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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: Xu, Xuezhu
Article Type: Research Article
Abstract: Sports events, as large-scale events that provide products and services, have received widespread attention for their economic benefits and influence. Event organizers expect to achieve high efficiency by providing high-quality products and services. The quality of competition products and services is mainly evaluated through the subjective feelings of the audience, and usually the audience’s evaluation of service quality is vague. Therefore, this article intends to establish an evaluation index system for the quality of spectator service in sports events, in order to provide a reasonable evaluation of the service products provided by sports event organizers. The audience service quality evaluation …for large-scale sports-events is a MAGDM problems. Recently, the EDAS and CRITIC technique has been employed to cope with MAGDM issues. The interval neutrosophic sets (INSs) are employed as a tool for characterizing uncertain information during the audience service quality evaluation for large-scale sports-events. In this paper, the interval neutrosophic number EDAS (INN-EDAS) technique based on the Hamming distance and Euclid distance is founded to manage the MAGDM under INSs. The CRITIC technique is employed to obtain the weight information based on the Hamming distance and Euclid distance under INSs. Finally, a numerical case study for audience service quality evaluation for large-scale sports-events is employed to validate the proposed technique. The main contributions of this paper are proposed: (1) The INN-EDAS technique based on the Hamming distance and Euclid distance is founded to manage the MAGDM under INSs; (2) The CRITIC technique is employed to obtain the weight information based on the Hamming distance and Euclid distance under INSs; (3) a numerical case study for audience service quality evaluation for large-scale sports-events is employed to validate the proposed technique. Show more
Keywords: Multiple attribute group decision making (MAGDM), interval neutrosophic sets (INSs), EDAS technique, CRITIC technique, audience service quality evaluation
DOI: 10.3233/JIFS-236124
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2357-2370, 2024
Authors: Liu, Mingtang | Zhang, Mengxiao | Zhang, Peng | Wang, Guanghui | Chen, Xiaokang | Zhang, Hao
Article Type: Research Article
Abstract: Aiming at the shortcomings of traditional water level prediction methods such as insufficient information mining ability and unclear mechanism of heuristic algorithms, this paper proposes for the first time a water level prediction method based on blockchain technology fused with long short-term memory (LSTM) network. The method utilizes blockchain and LSTM neural network to build a combined model, and directly uploads monitoring data such as import and export water flow and water level to predict the water level, which avoids the secondary error brought by the indirect calculation of flow. In this paper, the flow compensation strategy is proposed for …the first time, and the monitoring data with large deviations are compensated accordingly to reduce the prediction error from the source. The results show that the combined Blockchain-LSTM model has the smallest prediction error after adopting the compensation strategy, with the MAE of 0.290 and the RMSE of 0.490, which are smaller than those of other models, and has high prediction accuracy and practicability, which provides technical support for real-time scheduling of the South-to-North Water Diversion Reservoir. Show more
Keywords: LSTM, Blockchain-LSTM, water level prediction, compensation strategy
DOI: 10.3233/JIFS-231411
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2371-2380, 2024
Authors: Ameksa, Mohammed | Elamrani Abou Elassad, Zouhair | Elamrani Abou Elassad, Dauha | Mousannif, Hajar
Article Type: Research Article
Abstract: While road accidents’ prediction has been of crucial importance in the development of intelligent transportation technologies; a profound analysis within the driver-vehicle-environment system is no doubt of great interest and necessity. Three categories of features namely vehicle kinematics, driver inputs and environmental conditions collected using a desktop driving simulator have been systematically recorded in order to outline a fusion strategy based on various base classifiers and a Meta classifier that learns from base classifiers’ results to acquire more efficient accidents’ predictions. Highly heuristic optimized tree-based models namely AdaBoost, XGBoost, RF along with the MLP deep learning technique have been endorsed …to establish effective predictions. Furthermore, to ensure that the proposed system provide superior and stable decisions as road accidents are generally unexpected and occur rarely, an imbalance-learning approach was conducted to add to the current knowledge by adopting three performant balancing strategies: ROS, SMOTE and ADASYN. To the best our knowledge, there has been a limited interest at adopting a fusion-based system examining the impact of real-time features’ combinations and fused tree-based models along with deep learning technique as meta-classifier on the prediction of road accidents while taking into account class imbalance. The findings depict that the superior performance of the proposed fusion system with precision, recall and f1-score over 90%. As a whole, the results highlight the significance of the explanatory features related to potential accidents and can be employed in designing efficient intelligent transportation systems. Show more
Keywords: Crash prediction, machine learning, fusion framework, balancing techniques
DOI: 10.3233/JIFS-232078
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2381-2397, 2024
Authors: Arulalan, V. | Premanand, V. | Kumar, Dhananjay
Article Type: Research Article
Abstract: An efficient model to detect and track the objects in adverse weather is proposed using Tanh Softmax (TSM) EfficientDet and Jaccard Similarity based Kuhn-Munkres (JS-KM) with Pearson-Retinex in this paper. The noises were initially removed using Differential Log Energy Entropy adapted Wiener Filter (DLE-WF). The Log Energy Entropy value was calculated between the pixels instead of calculating the local mean of a pixel in the normal Wiener filter. Also, the segmentation technique was carried out using Fringe Binarization adapted K-Means Algorithm (FBKMA). The movement of segmented objects was detected using the optical flow technique, in which the optical flow was …computed using the Horn-Schunck algorithm. After motion estimation, the final step in the proposed system is object tracking. The motion-estimated objects were treated as the target that is initially in the first frame. The target was tracked by JS-KM algorithm in the subsequent frame. At last, the experiential evaluation is conducted to confirm the proposed model’s efficacy. The outcomes of Detection in Adverse Weather Nature (DAWN) dataset proved that in comparison to the prevailing models, a better performance was achieved by the proposed methodology. Show more
Keywords: Object detection, adverse weather, weiner filter, object tracking, Retinex
DOI: 10.3233/JIFS-233623
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2399-2413, 2024
Authors: Wang, Yao | Yu, Tao | Luo, Tianmin | Ye, Haojie | Pan, Yiru
Article Type: Research Article
Abstract: Fault detection and diagnosis in electrical machines are periodical for preventing operational interruptions and unexpected shutdowns. However, a Wavelet Feature-dependent Clustering Technique (WFCT) is introduced to address the cyclic fault detection between successive operation intervals. This technique identifies override features from the time-frequency operational wavelets throughout the machine running time. This grouping binds time and operational frequency for identifying override exceeding shutdown/ failure instances. Based on their revamping time, the identified instances are further grouped to prevent overrides in successive operational hours. The fuzzy clustering prevents variation features based on conventional to high-fuzzified extractions.
Keywords: Electrical machines, fault diagnosis, feature extraction, fuzzy clustering, time-frequency wavelet
DOI: 10.3233/JIFS-234256
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2415-2431, 2024
Authors: Muniz, Rafael Ninno | de Sá, José Alberto Silva | da Rocha, Brigida Ramati Pereira | Buratto, William Gouvêa | Nied, Ademir | da Costa Jr., Carlos Tavares
Article Type: Research Article
Abstract: Energy sustainability indicators are essential for evaluating and measuring energy systems’ environmental, social, and economic impact. These indicators can be used to assess the sustainability of different energy sources, such as renewable or fossil fuels, as well as the performance of energy systems in various regions or countries. The goal of this paper is to propose a new energy sustainability index based on fuzzy logic for the Amazon region. The fuzzy inference system enabled the operationalization of subjective sustainability concepts, resulting in a final index that can evaluate the performance of the states in the Legal Amazon and compare them …to each other. The results indicated that Mato Grosso had the highest ranking, followed by Tocantins, Amapá, Roraima, Rondônia, Pará, Acre, Maranhão, and Amazonas in the last position. These findings demonstrate that the selected indicators and the final index are effective tools for evaluating the energy sustainability of the Amazon region and can aid public managers in making decisions and proposing sustainable regional development policies for the region. Show more
Keywords: Amazon, energy planning, fuzzy logic, indicators, sustainability
DOI: 10.3233/JIFS-235750
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2433-2446, 2024
Authors: Sweatha, S. | Sindu Devi, S.
