Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

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. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Vasavi, J. | Abirami, M.S.

Article Type: Research Article

Abstract: Latent Lip groove application is been a notable topic in forensic applications like crime and other investigations. The detection of lip movement is been a challenging task since it is a smaller integral part of the human face. The conventional models operate on the available public or private dataset but it is constrained to the large population and unconstrained environment. The study aims at developing a deep learning model in a multimodal system using the deep U-Net Convolutional Neural Network architecture. It also aims at improving biometric authentication through a deep pattern recognition that involves the feature extraction of grooves …present in the human lips. An examination of grooves present in the input lip image is conducted by the present system to check the authenticity of the person entering the cyber-physical systems. The lip images are collected from the public security cameras via high-definition cameras in crowded areas that help the proposed method in forensic investigation and further, it considers various unconstrained scenarios to improve the efficacy of the system. The study involves initially pre-processing of lip image, and feature extraction of lip grooves to improve the efficacy of the lip trait. The simulation is conducted on the MATLAB tool to examine the efficacy of the model against various existing methods. Further, the study does not take into account the datasets available on the websites and lip images are only collected from a large set population in a real-time environment. The results of the simulation show that the proposed method achieves a higher degree of accuracy in extracting the grooves from the input lip images. Show more

Keywords: Biometric authentication, lip pattern, U-Net, grooves, multimodal

DOI: 10.3233/JIFS-223488

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Reeba Jennifer, R. | Albert Raj, A.

Article Type: Research Article

Abstract: An Intracranial cyst is an abnormal growth of mass in the brain that affects functioning of the nervous system and so an early detection of the lesion enables to avoid adverse effects. The processing unit in the Magnetic Resonance Imaging (MRI) system performs reading the images followed by primary image enhancement to suppress distortions thereby enhancing the feature quality in terms of its intensity, augmenting the resolution by image segmentation, post-processing by thresholding based on grayscale values and performing several morphological operations. With the existing methodologies, extracting the Region Of Interest (ROI) with the overlapping intensity values lead to inaccurate …results. A novel method in which the input image that is anisotropically diffused and blurred is converted into a sharp image. Further, fuzzy partitioning of pixels deployed on Global Thresholding –Clustering Methodology (GT-CM) based segmentation takes 4 clusters into account hence forth seperating the exterior portion of the skull, the border region of the skull, the ventricles which may include the lesion and the noise. Statistical results based on several metrics such as sensitivity, specificity, F measure, Jaccord Index, Dice Coefficient and precision show that the proposed method is far more effective. An accuracy of 99.26% is obtained in exactly locating and extracting the lesion along with its attributes. Show more

Keywords: MRI, image segmentation, ROI, fuzzy, GT-CM

DOI: 10.3233/JIFS-221947

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Li, Huan

Article Type: Research Article

Abstract: The difficulties in determining the compressive strength of concrete are inherited due to the various nonlinearities rooted in the mix designs. These difficulties raise dramatically considering the modern mix designs of high-performance concrete. Presents study tries to define a simple approach to link the input ingredients of concrete with the resulted compressive with a high accuracy rate and overcome the existing nonlinearity. For this purpose, the radial base function is defined to carry out the modeling process. The optimal results were obtained by determining the optimal structure of radial base function neural networks. This task was handled well with two …precise optimization algorithms, namely Henry’s gas solubility algorithm and particle swarm optimization algorithm. The results defined both models’ best performance earned in the training section. Considering the root mean square error values, the best value stood at 2.5629 for the radial base neural network optimized by Henry’s gas solubility algorithm, whereas the same value for the the radial base neural network optimized by particle swarm optimization was 2.6583 although both hybrid models provided acceptable output results, the radial base neural network optimized by Henry’s gas solubility algorithm showed higher accuracy in predicting high performance concrete compressive strength. Show more

Keywords: High-performance concrete, Henry’s gas solubility algorithm, particle swarm optimization algorithm, radial base function neural network

DOI: 10.3233/JIFS-221342

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Bi, Shunjie | Wu, Zhiyong | Gao, Peng | Ding, Hangqi

Article Type: Research Article

Abstract: Evolutionary multitasking algorithms (EMT) study how to solve multiple optimization tasks simultaneously by evolutionary computation, and investigate how knowledge sharing can accelerate the convergence of individual tasks, meaning that useful knowledge gained in solving one task can be used to solve other tasks. However, as the evolutionary search continues, the learnability among tasks may decrease, leading to a decrease in the efficiency of knowledge transfer and affecting the population evolution. To solve this problem, a new multifactorial evolutionary algorithm (MFEA-VOM) is proposed in this paper, which applies to three strategies, namely, implicit conversion strategy, opposition matrix strategy, and regulatory gene …fusion strategy. The implicit conversion strategy is applied to minimize the threat of negative knowledge migration and reduce the impact caused by negative knowledge migration. The proposed opposition matrix strategy explores more unknown areas of the population and improves the exploration ability of the population by further exploring and utilizing the unified search space, transforming the parent individuals into an appropriate task through mapping relationships, and reducing the gap between tasks. The proposed regulatory gene fusion strategy is applied to the reproduction of individuals to produce better individuals applicable to the task, submitting the efficiency of knowledge transfer. Through a comprehensive experimental analysis of the EMT optimization problem, the experimental results demonstrate the better performance of MFEA-VOM compared to other EMT algorithms. Show more

Keywords: Evolutionary multitasking, knowledge transfer, opposition matrix, implicit conversion, regulatory gene fusion

DOI: 10.3233/JIFS-222267

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2023

Authors: Jiang, Feng | Lin, Chunhua | Chen, Jing | Wu, Chutian

Article Type: Research Article

Abstract: New energy integration is thought to be one of the most potential solutions to support the power system with a sustainable energy infrastructure. However, new energy is an uncertain power generation resource, and the electricity generated by it has the characteristics of randomness, intermittency and reverse peak regulation. Its large-scale integration into the power grid makes the operation and reliability scheduling of the power system more challenging. It was important to build a wireless sensing and monitoring network to monitor the power and change trend of the new energy field (station) in real time. The energy consumption of wireless sensing …monitoring network is an important factor to improve the reliability of new energy scheduling. Based on the energy consumption of the wireless sensing monitoring network built by the new energy scheduling, the compression sensing technology was integrated and the network routing protocol (I-LEACH protocol) was optimized. The sampling data was transmitted by the cluster head node at the compression rate of 0.6, the improved OMP (Orthogonal Matching Pursuit) algorithm was reconstructed to achieve reliable data transmission, and the network energy consumption was further reduced. Compared with the I-LEACH routing protocol network, the experiments show that the network residual energy of the proposed method increased by 22% and the life cycle increased by about 30% . This method is helpful to improve the reliability of new energy power dispatching system and it can provide reference for realizing the reliability scheduling of new energy power system. Show more

Keywords: I-LEACH, cluster head node, OMP

DOI: 10.3233/JIFS-222980

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Haj Seyed Javadi, Mohammadreza | Haj Seyed Javadi, Hamidv | Rahmani, Parisa

