Journal of Intelligent & Fuzzy Systems - Volume 45, issue 1

Authors: Dey, Aniruddha | Ghosh, Manas | Chowdhury, Shiladitya | Kahali, Sayan

Article Type: Research Article

Abstract: This paper presents a novel decision-making method for face recognition where the features were extracted from the original image fused with its corresponding true and partial diagonal images. To extract features, we adopted the generalized two-dimensional FLD (G2DFLD) feature extraction technique. The feature vectors from a test image are given as input to neural network-based classifier. It is trained with the feature vectors of original image and diagonally fused images and thereby the merit weights with respect to different classes were generated. To address the factors that affect the face recognition accuracy and uncertainty related to raw biometric data, a …fuzzy score for each of the classes is generated by treating a type-2 fuzzy set. This type-2 fuzzy set is formed by the feature vectors of both the diagonally fused training samples and the test image of the respective classes. A concluding score for each of the classes under consideration is computed by fusing complemented merit weight with the complemented fuzzy score. These class-wise concluding scores are considered in the face recognition process. In this study, the well-known face databases (AT&T, UMIST and CMU-PIE) are used to evaluate the performance of the proposed method. The experimental results illustrate the fact that the proposed method has exhibited superior classification precision as compared with other state-of-art methods. Our T2FMFImg F method achieves highest face recognition accuracies of 99.41%, 98.36% and 89.80% in case of AT&T, UMIST and CMU-PIE (with expression), respectively while for CMU-PIE (with Light) the highest recognition accuracy is 97.957%. In addition to it, the presented method is quite successful in fusing and classifying textural information from the original and partial diagonal images by integrating them with type-2 fuzzy set-based treatment. Show more

Keywords: Image-level fusion, confidence factor, face recognition, fuzzy type-2

DOI: 10.3233/JIFS-224288

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 743-761, 2023

Authors: Badshah, Noor | Arif, Muhammad | Khan, Tufail Ahmad | Ullah, Asmat | Rabbani, Hena | Atta, Hadia | Begum, Nasra

Article Type: Research Article

Abstract: Segmenting outdoor images in the presence of haze, fog or smog (which fades the colors and diminishes the contrast of the observed objects) has been a challenging task in image processing with several important applications. In this paper, we propose a new fractional-order variational model that will be able to de-haze and segment a given image simultaneously. The proposed method incorporates the atmospheric veil estimation based on the dark channel prior (DCP). This transmission map can reduce significantly the edge artifacts and enhance estimation precision in the resulting image. The transmission map is then changed over to the high-quality depth …map, with which the new fractional-order variational model can be framed to look for the haze free segmenting image for both grey and color outdoor images. An explicit gradient descent scheme is employed to find efficiently the minimizer of the proposed energy functional. Experimental tests on real world scenes show that the proposed method can jointly de-haze and segment hazy or foggy images effectively and efficiently. Show more

Keywords: Foggy or hazy images, fractional-order total variation, image de-hazing, image segmentation, inhomogeneous intensity, object detection

DOI: 10.3233/JIFS-230385

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 763-781, 2023

Authors: Li, Dongping | Shen, Shikai | Yang, Yingchun | He, Jun | Shen, Haoru

Article Type: Research Article

Abstract: In order to solve the problems of inaccurate trajectory time prediction and poor privacy protection of dataset publishing mechanism, the study adds deep learning models into the trajectory time prediction model and designs the SLDeep model. Its performance is compared with LRD, STTM and DeepTTE models for experiments, and the results show that the SLDeep model. The lowest mean absolute error value was 116.357, indicating that it outperformed the other models. The study designed the Travelet publishing mechanism by incorporating differential privacy methods into the publishing mechanism, and compared it with Li’s and Hua’s publishing mechanisms for experiments. The results …show that the mutual information index value of Travelet publishing mechanism is 0.06, which is better than Li’s and Hua’s publishing mechanisms. The experimental results show that the performance of the trajectory time prediction model incorporating deep learning and the dataset publishing mechanism incorporating differential privacy methods has been greatly improved, which can provide new ideas to obtain a more accurate and all-round trajectory big data management system. Show more

Keywords: Deep learning, differential privacy, trajectory time prediction, release mechanism

DOI: 10.3233/JIFS-231210

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 783-795, 2023

Authors: Chola Raja, K. | Kannimuthu, S.

Article Type: Research Article

Abstract: Autism Spectrum Disorder (ASD) is a complicated neurodevelopment disorder that is becoming more common day by day around the world. The literature that uses machine learning (ML) and deep learning (DL) approaches gained interest due to their ability to increase the accuracy of diagnosing disorders and reduce the physician’s workload. These artificial intelligence-based applications can learn and detect patterns automatically through the collection of data. ML approaches are used in various applications where the traditional algorithms have failed to obtain better results. The major advantage of the ML algorithm is its ability to produce consistent and better performance predictions with …the help of non-linear and complex relationships among the features. In this paper, deep learning with a meta-heuristic (MH) approach is proposed to perform the feature extraction and feature selection processes. The proposed feature selection phase has two sub-phases, such as DL-based feature extraction and MH-based feature selection. The effective convolutional neural network (CNN) model is implemented to extract the core features that will learn the relevant data representation in a lower-dimensional space. The hybrid meta-heuristic algorithm called Seagull-Elephant Herding Optimization Algorithm (SEHOA) is used to select the most relevant and important features from the CNN extracted features. Autism disorder patients are identified using long-term short-term memory as a classifier. This will detect the ASD using the fMRI image dataset ABIDE (Autism Brain Imaging Data Exchange) and obtain promising results. There are five evaluation metrics such as accuracy, precision, recall, f1-score, and area under the curve (AUC) used. The validated results show that the proposed model performed better, with an accuracy of 98.6%. Show more

Keywords: Autism spectrum disorder, Meta-Heuristic, Deep learning, Convolution neural network, seagull and elephant herding optimization, LSTM, fMRI.

DOI: 10.3233/JIFS-223694

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 797-807, 2023

Authors: Wu, Chong | Mao, Zengli | Zhan, Baoqiang | Wu, Yahui

Article Type: Research Article

Abstract: The ocean plays a crucial role in human society’s survival and development. While China’s marine economy has grown rapidly in recent years, it has also led to serious problems inhibiting ecosystem sustainability. This paper proposes high-quality development of the marine economy and combines the improved entropy value method, fuzzy hierarchical analysis method (FAHP), and data envelopment analysis (DEA) method to establish a quadratic relative evaluation model. A two-layer comprehensive index framework with 19 indicators is built to measure various aspects of the marine economy, including innovation, coordination, green, openness, and sharing. Empirical analysis conducted on 11 coastal provinces in China …using data mainly collected from the Chinese Statistical Yearbook reveals significant spatial patchiness in the high-quality development level of the marine economy. This discrepancy is largely due to differences in geographical locations, resources, and government policies. The study analyzes four benchmark provinces of high-quality development and summarizes their experiences. The paper concludes by providing suggestions and implications to support government decision-making. Show more

Keywords: Marine economy, high-quality, DEA, quadratic relative evaluation

DOI: 10.3233/JIFS-224173

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 809-830, 2023

Authors: Sathish Kumar, P.J. | Ponnusamy, Muruganantham | Radhika, R. | Dhurgadevi, M.

