Journal of Intelligent & Fuzzy Systems - Volume 44, issue 6

Authors: Mudgil, Pooja | Gupta, Pooja | Mathur, Iti | Joshi, Nisheeth

Article Type: Research Article

Abstract: Social media platforms, namely Instagram, Facebook, Twitter, YouTube, etc. have gained a lot of attention as users used to share their views, and post videos, audio, and pictures for social networking. In near future, understanding the meaning and analyzing this enormously rising volume and size of online data will become a necessity in order to extract valuable information from them. In a similar context, the paper proposes an analysis model in two phases namely the training and the sentiment classification using the reward-based grasshopper optimization algorithm. The training architecture and context analysis of the tweet are presented for the sentiment …analysis along with the ground truth processing of emotions. The proposed algorithm is divided into two phases namely the exploitation and the exploration part and creates a reward mechanism that utilizes both phases. The proposed algorithm also uses cosine similarity, dice coefficient, and euclidean distance as the input set and further processes using the grasshopper algorithm. Finally, it presents a combination of swarm intelligence and machine learning for attribute selection in which the reward mechanism is further validated using machine learning techniques. The comparative performance in terms of precision, recall, and F-measure has been measured for the proposed model in comparison to existing swarm-based sentiment analysis works. Overall, simulation analysis showed that the proposed work based on grasshopper optimization outperformed the existing approaches for Sentiment 140 by 5.93% to 10.05% SemEval 2013 by 6.15% to 12.61% and COVID-19 tweets by 2.72% to 9.13%. Thus, demonstrating the efficiency of the context-aware sentiment analysis using the grasshopper optimization approach. Show more

Keywords: Grasshopper optimization, sentiment, social media, swarm intelligence, Twitter

DOI: 10.3233/JIFS-221879

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10275-10295, 2023

Authors: Zhi, Zhaodan | Tao, Juan

Article Type: Research Article

Abstract: In this study, the constrained interval arithmetic (CIA) is used as an effective mathematical tool for solving the stability analysis for interval two-dimensional semi-linear differential equations. Under certain assumptions, the origin is a focus of the interval semi-linear differential equations if it is a focus of the interval linear ones. Meanwhile, the origin can be a center, a center-focus or a focus of interval semi-linear differential equations if it is a center of the interval linear ones. On the other word, the types of equilibrium point are still determined by the linear part when a nonlinear disturbance is added to …the interval linear differential equations. Based on CIA, the stability results of interval differential equations are the same as those of the real differential equations. At last, three illustrative examples validate the stability results of the origin for interval two-dimensional semi-linear differential equations. Show more

Keywords: Constrained interval arithmetic (CIA), interval differential equations, semi-linear, stability

DOI: 10.3233/JIFS-222020

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10297-10310, 2023

Authors: Korkoman, Malak Jalwi | Abdullah, Monir

Article Type: Research Article

Abstract: Online services have advanced to the point where they have made our lives much easier, but many problems should be solved to make these services safer for consumers. Numerous transactions are conducted daily, and much personal information is published and shared on e-commerce and social media platforms. This makes security, privacy, and problematic reliability barriers to overcome. One of these problems is detecting credit card fraud because thieves aim to make all transactions legitimate by stealing credit card information. Imbalanced data is a potential problem in machine learning that impairs the performance of the classifiers used in real-world systems. For …example, anomaly detection and fraudulent transactions. The term “data imbalance” refers to the problem in which the sample distribution is skewed or skewed towards a particular class. Due to its inherent nature, the software failure prediction dataset falls into the same category as non-defective software modules. The main objective of this paper is to solve the problem of the imbalanced fraud credit card dataset for enhancing the detection accuracy of using machine learning algorithms. This paper provides a unique fraud detection model using the Particle Swarm Optimization (PSO) based on oversampling technique of the minority class to solve the imbalanced dataset problem compared with the Genetic Algorithm (GA) technique. Random Forest (RF) algorithm shows up with sensitivity, specificity, and accuracy. The experimental results achieved 99.3% and 99.4% for GA and PSO within seconds, respectively. Experiments show that the proposed methods outperform other methods, evidenced by the higher classification accuracy obtained. Show more

Keywords: Fraud detection, genetic algorithm, particle swarm optimization, oversampling, random forest

DOI: 10.3233/JIFS-222344

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10311-10323, 2023

Authors: Wang, Chunying | Zhang, Jiahui | Yang, Qi

Article Type: Research Article

Abstract: The traditional fuzzy C-means clustering technology only considers one performance Angle of image segmentation process when processing data, resulting in low accuracy of image segmentation. In this paper, the traditional FCM algorithm is analyzed, and the low clustering accuracy, noise interference and lack of flexibility and other problems are fully considered from the relationship between parameter components, non-local spatial information elements and noise sensitivity. Firstly, a distance calculation method based on robust statistics theory is proposed, which can deal with abnormal noise stably. Secondly, based on the extreme learning machine theory, the non-local spatial information coefficient is introduced to improve …the identification ability of the influence factors. This method not only guarantees the anti-noise performance of the algorithm, but also preserves the image data, improving the iteration efficiency and segmentation accuracy of the algorithm. The test results show that the accuracy of the improved C-means clustering algorithm for image segmentation is 95.5%, which is compared with the traditional C-means clustering technique and other optimization algorithms. Show more