Article Type: Research Article
Abstract: During the period of 2019–20, forecasting was of utmost priority for health care planning and to combat COVID-19 pandemic. Almost everyone’s life has been greatly impacted by COVID-19. Understanding how the disease spreads is crucial to know how the disease behaves dynamically. The aim of the research is to construct an SEI Q 1 Q 2 R model for COVID-19 with fuzzy parameters. The fuzzy parameters are the transmission rate, the infection rate, the recovery rate and the death rate. We compute the basic reproduction number, using next-generation matrix method, which will be used further to study the model’s …prediction. The COVID-free and endemic equilibrium points attain local and global stability when R0 < 1. A sensitivity analysis of the reproduction number against its internal parameter has been done. The results of this model showed that intervention measures. The numerical simulation along with graphical representations at COVID-free and endemic points are shown. The SEIQ 1 Q 2 R model is a successful model to analyse the spreading and controlling the epidemics like COVID-19. Show more
Keywords: Stability, fuzzy basic reproduction number, sensitivity analysis
DOI: 10.3233/JIFS-231945
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2447-2460, 2024
Authors: Saranya, D. | Bharathi, A.
Article Type: Research Article
Abstract: A sudden increase in electrical activity in the brain is a defining feature of one of the severe neurological diseases known as epilepsy. This abnormality appears as a seizure, and identifying seizures is an important field of research. An essential technique for examining the features of neurological issues brain activities, and epileptic seizures is electroencephalography (EEG). In EEG data, analyzing epileptic irregularities visually requires a lot of time from neurologists. For accurate detection of epileptic seizures, numerous scientific techniques have been used with EEG data, and most of these techniques have produced promising results. For EEG signal classification with a …high classification accuracy rate, the present research proposes an enhanced machine learning-based epileptic seizure detection model. The present research provides a hybrid Improved Adaptive Neuro-Fuzzy Inference System (IANFIS)-Light Gradient Boosting Machine (LightGBM) technique for automatically detecting and diagnosing epilepsy from EEG data. The experimental findings were supported by EEG records made available by the German University of Bonn and scalp EEG data acquired at Children’s Hospital Boston. The suggested IANFIS-LightGBM, according to the results, offers the most significant classification accuracy ratings in both situations. Show more
Keywords: Electroencephalography (EEG), epileptic seizure detection, machine learning, LightGBM, and accuracy rate
DOI: 10.3233/JIFS-233430
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2463-2482, 2024
Authors: Subbiah, Priyanga | Nagappan, Krishnaraj
Article Type: Research Article
Abstract: Since it satisfies all prerequisites for the growth of humanity, agriculture is currently regarded as being the most significant sector for civilization. One of the main forms of human energy production is thought to be plants, which also provide nutrients, cures, etc. Any damage or disease brought on by exposure to pathogens, viruses, bacteria, etc., while cultivating plants results in a decline in productivity, making it crucial to prevent such diseases and take the required precautions to avoid them. Accurately identifying such fatal diseases is a crucial first step for both the businesses and farmers. Six different Convolutional Neural Networks …(CNNs) that accept plant leaf images as input, along with the Enhanced Symbiotic Organism Search (ESOS) optimization algorithm, have been implemented in our research. We intend to extensively contrast the various models based on accuracy, precision, recall, and F1-score. In the area of image recognition and classification, convolutional neural networks (CNNs), in particular, and deep learning, in general, are developing. The literature contains a variety of CNN designs. The dataset size, the number of classes, the model’s weights, hypermeters, and optimizers are a few examples of the variables that have an impact on a CNN model’s performance. Because of its benefits, transfer learning and fine-tuning a pre-trained model are now very popular. This study examines the impact of six popular CNN models: DenseNet, MobileNet, EfficientNet, VGG19, ResNet and Inception. As a result, DenseNet demonstrates an optimal accuracy rate of 98% when compared to other models. Show more
Keywords: Plant disease detection, tomato plant leaf disease detection, deep learning, CNN, DenseNet, MobileNet, EfficientNet, VGG19, ResNet and inception
DOI: 10.3233/JIFS-232067
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2483-2494, 2024
Authors: Jenifer, L. | Radhika, S.
Article Type: Research Article
Abstract: Cardiovascular disease is the leading cause of death and more than half million people were died around the world. However, cardiovascular health monitoring is crucial for effective heart disease diagnosis and management. In this paper, a novel deep learning-based YOLO-ECG model is proposed to ECG arrhythmia classification method for portable monitoring. Initially, the ECG signals are gathered using 12-lead electrodes in the real time and these signals are denoised using two-dimensional stationary wavelet transform (2D-SWT). In SWT, zeros are inserted between filter taps rather than decimal points to eliminate repetitions and increase robustness. The denoised ECG signals are fed into …the deep learning-based YOLO network with Gaussian error linear unit (GELU) activation function for detecting the ECG abnormalities of arrythmia. ECG waveforms are analyzed for the local fractal dimension at each sample point before heartbeat waveforms are extracted within a set length window. A squeeze and excitation attention (SEAN) module is introduced in the YOLO network for selecting size of 1D convolution kernel, and the dimension is preserved during local cross-channel interactions, decrease network complexity and enhance model efficiency. The classification findings demonstrate that the proposed YOLO-ECG model performs better by ECG recordings from the MIT-BIH arrhythmia dataset. From the experimental analysis, the proposed YOLO-ECG model yields the overall accuracy of 99.16% for efficient classification of arrythmia ECG signals. Show more
Keywords: Arrythmia classification, ECG signal, deep learning, 2D stationary wavelet transform, YOLO network
DOI: 10.3233/JIFS-235858
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2495-2505, 2024
Authors: Jiang, Xianliang | Yang, Ze | Huang, Junkai | Jin, Guang | Yu, Guitao | Zhang, Xi | Qin, Zhen
Article Type: Research Article
Abstract: Rivers serve as vital water sources, maintain ecological equilibrium, and enhance landscapes. However, the looming issue of floating debris stemming from improper waste disposal and illegal discharge, poses an imminent threat to river ecosystems and their aesthetic appeal. Conventional human-led inspections prove labor-intensive, inefficient, and prone to errors. This study introduces an innovative approach for river debris detection, employing Unmanned Aerial Vehicles (UAVs) imagery in conjunction with a refined YOLOv5n model. This approach offers three key contributions. Primarily, the YOLOv5n model is bolstered by integrating the Efficient Channel Attention (ECA) module and reshaping the MobileNetV3 backbone to align with MobileNetV3S, …thereby significantly streamlining computational demands and model intricacy. Additionally, precision and speed are augmented by eliminating the detection head for larger targets, while decreasing computational requirements. Subsequently, to counter dataset scarcity, we curate a UAV-derived river debris dataset, encompassing five prevalent debris types, serving as an indispensable resource for method refinement and assessment. Lastly, the upgraded model’s evaluation on Jetson Nano yields an mAP of 87.2%, merely 0.7% lower than the original YOLOv5n model. Remarkably, the refined model achieves substantial reductions of 57.1% in parameters, 52.6% in volume, and 54.8% in GFLOPs. Additionally, inference time is abbreviated to 57.3ms per Jetson Nano image, 13.4ms faster than the original. These findings underscore edge computing’s potential in river restoration. In conclusion, the fusion of deep learning object detection and UAV imagery empowers adept river debris detection. Show more
Keywords: Rivers, floating debris, UAV Imagery, YOLOv5n model, edge computing
DOI: 10.3233/JIFS-234222
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2507-2520, 2024
Authors: Sruthi, S. | Anuradha, B.
