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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Chen, Zhipeng | Liu, Xiao | Qin, Jianhua
Article Type: Research Article
Abstract: To solve the problem that the walking jitter of quadruped robots leads to the degradation of clarity of visual imaging, a quadruped robot visual imaging jitter compensation algorithm based on the theory of walking jitter is proposed. The D-H coordinate transformation method is used to establish the coordinate system of each joint of the leg. The kinetic equations of the leg are derived from the relationship between the rotational velocity and the moment of the leg joint, and the kinetic equilibrium equations of the quadruped robot body are established based on the spatial moment equilibrium theorem; the spring-mass model of …the leg of the quadruped robot is used to construct the kinetic equations of the leg jittering, and the kinetic equations of the body jittering are derived using the moment equilibrium condition of the body center of gravity position and under the effect of the leg and body jitter to obtain the visual imaging device jitter quantity; finally, the tremor quantity is combined with the jitter quantity and rotation matrix to derive the walking jitter mathematical model of the quadruped robot visual imager, and the jitter compensation algorithm of quadruped robot visual imager is verified. The experimental results show that compared with the traditional Wiener filter algorithm for jitter compensation and the BP neural network jitter compensation algorithm, this algorithm improves the visual imaging by 10.8% and 3.3% in the two evaluation indexes of peak signal-to-noise ratio and structural similarity, respectively, and the de-jittering effect is better. Show more
Keywords: Quadruped robot, visual imaging, walking jitter, compensation algorithm
DOI: 10.3233/JIFS-235345
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8771-8782, 2024
Authors: Xiong, Haoyu | Yang, Leixin | Fang, Gang | Li, Junwei | Xiang, Yu | Zhang, Yaping
Article Type: Research Article
Abstract: Test-time augmentation (TTA) has become a widely adopted technique in the computer vision field, which can improve the prediction performance of models by aggregating the predictions of multiple augmented test samples without additional training or hyperparameter tuning. While previous research has demonstrated the effectiveness of TTA in visual tasks, its application in natural language processing (NLP) tasks remains challenging due to complexities such as varying text lengths, discretization of word elements, and missing word elements. These unfavorable factors make it difficult to preserve the label invariance of the standard TTA method for augmented text samples. Therefore, this paper proposes a …novel TTA technique called Defy, which combines nearest-neighbor anomaly detection algorithm and an adaptive weighting network architecture with a bidirectional KL divergence entropy regularization term between the original sample and the aggregated sample, to encourage the model to make more consistent and reliable predictions for various augmented samples. Additionally, by comparing with Defy, the paper further explores the problem that common TTA methods may impair the semantic meaning of the text during augmentation, leading to a shift in the model’s prediction results from correct to corrupt. Extensive experimental results demonstrate that Defy consistently outperforms existing TTA methods in various text classification tasks and brings consistent improvements across different mainstream models. Show more
Keywords: Test-time augmentation, test-time robustification, text classification, language model, anomaly detection
DOI: 10.3233/JIFS-236010
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8783-8798, 2024
Authors: Vijaya Lakshmi, A. | Vaitheki, K. | Suresh Joseph, K.
Article Type: Research Article
Abstract: Over the years, numerous optimization problems have been addressed utilizing meta-heuristic algorithms. Continuing initiatives have always been to create and develop new, practical algorithms. This work proposes a novel meta-heuristic approach employing the slender Loris optimization algorithm (SLOA), miming slender Loris behavior. The behavior includes foraging, hunting, migration and communication with each other. The ultimate goal of the devised algorithm is to replicate the food-foraging behaviour of Slender Loris (SL) and the quick movement of SL when threatened (i.e.) their escape from predators and also mathematically modelled the special communication techniques of SL using their urine scent smell. SLOA modelled …SL’s slow food foraging behaviour as the exploitation phase, and moving between the tree and escaping from a predator is modelled as the exploration phase. The Eyesight of slender Loris plays a vital role in food foraging during nighttime in dim light. The operator’s Eyesight is modelled based on the angle of inclination of SL. The urine scent intensity is used here to be instrumental in preventing already exploited territory activities, which improves algorithm performance. The suggested algorithm is assessed and tested against nineteen benchmark test operations and evaluated for effectiveness with standard widely recognized meta-heuristics algorithms. The result shows SLOA performing better and achieving near-optimal solutions and dominance in exploration–exploitation balance in most cases than the existing state-of-the-art algorithms. Show more
Keywords: Slender loris optimization algorithm, exploitation and exploration, optimization problems, swarm intelligence algorithm, metaheuristic
DOI: 10.3233/JIFS-236737
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8799-8810, 2024
Authors: Chen, Junzhuo | Lu, Zonghan | Kang, Shitong
Article Type: Research Article
Abstract: In the wake of the global spread of monkeypox, accurate disease recognition has become crucial. This study introduces an improved SE-InceptionV3 model, embedding the SENet module and incorporating L2 regularization into the InceptionV3 framework to enhance monkeypox disease detection. Utilizing the Kaggle monkeypox dataset, which includes images of monkeypox and similar skin conditions, our model demonstrates a noteworthy accuracy of 96.71% on the test set, outperforming conventional methods and deep learning models. The SENet module’s channel attention mechanism significantly elevates feature representation, while L2 regularization ensures robust generalization. Extensive experiments validate the model’s superiority in precision, recall, and F1 score, …highlighting its effectiveness in differentiating monkeypox lesions in diverse and complex cases. The study not only provides insights into the application of advanced CNN architectures in medical diagnostics but also opens avenues for further research in model optimization and hyperparameter tuning for enhanced disease recognition. Show more
Keywords: CNN, InceptionV3, SENet, L2 regularization, monkeypox disease, deep learning
DOI: 10.3233/JIFS-237232
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8811-8828, 2024
Authors: Zhou, Yinwei | Hu, Jun
Article Type: Research Article
Abstract: The rough set model has been extended to interval rough number decision systems, but the existing studies do not consider interval rough number decision systems with missing values. To this end, a rough set model of incomplete interval rough number decision systems (IIRNDSs) is proposed, and its uncertainty measures are investigated. Firstly, the similarity of two incomplete interval rough numbers (IIRNs) are defined by calculating their optimistic and pessimistic distances of the lower and upper approximation intervals of IIRNs. Then, the rough sets in IIRNDSs are constructed by the induced similarity relation. Next, four uncertainty measures, including approximation accuracy, approximation …roughness, conditional entropy, and decision rough entropy are given, which exhibit a monotonic variation with changes in the size of attribute sets, α, and θ. Finally, the experimental results demonstrate the proposed rough set model of IIRNDSs is feasible and effective. Show more
Keywords: Incomplete interval rough number decision systems, interval rough number, similarity relation, uncertainty measure, rough sets
DOI: 10.3233/JIFS-237320
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8829-8843, 2024
Authors: Guo, Hong | Yang, Jin | Yang, Jun
Article Type: Research Article
Abstract: This paper proposes a method of using machine learning and an evolutionary algorithm to solve the flexible job shop problem (FJSP). Specifically, a back propagation (BP) neural network is used as the machine learning method, the most widely used genetic algorithm (GA) is employed as the optimized object to address the machine-selection sub-problem of the FJSP, and particle swarm optimization (PSO) is utilized to solve the operation-order sub-problem of the FJSP. At present, evolutionary algorithms such as the GA, PSO, ant colony algorithm, simulated annealing algorithm, and their optimization algorithms are widely used to solve the FJSP; however, none of …them optimizes the initial solutions. Because each of these algorithms only focuses on solving a single FJSP, they can only use randomly generated initial solutions and cannot determine whether the initial solutions are good or bad. Based on these standard evolutionary algorithms and their optimized versions, the JSON object was introduced in this study to cluster and reconstruct FJSPs such that the machine learning strategies can be used to optimize the initial solutions. Specifically, the BP neural networks are trained so that the generalization of BP neural networks can be used to judge whether the initial solutions of the FJSPs are good or bad. This approach enables the bad solutions to be filtered out and the good solutions to be maintained as the initial solutions. Extensive experiments were performed to test the proposed algorithm. They demonstrated that it was feasible and effective. The contribution of this approach consists of reconstructing the mathematical model of the FJSP so that machine learning strategies can be introduced to optimize the algorithms for the FJSP. This approach seems to be a new direction for introducing more interesting machine learning methodologies to solve the FJSP. Show more
Keywords: Flexible job shop scheduling problem, mechanical engineering, evolutionary algorithms, machine learning
DOI: 10.3233/JIFS-224021
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8845-8863, 2024
Authors: Wang, Tianxiong | Xu, Mengmeng | Yang, Liu | Zhou, Meiyu | Sun, Xin
Article Type: Research Article
Abstract: Kansei Engineering (KE) is a product design method that aims to develop products to meet users’ emotional preferences. However, traditional KE faces the problem that the acquisition of Kansei factors does not represent the real consumers demands based on manual and reports, and using traditional methods to calculate relationship between Kansei factors and specific design elements, which can lead to the omission of key information. To address these problems, this study adopts text mining and backward propagation neural networks (BPNN) to propose a product form design method from a multi-objective optimization perspective. Firstly, Term Frequency-Inverse Document Frequency (TF-IDF) and WordNet …are used to extract key user Kansei requirements from online review texts to obtain more accurate Kansei knowledge. Secondly, the BPNN is used to establish the non-linear relationship between product Kansei factors and specific design elements, and a preference mapping prediction model is constructed. Finally, BPNN is transformed into an iterative prediction value of non-dominated sorting genetic algorithm-II (NSGA-II), and the model is solved through multi-objective evolutionary algorithm (MOEA) to obtain the Pareto optimal solution set that satisfies the user’s multiple emotional needs, and the fuzzy Delphi method is used to obtain the best product form design scheme that meets the user’s multiple emotional images. Using the example of electric bicycle form design could show that this proposed method can effectively complete multi-objective product solutions innovation design. Show more
Keywords: Text mining, Back propagation neural network (BPNN), Multi-objective evolutionary algorithm (MOEA), Non-dominated sorting genetic algorithm-II (NSGA-II), Kansei engineering
DOI: 10.3233/JIFS-230668
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8865-8885, 2024
Authors: Maleki, Monavareh | Ebrahimi, Mohamad | Davvaz, B.
Article Type: Research Article
Abstract: The concept of entropy and information gain of BE-algebras in scientific disciplines such as information theory, data science, supply chain and machine learning assists us to calculate the uncertanity of the scientific processes of phenomena. In this respect the notion of filter entropy for a transitive BE-algebra is introduced and its properties are investigated. The notion of a dynamical system on a transitive BE-algebra is introduced. The concept of the entropy for a transitive BE-algebra dynamical system is developed and, its characteristics are considered. The notion of equivalent transitive BE-algebra dynamical systems is defined, and it is proved the fact …that two equivalent BE-algebra dynamical systems have the same entropy. Theorems to help calculate the entropy are given. Specifically, a new version of Kolmogorov– Sinai Theorem has been proved. The study introduces the concept of information gain of a transitive BE-algebra with respect to its filters and investigates its properties. This study proposes the use of filter entropy to approximate the level of risk introduced by a BE-algebra dynamical system. This aim is reached by defining the information gain with respect to the filters of a BE-algebra. This methodology is well developed for use in engineering, especially in industrial networks. This paper proposes a novel approach to assess the quantity of uncertainty, and the impact of information gain of a BE-algebra dynamical system. Show more
Keywords: Generator, transitive BE-algebra, dynamical system, entropy, information gain
DOI: 10.3233/JIFS-232363
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8887-8901, 2024
Authors: Ma, Ping | Ni, Zhengwei
Article Type: Research Article
Abstract: Time series forecasting has a wide range of applications in various fields. To eliminate the need for time series data volume, a meta-learning-based few-shot time series forecasting method is proposed. This method uses a residual stack module as its backbone and connects the residuals forward and backward through a multilayer fully connected network so that the model and the meta-learning framework can be seamlessly combined. The Empirical knowledge of different time-sequence tasks is obtained through meta-training. To enable fast adaptation to new prediction tasks, a small meta-network is introduced to adaptively and dynamically generate the learning rate and weight decay …coefficient of each step in the network. This method can use sequences of different data distribution characteristics for cross-task learning, and each training task only needs a small number of time series to achieve sequence prediction for the target task. The results show that compared with the two baselines, the proposed method has improved performance on 67.07% and 58.53% of the evaluated tasks. Thus, this method can effectively alleviate the problems caused by insufficient data during training and has broad application prospects in the field of time series. Show more
Keywords: Time series forecasting, few-shot learning, meta learning, residual stack model
DOI: 10.3233/JIFS-233520
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8903-8916, 2024
Authors: Gul, Rimsha | Bashir, Maryam
Article Type: Research Article
Abstract: As the volume of data continues to grow, the significance of text classification is on the rise. This vast amount of data majorly exists in the form of texts. Effective data preparation is essential to extract sentiment data from this vast amount of text, as irrelevant and redundant information can impede valuable insights. Feature selection is an important step in the data preparation phase as it eliminates irrelevant and insignificant features from the huge features set. There exist a large body of work related to feature selection for image processing but limited research is done for text data. While some …studies recognize the significance of feature selection in text classification, but there is still need for more efficient sentiment analysis models that optimize feature selection and reduce computational. This manuscript aims to bridge these gaps by introducing a hybrid multi-objective evolutionary algorithm as a feature selection mechanism, combining the power of multiple objectives and evolutionary processes. The approach combines two feature selection techniques within a binary classification model: a filter method, Information Gain (IG), and an evolutionary wrapper method, Binary Multi-Objective Grey Wolf Optimizer (BMOGWO). Experimental evaluations are conducted across six diverse datasets. It achieves a reduction of over 90 percent in feature size while improving accuracy by nearly nine percent. These results showcase the model’s efficiency in terms of computational time and its efficacy in terms of higher classification accuracy which improves sentiment analysis performance. This improvement can be beneficial for various applications, including recommendation systems, reviews analysis, and public opinion observation. However, it’s crucial to acknowledge certain limitations of this study. These encompass the need for broader classifier evaluation, and scalability considerations with larger datasets. These identified limitations serve as directions for future research and the enhancement of the proposed approach. Show more
Keywords: Feature selection, sentiment analysis, multi-objective optimization, evolutionary algorithms
DOI: 10.3233/JIFS-234615
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8917-8932, 2024
Authors: Yang, Jiyun | Gui, Can
Article Type: Research Article
Abstract: Malware attack is a growing problem on the Android mobile platform due to its popularity and openness. Although numerous malware detection approaches have been proposed, it still remains challenging for malware detection due to a large amount of constantly mutating apps. The opcode, as the most fundamental part of Android app, possesses good resistance against obfuscation and Android version updates. Due to the large number of opcodes, most opcode-based methods employ statistical-based feature selection, which disrupts the correlation and semantic information among opcodes. In this paper, we propose an Android malware detection framework based on sensitive opcodes and deep reinforcement …learning. Firstly, we extract sensitive opcode fragments based on sensitive elements and then encode the features using n -gram. Next, we use deep reinforcement learning to select the optimal subset of features. During the process of handling opcodes, we focus on preserving semantic information and the correlation among opcodes. Finally, our experimental results show an accuracy of 0.9670 by using the 25 opcode features we obtained. Show more
Keywords: Android malware, deep reinforcement learning, feature selection, machine learning
DOI: 10.3233/JIFS-235767
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8933-8942, 2024
Authors: Chen, Hongan | Zhang, Zongfu | Luo, Qingjia | Chen, Rongbin | Zhao, Yang
Article Type: Research Article
Abstract: Existing methods for recognizing partial discharge patterns in power cables do not utilize fuzzy clustering of the discharge signals, resulting in poor quality and low recall and precision of the pattern recognition. To address this, we propose a new approach for partial discharge pattern recognition in cables using Gustafson-Kessel(GK) Fuzzy Clustering. The method involves acquiring signals from a power cable partial discharge monitoring system and then processing the signals with GK fuzzy clustering. The clustered discharge signals are filtered with wavelet packet transforms before input into an improved adaptive resonance theory(ART) neural network for final pattern recognition. Experiments demonstrate the …new technique achieves up to 98.7% recall and 85.6% precision for discharge pattern recognition, with discharge signal Signal Noise Ratio(SNR) between 55 dB and 62 dB and maximum recognition accuracy reaching 98%. The proposed fuzzy clustering-based pattern recognition approach significantly enhances partial discharge diagnostics for power cable monitoring. Show more
Keywords: Gustafson-Kessel(GK) fuzzy clustering, power cable, partial discharge, pattern recognition, wavelet packet transform
DOI: 10.3233/JIFS-235945
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8943-8959, 2024
Authors: Jianping, Liu | Yingfei, Wang | Jian, Wang | Meng, Wang | Xintao, Chu
Article Type: Research Article
Abstract: To better understand users’ behavior patterns in web search, numerous click models are proposed to extract the implicit interaction feedback. Most existing click models are heavily based on the implicit information to model user behaviors, ignoring the impact of explicit information between queries and documents in search sessions. In this paper, we fully consider the topic relevance between queries and documents in search sessions and propose a novel topic relevance-aware click model (TRA-CM) for web search. TRA-CM consists of a relevance estimator and an examination predictor. The relevance estimator consists of a topic relevance predictor and a click context encoder. …In the topic relevance predictor, we utilize the pre-trained BERT model to model the content information of queries and documents in search sessions. Meanwhile, we use transformer to encode users’ click behaviors in the click context encoder. We further apply a two-stage fusion strategy to obtain the final relevance scores. The examination predictor estimates the examination probability of each document. We further utilize learnable filters to attenuate log noise and obtain purer input features in both relevance estimator and examination predictor, and investigate different combination functions to integrate relevance scores and examination probabilities into click prediction. Extensive experiment results on two real-world session datasets prove that TRA-CM outperforms existing click models in both click prediction and relevance estimation tasks. Show more