Keywords: C-means, noise, clustering, image processing, fuzzy

DOI: 10.3233/JIFS-222912

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10325-10335, 2023

Authors: Wang, Weize | Feng, Yurui

Article Type: Research Article

Abstract:  There are various uncertainties in the multi-criteria group decision making (MCGDM) process, including the definition of the importance of decision information and the assignment of criterion assessment values, etc., which cause decision makers to be unconfident in their decisions. In this paper, an MCGDM approach based on the reliability of decision information is proposed in Fermatean fuzzy (FF) environment, allowing a decision to be made with confidence that the alternative chosen is the best performing alternative under the range of probable circumstances. First, we prove that the FF Yager weighted averaging operator is monotone with respect to the total order …and note the inconsistency between the monotonicity of some FF aggregation operators and their application in MCGDM. Second, we extend the divergence measure of FFS to order σ for calculating the variance of decision information and accordingly develop an exponential FF entropy measure to measure the uncertainty of decision information. Then, the reliability of decision information is defined, which accounts for the degree of variance of decision information across criteria from the criterion dimension and the uncertainty of the decision information from the alternative dimension. Following that, an integrated MCGDM framework is completed. Finally, the applications to a numerical example and comparisons with previous approaches are conducted to illustrate the validity of the established approach. Show more

Keywords: Multi-criteria group decision making, Fermatean fuzzy set, Divergence measure, Entropy measure, Supplier selection

DOI: 10.3233/JIFS-223014

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10337-10356, 2023

Authors: Upendra Raju, K. | Amutha Prabha, N.

Article Type: Research Article

Abstract: Reversible data hiding (RDH) based on Steganography is considered as one of the future related aspects in the field of security for the information hiding paradigm. Existing research work has been carried out based on secure data transmission as well as reducing the dataloss from one user to other users. But due to encryption data expansion over non-linear transformation, complexity in attacking caused due to keyspace, ineffective image compression, poor embedding ratio, poor quality, overflow/underflow problems, data loss etc., leads to inefficient data transmission causing a security risk. This paper proposes a novel method named Triple Secured Data Hiding Steganography …Model which provides solutions to the above challenges. This work is initiated with Hyper Chaos 2D Compressive Sensing that performs image compression and encryption simultaneously. It provides control over low dimension chaos system bearing secure risks with suffering from data encrypted expansion while adopt non-linear transformation. In addition to reduce the error rate and providing signal synchronization as well as system reliability over the transmission channel, Manchester Encoder/Decoder is initiated. To cope up with data embedding and extraction our work has proposed Circular Queue Exploiting Modification Direction(CQEMD). Thus, overall proposed model enhances effective secure data transmission under RDH by inhabiting a triple secured system. Show more

Keywords: Circular Queue Exploiting Modification Direction (CQEMD), Hyper Chaos 2D Compressive Sensing (CS), ManchesterEncoder/Decoder, Reversible Data Hiding (RDH), steganography

DOI: 10.3233/JIFS-223131

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10357-10367, 2023

Authors: Cabrera-Ponce, Aldrich A. | Martin-Ortiz, Manuel | Martinez-Carranza, Jose

Article Type: Research Article

Abstract:  Geo-localisation from a single aerial image for Uncrewed Aerial Vehicles (UAVs) is an alternative to other vision-based methods, such as visual Simultaneous Localisation and Mapping (SLAM), seeking robustness under GPS failure. Due to the success of deep learning and the fact that UAVs can carry a low-cost camera, we can train a Convolutional Neural Network (CNN) to predict position from a single aerial image. However, conventional CNN-based methods adapted to this problem require off-board training that involves high computational processing time and where the model can not be used in the same flight mission. In this work, we explore the …use of continual learning via latent replay to achieve online training with a CNN model that learns during the flight mission GPS coordinates associated with single aerial images. Thus, the learning process repeats the old data with the new ones using fewer images. Furthermore, inspired by the sub-mapping concept in visual SLAM, we propose a multi-model approach to assess the advantages of using compact models learned continuously with promising results. On average, our method achieved a processing speed of 150 fps with an accuracy of 0.71 to 0.85, demonstrating the effectiveness of our methodology for geo-localisation applications. Show more

Keywords: Continual learning, geo-localisation, aerial image, GPS

DOI: 10.3233/JIFS-223627

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10369-10381, 2023

Authors: Westarb, Gustavo | Stefenon, Stefano Frizzo | Hoppe, Aurélio Faustino | Sartori, Andreza | Klaar, Anne Carolina Rodrigues | Leithardt, Valderi Reis Quietinho

Article Type: Research Article

Abstract:  This paper presents the development and application of graph neural networks to verify drug interactions, consisting of drug-protein networks. For this, the DrugBank databases were used, creating four complex networks of interactions: target proteins, transport proteins, carrier proteins, and enzymes. The Louvain and Girvan-Newman community detection algorithms were used to establish communities and validate the interactions between them. Positive results were obtained when checking the interactions of two sets of drugs for disease treatments: diabetes and anxiety; diabetes and antibiotics. There were found 371 interactions by the Girvan-Newman algorithm and 58 interactions via Louvain.

Keywords: Drug interaction, graph neural network, communities detection

DOI: 10.3233/JIFS-223656

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10383-10395, 2023

Authors: Li, Dan | Gao, Fei

Article Type: Research Article

Abstract: Slime mould algorithm (SMA) is a novel meta-heuristic algorithm with fast convergence speed and high convergence accuracy. However, it still has some drawbacks to be improved. The exploration and exploitation of SMA is difficult to balance, and it easy to fall into local optimum in the late iteration. Aiming at the problems existing in SMA, a multistrategy slime mould algorithm named GCSMA is proposed for global optimization in this paper. First, the Logistic-Tent double chaotic map approach is introduced to improve the quality of the initial population. Second, a dynamic probability threshold based on Gompertz curve is designed to balance …exploration and exploitation. Finally, the Cauchy mutation operator based on elite individuals is employed to enhance the global search ability, and avoid it falling into the local optimum. 12 benchmark function experiments show that GCSMA has superior performance in continuous optimization. Compared with the original SMA and other novel algorithms, the proposed GCSMA has better convergence accuracy and faster convergence speed. Then, a special encoding and decoding method is used to apply GCSMA to discrete flexible job-shop scheduling problem (FJSP). The simulation experiment is verified that GCSMA can be effectively applied to FJSP, and the optimization results are satisfactory. Show more

Keywords: Slime mould algorithm, double chaotic map, Gompertz dynamic probability, Cauchy mutation, flexible job shop scheduling problem