Article Type: Research Article

Abstract: The Internet of Things (IoT) is a future-generation networking environment in which distributed smart objects can communicate directly and create a connection between different types of heterogeneous networks. Knowing the accurate localization of IoT-based devices is one of the most challenging issues in expanding the IoT network performance. This paper was done to propose a new fuzzy type2-based scheme to enhance the position accurateness of sensors deployed in the Internet of Things environments. Our proposed scheme is based on the weighted centralized localization strategy, in which the location of unknown nodes calculates using the fuzzy type-2 system. The flow measurement …via the wireless channel to calculate the separation distance between the sensor/anchor nodes is employed as the fuzzy system input. Also, the fuzzy membership functions to better adaptivity of our scheme with lossy IoT environments via learning automata algorithm are tuned. Then, in the proposed method, the fuzzy type-2 calculations are restricted by comparing the received signal strength with a predefined threshold value to extend the network lifetime. The effectiveness of the proposed scheme has been proven through extensive simulation. Based on the simulation results, our scheme, on average, reduced the localization error by 35.9% and 9.5% decreased the energy consumption by 13% and 7.2%, and reduced the convergence rate by 33.1% and 12.37 % compared to the HSPPSO and IMRL methods, respectively. Show more

Keywords: IoT, location, learning automata, fuzzy logic, signal strength

DOI: 10.3233/JIFS-223103

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2023

Authors: Yue, Guanli | Deng, Ansheng | Qu, Yanpeng | Cui, Hui | Liu, Jiahui

Article Type: Research Article

Abstract: Ensemble clustering helps achieve fast clustering under abundant computing resources by constructing multiple base clusterings. Compared with the standard single clustering algorithm, ensemble clustering integrates the advantages of multiple clustering algorithms and has stronger robustness and applicability. Nevertheless, most ensemble clustering algorithms treat each base clustering result equally and ignore the difference of clusters. If a cluster in a base clustering is reliable/unreliable, it should play a critical/uncritical role in the ensemble process. Fuzzy-rough sets offer a high degree of flexibility in enabling the vagueness and imprecision present in real-valued data. In this paper, a novel fuzzy-rough induced spectral ensemble …approach is proposed to improve the performance of clustering. Specifically, the significance of clusters is differentiated, and the unacceptable degree and reliability of clusters formed in base clustering are induced based on fuzzy-rough lower approximation. Based on defined cluster reliability, a new co-association matrix is generated to enhance the effect of diverse base clusterings. Finally, a novel consensus spectral function is defined by the constructed adjacency matrix, which can lead to significantly better results. Experimental results confirm that the proposed approach works effectively and outperforms many state-of-the-art ensemble clustering algorithms and base clustering, which illustrates the superiority of the novel algorithm. Show more

Keywords: Rough set, fuzzy-rough set, ensemble clustering, cluster reliability, spectral clustering

DOI: 10.3233/JIFS-223897

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

Authors: Jayapriya, P. | Umamaheswari, K. | Kavitha, A. | Ahilan, A.

Article Type: Research Article

Abstract: In recent years, finger vein recognition has gained a lot of attention and been considered as a possible biometric feature. Various feature selection techniques were investigated for intrinsic finger vein recognition on single feature extraction, but their computational cost remains undesirable. However, the retrieved features from the finger vein pattern are massive and include a lot of redundancy. By using fusion methods on feature extraction approaches involving weighted averages, the error rate is minimized to produce an ideal weight. In this research, a novel combinational model of intelligent water droplets is proposed along with hybrid PCA LDA feature extraction for …improved finger vein pattern recognition. Initially, finger vein images are pre-processed to remove noise and improve image quality. For feature extraction, Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) are employed to identify the most relevant characteristics. The PCA and LDA algorithms combine features to accomplish feature fusion. A global best selection method using intelligent water drops (GBS-IWD) is employed to find the ideal characteristics for vein recognition. The K Nearest Neighbour Classifier was used to recognize finger veins based on the selected optimum features. Based on empirical data, the proposed method decreases the equal error rate by 0.13% in comparison to existing CNN, 3DFM, and JAFVNet techniques. The overall accuracy of the proposed GBSPSO-KNN is 3.89% and 0.85% better than FFF and GWO, whereas, the proposed GBSIWD-KNN is 4.37% and 1.35% better than FFF and GWO respectively. Show more

Keywords: Principle component analysis, finger vein recognition, linear discriminant analysis, k-nearest neighbor, intelligent water drops

DOI: 10.3233/JIFS-222717

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Hatiboglu, Melek | Dayioglu, Habip | İssever, Halim | Ayvaz, Berk

Article Type: Research Article

Abstract: It is difficult to evaluate ergonomic risk factors in occupations with unpredictable tasks, random demands, and variable settings such as emergency medical services (EMS). This study deals with the problem of selecting an ergonomic risk-evaluation method with Pythagorean Fuzzy Sets (PFSs) based Pythagorean Fuzzy AHP (PF-AHP) and Pythagorean Fuzzy WASPAS (PF-WASPAS) methodology. The method selection criteria were obtained by consulting five different anonymous experts on the candidate criteria obtained from the literature review. The final four main criteria and ten sub-criteria were then decided. After the determination of the decision criteria, five experts were asked to evaluate the criteria and …to express their opinions on criteria-alternative scoring by means of a questionnaire for method selection. A two-step method is suggested for the selection of the ergonomic risk-evaluation method. In the first step, PF-AHP is utilized in order to identify the weight of criteria used in the method selection. In the second step, the PF-WASPAS method is proposed in order to OWAS, RULA, and REBA methods. The accuracy and validity of the suggested hybrid model is tested with real data in İstanbul Ambulance Service stations. A sensitivity analysis is carried out to test the reliability of the model. Moreover a comparative analysis is carried out with AHP and Fuzzy AHP methods to identify criteria weights. Study results show that REBA is the most appropriate ergonomic risk-evaluation method in EMS. Show more

Keywords: Ergonomic risk assessment method, Pythagorean fuzzy sets, AHP, WASPAS, emergency medical service

DOI: 10.3233/JIFS-222974

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Meenakshi, A. | Mythreyi, O. | Bramila, M. | Kannan, A. | Senbagamalar, J.

Article Type: Research Article

Abstract: Neutrosophic graphs deals with more complex, uncertain problems in real-life applications which provides more flexibility and compatibility than Intuitionistic fuzzy graphs. The aim of this paper is to enrich the efficiency of the network in accordance with productivity and quality. Here we develop two Neutrosophic graphs into a fully connected Neutrosophic network using the product of graphs. Such a type of network is formed from individuals with unique aspects in every field of work among them. This study proposes extending the other graph products and forming a single valued Neutrosophic graph to find the efficient productivity in the flow of …information on a single source network of a single valued Neutrosophic network. An Optimal algorithm is proposed and illustrated with an application. Show more

Keywords: Neutrosophic graph, graph operation, domination number, optimal network, score function

DOI: 10.3233/JIFS-223718

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Dhivya, S. | Mohanavalli, S. | Kavitha, S.