Article Type: Research Article

Abstract: Underwater wireless sensor networks (UWSNs) are designed to perform cooperative monitoring and data collection tasks by combining several elements, such as automobiles and sensors located in a particular acoustic area. Several studies have been carried out to improve energy efficiency and routing reliability. However, UWSN faces several challenges, such as high ocean interference and noise, long transmission delays, limited bandwidth, and low sensor node battery energy. In this work, a novel underwater clustering-based hybrid routing protocol (UC-HRP) has been proposed to address these issues. The overall process is carried out in three phases. In the first phase, the fuzzy-ELM approach …is used to initialize the cluster based on parameters such as Doppler spread, path loss, noise, and multipath. In the second phase, the cluster head is selected using Cluster Centre Cluster Head Selection (C3HS) based on Link quality, distance, node degree, and residual energy. In the third phase, Hybrid Artificial Bee Colony (HABC) algorithm is used for selecting an optimal route based on the parameters such as reliability, bandwidth effectiveness, average path loss, and average transmission latency. The performance of the proposed UC-HRP method is evaluated using a variety of parameters, including the network lifetime, packet delivery ratio, alive nodes, and energy consumption. The proposed technique improves the network lifetime by 14.03%, 16.25%, and 18.34% better than ACUN, ANC-UWSNS, and MERP respectively. Show more

Keywords: Underwater wireless sensor networks, fuzzy extreme learning machine, cluster centre cluster head selection, hybrid artificial bee colony algorithm

DOI: 10.3233/JIFS-230172

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 831-843, 2023

Authors: Chen, Guomin | Jin, Yingwei | Cheng, Shili | Jiao, Huihua

Article Type: Research Article

Abstract: Fuel Cells are novel devices that have been proposed as new power generation systems. The advantages of solid oxide fuel cells are higher efficiency, higher stability, fuel flexibility, lower emissions, and generally lower cost. In the present study, the fuzzy model is employed to build the model of the solid oxide fuel cell considering various sputtering power, thickness of electrolyte, and temperatures of cell. The maximum iterations for the adaptive neuro-fuzzy inference model was considered 50 iterations. About 3500 samples were applied for the training process, and almost 900 samples were utilized for the testing. After modeling process, the genetic …algorithm, particle swarm, simulated annealing, and hybrid firefly-particle swarm optimizers are applied to achieve the optimum value of current and power densities. The results showed that proposed fuzzy model could approximate the model the system with a good agreement with experimental data. Additionally, the obtained data confirm the accuracy, high convergence speed, and robustness of the proposed hybrid optimizer compared to three efficient optimization algorithms. Accordingly, the correlation factor for the proposed fuzzy model for the training and testing dataset was obtained to be 0.9298 and 0.9289, correspondingly. Show more

Keywords: Performance improvement of SOFC, adaptive neuro-fuzzy inference model, various optimization algorithms, experimental dataset accuracy, comparative study

DOI: 10.3233/JIFS-221125

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 845-862, 2023

Authors: Li, Lin

Article Type: Research Article

Abstract: In recent years, the use of Gas Turbines (GTs) to generate electricity has grown exponentially. Therefore, for the optimal performance of gas power plants, a lot of research has been done on modeling different parts of GTs, estimating model parameters, and controlling them. But most of the available methods are not accurate enough, like most linear methods, or are model-based, which require an accurate model of the system (like most nonlinear methods), or there is a constant need to adjust the controller parameters. To address these shortcomings, this study uses a new hybrid method including the brain emotional learning-based intelligent …controller, the nonlinear multivariate method in the form of feedback linearization, and an adaptive control method of mode predictive reference model used to quickly control the GT. The Rowen model is used to simulate the nonlinear model of the GT. Owing to the influence of exhaust temperature on the speed of GT and the multivariate system model, nonlinear multivariate controller design is considered. First, the adaptive control method of the state-predictive reference model for a multi-output multi-input system, in general, is presented, and then, the proposed method for a GT with real dynamic values is implemented. The simulation results show the ability of the proposed controller to control the GT. In order to prove the efficiency of the proposed method, the obtained results are compared with the PID industrial controller method and the classical reference model method. Show more

Keywords: GTs, speed control, brain emotional learning based intelligent controller, feedback linearization, dynamic simulation

DOI: 10.3233/JIFS-221408

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 863-876, 2023

Authors: Ammasaikutti, Pradeep | Palanisamy, Kannan

Article Type: Research Article

Abstract: A single phase Soft Switching-Solid State Transformer (SS-SST) design is proposed with H-bridge topology as an alternative solution to fulfil the demand of low (or) medium grid power applications. A medium/low frequency transformers fed with H-bridge circuit are incorporate without DC-voltage link, and it’s provided sinusoidal output voltage into the grid. An optimization of Cuckoo Search Firefly (CSF) algorithm was proposed in this research to find optimum switching angle and duty cycle in bridge circuit unit. At present optimum grid power is achieved a maximum efficiency of medium/low power frequency with the help of proposed SS-SST (MS4T) model. For proposed …design is used to electric aircraft, ship power systems, battery energy storage systems (BESS) and fast charging electric vehicles (EV). Which are appealing the networks of medium-voltage DC (MVDC). Proposed MS4T design is based on soft-switching transformer with low conduction loss, low EMI and high efficiency via H-bridge converter circuit. The capacitor voltage balancing control between cascade module and design of the component including a medium level voltage frequency transformer that is implement a 1 kV to 0.25 kV MS4T described. Therefore, the efficacy of the present investigations are established with MATLAB platform. The medium voltage Micro Grid (MG) output is estimated under different operation load conditions. A simulation result of the grid power is measured minimum harmonics level by using optimum switching angle, switching frequency and duty cycle arrangements. Show more

Keywords: Soft switching-solid state transformer, cuckoo search firefly algorithm, H-bridge circuit, medium level voltage, grid

DOI: 10.3233/JIFS-224393

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 877-890, 2023

Authors: Yang, Wendong | Wang, Jingyi | Yang, Sibo | Zhang, Kai

Article Type: Research Article

Abstract: Short-term load prediction has always played an increasingly important part in power system administration, load dispatch, and energy transfer scheduling. However, how to build a novel model to improve the accuracy of load forecasts is not only an extremely challenging problem but also a concerning problem for the power market. Specifically, the individual model pays no attention to the significance of data selection, data preprocessing, and model optimization. So these models cannot always satisfy the time series forecasting’s requirements. With these above-mentioned ignored factors considered, to enhance prediction accuracy and reduce computation complexity, in this study, a novel and robust …method were proposed for multi-step forecasting, which combines the power of data selection, data preprocessing, artificial neural network, rolling mechanism, and artificial intelligence optimization algorithm. Case studies of electricity power data from New South Wales, Australia, are regarded as exemplifications to estimate the performance of the developed novel model. The experimental results demonstrate that the proposed model has significantly increased the accuracy of load prediction in all quarters. As a result, the proposed method not only is simple, but also capable of achieving significant improvement as compared with the other forecasting models, and can be an effective tool for power load forecasting. Show more

Keywords: Short-term load prediction, data selection, data preprocessing, optimization, forecasting

DOI: 10.3233/JIFS-224567

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 891-909, 2023

Authors: Sun, Shuwan | Bian, Weixin | Xie, Dong | Xu, Deqin | Huang, Yi

Article Type: Research Article

Abstract: With the development of wireless communication technology and the rapid increase of user data, multi-server key agreement authentication scheme has been widely used. In order to protect users’ privacy and legitimate rights, a two-factor multi-server authentication scheme based on device PUF and users’ biometrics is proposed. The users’ biometrics are combined with the physical characteristics of the Physically Unclonable Functions (PUF ) as authentication factors, which not only ensures the security of the scheme, but it also is user-friendly without a password. The proposed scheme can be applied to telemedicine, smart home, Internet of Vehicles and other fields to …achieve mutual authentication and key agreement between users and servers. In order to prove the security of the proposed scheme, the widely accepted ROR model and BAN logic are used for formal security analysis. The scheme can effectively resist various security attacks, and the comparison with existing schemes shows that it has better performance in terms of communication cost and computational complexity. Show more

Keywords: Multi-server, physical unclonable function, password-free, mutual authentication, biometric security and privacy

DOI: 10.3233/JIFS-221354

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 911-928, 2023

Authors: Nishy Reshmi, S. | Shreelekshmi, R.