DOI: 10.3233/JIFS-223827

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10397-10415, 2023

Authors: Shan, Chuanhui | Ou, Jun | Chen, Xiumei

Article Type: Research Article

Abstract: As one of the main methods of information fusion, artificial intelligence class fusion algorithm not only inherits the powerful skills of artificial intelligence, but also inherits many advantages of information fusion. Similarly, as an important sub-field of artificial intelligence class fusion algorithm, deep learning class fusion algorithm also inherits advantages of deep learning and information fusion. Hence, deep learning fusion algorithm has become one of the research hotspots of many scholars. To solve the problem that the existing neural networks are input into multiple channels as a whole and cannot fully learn information of multichannel images, Shan et al. proposed …multichannel concat-fusional convolutional neural networks. To mine more multichannel images’ information and further explore the performance of different fusion types, the paper proposes new fusional neural networks called multichannel cross-fusion convolutional neural networks (McCfCNNs) with fusion types of “R+G+B/R+G+B/R+G+B” and “R+G/G+B/B+R” based on the tremendous strengths of information fusion. Experiments show that McCfCNNs obtain 0.07-6.09% relative performance improvement in comparison with their corresponding non-fusion convolutional neural networks (CNNs) on diverse datasets (such as CIFAR100, SVHN, CALTECH256, and IMAGENET) under a certain computational complexity. Hence, McCfCNNs with fusion types of “R+G+B/R+G+B/R+G+B” and “R+G/G+B/B+R” can learn more fully multichannel images’ information, which provide a method and idea for processing multichannel information fusion, for example, remote sensing satellite images. Show more

Keywords: Information fusion, fusion type “R+G+B/R+G+B/R+G+B”, fusion type “R+G/G+B/B+R”, CNN, McCfCNN

DOI: 10.3233/JIFS-224076

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10417-10436, 2023

Authors: Zhang, Dongping | Lan, Hao | Ma, Zhennan | Yang, Zhixiong | Wu, Xin | Huang, Xiaoling

Article Type: Research Article

Abstract: The key to solving traffic congestion is the accurate traffic speed forecasting. However, this is difficult owing to the intricate spatial-temporal correlation of traffic networks. Most existing studies either ignore the correlations among distant sensors, or ignore the time-varying spatial features, resulting in the inability to extract accurate and reliable spatial-temporal features. To overcome these shortcomings, this study proposes a new deep learning framework named spatial-temporal gated graph convolutional network for long-term traffic speed forecasting. Firstly, a new spatial graph generation method is proposed, which uses the adjacency matrix to generate a global spatial graph with more comprehensive spatial features. …Then, a new spatial-temporal gated recurrent unit is proposed to extract the comprehensive spatial-temporal features from traffic data by embedding a new graph convolution operation into gated recurrent unit. Finally, a new self-attention block is proposed to extract global features from the traffic data. The evaluation on two real-world traffic speed datasets demonstrates the proposed model can accurately forecast the long-term traffic speed, and outperforms the baseline models in most evaluation metrics. Show more

Keywords: Traffic speed forecasting, graph convolution operation, gated recurrent unit, self-attention block

DOI: 10.3233/JIFS-224285

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10437-10450, 2023

Authors: Han, Nana | Qiao, Junsheng

Article Type: Research Article

Abstract: Lately, Jiang and Hu (H.B. Jiang, B.Q. Hu, On ( O , G ) -fuzzy rough sets based on overlap and grouping functions over complete lattices, Int. J. Approx. Reason. 144 (2022) 18-50.) put forward ( O , G ) -fuzzy rough sets via overlap and grouping functions over complete lattices. Meanwhile, they showed the characterizations of O -upper and G -lower L -fuzzy rough approximation operators in ( O , G ) -fuzzy rough set …model based on some of specific L -fuzzy relations and studied the topological properties of the proposed model. Nevertheless, we discover that the partial results given by Jiang and Hu could be further optimized. So, as a replenish of the above article, in this paper, based on G -lower L -fuzzy rough approximation operator in ( O , G ) -fuzzy rough set model, we further explore several new conclusions on the relationship between G -lower L -fuzzy rough approximation operator and different L -fuzzy relations. In particular, the equivalent descriptions of relationship between G -lower L -fuzzy rough approximation operator and O -transitive ( O -Euclidean) L -fuzzy relations are investigated, which are not involved in above literature and can make the theoretical results of this newly fuzzy rough set model more perfect. Show more

Keywords: (𝔒, 𝔊)-fuzzy rough set, 𝔏-fuzzy relation, overlap function, grouping function, complete lattice

DOI: 10.3233/JIFS-224286

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10451-10457, 2023

Authors: Liu, Lin | Yang, Lijun

Article Type: Research Article

Abstract: The level of education in colleges is career and development-focused compared to that from high schools. Quality education relies on the teachers’ qualifications, knowledge, and experience over the years. However, the demand for technical and knowledge-based education is increasing with the world’s demands. Therefore, assessing the knowledge of teaching professionals to meet external demand becomes mandatory. This article introduces an Acceded Data Evaluation Method (ADEM) using Fuzzy Logic (FL) for teaching quality assessment. The proposed method inputs the teachers’ skills and students’ productivity for evaluation. The teachers’ knowledge and updated skills through training and self-learning are the key features for …evaluating the independents’ performance. The impact of the above features on the student qualifying ratio and understandability (through examination) are analyzed periodically. Depending on the qualifications and performance, the teachers’ knowledge update is recommended with the new training programs. In this evaluation process, fuzzy logic is implied for balancing and identifying the maximum validation criteria that satisfy the quality requirements. The recommendations using partial and fulfilled quality constraints are identified using the logical truth over the varying assessments. The proposed method is analyzed using the metrics evaluation rate, quality detection, recommendations, evaluation time, and data balancing. Show more

Keywords: Data balancing, decision recommendations, fuzzy logic, teaching quality