Article Type: Research Article

Abstract: Breast cancer can be successfully treated if diagnosed at its earliest, though it is considered as a fatal disease among women. The histopathology slide turned images are the gold standard for tumor diagnosis. However, the manual diagnosis is still tedious due to its structural complexity. With the advent of computer-aided diagnosis, time and computation intensive manual procedure can be managed with the development of an automated classification system. The feature extraction and classification are quite challenging as these images involve complex structures and overlapping nuclei. A novel nuclei-based patch extraction method is proposed for the extraction of non-overlapping nuclei patches …obtained from the breast tumor dataset. An ensemble of pre-trained models is used to extract the discriminating features from the identified and augmented non-overlapping nuclei patches. The discriminative features are further fused using p-norm pooling technique and are classified using a LightGBM classifier with 10-fold cross-validation. The obtained results showed an increase in the overall performance in terms of accuracy, sensitivity, specificity, and precision. The proposed framework yielded an accuracy of 98.3% for binary class classification and 95.1% for multi-class classification on ICIAR 2018 dataset. Show more

Keywords: Breast cancer, histopathology, nuclei-based patches, nuclei feature fusion, LightGBM

DOI: 10.3233/JIFS-222136

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Xue, Junxiao | Kong, Xiangyan | Wang, Gang | Dong, Bowei | Guan, Haiyang | Shi, Lei

Article Type: Research Article

Abstract: The problem of mixed static and dynamic obstacle avoidance is essential for path planning in highly dynamic environments. However, the paths formed by grid edges can be longer than the actual shortest paths in the terrain since their headings are artificially constrained. Existing methods can hardly deal with dynamic obstacles. To address this problem, we propose a new algorithm combining Model Predictive Control (MPC) with Deep Deterministic Policy Gradient (DDPG). Firstly, we apply the MPC algorithm to predict the trajectory of dynamic obstacles. Secondly, the DDPG with continuous action space is designed to provide learning and autonomous decision-making capability for …robots. Finally, we introduce the idea of the Artificial Potential Field to set the reward function to improve convergence speed and accuracy. In this paper, Matplotlib is used for simulation experiments. The results show that our method has improved considerably in accuracy by 8.11% -43.20% compared with theother methods, and on the length of the path and turning angle by reducing about 50 units and 340 degrees compared with DeepQ Network (DQN), respectively. We also employ Unity 3D to perform simulation experiments in highly uncertain environments, such as squares. Show more

Keywords: path planning, obstacle avoidance, MPC, DDPG, artificial potential field

DOI: 10.3233/JIFS-211999

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Agitha, T. | Sivarani, T.S.

Article Type: Research Article

Abstract: This research work focus on level control in quadruple tank systems based on proposed Deep Neural Fuzzy based Fractional Order Proportional Integral Derivative (DN-FFOPID) controller system. This is used for controlling the liquid level in these non- linear cylindrical systems. These model helps in identifying the dynamics of the tank system which gives the control signal feed forwarded from the reference liquid level. But, it fails to minimize the error and the system is also subjected to external disturbances. Hence, to minimize this drawback a novel controller must be introduced in it. The proposed Deep Neural model is a six …layered network which are optimized with the back-propagation algorithm. It effectively trains the system thus reducing the steady state error, offset model errors and unmeasured disturbances. This neural intelligent system maintains the liquid level which fulfils the required design criteria like time constant, no overshoot, less rise time and less settling time, which can be applied to various fields. MATLAB/simulink at FOMCON toolbox is used to perform the simulation. Real time liquid control experimental results and simulation results are demonstrated which proves the effectiveness and feasibility of the proposed methods for the quadruple tank system which finds applications in effluent treatment, petrochemical, pharmaceutical and aerospace fields. Show more

Keywords: Proposed deep neural fuzzy based fractional order proportional integral derivative controller, non- linear quadruple tank systems, back propagation, MATLAB/simulink –FOMCON toolbox

DOI: 10.3233/JIFS-221674

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 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. Pre-press, no. Pre-press, pp. 1-8, 2023

Authors: Meenakshi, A. | Mythreyi, O.

Article Type: Research Article

Abstract: Neutrosophic graphs deals with more complex, uncertain problems in real-life applications which provides more flexibility and compatibility than intuitionistic fuzzy graphs. The aim of this paper is to enrich the efficiency of the maximized network in accordance with time management and quality. Here we maximize three neutrosophic graphs into a fully connected Neutrosophic network using the Max product of graphs. Such a type of network is formed from individuals with unique aspects in every field of work among them. This study proposes the max product of three graphs and forming a single-valued neutrosophic graph to find the efficient time management …in the flow of information on a single source time-dependent network of single-valued neutrosophic network. The proposed approach is illustrated with applications. Also, a spanning tree algorithm comparative study is done with Said Broumi et al. [15 ] and enhanced the result by minimum score function. Show more

Keywords: Neutrosophic graph, max product, social network, minimal spanning tree, score function

DOI: 10.3233/JIFS-223484

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Kadeeja Mole, K.P. | Sameena, Kalathodi

Article Type: Research Article

Abstract: In this work, several operations on fuzzy graphs are introduced: u -product, strong edge product, and k th power. The relationship between the fuzzy chromatic number of resultant fuzzy graphs of operations union, join, and newly developed operations and the fuzzy chromatic number of associated fuzzy graphs is also investigated using fuzzy colouring techniques. The number of captures in a chess puzzle move is calculated using the fuzzy colouring approach.

Keywords: Fuzzy graph, fuzzy chromatic number, operations of fuzzy graphs, strong edge, fuzzy colouring

DOI: 10.3233/JIFS-223263

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Savitha, S. | Rajiv Kannan, A.

Article Type: Research Article

Abstract: Chronic Kidney Disease (CKD) is a crucial life-threatening condition due to impaired kidney functionality and renal disease. In recent studies, Kidney disorder is considered one of the essential and deadliest issues that threaten patients’ survival with the lack of earlier prediction and classification. The earlier prediction process and the proper diagnosis help delay or stop the chronic disease progression into its final stage, where renal transplantation or dialysis is a known way of saving the patient’s life. Global studies reveal that nearly 10% of the population is affected by Chronic Kidney Disease (CKD), and millions die because of non-affordable treatment. …Early detection of CKD from the biological parameters would save people from this crisis. Machine Learning algorithms are playing a predominant role in disease diagnosis and prognosis. This work generates compound features from CKD indicators by two novel algorithms: Correlation-based Weighted Compound Feature (CWCF) and Feature Significance based Weighted Compound Feature (FSWCF). Any learning algorithm is as good as its features. Hence, the features generated by these algorithms are validated on different machine learning algorithms as a test for generality. The simulation is done in MATLAB 2020a environment where various metrics like prediction accuracy gives superior results compared to multiple other approaches. The accuracy of CWCF over different methods like LR is 97.23%, Gaussian NB is 99%, SVM is 99.18%, and RF is 99.89%, which is substantially higher than the approaches without proper methods feature analysis. The results suggest that generated compound features improve the predictive power of the algorithms. Show more

Keywords: Feature selection, correlation, feature significance, chronic kidney disease, feature projection, mutual information

DOI: 10.3233/JIFS-222401

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Caroline Misbha, J. | Ajith Bosco Raj, T. | Jiji, G.

