic, UAV, sensors

DOI: 10.3233/JIFS-222630

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1153-1168, 2023

Authors: Subramanian, Kannimuthu | Kandhasamy, Premalatha

Article Type: Research Article

Abstract: Mining high utility itemsets (HUIs) from transaction databases is one of the current research areas in the data mining field. HUI mining finds itemsets whose utility meets a predefined threshold. It enables users to quantify the usefulness or preferences of products by utilizing different values. Since utility mining approaches do not satisfy the downward closure property, the cost of candidate generation for HUI mining in terms of time and memory space is excessive. This paper presents Genetic Algorithm based Particle Swarm Optimization (GA-PSO), which can efficiently prune down the number of candidates and optimally acquire the complete set of high …utility itemsets. The proposed algorithm’s performance is assessed using the synthetic dataset T20.I6.D100K and the real-time supermarket dataset, which comprises 38765 transactions and 167 unique products. It performs very effectively in terms of time and memory on large databases constituted of small transactions, which are challenging for existing high utility itemsets mining algorithms to manage. Experiments on real-world applications show the importance of high utility itemsets in business decisions, as well as the distinction between frequent and high utility itemsets. Show more

Keywords: Data mining, high utility itemset, genetic algorithm, particle swarm optimization, stagnation

DOI: 10.3233/JIFS-220871

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1169-1189, 2023

Authors: Riaz, Muhammad | Jamil, Nimra

Article Type: Research Article

Abstract: The idea of a cubic bipolar fuzzy set (CBFS ) is a new hybrid extension of the cubic set (CS) and the bipolar fuzzy set (BFS). A CBFS is a strong model to deal with bipolarity and fuzziness in terms of positive membership grades (PMGs) and negative membership grades (NMGs). A positive interval and a positive numbers represent a PMG to express the degree of belongingness of a specific property, and a negative interval and a negative number represent a NMG which defines the degree of non-belongingness of the specific property (or satisfaction level of its counter property). The …aim of this paper is to define the cubic bipolar fuzzy topology under P-order (CBFS P topology) as well as the cubic bipolar fuzzy topology under R-order (CBFS R topology). We investigate certain properties and results of CBFS P topology and CBFS R topology. Topological structures on CBFSs are helping in the development of new artificial intelligence (AI) techniques for healthcare domain strategies and investigating various critical diseases. Such techniques allow for the early detection and investigation of diseases, assisting clinicians in minimizing the possible risk factors. An extended linear assignment model (LAM) and superiority and inferiority ranking method (SIR method) are proposed for healthcare diagnosis based on newly developed structures. The proposed LAM and SIR method are successfully applied for investigation of critical diseases. Moreover, we discuss a comparison analysis of investigations made by suggested techniques with some existing approaches. Show more

Keywords: Cubic bipolar fuzzy set, cubic bipolar fuzzy topology, computational intelligence, linear assignment model, SIR method, healthcare

DOI: 10.3233/JIFS-222224

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1191-1212, 2023

Authors: Carmalatta, J. | Diwakaran, S. | Uma Maheswari, P. | Raja, S. | Robinson, Y. Harold | Julie, E. Golden | Kumar, Raghvendra | Son, Le Hoang | Le, Chung | Tung, Nguyen Thanh | Long, Hoang Viet

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-212214

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1213-1228, 2023

Authors: Esmaeili, Mahin

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-213121

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1229-1240, 2023

Authors: Wu, Meiqin | Chen, Ruixin | Fan, Jianping

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-220194

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1241-1256, 2023

Authors: Liu, Yitong | Mu, Xuewen

Article Type: Research Article

Abstract:  A new neural network is proposed to solve the second-order cone constrained variational inequality (SOCCVI) problems. Instead of the smoothed Fishcer-Burmeister function, a smooth regularized Chen-Harker-Kanzow-Smale (CHKS) function is used to handle relevant complementarity conditions. By using a neural network approach based on the CHKS function, the KKT conditions corresponding to the SOCCVI are solved. Some stability properties of the neural network can be verified by the Lyapunov method. When the parameters of the neural network are different, the achieved convergence speed will also vary. Further by controlling the corresponding parameters, the neural network can achieve a faster convergence speed …than a classical model. Numerical simulations are applied to examine the computing capability of the neural network as well as the influence of parameters on it. Show more