DOI: 10.3233/JIFS-224290

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10459-10475, 2023

Authors: Al-Andoli, Mohammed Nasser | Tan, Shing Chiang | Sim, Kok Swee | Goh, Pey Yun | Lim, Chee Peng

Article Type: Research Article

Abstract: Malicious software, or malware, has posed serious and evolving security threats to Internet users. Many anti-malware software packages and tools have been developed to protect legitimate users from these threats. However, legacy anti-malware methods are confronted with millions of potential malicious programs. To combat these threats, intelligent anti-malware systems utilizing machine learning (ML) models are useful. However, most ML models have limitations in performance since the training depth is usually limited. The emergence of Deep Learning (DL) models allow more training possibilities and improvement in performance. DL models often use gradient descent optimization, i.e., the Back-Propagation (BP) algorithm; therefore, their …training and optimization procedures suffer from local sub-optimal solutions. In addition, DL-based malware detection methods often entail single classifiers. Ensemble learning overcomes the shortcomings of individual techniques by consolidating their strengths to improve the performance. In this paper, we propose an ensemble DL classifier stacked with the Fuzzy ARTMAP (FAM) model for malware detection. The stacked ensemble method uses several heterogeneous deep neural networks as the base learners. During the training and optimization process, these base learners adopt a hybrid BP and Particle Swarm Optimization algorithm to combine both local and global optimization capabilities for identifying optimal features and improving the classification performance. FAM is selected as a meta-learner to effectively train and combine the outputs of the base learners and achieve robust and accurate classification. A series of empirical studies with different benchmark data sets is conducted. The results ascertain that the proposed ensemble method is effective and efficient, outperforming many other compared methods. Show more

Keywords: Ensemble learning, fuzzy ARTMAP, deep learning, malware detection, particle swarm optimization, backpropagation algorithm

DOI: 10.3233/JIFS-230009

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10477-10493, 2023

Authors: Rose, Biji | Aruna Devi, B.

Article Type: Research Article

Abstract: From the signal received on a particular frequency band, spectrum sensing (SS) is used in cognitive radio (CR) to assess whether the primary user (PU) is using the spectrum and, consequently, whether the secondary user (SU) can utilize the spectrum. The main issue with SS is determining the presence of the primary signal in a low signal-to-noise ratio (SNR). Compared to conventional technologies, machine learning techniques are more effective and accurate at identifying the qualities of input data. This paper proposes a machine learning (ML) based SS model for CR with effective feature extraction and reduction techniques. The proposed work …comprises five phases: noise removal, wavelet transform, feature extraction, dimensionality reduction, and classification. Firstly, noise filtering is done on the received signal to remove the noise present in the input signal using the filters such as moving median filter (MMF), Gaussian filter (GF), and Gabor filter (GBF). After that, the filtered signal is transformed into a wavelet domain using Discrete Wavelet Transform (DWT) algorithm. Then the statistical features such as average absolute value, wavelet energy, variance, standard deviation, and peak value features are extracted from the DWT. Next, the dimensionality reduction (DR) is performed using Linear Discriminant Analysis (LDA). Finally, the classification is performed using the ensemble ML classifiers such as Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbour (KNN), which classify whether the PU signal is active or not. Simulations are carried out to analyze the efficiency of the presented models for SS. The results proved that SVM obtains the best performance for SS with higher accuracy and lower SNR. Show more

Keywords: Cognitive radio, spectrum sensing, discrete wavelet transform, machine learning, signal-to-noise ratio

DOI: 10.3233/JIFS-230438

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10495-10509, 2023

Authors: Li, Huiru | Hu, Yanrong | Liu, Hongjiu

Article Type: Research Article

Abstract: Stock price volatility is influenced by many factors, including unstructured data that is not easy to quantify, such as investor sentiment. Therefore, given the difficulty of quantifying investor sentiment and the complexity of stock price, the paper proposes a novel LASSO-ATT-LSTM intelligent stock price prediction system based on multi-source data. Firstly, establish a sentiment dictionary in the financial field, conduct sentiment analysis on news information and comments according to the dictionary, calculate sentiment scores, and then obtain daily investor sentiment. Secondly, the LASSO (Least absolute shrinkage and selection operator) is used to reduce the dimension of basic trading indicators, valuation …indicators, and technical indicators. The processed indicators and investor sentiment are used as the input of the prediction model. Finally, the LSTM (Long short-term memory) model that introduces the attention mechanism is used for intelligent prediction. The results show that the prediction of the proposed model is close to the real stock price, MAPE, RMSE, MAE and R2 are 0.0118, 0.0685, 0.0515 and 0.8460, respectively. Compared with the existing models, LASSO-ATT-LSTM has higher accuracy and is an effective method for stock price prediction. Show more

Keywords: Stock price forecast, sentiment analysis, LSTM, attention, multi-source data

DOI: 10.3233/JIFS-221919

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10511-10521, 2023

Authors: Yanhu, Han | Huimin, Xin

Article Type: Research Article

Abstract: The location and capacity of precast concrete component factories (PC component factories) are not only the key factors for manufacturers to gain competitive advantage, but also the important factors affecting the operational efficiency of the prefabricated construction supply chain. This paper takes the capacitated location problem of PC component factories as the research object. Drawing on the model of traditional capacitated plant location problem, the model of capacitated location problem of PC component factories is constructed by setting the optional production scale by stages. According to the characteristics of this model, the optimal strategy of location is determined by using …the Tabu search algorithm. Taking the location problem of PC component factory in the Beijing-Tianjin-Hebei region as the object, the calculation example is designed, in which the influence of the distance parameters on the results of location problem is analyzed. The results can make the configuration of regional PC component factories more reasonable and balanced. Show more

Keywords: Prefabricated construction, location, PC component factories, capacity limitation

DOI: 10.3233/JIFS-222923

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10523-10535, 2023

Authors: Chiranjeevi, Phaneendra | Rajaram, A.