Keywords: BERT, click model, click prediction, deep learning, web search
DOI: 10.3233/JIFS-236894
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8961-8974, 2024
Authors: Sharmila Joseph, J. | Vidyarthi, Abhay
Article Type: Research Article
Abstract: One of the most common types of cancer is Laryngeal cancer, which has a high mortality rate. The primary malignant tumor responsible for this disease is squamous cell carcinoma (SCC). Early diagnosis is very important to avoid experiencing morbidity and mortality. Various tools and techniques are used to detect and monitor laryngeal cancers. Unfortunately, these tools and techniques have various limitations, for example, Existing tools and approaches Mask R-CNN for identifying laryngeal cancer have various performance limitations. These include the inability to accurately identify the disease in its early stages, the complexity of the computational environment, and the time-consuming process …of conducting patient screenings by utilizing diverse image datasets, but it lagging to detect large dataset. In this paper, we present a hybrid deep-learning model which can be used to analyze and monitor the different symptoms of laryngeal cancers. Proposed model takes Laryngeal cancer dataset as input; preprocessing is done using median filter, then data augmentation is applied to increase data diversity, then feature extraction is performed using LBP-KNN, finally cancer identification/classification is done using Mask-RCNN. Proposed model attains Accuracy:99.3%; Precision:97.99%; Recall:98.09% and F-measure: 97.01%. This method could be useful in providing clinical support to radiologists and doctors. The proposed model can be used to detect minor malignancies in patients in a fast and accurate manner. It can also help improve the efficiency of the clinical process by allowing clinicians to screen more patients. Show more
Keywords: Laryngeal cancer, squamous cell carcinoma, Mask R-CNN, local binary pattern, K-nearest neighbors (KNN)
DOI: 10.3233/JIFS-231154
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8975-8992, 2024
Authors: Alqudah, Rajaa | Al-Mousa, Amjed | Faza, Ayman
Article Type: Research Article
Abstract: Traffic on highways has increased significantly in the past few years. Consequently, this has caused delays for the drivers in reaching their final destination and increased the highway’s congestion level. Many options have been proposed to ease these issues. In this paper, a model of the highway drivers’ population was built based on several factors, including the behavioral patterns of the drivers, like drivers’ time flexibility to reach the destination, their carpool eligibility, and their tolerance to pay the toll price, in addition to the traffic information from the system. A fuzzy logic decision-making model is presented to emulate how …drivers would choose the lane to use based on the aforementioned factors and the current congestion levels of all the lanes on the highway. The presented model, along with the simulation results from applying the model to different simulation scenarios, show the usefulness of such a model in predicting an optimal toll value. Such optimal value would reduce congestion on the highway at one end while maximizing the revenue for the toll company. Show more
Keywords: Fuzzy logic, decision-making, probabilistic model, toll pricing, traffic management
DOI: 10.3233/JIFS-231352
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8993-9006, 2024
Authors: Singh, Surender | Sharma, Sonam
Article Type: Research Article
Abstract: A Single-valued neutrosophic set (SVNS) has recently been explored as a comprehensive tool to assess uncertain information due to varied human cognition. This notion stretches the domain of application of the classical fuzzy set and its extended versions. Various comparison measures based on SVNSs like distance measure, similarity measure, and, divergence measure have practical significance in the study of clustering analysis, pattern recognition, machine learning, and computer vision-related problems. Existing measures have some drawbacks in terms of precision and exclusion of information and produce unreasonable results in categorization problems. In this paper, we propose a generic method to define new …divergence measures based on common aggregation operators and discuss some algebraic properties of the proposed divergence measures. To further appreciate the proposed divergence measures, their application to pattern recognition has been investigated in conjunction with the prominent existing comparison measures based on SVNSs. The comparative assessment sensitivity analysis of the proposed measures establishes their edge over the existing ones because of appropriate classification results. Show more
Keywords: Single-valued neutrosophic set, aggregation operator, pattern recognition, divergence measure
DOI: 10.3233/JIFS-232369
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9007-9020, 2024
Authors: Dai, Songsong
Article Type: Research Article
Abstract: The well-known iterative boolean-like law a →(a → b ) = a → b can be generalized to the functional equation I (x , I (x , y )) = I (x , y ), where I is a fuzzy implication. In this paper, we discuss an approximation of the equation, I (x , I (x , y )) ≈ I (x , y ), i.e., the law is approximately valid. Furthermore, we study the property of approximation preserving with respect to compositions of fuzzy implications. Finally, we give a necessary condition and a sufficient condition for the approximate equation of (S , N )-implications.
Keywords: Functional equation, iterative boolean-like law, fuzzy implication, (S, N)-implication
DOI: 10.3233/JIFS-233435
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9021-9028, 2024
Authors: Li, Xiaoli | Du, Linhui | Yu, Xiaowei | Wang, Kang | Hu, Yongkang
Article Type: Research Article
Abstract: During the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems, precise energy consumption prediction plays an important role in achieving energy savings and optimizing system performance. However, the HVAC system is a complex and dynamic system characterized by a large number of variables that exhibit significant changes over time. Therefore, it is inadequate to rely on a fixed offline model to adapt to the dynamic changes in the system that consume tremendous computation time. To solve this problem, a deep neural network (DNN) model based on Just-in-Time learning with hyperparameter R (RJITL) is proposed in this paper to predict …HVAC energy consumption. Firstly, relevant samples are selected using Euclidean distance weighted by Spearman coefficients. Subsequently, local models are constructed using deep neural networks supplemented with optimization techniques to enable real-time rolling energy consumption prediction. Then, the ensemble JITL model mitigates the influence of local features, and improves prediction accuracy. Finally, the local models can be adaptively updated to reduce the training time of the overall model by defining the update rule (hyperparameter R ) for the JITL model. Experimental results on energy consumption prediction for the HVAC system show that the proposed DNN-RJITL method achieves an average improvement of 5.17% in accuracy and 41.72% in speed compared to traditional methods. Show more
Keywords: HVAC, energy consumption, weighted similarity measure, deep neural network, Just-in-Time learning
DOI: 10.3233/JIFS-233544
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9029-9042, 2024
Authors: Mohammed Mustafa, M. | Kalpana Devi, S. | Althaf Ali, A. | Gunavathie, M.A.
Article Type: Research Article
Abstract: Wireless body sensor networks have gained significant importance across diverse fields, including environmental monitoring, healthcare, and sports. This research is concentrated on sports applications, specifically exploring the viability of a wireless body area network tailored for high-performing athletes. The paper is divided into three sections. First, the design of the node location that is used for real-time monitoring of a sportsperson in which the node position, such as the human thigh, foot, arm, wrist, and chest, was estimated and the best position was selected. Second, the accuracy of an application when related to the other schemes such as TDMA with …ZigBee and RA-TDMA & PA-TDMA was done. The reliability using RA-TDMA performed well and showed approximately 98% reliability. Finally, the features of wireless communiqués that affect the presentation of the network for RA-TDMA were estimated, such as delay and jitter. These findings collectively contribute to advancing the understanding of optimizing wireless body sensor networks for sports applications, with notable achievements including the identification of the arm as the optimal sensor placement, achieving a 98% success rate, and surpassing alternative techniques in network performance parameters like packet delivery rate. Show more
Keywords: Location points, real time scheduling, RATDMA, BSN
DOI: 10.3233/JIFS-234275
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9043-9055, 2024
Authors: Qu, Ying | Wang, Xuming
Article Type: Research Article
Abstract: In order to effectively prevent and control accidents, it is essential to trace back the causes of gas explosions in cities. The DT-AR(decision tree-association rule) algorithm is proposed as a quantitative analysis of gas accident features and causality. First, 210 gas explosion accident investigation reports were taken as samples. The gas accident causation system is divided into three aspects, including environmental factors, management factors and physical factors. Management factors were sorted into organizational-level and individual-level factors from the investigation reports. Second, the CART decision tree model was used to compare location features, organizational causality features, and individual causality features of …the piped and bottled gas accidents, and a decision tree model with the gas system fault site as the root node was built to filter the key feature variables. In order to reveal factor correlations and deep-level causation, the Apriori algorithm is used to mine accident association rules. The combinations on the branches of the decision tree are used as constraints to filter the critical causality rule, which improves the efficiency of association rule screening and enhances prediction accuracy. The results demonstrate that the DT-AR algorithm can evaluate the importance of variables, quickly locate effective combinations of factors, and mine the complete causal chain. The association rule is screened based on the constraint of the key element combination of the decision tree, which compensates for the low efficiency of the Apriori algorithm for association rule mining. In addition, the accident-caused excavation results provide an effective path for gas companies, outsourced service companies and administrative departments to implement gas safety chain supervision, which can address the problem of gas accident safety management failures and provide decision support for accident prevention. Show more
Keywords: 24model, decision tree model, association rule, gas explosion
DOI: 10.3233/JIFS-234372
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9057-9068, 2024
Authors: Tomy, Navin | Johnson, T.P.
Article Type: Research Article
Abstract: This paper deals with lattice isomorphic L -topological spaces. We are concerned with a question: Under what conditions will a lattice isomorphic L -topological spaces be L -homeomorphic. We give contributions to this question in three different ways.
Keywords: L-homeomorphism, quasi L-homeomorphism, lattice isomorphism, pL-homeomorphism
DOI: 10.3233/JIFS-234375
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9069-9082, 2024
Authors: Liu, Gan | Qi, Guirong | Wan, Sanyu
Article Type: Research Article
Abstract: Imbalanced data is a serious binary classification difficulty in forecasting the well-being of the elderly. This paper improves the Smote algorithm from the algorithm and sample dimensions to tackle the issue of imbalanced distribution of questionnaire data. The k-means Smote is combined with RBFNN as K-RBFNN Smote in the algorithm dimension and add FCM link to resample the minority set in the sample dimension as FCM K-RBFNN Smote. In order to improve the generalization of models, the RUS module is added to the algorithm. Experiments are carried out on four improved Smote technologies and two existing Smote technologies combined with …XGBoost, which is superior than the other five conventional classification models. The experimental results indicate that the performance order is RUS FCM K-RBFNN Smote > K-RBFNN Smote > FCM K-RBFNN Smote > RUS K-RBFNN Smote > K-Means Smote > FCM Smote. The RUS FCM K-RBFNN method has been identified as the optimal approach for enhancing performance, resulting in a 98.58% accuracy rate. In conclusion, Smote algorithm undergoes the implementation of K-RBFNN shows greater performance and the enhancement of FCM and RUS relies on the structure of sampling. Show more
Keywords: RUS FCM K-RBFNN Smote, XGBoost, imbalanced data, elderly well-being classification
DOI: 10.3233/JIFS-235213
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9083-9102, 2024
Authors: Abraham, Asha | Kayalvizhi, R. | Mohideen, Habeeb Shaik
Article Type: Research Article
Abstract: Nowadays, cancer has become more alarming. This paper discusses the most significant Ovarian Cancer, Epithelial Ovarian Cancer (EOC), due to the low survival rate. The proposed algorithm for this work is a ‘Multi classifier ShapRFECV based EOC’ (MSRFECV-EOC) subtype analysis technique that utilized the EOC data from the National Centre for Biotechnology Information and Cancer Cell Line Encyclopedia websites for early identification of EOC using Machine Learning Techniques. This approach increases the data size, balances different classes of the data, and cuts down the enormous number of features unrelated to the disease of interest to prevent overfitting. To incorporate these …functionalities, in the data preprocessing stage, OC-related gene names were taken from the Cancermine database and other OC-related works. Moreover, OC datasets were merged based on OC genes, and missing values of EOC subtypes were identified and imputed using Iterative Logistic Imputation. Synthetic Minority Oversampling Technique with an Edited Nearest Neighbors approach is applied to the imputed dataset. Next, in the Feature Selection phase, the most significant features for subtypes of EOC were identified by applying the Shapley Additive Explanations based on the Recursive Feature Elimination Cross-Validation (ShapRFECV) algorithm, preserving predefined features while selecting new EOC features. Eventually, an accuracy of 97% was achieved with Optuna-optimized Random Forest, which outperformed the existing models. SHAP plotted the most prominent features behind the classification. The Pickle tool saves much training time by preserving hidden parameter values of the model. In the final phase, by using the Stratified K Fold Stacking Classifier, the accuracy was improved to 98.9%. Show more
Keywords: Machine learning, Ovarian cancer, Pickle, multi classification, Random Forest
DOI: 10.3233/JIFS-236197
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9103-9117, 2024
Authors: Jumde, Amol | Keskar, Ravindra
Article Type: Research Article
Abstract: With tremendous evolution in the internet world, the internet has become a household thing. Internet users use search engines or personal assistants to request information from the internet. Search results are greatly dependent on the entered keywords. Casual users may enter a vague query due to lack of knowledge of the domain-specific words. We propose a query reformulation system that determines the context of the query, decides on keywords to be replaced and outputs a better-modified query. We propose strategies for keyword replacements and metrics for query betterment checks. We have found that if we project keywords into the vector …space of word projection using word embedding techniques and if the keyword replacement is correct, clusters of a new set of keywords become more cohesive. This assumption forms the basis of our proposed work. To prove the effectiveness of the proposed system, we applied it to the ad-hoc retrieval tasks over two benchmark corpora viz TREC-CDS 2014 and OHSUMED corpus. We indexed Whoosh search engine on these corpora and evaluated based on the given queries provided along with the corpus. Experimental results show that the proposed techniques achieved 9 to 11% improvement in precision and recall scores. Using Google’s popularity index, we also prove that the reformulated queries are not only more accurate but also more popular. The proposed system also applies to Conversational AI chatbots like ChatGPT, where users must rephrase their queries to obtain better results. Show more
Keywords: Query reformulation, WordNet, word embedding, whoosh, TREC
DOI: 10.3233/JIFS-236296
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9119-9137, 2024
Authors: Selvakumar, B. | Abinaya, P. | Lakshmanan, B. | Sheron, S. | Smitha Rajini, T.
Article Type: Research Article
Abstract: Security and privacy are major concerns in this modern world. Medical documentation of patient data needs to be transmitted between hospitals for medical experts opinions on critical cases which may cause threats to the data. Nowadays most of the hospitals use electronic methods to store and transmit data with basic security measures, but these methods are still vulnerable. There is no perfect solution that solves the security problems in any industry, especially healthcare. So, to cope with the arising need to increase the security of the data from being manipulated the proposed method uses a hybrid image encryption technique to …hide the data in an image so it becomes difficult to sense the presence of data in the image while transmission. It combines Least Significant Bit (LSB) Algorithm using Arithmetic Division Operation along with Canny edge detection to embed the patient data in medical images. The image is subsequently encrypted using keys of six different chaotic maps sequentially to increase the integrity and robustness of the system. Finally, an encrypted image is converted into DNA sequence using DNA encoding rule to improve reliability. The experimentation is done on the Chest XRay image, Knee Magnetic Resonance Imaging (MRI) image, Neck MRI image, Lungs Computed Tomography (CT) Scan image datasets and patient medical data with 500 characters, 1000 characters and 1500 characters. And, it is evaluated based on time coefficient of encryption and decryption, histogram, entropy, similarity score (Mean Square Error), quality score (peak signal-to-noise ratio), motion activity index (number of changing pixel rate), unified average changing intensity, image similarity score (structure similarity index measurement) between original and encrypted images. Also, the proposed technique is compared with other recent state of arts methods for 500 characters embedding and performed better than those techniques. The proposed method is more stable and embeds comparatively more data than other recent works with lower Mean Square Error value of 4748.12 which is the main factor used to determine how well the data is hidden and cannot be interpreted easily. Also, it achieved a Peak Signal-Noise Ratio (PSNR) value of 71.34 dB, which is superior than other recent works, verifying that the image quality remains uncompromising even after being encrypted. Show more
Keywords: Hybrid image encryption, least significant bit algorithm, arithmetic division operation, canny edge detection algorithm, chaotic maps, DNA encoding
DOI: 10.3233/JIFS-236637
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9139-9153, 2024
Authors: Zhong, Yu | Shen, Bo | Wang, Tao
Article Type: Research Article
Abstract: Document-level relation extraction aims to uncover relations between entities by harnessing the intricate information spread throughout a document. Previous research involved constructing discrete syntactic matrices to capture syntactic relationships within documents. However, these methods are significantly influenced by dependency parsing errors, leaving much of the latent syntactic information untapped. Moreover, prior research has mainly focused on modeling two-hop reasoning between entity pairs, which has limited applicability in scenarios requiring multi-hop reasoning. To tackle these challenges, a syntax-enhanced multi-hop reasoning network (SEMHRN) is proposed. Specifically, the approach begins by using a dependency probability matrix that incorporates richer grammatical information instead of …a sparse syntactic parsing matrix to build the syntactic graph. This effectively reduces syntactic parsing errors and enhances the model’s robustness. To fully leverage dependency information, dependency-type-aware attention is introduced to refine edge weights based on connecting edge types. Additionally, a part-of-speech prediction task is included to regularize word embeddings. Unrelated entity pairs can disrupt the model’s focus, reducing its efficiency. To concentrate the model’s attention on related entity pairs, these related pairs are extracted, and a multi-hop reasoning graph attention network is employed to capture the multi-hop dependencies among them. Experimental results on three public document-level relation extraction datasets validate that SEMHRN achieves a competitive F1 score compared to the current state-of-the-art methods. Show more
Keywords: Attention mechanism, document-level relation extraction, syntactic information, multi-hop reasoning
DOI: 10.3233/JIFS-237167
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9155-9171, 2024
Authors: Amiri-Bideshki, M. | Hoskova-Mayerova, S. | Ameri, R.
