Journal of Intelligent & Fuzzy Systems - Volume 46, issue 1

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: This article has been retracted.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

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