Article Type: Research Article

Abstract: Recommender systems based on sentiment analysis become challenging due to the presence of enormous data available over the internet. With the lack of proper data cleaning and analysis methods, existing machine learning (ML) techniques fail to generate accurate recommendations. To overcome this issue, this paper proposes a Light Deep Learning (LightDL)-based recommender system that uses Twitter-based reviews. First, the data is collected from Twitter and cleaned by subsequent data cleaning processes. Then, this pre-processed data is fed into the LightDL model, which learns the important features like hashtags, unigrams, multigrams, etc. from each piece of data. Here, we have learned …about four groups of features, including semantic features, syntactic features, symbolic features, and tweet-based features. Finally, the data is classified into positive, negative, and neutral categories according to the learned features. On the basis of classified sentiment, the review is generated to the users. Finally, the model is evaluated in terms of accuracy, precision, recall, f-measure, and error rate through extensive experiments in Matlab. The proposed LightDL model outperforms in all performance measures; specifically, it achieves 95% accuracy for the Twitter dataset. Show more

Keywords: Lightweight Dl, sentiment analysis, recommender system, twitter data

DOI: 10.3233/JIFS-223871

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10537-10550, 2023

Authors: Weng, Zhi | He, Dongchang | Zheng, Yan | Zheng, Zhiqiang | Zhang, Yong | Gong, Caili

Article Type: Research Article

Abstract: As the basis of intelligent breeding management and animal husbandry insurance, the identification of individual cattle is important in animal husbandry management. Given the difficulty of data acquisition caused by the non-rigid and lacking cooperation of cattle, this study proposes a method for cattle face image acquisition and processing that can efficiently adapt to the harsh environment of cattle barns. When processing the non-rigid cow face, the method of approximating the cow face to a rigid body is used to establish the cow face image data set., and the cattle face image data set is established. The Three Dimensional(3D) reconstruction …method of cattle face uses a 3D image reconstruction method based on multiple perspectives. First, the scale-invariant feature transform algorithm is used to extract the image feature points. The fast library for approximate nearest neighbors algorithm is used to match feature points. The matching results are selected via random sampling consensus. Second, the structure of the motion method is used for the sparse reconstruction of point clouds, and the dense point cloud is then generated using the three-dimensional multi-view stereo vision algorithm. Finally, the Poisson surface reconstruction method is used for surface reconstruction. The results indicate that this method can effectively realize the three-dimensional reconstruction of cattle faces; the reconstructed images have obvious color, clear texture, and complete shape features. Show more

Keywords: 3D Reconstruction, approximate rigidity, multi-perspective, surface reconstruction

DOI: 10.3233/JIFS-224260

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10551-10563, 2023

Authors: Lin, Tiantai | Yang, Bin

Article Type: Research Article

Abstract: In social life, conflict situations occur frequently all the time. To analyse a conflict situation, not only the intrinsic reason of the conflict but also the resolution of the conflict should be given. In this paper, we propose a combine conflict analysis model under q -rung fuzzy orthopair information system that contain conflict resolution, which is called discern function-based three-way group conflict analysis. Firstly, we propose three novel form conflict distances which are induced by discern functions, and examine their properties, then the comprehensive conflict distances are given based on the normality and symmetry they share. Thus, the conflict analysis …and resolution method in our model can be directly gained based on these novel form conflict distances. Secondly, from the view of group decision, the comprehensive q -rung fuzzy loss function is attained by aggregating a group of q -rung fuzzy loss functions through the q -rung orthopair fuzzy weighted averaging operator in the procedure of conflict resolution. Finally, we employ an example of the governance of a local government to demonstrate the process of finding an optimal feasible strategy in our model. Show more

Keywords: Conflict analysis, resolution of conflict analysis, q-rung orthopair fuzzy set, three-way decisions

DOI: 10.3233/JIFS-224589

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10565-10580, 2023

Authors: Rao, Juan | Peng, Ling | Rao, Jingjing | Cao, Xiaofen

Article Type: Research Article

Abstract: The evaluation of college physical education (PE) teaching quality is an indispensable part of the teaching process. Building a scientific, comprehensive, reasonable and effective evaluation system is crucial to improving the quality of college PE classroom teaching. This process is not easy, and requires long-term efforts and persistence. The PE teaching quality evaluation in Colleges and Universities is frequently viewed as the multiple attribute decision making (MADM) issue. In such paper, Taxonomy method is designed for MADM under double-valued neutrosophic sets (DVNSs). First, the score function of DVNSs and Criteria Importance Through Intercriteria Correlation (CRITIC) method is used to derive …the attribute weights. Second, then, the optimal choice is obtained through calculating the smallest double-valued neutrosophic number (DVNN) development attribute values from the DVNN positive ideal solution (DVNNPIS). Finally, a numerical example for PE teaching quality evaluation is given to illustrate the built method. Show more

Keywords: Multiple attribute decision making (MADM), double-valued neutrosophic sets (DVNSs), taxonomy method, CRITIC method, PE teaching quality

DOI: 10.3233/JIFS-230118

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10581-10590, 2023

Authors: Guo, Tianlong | Shen, Derong | Kou, Yue | Nie, Tiezheng

Article Type: Research Article

Abstract:  Multi-view clustering that integrates the complementary information from different views for better clustering is a fundamental topic in data engineering. Most existing methods learn latent representations first, and then obtain the final result via post-processing. These two-step strategies may lead to sub-optimal clustering. The existing one-step methods are based on spectral clustering, which is inefficient. To address these problems, we propose a Multi-view fusion guided Matrix factorization based One-step subspace Clustering (MMOC) to perform clustering on multi-view data efficiently and effectively in one step. Specifically, we first propose a matrix factorization based multi-view fusion representation method, which adopts efficient matrix …factorization instead of time-consuming spectral representation to reduce the computational complexity. Then we propose a self-supervised weight learning strategy to distinguish the importance of different views, which considers both the gradient and the learning rate to make the learned weights closer to the real situation. Finally, we propose a one-step framework of MMOC, which effectively reduces the information loss by integrating data representation, multi-view data fusion, and clustering into one step. We conduct experiments on 5 real-world datasets. The experimental results show the effectiveness and the efficiency of our MMOC method in comparison with state-of-the-art methods. Show more

Keywords: multi-view clustering, matrix factorization, weight learning, subspace clustering

DOI: 10.3233/JIFS-224578

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10591-10604, 2023

Authors: Sudha, V. | Shanmugam, Sathiya Priya | Anitha, D. | Raja, R.