Keywords: Neural network, Second-order cone, Variational inequality, CHKS function, Lyapunov method

DOI: 10.3233/JIFS-220972

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1257-1268, 2023

Authors: Xu, Yunjian | Guo, Aiyin

Article Type: Research Article

Abstract: The orthophotos of Pinus tabulaeformis and seabuckthorn were collected by UAV, these images were used as test images, and the performance of six image segmentation algorithms were qualitatively analyzed and quantitatively compared such as fuzzy pixel clustering and watershed algorithms. The error rate, relative final measurement accuracy, and running time are used as evaluation criteria. The experimental results show that the segmentation algorithms’ performance of the affected forest image is closely related to the image-capturing height and noise. Finally, the guiding suggestions for the application of the orthophoto segmentation algorithm are given from unmanned aerial vehicles in the affected forest …area. Show more

Keywords: Forest diseases and insect pests, Unmanned aerial vehicle (UAV) orthographic image, fuzzy pixel clustering, watershed algorithm

DOI: 10.3233/JIFS-221403

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1269-1281, 2023

Authors: Pandurangan, Raji | Jayaseelan, Samuel Manoharan | Rajalingam, Suresh | Angelo, Kandavalli Michael

Article Type: Research Article

Abstract: The traffic signal recognition model plays a significant role in the intelligent transportation model, as traffic signals aid the drivers to driving the more professional with awareness. The primary goal of this paper is to proposea model that works for the recognition and detection of traffic signals. This work proposes the pre-processing and segmentation approach applying machine learning techniques are occurred recent trends of study. Initially, the median filter & histogram equalization technique is utilized for pre-processing the traffic signal images, and also information of the figures being increased. The contrast of the figures upgraded, and information about the color …shape of traffic signals are applied by the model. To localize the traffic signal in the obtained image, then this region of interest in traffic signal figures are extracted. The traffic signal recognition and classification experiments are managed depending on the German Traffic Signal Recognition Benchmark-(GTSRB). Various machine learning techniques such as Support Vector Machine (SVM), Extreme Learning Machine (ELM), Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), Convolutional neural network (CNN)- General Regression Neural Network (GRNN) is used for the classification process. Finally, the obtained results will be compare in terms of the performance metrics like accuracy, F1 score, kappa score, jaccard score, sensitivity, specificity, recall, and precision. The result shows that CNN-GRNN with ML techniques by attaining 99.41% accuracy compare to other intelligent methods. In this proposed technique is used for detecting and classifying various categories of traffic signals to improve the accuracy and effectiveness of the system. Show more

Keywords: Traffic signal images, traffic signs, median filter, gabor filter, forecasting

DOI: 10.3233/JIFS-221720

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1283-1303, 2023

Authors: Jin, Hui | Li, Jun-qing

Article Type: Research Article

Abstract: With the emphasis of the exhaust gas emission of the transportation vehicles, the mode of picking up and delivering products simultaneously has become a challenging issue in the vehicle routing problem (VRP). To remedy this issue, we investigate a special VRP with realistic constraints including product classification, pickup-delivery, and time window (PC-VRPSPDTW). Then, a hybrid algorithm combining tabu search and artificial immune algorithm (TS-AIA) is proposed. In the proposed algorithm, the earliest time and residual capacity (ETRC) heuristic is designed to generate the initial population. Then, two metaheuristics including variable neighborhood search and large neighborhood search are cooperated to balance …the exploration and exploitation abilities. Besides, a new crossover operator is designed to increase the population diversity. Finally, a series of comparative experiments on the extension version of the Solomon’s benchmarks are performed to verify the effectiveness of the proposed algorithm. Show more