Article Type: Research Article
Abstract: The purpose of this paper is to study some properties of modular hyperlattices. We state and prove some propositions (theorems) of [2 ] with a stronger condition(modularity) than distributivity. We prove that if hyperlattice L with bottom element 0 is modular, then 0 ∨ 0 =0 and there exists no element in x ∨ x greater than x . Also, we study pentagonal hyperlattice that is non-modular. Finally, some results of fundamental relation are given.
Keywords: Hyperlattice, modular element, pentagonal hyperlattice
DOI: 10.3233/JIFS-237912
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9173-9178, 2024
Authors: Khan, Younas | Ashraf, Shahzaib | Farman, Muhammad | Abdallah, Suhad Ali Osman
Article Type: Research Article
Abstract: Achieving household food security is the tumbling issue of the century. This article explores the factors affecting household food security and solutions by utilizing a synergy of statistical and mathematical models. The methodology section is divided into two portions namely sociological and mathematical methods. Sociologically, 379 household heads were interviewed through structured questions and further analyzed in terms of descriptive and binary logistic regression. The study found that 4 independent variables (poverty, poor governance, militancy, and social stratification) showed a significant association (P = 0.000) to explain variations in the dependent variable (household FS). The Omnibus test value (χ2 = 102.386; P … = 0.000) demonstrated that the test for the entire model against constant was statistically significant. Therefore, the set of predictor variables could better distinguish the variation in household FS. The Nagelkerke’s R Square (R2 = .333) helps to interpret that the prediction variable and the group variables had a strong relationship. Moreover, 23% to 33% variation in FS was explained by the grouping variables (Cox and Snell R2 = 0.237 and Nagelkerke’s R2 = 0.333). The significant value of Wald test results for each variable confirmed that the grouping variables (poor governance P = 0.004, militancy P = 0.000, social stratification P = 0.021 and poverty P = 0.000) significantly predicted FS at the household level. Mathematically, all the statistics were validated further through the application of spherical fuzzy mathematics (TOPIS and MADM) to explore what factors are affecting household FS. Thus, the study found that F 3 (poverty ) > F 2 (militancy) > F 4 (social stratification) > F 1 (poor governance) respectively. Thus, it could be concluded from these findings that the prevalence of poverty dysfunctional all the channels of household FS at the macro and micro levels. Therefore, a sound and workable model to eradicate poverty in the study area by ensuring social safety nets for the locals was put forward some of the policy implications for the government are the order of the day. Show more
Keywords: Food security, militancy, poor governance, social stratification, poverty, logistic regression, TOPIS, MADM, spherical fuzzy set
DOI: 10.3233/JIFS-237938
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9179-9195, 2024
Authors: Seethappan, K. | Premalatha, K.
Article Type: Research Article
Abstract: Even though various features have been investigated in the detection of figurative language, oxymoron features have not been considered in the classification of sarcastic content. The main objective of this work is to present a system that can automatically classify sarcastic phrases in multi-domain data. This multi-domain dataset consisting of 67850 sarcastic and non-sarcastic data is collected from various websites to identify sarcastic or non-sarcastic utterances. Multiple approaches are examined in this work to improve sarcasm identification: 1. A Combination of fasttext embedding, syntactic, semantic, lexical n-gram, and oxymoron features 2. TF-IDF feature weighting scheme 3. Three machine learning algorithms …(SVM, Multinomial Naïve Bayes, and Random Forest), three deep learning algorithms (CNN, LSTM, MLP), and one ensemble model (CNN + LSTM) The CNN + LSTM model achieves a Precision of 91.32%, Recall of 92.85%, F-Score of 92.08%, accuracy of 92.01%, and Kappa of 0.84 by combining the fasttext embedding, bigram, syntactic, semantic, and oxymoron features with TF-IDF method. These experimental results show CNN + LSTM with a combination of all features outperforms the other algorithms in classifying the sarcasm in both datasets. The sarcasm classification performance of our dataset and another sarcasm news dataset was compared while applying the above model. Show more
Keywords: Natural language processing, sarcasm, figurative language, deep learning, CNN, oxymoron
DOI: 10.3233/JIFS-224110
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9197-9207, 2024
Authors: Sangeetha, R. | Kuriakose, Bessy M. | Naveen, V. Edward | Jenefa, A. | Lincy, A.
Article Type: Research Article
Abstract: Classifying VoIP (Voice over Internet Protocol) traffic is vital for optimizing network performance and Quality of Service (QoS). This study introduces the Multivariate Statistical-Based Classification (MVSC) system, designed to classify network traffic with high accuracy and efficiency. As traditional methods struggle in the diverse and complex landscape of today’s network traffic, which includes voice, video, gaming, and data, the MVSC algorithm rises to the challenge. It employs Statistical Dissemination and leverages various statistical features such as Packet Size, Inter-Arrival Statistics, Packet and Data rates, Flow Length, and Five-tuple information to create nuanced profiles of network traffic packets. These packets are …then grouped into distinct clusters based on their statistical attributes through Application Flow Cluster Grouping. A unique aspect of the MVSC system is its approach to representing each application flow as points in a two-dimensional space, where distances to predefined application profiles are calculated. The nearest profile then determines the type of VoIP traffic. Experimental results using university traffic data (KU-IDS) underscore the system’s high accuracy, consistently around 98-99%. These findings affirm the system’s suitability for real-time deployment. In summary, the MVSC system offers a robust and efficient solution for VoIP traffic classification, significantly boosting network performance and QoS, and proving to be an invaluable asset in contemporary network management. Show more
Keywords: Statistical dissemination, artificial intelligence, clustering algorithms, semi-supervised models, statistical analysis, VoIP traffic
DOI: 10.3233/JIFS-231113
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9209-9223, 2024
Authors: Rezaei, Reza | Shahidi, Seyed-Ahmad | Abdollahzadeh, Sohrab | Ghorbani-Hasansaraei, Azade | Raeisi, Shahram Naghizadeh | Hayati, Jamileh
Article Type: Research Article
Abstract: Proper and systematic management of food industry failures can improve the quality of products and save a lot on the costs of organizations and people’s health. One of the conventional methods for risk assessment is the Failure Modes and Effects Analysis (FMEA) which is often performed in a phase or stage. Compared to the combined methods, this method is less accurate due to similar priorities of failure in the evaluation and the lack of consideration of the interaction between risks. The current research has applied an integrated approach based on two techniques, FMEA and Fuzzy Cognitive Map (FCM), in a …multi-stage manner to increase assessment accuracy and ranking of failures. By considering the risks of an industry in an uncertain environment and the causal relationships between failures, this approach can evaluate the industry’s risks better than conventional methods. In the research method, the initial prioritization of failures by the FMEA method is used as the input of the multi-stage FCM. The cause-and-effect relationship between the failures is determined by experts and the functional records of the processes, and the FCM is prepared. Since no research evaluates the risks of the malting industry step by step and considers the causal relationships between the risks, the present study has improved risk evaluation in the malting industry by using a multi-stage FCM. The ranking results with the proposed hybrid approach and its comparison with the conventional methods showed that the rating became more accurate, and the multiple priorities were improved. Managers of the malt beverage industry can make effective investment decisions to reduce or better control the risks of this industry by using the results of applying the proposed approach. Show more
Keywords: Fuzzy cognitive map, beverage industry failures, risk evaluation, FMEA
DOI: 10.3233/JIFS-233277
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9225-9247, 2024
Authors: Vasanthamani, K. | Pavai Madheswari, S.
Article Type: Research Article
Abstract: This paper deals with a discrete-time Geo/G/1 queue with repeated attempts and starting failure. If the server fails to start, it is sent for repair. During a repair process, alterations in the repair times is permitted based on current requirements. Customers are served on priority by the pre-emptive resume queue discipline. The distributions of the various system states when the system is in stable are analysed using the generating function technique. Analytical expressions are supported by numerical illustrations to exhibit the influence of the various parameters of the system on the performancemeasures.
Keywords: Discrete-time retrial queues, general retrial times, unreliable server, impatience, priority, replacement in repair times
DOI: 10.3233/JIFS-233406
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9249-9259, 2024
Authors: Selvy, R. | Vinod Kumar, P.B.
Article Type: Research Article
Abstract: It is observed that IFSs are defined based on the concept that the iterates take only an integer number of times. This work studies the dynamics of functions, where a function can iterate r times for every r ∈ R . Utilizing concepts from fuzzy sets, r -times iterates of a function are defined for r ∈ R . The study demonstrates that the chaotic property can be generalized to this new iterative concept. The chaotic behavior of a function is then extended using this iterative concept.
Keywords: Iterated function systems, fuzzy functions, chaotic functions
DOI: 10.3233/JIFS-236563
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9261-9270, 2024
Authors: Arunkumar, N. | Nagaraj, Balakrishnan | Keziah, M. Ruth
Article Type: Research Article
Abstract: Parkinson disease (PD) is a type of neurodegenerative disorder that affects the motor movement of the patient. But each technique has its own advantages or disadvantages. In gene, speech and handwriting data model, the feature extraction and reduction is an important step for efficient classification. These two steps require proper attention for selection and also require high processing time as compared to other data model like images. Because in image modality, the deep learning algorithm can be applied that can perform all process and automate the classification. As compared to these domains, the signal produces better and best results. Because …the electroencephalogram (EEG) signal are taken from the brain using electrodes and it helps to observe the brain signals effectively and immediately as compared to the other data modals. Hence, in this paper, the wavelet transform will be used to decompose the signals and statistical features will be extracted from the transformed signal. Here, the satin bower bird optimization will be used for both type of wavelet selection and feature reduction process for final classification. The reduced feature set will be classified using Ensemble Neural Network type including InceptionV3, DenseNet, MobileNet, Xception, and NasNet) recently proposed for medical image classification. The whole process will be realized using MATLAB R2021a software and its performance will be evaluated in terms of Accuracy and is compared against Automated Tunable Q-wavelet transform performance. The proposed ensemble method, employing EEG signal processing and neural networks, achieved a 97% success rate in discriminating PD datasets, surpassing Convolutional Neural Network (CNN) and Machine Learning (ML) classifications (88% –92%). Utilizing MATLAB R2021a, its superiority over Q-wavelet transform was evident, signifying improved PD dataset discrimination. Show more
Keywords: Parkinson diseases, EEG signals, wavelet transform, features, optimization, classifier
DOI: 10.3233/JIFS-236145
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9271-9290, 2024
Authors: Huang, Mengtao | Wang, Jiaxuan
Article Type: Research Article
Abstract: Pedestrian trajectory prediction plays a crucial role in autonomous driving, as its accuracy directly affects the autonomous driving system’s comprehension of the environment and subsequent decision-making processes. Current trajectory prediction methods tend to oversimplify pedestrians to mere point coordinates, utilizing positional information to infer interactions among individuals while overlooking the temporal correlations between them, thereby excessively simplifying pedestrian characteristics. To address the aforementioned issues, this paper proposes a trajectory prediction model for autonomous driving applications, that takes into account both pedestrian motion characteristics and scene interaction. The model optimizes the LSTM unit structure twice, serving to learn correlations among long …trajectories of pedestrians and to integrate multiple forms of information into the neighborhood interaction module. Furthermore, our model introduces dual attention mechanisms for individuals and scenes, focusing on the key motion points of individual pedestrians and their interactive behavior with others in busy scenarios. The efficacy of the model was validated on the MOT16 pedestrian dataset and the Daimler pedestrian path prediction dataset, outperforming mainstream methods with 8% and 10% reductions in Average Displacement Error and Final Displacement Error respectively. Show more
Keywords: Trajectory prediction, automated driving, CNN-LSTM, deep learning
DOI: 10.3233/JIFS-236271
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9291-9310, 2024
Authors: Wu, Hui-Yong | Zhou, Zi-Wei | Li, Hong-Kun | Yang, Tong-Tong
Article Type: Research Article
Abstract: In order to enhance the accuracy and reliability of fault diagnosis in chemical processes, this paper proposes a methodology for chemical process fault diagnosis based on an improved SE-ResNet-BiGRU neural network. Initially, the ResNet model is enhanced by incorporating the SENet mechanism, enabling the extraction of features from input data and selectively enhancing them, thereby strengthening the model’s ability to capture crucial features. Subsequently, the BiGRU model is employed to perform temporal modeling on the extracted features, allowing for better capture of dynamic changes in fault signals. In order to validate the effectiveness of this approach, experiments are conducted using …the TE chemical process dataset. The results are analyzed using methods such as ROC-AUC, confusion matrix, and t-SNE visualization. The improved SE-ResNet-BiGRU model achieves a testing accuracy of 97.78% and an average fault diagnosis rate of 97.24%. Compared to other deep learning methods, this methodology exhibits significant improvements in fault diagnosis rate and reliability. It holds promising potential as an essential tool for fault diagnosis in chemical processes, contributing to enhanced production safety, efficiency, and reduced risk of accidents. Show more
Keywords: Fault diagnosis, residual neural network, bidirectional gate recurrent unit, squeeze-and-excitation network, t-distributed Stochastic neighbor embedding
DOI: 10.3233/JIFS-236948
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9311-9328, 2024
Authors: Huang, Jui-Chan | Shu, Ming-Hung | Lin, Hsiang-Tsen | Day, Jen-Der
Article Type: Research Article
Abstract: With the fast advances of new energy vehicles, the EV battery technology needs to be further improved to follow the step. How to effectively diagnose the electric vehicle’s lithium battery fault becomes a hotspot in the academic circle. This study has proposed new method that uses the state of charge of the battery and self-coder depth to detect faults in the lithium battery group of electric vehicles. First, the study investigates the single lithium battery faults. Then, it builds a lithium battery group fault diagnosis model by integrating the battery charge state and denoising converter network. Finally, it uses a …dataset and retired battery group to validate the model’s performance. The results show that the proposed model achieves an accuracy of 93.18% and a recall rate of 93.73% in identifying the faults in the lithium batteries of the electric vehicles and its F1 value is as high as 0.95. Moreover, the modeling method has the lowest prediction error, indicating its high accuracy and robustness in diagnosing the faults of battery packs. This study aims to provide an effective solution for the fault diagnosis of lithium battery packs in electric vehicles. Show more
Keywords: Transformer framework, DAE, electric vehicle, lithium battery, fault diagnosis
DOI: 10.3233/JIFS-237796
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9329-9341, 2024
Authors: Razzaque, Huzaira | Ashraf, Shahzaib | Sohail, Muhammad | Abdeljawad, Thabet
Article Type: Research Article
Abstract: Spherical q-linear Diophantine fuzzy sets (Sq-LDFSs) showed a significant improvement to handling uncertainty in multi-criteria decision-making (MADM). It is advantageous for two-parametric data as well as for data with three variable parameters. One of the most crucial functions of supply chain management is to increase competitive pressure. The study’s standout innovation, Multi-Attributive Ideal Real Comparative Analysis (MAIRCA), has been implemented to give powerful group decision-making. An ecological perspective is becoming more prevalent due to the competitive climate and customer perception. Green supplier selection (GSS) has become a significant issue. In this study, we address the problem of GSS, which aims …for flexibility, robustness, ecological sensitivity, leanness, and feasibility. The feasibility criteria in recycling, environmental, carbon footprints, and water consumption are different from those in standard supplier selection. The aim of our work is to introduced the weighted Average/Geometric aggregation operators based on Sq-LDFSs. For this we defined some operational rules as a foundation of aggregation operators. Secondly we proposed a MAIRCA approach for Sq-LDFSs to address these issues. The MAIRCA strategy, which uses multi-criteria group decision-making (MCGDM) to evaluate and choose traditional and environmental conventionalities, is used to reduce instability and ambiguity. The spherical q-linear Diophantine fuzzy MAIRCA approach provides comparative analysis of decision-makers and criteria. By merging Sq-LDFS and MAIRCA, a hybrid strategy is formed, successfully selecting the best provider among options based on the order of significance. These numerical examples demonstrate the suggested MCGDM approaches that were applied in actual situations, giving a realistic appreciation of their efficacy. The comparative study of the final ranking further supports the idea that these strategies are dependable in decision-making processes in addition to being practical and usable. Show more
Keywords: Spherical q-linear Diophantine fuzzy set, MAIRCA technique, Spherical q-linear Diophantine fuzzy weighted aggregation operators based on algebraic norms, decision making
DOI: 10.3233/JIFS-235397
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9343-9366, 2024
Authors: Li, Chunling | Zhang, Yi
Article Type: Research Article
Abstract: The existing negative selection algorithms can not improve their detection performance by human intervention during the testing process. This paper proposes a negative selection algorithm with human-in-the-loop for anomaly detection. It uses self-sample clusters to train detectors with a nonrandom strategy. Its detectors and self-sample clusters fully cover state space without overlapping each other. It locally adjusts detectors and self-sample clusters with human intervention to improve its detection performance during the testing process. Experiments were performed on two synthetic datasets and the Iris dataset from the UCI repository to assess its performance. The results show that it outperforms the other …anomaly detection methods in most cases. Show more
Keywords: Negative selection algorithm, human-in-the-loop, anomaly detection, artificial immune algorithm, artificial immune system
DOI: 10.3233/JIFS-235724
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9367-9380, 2024
Authors: Wu, Xiaogang
Article Type: Research Article
Abstract: The similarity measure of intuitionistic fuzzy sets is a primary method for dealing with uncertainty and fuzzy problems and is commonly used in fuzzy decision-making and pattern recognition. The current mainstream similarity measure is based on the classical fuzzy set with only one negation, which often violates the intuitionistic problem in applications because the actual semantics of multiple negations are not considered. To solve the inconsistency and irrationality problems in the classical similarity methods, we introduce three negations (contradiction negation, opposition negation, and mediation negation) in the intuitionistic fuzzy set to obtain the generalized intuitionistic fuzzy set and prove its …related property theorem. On this basis, our similarity measure adopts a mediational negation to represent non-membership, which fully utilizes the multiple negation information of non-membership and hesitancy and avoids the loss of fuzzy information. We verify the method’s rationality, validity, and originality through pattern recognition experiments and numerical examples, which improves the performance of intuitionistic fuzzy set similarity in practical applications and provides a new approach for future research on intuitionistic fuzzy inference. Show more
Keywords: Generalized intuitionistic fuzzy sets (GIFS), three kinds of negation, similarity measure, fuzzy decision
DOI: 10.3233/JIFS-236510
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9381-9391, 2024
Authors: Balasubramaniyan, M. | Navaneethan, C.