Article Type: Research Article

Abstract: In this paper, we propose a method exploiting syntactic structure, semantic relations and word embeddings for recognizing textual entailment. The sentence pairs are analyzed using their syntactic structure and categorization of sentences in active voice, sentences in passive voice and sentences holding copular relations. The main syntactic relations such as subject, verb and object are extracted and lemmatized using a lemmatization algorithm based on parts-of-speech. The subject-to-subject, verb-to-verb and object-to-object similarity is identified using enhanced Wordnet semantic relations. Further similarity is analyzed using modifier relation, number relation, nominal modifier relation, compound relation, conjunction relation and negative relation. The experimental evaluation …of the method on Stanford Natural Language Inference dataset shows that the accuracy of the method is 1.4% more when compared to the state-of-the-art zero shot domain adaptation methods. Show more

Keywords: GloVe, natural language processing, textual entailment, Wordnet

DOI: 10.3233/JIFS-223275

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 929-939, 2023

Authors: Liu, Boting | Guan, Weili | Yang, Changjin | Fang, Zhijie

Article Type: Research Article

Abstract: Word vector is an important tool for natural language processing (NLP) tasks such as text classification. However, existing static language models such as Word2vec cannot solve the polysemy problem, leading to a decline in text classification performance. To solve this problem, this paper proposes a method for making Chinese word vector dynamic (MCWVD). The part of speech (POS) is used to solve the ambiguity problem caused by different POS. The POS structure graph is constructed and the syntactic structure information of POS features is extracted by GCN (Graph Convolutional Network). POS vector and word vector are concatenated into PW (POS-Word) …vector. Parametric matrix is added to improve the fusion effect of POS and word features. Multilayer attention is used to distinguish the importance of different features and further update the vector expression of word vectors about the current context. Experiments on Chinese datasets THUCNews and SogouNews show that MCWVD effectively improves the accuracy of text classification and achieves better performance than CoVe (Context Vectors) and ELMo (Embeddings from Language Models). MCWVD also achieves similar performance to BERT and GPT-1 (Generative Pre-Training), but with a much lower computational cost and only 4% of BERT parameters. Show more

Keywords: Word vector, Word2vec, part of speech, Graph Convolutional Network, multi-layer attention

DOI: 10.3233/JIFS-224052

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 941-952, 2023

Authors: Tong, Shekun | Peng, Jie

Article Type: Research Article

Abstract: In this work, with the aim of separating the genuine and forgery samples of the signature, we developed a new dual-path architecture using deep neural network and a traditional descriptor for feature extraction toward an automatic offline signature recognition. The proposed approach is an extended version of VGG-16, which is enhanced using our two paths architecture. In the first path, we explore features using a deep convolutional neural network, and in the second path, we discover global features using a traditional heuristic approach. For classical feature extraction, an innovative idea is presented, in which the descriptor is stable for some …common changes, such as magnification and epoch, in the signature samples. Our traditional approach extracts global features that are stable with rotation and scaling. The proposed method was analyzed and compared with three well-known databases of CEDAR, UTsig, and GPDS signature images. A dual-patched model architecture is significantly more accurate than the basic model when compared to the basic model. In agreement with the proposed method, the best signature recognition accuracy on the CEDAR database is in the range of 98.04-99.96%, while the best recognition accuracy on the GPDS and UTsig databases is 98.04% and 99.56%, respectively. Furthermore, this technique has been compared with four popular methods such as VGG-S, VGG-M, VGG-16, and LS2Net. The presented approach achieved a recognition rate of 99.96% using a diverse signature database. Experimental results demonstrate that the proposed VGG-16 based signature recognition system is superior over texture-based and deep-learning methods and also outperforms the existing state-of-the-art results in this regard. It is expected that the proposed system will provide fresh acumen to the researchers in developing offline signature verification and recognition systems in other scripts. Show more

Keywords: Signature recognition, offline, deep learning, VGG 16-layer neural network, feature extraction

DOI: 10.3233/JIFS-224326

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 953-964, 2023

Authors: Devika, M. | Shaby, Maflin

Article Type: Research Article

Abstract: One of the major challenge in Wireless Sensor Networks (WSN’s) deployment is efficient energy consumption. Critical distance, proper routing algorithm and error control coding techniques can be used for energy optimization. As WSN contains a large number of power constrained sensors, the sensed data from the environment should be transmitted in a cooperative way to the base station (BS). The pattern of the clustering structure can extend the sensor network life time, reduce the total consumed energy and regulate the data transmission. Clustering concept combines group of sensors which are located in the same communication range. Some of the routing …protocol like, SEED, LEACH, SEP, Z-SEP etc., suffers from idle listening problem, which cannot cope with an environment with sensor nodes. It leads to energy wastage across the network. To manage energy efficiency and traffic heterogeneity issues, a new routing protocol called enhanced energy efficient sleep awake aware intelligent sensor network (EEESAA) is proposed. Here, one sensor in each group will be in active mode whereas other sensors entered in sleep mode. Based on the nodes energy, sleep and awake node pairs will be altered. In the proposed method, one slot is allotted for group of pairs. The proposed approach is evaluated and compared against LEACH, SEP and Z-SEP protocols. Simulation results show that EEESAA protocol performs better than LEACH, SEP, Z-SEP in terms of cluster head selection, throughput, number of alive & dead nodes and network lifetime. Show more

Keywords: Wireless sensor network, enhanced energy efficient sleep awake aware intelligent sensor network (EEESAA), low-energy adaptive clustering hierarchy (LEACH), stable election protocol, zonal stable election protocol

DOI: 10.3233/JIFS-224380

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 965-973, 2023

Authors: Yao, Zhuangkai | Zeng, Bi | Hu, Huiting | Wei, Pengfei

Article Type: Research Article

Abstract: In recent mathematical reasoning tasks, self-attention has achieved better results in public datasets. However, self-attention performs poorly on more complex mathematical problems due to the lack of capacity to capture local features and the ill-conditioned training after deepening the number of layers. To tackle the problem and enhance its ability of extracting local features while learning the global contexts, we propose an implicit mathematical reasoning model that improves Transformer by combining self-attention and convolution to achieve joint modeling of global and local context. Also, by introducing Reweight connection and adversarial loss function, we prevent the model gradient from disappearing or …exploding in a deep neural network while ensuring the convergence speed and avoiding overfitting. Experimental results show that the proposed model improves the accuracy by 4.47% on average for complex mathematical problems compared to the best existing results. In addition, we verify the validity of our model using ablation analysis and further demonstrate the interpretability of the model by attention mapping and task role analysis. Show more

Keywords: Implicit mathematical reasoning, self-attention, depth separable convolution, causal language model, adversarial loss

DOI: 10.3233/JIFS-224598

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 975-988, 2023

Authors: Wang, Wei | Zhang, Ning | Peng, Weishi | Liu, Zhengqi

Article Type: Research Article

Abstract: Intonation evaluation is an important precondition that offers guidance to music practices. This paper present a new intonation quality evaluation method based on self-supervised learning to solve the fuzzy evaluation problem at the critical intonations. Firstly, the effective features of audios are automatically extracted by a self-supervised learning-based deep neural network. Secondly, the intonation evaluation of the single tones and pitch intervals are carried out by combining with the key local features of the audios. Finally, the intonation evaluation method characterized by physical calculations, which simulates and enhances the manual assessment. Experimental results show that the proposed method achieved the …accuracy of 93.38% which is the average value of multiple experimental results obtained by randomly assigning audio data, which is much higher than that of the frequency-based intonation evaluation method(37.5%). In addition, this method has been applied in music teaching for the first time and delivers visual evaluation results. Show more

Keywords: Music practice, intonation evaluation, self-supervised learning, deep neural network, audio feature extraction

DOI: 10.3233/JIFS-230165

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 989-1000, 2023

Authors: Lin, Fucai | Wu, Tingyi | Cao, Xiyan | Li, Jinjin

Article Type: Research Article

Abstract: The theory of knowledge spaces (KST) which is regarded as a mathematical framework for the assessment of knowledge and advices for further learning. Now the theory of knowledge spaces has many applications in education. From the topological point of view, we discuss the language of the theory of knowledge spaces by the axioms of separation and the accumulation points of pre-topology respectively, which establishes some relations between topological spaces and knowledge spaces; in particular, we show that the language of the regularity of pre-topology in knowledge spaces and give a characterization for knowledge spaces by inner fringe of knowledge states. …Moreover, we study the relations of Alexandroff spaces and quasi ordinal spaces; then we give an application of the density of pre-topological spaces in primary items for knowledge spaces, which shows that one person in order to master an item, she or he must master some necessary items. In particular, we give a characterization of a skill multimap such that the delineated knowledge structure is a knowledge space, which gives an answer to a problem in [14 ] or [18 ] whenever each item with finitely many competencies; further, we give an algorithm to find the set of atom primary items for any finite knowledge space. Show more