Keywords: Product classification, pickup and delivery, hybrid complementary metaheuristic, tabu search, artificial immune algorithm

DOI: 10.3233/JIFS-222118

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1305-1322, 2023

Authors: Chen, Tuantuan | Xin, Delin | Zhang, Zhongwen | Chen, Hu | Jiang, Qiangqiang

Article Type: Research Article

Abstract: Mineshaft development mode design and decision-making is a complex system theory problem with significant implications for the mine’s investment cost, operational safety, and production efficiency. Because of the many factors that influence mine shaft development mode decision-making, various decision-makers have different worries and inclinations, resulting in greater subjectivity and uncertainty in the decision-making process. The concept of a specialty chain was born out of a belief in group decision-making. By merging and assessing the decision-making information of different groups in the same specialty chain, a systematic decision-making index database of the mine shaft development model was created. To elucidate the …correlation model and hierarchical link between the decision-making indexes, the Interpretative Structure Model (ISM) was applied. The multilevel decision-making index system of mine shaft development mode was established. The decision-making group structure was optimized. The relative importance of the Analytical Hierarchy Process (AHP) was modified to determine the scale. A collaborative weight determination method of multiple decision-making groups was established to reduce the influence of individual subjective consciousness on decision-making results. The ISM-GAHP-FCA decision-making model of mine shaft development mode was built in conjunction with Fuzzy Comprehensive Analysis (FCA) to increase fuzzy decision-making information’s integration and analysis ability. The decision-making outcomes from the analysis of 10 typical mine shaft types in China are adaptable to the actual situation. The model can effectively express the hierarchy, significance, and fuzziness of mine shaft development mode decision-making indexes and limit the interference of decision-maker subjectivity on decision-making results. Show more

Keywords: Mine development mode, mineshaft, interpretive structural model, group decision making, analytic hierarchy process, fuzzy comprehensive evaluation

DOI: 10.3233/JIFS-212119

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1323-1336, 2023

Authors: Li, Jingmin | Xu, Shuzhen

Article Type: Research Article

Abstract: As an important basic industry of national economy, the iron and steel industry has provided an important raw material guarantee for a long time. However there are a large number of hazard sources which are prone to safety accidents in the production process. Then safety evaluation in the production system is highly needed to effectively prevent the occurrence of accidents in iron and steel enterprises. Hence we introduce a method based on deep learning model to evaluate safety of the enterprises. Firstly, the risk factors and casualties in production process are investigated, and a set of safety evaluation index system …is constructed.Secondly, since deep neural network model has the characteristics of strong feature extraction ability and simple model structure, we design a safety evaluation model based on deep neural network. The 25-dimensional evaluation index value is the input of the network, and the network output corresponds to five risk levels. On this basis, the optimization algorithm of deep neural network model is designed to explore the mapping relationship between risk characteristics and safety level. Tensorflow deep learning framework is used to build the evaluation model, classification loss function and network optimization method are designed to train the model. Finally, through experiments, the optimal model structure is determined by comparing the influence of different parameter optimization strategies, different hidden layer structures, and different activation functions on the safety evaluation performance. A three hidden layer structure with the Adam back propagation algorithm and LeakyRelu activation function is adopted to obtain higher accuracy and faster convergence rate. The experiments show that our evaluation model provides an operational method for evaluating the safety management status of iron and steel enterprises. Show more

Keywords: Iron and steel enterprises, safety assessment, neural network, optimization algorithm, deep learning

DOI: 10.3233/JIFS-220246

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1337-1348, 2023

Authors: Soumya, T.V. | Sabu, M.K.