Article Type: Research Article

Abstract: The research aims to provide network security so that it can be protected from several attacks, especially DoS (Denial-of-Service) or DDoS (Distributed Denial-of-Service) attacks that could at some point render the server inoperable. Security is one of the main obstacles. There are a lot of network risks and attacks available today. One of the most common and disruptive attacks is a DDoS attack. In this study, upgraded deep learning Elephant Herd Optimization with random forest classifier is employed for early DDos attack detection. The DDoS dataset’s number of characteristics is decreased by the proposed IDN-EHO method for classifying data learning …that works with a lot of data. In the feature extraction stage, deep neural networks (DNN) approach is used, and the classified data packages are compared to return the DDoS attack traffic characteristics with a significant percentage. In the classification stage, the proposed deep learning Elephant Herd Optimization with random forest classifier used to classify the data learning which deal with a huge amount of data and minimise the number of features of the DDoS dataset. During the detection step, when the extracted features are used as input features, the attack detection model is trained using the improved deep learning Elephant Herd Optimization. The proposed framework has the potential to be a promising method for identifying unidentified DDoS attacks, according to experiments. 99% recall, precision, and accuracy can be attained using the suggested strategy, according on the findings of the experiments. Show more

Keywords: Effective fuzzy, elephant herd optimization, DDoS attack, hybrid deep learning method

DOI: 10.3233/JIFS-224149

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Santhadevi, D. | Janet, B.

Article Type: Research Article

Abstract: Many Internet of Things (IoT) devices are susceptible to cyber-attacks. Attackers can exploit these flaws using the internet and remote access. An efficient Intelligent threat detection framework is proposed for IoT networks. This paper considers four key layout ideas while building a deep learning-based intelligent threat detection system at the edge of the IoT. Based on these concepts, the Hybrid Stacked Deep Learning (HSDL) model is presented. Raw IoT traffic data is pre-processed with spark. Deep Vectorized Convolution Neural Network (VCNN) and Stacked Long Short Term Memory Network build the classification model (SLSTM). VCNN is used for extracting meaningful features …of network traffic data, and SLSTM is used for classification and prevents the DL model from overfitting. Three benchmark datasets (NBaIoT-balanced, UNSW-NB15 & UNSW_BOT_IoT- imbalanced) are used to test the proposed hybrid technique. The results are compared with state-of-the-art models. Show more

Keywords: Hybrid stacked deep learning, stacked LSTM, Vectorized Convolutional Neural Network, IoT-network security, edge computing

DOI: 10.3233/JIFS-223246

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2023

Authors: Huang, Ying | Cao, Zhiying | Chen, Siyuan | Zhang, Xiuguo | Wang, Peipeng | Cao, Qilei

Article Type: Research Article

Abstract: Most existing Web service recommendation models based on machine learning do not fully consider the high-order features interaction between users and services and with poor interpretability. In this paper, an Interpretable Web Service Recommendation model based on Disentangled Representation Learning (WSR-DRL) is proposed. First of all, to make full use of the service description information to improve the accuracy of Web service recommendation, the features representation of service name is obtained by using BERT model, and the local and global features representation of service description information is further obtained by combining 2-D CNN and Bi-LSTM. Then the disentangled convolution neural …network is used to generate the high-order interaction features between users and services, and the neighborhood routing algorithm is used to mine the latent factors in these features. That improves the accuracy of Web service recommendation and make it interpretable. Finally, in order to verify the effectiveness of the model, several groups of experiments are carried out on real data sets. The experimental results show that compared with latest models such as DMF, DeepFM, DKN, GCMC, NDCG model and WSR-MGAT model, the WSR-DRL model proposed in this paper shows better performance on [email protected], [email protected], [email protected] and [email protected] evaluation metrics. Show more

Keywords: Web service recommendation, Disentangled representation learning, BERT

DOI: 10.3233/JIFS-223306

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Martinez-Gil, Jorge | Chaves-Gonzalez, Jose Manuel

Article Type: Research Article

Abstract: Recently, transfer learning strategies have become ideal for reusing acquired knowledge through a training phase. The key idea is that reusing such knowledge brings advantages such as increased accuracy and considerable resource savings. In this work, we design a novel strategy for effective and efficient transfer learning in semantic similarity. Our approach is based on generating and transferring optimal models obtained through a symbolic regression process being able to stack evaluation scores from several fundamental techniques. After an exhaustive empirical study, the results lead to high accuracy in addition to significant savings in terms of training time consumed in most …of the scenarios considered. Show more

Keywords: Knowledge engineering, Transfer learning, Semantic textual similarity

DOI: 10.3233/JIFS-230141

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Liu, Qian | Hou, Jundan | Dong, Qi

Article Type: Research Article

Abstract: Tourism is the most culturally loaded industry. In the new era, China’s tourism industry is developing rapidly and the public’s need for diversified culture is growing. The integration of culture and tourism, the development of new forms of cultural tourism industry, is also an important means to enhance the country’s cultural soft power, promote the development of China’s tourism culture, and solve the contradiction between cultural supply and demand. The industrial competitiveness evaluation of regional cultural tourism is looked as the multiple attribute group decision-making (MAGDM) problem. This paper proposed extended MARCOS method in probabilistic hesitant fuzzy sets (PHFSs) circumstance. …Meanwhile, the CRITIC method is used to evaluate the criterion weights. Then we give a case study for industrial competitiveness evaluation of regional cultural tourism to measure the novel model’s validity. Show more

Keywords: Multiple attributes group decision making (MAGDM), probabilistic hesitant fuzzy sets (PHFSs), MARCOS method, CRITIC method, industrial competitiveness evaluation

DOI: 10.3233/JIFS-224491

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Li, Fuxue | Chi, Chuncheng | Yan, Hong | Liu, Beibei | Shao, Mingzhi

Article Type: Research Article

Abstract: Transformer-based neural machine translation (NMT) has achieved state-of-the-art performance in the NMT paradigm. However, it relies on the availability of copious parallel corpora. For low-resource language pairs, the amount of parallel data is insufficient, resulting in poor translation quality. To alleviate this issue, this paper proposes an efficient data augmentation (DA) method named STA. Firstly, the pseudo-parallel sentence pairs are generated by translating sentence trunks with the target-to-source NMT model. Furthermore, two strategies are introduced to merge the original data and pseudo-parallel corpus to augment the training set. Experimental results on simulated and real low-resource translation tasks show that the …proposed method improves the translation quality over the strong baseline, and also outperforms other data augmentation methods. Moreover, the STA method can further improve the translation quality when combined with the back-translation method with the extra monolingual data. Show more

Keywords: Data augmentation, neural machine translation, sentence trunk, mixture, concatenation

DOI: 10.3233/JIFS-230682

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Noon, Serosh Karim | Amjad, Muhammad | Qureshi, Muhammad Ali | Mannan, Abdul

Article Type: Research Article

Abstract: For the last decade, the use of deep learning techniques in plant leaf disease recognition has seen a lot of success. Pretrained models and the networks trained from scratch have obtained near-ideal accuracy on various public and self-collected datasets. However, symptoms of many diseases found on various plants look similar, which still poses an open challenge. This work takes on the task of dealing with classes with similar symptoms by proposing a trained-from-scratch shallow and thin convolutional neural network employing dilated convolutions and feature reuse. The proposed architecture is only four layers deep with a maximum width of 48 features. …The utility of the proposed work is twofold: (1) it is helpful for the automatic detection of plant leaf diseases and (2) it can be used as a virtual assistant for a field pathologist to distinguish among classes with similar symptoms. Since dealing with classes with similar-looking symptoms is not well studied, there is no benchmark database for this purpose. We prepared a dataset of 11 similar-looking classes and 5, 108 images for experimentation and have also made it publicly available. The results demonstrate that our proposed model outperforms other recent and state-of-the-art models in terms of the number of parameters, training & inference time, and classification accuracy. Show more

Keywords: Plant disease, similar-looking symptoms, shallow CNN models, lightweight models, agriculture