Article Type: Research Article
Abstract: Artificial intelligence has played a significant role in the expansion of the agriculture industry in recent times by evaluating data and making recommendations for better production. An automated method for determining significant information in seed quality analysis is the peanut maturity analysis in image processing through sensory images. The majority of the time, changes in picture intensity result in feature independence and precise maturity level determination. Therefore, agricultural precision in identifying essential features is low. To address this issue, we suggest employing a Cross-Layer Multi-Perception Neural Network (CLMPNN) for hyperspectral sensory image feature observation in order to determine the optimal …assessment of peanut maturity in agriculture. The sensing unit first determines the angular cascade projection’s (ACP) structural dependencies for the peanut pod structure. With the aid of color-intensive saturation, the entity projection of pod growth is found using the Slicing Fragment Segmentation (SFS) technique. This generates the various entity variations by integrating relational maturity and non-maturity findings with spectral values. Next, cross-layer multi-perception neural networks are trained with hyperspectral values optimized by LSTM to distinguish between mature and immature pods. In comparison to the other system, this one does exceptionally well in precision agriculture, with a 98.6 well recall rate, a 97.3% classification accuracy, and a 98.9% production accuracy. Show more
Keywords: Peanut maturity, feature selection and classification, deep learning, cascade projection, slicing segmentation
DOI: 10.3233/JIFS-239332
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9393-9407, 2024
Authors: Xiao, Yanjun | Li, Rui | Zhao, Yue | Wang, Xiaoliang | Liu, Weiling | Peng, Kai | Wan, Feng
Article Type: Research Article
Abstract: The rapier loom works in a complex environment and operates at high speeds. It is inevitable that its performance will deteriorate during the production process, which in turn will cause faults. The development of maintenance has undergone the transition from “regular maintenance” and “post-event maintenance” to “predictive maintenance”. In order to achieve the synergistic optimization goal of ensuring operational safety and reducing operational costs, a predictive maintenance method driven by the fusion of digital twin and deep learning is proposed based on the idea of “combining the real with the virtual and controlling the real”. Firstly, a digital twin system …structure model of rapier weaving machine is constructed, and the overall architecture of digital twin is proposed according to the full operation cycle of rapier weaving machine. Then, the digital twin-driven process parameter evaluation and prediction and health state evaluation and prediction are investigated separately. In order to achieve the evaluation and prediction of process parameters to ensure the efficiency of weaving machine operation, the prediction method of IWOA optimized BP neural network driven by twin data is proposed and the model is updated and optimized based on the martingale distance approach. In order to achieve health state assessment and prediction, we use health index as an evaluation index to characterize the health condition of spindles, and use BiLSTM network to achieve prediction of remaining spindle life and then make maintenance decisions. The results show that there are greater advantages to combining deep learning and digital twin technology for intelligent predictive maintenance of rapier loom. Show more
Keywords: Digital twin, predictive maintenance, deep learning, rapier loom
DOI: 10.3233/JIFS-233863
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9409-9430, 2024
Authors: Zhou, Ning | Liu, Bin | Cao, Jiawei
Article Type: Research Article
Abstract: Facial expression recognition has long been an area of great interest across a wide range of fields. Deep learning is commonly employed in facial expression recognition and demonstrates excellent performance in large-sample classification tasks. However, deep learning models often encounter challenges when confronted with small-sample expression classification problems, as they struggle to extract sufficient relevant features from limited data, resulting in subpar performance. This paper presents a novel approach called the Multi-CNN Logical Reasoning System, which is based on local area recognition and logical reasoning. It initiates the process by partitioning facial expression images into two distinct components: eye action …and mouth action. Subsequently, it utilizes logical reasoning based on the inherent relationship between local actions and global expressions to facilitate facial expression recognition. Throughout the reasoning process, it not only incorporates manually curated knowledge but also acquires hidden knowledge from the raw data. Experimental results conducted on two small-sample datasets derived from the KDEF and RaFD datasets demonstrate that the proposed approach exhibits faster convergence and higher prediction accuracy when compared to classical deep learning-based algorithms. Show more
Keywords: Facial expression recognition, logic reasoning, few-shot learning, local area recognition
DOI: 10.3233/JIFS-233988
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9431-9447, 2024
Authors: Zhang, Yongzhi | He, Keren | Ge, Jue
Article Type: Research Article
Abstract: Pedestrian detection plays a crucial role in ensuring traffic safety within the domain of computer vision. However, accurately detecting pedestrians in complex environments proves to be a challenge due to issues such as occlusion. To address this issue, this paper presents an end-to-end pedestrian detection model founded on the DEtection TRansformer (DETR) architecture, effectively managing occlusion scenarios. The proposed model comprises a backbone Convolutional Neural Network (CNN) and a Transformer network. The backbone CNN incorporates variable convolution and U-Net design principles to enhance feature extraction capabilities, particularly for occluded pedestrians. Additionally, our innovative Adaptive Occlusion-Aware Attention Mechanism (AOAM) is embedded …within the Transformer network, allowing the model to dynamically adjust attention weights and enhance the localization and identification of occluded pedestrians. Extensive experiments on the Caltech and ETH datasets demonstrate the superior performance of our model compared to state-of-the-art approaches across four key evaluation metrics. This study provides effective methodologies and theoretical foundations for pedestrian detection in complex environments. Show more
Keywords: Pedestrian detection, occlusion-aware, attention mechanism, DETR
DOI: 10.3233/JIFS-235386
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9449-9464, 2024
Authors: Li, Junwei | Lian, Mengmeng | Jin, Yong | Xia, Miaomiao | Hou, Huaibin
Article Type: Research Article
Abstract: To address the issue of unknown expert and attribute weights in the comprehensive assessment of hospitals, as well as the potential challenges posed by distance measures, this paper presents a probabilistic language multi-attribute group decision-making (MAGDM) approach that utilizes correlation coefficients and improved entropy. First, the correlation function, called the probabilistic linguistic correlation coefficient, is introduced into the probabilistic linguistic term set(PLTS) to measure the consistency among experts, so as to obtain the weights of experts. Next, based on Shannon entropy, an improved probabilistic linguistic entropy is proposed to measure the uncertainty of PLTS considering the number of alternatives and …information quantity. Then, based on the correlation coefficient and improved entropy, the attribute weights are obtained. In addition, in order to overcome the counter-intuitive problem of existing distance measurement, this paper proposes a probabilistic language distance measurement method based on the Bray-Curtis distance to measure the differences between PLTSs. On this basis, by applying the technique for order preference by similarity to ideal solution (TOPSIS) method and using PLTSs to construct the MAGDM method, the ranking of alternative schemes is generated. Finally, the improved MAGDM method is applied to an example of the comprehensive evaluation of the smart medical hospitals. The results show that compared with the existing methods, this method can determine the weight information more reasonably, and the decision-making results are not counter-intuitive, so it can evaluate the hospital more objectively. Show more
Keywords: Probabilistic linguistic term set (PLTS), multi-attribute group decision-making (MAGDM), expert weights, attribute weights, correlation coefficient
DOI: 10.3233/JIFS-235593
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9465-9478, 2024
Authors: Mahmood, Tahir | Hussain, Kashif | Ahmmad, Jabbar | Shahab, Sana | ur Rehman, Ubaid | Anjum, Mohd
Article Type: Research Article
Abstract: The notion of a T-bipolar soft set (T - BS ft S ) is the structure that has the ability to discuss the two-sided aspects of certain situations like the effects and side effects of a medicine. Moreover, T - BS ft S has the ability to discuss the parametrization tool as well. Also, notice that a group is an algebraic structure that is the key tool in many branches of mathematics. In many decision-making situations, we have to discuss the two-sided aspects of a certain situation and we can see that T - BS ft S is …the only structure that can handle it. So based on a characteristic of T - BS ft S and groups theory there is a need to define the combined notion of T - BS ft S and group. So, based on these valuable structures, in this manuscript, we aim to introduce the notion of T-bipolar soft groups by generalizing T-bipolar soft sets. Based on this newly defined structure, we have defined the basic operational laws like extended union, extended intersection, restricted union, restricted intersection, AND product, and OR product for T-bipolar soft groups. Moreover, we have observed the impact of these newly defined notions on T-bipolar soft groups. Show more
Keywords: Soft set, soft groups, T-bipolar soft set, T-bipolar soft groups
DOI: 10.3233/JIFS-236150
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9479-9490, 2024
Authors: Madhubala, P. | Ghanimi, Hayder M.A. | Sengan, Sudhakar | Abhishek, Kumar
Article Type: Research Article
Abstract: The medical domain faces unique challenges in Information Retrieval (IR) due to the complexity of medical language and terminology discrepancies between user queries and documents. While traditional Keyword-Based Methods (KBM) have limitations, the integration of semantic knowledge bases and concept mapping techniques enhances data organization and retrieval. Addressing the growing demands in the biomedical field, a novel medical Information Retrieval System (IRS) is proposed that employs Deep Learning (DL) and KBM. This system comprises five core steps: pre-processing of texts, document indexing using DL (ELMo) and KBM, advanced query processing, a BiLSTM-based retrieval network for contextual representation, and a KR-R …re-ranking algorithm to refine document relevance. The purpose of the system is to give users improved biomedical search results through the integration of all of these techniques into a method that takes into consideration the semantic problems of medical records. An in-depth examination of the TREC-PM track samples from 2017 to 2019 observed an impressive leading MRR score of 0.605 in 2017 and a best-in-class rPrec score of 0.350 in 2019, proving how well able the system is to detect and rank relevant medical records accurately. Show more
Keywords: Biomedical information retrieval, BiLSTM, DL, accuracy, query semantics, kernel ridge regression
DOI: 10.3233/JIFS-237056
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9491-9510, 2024
Authors: Gao, Yanbing | Ma, Rui
Article Type: Research Article
Abstract: With the deepening development of the financial market, the role of regulatory systems in ensuring green and safe financial environment is becoming increasingly prominent. The traditional intelligent financial regulatory systems on the market lack precise and effective real-time monitoring and recognition capabilities, making it difficult to effectively process and analyze large-scale financial data. In order to improve the real-time recognition of abnormal situations or potential risks, achieve automation and intelligence of supervision, this article combines deep learning technology to study the deep practice of IoT image recognition technology in intelligent financial supervision systems. In response to the “data silos” and …cross regional linkage issues faced by financial industry regulation, this article designs and implements an intelligent regulatory system based on IoT image recognition technology through deep learning. Using Convolutional Neural Network (CNN) algorithm to classify and analyze system images for regulatory and risk control purposes. The research results indicate that the intelligent financial regulatory system constructed in this article has high stability and responsiveness, which can effectively meet the real-time regulatory needs of finance and help promote the healthy development of the financial market. Show more
Keywords: Financial supervision system, internet of things, image recognition technology, deep learning, artificial intelligence
DOI: 10.3233/JIFS-237692
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9511-9523, 2024
Authors: Ren, Zhenxing | Zhang, Jia | Zhou, Yu | Ji, Xinxin
Article Type: Research Article
Abstract: Over the past several decades, several air pollution prevention measures have been developed in response to the growing concern over air pollution. Using models to anticipate air pollution accurately aids in the timely prevention and management of air pollution. However, the spatial-temporal air quality aspects were not properly taken into account during the prior model construction. In this study, the distance correlation coefficient (DC) between measurements made in various monitoring stations is used to identify appropriate correlated monitoring stations. To derive spatial-temporal correlations for modeling, the causality relationship between measurements made in various monitoring stations is analyzed using Transfer Entropy …(TE). This work explores the process of identifying a piecewise affine (PWA) model using a larger dataset and suggests a unique hierarchical clustering-based identification technique with model structure selection. This work improves the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) by introducing Kullback-Leibler (KL) Divergence as the dissimilarity between clusters for handling clusters with arbitrary shapes. The number of clusters is automatically determined using a cluster validity metric. The task is formulated as a sparse optimization problem, and the model structure is selected using parameter estimations. Beijing air quality data is used to demonstrate the method, and the results show that the proposed strategy may produce acceptable forecast performance. Show more
Keywords: PWA model, prediction of air pollutants, spatial-temporal features, hierarchical clustering-based identification
DOI: 10.3233/JIFS-238920
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9525-9542, 2024
Authors: Yang, Xingyao | Chang, Mengxue | Yu, Jiong | Wang, Dongxiao | Dang, Zibo
Article Type: Research Article
Abstract: Social recommendations enhance the quality of recommendations by integrating social network information. Existing methods predominantly rely on pairwise relationships to uncover potential user preferences. However, they usually overlook the exploration of higher-order user relations. Moreover, because social relation graphs often exhibit scale-free graph structures, directly embedding them in Euclidean space will lead to significant distortion. To this end, we propose a novel graph neural network framework with hypergraph and hyperbolic embedding learning, namely HMGCN. Specifically, we first construct hypergraphs over user-item interactions and social networks, and then perform graph convolution on the hypergraphs. At the same time, a multi-channel setting …is employed in the convolutional network, with each channel encoding its corresponding hypergraph to capture different high-order user relation patterns. In addition, we feed the item embeddings and the obtained high-order user embeddings into a hyperbolic graph convolutional network to extract user and item representations, enabling the model to better capture the hierarchical structure of their complex relationships. Experimental results on three public datasets, namely FilmTrust, LastFM, and Yelp, demonstrate that the model achieves more comprehensive user and item representations, more accurate fitting and processing of graph data, and effectively addresses the issues of insufficient user relationship extraction and data embedding distortion in social recommendation models. Show more
Keywords: Social recommendation, hypergraph learning, hyperbolic embedding, graph convolutional network, data mining
DOI: 10.3233/JIFS-235266
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9543-9557, 2024
Authors: Liu, Zhanpeng | Xiao, Wensheng | Cui, Junguo | Mei, Lianpeng
Article Type: Research Article
Abstract: The drilling permanent magnet synchronous motor (DPMSM) contains multiple subsystems with identical structures and has a high probability of failure because the downhole working conditions are harsh. Therefore, the quick localization of faults is difficult to determine although the fault type may be identified in time. The system diagnostic model based on the Bayesian network (BN) can be used for fault diagnosis and localization for components in subsystems, but it is difficult to build and modify due to the complex system in practice. New methods are necessary to reduce the difficulty of building and modifying models. In this study, object-oriented …ideas are introduced into the BN to establish a system diagnostic model based on an Object-oriented Bayesian network (OOBN) for the DPMSM. First, the fault diagnostic models for subsystems based on BN are established, respectively. Then, submodels of forward and backward based on BN are instantiated as instance nodes. Next, instance nodes are connected through input nodes and output nodes to establish the OOBN-based system diagnosis model. Finally, the system diagnosis model is validated by sensitivity analysis and the effectiveness is discussed in Cases. The system diagnosis model can effectively reduce the difficulties of modeling and modifying. Show more
Keywords: Object-oriented Bayesian network, fault diagnosis, instance nodes, sensitivity analysis
DOI: 10.3233/JIFS-236850
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9559-9576, 2024
Authors: Hu, Yuanyuan
Article Type: Research Article