Article Type: Research Article

Abstract: The cogent area, Probabilistic rough sets, offers methods that are used to trisect the data into positive, negative and boundary regions for optimum (α, β) pairs. These basic methods generate three regions based on a single quality, including cost, entropy, impurity, correlation and variance, thereby the best (α, β) pair is generated. The optimization of multiple qualities has significance in real-life applications; however, experiments rarely discussed the optimization of different criteria together in probabilistic rough sets. This probe conducts multi-objective optimization of uncertainty, impurity and correlation, to determine a trisection at optimal (α, β) pairs. For that, this work proposes …a hybrid method that involves Weighted Sum and Artificial Bee Colony Algorithm to optimize the thresholds. The results are compared with the Information-theoretic rough sets and Game-theoretic rough sets. The proposed method outperforms regarding optimal qualities, multiple optimum thresholds, minimal size of boundary regions, and better evaluation results. By attesting the study on experimental data sets, optimal (α, β) pairs are obtained at which the uncertainty and impurity are minima. Moreover, the correlation at this threshold is reasonable. From the application viewpoint, it reduces the cost of further analysis by generating the minimum delayed decision and maximizes the benefit with optimal decisions by considering multiple optimized qualities simultaneously. Show more

Keywords: Multi-objective optimization, probabilistic rough sets, artificial bee colony algorithm, entropy, gini index

DOI: 10.3233/JIFS-221359

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1349-1367, 2023

Authors: Xu, Wei | Mao, Jun-Jun | Zhu, Meng-Meng

Article Type: Research Article

Abstract:  The group decision-making problem usually involves decision makers (DMs) from different professional backgrounds, which leads to a considerable point, that it is the fact that there will be a certain difference in the professional cognition, risk preference and other hidden inherent factors of these DMs to the objective things that need to be evaluated. To improve the reasonability of decision-making, these hidden inherent preference (HIP) of DMs should be determined and eliminated prior to decision making. As a special form of fuzzy set, q-rung orthopair fuzzy numbers (q-ROFNs) is a useful tool to process uncertain information in decision making problems. …Hence, under the environment of q-ROFNs, the determination of HIP based on distance from average score is proposed and a risk model is established to eliminate the HIP by analyzing the possible impact. Meanwhile, a dominant function is proposed, which extends the comparison method between q-ROFNs and an integrated decision-making method is provided. Finally, considering the application background of double carbon economy, an example by selecting the best design of electric vehicles charging station (EVCS) is conducted to illustrate the proposed method, and the feasibility and efficiency are verified. Show more

Keywords: group decision-making, q-ROFNs, hidden inherent preference, risk model, double carbon economy

DOI: 10.3233/JIFS-221702

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1369-1384, 2023

Authors: Al Ghour, Samer

Article Type: Research Article

Abstract: In this paper, we introduce soft somewhat ω-continuous soft mappings and soft somewhat ω-open soft mappings as two new classes of soft mappings. We characterize these two concepts. Also, we prove that the class of soft somewhat ω-continuous (resp. soft somewhat ω-open) soft mappings contains the class of soft somewhat continuous (resp. soft somewhat open) soft mappings. Moreover, we obtain some sufficient conditions for the composition of two soft somewhat ω-continuous (resp. soft somewhat ω-open) soft mappings to be a soft somewhat ω-continuous (resp. a soft somewhat ω-open) soft mapping. Furthermore, we introduce some sufficient conditions for restricting a soft …somewhat ω-continuous (resp. soft somewhat ω-open) soft mapping to being a soft somewhat ω-continuous (resp. soft somewhat ω-open) soft mapping. In addition to these, we introduce extension theorems regarding soft somewhat ω-continuity and soft somewhat ω-openness. Finally, we investigate the correspondences between the novel notions in soft topology and their general topological analogs. Show more

Keywords: Soft somewhat continuous soft mapping, soft ω-continuous soft mappings, soft somewhat open soft mapping, soft generated soft topological space