Article Type: Research Article

Abstract: An intelligent segmentation and identification of edemas diseases constitutes a most important crucial ophthalmological issues since they provide important information for the diagnosis process in accordance to the disease severity. But diagnosing the different edema diseases using the OCT-images are considered to be daunting challenge among the researchers. The implementation of computational intelligence techniques such as machine learning, deep learning, bio inspired algorithms and image processing techniques may help the doctors for some extent in improving the automatic extraction and diagnosis process consequently improving patients’ life quality. But, these are liable to more errors and less performance, which requires further …improvisation in designing the intelligent systems for an effective classification of edema diseases. In this context, this paper proposes the hybrid intelligent framework for the identification, segmentation and classification of three types of edemas such as using the retinal optical coherence tomography (OCT) Images. In this process, Single Feed Forward Training networks (SLFTN) are integrated with Convolutional Layers whose hyperparameters are tuned by using Lion Optimization algorithm. An intensive experimentation is carried out using the Kaggle Retinal OCT Image datasets-2020 with Tensor flow and the proposed framework is trained with the different set of 84,494 images in which performance metrics such as accuracy, sensitivity, specificity, recall and f1score are calculated. Results shows the proposed system has provided satisfactory performance, reaching the average highest accuracy of 99.9% in identifying and classifying the respectively. Show more

Keywords: Machine learning, deep learning, retinal optical coherance tomography images, convolutional layers, lion optimization algorithm

DOI: 10.3233/JIFS-230128

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10605-10620, 2023

Authors: Ye, Tingqing | Zheng, Haoran

Article Type: Research Article

Abstract:  Uncertain statistics is a set of mathematical techniques to collect, analyze and interpret data based on uncertainty theory. In addition, probability statistics is another set of mathematical techniques based on probability theory. In practice, when to use uncertain statistics and when to use probability statistics to model some quality depends on whether the distribution function of the quality is close enough to the actual frequency. If it is close enough, then probability statistics may be used. Otherwise, uncertain statistics is recommended. In order to illustrate it, this paper employs uncertain statistics, including uncertain time series analysis, uncertain regression analysis and …uncertain differential equation, to model the birth rate in China, and explains the reason why uncertain statistics is used instead of probability statistics by analyzing the characteristics of the residual plot. In addition, uncertain hypothesis test is used to determine whether the estimated uncertain statistical models are appropriate. Show more

Keywords: Uncertainty theory, uncertain time series analysis, uncertain regression analysis, uncertain differential equation, birth rate

DOI: 10.3233/JIFS-230179

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10621-10632, 2023

Authors: Khan, Asghar | Aslam, Muhammad | Iqbal, Quaid

Article Type: Research Article

Abstract: Many unknowable elements make it difficult to measure cyclone disasters, traditional methods are insufficient to measure these factors. Fuzzy set theory and its expansions are effective ways to measure these uncertainties for these kinds of uncertainty. An evaluation of the cyclone disaster’s spatial vulnerability is necessary in order to build disaster damage reduction methods. In real life, we may come into a hesitant environment when making decisions. To explore such environments, we introduce hesitant fuzzy set (HFS) into Fermatean fuzzy set (FFS) and extend the existing research effort on FFSs in light of the effective tool of HFSs for expressing …the hesitant condition. In this study, we develop a comprehensive tropical cyclone disaster assessment by applying Fermatean hesitant fuzzy (FHF) information. In this paper, various unique aggregation strategies for the analysis of decision-making problems are introduced. As a result, Fermatean hesitant fuzzy average (FHFWA), Fermatean hesitant fuzzy ordered weighted average (FHFOWA), Fermatean hesitant fuzzy weighted geometric (FHFWG), and Fermatean hesitant fuzzy ordered weighted geometric (FHFOWG) operators have been developed. We also go over some of the most important features of these operators. Furthermore, we establish an algorithm for addressing a multiple attribute decision-making issue employing Fermatean hesitant fuzzy data by using these operators. and attribute prioritizing. A real-world problem of cyclone disaster damages in several parts of Pakistan is explored to test the applicability of these operators. In the final section, we expand the TOPSIS approach to a Fermatean hesitant fuzzy environment and compare the outcomes of the extended TOPSIS method with operators established in the FHF-environment. Show more

Keywords: Cyclone disaster, FHFSs, Aggregation operatos, TOPSIS method, MADM

DOI: 10.3233/JIFS-222144

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10633-10660, 2023

Authors: Zou, Yuan

Article Type: Research Article

Abstract: Bayesian decision models use probability theory as a commonly technique to handling uncertainty and arise in a variety of important practical applications for estimation and prediction as well as offering decision support. But the deficiencies mainly manifest in the two aspects: First, it is often difficult to avoid subjective judgment in the process of quantization of priori probabilities. Second, applying point-valued probabilities in Bayesian decision making is insufficient to capture non-stochastically stable information. Soft set theory as an emerging mathematical tool for dealing with uncertainty has yielded fruitful results. One of the key concepts involved in the theory named soft …probability which is as an immediate measurement over a statistical base can be capable of dealing with various types of stochastic phenomena including not stochastically stable phenomena, has been recently introduced to represent statistical characteristics of a given sample in a more natural and direct manner. Motivated by the work, this paper proposes a hybrid methodology that integrates soft probability and Bayesian decision theory to provide decision support when stochastically stable samples and exact values of probabilities are not available. According to the fact that soft probability is as a special case of interval probability which is mathematically proved in the paper, thus the proposed methodology is thereby consistent with Bayesian decision model with interval probability. In order to demonstrate the proof of concept, the proposed methodology has been applied to a numerical case study regarding medical diagnosis. Show more