Article Type: Research Article
Abstract: Fire poses a significant threat to both lives and property, necessitating effective early detection measures. Despite challenges in identifying smoke and fire in their initial stages, we have devised a cost-efficient visual detection system. Early fire detection enhances its potential effectiveness. CCTV surveillance systems are now commonplace in developed countries, serving as tools for periodic monitoring of various locations. However, fluctuating ambient light conditions, camera angles, and seasonal variations can introduce data distortions, occlusions, and impact model accuracy. To address these issues, we’ve implemented a method combining deep learning networks and machine learning strategies for flame detection and direction classification. …Our innovative QuickDenseNet extracts dense features from segmented flame video frames. We introduce the Ensemble Score Voted SVM (ESV-SVM), employing SVM as the primary learner and score voting as the auxiliary learner. Our approach is rigorously evaluated through simulations, measuring accuracy and various Key Performance Indices (KPIs), including Precision, F1-score, Recall, Correlation, Error, FPR, and Correlation Coefficients. Remarkably, our proposed method achieves an impressive precision rate of approximately 99.5%. Show more
Keywords: Fire detection, ensemble learning, deep feature, CNN, video surveillance, color segmentation, dense network
DOI: 10.3233/JIFS-236387
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2521-2535, 2024
Authors: Kaur, Ranjeet | Tripathi, Alka
Article Type: Research Article
Abstract: The present work is an effort to support the typographical errors of keywords that are not supported by existing compilers and integrated development environment(IDE) in ’C’ language. The fuzzy automata modelling approximate string matching is proposed for error handling during lexical analysis. By introducing fuzziness to lexemes the typographical errors can be rectified at the time of compilation and flexibility of lexical analyser can be greatly improved. The recognition of fuzzy tokens during lexical analysis is described in order to correct errors caused by sticking key, deletion, typing and swapping key in keywords during C programming. Algorithms and pseudo code …are being developed to measure the degree of membership of crisp and fuzzy lexemes. Accuracy is tested and examined once the fuzzy lexemes are trained using a neural network. The proposed method is an add on feature that can be incorporated in existing compilers and IDEs to increase their flexibility. Show more
Keywords: Fuzzy lexemes, fuzzy automata, error handling, approximate string matching, fuzzy lexical analysis
DOI: 10.3233/JIFS-223021
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2537-2546, 2024
Authors: Konduru, Ashok Kumar | Mazher Iqbal, J.L.
Article Type: Research Article
Abstract: Emotion recognition from speech signals serves a crucial role in human-computer interaction and behavioral studies. The task, however, presents significant challenges due to the high dimensionality and noisy nature of speech data. This article presents a comprehensive study and analysis of a novel approach, “Digital Features Optimization by Diversity Measure Fusion (DFOFDM)”, aimed at addressing these challenges. The paper begins by elucidating the necessity for improved emotion recognition methods, followed by a detailed introduction to DFOFDM. This approach employs acoustic and spectral features from speech signals, coupled with an optimized feature selection process using a fusion of diversity measures. The …study’s central method involves a Cuckoo Search-based classification strategy, which is tailored for this multi-label problem. The performance of the proposed DFOFDM approach is evaluated extensively. Emotion labels such as ‘Angry’, ‘Happy’, and ‘Neutral’ showed a precision rate over 92%, while other emotions fell within the range of 87% to 90%. Similar performance was observed in terms of recall, with most emotions falling within the 90% to 95% range. The F-Score, another crucial metric, also reflected comparable statistics for each label. Notably, the DFOFDM model showed resilience to label imbalances and noise in speech data, crucial for real-world applications. When compared with a contemporary model, “Transfer Subspace Learning by Least Square Loss (TSLSL)”, DFOFDM displayed superior results across various evaluation metrics, indicating a promising improvement in the field of speech emotion recognition. In terms of computational complexity, DFOFDM demonstrated effective scalability, providing a feasible solution for large-scale applications. Despite its effectiveness, the study acknowledges the potential limitations of the DFOFDM, which might influence its performance on certain types of real-world data. The findings underline the potential of DFOFDM in advancing emotion recognition techniques, indicating the necessity for further research. Show more
Keywords: Hidden markov model, emotion detection, speech signal, artificial intelligence, cuckoo search, distributed diversity measures
DOI: 10.3233/JIFS-231263
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2547-2572, 2024
Authors: Gao, Lijun | Zhu, Jialong | Zhang, Xuedong | Wu, Jiehong | Yin, Hang
Article Type: Research Article
Abstract: Deep neural networks have been extensively applied in fields such as image classification, object detection, and face recognition. However, research has shown that adversarial samples with subtle perturbations can effectively deceive these networks. Existing methods for generating such adversarial images often lack stealth and robustness. In this study, we present an enhanced attack strategy based on traditional Generative Adversarial Networks (GANs). We integrate image texture into the unsupervised training scheme, guiding the model to focus perturbations in high-texture areas. We also introduce a dynamic equilibrium training strategy that employs Differential Evolution algorithms to adaptively adjust both network weight parameters and …the training ratio between the generator and discriminator, achieving a self-balancing training process. Further, we propose an image local optimization algorithm to eliminate perturbations in non-sensitive areas through weighted filtering. The model is validated using benchmark datasets such as MNIST, ImageNet and SVHN. Through extensive experimental evaluations, our approach shows a 4.93% improvement in attack success rate against conventional models and a 10.23% increase against defense models compared to state-of-the-art attack methods. Show more
Keywords: Adversarial samples, texture sensitive region, GAN networks, micro parallax, optimization algorithm
DOI: 10.3233/JIFS-231653
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2573-2584, 2024
Authors: Liu, Cong | She, Wenhao
Article Type: Research Article
Abstract: Defect detection in mobile phone cameras constitutes a critical aspect of the manufacturing process. Nonetheless, this task remains challenging due to the complexities introduced by intricate backgrounds and low-contrast defects, such as minor scratches and subtle dust particles. To address these issues, a Bilateral Feature Fusion Network (BFFN) has been proposed. This network incorporates a bilateral feature fusion module, engineered to enrich feature representation by fusing feature maps from multiple scales. Such fusion allows the capture of both fine and coarse-grained details inherent in the images. Additionally, a Self-Attention Mechanism is deployed to garner more comprehensive contextual information, thereby enhancing …feature discriminability. The proposed Bilateral Feature Fusion Network has been rigorously evaluated on a dataset of 12,018 mobile camera images. Our network surpasses existing state-of-the-art methods, such as U-Net and Deeplab V3+, particularly in mitigating false positive detection caused by complex backgrounds and false negative detection caused by slight defects. It achieves an F1-score of 97.59%, which is 1.16% better than Deeplab V3+ and 0.99% better than U-Net. This high level of accuracy is evidenced by an outstanding precision of 96.93% and recall of 98.26%. Furthermore, our approach realizes a detection speed of 63.8 frames per second (FPS), notably faster than Deeplab V3+ at 57.1 FPS and U-Net at 50.3 FPS. This enhanced computational efficiency makes our network particularly well-suited for real-time defect detection applications within the realm of mobile camera manufacturing. Show more
Keywords: Defect detection, image segmentation, feature fusion, deep learning, mobile camera
DOI: 10.3233/JIFS-232664
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2585-2594, 2024
Authors: Jiang, Li | Yang, Lu | Zang, Xiaoning | Dong, Junfeng | Lu, Wenxing
Article Type: Research Article