Abstract: Music education has a rich historical background. Nevertheless, the introduction of modern teaching methods is relatively delayed. In recent years, there has been a remarkable acceleration in the advancement of music education. A promising tool that has emerged to revolutionize education as a whole is Virtual Reality (VR) technology, which offers immersive and interactive experiences across various disciplines. At the university level, integrating VR technology into music education opens up exciting opportunities to enhance practical teaching methods and provide students with enriched musical experiences. Virtual Reality together with Internet of Things (IoT) demonstrates its capabilities in various tasks, but its …widespread availability in online learning remainders a pressing challenge that needs to be addressed. In pre-processing, it removes noise data using Dynamic Context-Sensitive Filtering (DCSF). VR technology creates an unparalleled learning environment, it transporting students to virtual concert halls, recording studios, or historical music venues. Hence the Multiscale deep bidirectional gated recurrent neural Network (MDBGNN) improves the practical teaching of music course concept, like Music theory, harmony, and rhythm can be visualized and experienced in VR. Finally, Dung Beetle Optimization Algorithm (DBOA) is employed to optimize the weight parameters of MDBGNN. The proposed MDBGNN-DBO-UMC-VRT is implemented in Python. The proposed method is analysed with the help of performance metrics, like precision, accuracy, F1-score, Recall (Sensitivity), Specificity, Error rate, Computation time and RoC. The proposed MDBGNN-DBO-UMC-VRT method attains 13.11%, 18.12% and 18.73% high specificity, 11.13%, 11.04% and 19.51% lower computation Time, 15.29%, 15.365% and 14.551% higher ROC and 13.65%, 15.98%, and 17.15% higher Accuracy compared with existing methods, such as Enhancing Vocal Music Teaching through the Fusion of Artificial Intelligence Algorithms and VR Technology (CNN-UMC-VRT), Exploring the Efficacy of VR Technology in Augmenting Music Art Teaching (BPNN-UMC-VRT) and Implementing an Interactive Music-Assisted Teaching System Using VR Technology (DNN-UMC-VRT) respectively. Show more
Keywords: Dung Beetle optimization, Dynamic Context-Sensitive Filtering, multiscale deep bidirectional gated recurrent neural network, Virtual Reality, music course
DOI: 10.3233/JIFS-236893
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9577-9590, 2024
Authors: Zhao, Xue | Li, Qiaoyan | Xing, Zhiwei | Dai, Xuezhen
Article Type: Research Article
Abstract: Traditional multi-label feature selection is usually performed under the condition of given label information, but nowadays, labeling multi-label data is a huge project, which is both time-consuming and labor-intensive, but if there is no label information condition, it will lead to poor feature selection, in order to solve this problem, this paper proposes a new semi-supervised multi-label feature selection method, i.e., semi-supervised multi-label feature selection algorithm based on dual dynamic graph. In this paper, a semi-supervised multi-label feature selection algorithm is proposed by constructing a dual dynamic graph. First, the method selects the most discriminative features for dimensionality reduction through …the feature selection method of least squares regression, combined with the redundancy penalty of highly correlated features. Second, the label information is added to the construction of sample matrix similarity to learn the similarity. A semi-supervised multi-label feature selection framework is constructed by designing iterative updates of dual dynamic graphs to learn more accurate pseudo-label matrices to guide feature selection. Finally, the paper validates the above model using the alternating iteration optimization algorithm and verifies the effectiveness of the algorithm through experiments. Show more
Keywords: Multi-label learning, semi-supervised, feature selection, dual dynamic graph, redundant regular terms
DOI: 10.3233/JIFS-237146
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9591-9605, 2024
Authors: Kang, Xiaoqiang
Article Type: Research Article
Abstract: The Internet of Things (IoT) refers to a vast network of interconnected devices, objects, and systems powered by sensors, software, and connectivity capabilities. The interconnectivity of IoT devices has led to a substantial increase in data production. Efficiently managing and analyzing large data volumes is a significant challenge for IoT systems. To address this challenge, data aggregation is the primary process. IoT data aggregation aims to provide high-quality service by ensuring fast data transmission, high reliability, minimal energy consumption, and data priority consideration. Data aggregation involves collecting data from multiple sensors and devices and then integrating it using a function …to minimize system traffic. This paper thoroughly examines data aggregation techniques in the IoT context. Techniques are grouped according to underlying principles, and their potential applications, advantages, and limitations are discussed. Show more
Keywords: Internet of things, data aggregation, data transmission, energy consumption
DOI: 10.3233/JIFS-238284
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9607-9623, 2024
Authors: Yu, Peng | Jing, Fengwei | Guo, Jin
Article Type: Research Article
Abstract: This paper studies the security control problem of semi-Markov jump systems. First, the parameter uncertainty, the time-varying delay, the nonlinear function and the cyber attack are considered in the system. Second, the nonlinear function is linearized by the fuzzy logic rule. A sliding mode surface is designed to obtain an equivalent controller and get a sliding mode dynamic system. By constructing Lyapunov functions of the mode dependence, a sufficient condition for H ∞ asymptotic stability of the system is obtained. Then, an adaptive sliding mode controller is established, and the original system reaches the sliding mode surface in a …finite time. Finally, two examples verify the correctness and practicality of the proposed theory. Show more
Keywords: Semi-Markov jump system, sliding mode control, cyber attack, fuzzy logic
DOI: 10.3233/JIFS-238994
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9625-9638, 2024
Authors: Hussain, Abrar | Amjad, Alina | Ullah, Kifayat | Pamucar, Dragan | Ali, Zeeshan | Al-Quran, Ashraf
Article Type: Research Article
Abstract: Supplier selection is a very crucial process within a business or commercial enterprise because it depends upon different components like reliability, customer need, services, cost and reputation. A suitable supplier is familiar with developing a relationship between customer needs and business. To serve this purpose, the multiple attribute group decision-making (MAGDM) technique is a well-known and efficient aggregation model used to evaluate flexible optimal options by considering some appropriate criteria or attributes. Experts face some sophisticated challenges during the decision-making process due to uncertain and ambiguous information about human opinions. To address such conditions, we explore the notion of spherical …fuzzy sets (SFS) and their reliable operations. Some flexible operational laws of Dombi t-norms are also developed in light of spherical fuzzy (SF) information. Combining the theory of Hamy mean (HM) models and Dombi aggregation tools, some robust strategies are also studied in this research work. The main objectives of this article are to propose some dominant strategies in the presence of SF information including spherical fuzzy Dombi Hamy mean (SFDHM), spherical fuzzy Dombi weighted Hamy mean (SFDWHM), spherical fuzzy Dombi Dual Hamy mean (SFDDHM) and spherical fuzzy Dombi weighted Dual Hamy mean (SFDWDHM) operators. The MAGDM techniques are utilized to evaluate the flexibility of our derived methodologies under considering SF information. An experimental case study is utilized to evaluate a notable supplier enterprise under consideration of our developed methodologies. Finally, a comprehensive overview of our research work is also presented. Show more
Keywords: Spherical fuzzy values, Dombi aggregation tools, Hamy mean and Dual Hamy mean, multi-attribute group decision-making system
DOI: 10.3233/JIFS-234514
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9639-9662, 2024
Authors: Indupalli, Manjula Rani | Pradeepini, Gera
Article Type: Research Article
Abstract: Symptom-based disease identification is crucial to the diagnosis of the disease at the early stage. Usage of traditional stacking and blending models i.e., with default values of the models cannot handle the multi-classification data properly. Some of the existing researchers also implemented tuning with the help of a grid search approach but it consumes more time because it checks all the possible combinations. Suppose if the model has n estimators with k values it needs to check (n*k)! elements combination, this makes the learning time high. The proposed model chooses the estimators to train the model with in a considerable …amount of time using an advanced tuning technique known as “Bayes-Search” on an ensemble random forest and traditional, support vector machine. The advantage of this model is its capability to store the best evaluation metrics from the previous model and utilise them to store the new model. This model chooses the values of the estimator based on the probability of selection, which reduces the elements in search space i.e., (< (n-k)!). The proposed model defines the objective function with a minimum error rate and predicts the error rate with the selected estimators for different distributions. The model depending on the predicted value decides whether to store the value or to return the value to the optimizer. The Bayes search optimization has achieved +9.21% accuracy than the grid search approach. Among the two approaches random forest has achieved good accuracy and less loss using Bayes search with cross-validation. Show more
Keywords: Grid search, bayes search, objective function, error minimization, search space
DOI: 10.3233/JIFS-236137
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9663-9676, 2024
Authors: Pang, Yufeng | Li, Xiaojuan
Article Type: Research Article
Abstract: Traditional fault detection methods in acoustic signal feature extraction of rolling bearings often make the signal denoising process complex due to low signal-to-noise ratio and weak fault features, making this method difficult to meet real-time requirements. Therefore, a fault detection model based on Fast-Renoriented SIFT feature extraction is proposed, which can quickly extract a large number of features from the original signal without the need for noise reduction processing and can effectively improve the efficiency and accuracy of fault detection. At the same time, to adapt to the fault detection of rolling bearings under multiple working conditions, this study also …proposes an adaptive extended word bag model that combines local kurtosis and local 2-dimensional information entropy features, improving the adaptability and flexibility of the new model. It obtained a 100% overall recognition rate and a fault detection time of no more than 0.5 seconds in a 5-fold cross-validation experiment, verifying the excellent recognition accuracy, stability, and operational efficiency of the detection model. Its recognition accuracy in the multi-working condition rolling bearing fault detection experiment was above 97%, which was improved by about 21.25% compared to the traditional word bag model and had significant advantages in fault recognition accuracy and computational efficiency. Show more
Keywords: Acoustic signal, bearing fault detection, fast-unoriented SIFT algorithm, feature extraction, word bag model
DOI: 10.3233/JIFS-237331
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9677-9691, 2024
Authors: Shen, Xiaohui
Article Type: Research Article
Abstract: Analyzing Lexical Semantic Changes (LSC) in Educational Texts (ET) refers to examining how the meanings of words, terms, or phrases used in ET have evolved. It involves learning shifts in the semantic content, connotations, and language associations within educational resources such as textbooks, research articles, and instructional content. The analysis can reveal how educational models, pedagogical methods, and terminology have transformed in response to technological innovations, societal changes, and pedagogical developments. This analysis provides visions into the dynamic nature of educational discourse, helping researchers, educators, and policymakers understand how language has adapted to reflect changes in educational paradigms and the …broader educational context. This research investigates the semantic analysis and classification performance within ET, employing the innovative Decision Tree + Feed Forward Neural Networks (DT + FFNNs) framework. This research shows the dynamic semantic relationships inherent in educational terminology by diverse semantic similarity measures and contextualized embeddings. It looks at how educational language changes to reflect changes in society, technology, and pedagogy. The study uses a DT + FFNN framework for semantic analysis and classification. The study uses several embeddings and semantic similarity metrics, and Spearman’s Correlation Coefficient (SCC) is employed to evaluate their effectiveness. This study highlights the DT + FFNN framework’s capacity to capture complex semantics in an educational setting and offers insights into the adaptive nature of educational discourse. SCC serves as a guiding metric, offering insights into the efficiency of several embeddings and measures. The findings show the pivotal role of fine-tuning in significantly enhancing the accuracy of DT + FFNNs across measures, revealing its remarkable potential in capturing semantics within an educational context. Show more
Keywords: Semantic analysis, education, spearman correlation, machine learning, decision tree, and accuracy
DOI: 10.3233/JIFS-237410
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9693-9707, 2024
Authors: Zhang, Weidong | Tan, Huadi
Article Type: Research Article
Abstract: Smart farming is revolutionizing agriculture by integrating advanced technologies to enhance productivity, efficiency, and sustainability. This paper proposes a novel, 5G-enabled Pest and Disease Detection and Response System (PDDRS) that synergizes environmental sensor data with image analytics for comprehensive Plant Disease Detection (PDD). By leveraging the high bandwidth and ultra-low latency capabilities of 5G, our integrated system surpasses traditional communication technologies, facilitating real-time data analytics and immediate intervention strategies. We introduce two Machine Learning (ML) models: an image-based Mask R-CNN with FPN, which achieves a precision of 91.1% and an accuracy of 95.1%, and an environmental-based FFNN + LSTM model, evaluated for …ACC, AUC, and F1-Score, showing promising results in disease forecasting. Our experiments demonstrate that the PDDRS significantly enhances throughput and latency performance under various connected devices, showcasing a scalable, cost-effective solution suitable for next-generation smart farming. These advancements collectively empower the PDDRS to deliver actionable insights, enabling targeted applications such as precise pesticide deployment, and stand as a testament to the potential of 5G in agricultural innovation. Show more
Keywords: IoT, 5G, machine learning, smart farming, accuracy, plant disease prediction, WSN
DOI: 10.3233/JIFS-237482
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9709-9726, 2024
Authors: Zhang, Guili | Li, Pengxi | Zhang, Hanyue | Yu, Yinglong | Liang, Zhao
Article Type: Research Article
Abstract: Our society is being transformed by the technology emergence and the industrial revolution. The advances in the internet and artificial intelligence are reshaping the means of education, profoundly changing the ways of teaching and learning. This paper studies the pattern of how the new 5th generation blended campus network is applied to aid the new generation of intelligence teaching. This pattern is the implementation of national major policies and the measure of cultivating people. This paper introduces a new model for the intelligence teaching system. Based on the new model, distance interaction teaching system, VR practicing teaching system, intelligence testing …system, and higher education intelligence decision system are developed. This model can be the basis of the informatization of future education. Show more
Keywords: 5G, Blended campus network, intelligence teaching, VR practice teaching system
DOI: 10.3233/JIFS-237768
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9727-9738, 2024
Authors: He, Liu | Zhu, Yuanguo | Ye, Tingqing
Article Type: Research Article
Abstract: In recent years, uncertain fractional differential equations was proposed for the description of complex uncertain dynamic systems with historical characteristics. For wider applications of uncertain fractional differential equations, researches on parameter estimation for uncertain fractional differential equations are of great importance. In this paper, based on the thought of least squares estimation and uncertain hypothesis test, an algorithm of parameter estimation for uncertain fractional differential equations is discussed. Finally, we consider the application of uncertain fractional differential equations based model to predict the forecasting stock price of three major indexes of U.S. stocks and make a comparison between uncertain fractional …differential equations, uncertain differential equations and stochastic differential equations. Show more
Keywords: Uncertainty theory, Uncertain fractional differential equations, Parameter estimation, Least squares estimation, Uncertain stock price model
DOI: 10.3233/JIFS-237977
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9739-9753, 2024
Authors: Sung, Tien-Wen | Zhao, Baohua | Zhang, Xin | Lee, Chao-Yang | Fang, Qingjun
Article Type: Research Article
Abstract: Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a kind of swarm-based collaborative optimization algorithm that solves the problem of a position deviation in a DE search by using the co-evolution matrix M instead of the cross-control parameter CR in the differential evolution algorithm (DE). However, QUATRE shares some of the same weaknesses as DE, such as premature convergence and search stagnation. Inspired by the artificial bee colony algorithm (ABC), we propose a new QUATRE algorithm to improve these problems that ranks all the individuals and evolves only the poorer half of the population. In an evolving population, individuals of …different levels intersect with dimensions of different sizes to improve search efficiency and accuracy. In addition, we establish a better selection framework for the parent generation individuals and select more excellent parent individuals to complete the evolution for the individuals trapped in search stagnation. To verify the performance of the new QUATRE algorithm, we divide the comparison algorithm into three groups, including ABC variant group, DE variant group, and QUATRE variant group, and the CEC2014 test suite is used for the comparison. The experimental results show the new QUATRE algorithm performance is competitive. We also successfully apply the new QUATRE algorithm on the 3D path planning of UAV, and compared with the other famous algorithm performance it is still outstanding, which verifies the algorithm’s practicability. Show more
Keywords: QUATRE algorithm, swarm-based optimization, fixed dimension updating, 3D path planning, unmanned aerial vehicle
DOI: 10.3233/JIFS-230928
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9755-9781, 2024
Authors: Amsaprabhaa, M.