Keywords: Soft probability, interval probability, Bayes rule, interval numbers, possibility degree

DOI: 10.3233/JIFS-223020

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10661-10673, 2023

Authors: Qureshi, Saima Siraj | He, Jingsha | Qureshi, Sirajuddin | Zhu, Nafei | Zardari, Zulfiqar Ali | Mahmood, Tariq | Wajahat, Ahsan

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-220932

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10675-10687, 2023

Authors: Guo, Xiaoyong | Zhang, Kai | Peng, Jiahan | Chen, Xiaoyan | Guo, Guangjie

Article Type: Research Article

Abstract: This paper proposes that the task of single-image low-light enhancement can be accomplished by a straightforward method named Opt2Ada. It contains a series of pixel-level operations, including an opt imized illuminance channel decomposition, an ada ptive illumination enhancement, and an ada ptive global scaling. Opt2Ada is traditional and it does not rely on architecture engineering, super-parameter tuning, or specific training dataset. Its parameters are generic and it has better generalization capability than existing data-driven methods. For evaluation, both the full-reference, non-reference, and semantic metrics are calculated. Extensive experiments on real-world low-light images demonstrate the superiority of Opt2Ada over recent traditional …and deep learning algorithms. Due to its flexibility and effectiveness, Opt2Ada can be deployed as a pre-processing subroutine for high-level computer vision applications. Show more

Keywords: Low-light image enhancement, Image processing, Traditional method

DOI: 10.3233/JIFS-222644

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10689-10702, 2023

Authors: Akbaba, Ümmügülsün | Hikmet Değer, Ali

Article Type: Research Article

Abstract: In this study, new matrices which produce the Pell and Pell-Lucas numbers are given. By using these matrices, new identities and relations related to the Pell and Pell-Lucas numbers are obtained.

Keywords: Pell numbers, Pell-Lucas numbers, matrices

DOI: 10.3233/JIFS-222957

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10703-10707, 2023

Authors: He, Zihang | Zhao, Kaiyan | Li, Bohan | Li, Yong

Article Type: Research Article

Abstract: This paper proposes an approach that regulates the confidence of predicted boxes for corner-based detection methods. Corner-based methods have achieved state-of-the-art performance on MS-COCO by predicting corners and grouping them to generate boxes. However, the box confidence is simply defined to be the average score of grouped corners, ignoring the score and tag discrepancy between them. The discrepancy may lead to the generation of more false positives (FPs) since a larger discrepancy often means that the grouped corners less likely belong to the same object. Observing this, this paper proposes introducing the discrepancy of corners (DoC) to decrease the box …confidence. Also, the score and location of center (SLoC) of a detection box is utilized to further finely regulate the confidence. DoC and SLoC can effectively reduce FPs and missings and hence improve the detection performance without changing any model parameter. Experimental results on MS-COCO also show improvements. Show more

Keywords: Object detection, anchor-free, corner-based

DOI: 10.3233/JIFS-212804

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10709-10720, 2023

Authors: Li, Feng

Article Type: Research Article

Abstract: With the advent of the information age, the development direction of automobiles has gradually changed, both from the domestic and foreign policy support attitude, or from the actual actions of the automotive industry and scientific research institutes’ continuous efforts, it is not difficult to see that driverless vehicle. At this time, the testing and evaluation of the intelligent behavior of driverless vehicles is particularly important. It is particularly important not only to regulate the intelligent behavior of unmanned vehicles, but also to promote the key It can not only regulate the intelligent behavior of unmanned vehicles, but also promote the …improvement of key technologies of unmanned vehicles and the research and development of driver assistance systems. The evaluation of comprehensive obstacle-avoiding behavior for unmanned vehicles is often considered as a multi-attribute group decision making (MAGDM) problem. In this paper, the EDAS method is extended to the interval neutrosophic sets (INSs) setting to deal with MAGDM and the computational steps for all designs are listed. Then, the criteria importance through intercriteria correlation (CRITIC) is defined to obtain the attribute’s weight. Finally, the evaluation of comprehensive obstacle-avoiding behavior for unmanned vehicles is given to demonstrate the interval neutrosophic number EDAS (INN-EDAS) model and some good comparative analysis is done to demonstrate the advantages of INN-EDAS. Show more

Keywords: Multi-attribute group decision making (MAGDM), interval neutrosophic sets (INSs), EDAS method, comprehensive obstacle-avoiding behavior, unmanned vehicles

DOI: 10.3233/JIFS-223370

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10721-10732, 2023

Authors: Sridevi, A. | Preethi, M.

Article Type: Research Article

Abstract: The technologically adapted agricultural procedures convert conventional farming practices and introduce smart farming or smart agriculture. Manual interventions in farming are unavoidable, however, it was reduced due to the Internet of Things (IoT). Sensors are used to monitor the farms which reduce the manpower requirements as well the cost. In this research work, a smart monitoring and prediction system was developed using IoT along with Fog computing. The physical data from farms are collected through IoT sensors and processed using a novel correlation-based ensemble classifier. Fog computing is adopted in the proposed work to reduce the data transmission delay and …computation complexities. Simulation analysis using benchmark datasets demonstrates the proposed model performance in terms of precision, recall, F1-score, and accuracy. Comparative analysis with conventional techniques like neural networks, extreme learning machine, and hybrid particle swarm optimization algorithm, validates the superior performance of the proposed model. With maximum accuracy of 96.67% proposed model outperforms conventional approaches. Show more

Keywords: Internet of Things (IoT), fog computing, latency, monitoring, feature extraction, prediction, correlation-based approach, ensemble classifier