Keywords: Knowledge space, knowledge structure, learning space, pre-topological space, skill multimap, quasi ordinal space, Alexandroff space, separation of axiom, primary item, Primary 54A05, secondary 54A25, 54B05, 54B10, 54D05, 54D70

DOI: 10.3233/JIFS-230498

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1001-1013, 2023

Authors: Wang, Encheng | Liu, Xiufeng | Wan, Jiyin

Article Type: Research Article

Abstract: Received Signal Strength Indication (RSSI) fluctuates with the change of indoor noise, resulting in a large positioning error of the trained Back Propagation Neural Network (BPNN). An adaptive indoor positioning model based on Cauchy particle swarm optimization (Cauchy-PSO) BPNN is proposed to solve the problem. In the off-line training phase, the signal with less noise intensity acquired in a good environment is selected as the original training set in the localization phase. The variance of the received set of signals is used as a measure of the noise intensity of the current environment. In the localization phase, the variance of …each set of signals received is calculated at equal intervals. If the variance of adjacent intervals differs significantly, the system adjusts the original training set data according to the current noise intensity and re-trains the BP model online. Meanwhile, the particle swarm optimization algorithm using Cauchy variance to optimize the BP network tends to fall into the disadvantage of local optimum. Considering that the collected fingerprint database may generate “high-dimensional disasters”, Principal Component Analysis (PCA) is used to select and downscale the features of the wireless Access Point (AP). The proposed adaptive localization model can be trained online. The improved Cauchy-PSO algorithm and data dimensionality reduction can further improve the localization accuracy and training speed of the BP model. The experimental results show that the adaptive indoor localization model has strong adaptive capability in a noise-varying environment. Show more

Keywords: RSSI, adaptive BP model (AI-BP), BPNN, PCA, Cauchy-PSO

DOI: 10.3233/JIFS-231082

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1015-1027, 2023

Authors: Qiao, Wenbao

Article Type: Research Article

Abstract: Computer network security evaluation is a basic work to determine the security performance of the network system and implement the network security management. It involves organizational management, network technology, personnel psychology, social environment and other factors. In recent years, with the rapid development of information technology in China, the problem of computer network security has become increasingly prominent. Although domestic and foreign scholars have sought effective methods of network security evaluation from different aspects and using different methods, many factors involved in network security are difficult to quantify, so far, there is no relatively mature quantitative evaluation method of network …security. The computer network security evaluation is classical multiple attribute decision making (MADM) problems. In this article, based on projection measure, we shall introduce the projection models with q-rung orthopair fuzzy information. First of all, the definition of q-rung orthopair fuzzy sets (q-ROFSs) is introduced. In addition, to fuse overall q-rung orthopair fuzzy evaluation information, two aggregation operators including q-ROFWA and q-ROFWG operators is introduced. Furthermore, combine projection with q-ROFSs, we develop the projection models with q-rung orthopair fuzzy information. Based on developed weighted projection models, the multiple attribute decision making model is established and all computing steps are simply depicted. Finally, a numerical example for computer network security evaluation is given to illustrate this new model and some comparisons between the new proposed models and q-ROFWA and q-ROFWG operators are also conducted to illustrate advantages of the new built method. Show more

Keywords: Multiple attribute decision making (MADM) problems, q-rung orthopair fuzzy sets (q-ROFSs), q-rung orthopair fuzzy projection model, computer network security evaluation

DOI: 10.3233/JIFS-231351

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1029-1038, 2023

Authors: Wajid, Mohd Anas | Zafar, Aasim | Terashima-Marín, Hugo | Wajid, Mohammad Saif

Article Type: Research Article

Abstract: Recent advances in technology and devices have caused a data explosion on the Internet and on our home PCs. This data is predominantly obtained in various modalities (text, image, video, etc.) and is essential for e-commerce websites. The products on these websites have both images and descriptions in text form, making them multimodal in nature. Earlier categorization and information retrieval methods focused mostly on a single modality. This study employs multimodal data for classification using neutrosophic fuzzy sets for uncertainty management for information retrieval tasks. This effort utilizes image and text data and, inspired by past techniques of embedding text …over an image, attempts to classify the images using neutrosophic classification algorithms. For classification tasks, Neutrosophic Convolutional Neural Networks (NCNNs) are used to learn feature representations of the produced images. We demonstrate how a pipeline based on NCNN can be utilized to learn representations of the innovative fusion method. Traditional convolutional neural networks are vulnerable to unknown noisy conditions in the test phase, and as a result, their performance for the classification of noisy data declines. Comparing our method against individual sources on two large-scale multi-modal categorization datasets yielded good results. In addition, we have compared our method to two well-known multi-modal fusion methodologies, namely early fusion and late fusion. Show more

Keywords: Multimodal data, early & late fusion, fuzzy logic, neutrosophic logic, convolutional neutral network

DOI: 10.3233/JIFS-223752

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1039-1055, 2023

Authors: Thao, Le Quang | Linh, Le Khanh | Thien, Nguyen Duy | Cuong, Duong Duc | Bach, Ngo Chi | Dang, Nguyen Ha Thai | Hieu, Nguyen Ha Minh | Minh, Nguyen Trieu Hoang | Diep, Nguyen Thi Bich

Article Type: Research Article

Abstract: The detection and prediction of cleaning conditions in school restrooms are crucial for reducing health risks and improving service quality. Traditional methods like manual hygienic inspection, fixed cleaning schedules, and automatic flushing devices have required large investments of money and effort from cleaning businesses to maintain cleanliness in school restrooms. To address this issue, we propose a prediction model based on Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) architecture. The model uses a dataset obtained from real-time conditions of the toilet via a wireless sensor network, enabling more efficient scheduling of toilet cleaning tasks. By predicting patterns of …Ammoniac (NH3) concentrations and Relative Humidity (RH) levels over time, our LSTM model is superior to the RNN model in performance, significantly reducing deviations in the NH3 and RH values with RMSE values of 3.32 and 2.85 , respectively. Furthermore, the model’s flexibility allows a variety of inputs to evaluate the need for cleaning at specific times, achieving maximum efficiency without requiring excessive neurons. Show more

Keywords: Wireless sensor network, manage clean restroom, LSTM, prediction

DOI: 10.3233/JIFS-230056

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1057-1065, 2023

Authors: Thao, Nguyen Xuan | Duong, Truong Thi Thuy

Article Type: Research Article

Abstract: Online reviews play a vital role in providing multidimensional information for tourists. It also has an effect on the ranking and overall score of hotels. As a powerful tool, the Fermatean fuzzy set efficiently models dealing with uncertain information. Considering that there is no study using the correlation coefficient in Fermatean fuzzy context to assess the effect of online reviews on ratings and overall score of hotels. Therefore, a correlation coefficient measure is put forward to determine the relationship between two Fermaten fuzzy numbers and then they are utilized to assess the impact of online reviews on ranking and overall …rating of hotels. The paper first introduces the TOPSIS–based ranking model using a new distance under Fermatean set. Then, we construct a new correlation coefficient between two Fermatean fuzzy numbers to measure the effect of online reviews with ranking, overall score and score of hotels under given criteria. A case study on TripAdvisor.com is performed to illustrate the proposed operator and model. Show more

Keywords: Hotels, decision making, picture fuzzy set, intuitionistic fuzzy set, Fermatean fuzzy set, correlation coefficient

DOI: 10.3233/JIFS-230667

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1067-1087, 2023

Authors: Babiyola, A. | Aruna, S. | Sumithra, S. | Buvaneswari, B.