DOI: 10.3233/JIFS-222098

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1385-1396, 2023

Authors: Li, Fang | Lu, Weihua | Yang, Xiyang | Guo, Chong

Article Type: Research Article

Abstract: In the existing short-term forecasting methods of time series, two challenges are faced: capture the associations of data and avoid cumulative errors. For tackling these challenges, the fuzzy information granule based model catches our attention. The rule used in this model is fuzzy association rule (FAR), in which the FAR is constructed from a premise granule to a consequent granule at consecutive time periods, and then it describes the short-association in data. However, in real time series, another association, the association between a premise granule and a consequent granule at non-consecutive time periods, frequently exists, especially in periodical and seasonal …time series. While the existing FAR can’t express such association. To describe it, the fuzzy long-association rule (FLAR) is proposed in this study. This kind of rule reflects the influence of an antecedent trend on a consequent trend, where these trends are described by fuzzy information granules at non-consecutive time periods. Thus, the FLAR can describe the long-association in data. Correspondingly, the existing FAR is called as fuzzy short-association rule (FSAR). Combining the existing FSAR with FLAR, a novel short-term forecasting model is presented. This model makes forecasting at granular level, and then it reduces the cumulative errors in short-term prediction. Note that the prediction results of this model are calculated from the available FARs selected by the k-medoids clustering based rule selection algorithm, therefore they are logical and accurate. The better forecasting performance of this model has been verified by comparing it with existing models in experiments. Show more

Keywords: Trend fuzzy information granule, fuzzy long-association rule, long-association, k-medoids clustering based rule selection algorithm, short-term forecasting

DOI: 10.3233/JIFS-222721

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1397-1411, 2023

Authors: Amini, Mohammad | Targhi, Alireza Tavakoli | Hosseinzadeh, Mehdi | Farivar, Faezeh | Bidaki, Reza

Article Type: Research Article

Abstract: Handwriting problems, also known as dysgraphia, are defined as a disorder or difficulty in producing written language associated with writing mechanics. The occurrence of handwriting problems among elementary students varies from 10 to 34%. With negative impacts on educational performance, handwriting problems cause low self-confidence and disappointment in the students. In this research, a pen-tablet was employed to sample children’s handwriting, which revealed online features of handwriting such as kinematic and temporal features as well as wrist and hand angles and pen pressure on the surface. This digitizer could also extract the online handwriting features when the pen was not …in contact with the surface. Such features are called in-air features. The purpose of this study was to propose a method for diagnosing dysgraphia along with an evaluation of the impact of in-air features on the diagnosis of this disorder. A rich dataset (OHF-1) of online handwriting features of dysgraphic and non-dysgraphic students was prepared. After the extraction of a huge set of features and choosing a feature selection method, three machine learning methods, i.e. SVM, Random Forest and AdaBoost were compared and with the SVM method, an accuracy of 85.7% in diagnosing dysgraphia was achieved, when both in-air and on-surface features were included. However, while using purely in-air data or merely on-surface features, accuracies of 80.9% and 71.4% were achieved, respectively. Our findings showed that in-air features had a significant amount of information related to the diagnosis of dysgraphia. Consequently, they might serve as a significant part of the dysgraphia diagnosis. Show more

Keywords: Handwriting Analysis, Identification of Dysgraphia, In-Air Analysis, Machine Learning, Online Handwriting Features

DOI: 10.3233/JIFS-221708

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1413-1424, 2023

Authors: Jebin Bose, S. | Kalaiselvi, R.

Article Type: Research Article

Abstract: In today’s world, Android has become the most significant and standard operating system for smartphones. The acceptance of the rapidly growing android system has outcome in a significant enhancement in the number of malware on comparing earlier days. There were several antimalware programs that are designed efficiently for protecting the sensitive data of the user in a mobile system from the occurrence of such attacks. Detection of malware system based on deep learning model along with the use of optimization technique is presented in this work. Initially, android malware dataset input is acquired and the normalization process is done. The …feature selection is carried along with the optimization technique Recurrent Tuna Swarm Optimization. By this, an optimal selection of features can be attained. Show more

Keywords: Android system, malware detection, deep learning model, recurrent tuna swarm optimization, dynamic attention-based LSTM

DOI: 10.3233/JIFS-220828

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1425-1438, 2023

Authors: Manju, S.C. | Geetha, J. | Somasundaram, K.