Article Type: Research Article
Abstract: Human pose recognition from videotapes has become an emerging research topic for tracking human movements. The objective of this work is to develop a deep multimodal Spatio-Temporal Harris Hawk Optimized Pose Recognition (STHHO-PR) framework for self-learning fitness exercises. The presented STHHO-PR framework uses audio modality and visual modality to classify the different poses. In audio modality, the VGG-16 network paradigm is used to extract the audio traits for fitness pose recognition. In visual modality, Harris Hawks Optimization (HHO) along with the Minimum Cross Entropy (MCE) method is employed to find out the optimum threshold values for body parts segmentation. These …segmented body parts highlight the human joint points that are connected through the skeletonization process to extract the skeletal information. The extracted spatio-temporal features from audio modality and visual modality are optimally fused and used in the classification process. Weighted Majority Voting Ensemble (WMVE) classifier is adopted to build the classification model. This work is experimented with yoga videos acquired from publicly available datasets. The results show that the presented STHHO-PR framework outperforms other state-of-art procedures in terms of prediction accuracy. Show more
Keywords: Harris Hawks Optimization, Minimum Cross Entropy, Weighted Majority Voting Ensemble classifier, yoga video, yoga poses classification
DOI: 10.3233/JIFS-233286
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9783-9805, 2024
Authors: Cui, Wanqiu
Article Type: Research Article
Abstract: Graph data storage has a promising prospect due to the surge of graph-structure data. Especially in social networks, it is widely used because hot public opinions trigger some network structures consisting of massively associated entities. However, the current storage model suffers from slow processing speed in this dense association graph data. Thus, we propose a new storage model for dense graph data in social networks to improve data processing efficiency. First, we identify the public opinion network formed by hot topics or events. Second, we design the germ elements and public opinion bunching mapping relationship based on equivalence partition. Finally, …the Public Opinion Bunching Storage(POBS) model is constructed to implement dense graph data storage effectively. Extensive experiments on Twitter datasets demonstrate that the proposed POBS performs favorably against the state-of-the-art graph data models for storage and processing. Show more
Keywords: Graph data storage, social networks, topic cluster, equivalent partition
DOI: 10.3233/JIFS-233540
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9807-9818, 2024
Authors: Narendiranath Babu, T. | Kothari, Ayush Jain | Rama Prabha, D. | Mokashe, Rohan | Kagita, Krish Babu | Raj kumar, E.
Article Type: Research Article
Abstract: In the modern world, condition monitoring is crucial to the predictive maintenance of machinery. Gearboxes are widely used in machineries and auto motives to achieve the variable speeds. The major problem in gearbox is catastrophic failure due to heavy loads, corrosion and erosion, results in economic loss and creates high safety risks. So, it is necessary to provide condition monitoring technique to detect and diagnose failures, to achieve cost benefits to industry. The main purpose of this study to use Machine Learning (ML) algorithms and Artificial Neural Network (ANN) which are very powerful and reliable tool for fault detection and …its most important attribute is its ability to efficiently detect non-stationary, non-periodic, transient features of the vibration signal. To do the vibration study, an experimental setup was created, and various faults were induced faults of various kinds that usually occurred in the gearbox. The gear in the gear train was subjected to vibration analysis which was captured via a sensor. Signal processing was carried out using MATLAB Toolbox. To automatically identify the flaws in the helical gearbox, an artificial neural network (ANN) and several machines learning methods, including KNN, decision tree, random forest, and SMV, were trained by creating a database from the experiment conducted. The outcomes showed potential in accurately classifying the faults. The results show that ANN has the highest accuracy of 99.6% with a 6.5662 seconds computational time while SVM has the lowest accuracy of 96% among them along with the highest computational time of 21.324 seconds. Show more
Keywords: Helical gearbox, vibration analysis, signal processing, fault diagnosis, artificial neural network, K-nearest neighbor, support vector machine, decision tree, random forest
DOI: 10.3233/JIFS-233602
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9819-9840, 2024
Authors: Guo, Sheng | Tan, Mian | Cai, Shan | Zhang, Zaijun | Liang, Yihui | Feng, Hongxi | Zou, Xue | Wang, Lin
Article Type: Research Article
Abstract: Although facial expression recognition (FER) has a wide range of applications, it may be difficult to achieve under local occlusion conditions which may result in the loss of valuable expression features. This issue has motivated the present study, as a part of which an effective multi-feature cross-attention network (MFCA-Net) is proposed. The MFCA-Net consists of a two-branch network comprising a multi-feature convolution module and a local cross-attention module. Thus, it enables decomposition of facial features into multiple sub-features by the multi-feature convolution module to reduce the impact of local occlusion on facial expression feature extraction. In the next step, the …local cross-attention module distinguishes between occluded and unoccluded sub-features and focuses on the latter to facilitate FER. When the MFCA-Net performance is evaluated by applying it to three public large-scale datasets (RAF-DB, FERPlus, and AffectNet), the experimental results confirm its good robustness. Further validation is performed on a real FER dataset with local occlusion of the face. Show more
Keywords: Facial expression recognition, deep convolution, multi-feature convolution module, local cross-attention module
DOI: 10.3233/JIFS-233748
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9841-9856, 2024
Authors: Geng, Xiuli | Du, Yuanhao | Cao, Shuyuan | Cheng, Sheng
Article Type: Research Article
Abstract: Against the backdrop of increasing global demand for reducing greenhouse gas emissions, promoting the use of energy-saving and environmentally friendly products has become a crucial aspect of low-carbon economic development. Customer satisfaction plays a vital role in the promotion of these products. To address the challenges of dealing with big data in the conventional customer satisfaction analysis tool, Importance Performance Analysis (IPA), a machine learning-based method is proposed to improve IPA. Firstly, the Latent Dirichlet Allocation (LDA) model is used to capture users’ opinions on different product topics. Then, the Support Vector Machine (SVM) and Random Forest (RF) algorithms are …employed respectively to assess the satisfaction and importance of product attributes, enabling an objective measurement of customer satisfaction and adapting to the current trend of big data. The proposed method is applied to the analysis of water heater satisfaction on the JD platform, obtaining satisfaction levels for 10 topics. The research findings demonstrate that the improved IPA method based on SVM-RF effectively explores customer satisfaction and can provide some improvement strategies for platform managers and manufacturers. Show more
Keywords: Low-carbon, customer satisfaction, importance performance analysis, latent dirichlet allocation, support vector machine, random forest
DOI: 10.3233/JIFS-235074
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9857-9871, 2024
Authors: Ratmele, Ankur | Thakur, Ramesh
Article Type: Research Article
Abstract: As more people express their thoughts on products on various online shopping platforms, the feelings expressed in these opinions are becoming a significant source of information for marketers and buyers. These opinions have a big impact on consumers’ decision to buy the best quality product. When there are too many features or a small number of records to analyze, the decision-making process gets difficult. A recent stream of study has used the conventional quantitative star score ratings and textual content reviews in this context. In this research, a decision-making framework is proposed that relies on feature-based opinions to analyze the …textual content of reviews and classify buyer’s opinions, thereby assisting consumers in making long-term purchases. The framework is proposed in this paper for product purchase decision making based on feature-based opinions and deep learning. Framework consists of four components: i) Pre-processing, ii) Feature extraction, iii) Feature-based opinion classification, and iv) Decision-making. Web scraping is used to obtain the dataset of Smartphone reviews, which is subsequently clean and pre-processed using tokenization and POS tagging. From the tagged dataset, noun labeled words are retrieved, and then the probable product’s features are extracted. These feature-based sentences or reviews are processed using a word embedding to generate review vectors that identify contextual information. These word vectors are used to construct hidden vectors at the word and sentence levels using a hierarchical attention method. With respect to each feature, reviews are divided into five classes: extremely positive, positive, extremely negative, negative, and neutral. The proposed method may readily detect a customer’s opinion on the quality of a product based on a certain attribute, which is beneficial in making a purchase choice. Show more
Keywords: Opinions, Opinion Extraction (OE), product features, decision making, hierarchical attention mechanism, GloVe
DOI: 10.3233/JIFS-235389
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9873-9887, 2024
Authors: Mi, Xiaodong | Luo, Qifang | Zhou, Yongquan
Article Type: Research Article
Abstract: Panchromatic and multi-spectral image fusion, called panchromatic sharpening, is the process of combining the spatial and spectral information of the source image into the fused image to give the image a higher spatial and spectral resolution. In order to improve the spatial resolution and spectral information quality of the image, an adaptive multi-spectral image fusion method based on an improved arithmetic optimization algorithm is proposed. This paper proposed improved arithmetic optimization algorithm, which uses dynamic stochastic search technique and oppositional learning operator, to perform local search and behavioral complementation of population individuals, and to improve the ability of population individuals …to jump out of the local optimum. The method combines adaptive methods to calculate the weights of linear combinations of panchromatic and multi-spectral gradients to improve the quality of fused images. This study not only improves the quality and effect of image fusion, but also focuses on optimizing the operation efficiency of the algorithm to have real-time and high efficiency. Experimental results show that the proposed method exhibits strong performance on different datasets, improves the spatial resolution and spectral information quality of the fused images, and has good adaptability and robustness. The source code is available at: https://github.com/starboot/IAOA-For-Image-Fusion . Show more
Keywords: Image fusion, multi-spectral image, panchromatic image, oppositional learning operator, arithmetic optimization algorithm, meta-heuristic
DOI: 10.3233/JIFS-235607
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9889-9921, 2024
Authors: Guo, Yan | Tang, Dezhao | Cai, Qiqi | Tang, Wei | Wu, Jinghua | Tang, Qichao
Article Type: Research Article
Abstract: Under the influence of the coronavirus disease and other factors, agricultural product prices show non-stationary and non-linear characteristics, making it increasingly difficult to forecast accurately. This paper proposes an innovative combinatorial model for Chinese hog price forecasting. First, the price is decomposed using the Seasonal and Trend decomposition using the Loess (STL) model. Next, the decomposed data are trained with the Long Short-term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Finally, the prepared data and the multivariate influence factors after Factor analysis are predicted using the gated recurrent neural network and attention mechanisms (AttGRU) to obtain the …final prediction values. Compared with other models, the STL-FA-AttGRU model produced the lowest errors and achieved more accurate forecasts of hog prices. Therefore, the model proposed in this paper has the potential for other price forecasting, contributing to the development of precision and sustainable agriculture. Show more
Keywords: Machine learning, precision agriculture, digital agriculture, STL, attentional mechanisms
DOI: 10.3233/JIFS-235843
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9923-9943, 2024
Authors: Chen, Liang-Ching | Chang, Kuei-Hu | Wu, Chia-Heng | Chen, Shin-Chi
Article Type: Research Article
Abstract: Although natural language processing (NLP) refers to a process involving the development of algorithms or computational models that empower machines to understand, interpret, and generate human language, machines are still unable to fully grasp the meanings behind words. Specifically, they cannot assist humans in categorizing words with general or technical purposes without predefined standards or baselines. Empirically, prior researches have relied on inefficient manual tasks to exclude these words when extracting technical words (i.e., terminology or terms used within a specific field or domain of expertise) for obtaining domain information from the target corpus. Therefore, to enhance the efficiency of …extracting domain-oriented technical words in corpus analysis, this paper proposes a machine-based corpus optimization method that compiles an advanced general-purpose word list (AGWL) to serve as the exclusion baseline for the machine to extract domain-oriented technical words. To validate the proposed method, this paper utilizes 52 COVID-19 research articles as the target corpus and an empirical example. After compared to traditional methods, the proposed method offers significant contributions: (1) it can automatically eliminate the most common function words in corpus data; (2) through a machine-driven process, it removes general-purpose words with high frequency and dispersion rates –57% of word types belonging to general-purpose words, constituting 90% of the total words in the target corpus. This results in 43% of word types representing domain-oriented technical words that makes up 10% of the total words in the target corpus are able to be extracted. This allows future researchers to focus exclusively on the remaining 43% of word types in the optimized word list (OWL), enhancing the efficiency of corpus analysis for extracting domain knowledge. (3) The proposed method establishes a set of standard operation procedure (SOP) that can be duplicated and generally applied to optimize any corpus data. Show more
Keywords: Corpus, natural language processing (NLP), technical word, advanced general-purpose word list (AGWL), COVID-19
DOI: 10.3233/JIFS-236635
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9945-9956, 2024
Authors: de Oliveira, Heveraldo R. | Vieira, Antônio Wilson | Santos, Laércio Ives | Filho, Murilo César Osório Camargos | Ekel, Petr Ya. | D’Angelo, Marcos Flávio S.V.
Article Type: Research Article
Abstract: When providing patient care, healthcare professionals often rely on interpreting laboratory and clinical test results. However, their analysis is constrained by human capacity, leading to uncertainties in diagnoses. Machine learning has the potential to evaluate a larger amount of data and identify patterns and relationships that may otherwise go unnoticed. However, popular machine learning algorithms typically require abundant and labeled data, which is not always available. To address this challenge, the adoption of active learning allows for the selection of the most relevant instances for training, reducing the need for extensive labeling. Additionally, fuzzy logic offers the ability to handle …uncertainties. This paper proposes a novel approach that utilizes fuzzy membership functions to transform data as a pre-processing step for active learning. The objective is to approximate similar instances, specifically for the purpose of prediction, thereby minimizing the workload of human experts in labeling data for model training. The results of this study demonstrate the effectiveness of this approach in predicting heart disease and highlight the potential of using membership functions to enhance machine learning models in the analysis of medical information. By incorporating fuzzy logic and active learning, healthcare professionals can benefit from improved accuracy and efficiency in diagnosing and predicting pacients’ health conditions. Show more
Keywords: Active learning, fuzzy logic, cardiovascular diseases
DOI: 10.3233/JIFS-237047
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9957-9973, 2024
Article Type: Research Article
Abstract: In order to ensure the safety of life and property in large buildings, the design of emergency evacuation routes for large buildings based on cloud computing and GIS big data is studied. Combining cloud computing and GIS big data, a command model for emergency evacuation of large buildings is built. Emergency evacuation functions are realized through the access layer, business logic layer, cloud computing layer and data layer. GIS big data of large buildings is stored in the model data layer. GIS geographic data is clustered through the MapReduce based parallel K-means clustering algorithm in the cloud computing layer. After …clustering, the emergency evacuation road network of large buildings is constructed through GIS in the business logic layer. On the road network, the emergency evacuation route selection method combining Dikstra algorithm and ant colony algorithm is used to realize the optimal route selection of emergency evacuation of large buildings. Experiments show that this method can effectively select the best evacuation path in large buildings, and the evacuation speed of the selected path is fast, which can ensure the safety of people in buildings. Show more
Keywords: Cloud computing, GIS big data, large buildings, emergency evacuation route, K-means clustering, ant colony algorithm
DOI: 10.3233/JIFS-237834
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9975-9986, 2024
Authors: Xiao, Yan | Liang, Jinqian
Article Type: Research Article
Abstract: In many real production scenarios, departmental organizations often exhibit a hierarchical structure, where departments cooperate with subordinate departments to optimize resource allocation and maximize their respective benefits. However, due to a lack of information or data, many model parameters in the allocation process cannot be precisely defined. In response to this challenge, an interval n -person hierarchical resource allocation model is proposed to achieve maximum economic benefit in uncertain environments. Based on the concepts of satisfactory degrees of comparing intervals and interval-valued cores of interval-valued n -person cooperative games, an auxiliary nonlinear programming model and method are developed to solve …the interval-valued cores of such cooperative games. The approach explicitly considers the inclusion and/or overlap relations between intervals, whereas the traditional interval ranking method may not guarantee the existence of interval-valued cores. The proposed method offers cooperative opportunities under uncertain conditions. Finally, the feasibility and applicability of the models and methods are demonstrated through a numerical example and comparison with other methods. Show more
Keywords: Hierarchical structure, resource allocation, uncertain environment, interval n-person cooperative game, nonlinear programming model
DOI: 10.3233/JIFS-191941
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9987-9998, 2024
Authors: Chau, Vinh Huy | Vo, Anh Thu | Ngo, Huu Phuc
Article Type: Research Article
Abstract: This paper discusses the use of an improved random forest regression algorithm (RFRA) to predict the total score of powerlifters. The paper collected the age, weight, and total score of multiple powerlifters, and then used an improved RFRA to build a predictive model. The parameters of this model are optimized by a differential squirrel search algorithm. The experimental results show that our proposed method can effectively predict the total score of powerlifters with an error of less than 10%, which can provide a reference for experts and athletes before training or competition.