DOI: 10.3233/JIFS-223554

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 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. Pre-press, no. Pre-press, pp. 1-16, 2023

Authors: Shu, Wenhao | Chen, Ting | Qian, Wenbin | Yan, Zhenchao

Article Type: Research Article

Abstract: Feature selection focuses on selecting important features that can improve the accuracy and simplification of the learning model. Nevertheless, for the ordered data in many real-world applications, most of the existing feature selection algorithms take the single-measure into consideration when selecting candidate features, which may affect the classification performance. Based on the insights obtained, a multi-measure feature selection algorithm is developed for ordered data, which not only considers the certain information by the dominance-based dependence, but also uses the discern information provided by the dominance-based information granularity. Extensive experiments are performed to evaluate the performance of the proposed algorithm on …UCI data sets in terms of the number of selected feature subset and classification accuracy. The experimental results demonstrate that the proposed algorithm not only can find the relevant feature subset but also the classification performance is better than, or comparably well to other feature selection algorithms. Show more

Keywords: Ordered decision system, dominance-based rough set, multi-measure, feature selection

DOI: 10.3233/JIFS-224474

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 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. Pre-press, no. Pre-press, pp. 1-10, 2023

Authors: Shen, Xin | Xu, Qianhui | Liu, Qiao | Leibercht, Markus

Article Type: Research Article

Abstract: With the acceleration of technological change and globalization, companies face increasing environmental uncertainty and complexity. The COVID-19 pandemic has severely damaged the global supply chain and aggravated the operational risks of supply chains. Industry and academia have conducted studies on the construction of resilient and integrated supply chains, and to date a bulk of empirical literature has already been accumulated. A notable feature of existing literature is the heterogeneity in the characterization of the relationship between supply chain resilience, supply chain integration, and supply chain performance. In this study meta-analysis and structural equation modeling (MASEM) methods are integrated to construct …a theoretical framework of supply chain resilience, supply chain integration, and supply chain performance. 45 empirical studies (73 effect size data, 2092 samples) are selected from 10,623 papers published over the years 2013 to 2021 to explore the transmission mechanisms, the role of mediator variable, and boundary conditions of the relationship between supply chain resilience and supply chain performance. The results show that supply chain resilience can promote supply chain performance. Moreover, supply chain integration (supplier integration, internal integration, and customer integration) plays a partial mediating role for the impact of supply chain resilience on supply chain performance. Situations and measurement factors such as industry type, national culture (power distance), sampling area, and logistics performance have a certain impact on the relationship, and the usage of different indicators may lead to marked differences in conclusions regarding the relationship. By extracting the conclusions of existing empirical studies, this study proposes new insights into the mechanism of action of supply chain resilience, supply chain integration, and supply chain performance and provides specific suggestions for future supply chain management. Show more

Keywords: Supply chain resilience, supply chain integration, supply chain performance, meta-analysis, structural equation model

DOI: 10.3233/JIFS-220649

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2023

Authors: Li, Hui

Article Type: Research Article

Abstract: The scientific research work of colleges and universities has attracted more and more social attention because of its large number of multidisciplinary scientific and technological talents, hardware facilities and good scientific research environment, and the quality of scientific and technological management work of colleges and universities directly affects the level of scientific and technological work of colleges and universities. Starting from the common problems of scientific research management in colleges and universities, this paper explores the ideas and methods to further promote scientific research work by improving the quality of scientific research management. The quality evaluation of scientific research management …in application-oriented universities is classical multiple attribute group decision making (MAGDM). Based on this, we extend the traditional CODAS method to the Pythagorean 2-tuple linguistic sets (P2TLSs) and propose the Pythagorean 2-tuple linguistic CODAS (P2TL-CODAS) method for quality evaluation of scientific research management in application-oriented universities. The P2TL-CODAS method is established and all computing steps are simply presented. Furthermore, we apply the P2TL-CODAS method to evaluate the quality evaluation of scientific research management in application-oriented universities. Show more

Keywords: Multiple attribute group decision making (MAGDM), Pythagorean 2-tuple linguistic sets (P2TLSs), CODAS method, P2TL-CODAS model, quality evaluation of scientific research management

DOI: 10.3233/JIFS-230629

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Kong, Lingxing | Liu, Kailong | Fu, Deyi | Liu, Boyong | Ma, Jingkai | Sun, Huini | Bai, Shuang

Article Type: Research Article

Abstract: Accurately evaluating the technological improvement effects of wind turbines is crucial for wind farm operators. To this end, this paper proposes an innovative approach that employs a wind power regression model which leverages external environmental information to predict the output power of wind turbines. The effectiveness of technological improvements can be evaluated by comparing the predicted output power with the measured output power. In this paper, a model called stacked LSTM networks with attention mechanisms is designed. In the proposed model, the stacked LSTM networks are used to enhance the nonlinear fitting ability and capture deeper features of the input …sequence. Furthermore, temporal attention mechanisms are employed to make the model focus on important time-series information of the data. In addition, a hierarchical attention mechanism is designed to explore the correlation among the outputs of the stacked LSTM networks and enrich the model’s output information. The experiments on the data from a wind farm show that the proposed method outperforms various wind power prediction benchmarks, achieving lower RMSE, MAE, and MAPE values of 142.82, 104.2, and 4.85%, respectively. Show more

Keywords: Wind power regression prediction, evaluation of technological improvement effect, stacked LSTMs, temporal attention mechanism, hierarchical attention mechanism

DOI: 10.3233/JIFS-230403

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Liu, Pingqing | Wang, Hongjun | Ning, Baoquan | Wei, Guiwu

Article Type: Research Article

Abstract: The recruitment of university researchers can be considered a multi-attribute group decision-making (MAGDM) problem. MAGDM is a familiar issue with uncertainty and fuzziness in the decision-making field. Generalized hesitation fuzzy numbers (GHFNs) as a new expanded form of hesitation fuzzy numbers (HFNs) can better express the uncertain information in MAGDM. The TODIM is a very classical and widely used method to deal with the MAGDM issue. In this paper, we integrate cumulative prospect theory (CPT) into TODIM to consider not only decision makers’ subjective risk preferences but also their confidence level to obtain more reasonable choices under risk conditions. Therefore, …we propose the GHF CPT-TODIM approach to tackle the MAGDM issue. Meanwhile, in the GHF environment, it is proposed to use the volatility of attribute information (entropy weighting method) to obtain the importance of attributes, obtain the unknown attribute weight, and enhance the rationality of weight information. Finally, the validity and usefulness of the technique are verified by applying the GHF CPT-TODIM technique to the recruitment of university researchers and comparing it with the existing GHF MAGDM method, which offers a new way to solve the MAGDM problem with GHFNs. Show more

Keywords: Multi-attribute group decision-making (MAGDM), generalized hesitant fuzzy numbers (GHFNs), TODIM, cumulative prospect theory (CPT), recruitment of university researchers

DOI: 10.3233/JIFS-224437

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

Authors: Rana, Anurag | Vaidya, Pankaj | Kautish, Sandeep | Kumar, Manoj | Khaitan, Supriya