Article Type: Research Article

Abstract:  Topological indices play a significant role in molecular chemistry, spectral graph theory, network theory, etc. We aim to contribute some new results on PI and weighted PI indices. The vertex PI index of a graph G is given by, PI (G ) = ∑e ∈E (G ) (|V (G ) | - N G (e )). The weighted PI index of a graph G is given by, PI w (G ) = ∑e =(u ,v )∈E (G ) (d G (u ) + d G (v ))(|V (G ) | - N G (e )). We obtained the PI and weighted PI indices for powers …of paths, cycles, and their complements in this study. Also, for a regular graph, a relationship between PI and weighted PI indices is established, and using this relation the weighted PI index is calculated for k th power of a cycle. Show more

Keywords: PI index, Weighted PI index, Power of a graph, Complement of a graph., AMS subject classification: 05C07, 05C09, 05C12.

DOI: 10.3233/JIFS-221436

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1439-1452, 2023

Authors: Al-Sharqi, Faisal | Ahmad, Abd Ghafur | Al-Quran, Ashraf

Article Type: Research Article

Abstract: Interval complex neutrosophic soft set (ICNSS) is the generalization of complex neutrosophic soft set (CNSS) as it provides an interval-based membership structure to handle the complex neutrosophic soft data. However, in the definition of the ICNSS, parameters set is a classical set, and the parameters have the same degree of importance which is considered as 1. This poses a limitation in modeling of some problems. Therefore, we introduce the concept of fuzzy parameterized interval complex neutrosophic soft set (FP-ICNSS) based on idea that each of elements of parameters set has got an importance degree. The basic theoretical operations and properties …are defined and verified on FP-ICNSS. For FP-ICNSS, we conceptualize the relevant mapping and study the properties of the FP-ICNSS images and inverse images. Then, we propose a new algorithm that is applicable in the field of medical diagnosis and decision-making problems for selection right product. Moreover, an illustrative example is presented which depicts its validity for successful application to the problems involving vagueness and uncertainties. Eventually, a comparison between the proposed model and the existing methods is conducted to clarify the importance of this model. Show more

Keywords: Complex neutrosophic set, fuzzy parameterized-interval neutrosophic soft set, interval neutrosophic set, interval-complex neutrosophic soft set, soft set

DOI: 10.3233/JIFS-221579

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1453-1477, 2023

Authors: Ahmad, Uzma | Nawaz, Iqra

Article Type: Research Article

Abstract: In this paper, we introduced Wiener index ( WI ) and average Wiener index ( AWI ) of directed rough fuzzy graph (DRFG). WI is the most extensively used index in graph theory. This index is based on the geodesic distance between two vertices. If there is no directed path from vertex x to vertex y in DRFG, we assume that the weight of geodesic from vertex x to vertex y is zero. In this paper, we investigate the connection between …WI and connectivity index ( CI ), which is one of the most prominent index, by presenting several examples and results. We introduced the concept of complete directed rough fuzzy graph (CDRFG) along with some useful results like CDRFG have no weak edges. We also compute the WI for CDRFG. Moreover, we discussed three types of vertices: Wiener enhancing vertex (WEV), Wiener reducing vertex (WRV), and Wiener neutral vertex (WNV). The proposed study of DRFG is suitable for modeling uncertainties and unclear data information in the real life circumstances. In the end, we proposed an application of the WI in the human trafficking network. We also presented a detailed comparative analysis and comparison table by comparing our result for both CI and WI for the same human trafficking network. Show more

Keywords: Directed rough fuzzy graph, connectivity index, wiener index, human trafficking, AMS (MSC): 03E72, 68R10, 68R05

DOI: 10.3233/JIFS-221627

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1479-1495, 2023

Authors: Venkataramanan, K. | Kannan, P. | Sivakumar, M.