Keywords: Artificial intelligence, random forest, powerlifting, total score
DOI: 10.3233/JIFS-230032
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9999-10004, 2024
Authors: Li, Guofa | Wang, Jinfu | He, Jialong | Wang, Jili | Hou, Tianwei
Article Type: Research Article
Abstract: The reliability of machine tool components, particularly the tool magazine manipulator, significantly affects the overall performance of the machine tool. To address the challenge of accurately evaluating the manipulator’s health status using a single performance indicator, this study proposes a method that combines Fuzzy Comprehensive Evaluation (FCE) and a Combined Weighting Method (CWM). By considering both subjective and objective factors, this method provides a comprehensive evaluation of the manipulator’s health status, enhancing the accuracy and reliability of the assessment. The method utilizes fuzzy distribution to construct membership matrices for different health levels and adopts the CWM that combines the Entropy …Weight Method (EWM) and Analytic Hierarchy Process (AHP) to determine the combined weights of the health evaluation indices. This approach improves the accuracy and reliability by considering multiple indicators and objectively weighting them based on their importance. The current health status of the manipulator is evaluated using the fuzzy weighted average operator and the maximum membership principle. Moreover, a fault prediction method based on Particle Swarm Optimization (PSO) and GM(1,1) is proposed to overcome the information gap and small sample problems. The proposed model’s prediction accuracy is verified by comparing it with other models, demonstrating its effectiveness and reliability. Show more
Keywords: Health status evaluation, fault prediction, fuzzy comprehensive evaluation, grey model, particle swarm optimization
DOI: 10.3233/JIFS-233028
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10005-10018, 2024
Authors: Zhang, Min
Article Type: Research Article
Abstract: Vehicle safety on roadsides is vital for preventing collisions, controlling failures and accidents, and ensuring driver and passenger wellness. Finite Element Analysis (FEA) in vehicle safety relies on the vehicle’s physical attributes for predicting and preventing collisions. This article introduces a Differential FEA (DFEA) model for predicting vehicle collisions regardless of the speed and direction for driver/ passenger safety. The proposed model induces a vehicle’s speed, direction, and displacement from two perspectives: self and approaching vehicle. The collision probability with the trailing or persuading vehicle is calculated by distinguishing the forward and rear features. The differential calculus for the point …of deviation and distance metrics are significantly estimated for a vehicle’s front and rear ends. Such calculus generates a maximum and minimum possibility for self and approaching vehicle contact. This contact is further split based on the collision threshold; the threshold is determined using the safe distance between two vehicles for collision-less driving. The threshold exceeding vehicles are alerted for their change in direction/ speed through contact point (rear/front) differential derivatives. This ensures collision detection under fewer contact errors, leveraging the safety of the duo vehicles. Show more
Keywords: Collision, contact threshold, differential equation, finite element analysis, vehicle safety
DOI: 10.3233/JIFS-233628
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10019-10034, 2024
Authors: Nagamani, T. | Logeswari, S.
Article Type: Research Article
Abstract: A common cardiovascular illness with high fatality rates is coronary artery disease (CAD). Researchers have been exploring alternative methods to diagnose and assess the severity of CAD that are less invasive, cost-effective, and utilize noninvasive clinical data. Machine learning algorithms have shown promising and potential results. Accordingly, this study focuses on assisting medical practitioners with CAD detection by using a hybrid classification system combining XGBoost and Adam optimization. The primary approach incorporates One-Hot encoding to transform categorical attributes within the dataset, enhancing the precision of predictions. The secondary approach constitutes a hybrid classification model integrating XGBoost and employing Adam optimizations …for CAD detections. The efficacy of the recommended method is assessed using the cleveland, Hungarian, and Statlog heart-disease data sets. The proposed system and the standard Grid and Random Search classifiers are compared. The experimental outcomes indicate that the suggested model achieves a notable prediction accuracy of 94.19%. This represents an improvement of 7 to 8% over the existing grid search algorithm and 2 to 3% improvement over the random search algorithm for the above all datasets. Hence, the proposed system can be a valuable tool for identifying CAD patients, offering enhanced prediction accuracy. Show more
Keywords: Adam optimization, coronary artery disease, grid search, one hot encoding, random search, XGBoost
DOI: 10.3233/JIFS-233804
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10035-10044, 2024
Authors: Pan, Hongyan
Article Type: Research Article
Abstract: In addition to providing learners with a large amount of teaching resources, online teaching platforms can also provide learning resources and channels such as video courseware, Q&A tutoring groups, and forums. However, currently, there are still shortcomings in depth and dimensionality in mining student learning behavior data on the platform. In view of this situation, based on the learning interaction behavior, this study established the difficulty similarity model of knowledge points, and used spectral clustering to classify their difficulty. In addition, the study intended to use the maximum frequent subgraph under the Gspan framework to characterize learners’ implicit learning patterns. …The outcomes expressed that the algorithm put forward in the study achieved the highest accuracy index of 98.8%, which was 1.4%, 4.0%, and 8.6% higher than Apriori-based graph mining algorithms, K-means, and frequent subgraph discovery algorithms. In terms of F1 index, the convergence value of the algorithm proposed in the study was 95.5%, which was about 2.5% higher than the last three algorithms. In addition, learners of all three cognitive levels had the highest maximum number of frequent subgraphs with sizes above 100 when the minSup value was 60%. And when the number of clusters was 3, the clustering accuracy of the three learners was the highest. In similarity calculation, the calculation method used in the study was at the minimum in terms of root mean square error and absolute error average index, which were 0.048% and 0.01% respectively. This indicated that the model proposed by the research had better classification effect on the difficulty of knowledge points for learners of different cognitive levels, and had certain application potential. Show more
Keywords: Similarity, knowledge points, clustering, multidimensional, Gspan, accuracy
DOI: 10.3233/JIFS-234274
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10045-10058, 2024
Authors: Liu, Jianping | Chu, Xintao | Wang, Jian | Wang, Meng | Wang, Yingfei
Article Type: Research Article
Abstract: Due to the polysemy and complexity of the Chinese language, Chinese machine reading comprehension has always been a challenging task. To improve the semantic understanding and robustness of Chinese machine reading comprehension models, we propose a model that utilizes adversarial training algorithms and Permuted Language Model (PERT). Firstly, we employ the PERT pre-training model to embed paragraphs and questions into vector space to obtain corresponding sequential representations. Secondly, we use a multi-head self-attention mechanism to extract key textual information from the sequence and employ a Bi-GRU network to semantically fuse the output feature vectors, aiming to learn deep semantic representations …in the text. Finally, we introduce perturbations into the model training process. We achieve this by utilizing adversarial training algorithms such as Fast Gradient Method (FGM) and Projected Gradient Descent (PGD). These algorithms generate adversarial samples to enhance the model’s robustness and stability when facing diverse inputs. We conducted comparative experiments on the publicly available Chinese reading comprehension datasets CMRC2018 and DRCD. The experimental results show that our proposed model has achieved significant improvements in both EM and F1-Score compared to the baseline model. To validate the model’s generalization and robustness, we utilized ChatGPT to construct a scientific dataset that includes a large number of domain-specific terms, sentences with mixed Chinese and English, and complex comprehension tasks. Our model also performed remarkably well on the self-built dataset. In conclusion, the proposed model not only effectively enhances the understanding of semantic information in Chinese text but also demonstrates a certain level of generalization capability. Show more
Keywords: Machine reading comprehension, pre-trained model, adversarial training, Bi-GRU, multi-head self-attention mechanism
DOI: 10.3233/JIFS-234417
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10059-10073, 2024
Authors: Zhu, Meng-Meng | Mao, Jun-Jun | Xu, Wei
Article Type: Research Article
Abstract: Linguistic preference relations with self-confidence (LPRs-SC) are the preference relation that can reflect the decision maker’s (DM) confidence psychology and has received widespread attention for their simple form and multiple information. Currently, arithmetic studies of LPRs-SC are conducted separately for preference relations and self-confidence. In addition, personalized individual semantics (PIS) is an important tool in large-scale decision-making to reflect the differences in the semantic understanding of DMs. However, the confidence level in LPRs-SC limits the preference relation to a certain extent and the linguistic representations of these two components are usually different. This means that it is not only necessary …to propose an arithmetic rule that can express the restrictive relationship between the two but also to construct a model that can extract the PIS of preference relation and confidence respectively. Besides, we constructed a two-stage consensus reaching process (CRP) based on the specificity of the LPRs-SC structure when enhancing group harmony. The process takes self-confidence as an independent source of information, delineates the adjusted categories in detail, and builds an adjustment model accordingly. Finally, the example and comparative analyses verify the merits of the proposed PIS in terms of consistency enhancement and CRP in terms of speed and accuracy harmonization. Show more
Keywords: Personalized individual semantics, linguistic preference relations with self-confidence, consensus reaching process, large scale decision making
DOI: 10.3233/JIFS-236552
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10075-10093, 2024
Authors: Peng, Jun long | Liu, Xiao
Article Type: Research Article
Abstract: This study explores the impact of public health events, multi-modal projects, multi-project environments, and multi-capacity resource constraints on project scheduling. It describes the comprehensive resource-constrained project scheduling problem (MCMRCMPSP) specifically for public health events, and proposes two approaches for modelling and solving the problem. The objective is to enhance the practical relevance of project scheduling and enrich the problem itself. To improve efficiency and the algorithm for scheduling problems, an enhanced quantum algorithm based on the quantum particle swarm algorithm (QPSO) is proposed. The enhancements include Gaussian variation and a tournament selection strategy. Furthermore, the article integrates multiple heuristic rules …with the algorithm to minimize illogical computations, improve computational efficiency, and enhance solution quality. The proposed algorithm’s effectiveness is validated through performance tests and practical application experiments. The results show that the algorithm has superior convergence performance and solution accuracy compared with the traditional QPSO, particle swarm algorithm (PSO), genetic algorithm, ant colony algorithm, and cuckoo algorithm. Thus, the algorithm provides a targeted resource scheduling plan for real-world cases. This research contributes to the field of project scheduling problems and proposes a new solution. Show more
Keywords: Public health events, improved quantum algorithm, multi-mode, multi-project, multi-capability resource-constrained project scheduling
DOI: 10.3233/JIFS-236757
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10095-10114, 2024
Authors: Kahraman, Cengiz
Article Type: Research Article
Abstract: Intuitionistic fuzzy sets aims at taking the hesitancy of an expert into account in assigning a membership degree or a non-membership degree. The direct assignment of decimal numbers for membership and non-membership degrees of an element in intuitionistic fuzzy sets is not practical. Besides, the assigned degrees are generally composed of one digit or at most two digits after dot. This problem has not been addressed as much as it deserves in the literature. The hypothesis of the paper is that the determination of proportional relationships between membership and non-membership degrees is more appropriate than the direct assignment to obtain …the degrees. Proportional intuitionistic fuzzy (PIF) sets require only the proportion relations between an intuitionistic fuzzy set’s parameters. The accuracy of the results obtained with multi-criteria decision-making models definitely depends on how accurately the membership degrees are determined. In this paper, we extend Combinative distance-based assessment (CODAS) method by using single-valued proportional intuitionistic fuzzy sets. We compare the proposed PIF CODAS method with ordinary fuzzy CODAS method. A cloud service provider selection problem is handled to show the validity of the proposed PIF CODAS method. Additionally, a comparative analysis and a sensitivity analysis together with a discussion are presented. Show more
Keywords: Proportional intuitionistic fuzzy sets, aggregation operators, multi-criteria decision making, CODAS, Cloud service provider selection
DOI: 10.3233/JIFS-237389
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10115-10133, 2024
Authors: Chang, Chih-Yung | Yang, Yu-Ting | Zhang, Qiaoyun | Lin, Yi-Ti | Roy, Diptendu Sinha
Article Type: Research Article
Abstract: With the field of technology has witnessed rapid advancements, attracting an ever-growing community of researchers dedicated to developing theories and techniques. This paper proposes an innovative ICRM (Intelligent Citation Recommendation Mechanism), designed to automate the process of suggesting the appropriate number of citations for individual brackets within a document. The proposed ICRM comprises three phases: Coarse-grained Weighted Bag of Word (WCBW), Fine-grained SciBERT (FSB) and Citation Adjustment phases. Firstly, the WCBW phase employs TF-IDF to extract keywords from both target and candidate documents, forming vectors that capture word significance along with metadata like authorship, keywords, and titles. It aims to …identify relevant papers from a database, serving as initial candidates for each bracket. Secondly, the FSB phase employs the SciBERT model to assess the similarity between candidate documents and the local context around brackets, enhancing the precision of recommendations. It refines this selection by analyzing candidate-document relationships within the proximity of the brackets. Lastly, the Citation Adjustment phase tackles overlapping citations and ensures that recommended citation numbers align with user-defined criteria, resolving issues of imbalance. The simulation results demonstrate that the proposed ICRM outperforms existing models significantly in terms of precision, recall and F1-score. Show more
Keywords: Citation recommendation, TF-IDF, weighted bag of word, BERT
DOI: 10.3233/JIFS-237975
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10135-10150, 2024
Authors: Ping, Yang
Article Type: Research Article
Abstract: This study delves into a novel approach for energy conservation and environmental pollution reduction through modern environmental art design, guided by the ecological civilization concept and powered by artificial intelligence (AI) technology. The environmental art framework, aligning with the ecological civilization paradigm, is intricately designed. The data acquisition layer employs diverse sensors to gather equipment status, environmental, and pollution data, transmitting it to the executive controller layer via internal WIFI connectivity. The collected data undergoes meticulous analysis and processing within the data layer before reaching the actuator control layer. Leveraging support vector machines in artificial intelligence, the executive controller layer …amalgamates the analyzed equipment and environmental data to devise energy-saving equipment and environmental pollution control schemes. Real-time visualization of these outcomes is achieved through the display operation layer. Findings affirm the effectiveness of this method in acquiring pertinent data for modern environmental art design and managing equipment states. Implementation of this approach successfully diminishes power consumption, dust concentration, and formaldehyde levels in the modern environmental art design zone, showcasing its prowess in energy conservation and pollution control. The integration of AI within the ecological civilization framework highlights its potential in fostering sustainable and environmentally conscious practices in modern art creation. Show more
Keywords: Artificial intelligence technology, ecological civilization concept, modern environmental art, support vector machine, energy saving control, environmental pollution
DOI: 10.3233/JIFS-239687
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10151-10165, 2024
Authors: Du, Xin-Feng | Wang, Jie-Sheng | Sun, Wei-Zhen | Zhang, Zhi-Hao | Zhang, Yun-Hao
Article Type: Research Article
Abstract: Accurate blood vessel segmentation on retinal blood vessel images is helpful for the early detection of ophthalmic diseases such as diabetes, hypertension, cardiovascular and cerebrovascular diseases, and inhibits the deterioration of the disease. In current research within the field of retinal blood vessel segmentation, significant challenges exist in accurately segmenting small blood vessels and maintaining blood vessel continuity. The segmentation algorithm proposed in this article offers substantial improvements to address these issues. To enhance the segmentation performance of retinal blood vessels and facilitate more accurate diagnosis of fundus diseases by ophthalmologists, this paper introduces a novel bidirectional convolutional long short-term …memory (LSTM) residual U-Net segmentation algorithm, incorporating improvements to the Focal loss function. Firstly, in the encoding part of U-Net, the multi-scale convolution kernels and Bi-ConvLSTM were adopted to improve the residual structure, obtain richer blood vessel features and enhance the detection ability of micro vessels and the continuity of blood vessel characteristics. At the same time, the class balanced cross entropy loss function was improved and the proportional modulation factor is introduced to enhance the learning ability of the network for difficult samples. By adding the Bi-ConvLSTM to the residual structure and introducing the proportional modulation coefficient to the loss function, the network structure realizes better feature information detection and greatly enhances the detection ability of small blood vessels. The experimental analysis on the DRIVE and CHASE_DB1 data sets showed that the sensitivity, specificity, accuracy and AUC reached 0.7961, 0.9796, 0.9563, 0.9792; 0.8344, 0.9665, 0.9547, 0.9758, respectively. The experimental results fully show that the Bi-ConvLSTM residual U-Net segmentation algorithm based on the improved Focal loss function enhances the detection ability of small blood vessel features, improves the continuity of blood vessel features and the network segmentation performance, and is superior to U-Net algorithm and some current mainstream retinal blood vessel segmentation algorithms. Show more
Keywords: Retinal blood vessel segmentation, bi-directional convolution long and short time memory network, residual block, Multi-scaleconvolution, U-Net, proportional modulation coefficient
DOI: 10.3233/JIFS-236702
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10167-10186, 2024
Authors: Zong, Xinlu | Li, Hejing | Liu, Aiping | Xu, Hui
Article Type: Research Article
Abstract: Emotion is a crucial factor which influences evacuation effects. However, the studies and quantitative analysis of evacuation emotions, including the emotion generated by external factors and internal personality or cognition levels, emotional contagion evolution, and the regulation mechanism of pedestrians to negative emotion, are still rare. In this paper, an evacuation model based on emotional cognition and contagion (EMECC) is presented. Firstly, individual’s emotion is generated and quantified based on Lazarus’s cognitive theory. Secondly, the emotional contagion between individuals is simulated by SIS (Susceptible Infected Susceptible) infectious disease model. Combining with cellular automata model, an emotion-driven moving rule is proposed …to guide pedestrians move towards the directions with more positive individuals so that positive emotions can be spread effectively. Various experiments on model parameters, obstacles, and emotional contagion process are implemented to verify the effectiveness of the EMECC model. The simulation and experimental results show that emotional regulation mechanism can improve pedestrian’s decision-making ability and contagion of positive emotion can accelerate evacuation process. The EMECC model can simulate emotional changes dynamically and guide pedestrians efficiently and reasonably in emergency evacuation. Show more
Keywords: Emergency evacuation, crowd simulation, emotion, emotional contagion
DOI: 10.3233/JIFS-237147
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10187-10200, 2024
Authors: Wang, Qian
Article Type: Research Article
Abstract: Neuroimaging technology is considered a non-invasive method research the structure and function of the brain which have been widely used in neuroscience, psychiatry, psychology, and other fields. The development of Deep Learning Neural Network (DLNN), based on the deep learning algorithms of neural imaging techniques in brain disease diagnosis plays a more and more important role. In this paper, a deep neural network imaging technology based on Stack Auto-Encoder (SAE) feature extraction is constructed, and then Support Vector Machine (SVM) was used to solve binary classification problems (Alzheimer’s disease [AD] and Mild Cognitive Impairment [MCI]). Four sets of experimental data …were employed to perform the training and testing stages of DLNN. The number of neurons in each of the DLNNs was determined using the grid search technique. Overall, the results of DLNNs performance indicated that the SAE feature extraction was superior over (Accuracy Rate [AR] = 74.9% with structure of 93-171-49-22-93) shallow layer features extraction (AR = 70.8% with structure of 93-22-93) and primary features extraction (AR = 69.2%). Show more
Keywords: Deep learning neural network, neuroimaging technology, brain diseases, disease diagnosis, feature extraction
DOI: 10.3233/JIFS-237979
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10201-10212, 2024
Authors: Qiao, Gongzhe | Zhuang, Yi | Ye, Tong | Qiao, Yuan
Article Type: Research Article
Abstract: The intelligent network information systems, such as smart grid systems, face many security problems in the aspects of sensing, communication and computing. Information security risk assessment is an important way to assess the threats faced by information systems before risk events occur and ensure the security of assets. However, a comprehensive risk assessment of the system is a very resource-consuming process. Many existing risk assessment methods rely on a large number of experts and computing resources. Their assessment results are vulnerable to the differences in experts’ subjective judgments. Therefore, we propose FRAMB, a novel man-machine collaborative risk assessment method based …on fitting upper and lower bounds. Firstly, we present a risk assessment criterion including four categories and sixteen risk factors following the ISO/IEC 27005:2018 standard. On this basis, we present the DFAHP and CM-NN assessment models to obtain the upper and lower bounds of the risk assessment value, which provides a reference for expert assessment. FRAMB integrates the experts’ assessment value and the values of upper and lower bounds, and adjusts the weights of these values to give the final risk assessment value. We introduce the risk assessment process of FRAMB in detail through a case study of the smart grid system risk assessment. We evaluate the effectiveness and accuracy of FRAMB through experiments. The experimental results show that FRAMB can effectively and accurately assess the security risks of the intelligent network information systems. Show more
Keywords: Risk assessment, information systems, neural network, analytic hierarchy process, expert evaluation
DOI: 10.3233/JIFS-231880
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10213-10229, 2024
Authors: Zhan, Qiuyan | Saeid, A. Borumand | Davvaz, Bijan
Article Type: Research Article
Abstract: The aim of this paper is to investigate several operators on L -algebras. At first, closure (interior) operators on L -algebras are defined and some properties of them are obtained. Then, existential operators and universal operators on L -algebras are studied, a one-to-one correspondence between the set of all quantifier operators and the set of all relative complete subalgebras of CKL -algebras is constructed. Furthermore, very true operators on L -algebras are investigated and by giving a very true ideal of a very true L -algebra, quotient structures on very true L -algebras are established.