DOI: 10.3233/JIFS-224225

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10733-10746, 2023

Authors: Diao, Xiu-Li | Zheng, Cheng-Hao | Zeng, Qing-Tian | Duan, Hua | Song, Zheng-guo | Zhao, Hua

Article Type: Research Article

Abstract: With the increase in needs for personalized learning of online students, knowledge tracing (KT), a technique aimed at tracing the state of a student’s knowledge mastery and predicting performance in future exercises, has become a hot topic in personalized learning research. The behavioral features exhibited during students’ learning process bear information that impacts the state of a student’s knowledge mastery. To study the influence of learning behaviors on students’ knowledge mastery state in the learning process, we propose a Precise Modeling of Learning Process based on M ultiple B ehavioral F eatures for K nowledge T racing model (MBFKT), which …models a student’s learning process by making use of these behavioral features. MBFKT initially processes these features through multi-head attention networks, memory networks, and recurrent neural networks to model students’ learning process into three memory links: memory decline link, memory enhancement link, and memory update link. Various update strategies are designed for each memory link, and the performance of numerous possible combinations of behavioral features in the memory links is compared, for the rules of learning and forgetting to be explained. Furthermore, we also study the contribution and degree of influence of different behavioral features on a student’s knowledge mastery state, by which MBFKT is improved, thus enhancing the accuracy of prediction. Through experiments on real online education datasets and comparison with existing benchmark methods, it is observed that MBFKT has evident advantages in predicting performance with good interpretability. Show more

Keywords: personalized learning, knowledge tracing, multiple behavioral features, memory links, educational data mining

DOI: 10.3233/JIFS-224351

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10747-10764, 2023

Authors: Sivaranjani, S. | Vivek, C.

Article Type: Research Article

Abstract: Spectrum sensing will be an essential component in developing cognitive radio networks, which will be an essential component of the subsequent generation of wireless communication systems. Over the course of several decades, a great deal of different strategies, including cyclo-stationary, energy detectors, and matching filters, have been put up as potential solutions. Obviously, each of these methods comes with a few of negatives that you have to take into consideration. When the Signal-to-Noise Ratio (SNR) changes, energy detectors work poorly; cyclo-stationary detectors are technically sophisticated; and employing matching filters needs experience with Primary User (PU) signals. Researchers have recently been …devoting a great deal of attention to Machine Learning (ML) and Deep Learning (DL) algorithms as a result of the potential uses that these algorithms may have in the development of exceptionally accurate spectrum sensing models. The capacity to learn from data in a way that traditional learning algorithms are unable to has led to the rise in prominence of these types of algorithms. The Hybrid Model of Improved Long Short Term Memory with Improved Extreme Learning Models (HILSTM-IELM), to be more specific, is what is being suggested since it reduces the amount of energy that is used during data transmission as well as the range and the duty cycle. Because of this, the disadvantage in existing methodology, proposed technique reduced to a certain level in energy consumption. In the last step of this analysis, the performance of the HILSTM-IELM-based spectrum sensing is compared to that of a variety of different methods that are currently in use. According to the findings of recent studies, the spectrum sensing method that was created provides superior performance to that of technologies in terms of the accuracy, sensitivity, and specificity of data transmission systems. Show more

Keywords: Improved long short-term memory, improved extreme learning machines, energy detectors, cyclo-stationary features, machine learning, deep learning algorithms

DOI: 10.3233/JIFS-224376

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10765-10779, 2023

Authors: Wang, Zhiyong

Article Type: Research Article

Abstract: Assessment of energy needed for crack growth in concrete structures has been an interesting topic since the use of fracture mechanics to concrete. However, experimental procedures need time, cost and efforts. Based on historical data, regression approaches were created using mechanical characteristics and mixed design factors to quantify the concrete preliminary (Gf ) and whole (GF ) fracture energy. This work combined support vector regression (SVR) analysis with antlion optimization (ALO) and Harris Hawks optimization (HHO) approaches to build a hybridized SVR evaluation to fully comprehend Gf and GF . Evaluation metrics demonstrate that both optimized ALO-SVR and HHO-SVR …assessments could perform wonderfully throughout the estimation mechanism. Whenever the superior SVR investigation was contrasted to the literature, it was observed that the uniquely developed ALO-SVR regression also provides a reasonable boost in effectiveness, with benefits across the board. Finally, although the HHO-SVR technique has its particular capabilities in the simulating procedure, the ALO-SVR analysis seems to be highly reliable for determining Gf and GF . Show more

Keywords: Preliminary and entire fracture energy, concrete, SVR analysis, metaheuristic optimization algorithms

DOI: 10.3233/JIFS-224464

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10781-10798, 2023

Authors: Kasture, Neha | Jain, Pooja

Article Type: Research Article

Abstract:  Speech Recognition and its potential applications in terms of “talking devices” have become indispensable in today’s world. Technological advances like mobiles, smart home assistants or tablets extensively use the techniques of automatic speech recognition that works good for adults but cannot always follow and understand children’s speech. The primary goal of this paper is to bridge the gap of communication between voice assistants and Indian children speaking English as secondary language. The issue of lack of children’s speech corpora with English as non-native language, is addressed by creating a dataset of children in the age group of 5-15 years, speaking …Hindi or Marathi as their mother tongue and English as their second language. The analysis and implementation of the proposed work shows the accuracy of approximately 96% and potential for further scope by increasing the size of dataset in lower age group. The key contributions of our work are (i) creating speech dataset of Indian children whose mother-tongue is Hindi or Marathi, (ii) employing and evaluating hybrid Convolutional Neural Network (CNN) as an age classifier, (iii) language modeling to customize children vocabulary, (iv) checking accuracy and performance of the system. Show more

Keywords: Analysis of Children’s Speech, Automatic Speech Recognition, Child-Machine Interaction, Children’s Speech Recognition, Convolutional Neural Network

DOI: 10.3233/JIFS-224472

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10799-10813, 2023