Article Type: Research Article

Abstract: The need for a monitoring system has grown as a result of rising crime and anomalous activity. To avoid unusual incidents, the common man initiated video surveillance of important areas, which was then passed on to the government. In typical surveillance operations, surveillance devices create a vast volume of data that must be manually analysed. Manually handling huge data sets in real time results in information loss. To prevent abnormal incidents, the actions in sensitive areas can be properly monitored, evaluated, and alerted to the appropriate authorities. Previous deep learning-based activity identification methods have appeared, but the findings are inaccurate, …and the proposed Hybrid Machine Learning Algorithms (HMLA) incorporate two detection methods for surveillance videos like as Transfer Learning (TL) and Continual Learning (CL). As a result, the suspicious activity in the video may be missed. Consequently, numerous image processing and computer vision technologies were used in activity detection to decrease human effort and mistakes in surveillance operations. Activities in sensitive areas can be properly monitored and evaluated to avoid unusual incidents, and the appropriate authorities may be alerted. Hence, in order to decrease human error and effort in surveillance operations, activity recognition embraced a variety of image processing and computer vision technologies. In this present work, the capacity has constraints that impact recognition accuracy. Consequently, this research paper presents a HMLA based technique that uses feature extraction using multilayer (Long Short Term Memory) LSTM, Convolutional Neural Networks (CNN), and Temporal feature extraction using multilayer LSTM to improve identification accuracy by 96% while requiring minimal execution time. To show the superior performance of the proposed hybrid machine learning technique, a standard UCF crime dataset was utilised for experimental analysis and compared to existing deep learning algorithms. Show more

Keywords: Hybrid machine learning algorithms, surveillance videos, transfer learning, continual learning, recognition abnormal events

DOI: 10.3233/JIFS-231187

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1089-1102, 2023

Authors: Deng, Wentao | Ma, Guoqing

Article Type: Research Article

Abstract: The quality evaluation of Chinese universities ideological and political (IAP) education has gone through the stages of defining tasks, proposing standards and exploring and carrying out, and has completed the stage tasks and accumulated practical experience. To construct the quality evaluation system of IAP education of Chinese universities in the new era, it is necessary to find the quality positioning in the fundamental task of establishing moral education and pay attention to the synergy between the internal and external parts of the quality of IAP education of Chinese universities. The IAP education quality evaluation of Chinese universities are the multiple-attribute …decision-making (MADM) issue. In this paper, we extend the geometric Heronian mean (GHM) operator to fuzzy number intuitionistic fuzzy numbers (FNIFNs) to propose the fuzzy number intuitionistic fuzzy weighted geometric HM (FNIFWGHM) operator. Then, the MADM method are built on FNIFWGHM operator. Finally, a numerical example for IAP education quality evaluation of Chinese universities and some comparative studies are used to prove the built methods’ credibility and reliability. Show more

Keywords: Multiple-attribute decision-making (MADM), Fuzzy number intuitionistic fuzzy numbers (FNIFNs), FNIFWHM operator, education quality evaluation

DOI: 10.3233/JIFS-224145

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1103-1118, 2023

Authors: Subha, K.J. | Rajavel, R. | Paulchamy, B.

Article Type: Research Article

Abstract: The Retinal image analysis has received significant attention from researchers due to the compelling need of early detection systems that aid in the screening and treatment of diseases. Several automated retinal disease detection studies are carried out as part of retinal image processing. Heren an Improved Ensemble Deep Learning (IEDL) model has been proposed to detect the various retinal diseases with a higher rate of accuracy, having multiclass classification on various stages of deep learning algorithms. This model incorporates deep learning algorithms which automatically extract the properties from training data, that lacks in traditional machine learning approaches. Here, Retinal Fundus …Multi-Disease Image Dataset (RFMiD) is considered for evaluation. First, image augmentation is performed for manipulating the existing images followed by upsampling and normalization. The proposed IEDL model then process the normalized images which is computationally intensive with several ensemble learning strategies like heterogeneous deep learning models, bagging through 5-fold cross-validation which consists of four deep learning models like ResNet, Bagging, DenseNet, EfficientNet and a stacked logistic regression for predicting purpose. The accuracy rate achieved by this method is 97.78%, with a specificity rate of 97.23%, sensitivity of 96.45%, precision of 96.45%, and recall of 94.23%. The model is capable of achieving a greater accuracy rate of 1.7% than the traditional machine learning methods. Show more

Keywords: Improved Ensemble Deep learning (IEDL), bagging through 5-fold cross-validation, Retinal Fundus Multi-Disease Image Dataset (RFMiD), Stacked logistic regression

DOI: 10.3233/JIFS-230912

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1119-1130, 2023

Authors: Yang, Xu

Article Type: Research Article

Abstract: The Petri net structure of workflow is used to model, and the moment generating function is used to analyze the time performance of workflow, and the complexity of analysis is given. It provides basic theory and basis for analysis and verification. The calculation of time complexity is given for sequence, concurrency, cycle, conflict (selection) and mutual exclusion. The performance analysis method based on moment generating function can be used to analyze the performance of arbitrarily distributed bounded or unbounded random Petri nets. Establish a broad-random Petri net model that conforms to the concept of workflow. Then, based on statistical analysis …and experience estimation of relevant data in the actual system, analyze the time nature of the on-demand service based on the analysis method based on behavioral expression, and obtain some valuable performance and index information. A necessary and sufficient condition for maintaining reliability of a workflow network model is given; A polynomial decomposition algorithm for P-invariants is proposed; Combining the moment function, a performance analysis method for workflow systems is established. An example is given to verify the effectiveness of the algorithm. Show more

Keywords: Performance analysis, workflow net, concurrent selection structure, read arcs, loop structure

DOI: 10.3233/JIFS-231137

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1131-1139, 2023

Authors: Al Ghour, Samer

Article Type: Research Article

Abstract: We use soft ω s -open sets to define soft ω s -irresoluteness, soft ω s -openness, and soft pre-ω s -openness as three new classes of soft mappings. We give several characterizations for each of them, specially via soft ω s -closure and soft ω s -interior soft operators. With the help of examples, we study several relationships regarding these three notions and their related known notions. In particular, we show that soft ω s -irresoluteness is strictly weaker than soft ω s -continuity, soft ω s -openness lies strictly …between soft openness and soft semi-openness, pre-ω s -openness is strictly weaker than ω s -openness, soft ω s -irresoluteness is independent of each of soft continuity and soft irresoluteness, soft pre-ω s -openness is independent of each of soft openness and soft pre-semi-openness, soft ω s -irresoluteness and soft continuity (resp. soft irresoluteness) are equivalent for soft mappings between soft locally countable (resp. soft anti-locally countable) soft topological spaces, and soft pre-ω s -openness and soft pre-semi-continuity are equivalent for soft mappings between soft locally countable soft topological spaces. Moreover, we study the relationship between our new concepts in soft topological spaces and their topological analog. Show more

Keywords: Soft ωs-open sets, soft ωs-continuous function, soft irresolute soft mapping, soft semi-open soft mapping, soft pre-semi-open soft mapping

DOI: 10.3233/JIFS-223332

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1141-1154, 2023

Authors: Tan, Guimei | Yu, Xichang

Article Type: Research Article

Abstract: As a powerful tool to model some unsharp concepts in real life, uncertain sets have been studied by more and more scholars. In order to characterize the degree of difficulty of uncertain sets, the hyperbolic entropy of an uncertain set and the hyperbolic relative entropy of uncertain sets are introduced in this paper. After that, this paper derived a key formula to calculate the hyperbolic entropy of an uncertain set via membership function, and some mathematical properties of hyperbolic entropy are also investigated in this paper. Finally, the hyperbolic entropy is applied in some research fields such as uncertain learning …curve, clustering of rare books and portfolio selection of collecting rare books. Show more

Keywords: Uncertainty theory, uncertain set, hyperbolic entropy, uncertain learning curve

DOI: 10.3233/JIFS-223626

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1155-1168, 2023

Authors: Xie, Wenxuan | Wu, Jiali | Sheng, Yuhong

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-223641

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1169-1178, 2023

Authors: Bhuvanya, R. | Kavitha, M.