Keywords: L-algebra, closure (interior) operator, existential (universal) operator, very true operator
DOI: 10.3233/JIFS-234370
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10231-10241, 2024
Authors: Ajitha Gladis, K.P. | Srinivasan, R. | Sugashini, T. | Ananda Raj, S.P.
Article Type: Research Article
Abstract: Visual impairment people have many difficulties in everyday life, including communicating and getting information, as well as navigating independently and safely. Using auditory alerts, our study hopes to improve the lives of visually impaired individuals by alerting them to items in their path. In this research, a Video-based Smart object detection model named Smart YOLO Glass has been proposed for visually impaired persons. A Paddling - Paddling Squeeze and Attention YOLO Network model is trained with multiple images to detect outdoor objects to assist visually impaired people. In order to calculate the distance between a blind person and obstacles when …moving from one location to another, the proposed method additionally included a distance-measuring sensor. The visually impaired will benefit from this system’s information about around objects and assistance with independent navigation. Recall, accuracy, specificity, precision, and F-measure were among the metrics used to evaluate the proposed strategy. Because there is less time complexity, the user can see the surrounding environment in real time. When comparing the proposed technique to Med glasses, DL smart glass, and DL-FDS, the total accuracy is improved by 7.6%, 4.8%, and 3.1%, respectively. Show more
Keywords: Visual impairment, deep learning, outdoor object detection, wearable system, YOLO network
DOI: 10.3233/JIFS-234453
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10243-10256, 2024
Authors: Yu, Dan | Wu, Jun | He, Yongling
Article Type: Research Article
Abstract: The distributed robust optimal allocation method for multi-microgrid interconnected systems usually involves a large number of variables and constraints, and the computational complexity is high in practical applications, which makes it difficult to solve the problem. Therefore, a distributed robust optimal allocation method for multi-microgrid interconnection systems based on multi-objective swarm algorithm is proposed. A distributed robust optimization configuration constraint index model for multi-microgrid interconnection system is established. Considering the influence of energy storage technology operation characteristics on its service life, a micro-grid hybrid energy storage capacity optimization configuration model with the minimum annual comprehensive energy storage cost as the …objective function is established with charge and discharge power and residual power as the constraint conditions. The multi-objective swarm algorithm is used to realize the optimization model of distributed robust configuration microgrid interconnection system. By determining the power capacity configuration of the optimal energy storage system and the corresponding frequency dividing points, the power capacity configuration of the optimal energy storage system and the corresponding frequency dividing points are determined. The hybrid energy storage configuration model of multi-microgrid interconnection system is established with the minimum alternative operating cost as the objective function, so as to realize the distributed robust optimal configuration of multi-microgrid interconnection system. The simulation results show that the distributed configuration of multi-microgrid interconnection system with the proposed method has good robustness and strong optimization control ability. Show more
Keywords: Multi-objective bee colony algorithm, multi-microgrid, interconnection system, robust allocation
DOI: 10.3233/JIFS-235092
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10257-10268, 2024
Authors: Mohan, Prakash | Aishwarya, S.
Article Type: Research Article
Abstract: Price changes in construction materials have a significant impact on building construction projects. Such price variations occur at random and at varying rates over time. A system that can estimate the magnitude and quantity of the change in material prices with reasonable accuracy is required. The primary goal is to create a machine-learning model that can predict the type of building material chosen based on environmental factors. The compressive strength of concrete is critical in defining its mechanical qualities. Long laboratory testing is needed to determine the compressive strength of concrete. The capacity of powerful machine learning algorithms to forecast …concrete compressive strength speeds up these lengthy experimental methods while also lowering expenses. This study provides abilities to precisely anticipate and categorize numerous qualities and traits of distinct materials. The framework includes a broad dataset that details materials, composition, and performance characteristics. Machine learning algorithms such as logistic regression (LR), decision trees (DT), and random forests (RF) train models on the training data. The models are hyper-parameter tweaked and feature developed to achieve the most outstanding performance. The k-fold method is used throughout the training and assessment phase to guarantee robustness and reduce bias. The F1 score and Receiver Operating Characteristic-Area Under Curve (ROC-AUC) curve are two performance measures used to measure how accurate and predictive the trained models are. The study findings provide insights into the qualities of the materials, facilitating improved material selection, quality assurance, and decision-making in the building sector. In the analyses, the best accuracy value was 99.92%, and the precision value was 88.83% using the LR algorithm. As a result, it was determined that the LR algorithm had the least execution 57.826 ms, and is thus the most suitable for use in concrete compressive strength estimation. Show more
Keywords: Building materials, machine learning algorithms, feature selection, model training, K-fold, performance evaluation
DOI: 10.3233/JIFS-236111
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10269-10285, 2024
Authors: Shrivastava, Ankit | Poonkuntran, S.
Article Type: Research Article
Abstract: Ensuring real-time performance while leveraging pedestrian detection is a crucial prerequisite for intelligent driving technology. The development of lightweight models with good detection accuracy is also crucial. This work proposes a novel method, the Attention Digital Filter with Anchor-Free Feature Pyramid Learning Model (ADFAFPLM), to meet these needs. The suggested method consists of combining two networks: one is a digital filter based on an attention network that eliminates noise and other picture distortions. The attention-based residual network digital filters are chosen for their enhanced filtering performance, adaptability, efficient learning through residual connections, noise suppression, interpretability, and generalization capabilities. Next, from …the input crowded and occluded photos, the pedestrian is identified using an anchor-free feature pyramid network. The Eurocity person dataset was used to train the model, and it was also tested on other datasets like CityPersons, INRIA, PennFudan, and Eurocity. The investigation was expanded to include images in hazy, noisy, and occlusion environments, among other environmental conditions. The image resolutions were also considered for analysis and it was observed that with increasing image resolution, the mAP increases. Based on the ablation study, the ADF-AFPLM adopted YOLOv8n with batch size 16, and image size 640 is considered for efficient result with different testing datasets. The model achieved a mean average precision (mAP) of approx. 87% and shows its efficacy over state-of-art models. Show more
Keywords: Object detection, pedestrian, deep learning, feature pyramid network, YOLO
DOI: 10.3233/JIFS-237639
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10287-10303, 2024
Authors: Tang, Sicong | Wang, Hailong
Article Type: Research Article
Abstract: With the continuous deepening of the urbanization process and the progress of science and technology, people transform nature and develop nature on a larger and larger scale, among which the most iconic transformation is a variety of building structures built by people. And with the passage of time, the building structure in the perennial wind and sun, there will be signs of “illness”, if not timely treatment, it will have a huge impact on the stability and safety of the building structure. Based on this, in this paper, according to the characteristics of crack identification on the surface of concrete …structure, background subtraction algorithm is selected for image noise reduction processing. Through three steps of digital image noise reduction, crack extraction and crack parameter identification, the quantitative recognition of cracks is completed and a complete system of crack parameter identification is formed. The experimental results show that the machine learning model of building structure health monitoring and damage recognition algorithm proposed in this paper has excellent statistical performance, and the relative error accuracy of recognition can be controlled within 10%. Show more
Keywords: Image processing, building structure, health monitoring, damage identification, crack identification
DOI: 10.3233/JIFS-239655
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10305-10314, 2024
Authors: Wang, Yu-Lin | Wang, Jin-Heng
Article Type: Research Article
Abstract: Virtual machine (VM) consolidation and migration that only consider current workload can result in excessive unnecessary migrations. To address this issue, a VM consolidation algorithm based on resource utilization prediction is proposed. An improved K-nearest neighbor (KNN) classification algorithm weighted by attribute inconsistency is proposed to predict the workload of both the host and the VMs. Firstly, two distributions are partitioned according to the neighboring relationship for comparing consistency. Then, an inconsistency evaluation function based on earth mover’s distance (EMD) is designed to measure the inconsistency between the neighboring sample set of each sample under each attribute and the equivalent …partition refined by the decision attribute. Finally, the inconsistency level of the neighboring samples is transformed into the importance of the corresponding attribute to implement the attribute weighting KNN classifier. When selecting the source host and target host for VM migration, both current and predicted overloads are considered to avoid unnecessary VM migrations. Simulation tests were performed with random and realistic workloads, and the results show that the proposed method can reduce the overall energy consumption of the host, while also reducing service level agreement (SLA) violations and VM migration. Show more
Keywords: Cloud computing, virtual machine consolidation, improved K-nearest neighbor regression, earth mover’s distance, attribute weighting
DOI: 10.3233/JIFS-239851
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10315-10328, 2024
Authors: Li, Zhigang | Nian, Wenhao | Sun, Xiaochuan | Li, Shujie
Article Type: Research Article
Abstract: Military object military object detection technology serves as the foundation and critical component for reconnaissance and command decision-making, playing a significant role in information-based and intelligent warfare. However, many existing military object detection models focus on exploring deeper and more complex architectures, which results in models with a large number of parameters. This makes them unsuitable for inference on mobile or resource-constrained combat equipment, such as combat helmets and reconnaissance Unmanned Aerial Vehicles (UAVs). To tackle this problem, this paper proposes a lightweight detection framework. A CSP-GhostnetV2 module is proposed in our method to make the feature extraction network more …lightweight while extracting more effective information. Furthermore, to fuse multiscale information in low-computational scenarios, GSConv and the proposed CSP-RepGhost are used to form a lightweight feature aggregation network. The experimental results demonstrate that our proposed lightweight model has significant advantages in detection accuracy and efficiency compared to other detection algorithms. Show more
Keywords: Deep learning, convolutional neural network, lightweight network, military object detection
DOI: 10.3233/JIFS-234127
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10329-10343, 2024
Authors: Saichand, N. Venkata | Naik, S. Gopiya
Article Type: Research Article
Abstract: Epilepsy is considered a most general neurological disorder related to brain activity disruption. In epileptic seizures detection and classification, EEG (Electroencephalogram) measurements that record the brain’s electrical activities are used frequently. Generally, physicians investigate the abnormalities in the brain. However, this technique is time-consuming, faced complexity in seizure detection, and poor consistency because of data imbalance. To overcome these difficulties, Improved Empirical Mode Decomposition for feature extraction and Improved Weight Updated KNN (K-Nearest Neighbor) algorithm for classification are proposed. In the case of pre-processing, a rule-based filter, namely a wiener scalar filter with integer wavelet transform is used for multiple …channels conversion and further signal to noise ratio is increased. Further in feature extraction, better features are extracted using an improved empirical mode decomposition-based bandpass filter. By using the Improved Weight updated KNN, feature extracted samples are classified incorrect manner, avoiding data imbalance issues. Feature vectors’ effective classification is performed attains higher computational speed and sensitivity. The EEG input signal of the proposed study utilizing the BONN dataset and different performance metrics such as accuracy, sensitivity, specificity, recall, f-score, and error values were performed and compared with various existing studies. From the results, it is clear that the proposed method provides effective detection for seizure and non-seizure patients compared with existing studies. Show more
Keywords: Seizure detection, bandpass filter, rule-based filter, improved empirical mode decomposition, improved weight updated KNN
DOI: 10.3233/JIFS-222960
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10345-10358, 2024
Authors: Li, Zheming | Chen, Yidan | Yang, Bo | Li, Chenwei | Zhang, Shihua | Li, Wei | Zhang, Hengwei
Article Type: Research Article
Abstract: Abstract Adversarial examples are often used to test and evaluate the security and robustness of image classification models. Though adversarial attacks under white-box setting can achieve a high attack success rate, due to overfitting, the success rate of black-box attacks is relatively low. To this end, this paper proposes diversified input strategies to improve the transferability of adversarial examples. In this method, various transformation methods are applied to randomly transform the original image multiple times, thereby generating a batch of transformed images. Then, in the process of back-propagation, the loss function gradient of the transformed images is calculated, and a weighted …average of the obtained gradient values is performed to generate adversarial perturbation, which is iteratively added to the original image to generate adversarial examples. Meanwhile, by increasing the variety of data augmentation transformation types and the number of input images, the proposed method effectively alleviates overfitting and improves the transferability of adversarial examples. Extensive experiments on the ImageNet dataset indicate that the proposed method can perform black-box attacks better than benchmark methods, with an average of 97.2% success rate attacking multiple models simultaneously. Show more
Keywords: Deep neural network, image classification, adversarial examples, black-box attacks, diversified input
DOI: 10.3233/JIFS-223584
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10359-10373, 2024
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