Abstract: This paper focuses on addressing the “last 100 metres” home delivery in rural areas, using a cooperated delivery method of drones and truck. Considering the constraints of drone load, drone energy consumption and customer time window, a mixed integer linear programming model is established to minimize the delivery cost. Owing to the computational complexity of this problem, a double ant colony optimization with neighbourhood search is proposed. First, the raw data are sorted and encoded. Second, the ant colony optimization with search operators is used to solve drone routes and truck route. Finally, the local search algorithm with search operators …is used to solve the connection point between the drones and truck to obtain the cooperated delivery routes. Extensive experiments are conducted on the instances randomly generated in the Solomon dataset, and results demonstrate the proposed algorithm effectively solves problems within reasonable runtimes. Sensitivity analysis is conducted on factors that may affect the delivery cost of the solution and provide insights about drones participating in the “last hundred metres” home delivery service. Show more
Keywords: Collaborative distribution, “last 100 metres” delivery, ant colony optimization, neighbourhood search
DOI: 10.3233/JIFS-233045
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2595-2614, 2024
Authors: Zheng, Lingfei | Hu, Zhubing | Yao, Meiling | Xu, Pengwei | Ma, Jing
Article Type: Research Article
Abstract: Hand gesture recognition is important in human-computer interaction with wide applications in many fields. Different from common hand gesture recognition based on 2D images acquired from RGB camera, the utilization of 3D images provides additional spatial information about the target and attracts more and more attention in hand gesture recognition. However, most 3D images for hand gesture recognition are based on depth maps, which only take the distance information as a channel of 2D images, without taking full use of the 3D information. Besides, greater data volume of 3D images brings challenges to the arithmetic facility of hand gesture recognition. …Here, we proposed a point cloud based method for hand gesture recognition. To fully use the 3D information, plane points for template matching were extracted based on their normal distributions, which leads to the average recognition rate over 97%. Pre-classification was implemented to ensure a high-efficient recognition without additional requirements for the computer. The proposed method may provide approach for accurate and efficient hand gesture recognition based on 3D images. Show more
Keywords: Hand gesture recognition, point cloud, 3D images, template matching
DOI: 10.3233/JIFS-233120
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2615-2627, 2024
Authors: Hameed, Saira | Ahmad, Uzma | Ullah, Samee | Shah, Abdul Ghafar
Article Type: Research Article
Abstract: Fuzzy graphs are of great significance in the modeling and analysis of complex systems characterized by uncertain and imprecise information. Among various types of fuzzy graphs, cubic fuzzy graphs stand out due to their ability to represent the membership degree of both vertices and edges using intervals and fuzzy numbers, respectively. The study of connectivity in fuzzy graphs depends on understanding key concepts such as fuzzy bridges, cutnodes and trees, which are essential for analyzing and interpreting intricate networks. Mastery of these concepts enhances decision-making, optimization and analysis in diverse fields including transportation, social networks and communication systems. This paper …introduces the concepts of partial cubic fuzzy bridges and partial cubic fuzzy cutnodes and presents their relevant findings. The necessary and sufficient conditions for an edge to be a partial cubic fuzzy bridge and cubic fuzzy bridge are derived. Furthermore, it introduces the notion of cubic fuzzy trees, provides illustrative examples and discusses results relevant to cubic fuzzy trees. The upper bonds for the number of partial cubic fuzzy bridges in a complete CFG is calculated. As an application, the concept of partial cubic fuzzy bridges is used to identify cities most severely affected by traffic congestion resulting from accidents. Show more
Keywords: Fuzzy graph, connectivity, bridges, trees, cubic fuzzy graph
DOI: 10.3233/JIFS-233142
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2629-2647, 2024
Authors: Mohamed Nusaf, A. | Kumaravel, R.
Article Type: Research Article
Abstract: Air pollution exerts a profound impact on both public health and the natural environment. In India, festivals like Diwali also contaminate the air by releasing pollutants into the atmosphere. It is essential to identify the most polluted region by estimating these pollutants. Since air quality assessment involves multiple air pollutants, there may be inherent uncertainty associated with data. This study employs a fuzzy Multi Attribute Decision Making (MADM) framework fuzzy Analytical Hierarchy Process-Entropy-fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (FAHP-Entropy-FVIKOR) to model the impact of air pollution as a decision-making problem to address the uncertainty and assess the air quality during …the Diwali festival from 2019 to 2021 in Tamil Nadu, India. An integrated weighting approach is utilised to determine the weights of the air pollutants using a fuzzy Analytical Hierarchy Process and Entropy methods. Mainly, the fuzzy VIKOR approach is employed to rank the polluted regions. The validation of the proposed model is established through a comparative analysis using Spearman’s rank correlation with two other existing fuzzy MADM methods. Furthermore, a sensitivity analysis is conducted to evaluate the influence of priority weights and the interdependence of pollutants in determining regional rankings. The results conclude that a strong positive correlation is attained between the proposed and existing methods and the highest levels of air pollution during the festival period are observed in Gandhi Nagar (2019), Rayapuram (2020), T. Nagar, Sowcarpet and Triplicane (2021) in their respective years. These findings substantiate the consistency and effectiveness of the proposed approach. Show more
Keywords: Air pollution, entropy, fuzzy MADM, fuzzy VIKOR, fuzzy AHP
DOI: 10.3233/JIFS-233593
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2649-2663, 2024
Authors: Zhang, Zhi-Hao | Wang, Jie-Sheng | Chen, Lin
Article Type: Research Article
Abstract: The colony is one of the important research objects in microbial technology, which can realize the evaluation of food safety level, environmental pollution degree, therapeutic effect of medical drugs, and characteristics of agricultural fungicides. Traditional colony image research requires human visual observation and statistics, which will result in low work efficiency and high work intensity. Colony image edge detection is an important basis for colony image research. Traditional edge detection operators cannot meet the accuracy requirements of the detection results. This paper proposes a Mediocrity Ant Colony Algorithm (MACA) to achieve edge detection of colony images. MACA combines the mediocrity …rule, uses empirical functions to establish a pheromone database that can be used as a pheromone update reference table, adopts the Chebyshev distance as a weight that affects pheromone update, and combines heuristic information acquisition with maximum variance classification method and local path weights. The method that jointly affects the ant transition probability incorporates feedback rules for obtaining path weights to improve the edge detection effect. By performing edge detection simulation experiments on six colonies of three types of bacteria, and comparing with the classic edge detection operators and two classic ant colony edge detection algorithms, the detection performance, detection results and running time are proposed. The stability and accuracy of MACA algorithm is better than other methods, and the ideal results of the colony image edge detection by the ant colony algorithm are obtained. Show more
Keywords: Colony image, mediocrity ant colony algorithm, edge detection
DOI: 10.3233/JIFS-233769
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2665-2691, 2024
Authors: Wang, Bing | Yue, Wei | Zhang, Lu
Article Type: Research Article
Abstract: The California Bearing Ratio (CBR) holds significant importance in the design of flexible pavements and airport runways, serving as a critical soil parameter. Moreover, it offers a means to gauge the soil response of subgrades through correlation, an aspect pivotal in soil engineering, particularly in shaping subgrade design for rural road networks. The CBR value of soil is influenced by numerous factors, encompassing variables like maximum dry density (MDD), optimum moisture content (OMC), liquid limit (LL), plastic limit (PL), plasticity index (PI), soil type, and soil permeability. The condition of the soil, whether soaked or unsoaked, also contributes to this …value. It is worth noting that determining CBR is time-consuming and extensive. Acknowledging the gravity of this determination, the study introduces a pioneering approach employing machine learning. This innovative technique uses a foundational multi-layer perceptron model, harnessing the algorithm’s robust capabilities in addressing regression challenges. A hybridization approach enhances the multi-layer perceptron’s performance and achieves optimal results. This approach integrates the Bonobo Optimizer (BO), Smell Agent Optimization (SAO), Prairie Dog Optimization (PDO), and Gold Rush Optimizer (GRO). The hybrid models proposed in this study exhibit promising results in predicting CBR values. The MLAO3 hybrid model is particularly noteworthy, emerging as the most accurate predictor among the range of models, with an impressive R2 value of 0.994 and an RMSE value of 2.80. Show more