Article Type: Research Article

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

DOI: 10.3233/JIFS-223754

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1179-1193, 2023

Authors: Jannu, Chaitanya | Vanambathina, Sunny Dayal

Article Type: Research Article

Abstract: Over the past ten years, deep learning has enabled significant advancements in the improvement of noisy speech. Due to the short time stability of speech signal, previous speech enhancement (SE) methods concentrated only on magnitude estimation, and these methods added a phase of the mixture in reconstructing the speech. The performance is limited in these approaches since the phase will also carry some of the speech information. Some of the speech enhancement approaches were developed later to jointly estimate both magnitudes as well as phases. Recently, complex-valued models, like deep complex convolution recurrent network (DCCRN), are proposed, but the computation …of the model is very huge. In this work, we propose a Discrete Cosine Transform-based Densely Connected Convolutional Gated Recurrent Unit (DCTDCCGRU) model using dilated dense block and stacked GRU. The dense connectivity strengthens the gradient propagation by concatenating features from previous layers at the input. The advantage of the dense block is that at various resolutions, the dilated convolutions aid with context aggregation, and the dense connectivity provides a feature map with more precise target information by passing through multiple layers. To represent the correlation between neighboring noisy speech frames, a two Layer GRU is added in the bottleneck of U-Net. The experimental findings demonstrate that the proposed model outperformed the other existing models in terms of STOI (short-time objective intelligibility), PESQ (perceptual evaluation of the speech quality), and output SNR (signal-to-noise ratio). Show more

Keywords: SE-Speech enhancement, DTC-Discrete cosine transform, SNR-Signal to noise ratio, dense block

DOI: 10.3233/JIFS-223951

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1195-1208, 2023

Authors: Bektur, Gulcin

Article Type: Research Article

Abstract: In this study, an energy-efficient distributed flow shop scheduling (DFSS) problem with total tardiness minimisation and machine-sequence dependent setup times is addressed. A mixed integer linear programming (MILP) model is proposed for the problem. A variant of the NSGA II algorithm is suggested for the solution of large scale problems. The proposed algorithm is compared with the state-of-the-art NSGA II, SPEA II, and multiobjective iterated local search algorithm. The computational results show that the proposed algorithm is efficient and effective for the problem. This is the first study to propose a heuristic algorithm for the distributed flow shop scheduling problem …with total tardiness minimisation, speed scaling and setups. Show more

Keywords: Energy efficient scheduling, distributed flow shop scheduling, multiobjective optimisation, heuristic algorithms, minimisation of total tardiness, speed scaling mechanism

DOI: 10.3233/JIFS-224199

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1209-1222, 2023

Authors: Xu, Qian

Article Type: Research Article

Abstract: As the problem of sub-health continues to expand among urban residents, forestry tourism has been further developed, and forest wellness travel for the purpose of recuperation has gradually become the focus of transformation and upgrading of the current big health industry. In order to refine the evaluation of the development potential of regional forest health tourism and achieve further promotion of regional forest health tourism, the study first established the construction principles of the evaluation system, combined with expert consultation and theoretical analysis methods to select evaluation indicators, and used analytic hierarchy process to obtain the weight of each indicator. …An adaptive variational genetic algorithm was then proposed to improve the BP neural network to form the AGA-BP model, which was finally applied to the assessment of the progression potentiality of forest wellness travel. The outcomes demonstrate that among the assessment indicators of forest wellness travel progression potentiality, the environmental quality has the largest weight of 0.4598; the convergence and precision of the AGA-BP model proposed by the research have been upgraded by 80% and 50% respectively, with a faster global search speed; in the assessment of the regional forest wellness travel progression potentiality, the method is highly consistent with the actual assessment outcomes, with an average precision rate of 98% indicating that it can accurately and effectively conduct potentiality assessment, providing a methodological reference for the sustainable progression of forest wellness travel. Show more

Keywords: Forest recreation, tourism, progression potentiality, BP neural network

DOI: 10.3233/JIFS-230582

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1223-1234, 2023

Authors: Zou, Wang | Zhang, Wubo | Tian, Zhuofeng | Wu, Wenhuan

Article Type: Research Article

Abstract: In the field of text classification, current research ignores the role of part-of-speech features, and the multi-channel model that can learn richer text information compared to a single model. Moreover, the method based on neural network models to achieve final classification, using fully connected layer and Softmax layer can be further improved and optimized. This paper proposes a hybrid model for text classification using part-of-speech features, namely PAGNN-Stacking1 . In the text representation stage of the model, introducing part-of-speech features facilitates a more accurate representation of text information. In the feature extraction stage of the model, using the multi-channel attention …gated neural network model can fully learn the text information. In the text final classification stage of the model, this paper innovatively adopts Stacking algorithm to improve the fully connected layer and Softmax layer, which fuses five machine learning algorithms as base classifier and uses fully connected layer Softmax layer as meta classifier. The experiments on the IMDB, SST-2, and AG_News datasets show that the accuracy of the PAGNN-Stacking model is significantly improved compared to the benchmark models. Show more

Keywords: Text classification, part-of-speech features, multi-channel, stacking algorithm

DOI: 10.3233/JIFS-231699

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1235-1249, 2023

Authors: Chen, Huakun | Jia, Qianlei | Huang, Wei | Shi, Jingping | Safwat, Ehab

Article Type: Research Article

Abstract: There is a growing body of literature that recognises the importance of Z-numbers proposed by Prof Zadeh. However, due to the complicated structure and short presentation time, many unknowns about Z-numbers still exist. To fill these gaps, this study aims to make use of rectangular coordinate system to express linguistic Z-numbers. Simultaneously, this study sets out to design a score function to quantify the information contained in different Z-numbers. Subsequently, distance measure and similarity measure are also presented from the perspective of coordinate system. Besides, linguistic discrete Z-numbers and belief rule base (BRB) model are combined to construct a novel …reasoning model on the basis of implication operators. To verify the validity of the proposed method, three representative examples of epidemic level assessment, multicriteria group decision-making (MCGDM), and network security assessment are employed. The comparison with other widely used methods are performed to further demonstrate the superiority of the proposed method. Show more

Keywords: Linguistic Z-numbers, score function, rectangular coordinate system, distance measure, similarity measure

DOI: 10.3233/JIFS-223025

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1251-1268, 2023

Authors: Zhu, Zhihui | Zhu, Meifang

Article Type: Research Article

Abstract: In recent years, chronic diseases, an aging population, and high healthcare costs have become global concerns. The Internet of Things (IoT) is transforming society by enabling physical objects to sense and collect data about their surroundings. It has evolved to encompass a wide range of sensing strategies, and it continues to improve in terms of sophistication and cost reduction. IoT can play an important role in enhancing human health through remote healthcare. The application of advanced IoT technology in healthcare is still a significant challenge due to a number of issues, such as the shortage of cost-effective and accurate smart …medical sensors, the absence of standardized IoT architectures, the heterogeneity of connected wearable devices, the multidimensionality of data generated, and the need for interoperability. In order to provide insight into the advance of IoT technologies in healthcare, this paper presents a comprehensive discussion on IoT device capabilities, focusing on the hardware and software systems, as well as the processing abilities, operating systems, and built-in tools. Show more

Keywords: Healthcare, internet of things, medical device, wireless sensor networks, data management, literature review

DOI: 10.3233/JIFS-224166

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1269-1288, 2023

Authors: Yuan, Pei

Article Type: Research Article

Abstract: With the globalization of the world’s economy, culture, science and technology, and the increasing frequency of international cooperation and exchanges, English will play an increasingly important role. For non-English majors in Chinese colleges and universities, college English is a public compulsory basic course, which plays a very important role in expanding students’ knowledge, improving foreign language cultural literacy and comprehensive language use ability. An important part of college English classroom teaching is teaching evaluation, which not only helps teachers obtain teaching feedback information, improve teaching management, and ensure teaching quality, but also effectively helps students adjust learning strategies, improve learning …methods, and improve learning efficiency. The English classroom teaching quality evaluation could be deemed as a classic multiple attribute group decision making (MAGDM) problem. In this paper, as a useful outranking approach, the extended QUALIFLEX method is utilized to address some MAGDM issues by using picture 2-tuple linguistic sets (P2TLSs). In addition, integrating the QUALIFLEX method with P2TLSs, the extended QUALIFLEX method with P2TLNs is constructed and all calculating procedures are simply depicted. Eventually, an empirical application of English classroom teaching quality evaluation has been offered to demonstrate this novel method. Show more