Article Type: Research Article

Abstract: This manuscript proposes a hybrid method for optimum sizing and energy management (EM) of hybrid energy storage systems (HESSs) in Electric vehicle (EV). The proposed hybrid method is combined performance of Honey Badger Algorithm (HBA) and recalling-enhanced recurrent neural network (RERNN), commonly called HBA-RERNN method. The major objective of proposed system is reducing the vehicle life time cost. The HESSs are incorporated with battery and super capacitor (SC). The proposed method is utilized to solve combined energy management and optimization size. Based on the variables, such as size of battery pack and super capacitor pack, HESS size is reflected. Depend …on various sensitivity factors, optimum hybrid energy storage systems size and financial costs are analyzed. At last, the performance of proposed system is implemented on MATLAB site and compared with several existing systems. From this simulation outcome, it concludes that the proposed system diminishes the overall cost and battery degradation cost as 66625 USD than the existing systems. The efficiency of the proposed system achieves 94.8763%. Show more

Keywords: Electric vehicle, hybrid energy storage system, energy management, cost Reduction, sizing, vehicle life time, sensitivity analysis, battery pack

DOI: 10.3233/JIFS-222503

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1497-1515, 2023

Authors: Swathi, P. | Kumar, G. | Jeyabalan, R. | Nishanthini, R.

Article Type: Research Article

Abstract: An one-one correspondence function λ from V (G ) ∪ E (G ) to the set {1, 2, …, |V (G ) | + |E (G ) |} is a total labeling of a finite undirected graph G without loops and multiple edges, where |V (G ) |and |E (G ) | are the cardinality of vertex and edge set of G respectively. A perfectly antimagic total labeling is a totally antimagic total labeling whose vertex and edge-weights that are also pairwise distint. Perfectly antimagic total (PAT) graph is a graph having such labeling. The topic of discovering perfectly antimagic total labeling of some families of …graphs is discussed in this paper. We also came up with certain conclusions about dual of a perfectly antimagic total graphs. Finally, we provided that the necessary and sufficient condition for a dual of a regular and irregular PAT graph to be a PAT graph. Show more

Keywords: Totally antimagic total labeling, antimagic total labeling, total labeling

DOI: 10.3233/JIFS-221279

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1517-1523, 2023

Authors: Javid, Irfan | Ghazali, Rozaida | Zulqarnain, Muhammad | Hassan, Norlida

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-220061

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1525-1545, 2023

Authors: Ramshankar, N. | Joe Prathap, P.M.

Article Type: Research Article

Abstract: Nowadays, people always use online promotions to know about best shops to buy the best products. This shopping experience and shopper’s opinion about the shop can be observed by the customer-experience shared on social media. A new customer when searching a shop needs information about manufacturing date (MRD) and manufacturing price (MRP), offers, quality, and suggestions which are only provided by the previous customer experience. Several approaches were used previously for predicting the product details, but no one approach provides accurate information. To overcome these issues, Reviewer Reliability and XGboost whale Optimized Sentiment Analysis for Online Product Recommendation is proposed …in this manuscript.Initially, Amazon Product recommendation datathe data are preprocessed and given to XGboost Classifier that classifies the product recommendation result as, good, bad and average. Generally the XGboost Classifier does not reveal any adoption of optimization techniques for computing the optimal parameters for assuring accurate classification of product recommendation. Therefore in this work, proposed Whale optimization algorithm utilized to optimize the weight parameters of the XGboost. Then the proposed model is implemented in MATLAB. The proposed method attains 18.31%, 12.81%, 45.75%, 26.97% and 25.55% lower Mean Absolute error, 18.31%, 12.81%, 27.97%, 25.97%, and 25.55% higher Mean absolute percentage error and 15.31%, 10.33%, 25.86%, 22.86% and 15.22% lower Mean Square Error than the existing methods. Show more

Keywords: Whale optimization algorithm (WOA), XGBoost classifier, sentiment analysis, online product shopping reviews, recommendation system

DOI: 10.3233/JIFS-221633

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1547-1562, 2023

Authors: Peng, Su-Mian

Article Type: Retraction

DOI: 10.3233/JIFS-219324

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1563-1563, 2023