Article Type: Research Article

Abstract: Parameters related to earthquake origins can be broken down into two broad classes: source location and source dimension. Scientists use distance curves versus average slowness to approximate the epicentre of an earthquake. The shape of curves is the complex function to the epicentral distance, the geological structures of Earth, and the path taken by seismic waves. Brune’s model for source is fitted to the measured seismic wave’s displacement spectrum in order to estimate the source’s size by optimising spectral parameters. The use of ANFIS to determine earthquake magnitude has the potential to significantly alter the playing field. ANFIS can learn …like a person using only the data that has already been collected, which improves predictions without requiring elaborate infrastructure. For this investigation’s FIS development, we used a machine with Python 3x running on a core i5 from the 11th generation and an NVIDIA GEFORCE RTX 3050ti GPU processor. Moreover, the research demonstrates that presuming a large number of inputs to the membership function is not necessarily the best option. The quality of inferences generated from data might vary greatly depending on how that data is organised. Subtractive clustering, which does not necessitate any type of normalisation, can be used for prediction of earthquakes magnitude with a high degree of accuracy. This study has the potential to improve our ability to foresee quakes larger than magnitude 5. A solution is not promised to the practitioner, but the research is expected to lead in the right direction. Using Brune’s source model and high cut-off frequency factor, this article suggests using machine learning techniques and a Brune Based Application (BBA) in Python. Application accept input in the Sesame American Standard Code for Information Interchange Format (SAF). An application calculates the spectral level of low frequency displacement (Ω 0 ), the corner frequency at which spectrum decays with a rate of 2(f c ), the cut-off frequency at which spectrum again decays (f max ), and the rate of decay above f max on its own (N ). Seismic moment, stress drop, source dimension, etc. have all been estimated using spectral characteristics, and scaling laws. As with the maximum frequency, fmax, its origin can be determined through careful experimentation and study. At some sites, the moment magnitude was 4.7 0.09, and the seismic moment was in the order of (107 0.19) 1023. (dyne.cm). The stress reduction is 76.3 11.5 (bars) and the source-radius is (850.0 38.0) (m). The ANFIS method predicted pretty accurately as the residuals were distributed uniformly near to the centrelines. The ANFIS approach made fairly accurate predictions, as evidenced by the fact that the residuals were distributed consistently close to the centerlines. The R2, RMSE, and MAE indices demonstrate that the ANFIS accuracy level is superior to that of the ANN. Show more

Keywords: Artificial neural networks, brune based application, adaptive neuro fuzzy inference system, source dimension, earthquake occurrence, prediction

DOI: 10.3233/JIFS-224423

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2023

Authors: Karuppuchamy, V. | Palanivelrajan, S.

Article Type: Research Article

Abstract: Chronic diseases like diabetes, Heart Failure (HF), malignancy, and severe respiratory sickness are the leading cause of mortality around the globe. Dissimilar indications or traits are extremely difficult to identify in HF patients. IoT solutions are becoming increasingly commonplace as smart wearable gadgets become more popular. Sudden heart attacks have a short life expectancy, which is terrible. As a result, a patient monitoring of heart patients based on IoT-centered Machine Learning (ML) is presented to help with HF prediction, and treatment is administered as necessary. Verification, Encryption, and Categorization are the three phases that make up this developed model. Initially, …the datasets from the IoT sensor gadget are gathered by authenticating with a specific hospital through encryption. The patient’s integrated IoT sensor module then transfers sensing information to the cloud. The Improved Blowfish Encryption (IBE) approach is used to protect the sensor data transfer to the cloud. Then the encrypted data is decrypted, and the classification is performed using the Adaptive Fuzzy-Based Long Short-Term Memory with Recurrent Neural Network (AF-LSTM-RNN) algorithm. The results are classed as malignant or benign. It assesses the patient’s cardiac state and sends an alert text to the doctor for treatment. The AF-LSTM-RNN-based HF prediction outperforms the existing techniques. Accuracy, sensitivity, specificity, precision, F-measure and Matthews Correlation Coefficient (MCC) are compared to existing procedures to ensure the planned research is genuine. Using the Origin tool, these metrics are shown as research findings. Show more

Keywords: Heart failure (HF), IoT, machine learning, improved blowfish encryption (IBE), adaptive fuzzy-based long short-term memory with recurrent neural network (AF-LSTM-RNN), origin tool

DOI: 10.3233/JIFS-224298

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2023

Authors: Bekhouche, Maamar | Haouassi, Hichem | Bakhouche, Abdelaali | Rahab, Hichem | Mahdaoui, Rafik

Article Type: Research Article

Abstract: Feature Selection (FS) for Sentiment Analysis (SA) becomes a complex problem because of the large-sized learning datasets. However, to reduce the data dimensionality, researchers have focused on FS using swarm intelligence approaches that reflect the best classification performance. Crocodiles Hunting Strategy (CHS), a novel swarm-based meta-heuristic that simulates the crocodiles’ hunting behaviour, has demonstrated excellent optimization results. Hence, in this work, two FS algorithms, i.e., Binary CHS (BCHS) and Improved BCHS (IBCHS) based on original CHS were applied for FS in the SA field. In IBCHS, the opposition-based learning technique is applied in the initialization and displacement phases to enhance …the search space exploration ability of the IBCHS. The two proposed approaches were evaluated using six well-known corpora in the SA area (Semeval-2016, Semeval-2017, Sanders, Stanford, PMD, and MRD). The obtained result showed that IBCHS outperformed BCHS regarding search capability and convergence speed. The comparison results of IBCHS to several recent state-of-the-art approaches show that IBCHS surpassed other approaches in almost all used corpora. The comprehensive results reveal that the use of OBL in BCHS greatly impacts the performance of BCHS by enhancing the diversity of the population and the exploitation ability, which improves the convergence of the IBCHS. Show more

Keywords: Sentiment analysis, Opinion mining, feature selection, swarm-based intelligence, crocodiles hunting strategy optimization algorithm, Opposition-based learning

DOI: 10.3233/JIFS-222192

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2023

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. Pre-press, no. Pre-press, pp. 1-19, 2023

Authors: Xu, Wenxiang | Wang, Lei | Liu, Dezheng | Tang, Hongtao | Li, Yibing

Article Type: Research Article

Abstract: Multi-agent collaborative manufacturing, high energy consumption and pollution, and frequent operation outsourcing are the three main characteristics of large complex equipment manufacturing enterprises. Therefore, the production scheduling problem of large complex equipment to be studied is a distributed flexible job shop scheduling problem involving operation outsourcing (Oos-DFJSP). Besides, the influences of each machine on carbon emission and job scheduling at different processing speeds are also involved in this research. Thus the Oos-DFJSP of large complex equipment consists of the following four sub-problems: determining the sequence of operations, assigning jobs to manufactories, assigning operations to machines and determining the processing speed …of each machine. In the Oos-DFJSP, if a job is assigned to a manufactory of a group manufacturing enterprise, and the manufactory cannot complete some operations of the workpiece, then these operations will be assigned to other manufactories with related processing capabilities. Aiming at solving the problem, a multi-objective mathematical model including costs, makespan and carbon emission was established, in which energy consumption, power generation of waste heat and treatment capacity of pollutants were considered in the calculation of carbon emission. Then, a multi-objective improved hybrid genetic artificial bee colony algorithm was developed to address the above model. Finally, 45 groups of random comparison experiments were presented. Results indicate that the developed algorithm performs better than other multi-objective algorithms involved in the comparison experiments not only on quality of non-dominated solutions but also on Inverse Generational Distance and Error Ratio. That is, the proposed mathematical model and algorithm were proved to be an excellent method for solving the multi-objective Oos-DFJSP. Show more