Keywords: Multiple attribute group decision making (MAGDM), picture fuzzy sets (PFSs), picture 2-tuple linguistic sets (P2TLSs), the extended QUALIFLEX method, English classroom teaching quality evaluation

DOI: 10.3233/JIFS-230969

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1289-1302, 2023

Authors: Jin, Xiaofang

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-231191

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1303-1312, 2023

Authors: Li, Yan

Article Type: Research Article

Abstract: With the development of socialist market economy, the exhibition industry has emerged as the tertiary industry matures in a globalized economic environment. As a new economic form, the exhibition economy presents new opportunities for economic development. The research on the exhibition industry at home and abroad has been relatively mature, and there has been a scientific analysis of the industrial linkage effect of the exhibition industry. The strong industrial linkage effect has made the exhibition industry occupy a very important position in the economic development of cities. However, in the development of China’s urban exhibition industry today, it is no …longer a simple question of developing and enhancing the strategic position of the exhibition industry in economic development, but rather a question of how to enhance the competitiveness of China’s urban exhibition industry. Only when the level of competitiveness is improved can the economic and social benefits brought by the exhibition industry be brought into full play. The fuzzy comprehensive competitiveness evaluation of urban exhibition industry is a classical multiple attribute decision making (MADM) problems. Recently, the TODIM and VIKOR method has been used to cope with MAGDM issues. The hesitant fuzzy sets (HFSs) are used as a tool for characterizing uncertain information during the fuzzy comprehensive competitiveness evaluation of urban exhibition industry. In this manuscript, the hesitant fuzzy TODIM-VIKOR (HF-TODIM-VIKOR) method is built to solve the MADM under HFSs. In the end, a numerical case study for fuzzy comprehensive competitiveness evaluation of urban exhibition industry is given to validate the proposed method. Show more

Keywords: Multiple attribute decision making(MAGDM), Hesitant fuzzy sets (HFSs), TODIM, VIKOR, urban exhibition industry

DOI: 10.3233/JIFS-231672

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1313-1323, 2023

Authors: Liu, Ting

Article Type: Research Article

Abstract: In this paper, a new timing synchronization algorithm for the main synchronous signal (PSS) is proposed for the risk identification of cross-border e-commerce in the Internet of things, aiming at the problems of poor performance of anti frequency bias and high computational complexity of the improved PSS timing synchronization algorithm. Based on the piecewise correlation algorithm, the normalized frequency deviation of PSS sequence is preset. The segmented ones are pre stored at the terminal by using the conjugate symmetry of PSS sequence. The fast correlation of each segment correlation window is realized by combining convolution and overlapping reservation block method. …Then, the threshold judgment is made after the time delay accumulation of the correlation values of the segments is made, so as to complete the joint detection of timing synchronization and coarse frequency deviation. The simulation results show that the algorithm can improve the performance of the system anti frequency offset effectively, reduce the complexity of the calculation and show that the timing synchronization conditions of the Internet can be satisfied. At the same time, under the background of the current development of cross-border logistics, this paper reviews the current research status of Transnational E-commerce logistics and Transnational E-commerce logistics risk. By comparing the advantages and disadvantages of various risk assessment methods, neural network and genetic algorithm are selected as the basic risk assessment methods in this paper. Based on the improved PSS timing synchronization algorithm and the Internet of things, the risk indicators of e-commerce logistics transnational will be selected from five risk dimensions: platform risk, customs clearance risk, organizational risk, process risk and environmental risk. Through the comprehensive literature and expert’s opinion, the logistics risk assessment index system of cross-border e-commerce is established. Show more

Keywords: PSS timing synchronization algorithm, internet of things, cross border e-commerce, risk identification

DOI: 10.3233/JIFS-221194

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1325-1340, 2023

Authors: Gao, Pengcheng | Chen, Mingxian | Zhou, Yu | Zhou, Ligang

Article Type: Research Article

Abstract: In order to estimate the deficiency of a city in its ability to prevent and control risks, as well as to evaluate the corresponding measures, this paper focuses on multi-attribute decision making based on LINMAP method and Manhattan distance at linguistic q-rung orthopair fuzzy. Manhattan distance is a new product that combines clustering distance with linguistic q-rung orthopair fuzzy to be able to use the data more effectively for measurement. LINMAP method is a decision making method based on ideal points, which can solve the weights as well as provide ideal solutions by linear programming model. The combination of the …two can create a new decision-making method, which can effectively evaluate the decision scheme of social public facilities according to the actual needs of decision-makers. The new method has the following advantages: (1) the conditions of linguistic fuzzy numbers can be applied more comprehensively, making the decision more realistic and effective; (2) the Manhattan distance is more in line with the human way of thinking and closer to life; (3) after comparative study, the results produced by this method have certain reliability. Show more

Keywords: Multi-attribute decision making, linguistic q-rung orthopair fuzzy, LINMAP method, Manhattan distance

DOI: 10.3233/JIFS-221750

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1341-1355, 2023

Authors: Padmanaban, K. | Shunmugalatha, A.

Article Type: Research Article

Abstract: A novel metaheuristic algorithm has been presented based on the physical significance of palm tree leaves and petioles, which can themselves water and fertilize with their unique architecture. Palm tree leaves collect almost all the raindrops that fall on the tree, which drags the nutrient-rich dropping of crawlers and birds that inhabit it and funnel them back to the palm tree’s roots. The proposed Palm Tree Optimization (PTO) algorithm is based on two main stages of rainwater before it reaches the trunk. Stage one is that the rainwater drops search for petioles in the local search space of a particular …leaf, and stage two involves that the rainwater drops after reaching the petioles search for trunk to funnel back to the root along with nutrients. The performance of PTO in searching for global optima is tested on 33 Standard Benchmark Functions (SBF), 29 constrained optimization problems from IEEE-CEC2017 and real-world optimization problems from IEEE-CEC2011 competition especially for testing the evolutionary algorithms. Mathematical benchmark functions are classified into six groups as unimodal, multimodal, plate & valley-shaped, steep ridges, hybrid functions and composition functions which are used to check the exploration and exploitation capabilities of the algorithm. The experimental results prove the effectiveness of the proposed algorithm with better search ability over different classes of benchmark functions and real-world applications. Show more

Keywords: PTO-palm tree optimization, exploration, exploitation, petioles, crankshaft

DOI: 10.3233/JIFS-222413

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1357-1385, 2023

Authors: Wang, Wei | Zhang, Weidong | Zhang, Zhe

Article Type: Research Article

Abstract: The complexity of the cohesive soil structure necessitates settlement modeling beneath shallow foundations. The goal of this research is to use recently discovered machine learning techniques called the hybridized radial basis function neural network (RBFNN ) with sine cosine algorithm (SCA ) and firefly algorithm (FFA ) to detect settlement (S m ) of shallow foundations. The purpose of using optimization methods was to find the optimal value for the primary attributes of the model under investigation. With R 2 values of at least 0.9422 for the learning series and 0.9271 for the assessment series, both the produced …SCA  - RBFNN and FFA  - RBFNN correctly replicated the S m , which indicates a considerable degree of efficacy and even a reasonable match between reported and modeled S m . In comparison to FFA  - RBFNN and ANFIS  - PSO , the SCA  - RBFNN is believed to be the more correct method, with the values of R 2 , RMSE and MAE was 0.9422, 7.2255 mm and 5.1257 mm, which is superior than ANFIS  - PSO and FFA  - RBFNN . The SCA  - RBFNN could surpass FFA one by 25% for the learning component and 14.2% for the test data, according to the values of PI index. Ultimately, it is apparent that the RBFNN combined with SCA could score higher than the FFA and even the ANFIS  - PSO , which is the proposed system in the S m forecasting model, after assessing the reliability and considering the assumptions. Show more

Keywords: Shallow foundation settlement, prediction, RBF neural network, sine cosine algorithm

DOI: 10.3233/JIFS-223907

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1387-1396, 2023

Authors: Keerthika, V. | Muhiuddin, G. | Jun, Y. B. | Elavarasan, B.