Keywords: California bearing ratio, multi-layer perceptron, meta-heuristic algorithms, hybrid machine learning
DOI: 10.3233/JIFS-233794
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2693-2711, 2024
Authors: Zhang, Benfei | Huang, Lijun | Wang, Jie | Zhang, Li | Wu, Yue | Jiang, Yizhang | Xia, Kaijian
Article Type: Research Article
Abstract: In this paper, a novel semi-supervised fuzzy clustering algorithm, MFM-SFCM, based on a membership fusion mechanism is proposed for Diffusion-weighted imaging (DWI) brain infarction lesion segmentation. The proposed MFM-SFCM algorithm addresses the issue of weakened constraints and insufficient influence of labeled samples on the clustering process that arises in the semi-supervised fuzzy C-means clustering (SFCM) when emphasizing supervised information. By using a new membership fusion mechanism, MFM-SFCM eliminates this issue, greatly improving the accuracy of clustering results and accelerating convergence speed. This allows fuzzy clustering to achieve good results in the segmentation of DWI brain infarction lesions using a small …amount of labeled information. The effectiveness of the MFM-SFCM algorithm is demonstrated through experiments conducted on a real-world dataset of DWI brain images. Show more
Keywords: Semi-supervised clustering, supervised information, FCM, membership fusion mechanism, medical image segmentation
DOI: 10.3233/JIFS-234148
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2713-2726, 2024
Authors: Zhang, Ju | Zhang, Tao | Xiang, Yanpeng | Liu, Jiahao | Zhang, Yu
Article Type: Research Article
Abstract: Information hiding is a crucial technology in the field of information security. Embedding capacity and stego-image quality are two key performance metrics in information hiding. In recent years, many information-hiding methods have been proposed to enhance embedding capacity and stego-image quality. However, through the study of these methods, we found that there is still room for improvement in terms of performance. This paper proposes a high-capacity information-hiding method based on a chunking matrix (CM). CM divides a 256×256 matrix into blocks, where each block contains k ×k corresponding secret numbers. A pair of pixels is extracted from the original …image and used as the coordinates for the matrix. In the search domain at that coordinate position, the corresponding secret number is found, and the matrix coordinates of the secret information are used as the pixel value for the stego-image. This paper evaluates the security and effectiveness of CM through measures such as embedding capacity, peak signal-to-noise ratio (PSNR), and bit-plane analysis. CM achieves a maximum embedding capacity of 4.806 bits per pixel (bpp ) and maintains a PSNR value of more than 30 dB. Furthermore, the bit-plane analysis fails to detect the presence of the information hidden using CM method. Show more
Keywords: Information hiding, security, chunking matrix, block, stego-image
DOI: 10.3233/JIFS-234236
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2727-2741, 2024
Authors: Wang, Ling | Ni, Zhiyun
Article Type: Research Article
Abstract: In recent years, the smart city concept has become popular due to its ability to improve the quality of life for urban residents. Smart community, smart transportation, and smart healthcare are among the several fields the idea covers. Integrating cloud computing technology into the healthcare industry has revolutionized healthcare delivery, enabling efficient data storage, analysis, and remote access to critical medical resources. However, choosing high-quality healthcare services from many cloud service providers remains challenging. This study presents the Quality of Service-driven Cloud Healthcare Services Selection (QCHSS) framework, underpinned by deep reinforcement learning, to tackle the intricate challenge of optimizing cloud-based …healthcare services. QCHSS prioritizes Quality of Service (QoS) criteria, elevating patient experiences and outcomes. Leveraging Deep Reinforcement Learning (DRL), particularly the Deep Q-network (DQN) technique, we intelligently select cloud healthcare services, resulting in substantial improvements in availability, reliability, energy efficiency, and throughput. This research not only advances cloud-based healthcare service selection but also underscores the transformative potential of DRL in complex decision-making processes, offering a significant contribution to the field and enhancing healthcare service quality. Show more
Keywords: Healthcare services, cloud computing, reinforcement learning, neural network
DOI: 10.3233/JIFS-234582
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2743-2757, 2024
Authors: Sun, Haibin | Li, Zheng
Article Type: Research Article
Abstract: Millions of traffic accidents occur worldwide each year, resulting in tens of thousands of deaths. The primary cause is the distracted behavior of drivers during the driving process. If the distracted behaviors of drivers during driving can be detected and recognized in time, drivers can regulate their driving and the goal of reducing the number of traffic fatalities can be achieved. A deep learning model is proposed to detect driver distractions in this paper. The model can identify ten behaviors including one normal driving behavior and nine distracted driving behaviors. The proposed model consists of two modules. In the first …module, the cross-domain complementary learning (CDCL) algorithm is used to detect driver body parts in the input images, which reduces the impact of environmental factors in vehicles on the convolutional neural network. Then the output images of the first module are sent to the second module. The Resnet50 and Vanilla networks are ensembled in the second module, and then the driver behavior can be classified. The ensemble architecture used in the second module can reduce the sensitivity of only a single network on the data, and then the detection accuracy can be improved. Through the experiments, it can be seen that the proposed model in this paper can achieve an average accuracy of 99.0%. Show more
Keywords: Deep learning, neural networks, distracted behavior, ensemble learning, semantic segmentation
DOI: 10.3233/JIFS-234593
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2759-2773, 2024
Authors: Wang, Fang
Article Type: Research Article
Abstract: The rapid development of cultural tourism in recent years refers to a process of cultural experience of tourist objects with cultural characteristics. It can not only vigorously carry forward the rich and colorful history and cultural deposits, but also combine the huge economic and cultural benefits generated by tourism, and promote the rapid development of cultural construction. Cultural tourism is a kind of way that all kinds of social groups enjoy, and it is a deep and lasting way of communication, which can promote the communication between people of different social strata. The existing literature has explored the influence of …tourists’ psychological carrying capacity, but failed to explain the process and degree of influence. Based on behavioral and experience theories, this paper proposes that culture has a positive impact on tourists’ psychological carrying capacity through tourist experience, and tests relevant hypotheses. The primary psychological traits of historical and cultural tourists include curiosity about historical mysteries, the desire for historical knowledge, motivation to collect spiritual enrichment, academic interest in cultural heritage exploration, and an aesthetic appreciation for classical history. Key determinants include the scale and conservation of historical and cultural resources, their combination with natural attractions, and the personal qualities of tourists and the cultural competence of tour guides. The mental health care model combines tourism and psychology to facilitate both physical and mental well-being through professional psychological counseling services, aiding tourists in their recovery and self-healing. This integrated approach offers a broad scope and potential as an effective tool for addressing negative emotions, with demonstrated therapeutic effects focusing on psychological and social factors. Show more
Keywords: Role of psycho-occupational therapy, cultural tourism, tourists, mode of physical and mental recovery
DOI: 10.3233/JIFS-235010