Keywords: Large complex equipment manufacturing, operation outsourcing, distributed flexible job shop scheduling, carbon emission, multi-objective improved hybrid genetic artificial bee colony algorithm

DOI: 10.3233/JIFS-223435

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-29, 2023

Authors: Wang, Ning | Zhu, Ping

Article Type: Research Article

Abstract: The three-way decision model based on linguistic term sets has been extensively investigated since decision makers frequently utilize natural language to evaluate in an actual decision-making process. The existing models require decision makers to select appropriate linguistic terms from a given linguistic term set. However, making such a choice is not always simple, and decision makers occasionally choose words that are related to their own experience. In order to deal with this kind of decision problem, we appeal to the theory of computing with words pioneered by Zadeh and establish a three-way decision model based on computing with words in …this paper. The paper focuses on how to deal with more general linguistic information using the theory of computing with words. Initially, using the concept of computing with words, we translate more broad linguistic information into a linguistic distribution assessment on a balanced linguistic term set in order to better analyze linguistic information. The three-way decision based on computing with words is then discussed. Decision-theoretic rough fuzzy sets take into account the ambiguity of the decision target as a generalization of the classical decision-theoretic rough sets. This is what motivated us to develop a three-way decision based on decision-theoretic rough fuzzy sets using computing with words. Additionally, a fabricated example demonstrates that our three-way decision model is more adaptable in processing linguistic information and can handle more general linguistic information provided by decision makers. Show more

Keywords: Computing with words, Three-way decision, Linguistic distribution assessments, Decision-theoretic rough fuzzy sets, Linguistic term sets

DOI: 10.3233/JIFS-224215

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 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. Pre-press, no. Pre-press, pp. 1-20, 2023

Authors: Kalawi, Dana | Cakar, Tarık | Gurul, Binnur

Article Type: Research Article

Abstract: This study aims to investigate the sustainable campus criteria, the variations made or require to be done to become an ecologically sustainable campus. In this context, the major goal of the research is assessing the sustainable campus design principles and indicators, setting the targets and deciding the precedencies with the Fuzzy Multi-Criteria Decision-Making methods (MCDM) for the sustainable campus design at Istanbul Gelisim University. In this study, model-based methods have been used to evaluate the sustainable campus performance of universities. In this respect, the study differs from other studies in the literature. Another difference of this study is that three …different Fuzzy Multi-Criteria Decision-Making methods has been used, these methods are Fuzzy-AHP, Fuzzy-TOPSIS and Fuzzy-ELECTRE. All three have different inference mechanisms. A common solution has been obtained by using the results of these three different Fuzzy-MCDM methods as hybrid dominance and superiority criteria. Here, the Copeland method, which takes the superiority criterion as a reference, has been used in the options where we could not provide the dominance criterion. At the end of this study, a recommendation report has been prepared according to these results. Show more

Keywords: Sustainable campus, fuzzy multicriteria decision making, fuzzy AHP, fuzzy TOPSIS, fuzzy ELECTRE

DOI: 10.3233/JIFS-223778

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-23, 2023

Authors: Duman, Ekrem

Article Type: Research Article

Abstract: The main function of the internal control department of a bank is to inspect the banking operations to see if they are performed in accordance with the regulations and bank policies. To accomplish this, they pick up a number of operations that are selected randomly or by some rule and, inspect those operations according to some predetermined check lists. If they find any discrepancies where the number of such discrepancies are in the magnitude of several hundreds, they inform the corresponding department (usually bank branches) and ask them for a correction (if it can be done) or an explanation. In …this study, we take up a real-life project carried out under our supervisory where the aim was to develop a set of predictive models that would highlight which operations of the credit department are more likely to bear some problems. This multi-classification problem was very challenging since the number of classes were enormous and some class values were observed only a few times. After providing a detailed description of the problem we attacked, we describe the detailed discussions which in the end made us to develop six different models. For the modeling, we used the logistic regression algorithm as it was preferred by our partner bank. We show that these models have Gini values of 51 per cent on the average which is quite satisfactory as compared to sector practices. We also show that the average lift of the models is 3.32 if the inspectors were to inspect as many credits as the number of actual problematic credits. Show more

Keywords: Predictive modeling, multi-classification, banking, internal control, data mining

DOI: 10.3233/JIFS-223679

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2023

Authors: Jeyalakshmi, P. | Karuppasamy, K.

Article Type: Research Article

Abstract: A signed graph Σ = (G , σ) is a graph with a sign attached to each arc. A subset S of V  (Σ) is called a dominating set of Σ if |N +  (v )  ∩ S | > |N -  (v )  ∩ S | for all v  ∈ V  - S  . A dominating set S  ⊆ V is a connected dominating set of Σ if <S > is connected. The minimum cardinality of a connected dominating set of Σ denoted by γ sc , is called the connected domination number of Σ . In this paper, we introduce the connected domination …number in a signed graph Σ and study different bounds and characterization of the connected domination number in a signed graph Σ . Furthermore, we find the best possible upper and lower bounds for γ sc ( Σ ) + γ sc ( Σ α c ) where Σ is connected. Show more

Keywords: Signed graph, dominating set, connected dominating set, connected domination number

DOI: 10.3233/JIFS-223857

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Tran, Van Quan

Article Type: Research Article

Abstract: The unconfined compressive strength (Qu) is one of the most important criteria of stabilized soil to design in order to evaluate the effective of soft soil improvement. The unconfined compressive strength of stabilized soil is strongly affected by numerous factors such as the soil properties, the binder content, etc. Machine Learning (ML) approach can take into account these factors to predict the unconfined compressive strength (Qu) with high performance and reliability. The aim of this paper is to select a single ML model to design Qu of stabilized soil containing some chemical stabilizer agents such as lime, cement and bitumen. …In order to build the single ML model, a database is created based on the literature investigation. The database contains 200 data samples, 12 input variables (Liquid limit, Plastic limit, Plasticity index, Linear shrinkage, Clay content, Sand content, Gravel content, Optimum water content, Density of stabilized soil, Lime content, Cement content, Bitumen content) and the output variable Qu. The performance and reliability of ML model are evaluated by the popular validation technique Monte Carlo simulation with aided of three criteria metrics including coefficient of determination R2, Root Mean Square Error (RMSE) and Mean Square Error (MAE). ML model based on Gradient Boosting algorithm is selected as highest performance and highest reliability ML model for designing Qu of stabilized soil. Explanation of feature effects on the unconfined compressive strength Qu of stabilized soil is carried out by Permutation importance, Partial Dependence Plot (PDP 2D) in two dimensions and SHapley Additive exPlanations (SHAP) local value. The ML model proposed in this investigation is single and useful for professional engineers with using the mapping Maximal dry density-Linear shrinkage created by PDP 2D. Show more

Keywords: Machine learning, unconfined compressive strength, stabilized soil, gradient boosting, monte carlo simulation, local SHAP value

DOI: 10.3233/JIFS-222899

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

Authors: Li, Weidong | Yu, Yongbo | Meng, Fanqian | Duan, Jinlong | Zhang, Xuehai