Article Type: Research Article

Abstract: Fuzzy sets, soft sets, and their generalisations have always been important tools for mathematicians and researchers working with uncertainty. Jun proposed a hybrid structure that combined the concepts of a fuzzy set and a soft set. It should be noted that hybrid structures are a combination of soft set and fuzzy set speculation. Our aim is to explore the concept of hybrid ordered ideals and hybrid interior ideals in ordered semirings, as well as look at some of their related properties, which is one of the important aspects of this paper. In order to investigate the structure theory of hybrid …ideals in ordered semirings, we define hybrid composition and hybrid addition. We also establish and characterise the regularity of ordered semirings in terms of hybrid structures. Show more

Keywords: Semiring, ideals, hybrid structure, hybrid interior ideals, ordered semirings

DOI: 10.3233/JIFS-224060

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1397-1408, 2023

Authors: Singh, Nitin Kumar | Singh, Pardeep | Das, Prativa | Chand, Satish

Article Type: Research Article

Abstract: Social media platforms allow people across the globe to share their thoughts and opinions and conveniently communicate with each other. Apart from various advantages of social media, it is also misused by a set of users for hate-mongering with toxic and offensive comments. The majority of the earlier proposed toxicity detection methods are primarily focused on the English language, but there is a lack of research on low-resource languages and multilingual text data. We propose an XRBi-GAC framework comprising XLM-RoBERTa, Bi-GRU with self-attention and capsule networks for multilingual toxic text detection. A loss function is also presented, which fuses the …binary cross-entropy loss and focal loss to address the class imbalance problem. We evaluated the proposed framework on two datasets, namely, the Jigsaw Multilingual Toxic Comment dataset and HASOC 2019 dataset and achieved F1-score of 0.865 and 0.829, respectively. The results of the experiments show that the proposed framework has outperformed the state-of-the-art multilingual models XLM-RoBERTa and mBERT on both datasets, which shows the versatility and robustness of the proposed XRBi-GAC framework. Show more

Keywords: Toxicity, multilingual text, XLM-RoBERTa, Bi-GRU, self-attention, capsule network

DOI: 10.3233/JIFS-224536

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1409-1421, 2023

Authors: Xu, Siyu | Qin, Keyun | Pan, Xiaodong | Fu, Chao

Article Type: Research Article

Abstract:  Both fuzzy set and rough set are important mathematical tools to describe incomplete and uncertain information, and they are highly complementary to each other. What is more, most fuzzy rough sets are obtained by combining Zadeh fuzzy sets and Pawlak rough sets. There are few reports about the combination of axiomatic fuzzy sets and Pawlak rough sets. For this reason, we propose the axiomatic fuzzy rough sets (namely rough set model with respect to the axiomatic fuzzy set) establishing on fuzzy membership space. In this paper, we first present a similarity description method based on vague partitions. Then the concept …of similarity operator is proposed to describe uncertainty in the fuzzy approximation space. Finally, some characterizations concerning upper and lower approximation operators are shown, including basic properties. Furthermore, we give a algorithm to verify the effectiveness and efficiency of the model. Show more

Keywords: Rough sets, axiomatic fuzzy rough sets, residuated lattices, fuzzy relations, approximation operators

DOI: 10.3233/JIFS-223643

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1423-1436, 2023

Authors: Sharma, Rahul | Singh, Amar

Article Type: Research Article

Abstract: In the recent decade, plant disease classification using convolution neural networks has proven to be superior because of its ability to extract key features. Obtaining the optimum feature subset with the necessary discriminant information is challenging. The main objective of this paper is to design an efficient hybrid plant disease feature selection approach and validate it on standard image datasets. The raw input image features were transformed into 8192 learned features by employing the VGG16. To reduce the training time and enhance classification accuracy, the dimensionality reduction technique Principal Component Analysis (PCA) is integrated with the big bang-big crunch (BBBC) …optimization algorithm. The PCA-BBBC feature selection method reduces computing time by eliminating unnecessary and redundant features. The proposed approach was evaluated on plant diseases and benchmarked image datasets. Experimental results reveal that the Artificial Neural Network (ANN) classifier integrated with the VGG16-PCA-BBBC approach enhanced the performance of the classifier. The proposed approach outperformed the VGG16-PCA-ANN method and other popular image classification techniques. For the rice disease dataset, the proposed hybrid approach reduced the VGG16 extracted 8192 deep features to 200 relevant principal components. The recommended reduced features were used for training ANN. The test dataset was classified by ANN with an accuracy of 99.12%. Experimental results demonstrate that the proposed approach improved the performance of the classifier and accurately labeled image and plant diseases datasets aiding farmers to adopt remedial measures. Show more

Keywords: BBBC, dimensionality reduction, feature selection, PCA, plant disease detection

DOI: 10.3233/JIFS-222517

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1437-1451, 2023

Authors: Vinodha, D. | Mary Anita, E.A.

Article Type: Research Article

Abstract: Industrial revolutions and demand of novel applications drive the development of sensors which offer continuous monitoring of remote hostile areas by collecting accurate measurement of physical phenomena. Data aggregation is considered as one of the significant energy-saving mechanism of resource constraint Wireless Sensor Networks (WSNs) which reduces bandwidth consumption by eliminating redundant data. Novel applications demand WSN to provide information about the monitoring region in multiple aspects in large scale. To meet this requirement, different kinds of sensors of different parameters are deployed in the same region which in turn demands the aggregator node to integrate diverse data in a …smooth and secure manner. Novelty in applications also requires Base station (BS) to apply multiple statistical functions. Hence, we propose to develop a novel secure cost-efficient data aggregation scheme based on asymmetric privacy homomorphism to aggregate data of multiple parameters and facilitate the BS to compute multiple functions in one round of data collection by providing elaborated view of monitoring region. To meet the claim of large scale WSN which requires dynamic change in size, vector-based data collection method is adopted in our proposed scheme. The security aspect is strengthened by allowing BS to verify the authenticity of source node and validity of data received. The performance of the system is analyzed in terms of computation and communication overhead using the mathematical model and simulation results. Show more

Keywords: Wireless sensor networks, secured data aggregation, privacy homomorphism

DOI: 10.3233/JIFS-223511

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1453-1472, 2023

Authors: Tian, Chang | Liu, Yanjung | Li, Meng | Fen, Chaofan

Article Type: Research Article

Abstract:  The key step in the intelligence of tongue diagnosis is the segmentation of the tongue image, and the accuracy of the segmented edges has a significant impact on the subsequent medical judgment. Deep learning can predict the class of pixel points to achieve pixel-level segmentation of images, so it can be used to handle tongue segmentation tasks. However, different models have different segmentation effects, and they did not learn the connection between space and channels, resulting in inaccurate tongue segmentation. This paper first discussed the choice of model and loss function and then compared the results of different options to …find the better model. Associating the red feature of the tongue is very conducive to segmentation as a feature, this paper tested many methods to try to get the color features of the original image to be paid attention to. Finally, this paper proposed an improved Encoder-Decoder network model to solve the problem based on the results. Start with Resnet as the backbone network, then introduce the U-Net model, and then we fused the attention layer, obtained from the source image through convolution and CBAM attention mechanism, and the feature layer obtained from the last upsampling in U-Net. Experimental results show that: The new, improved algorithm results are 2-3 percentage points higher than the popular algorithm, making it more suitable for tongue segmentation tasks. Show more

Keywords: Deep convolutional neural network, attention mechanism, tongue image, image segmentation

DOI: 10.3233/JIFS-221411

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1473-1480, 2023