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2775-2788, 2024
Authors: Zhaoxian, Ren | Min, Qu
Article Type: Research Article
Abstract: People’s demands for a higher quality of life are increasing, and furniture remains an essential part of daily life. In traditional furniture design methods, designers typically rely on their experience, leading to significant disparities between design solutions and user expectations. A comprehensive model is proposed with combination of Fuzzy KANO (FKANO) method, the Criteria Importance through Intercriteria Correlation (CRITIC) method, and the Coupling Coordination Degree (CCD) method for furniture design and evaluation, using desk design as an example. Firstly, FKANO model is applied to classify and filter user requirements, identifying crucial user needs as the basis for subsequent design. Secondly, …three desk design proposals that align with user requirements are formulated. Thirdly, the CRITIC method is introduced, using the filtered user requirements to construct an evaluation system and calculate the weights of various indicators. Lastly, the CCD method is applied to select the optimal desk design from five samples, including three designed by this study and two existing on the market. This comprehensive approach contains critical stages such as requirement identification, weight determination, and solution selection, achieving comprehensive research objectives. Besides, sensitivity analysis was conducted to validate the effectiveness of this integrated model, demonstrating its ability to balance different user requirements under different weight settings. The results indicate that the proposed approach enhances the scientific rigor, systematization, and user satisfaction of the furniture design and decision-making process. It offers valuable guidance for furniture manufacturers and designers, allowing furniture products to more effectively align with market demands, thus enhancing their competitiveness. Show more
Keywords: FKANO model, CRITIC method, CCD technique, design and evaluation, furniture design
DOI: 10.3233/JIFS-235272
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2789-2810, 2024
Authors: Tran-Anh, Dat | Nguyen Huu, Quynh | Nguyen Thi Phuong, Thao | Dao Thi Thuy, Quynh
Article Type: Research Article
Abstract: The wilting of leaves caused by disease poses risks to both harvest yield and the environment. Therefore, the timely detection of disease signs on leaves is crucial to enable farmers to prevent disease outbreaks and safeguard their crops. However, manually observing all diseased leaves on a large scale demands substantial time and human effort. In this study, we propose an effective method for automated disease detection on leaves. Specifically, this method utilizes images captured from mobile phones. The proposed technique combines four models (ensemble of models) with distinct features: (1) ResNeXt50 model with a high-quality image processing, (2) ViT model …with a low-quality image processing, (3) Efficientnet B5 model combines a self-learning with noisy input, and (4) Mobilenet V3 model with image segmentation. Experimental results demonstrate that the proposed method outperforms some of the state-of-the-art methods on TLU-Leaf dataset (ours) with F1-score of 90% and Cassava Leaf Disease dataset with F1-score of 87%. Show more
Keywords: Convolutional neural network, deep learning, multiple-model, leaf disease classification
DOI: 10.3233/JIFS-235940
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2811-2823, 2024
Authors: Hu, Huixian | Wang, Xiu | Li, Tian
Article Type: Research Article
Abstract: In the IP sector, the combination of visible image fusion (VIF) with infrared (IR) gives a more comprehensive and accurate description of a target image. To get over the problems of detail and energy loss during the fusion process caused by current deep learning fusion approaches, it is proposed to use a fusion strategy of IR and visible pictures based on full convolutional network (FCN) applying transfer learning. FCN model can take any size of the input and generate constant size of the output with desired rules. Through effective inference and learning procedure, the ability of features extraction and energy …conservation can be enhanced a lot. Experimental results demonstrate that the suggested method succeeds in improving IF quality over the other two comparable methods by preserving high light intensity and retrieving detail information. This also confirms its dominance across five different objective assessment indices: mutual information (MI), entropy (EN), edge-based similarity measure (Qabf), sum of correlations of differences (SCD), and multi-scale structural similarity for image (MS-SSIM). Show more
Keywords: Image fusion, full convolutional network, transfer learning, zero-phase component analysis
DOI: 10.3233/JIFS-236094
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2825-2834, 2024
Authors: Awodutire, Phillip Oluwatobi | Sule, Ibrahim
Article Type: Research Article
Abstract: In this work, a new family of distribution, which generalizes the Beta Weibull-G family by the introduction of a shape parameter to enhance better fit and flexibility, called the Modified Beta Weibull-G family of distributions is obtained. The mixture representation of the derived family of distributions was discussed, with the results effective in studying moments, moment generating functions, order statistics. Parameters of the family of distributions were estimated using the maximum likelihood estimation method. By utilizing this modified class of distributions, we build a new distribution called the modified beta Weibull Weibull and applied it to engineering datasets. Application revealed …a better performance in model fit, compared to some other distributions. Show more
Keywords: Modified Beta-G Distribution, Weibull-G, Modified Beta Weibull G distribution, estimation, real life data
DOI: 10.3233/JIFS-223042
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2835-2850, 2024
Authors: Che, Gaofeng | Yu, Zhen
Article Type: Research Article
Abstract: This work investigates trajectory-tacking control problem for underactuated autonomous underwater vehicles (AUV) with unknown dynamics. Due to the unknown dynamics, an action-critic networks based adaptive dynamic programming (ADP) scheme combined with backstepping approach is designed, which can achieve high-level system stability and tracking control accuracy. Firstly, the backstepping approach is introduced into the kinematic model of underactuated AUV and produces a virtual velocity control which is taken as the desired velocity input of the dynamic model of underactuated AUV. Secondly, the error tracking system is constructed according to the dynamic model of underactuated AUV. Thirdly, the critic neural network and …the action neural network are employed to transform the trajectory-tracking control problem into optimal control problem based on policy iteration algorithm. At last simulation results are given to verify the effectiveness of the proposed control scheme. Show more
Keywords: Adaptive dynamic programming (ADP), backstepping approach, tracking control, autonomous underwater vehicle (AUV)
DOI: 10.3233/JIFS-230232
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2851-2863, 2024
Authors: Xie, Tian-Tian | Wang, Rui-Ying
Article Type: Research Article
Abstract: Connectivity is one of the most essential notions in general topology. Convex structures are topological-like structures. Many properties in topological spaces have been generalized to convex structures, such as separation. However, connectivity has not been studied in convex structures yet. In this paper, firstly, based on the consideration to hull operators, separatedness is defined in classical convex structures, and then we provide the concept of connectivity. Secondly, some equivalent characterizations of connectivity are discussed, and we investigate the related properties of connectivity. In additional, through (L , M )-fuzzy convex hull operators, we propose the separatedness degrees of (L , …M )-fuzzy convex structures. Furthermore, the notion of connectedness degrees of (L , M )-fuzzy convex structures is introduced. Finally, many properties of connectivity in general convex structures can be generalized to (L , M )-fuzzy convex structures. Show more
Keywords: Convex structure, connectivity, (L, M)-fuzzy convex structure, separatedness degree, connectedness degree
DOI: 10.3233/JIFS-232309
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2865-2876, 2024
Authors: Yan, Guangzhou | Ni, Yaodong
Article Type: Research Article
