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