Article Type: Research Article

Abstract: Some subtle features of planting structures in irrigation areas could only be visible on high-resolution panchromatic spectral images. However, low spatial resolution multispectral image makes it hard to recognize them. It is challenging to accurately obtain crop planting structure when using traditional methods. This paper proposes an extraction method of crop planting structure based on image fusion and U-Net depth semantic segmentation network, which can automatically and accurately extract multi-category crop planting structure information. This method takes Landsat8 commercial multispectral satellite data set as an example, chooses RGB pseudo-color synthetic image which highlights vegetation characteristics, and uses HLS(Hue, Luminance, Saturation), …NND(Nearest-Neighbor Diffusion) and G-S(Gram-Schmidt) methods to fuse panchromatic band to obtain 15m high-resolution fusion image to obtain training set and test set, six types of land features including cities and rivers were labeled by manual to obtain the verification set. The training and validation sets are cut and enhanced to train the U-Net semantic segmentation network. Taking the Xiaokaihe irrigation area in Binzhou City, Shandong Province, China, as an example, the planting structure was classified, and the overall accuracy was 87.7%, 91.2%, and 91.3%, respectively. The accuracy of crop planting structures (wheat, cotton, woodland) was 74.2%, 82.5%, 82.3%, and the Kappa coefficient was 0.832, 0.880, and 0.881, respectively. The results showed that the NND-UNet method was suitable for large-scale continuous crop types (wheat, cotton), and the GS-UNet method had a better classification effect in discrete areas of cash crops (Jujube and many kinds of fruit trees). Show more

Keywords: Multispectral remote sensing, U-Net network, crop planting structures, multi category, image fusion

DOI: 10.3233/JIFS-230041

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Yan-e, Hou | Chunxiao, Wang | Congran, Wang | Gaojuan, Fan

Article Type: Research Article

Abstract: Multi-compartment vehicle routing problem (MCVRP) is an extension of the classical capacitated vehicle routing problem where products with different characteristics are transported together in one vehicle with multiple compartments. This paper deals with this problem, whose objective is to minimize the total travel distance while satisfying the capacity and maximum route length constraints. We proposed a hybrid iterated local search metaheuristic (HILS) algorithm to solve it. In the framework of iterated local search, the current solution was improved iteratively by five neighborhood operators. For every obtained neighborhood solution after the local search procedure, a large neighborhood search-based perturbation method was …executed to explore larger solution space and get a better neighborhood solution to take part in the next iteration. In addition, the worse solutions found by the algorithm were accepted by the nondeterministic simulated annealing-based acceptance rule to keep the diversification of solutions. Computation experiments were conducted on 28 benchmark instances and the experimental results demonstrate that our presented algorithm finds 16 new best solutions, which significantly outperforms the existing state-of-the-art MCVRP methods. Show more

Keywords: Multi-compartment vehicle routing problem, hybrid metaheuristic, iterated local search, large neighborhood search, simulated annealing

DOI: 10.3233/JIFS-223404

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: he, Jia-long | zhang, Xiao-Lin | wang, Yong-Ping | zhang, Huan-Xiang | gao, Lu | xu, En-Hui

Article Type: Research Article

Abstract:  In recent years, contrastive learning has been very successful in unsupervised tasks of representation learning and has received a lot of attention in supervised tasks. In supervised tasks, the discrete nature of natural language makes the construction of sample pairs difficult and the models are poorly robust to adversarial samples, so it remains a challenge to make contrastive learning effective for text classification tasks and to guarantee the robustness of the models. This paper presents a contrastive adversarial learning framework built using data augmentation with labeled insertion data. Specifically,By adding perturbation to the word-embedding matrix, adversarial samples are generated as …positive examples of contrastive learning, and external semantic information is introduced to construct negative examples. Contrastive learning is used to improve the sensitivity and generalization ability of the model, and adversarial training is used to improve robustness, thereby improving the classification accuracy. In addition, the momentum contrast from unsupervised tasks is also introduced into the text classification task to increase the number of sample pairs. Experimental results on several datasets show that the proposed approach outperforms the baseline comparison approach, and in addition some experiments are conducted to verify the effectiveness of the proposed framework under low-resource conditions. Show more

Keywords: Contrastive learning, adversarial training, text classification

DOI: 10.3233/JIFS-230787

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Lei, Deming | Du, Haoyang | Tang, Hongtao

Article Type: Research Article

Abstract: Distributed assembly flow shop scheduling problem (DAFSP) has been extensively considered; however, DAFSP with Pm  → 1 layout, in which m parallel machines are at fabrication stage and one machine is at assembly stage, is seldom handled. In this study, DAFSP with the above layout and transportation time is studied and an imperialist competitive algorithm with cooperation and division (CDICA) is presented to minimize makespan. Feature of the problem is used and a heuristic is applied to produce initial solution. Adaptive assimilation and evolution are executed in the weakest empire and adaptive cooperation is implemented between the winning empire and …the weakest empire in imperialist competition process. Empire division is performed when a given condition is met. Many experiments are conducted. The computational results demonstrate that new strategies are effective and CDICA is a very competitive in solving the considered DAFSP. Show more

Keywords: Assembly scheduling problem, distributed scheduling, imperialist competitive algorithm, cooperation

DOI: 10.3233/JIFS-223929

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2023

Authors: Liu, Wei | Liu, Qihan | Ye, Guoju | Zhao, Dafang | Guo, Yating | Shi, Fangfang

Article Type: Research Article

Abstract: The interval rough number rough sets model is the generalization of the classical rough sets. Since the lower approximation condition of interval rough number rough sets model is a full inclusion relation which is too strict to tolerate noisy data, strict conditions increase the possibility of a sample classified into a wrong class. To overcome the above shortcomings, an interval rough number variable precision rough sets model is proposed in this paper, which is combined with interval rough number similarity and the concept of variable precision rough sets. The model introduces the error parameter and can improve the tolerance of …noise data. Then the related properties of the model are also proved. Moreover, we construct a maximal positive domain attribute reduction method based on the proposed model, which can process the data type of interval rough number without discretization. Finally, numerical examples are given to verify the rationality of the model. Show more

Keywords: Similarity, interval rough number, variable precision rough set, attribute reduction

DOI: 10.3233/JIFS-222781

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 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. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Sherubha, P. | Ahmed, L. Jubair | Kannan, K.S. | Sasirekha, S.P.

Article Type: Research Article

Abstract: The aggressive form of cancer commonly in breast cells is breast cancer. The highly aggressive form of cancer is frequently created in breast cells. The need for the predictive model to accurately measure the prognosis prediction of breast cancer in the earlier stage is highly recommended. This development of methods for protecting people from fatal diseases by the researchers from the different disciplines who are all working altogether. An accurate breast cancer prognosis prediction is made by using a good predictive model to assist Medical Internet of Things (mIoT). Various advantages such as cancer detection in an earlier stage, medical …expenses related to treatment, and having unwanted treatment gives the accurate prediction attains spare patients. Existing models lie on the uni-modal data such as chosen gene expression to predict the model’s design. Few learning-based predictive models are used in the proposed method to improve breast cancer prognosis prediction from the current data sets. Most of the peculiar benefits of the suggested method rely on the model’s architecture. Here, a novel adaptive boosting model (a-BM) is used to measure the loss function of every individual and intends to reduce the error rate. Various performances metrics are used to evaluate the predictive performance, which provides the model gives a good outcome rather than the previous techniques. Show more

Keywords: Machine learning, breast cancer, prediction rate, loss function, error rate

DOI: 10.3233/JIFS-230086

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023