Abstract: This paper studies the pricing and low-carbon decision problems in a supply chain containing a manufacturer and a downstream retailer. The manufacturer produces a single product under the cap-and-trade scheme. We formulate the price and carbon-concerned demand function. To maximize their revenue, the manufacturer and the retailer determine their selling prices and carbon emission reduction rates separately. Due to the fast product updates speed, some parameters do not have enough historical data. For example, the sales cost of the retailer, the demand of consumers, and the total carbon emissions of manufacturers are far from frequency stability. This fact makes the …distribution function obtained in practice usually deviate from the frequency. They are all uncertain variables whose distributions are estimated from the empirical data of experts or managers. In this paper, we give three decentralized game models to explore the equilibrium behaviors in the corresponding decision environment under an uncertain environment. Corresponding analytical solutions are offered under different game scenarios. Finally, numerical experiments are performed to illustrate the effectiveness of the established models and yield some remarkable insights. Show more
Keywords: Supply chain management, Pricing decision, Cap-and-trade, Low-carbon, Stackelberg game
DOI: 10.3233/JIFS-232607
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2877-2897, 2024
Authors: Li, Shi | Zhang, Yongkang
Article Type: Research Article
Abstract: Entity linking is an important task for information retrieval and knowledge graph construction. Most existing methods use a bi-encoder structure to encode mentions and entities in the same space, and learn contextual features for entity linking. However, this type of system still faces some problems: (1) the entity embedding part of the model only learns from the local context of the target entity, which is too unique for entity linking model to learn the context commonality of information; (2) the entity disambiguation part only uses similarity calculation once to determine the target entity, resulting in insufficient interaction between the mentions …and candidate entities, and ineffective recall of real entities. We propose a new entity linking model based on graph neural network. Different from other bi-encoder retrieval systems, this paper introduces a fine-grained semantic enhancement information into the entity embedding part of the bi-encoder to reduce the specificity of the model. Then, the cross-attention encoder is used to re-rank the target mention and each candidate entity after the entity retrieval model. Experimental results show that although the model is not optimal in inference speed, it outperforms all baseline methods on the AIDA-CoNLL dataset, and has good generalization effects on four datasets in different fields such as MSNBC and ACE2004. Show more
Keywords: Entity linking, semanic reinforcement, cross-attention mechanism, graph convolutional network
DOI: 10.3233/JIFS-233124
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2899-2910, 2024
Authors: Dineshkumar, R. | Alphy, Anna | Kalaivanan, C. | Bashkaran, K. | Pattanaik, Balachandra | Logeswaran, T. | Saranya, K. | Deivasikamani, Ganeshkumar | Johny Renoald, A.
Article Type: Research Article
Abstract: Microgrids (MGs) have become a reliable power source for supplying energy to rural areas in a secure, consistent, and low-carbon emission manner. Power quality disturbance (PQD) is a common issue that reduces the MGs networks’ reliability and restricts its usage on a small scale. The performance, reliability and lifetime of the various power devices can be affected due to the problem of PQD in the network. Researchers have proposed numerous PQD monitoring techniques based on artificial intelligence. However, they are limited to low margins and accuracy. So, this paper suggests a novel hyperparameter-tuned or optimized deep learning model with an …attention-based feature learning mechanism for PQD prediction. The critical stages of the proposed work, such as data collection, feature extraction, and PQD prediction, are as follows. The PQD signals are first produced using the IEEE 1159 standard. Following that, the original time-domain features are directly recovered from the dataset, and the frequency-domain features using discrete wavelet transform (DWT). The extracted features were fed into visual geometry group 16 with multi-head attention and optimal hyperparameter-based bidirectional long short-term memory (V16MHA-OHBM) to perform spatial and temporal feature extraction. These extracted features are concatenated and given to the fully connected layer to forecast the PQD. The results showed that the suggested approach surpasses the prior state-of-the-art algorithms when trained and tested using 16 different types of synthetic noise PQD data produced using mathematical models in line with IEEE 1159. Show more
Keywords: Micro-grids, power quality disturbance, PQD prediction, data acquisition, IEEE 1159
DOI: 10.3233/JIFS-233263
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2911-2927, 2024
Authors: Jiang, Jianming | Ban, Yandong | Li, Jiayi | Zhou, Yane
Article Type: Research Article
Abstract: Accurate prediction of the aging population can provide valuable reference and corresponding theoretical support for the adjustment of national population development policy and economic development strategy. To explore the future development trend of China’s aging population, this paper establishes a novel fractional grey prediction model with the time power term (abbreviated as FGM (1, 1, t α ) model) to study China’s aging population. FGM (1, 1, t α ) has the properties of fractional order accumulation operation and GM (1, 1, t α ) model, which makes it good at capturing nonlinear features in time series. …Furthermore, the quantum genetic algorithm is used to search for unknown parameters in the model to facilitate the solving task of the model. Data on China’s aging population from 2000 to 2009 are used to train the prediction models, and data from 2010 to 2019 are used to evaluate the models’ prediction performance. The results show that the FGM (1, 1, t α ) model outperforms the other competing models, which means that it has good generalization. Finally, the FGM (1, 1, t α ) model is used to forecast China’s aging population from 2020 to 2029. Show more
Keywords: Grey system theory, grey prediction model, china’s elderly population, simpson formula, fractional order accumulation
DOI: 10.3233/JIFS-234205
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2929-2939, 2024
Authors: Sri Vinitha, V. | Renuka, D. Karthika
Article Type: Research Article
Abstract: Spam Email is a serious concern which can steal user’s personal information and cause huge financial loss due to the increasing rate of internet users. Therefore, the demand for accurate spam filtering has become more sophisticated for the Email spam detection. In the existing techniques, it is difficult to intricate the relationship between words in the Email using certain word embedding techniques and learning rate tuning is one of the greatest challenges of stochastic optimization. To overcome this difficulty, the proposed framework uses diverse ensemble based Email spam classification by incorporating multiple word embedding’s with Continuous Coin Betting optimizer. Word2Vec …is used to produce the first set of 200D, next set of 200D word embedding is produced by Glove and 768D is produced by using Bidirectional Encoder Representations from Transformers (BERT) respectively. After generating word embedding, then it is classified through diverse ensemble based classifier with base level classifier consists of Long Short Term Memory (LSTM) Networks, Gated Recurrent Unit (GRU) and Bi-directional Gated Recurrent Unit (Bi-GRU) and LSTM as Meta-classifier using COCOB optimizer. Experiments were conducted on 3 benchmark Email dataset and result shows that the proposed system outperforms well with a low false positive rate. Show more
Keywords: Word2Vec, bidirectional encoder representations from transformers, global vectors, gated recurrent unit, bi-directional gated recurrent unit, long short term memory, continuous coin betting
DOI: 10.3233/JIFS-235464
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2941-2954, 2024
Authors: Hu, Kuang-Hua | Chen, Fu-Hsiang | Zeng, Jhih-Hong | Lin, Sin-Jin
Article Type: Research Article
Abstract: Blockchain technology holds considerable amount of potential for all types of industries by executing transactions in a verifiable, efficient, and permanent channel. It has been widely viewed as a standard requirement for making industry ready for the future, but when it comes to practical applications, it still arouses numerous risks/challenges that need to be addressed. Therefore, it is essential to address this gap and establish a comprehensive and effective practical framework to align the information technology revolution with sustainable value creation. The purpose of this research is to realize to what extent an enterprise legacy system’s transformation benefits a blockchain-based …system and to minimize its specific risk through a hybrid fuzzy MRDM (multiple rule-based decision making) model that integrates data envelopment analysis with rough set theory (DEA-RST) and the fuzzy DEMATEL approach grounded on a questionnaire derived from domain experts. We aim to point out the inherent risks of blockchain-based technology adoption and to assist senior engineers in designing or adopting a suitable architecture for practical operation and planning of any future integration and development. The potential risk evaluation of business blockchain adoption reveals that the priority improvement sequence based on dimensions is smart contract risk, value transfer risk, and standard risk. Furthermore, law and regulation are the most critical criteria. Show more
Keywords: Blockchain-based technology adoption risk, decision making, multiple rule-based decision making, data envelopment analysis, risk management
DOI: 10.3233/JIFS-223381
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2955-2969, 2024
Authors: Li, Dan | Chen, Ming | Peng, Kaixiang | Wu, Libing
Article Type: Research Article
Abstract: As for the problem of trajectory tracking of a multi-joint serial manipulator, a novel fixed-time control scheme is proposed based on non-singular fast terminal sliding mode control. By employing a fast terminal sliding mode surface, we solve the singularity problem existed in traditional terminal sliding mode surface. In the meantime, in order to improve the rapidity of the system, the fixed-time control is incorporated with the fast terminal sliding mode surface control. Theoretical analysis proves that the proposed control scheme guarantees that better tracking performance is obtained, and its convergence time upper limit is not affected by the initial states. …In addition, a reaching law with the exponential approach characteristic is added to the control law, which effectively reduces the chattering phenomenon in the controller design. Finally, the effectiveness and feasibility of the designed controller are verified through a numerical simulation. Show more
Keywords: Fixed-time control, non-singular fast terminal sliding mode, trajectory tracking
DOI: 10.3233/JIFS-231664
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2971-2979, 2024
Authors: Khatun, Jasminara | Amanathulla, Sk | Pal, Madhumangal
Article Type: Research Article
Abstract: In the realm of handling imprecise information, picture fuzzy cubic sets have emerged as a more versatile tool compared to cubic sets, cubic intuitionistic fuzzy sets, and similar models. These sets offer better adaptability, precision and compatibility with the system than existing fuzzy models. This paper extends the concept of picture fuzzy cubic sets to the domain of graph theory, introducing the novel concept of picture fuzzy cubic graphs that surpasses previous results in terms of generality. The paper explores various essential operations, including composition, the Cartesian product, P -join, R -join, P -union, R -union of picture fuzzy cubic …graphs. It also investigates the order and degree of picture fuzzy cubic graphs. Furthermore, this work presents two practical applications of picture fuzzy cubic graphs. The first application involves computing the impact of other companies on a specific company and the second application focuses on evaluating the overall impact within a group of companies. Show more
Keywords: Picture fuzzy cubic set, picture fuzzy cubic graph, R-union, R-join
DOI: 10.3233/JIFS-232523
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2981-2998, 2024
Authors: Mutar, Emad Kareem
Article Type: Research Article
Abstract: In reliability analysis, the structure-function is a commonly used mathematical representation of the studied system. A signature vector is used for systems with independently and identically distributed (i.i.d.) component lifetimes. Each element in the signature represents the probability that the failure of the corresponding component will fail the entire system. This paper aims to provide a comprehensive understanding of assessing the performance of two complex systems for optimal communication design. The study compares two systems with the same components using signatures, expected cost rate, survival signature, and sensitivity to determine which system is preferred. It also provides several sufficient conditions …for comparing the lifetimes of two systems based on the usual stochastic order. The results are applied to two communication systems that have the same components. The mathematical properties presented in the study have been proven to enable efficient weighting of the optimal design. Show more
Keywords: Coherent system, signature, survival signature, sensitivity, stochastic order
DOI: 10.3233/JIFS-234456
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2999-3011, 2024
Authors: Peng, Yaxin | Yang, Keni | Zhao, Fangrong | Shen, Chaomin | Zhang, Yangchun
Article Type: Research Article
Abstract: Domain adaptation solves the challenge of inadequate labeled samples in the target domain by leveraging the knowledge learned from the labeled source domain. Most existing approaches aim to reduce the domain shift by performing some coarse alignments such as domain-wise alignment and class-wise alignment. To circumvent the limitation, we propose a coarse-to-fine unsupervised domain adaptation method based on metric learning, which can fully utilize more geometric structure and sample-wise information to obtain a finer alignment. The main advantages of our approach lie in four aspects: (1) it employs a structure-preserving algorithm to automatically select the optimal subspace dimension on the …Grassmannian manifold; (2) based on coarse distribution alignment using maximum mean discrepancy, it utilizes the smooth triplet loss to leverage the supervision information of samples to improve the discrimination of data; (3) it introduces structure regularization to preserve the geometry of samples; (4) it designs a graph-based sample reweighting method to adjust the weight of each source domain sample in the cross-domain task. Extensive experiments on several public datasets demonstrate that our method achieves remarkable superiority over several competitive methods (more than 1.5% improvement of the average classification accuracy over the best baseline). Show more
Keywords: Domain adaptation, metric learning, triplet loss, structure regularization, sample reweighting
DOI: 10.3233/JIFS-235912
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 3013-3027, 2024
Authors: Zekrifa, Djabeur Mohamed Seifeddine | Saravanakumar, R. | Nair, Sruthi | Pachiappan, Krishnagandhi | Vetrithangam, D. | Kalavathi Devi, T. | Ganesan, T. | Rajendiran, M. | Rukmani Devi, S.
Article Type: Research Article
Abstract: The increasing need for effective energy storage solutions has led to the prominence of lithium-ion batteries as a crucial technology across multiple industries. The proficient administration of these batteries is imperative in order to guarantee maximum efficiency, prolong their longevity, and uphold safety measures. This study presents a novel methodology for enhancing battery management systems (BMS) through the integration of cloud-based solutions, artificial intelligence (AI), and machine learning approaches. In this study, we present a conceptual framework that utilises cloud computing to augment the practical functionalities of battery management systems (BMS) specifically in the context of lithium-ion batteries. The incorporation …of cloud computing facilitates the implementation of scalable data storage, remote monitoring, and processing resources, hence enabling the execution of real-time analysis and decision-making processes. By leveraging the capabilities of machine learning and artificial intelligence, our methodology focuses on addressing crucial battery metrics, including the state of charge (SoC) and state of health (SoH). Through the ongoing collection and analysis of data obtained from battery systems that are deployed in real-world settings, the framework iteratively improves its predictive models, hence facilitating precise assessment of battery states. Ensuring safety is a crucial element in the management of batteries. The solution we propose utilises anomaly detection algorithms driven by artificial intelligence to detect potential safety issues, facilitating prompt responses and mitigating dangerous circumstances. In order to showcase the efficacy of our methodology, we offer practical implementations in several industries, encompassing the integration of renewable energy, use of electric vehicles, and optimisation of industrial processes. Through the utilisation of cloud-based machine learning techniques, we are able to enhance the efficiency of energy storage and consumption, while simultaneously enhancing the dependability and security of battery systems. This study highlights the potential of the proposed framework to revolutionise battery management paradigms, thereby guaranteeing secure and efficient energy prospects for a sustainable future. Show more
Keywords: Battery management system, state of health, state of charge, artificial intelligence, machine learning, cloud-based solutions
DOI: 10.3233/JIFS-236391
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 3029-3043, 2024
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china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl