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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: Du, Rong | Cheng, Yan
Article Type: Research Article
Abstract: This research paper highlights the significance of vehicle detection in aerial images for surveillance systems, focusing on deep learning methods that outperform traditional approaches. However, the challenge of high computation complexity due to diverse vehicle appearances persists. The motivation behind this study is to highlight the crucial role of vehicle detection in aerial images for surveillance systems, emphasizing the superior performance of deep learning methods compared to traditional approaches. To address this, a lightweight deep neural network-based model is developed, striking a balance between accuracy and efficiency enabling real-time operation. The model is trained and evaluated on a standardized dataset, …with extensive experiments demonstrating its ability to achieve accurate vehicle detection with significantly reduced computation costs, offering a practical solution for real-world aerial surveillance scenarios. Show more
Keywords: Aerial images, vehicle detection, surveillance system, deep learning, real-time processing
DOI: 10.3233/JIFS-236059
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Pavithra, R. | Ramachandran, Prakash
Article Type: Research Article
Abstract: The Hilbert spectrum images of intrinsic mode functions (IMF) of empirical mode decomposition (EMD) analysis and variational mode decomposition (VMD) analysis of faulty machine vibration signals are used in deep convolutional neural network (DCNN) for machine fault classification in which the DCNN automatically learns the features from spectral images using convolution layer. Though both EMD and VMD analysis suit well for non-stationary signal analysis, VMD has the merit of aliasing free IMFs. In this paper, the performance improvement of DCNN classification for a non-stationary vibration signal dataset using VMD is brought out. The numerical experiment uses the Hilbert spectrum images …of 4 EMD-IMFs and 4 VMD-IMFs in DCNN to classify 10 different faults of the Case Western Reserve University (CWRU) bearing dataset. The confusion matrices are obtained and the plot of model accuracies in terms of epochs for the DCNN is analysed. It is shown that the spectrum images of one of the four EMD-IMFs, IMF0 , give a validation accuracy of 100% and in the case of VMD the spectrum images of two of the four VMD-IMFs, IMF0 , and IMF1 give a validation accuracy of 100%. This reveals that non-aliasing IMFs of VMD are better at classifying bearing faults. Further to bring out the merits of VMD analysis for non-stationary signals the numerical experiment is conducted using VMD analysis for binary fault classification of the milling dataset which is more non-stationary than the bearing dataset which is proved by plotting the statistical parameters of both datasets against time. It is found that the DCNN classification is 100% accurate for IMF3 of VMD analysis which is much better than the 81% accuracy provided by EMD analysis as per existing literature. The performance comparison highlights the merits of VMD analysis over EMD analysis and other state-of-the-art methods and ensemble learning methods. Show more
Keywords: Deep convolution neural network, empirical mode decomposition, hilbert transform, intrinsic mode function, variational mode decomposition, ensemble learning
DOI: 10.3233/JIFS-237546
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Nawshin, Sabila | Islam, Salekul | Shatabda, Swakkhar
Article Type: Research Article
Abstract: Software Defined Networking (SDN) proposes a centralized network paradigm where a central controller manages the network. While this centralizes scheme opens up previously unachievable opportunities, it also makes the network more susceptible to a varying range of cyber threats. The development of effective Intrusion Detection Systems (IDS) designed for the SDN topology is a critical need to address the different vulnerabilities SDN faces. Towards that purpose, the inSDN dataset was specifically curated for intrusion detection in SDN with various attack scenarios unique to the SDN topology. This study leveraged the inSDN dataset to introduce an innovative Intrusion Detection …System (IDS) model that amalgamates Principal Component Analysis (PCA), a dimensionality reduction technique widely employed in traditional Machine Learning (ML) to extract the principal features of the dataset and couples it with Artificial Neural Networks (ANN) to classify network traffic based on the extracted features. The proposed model attains an exceptional accuracy rate of 99.95% for multi-class classification and demonstrate that it surpasses the current state-of-the-art techniques while operating within a much simpler framework. This significantly diminishes the necessity for complex models that demand extensive computational resources when dealing with the inSDN attack dataset. The analysis of the dataset carried out in this study also provides insights into the redundancy present in the dataset and identifies the core features that contains most of the information in the dataset. Show more
Keywords: Software Defined Networking (SDN), Intrusion Detection Systems (IDS), Principle Component Analysis (PCA), Artificial Neural Network (ANN)
DOI: 10.3233/JIFS-236340
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Kumar, Geethu S. | Ankayarkanni, B.
Article Type: Research Article
Abstract: Facial Emotion Recognition (FER) is a powerful tool for gaining insights into human behaviour and well-being by precisely quantifying a wide range of emotions especially stress, through the analysis of facial images. Detecting stress using FER entails meticulously examining subtle facial cues, such as changes in eye movements, brow furrowing, lip tightening, and muscle contractions. To assure effectiveness and real-time processing, FER approaches based on deep learning and artificial intelligence (AI) techniques was created using edge modules. This research introduces a novel approach for identifying stress, leveraging the Conv-XGBoost Algorithm to analyse facial emotions. The proposed model sustain rigorous evaluation …techniques, for employing key metrics examination such as the F1 score, validation accuracy, precision, and recall rate to assess its real-world reliability and robustness. This comprehensive analysis and validation proved the model’s practical utility in facial analysis. Integrating the Conv-XGBoost Algorithm with facial emotion analysis represents a promising and highly accurate solution for efficient stress detection. The method surpasses existing literature and demonstrate significant potential for practical applications based on well-validated data. Show more
Keywords: Stress, emotion recognition, Conv-XGBoost, deep learning, facial expression
DOI: 10.3233/JIFS-237820
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Martínez Felipe, Miguel de JesÚs | Martínez Castro, JesÚs Alberto | Montiel Pérez, JesÚs Yaljá | Chaparro Amaro, Oscar Roberto
Article Type: Research Article
Abstract: In this work, the image block matching based on dissimilarity measure is investigated. Moreover, an unsupervised approach is implemented to yield that the algorithms have low complexity (in numbers of operations) compared to the full search algorithm. The state-of-the-art experiments only use discrete cosine transform as a domain transform. In addition, some images were tested to evaluate the algorithms. However, these images were not evaluated according to specific characteristics. So, in this paper, an improved version is presented to tackle the problem of dissimilarity measure in block matching with a noisy environment, using another’s domain transforms or low-pass filters to …obtain a better result in block matching implementing a quantitive measure with an average accuracy margin of ± 0.05 is obtained. The theoretical analysis indicates that the complexity of these algorithms is still accurate, so implementing Hadamard spectral coefficients and Fourier filters can easily be adjusted to obtain a better accuracy of the matched block group. Show more
Keywords: Block matching, Walsh-Hadamard discrete transform, Fourier filter, dissimilarity measure, unsupervised machine learning
DOI: 10.3233/JIFS-219341
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Ensastegui-Ortega, Maria Elena | Batyrshin, Ildar | Cárdenas–Perez, Mario Fernando | Kubysheva, Nailya | Gelbukh, Alexander
Article Type: Research Article
Abstract: In today’s data-rich era, there is a growing need for developing effective similarity and dissimilarity measures to compare vast datasets. It is desirable that these measures reflect the intrinsic structure of the domain of these measures. Recently, it was shown that the space of finite probability distributions has a symmetric structure generated by involutive negation mapping probability distributions into their “opposite” probability distributions and back, such that the correlation between opposite distributions equals –1. An important property of similarity and dissimilarity functions reflecting such symmetry of probability distribution space is the co-symmetry of these functions when the similarity between probability …distributions is equal to the similarity between their opposite distributions. This article delves into the analysis of five well-known dissimilarity functions, used for creating new co-symmetric dissimilarity functions. To conduct this study, a random dataset of one thousand probability distributions is employed. From these distributions, dissimilarity matrices are generated that are used to determine correlations similarity between different dissimilarity functions. The hierarchical clustering is applied to better understand the relationships between the studied dissimilarity functions. This methodology aims to identify and assess the dissimilarity functions that best match the characteristics of the studied probability distribution space, enhancing our understanding of data relationships and patterns. The study of these new measures offers a valuable perspective for analyzing and interpreting complex data, with the potential to make a significant impact in various fields and applications. Show more
Keywords: Dissimilarity function, co-symmetry, correlation, probability distribution, negation
DOI: 10.3233/JIFS-219363
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Xu, Zhigang | Li, Yugen
Article Type: Research Article
Abstract: Construction site environment helmet detection is of great significance for protecting workers’ lives and realizing the automation of safety management. Aiming at the current object detection methods for the complex construction site environment in the small-scale helmet object detection ability is insufficient. This paper proposes a construction site environment helmet detection method based on multi-scale context and attention fusion. The method is able to aggregate the multi-scale contextual semantics of deep image features through the proposed multi-scale context module and expand the receptive field in order to improve the network’s discriminative learning ability for small-scale helmet objects. Meanwhile, the proposed …attention feature fusion module dynamically fuses features from shallow features and network decoding features to enhance the network’s ability to learn the expression of global feature dependencies and local spatial detail features of helmet objects, and further improve the network’s detection precision of helmet objects. The experimental results show that on the constructed safety helmet wearing dataset, the proposed method in this paper has good detection effect and balanced detection speed compared with the existing mainstream object detection methods. Show more
Keywords: Construction site, helmet detection, CenterNet, multi-scale context, attention feature fusion
DOI: 10.3233/JIFS-236385
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Wei, Tao | Yang, Changchun | Zheng, Yanqi | Zhang, Jingxue
Article Type: Research Article
Abstract: Recently, Graph Neural Networks (GNNs) using aggregating neighborhood collaborative information have shown effectiveness in recommendation. However, GNNs-based models suffer from over-smoothing and data sparsity problems. Due to its self-supervised nature, contrastive learning has gained considerable attention in the field of recommendation, aiming at alleviating highly sparse data. Graph contrastive learning models are widely used to learn the consistency of representations by constructing different graph augmentation views. Most current graph augmentation with random perturbation destroy the original graph structure information, which mislead embeddings learning. In this paper, an effective graph contrastive learning paradigm CollaGCL is proposed, which constructs graph augmentation by …using singular value decomposition to preserve crucial structure information. CollaGCL enables perturbed views to effectively capture global collaborative information, mitigating the negative impact of graph structural perturbations. To optimize the contrastive learning task, the extracted meta-knowledge was propagate throughout the original graph to learn reliable embedding representations. The self-information learning between views enhances the semantic information of nodes, thus alleviating the problem of over-smoothing. Experimental results on three real-world datasets demonstrate the significant improvement of CollaGCL over state-of-the-art methods. Show more
Keywords: Self-supervised learning, recommendation, contrastive learning, data augmentation
DOI: 10.3233/JIFS-236497
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Yang, Dianqing | Wang, Wenliang
Article Type: Research Article
Abstract: Unmanned aerial vehicle (UAV) remote-sensing images have a wide range of applications in wildfire monitoring, providing invaluable data for early detection and effective management. This paper proposes an improved few-shot target detection algorithm tailored specifically for wildfire detection. The quality of UAV remote-sensing images is significantly improved by utilizing image enhancement techniques such as Gamma change and Wiener filter, thereby enhancing the accuracy of the detection model. Additionally, ConvNeXt-ECA is used to focus on valid information within the images, which is an improvement of ConvNeXt with the addition of the ECANet attention mechanism. Furthermore, multi-scale feature fusion is performed by …adding a feature pyramid network (FPN) to optimize the extracted small target features. The experimental results demonstrate that the improved algorithm achieves a detection accuracy of 93.2%, surpassing Faster R-CNN by 6.6%. Moreover, the improved algorithm outperforms other target detection algorithms YOLOv8, RT-DETR, YoloX, and SSD by 3.4%, 6.4%, 7.6% and 21.1% respectively. This highlights its superior recognition accuracy and robustness in wildfire detection tasks. Show more
Keywords: Fire target detection, ConvNeXt-ECA, UAV remote-sensing image, feature pyramid network
DOI: 10.3233/JIFS-240531
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Singh, Pratibha | Kushwaha, Alok Kumar Singh | Varshney, Neeraj
Article Type: Research Article
Abstract: Precise video moment retrieval is crucial for enabling users to locate specific moments within a large video corpus. This paper presents Interactive Moment Localization with Multimodal Fusion (IMF-MF), a novel interactive moment localization with multimodal fusion model that leverages the power of self-attention to achieve state-of-the-art performance. IMF-MF effectively integrates query context and multimodal features, including visual and audio information, to accurately localize moments of interest. The model operates in two distinct phases: feature fusion and joint representation learning. The first phase dynamically calculates fusion weights for adapting the combination of multimodal video content, ensuring that the most relevant features …are prioritized. The second phase employs bi-directional attention to tightly couple video and query features into a unified joint representation for moment localization. This joint representation captures long-range dependencies and complex patterns, enabling the model to effectively distinguish between relevant and irrelevant video segments. The effectiveness of IMF-MF is demonstrated through comprehensive evaluations on three benchmark datasets: TVR for closed-world TV episodes and Charades for open-world user-generated videos, DiDeMo dataset, Open-world, diverse video moment retrieval dataset. The empirical results indicate that the proposed approach surpasses existing state-of-the-art methods in terms of retrieval accuracy, as evaluated by metrics like Recall (R1, R5, R10, and R100) and Intersection-of-Union (IoU). The results consistently demonstrate IMF-MF’s superior performance compared to existing state-of-the-art methods, highlighting the benefits of its innovative interactive moment localization approach and the use of self-attention for feature representation and attention modeling. Show more
Keywords: Multimedia data retrieval, query-dependent fusion, ranking system, multimodal retrieval, video segment localization
DOI: 10.3233/JIFS-233071
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Maheswari, M. | Anitha, D. | Sharma, Aditi | Kaur, Kiranpreet | Balamurugan, V. | Garikapati, Bindu | Dineshkumar, R. | Karunakaran, P.
Article Type: Research Article
Abstract: Anomaly detection, a critical aspect of data analysis and cybersecurity, aims to identify unusual patterns that deviate from the expected norm. In this study, we propose a hybrid approach that combines the strengths of Autoencoder neural networks and Multiclass Support Vector Machines (SVM) for robust anomaly detection. The Autoencoder is utilized for feature learning and extraction, capturing intricate patterns in the data, while the Multiclass SVM provides a discriminative classification mechanism to distinguish anomalies from normal patterns. Specifically, the Autoencoder is trained on normal data to acquire a compact and efficient representation of the underlying patterns, with the reconstruction errors …serving as indicative measures of anomalies. Concurrently, a Multiclass SVM is trained to classify instances into multiple classes, including an anomaly class. The anomaly scores from the Autoencoder and the decision function of the Multiclass SVM, along with that of the Random Forest Neural Network (AE-RFNN), are combined, leveraging their complementary strengths. A thresholding mechanism is then employed to classify instances as normal or anomalous based on the combined scores. The performance of the hybrid model is evaluated using standard metrics such as precision, recall, F1-score, and the area under the Receiver Operating Characteristic (ROC) curve. The proposed hybrid anomaly detection approach demonstrates effectiveness in capturing complex patterns and discerning anomalies across diverse datasets. Additionally, the model offers flexibility for adaptation to evolving data distributions. This study contributes to the advancement of anomaly detection methodologies by presenting a hybrid solution that combines feature learning and discriminative classification for improved accuracy and generalization. Show more
Keywords: Anomaly detection, Autoencoder, Multiclass SVM, feature learning, hybrid model, cybersecurity
DOI: 10.3233/JIFS-240028
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Ren, Xinyu | Yang, Wanhe | Yang, Hui
Article Type: Research Article
Abstract: With the increasing demand for tourism, people’s travel modes are more and more diversified, and the tourism recommendation system also arises at the historical juncture. However, the current recommendation system is only recommended for a single user and does not realize the group travel recommendation. To achieve the goal of recommending its preferred attractions for multiple users, the time decay characteristics and Pearson correlation coefficient in Newton’s cooling law are used to obtain the user similarity with spatial distance factor and temporal decay factor and to obtain the score prediction results based on spatiotemporal fusion. In addition, the trust of …user communication is used to recommend, and the weights of the two scoring results are added to obtain the personalized recommendation results of member users. Finally, the study used the fusion strategy to integrate the personalized recommendation results for group preference and obtained the final group travel recommendation list. Therefore, a group travel recommendation model based on spatio-temporal integration factors was constructed. According to the experimental analysis, we can see that the average HR value of the constructed model is 0.8124, and the average NDCG value is 0.7284, which can accurately judge users’ preferences and get the most suitable group travel recommendation results, thus facilitating users to make the next plan for the tourism project. Show more
Keywords: Group recommendation, spatio-temporal fusion, score prediction, fusion strategy
DOI: 10.3233/JIFS-239548
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Shehzadi, Maham | Fahmi, Aliya | Abdeljawad, Thabet | Khan, Aziz
Article Type: Research Article
Abstract: This paper investigates the detailed analysis of linear diophantine fuzzy Aczel-Alsina aggregation operators, enhancing their efficacy and computational efficiency while aggregating fuzzy data by using the fuzzy C-means (FCM) method. The primary goal is to look at the practical uses and theoretical foundations of these operators in the context of fuzzy systems. The aggregation process is optimised using the FCM algorithm, which divides data into clusters iteratively. This reduces computer complexity and enables more dependable aggregation. The mathematical underpinnings of Linear Diophantine Fuzzy Aczel-Alsina aggregation operators are thoroughly examined in this study, along with an explanation of their purpose in …handling imprecise and uncertain data. It also investigates the integration of the FCM method, assessing its impact on simplifying the aggregation procedure, reducing algorithmic complexity, and improving the accuracy of aggregating fuzzy data sets. This work illuminates these operators performance and future directions through extensive computational experiments and empirical analysis. It provides an extensive framework that shows the recommended strategy’s effectiveness and use in a variety of real-world scenarios. We obtain our ultimate outcomes through experimental investigation, which we use to inform future work and research. The purpose of the study is to offer academics and practitioners insights on how to improve information fusion techniques and decision-making processes. Show more
Keywords: Linear diophantine fuzzy set, Aczel-Alsina operational laws, linear diophantine fuzzy Aczel-Alsina aggregation operators, fuzzy C-means algorithm
DOI: 10.3233/JIFS-238716
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Chongjuan, Wang
Article Type: Research Article
Abstract: The convergence of visual communication design with unique effects, graphic design, as well as virtual reality, which is becoming progressively more popular, has created a new paradigm for education in recent years. However, emerging evidence indicates that their integration into the world of learning is a somewhat gradual and intricate process. The present research proposes a novel algorithm and a functional model of artificial intelligence technology design to automatically arrange graphic language in visual communication design. In visual communication design, the goal orchestration function used to determine the display size of buffer images is the difference between the minimum and …maximum values of the number of orchestration screens. An ant colony method is used in visual communication design to identify the optimal locations for visuals to be presented, and ASM semantics is used to characterize the visual languages. In order to accomplish the invention and development of a visual communication design style, the suggested algorithm has to be programmed and executed. It employs sequential decision marking to characterize the visual vocabulary and accomplishes automated organization. According to the trial results, visual saturation based on AI technology can reach up to 97%, and the average user satisfaction score is 7.65. It is evident that a creative visual thinking approach can maximize the visual communication design effect and communicate fresh design concepts. Show more
Keywords: Innovation and entrepreneurship, visual communication design (VCD), hybrid optimization, adaptive network-based fuzzy inference system (ANFIS), Statistical analysis, t-test and correlation
DOI: 10.3233/JIFS-235930
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Bakhshi, Mahmood | Ahn, Sun Shin | Jun, Young Bae | Borzooei, Rajab Ali
Article Type: Research Article
Abstract: Some kinds of pseudo valuations such as positive implicative pseudo valuation, (weak) implicative pseudo valuation, and commutative pseudo valuation of various types are introduced. Several examples, properties and characterizations of them are given as well. The relationships between them and the substructures of hyper BCK -algebras are investigated, too. Finally, by giving various examples and theorems, the relationships among the proposed pseudo valuations are investigated and characterized, especially in hyper BCK -algebras with three elements.
Keywords: Hyper BCK -algebra, pseudo valuation, positive implicative pseudo valuation, implicative pseudo valuation, commutative pseudo valuation
DOI: 10.3233/JIFS-233898
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Selvaraj, Sunil Kumar | Bhat Pundikai, Venkatramana
Article Type: Research Article
Abstract: BACKGROUND: The increased depletion of ground water resources poses the risk of higher moisture stress environment for agriculture crops. The rapid increase in the moisture stress situation imposes the need of efficient agricultural research on determining the impact of moisture stress on variety of crops. OBJECTIVE: The prime objective of the proposed work is building an IoT based Plant Phenotyping Device for moisture stress experimental study on variety of crops with deep learning model for stress response detection. METHODS: In this work, IoT technology is used for building a proposed system for conducting …the moisture stress experiments on plants and adopting the image processing and convolution neural network based model for stress prediction. RESULTS: The accuracy of the proposed system was experimentally evaluated and empirical results were satisfactory in maintaining the desired level of moisture stress. Performance analysis of LeNet, AlexNet, customized AlexNet and GoogLeNet CNN models were carried out with hyper-parameters variations on the leaf images. GoogLeNet achieved a better validation accuracy of 96% among other models. The trained GoogLeNet model is used for predicting the moisture stress response and predicted results were matched with manual observation of stress response. SIGNIFICANCE: The affirmative results of proposed system would increases its adoption for in-house precision agriculture and also for conducting various moisture stress experiments on variety of crops. The confirmative detection of moisture stress tolerance level of plant provides knowledge on minimum level of water requirement for plant growth, which in-turn save the water by avoiding excess watering to plants. Show more
Keywords: IoT, sensors, Raspberry Pi, moisture stress, deep learning
DOI: 10.3233/JIFS-236885
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Ashwin, P.V. | Ansal, K.A.
Article Type: Research Article
Abstract: Image classification using polarimetric synthetic aperture radar (Pol-SAR) is becoming more important in image processing for remote sensing applications. However, in the existing techniques, during the feature extraction process, there exist some limitations including laborious endeavour for Pol-SAR image classification, identifying intrinsic features for target recognition is difficult in feature selection, and pixel-level Pol-SAR image classification is difficult for obtaining more precise and coherent interpretation consequences. Hence to overcome these issues, a novel Multifarious Stratification Stratagem in machine learning is proposed to achieve pixel-level Pol-SAR classification. In this proposed model, a novel Scrumptious Integrant Wrenching method is used for efficient …feature extraction. It is compatible with the orientation-sensitive of the Pol-SAR image which increases the variety of intra-layer features. To remove the difficulty in feature selection, a novel Episodicical Proximity Selection method is proposed in which a Split-level parallel feature selection strategy is used to select the best qualities from the extracted features. To tackle the difficulty in classification, an Elastic Net Classifier (ENC) is used that find the coefficient vector for the linear combination of the training sets. This efficiently classified the best features in the Pol-SAR images and improved the proposed system’s accuracy. As a result, the performance measures of the proposed system demonstrate that the accuracy is increased by 99.69%, precision is increased by 98.99%, recall is increased by 98.99%, sensitivity is increased by 98.99%, and F1-score is increased by 98.99% as a response. Show more
Keywords: Feature extraction, feature selection, elastic net classifier, principle component analysis, convolution layer, max-pooling layer
DOI: 10.3233/JIFS-222403
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Ning, Tao | Zhang, Tingting | Huang, Guowei
Article Type: Research Article
Abstract: Folk dance is an important intangible cultural heritage in China. In the environment where movement recognition technology is widely used, there is still no research field on the protection and inheritance of folk dance culture. In order to better protect and inherit the minority dance, screening the typical movements of 5 types of minority dance, through the dance video frame processing, obtain the key movements of 19 class dance sequence, build the national dance typical action data set, put forward a 3D CNN fusion Transformer national dance recognition network model (FCTNet), the recognition rate of 96.7% in the experiment. The …results show that the construction method of the folk dance data set is reasonable, the identification model has good performance for the classification of folk dance, and can effectively identify and record the folk dance movements, which also makes new contributions to the digital protection of folk dance. Show more
Keywords: Transformer, folk dance, cultural protection
DOI: 10.3233/JIFS-235302
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-09, 2024
Authors: Shao, Shuai | Li, Dongwei
Article Type: Research Article
Abstract: As technology evolves, the allocation and use of educational resources becomes increasingly complex. Due to the many factors involved in recommending and matching English education resources, traditional predictive control models are no longer adequate. Therefore, fuzzy predictive control models based on neural networks have emerged. To increase the effectiveness and efficiency of using English educational resources (EER), this research aims to create a neural network-based fuzzy predictive control model (T-S-BPNN) for resource suggestion and matching. The results of the study show that the T-S-BPNN model α proposed in the study starts from 0 and increases sequentially by 0.1 up to …1, observing the change in MAE values. The experiment’s findings demonstrate that the value of MAE is lowest at values around 0.5. The T-S-BPNN model, on the other hand, gradually plateaued in its adaptation rate up to 7 runs, reaching about 9.8%. The accuracy rate peaked at 0.843 when the number of recommendations reached 7. The recall rate also peaked at 0.647 when the number of recommended English courses reached 7. The R-value for each set hovered around 0.97, which is a good fit. And the R-value of the training set is 0.97024, which can indicate that the T-S-BPNN model model proposed in the study fits well. It indicates that the algorithm proposed in the study is highly practical. Show more
Keywords: Resource recommendation, english teaching, fuzzy predictive control, recommended evaluation, neural network
DOI: 10.3233/JIFS-233265
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Ammavasai, S.K.
Article Type: Research Article
Abstract: The rapid growth of the cloud computing landscape has created significant challenges in managing the escalating volume of data and diverse resources within the cloud environment, catering to a broad spectrum of users ranging from individuals to large corporations. Ineffectual resource allocation in cloud systems poses a threat to overall performance, necessitating the equitable distribution of resources among stakeholders to ensure profitability and customer satisfaction. This paper addresses the critical issue of resource management in cloud computing through the introduction of a Dynamic Task Scheduling with Virtual Machine allocation (DTS-VM) strategy, incorporating Edge-Cloud computing for the Internet of Things (IoT). …The proposed approach begins by employing a Recurrent Neural Network (RNN) algorithm to classify user tasks into Low Priority, Mid Priority, and High Priority categories. Tasks are then assigned to Edge nodes based on their priority, optimizing efficiency through the application of the Spotted Hyena Optimization (SHO) algorithm for selecting the most suitable edge node. To address potential overloads on the edge, a Fuzzy approach evaluates offloading decisions using multiple metrics. Finally, optimal Virtual Machine allocation is achieved through the application of the Stable Matching algorithm. The seamless integration of these components ensures a dynamic and efficient allocation of resources, preventing the prolonged withholding of customer requests due to the absence of essential resources. The proposed system aims to enhance overall cloud system performance and user satisfaction while maintaining organizational profitability. The effectiveness of the DTS-VM strategy is validated through comprehensive testing and evaluation, showcasing its potential to address the challenges posed by the diverse and expanding cloud computing landscape. Show more
Keywords: Task scheduling, priority, classification, edge computing, cloud, VM allocation, IoT
DOI: 10.3233/JIFS-236838
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Du, Baigang | Zhang, Fujiang | Guo, Jun | Sun, Xiang
Article Type: Research Article
Abstract: The actual operating environment of rotating mechanical device contains a large number of noisy interference sources, leading to complex components, strong coupling, and low signal to noise ratio for vibration. It becomes a big challenge for intelligent fault diagnosis from high-noise vibration signals. Thus, this paper proposes a new deep learning approach, namely decomposition-enhance Fourier residual network (DEFR-net), to achieve high noise immunity for vibration signal and learn effective features to discriminate between different types of rotational machine faults. In the proposed DEFR-net, a novel algorithm is proposed to explicitly model high-noise signals for noisy data filtering and effective feature …enhancement based on a hard threshold decomposition function and muti-channel self-attention mechanism. Furthermore, it deeply integrates complementary analysis based on fast Fourier transform in the time-frequency domain and extends the breadth of network. The performance of the proposed model is verified by comparison with five state-of-the-art algorithms on two public datasets. Moreover, the noise experimental results show that the fault diagnosis accuracy is still 85.91% when the signal-to-noise-ratio reaches extreme noise of –8 dB. The results demonstrate that the proposed method is a valuable study for intelligent fault diagnosis of rotating machines in high-noise environments. Show more
Keywords: Intelligent fault diagnosis, high noise immunity, fourier residual network, decompose-enhance algorithm, attention mechanism
DOI: 10.3233/JIFS-233190
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Shao, Changshun | Yu, Zhenglin | Tang, Jianyin | Li, Zheng | Zhou, Bin | Wu, Di | Duan, Jingsong
Article Type: Research Article
Abstract: The main focus of this paper is to solve the optimization problem of minimizing the maximum completion time in the flexible job-shop scheduling problem. In order to optimize this objective, random sampling is employed to extract a subset of states, and the mutation operator of the genetic algorithm is used to increase the diversity of sample chromosomes. Additionally, 5-tuple are defined as the state space, and a 4-tuple is designed as the action space. A suitable reward function is also developed. To solve the problem, four reinforcement learning algorithms (Double-Q-learning algorithm, Q-learning algorithm, SARS algorithm, and SARSA(λ ) algorithm) are …utilized. This approach effectively extracts states and avoids the curse of dimensionality problem that occurs when using reinforcement learning algorithms. Finally, experimental results using an international benchmark demonstrate the effectiveness of the proposed solution model. Show more
Keywords: Reinforcement learning, flexible job-shop scheduling, maximum completion time, Variation
DOI: 10.3233/JIFS-236981
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Lin, Zhiwei | Zhang, Songchuan | Zhou, Yiwei | Wang, Haoyu | Wang, Shilei
Article Type: Research Article
Abstract: Current mainstream deep learning optimization algorithms can be classified into two categories: non-adaptive optimization algorithms, such as Stochastic Gradient Descent with Momentum (SGDM), and adaptive optimization algorithms, like Adaptive Moment Estimation with Weight Decay (AdamW). Adaptive optimization algorithms for many deep neural network models typically enable faster initial training, whereas non-adaptive optimization algorithms often yield better final convergence. Our proposed Adaptive Learning Rate Burst (Adaburst) algorithm seeks to combine the strengths of both categories. The update mechanism of Adaburst incorporates elements from AdamW and SGDM, ensuring a seamless transition between the two. Adaburst modifies the learning rate of the SGDM …algorithm based on a cosine learning rate schedule, particularly when the algorithm encounters an update bottleneck, which is called learning rate burst. This approach helps the model to escape current local optima more effectively. The results of the Adaburst experiment underscore its enhanced performance in image classification and generation tasks when compared with alternative approaches, characterized by expedited convergence and elevated accuracy. Notably, on the MNIST, CIFAR-10, and CIFAR-100 datasets, Adaburst attained accuracies that matched or exceeded those achieved by SGDM. Furthermore, in training diffusion models on the DeepFashion dataset, Adaburst achieved convergence in fewer epochs than a meticulously calibrated AdamW optimizer while avoiding abrupt blurring or other training instabilities. Adaburst augmented the final training set accuracy on the MNIST, CIFAR-10, and CIFAR-100 datasets by 0.02%, 0.41%, and 4.18%, respectively. In addition, the generative model trained on the DeepFashion dataset demonstrated a 4.62-point improvement in the Frechet Inception Distance (FID) score, a metric for assessing generative model quality. Consequently, this evidence suggests that Adaburst introduces an innovative optimization algorithm that simultaneously updates AdamW and SGDM and incorporates a learning rate burst mechanism. This mechanism significantly enhances deep neural networks’ training speed and convergence accuracy. Show more
Keywords: Convolutional neural networks (CNNs), MNIST, CIFAR, deep learning, optimization algorithms, person image generation, diffusion models
DOI: 10.3233/JIFS-239157
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Gonzalez, Claudia I. | Torres, Cesar
Article Type: Research Article
Abstract: This paper presents an approach incorporating fuzzy logic techniques inside a convolutional neural network to manage uncertainty present in the multiple data sources that the model handles when training. The implementation considers the use of information and filters in the fuzzy spectrum, as well as the creation of a new layer to replace the traditional convolution layer with a fuzzy convolutional layer. The aim is to design artificial intelligence algorithms that combine the potential of deep convolutional neural networks and fuzzy logic to create robust systems that allow modeling the uncertainty present in the sources of data and that are …applied to classification problems. The fuzzification process is developed using three membership functions, including the Triangular, Gaussian, and S functions. The work was tested in databases oriented to traffic signs, due to the complexity of the different circumstances and factors in which a traffic sign can be found. Show more
Keywords: Fuzzy-neural network, fuzzy CNN, fuzzy deep learning model, fuzzy data, fuzzy convolutional
DOI: 10.3233/JIFS-219369
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Sun, Haibin | Zhang, Wenbo
Article Type: Research Article
Abstract: The ability of deep learning based bearing fault diagnosis methods is developing rapidly. However, it is difficult to obtain sufficient and comprehensive fault data in industrial applications, and changes in vibration signals caused by machine operating conditions can also hinder the accuracy of the model. The problem of limited data and frequent changes in operating conditions can seriously affect the effectiveness of deep learning methods. To tackle these challenges, a novel transformer model named the Differential Window Transformer (Dwin Transformer), which employs a new differential window self-attention mechanism, is presented in this paper. Meanwhile, the model introduces a hierarchical structure …and a new patch merging to further improve performance. Furthermore, a new fault diagnosis model dealing with limited training data is proposed, which combines the Auxiliary Classifier Generative Adversarial Network with the Dwin Transformer(DT-ACGAN). The DT-ACGAN model can generate high-quality fake samples to facilitate training with limited data, significantly improving diagnostic capabilities. The proposed model can achieve excellent results under the dual challenges of limited data and variable working conditions by combining Dwin Transformer with GAN. The DT-ACGAN owns superior diagnostic accuracy and generalization performance under limited sample data and varying working environments when compared with other existing models. A comparative test about cross-domain ability is conducted on the Case Western Reserve University dataset and Jiang Nan University dataset. The results show that the proposed method achieves an average accuracy of 11.3% and 3.76% higher than other existing methods with limited data respectively. Show more
Keywords: Transformer, generative adversarial network, cross-domains, limited data, fault diagnosis
DOI: 10.3233/JIFS-236787
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Dai, Jinpeng | Zhang, Zhijie | Yang, Xiaoyuan | Wang, Qicai | He, Jie
Article Type: Research Article
Abstract: This study explores nine machine learning (ML) methods, including linear, non-linear and ensemble learning models, using nine concrete parameters as characteristic variables. Including the dosage of cement (C), fly ash (FA), Ground granulated blast furnace slag (GGBS), coarse aggregate (G), fine aggregate (S), water reducing agent (WRA) and water (W), initial gas content (GC) and number of freeze-thaw cycles (NFTC), To predict relative dynamic elastic modulus (RDEM) and mass loss rate (MLR). Based on the linear correlation analysis and the evaluation of four performance indicators of R2 , MSE, MAE and RMSE, it is found that the nonlinear model has …better performance. In the prediction of RDEM, the integrated learning GBDT model has the best prediction ability. The evaluation indexes were R2 = 0.78, MSE = 0.0041, MAE = 0.0345, RMSE = 0.0157, SI = 0.0177, BIAS = 0.0294. In the prediction of MLR, ensemble learning Catboost algorithm model has the best prediction ability, and the evaluation indexes are R2 = 0.84, MSE = 0.0036, RMSE = 0.0597, MAE = 0.0312, SI = 5.5298, BIAS = 0.1772. Then, Monte Carlo fine-tuning method is used to optimize the concrete mix ratio, so as to obtain the best mix ratio. Show more
Keywords: Machine learning, relative dynamic elastic modulus, mass loss rate, sensitivity analysis, optimization design of mix proportions
DOI: 10.3233/JIFS-236703
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-26, 2024
Authors: Yuan, Weihao | Yang, Mengdao | Gu, Hexu | Xu, Gaojian
Article Type: Research Article
Abstract: There is scope to enhance agricultural measurement and control systems user interactivity, which typically necessitates training for users to perform specific operations successfully. With the continuous development of natural language semantic processing technology, it has become essential to augment the user-friendliness of multifaceted control and query operations in the agricultural measurement and control sector, ultimately leading to reduced operation costs for users. The study aims to focus on command parsing. The proposed AMR-OPO semantic parsing framework is based on the natural language understanding method of Abstract Meaning Representation of Rooted Markup Graphs (AMR). It transforms the user’s natural language inputs …into structured ternary (OPO) statements (operation-place-object) and converts the corresponding parameters of the user’s input commands. The framework subsequently sends the transformed commands to the relevant devices via the IoT gateway. To tackle the intricate task of parsing instructions, we developed a BERT-BiLSTM-ATT-CRF-OPO entity recognition model. This model can detect and extract entities from agricultural instructions, and precisely populate them into OPO statements. Our model shows exceptional accuracy in instruction parsing, with precision, recall, and F-value all measuring at 92.13%, 93.12%, and 92.76%, correspondingly. The findings from our experiment reveal outstanding and precise performance of our approach. It is anticipated that our algorithm will enhance the user experience offered by agricultural measurement and control systems, while also making them more user-friendly. Show more
Keywords: Natural language processing, abstract meaning representation, entity recognition, natural language understanding, human-computer interaction
DOI: 10.3233/JIFS-237280
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Li, Yingjie | Sun, Rongrong | Huang, Guangrong | Deng, Yuanbin | Zhang, Haixuan | Zhang, Delong
Article Type: Research Article
Abstract: In response to a series of issues in the distribution network, such as inadequate and inflexible utilization of flexible loads, delayed response to demand participation, and the uncertainty of new energy source output, a differentiated objective-based method for optimizing distribution network operations is proposed. Firstly, flexible loads are categorized, and corresponding mathematical models are established. Secondly, by employing chance-constrained programming to account for the uncertainty in new energy source output, a multi-objective optimization model is developed to reduce distribution network economic costs, decrease network losses, and enhance power supply reliability. Subsequently, an improved NSGA-III algorithm is introduced to address the …multi-objective model. Finally, an 11-node distribution network is used as a case study, and three different algorithms are comprehensively compared. This confirms the rationality of the optimized scheduling scheme proposed in this paper. Show more
Keywords: Distribution network, flexible load, multi-objective optimization, chance-constrained programming
DOI: 10.3233/JIFS-238367
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Yan, HongJu
Article Type: Research Article
Abstract: To solve the problem of lack of practice in Japanese teaching, a design of a Japanese remote interactive practical teaching platform based on the modern edge computing-based wireless sensor network is proposed. In terms of hardware, it mainly refits interactive mobile edge computing, wireless sensor networks, microprocessors, and other equipment, and adjusts the interface circuit. The Japanese teaching data and relevant Japanese teaching resources generated in the process of Japanese Teaching of practical courses are stored in the corresponding database table according to a certain format, and the logical relationship between database tables is used to update the database. The …software function of the platform is designed with the support of a database and hardware equipment. It consists of multiple modules, including platform user roles, interactive practical teaching and management, practical resource management and distribution, practice project information release, practice investigation statistics, and platform operation safety. Through the above design, the operation of a Japanese remote interactive practical teaching platform is realized. The test results show that there is no significant difference in the function realization of the design platform, but when multiple users are online at the same time, the interaction performance of the design platform is stronger, that is, the operation performance of the platform has obvious advantages. Show more
Keywords: Mobile edge computing, wireless sensor network, Japanese teaching platform, remote interactive learning, microprocessor, platform user roles, practical teaching, database management, interaction performance
DOI: 10.3233/JIFS-238196
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Ahani, Zahra | Shahiki Tash, Moein | Ledo Mezquita, Yoel | Angel, Jason
Article Type: Research Article
Abstract: Super-enhancers are a category of active super-enhancers densely occupied by transcription factors and chromatin regulators, controlling the expression of disease-related genes and cellular identity. Recent studies have demonstrated the formation of complex structures by various factors and super-enhancers, particularly in various cancers. However, our current knowledge of super-enhancers, such as their genomic locations, interaction with factors, functions, and distinction from other super-enhancers regions, remains limited. This research aims to employ deep learning techniques to detect and differentiate between super-enhancers and enhancers based on genomic and epigenomic features and compare the accuracy of the results with other machine learning methods In …this study, in addition to evaluating algorithms, we trained a set of genomic and epigenomic features using a deep learning algorithm and the Python-based cross-platform software to detect super-enhancers in DNA sequences. We successfully predicted the presence of super-enhancers in the sequences with higher accuracy and precision. Show more
Keywords: Super-enhancer, enhancer, genomic, epigenomic, deep learning
DOI: 10.3233/JIFS-219356
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Shahbazova, Shahnaz N. | Rzayev, Ab.G. | Asadova, R.Sh. | Jabiyev, K.M.
Article Type: Research Article
Abstract: The paper gives a systems analysis in the field of heat transfer and temperature distribution (TD) along the length of oil production wells (OPW). The analysis shows that the existing mathematical models are suitable only for determining TD along the length of casing string (CS) and are not suitable for determining TD along the length of the tubing run, since the existence of the interfacial (between the CS and the tubing) annulus of the fluid and gas layers with heat capacity and thermal conductivity that differ significantly from the heat capacity and thermal conductivity of rocks surrounding the CS. Given …the above, mathematical models taking into account the impact of the fluid and gas layers in the annulus on the heat transfer from the upward fluid flow to the tubing wall and from the wall to the interfacial annulus are developed. To ensure the technological effectiveness of the obtained model, formulas for quantitative estimation of the heat transfer of the fluid flow into the surrounding environment are given. Show more
Keywords: Heat exchange, heat transfer, heat dissipation, thermal conductivity, temperature distribution, oil production well.
DOI: 10.3233/JIFS-219366
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024
Authors: Bai, Yu | Hu, Qijun | Zhou, Zhenxiang | Cai, Qijie | He, Leping
Article Type: Research Article
Abstract: The interaction of several workers with intelligent construction machinery can lead to serious collisions. Typically, the safety distance is used as an indicator of the safety of worker–machine interactions (WMI). However, the degree of risk does not increase linearly with decreasing worker–machine distances. To further reveal the essence of WMI safety, this study proposes a new method for assessing the safety state of WMIs, namely, the construction safety potential field. It is used to describe the factors and patterns associated with the spatial overlap and decay of hazardous energy in WMI operations. The proposed method was tested in an earthworks …construction WMI operation and the results were valid. A preliminary discussion of the relevant parameters constituting the construction safety potential field model is presented. The contributions of the research is proposing a generic energy-based model, which provides a novel idea for the interpretation of safety issues in construction WMI operations and opens up a new foundation for the development of active safety control. Show more
Keywords: Construction site, worker–machine safety, safety field, potential function
DOI: 10.3233/JIFS-236423
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Zhang, Qian | Bai, Enrui | Shao, Mingwen | Liang, Hong
Article Type: Research Article
Abstract: Convolutional neural networks (CNNs) and Transformer architectures have traditionally been recognized as the preferred models for addressing computer vision tasks. However, there has been a recent surge in the popularity of networks based on multi-layer perceptron (MLP) structures that do not rely on convolution or attention mechanisms. These MLP architectures have demonstrated exceptional performance in image classification tasks, exhibiting lower time complexity while maintaining high accuracy. In contrast, video classification tasks involve larger amounts of data and necessitate more intricate feature extraction, resulting in increased time and resource consumption. To enhance computational efficiency and minimize resource utilization, we propose a …convolution-free and Transformer-free architecture for video classification called Video-MLP for video classification. Video-MLP utilizes a simple MLP structure to learn video features. Specifically, it comprises two types of layers: Spatial-Mixer and Temporal-Mixer, which respectively capture spatial and temporal information. The Spatial-Mixer extracts spatial information from each frame along the height and width dimensions, while the Temporal-Mixer models temporal information for the same spatial positions across frames. To improve the efficiency of spatial-temporal modeling in our network, we used a spatial-temporal information fusion approach to integrate information at different scales. Additionally, we grouped the input data along the time dimension and designed three different grouping schemes when extracting temporal information. The experimental results indicate that Video-MLP achieved accuracy rates of 87.2% on the Kinetics-400 dataset and 75.3% on the SomethingV2 dataset, outperforming models with equivalent computational complexity. Notably, Video-MLP achieved these results without using convolution and attention mechanisms, and without pre-training on large-scale image and video datasets. Show more
Keywords: MLP-based-model, video classification, computer vision, deep learning
DOI: 10.3233/JIFS-240310
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Ma, Nana | Wang, Lili | Long, Yuting
Article Type: Research Article
Abstract: Music has been utilized throughout history as a medium for cultural communication and artistic expression, embodying various nations’ and societies’ ideologies and experiences. Music culture communication is crucial for encouraging cultural diversity and understanding and developing social cohesion and community building among people. Music teaching management is the process of setting up, arranging, and executing music education programs in a manner that successfully teaches students the essential skills and information necessary for becoming proficient musicians. Users’ exact preferences for various areas of attraction cannot be determined, nor are users’ choices for traditional music recommendations sufficiently accurate. A recommender system estimates …or anticipates people’s preferences and offers appropriate recommendations. First, the sparsity problem emerges when insufficient data is accessible for the recommendation, and the coverage is one of the key drawbacks of social labeling. Cold start issues might be difficult since new music learners might not have given sufficient details about their musical tastes. Hence, the Hybridized Fuzzy logic-based Content and Collaborative Music Recommendation (HFC2MR) system is proposed to create personalized music teaching plans that are effective and engaging for each student based on their music preferences and learning outcomes. Enhanced Fuzzy C-Means clustering is used in collaborative recommendations to group users based on their shared musical tastes and to provide each user with more individualized, accurate music recommendations based on other users’ listening habits and preferences in the same cluster. Subsequently, an assessment of the recommender system using parameters like accuracy, precision, f1-score, and recall ratio is shown with optimal cluster selection. The coverage ratio is used to compare experimental data based on skill capacity covered through the assessment of music teaching. RMSE metric is used to evaluate the accuracy of students’ performance based on music attributes related to teaching goals. Show more
Keywords: Music teaching management, fuzzy logic, recommender system, clustering and similarity
DOI: 10.3233/JIFS-232422
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Zhou, Yue | Chen, Qiwei
Article Type: Research Article
Abstract: Studying the evolution of karst rocky desertification (KRD) in control areas of diverse geomorphologic types and its correlation with land use provides valuable insights for identifying priority areas and implementing effective treatment measures. Employing Remote Sensing (RS) and GIS, this research quantitatively examines the evolution of KRD and its relationship with land use in the karst mountain and gorge areas of Guizhou Province over the period 2010 to 2020. The findings reveal continuous improvement in KRD across the study areas, albeit with noticeable regional disparities. Notably, the karst mountain region exhibited significantly higher change areas and rates of KRD, non-KRD, …light KRD, and moderate KRD compared to the gorge area, underscoring better desertification control in the former region. A discernible correlation emerges between different karst geomorphologic types, the distribution and changes in land use types, and the evolution of KRD. Land use change emerges as a pivotal factor influencing the improvement of KRD in these areas. Changes in land use patterns corresponded with a decrease in KRD in dry land, other woodland, grassland, and bare land across both regions. However, the response of KRD to land use patterns varied across control areas with different geomorphologic environments, resulting in geographical differentiation in KRD evolution. Key land use conversions, notably from shrubland to forestland and dry land to garden land in the gorge, and shrubland to forestland in the mountain, contributed significantly to KRD dynamics in these regions. Notably, in the gorge area, KRD primarily occurred in garden land, other woodland, dry land, and grassland. In contrast, in the mountain area, KRD was prevalent in shrubland, dry land, and grassland, highlighting distinct responses and contributions to its evolution. The study observes substantial land use change in KRD-improved areas, particularly in the gorge region. Notably, the responsiveness of KRD to woodland conversions (shrubland, forestland, other woodland) varied across different geomorphologic environments. The dynamics of rocky desertification occurrence (RDO) and the occurrence structure of KRD in various land use types exhibited significant differences between the two regions. The gorge area demonstrated generally higher RDO, with a relatively stable and simpler occurrence structure of KRD compared to the more dynamic and varied structure observed in the mountain area. The sequencing of KRD occurrence in both areas displayed stability in specific land use types, with varying intensities noted between them. Show more
Keywords: Karst, rocky desertification, land use, evolution, geomorphology
DOI: 10.3233/JIFS-241536
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Qin, Hao | Zou, Yanli | Yu, Guoliang | Liu, Huipeng | Tan, Yufei
Article Type: Research Article
Abstract: In the process of mapping outdoor undulating and flat roads, existing LiDAR SLAM systems often encounter issues such as map distortion and ghosting. These problems arise due to the low vertical resolution of multi-line LiDAR, which easily leads to the occurrence of odometry height drift during the mapping process. To address this challenge, this study propose a novel LiDAR SLAM system named SOHD-LOAM, designed specifically to suppress odometry height drift. This system encompasses several critical components, including data preprocessing, front-end LiDAR odometry, back-end LiDAR mapping, loop detection, and graph optimization. SOHD-LOAM leverages the road gradient limitation algorithm and the height …smoothing algorithm as its core, while also integrating the Kalman filter, loop detection, and graph optimization techniques. To evaluate the performance of SOHD-LOAM, the comprehensive experiments are conducted with using KITTI datasets and real-world scenes. The experimental results demonstrate that SOHD-LOAM achieves superior accuracy and robustness in global odometry compared to the state-of-the-art LEGO-LOAM. Specifically, the height error of the sequences 00, 05 experiment was found to be 40.62% and 61.92% lower than that of LEGO-LOAM. Additionally, the maps generated by SOHD-LOAM exhibit no distortion or ghosting, thereby significantly enhancing map quality. Show more
Keywords: Autonomous driving, SLAM, odometry height drift, road gradient limitation, height smoothing, loop detection
DOI: 10.3233/JIFS-235708
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Wei, YuHan | Kim, Young-Ju
Article Type: Research Article
Keywords: Camel herd algorithm (CHA), camel-bat swarm optimization (CBSO), cultural and creative product (CCP) Design, graphic design
DOI: 10.3233/JIFS-236320
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Lalitha, S. | Sridevi, N. | Deekshitha, Devarasetty | Gupta, Deepa | Alotaibi, Yousef A. | Zakariah, Mohammed
Article Type: Research Article
Abstract: Speech Emotion Recognition (SER) has advanced considerably during the past 20 years. Till date, various SER systems have been developed for monolingual, multilingual and cross corpus contexts. However, in a country like India where numerous languages are spoken and often humans converse in more than one language, a dedicated SER system for mixed-lingual scenario is more crucial to be established which is the focus of this work. A self-recorded database that includes speech emotion samples with 11 diverse Indian languages has been developed. In parallel, a mixed-lingual database is formed with three popular standard databases of Berlin, Baum and SAVEE …to represent mixed-lingual environment for western background. A detailed investigation of GeMAPS (Geneva Minimalistic Acoustic Parameter Set) feature set for mixed-lingual SER is performed. A distinct set of MFCC (Mel Frequency Cepstral Coefficients) coefficients derived from sine and cosine-based filter banks enriches the GeMAPS feature set and are proven to be robust for mixed-lingual emotion recognition. Various Machine Learning (ML) and Deep Learning (DL) algorithms have been applied for emotion recognition. The experimental results demonstrate GeMAPS features classified from ML has been quite robust for recognizing all the emotions across the mixed-lingual database of the western languages. However, with diverse recording conditions and languages of the Indian self-recorded database the GeMAPS with enriched features and classified using DL are proven to be significant for mixed-lingual emotion recognition. Show more
Keywords: Emotion, GeMAPS, mixed-lingual, sine, cosine filter bank
DOI: 10.3233/JIFS-219390
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Bisht, Akhilesh | Gupta, Deepa
Article Type: Research Article
Abstract: Neural Machine Translation (NMT) for low resource languages is a challenging task due to unavailability of large parallel corpus. The efficacy of Transformer based NMT models largely depends on scale of the parallel corpus and the configuration of hyperparameters implemented during model training. This study aims to delve into and elucidate the impact of hyperparameters on the performance of NMT models for low resource languages. To accomplish this, a series of experiments are conducted using an open-source Hindi-Kangri corpus to train both supervised and semi-supervised NMT models. Throughout the experimentation process, a significant number of discrepancies were identified within the …data-set, necessitating manual correction. The best translation performance evaluated with respect to the metrics such as BLEU (0–1), SacreBLEU (0–100), Chrf (0–100), Chrf+ (0–100), Chrf++ (0–100) and TER (%) is (0.15, 14.98, 41.43, 41.49, 38.77, 68.20) for Hindi to Kangri direction, and (0.283, 28.17, 49.71, 50.64, 48.63, 51.25) for Kangri to Hindi direction. Show more
Keywords: Neural machine translation, low resource language, low resource MT, transformers, semi-supervised MT, Kangri, natural language processing
DOI: 10.3233/JIFS-219384
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Momena, Alaa Fouad | Gazi, Kamal Hossain | Mukherjee, Asesh Kumar | Salahshour, Soheil | Ghosh, Arijit | Mondal, Sankar Prasad
Article Type: Research Article
Abstract: Use of the Internet of Everything (IoE), the number of smart gadgets increasing rapidly giving the side effect of huge data, which has led to issues with traditional cloud computing models like inadequate security, slow response times, poor privacy, and bandwidth overload. Conventionally, cloud computing is no longer adequate for supporting the diversified needs of the user and the extraordinary society of data processing, so edge computing technologies have been revealed. This study considers edge computing in an educational institute in a scientific way. Multi criteria decision making (MCDM) is one of the most suitable decision making processes that propose …to choose optimal alternatives by considering multiple conflicting criteria. Entropy weighted method is considered to evaluate factor weight. Weighted Aggregated Sum Product Assessment (WASPAS) and Combined Compromise Solution (CoCoSo) based MCDM methodologies examine the ranking of alternatives for this study. Multiple decision makers (DMs) give opinions with Pentagonal Fuzzy Soft Set (PFSS) to express the uncertainty and fuzziness of the data set. The set operations and arithmetic operations of PFSS are discussed in detail. Also, a new de-fuzzification method of PFSS is proposed in this study. Calculated the criteria weight and prioritized the alternative based on source data. Lastly, sensitivity analysis and comparative analysis are conducted to check the stability of the result. Show more
Keywords: Edge computing, Academic institute, PFSS, Entropy, WASPAS, CoCoSo
DOI: 10.3233/JIFS-239887
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Jaiseeli, C. | Raajan, N.R.
Article Type: Research Article
Abstract: Medical and satellite image analysis require incredibly high resolution. Super-resolution combines several low-resolution images of the same scene to generate a high-resolution image. The Super resolution employing deep learning techniques still has an illumination issue. This paper proposes a novel CGIHE-VDSR algorithm that integrates the Very Deep Super Resolution (VDSR) Network with Color Global Image Histogram Equalization (CGIHE) to improve image resolution. In the proposed method, the low-resolution image is first histogram equalized using the CGIHE algorithm. Then, the VDSR network is applied to the histogram equalized image for super-resolution. The comparison of real-time data with the benchmark images is …done using the proposed algorithm in the MATLAB platform. The PSNR and SSIM metrics demonstrate that the super resolution image obtained using the proposed method is significantly better than the existing methods. Show more
Keywords: Histogram equalization, super-resolution, CNN, subsample image, VDSR, residual
DOI: 10.3233/JIFS-219392
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Javed, Hira | Sufyan Beg, M.M. | Akhtar, Nadeem | Alroobaea, Roobaea
Article Type: Research Article
Abstract: Vlogs, Recordings, news, sport coverages are huge sources of multimodal information that do not just limit to text but extend to audio, images and videos. Applications such as summary generation, image/video captioning, multimodal sentiment analysis, cross modal retrieval requires Computer Vision along with Natural Language Processing techniques to extract relevant information. Information from different modalities must be leveraged in order to extract quality content. Hence, reducing the gap between different modalities is of utmost importance. Image to text conversion is an emerging field and employs the use of encoder decoder architecture. Deep CNNs extract the feature of images and sequence …to sequence models are used to generate text description. This paper is a contribution towards the growing body of research in multimodal information retrieval. In order to generate the textual description of images, we have performed 5 experiments using the benchmark Flickr8k dataset. In these experiments we have utilized different architectures - simple sequence to sequence model, attention mechanism, transformer-based architecture to name a few. The results have been evaluated using BLEAU score. Results show that the best descriptions are attained by making use of transformer architecture. We have also compared our results with the pretrained visual model vit-gpt2 that incorporates visual transformer. Show more
Keywords: Multimodal, captioning, summarization, etc
DOI: 10.3233/JIFS-219394
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Kostiuk, Yevhen | Tonja, Atnafu Lambebo | Sidorov, Grigori | Kolesnikova, Olga
Article Type: Research Article
Abstract: In this paper, we investigate the issue of hate speech by presenting a novel task of translating hate speech into non-hate speech text while preserving its meaning. As a case study, we use Spanish texts. We provide a dataset and several baselines as a starting point for further research in the task. We evaluated our baseline results using multiple metrics, including BLEU scores. We used a cross-validation approach and an average of the metrics per fold for evaluation. We achieved a 0.236 sentenceBLEU score on four folds. This study aims to contribute to developing more effective methods for reducing the …spread of hate speech in online communities. Show more
Keywords: Hate speech, translation, Spanish
DOI: 10.3233/JIFS-219348
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: George, Neetha | Ramachandran, Sivakumar | Jiji, C.V.
Article Type: Research Article
Abstract: Macula is the part of retina responsible for sharp and clear vision. Macular edema is caused by the accumulation of intraretinal fluid (IRF) in the macula, which is further distinguished by the compromised integrity of the blood-retinal barrier, particularly evident in the retinal vasculature. This results in swelling, that may lead to vision impairment and is the dominant sign of several ocular diseases, including age-related macular degeneration, diabetic retinopathy, etc. Quantitative analysis of the fluid regions in macular edema helps in ascertaining the severity as well as the response to treatment of the diseases. Optical coherence tomography (OCT) is a …major tool used by ophthalmologists for visualizing edema. The prevalent practice for diagnosing and treating macular edema involves measuring Central Retinal Thickness (CRT). Segmenting the IRF in OCT images offers the potential for a more accurate and better quantification of macular edema. This paper proposes a novel method combining convolutional neural network (CNN) and active contour model for segmenting the IRF to ascertain the severity of macular edema. The IRF region is initially segmented using an encoder-decoder architecture. Contour evolution is then performed on this segmented image to demarcate the IRF boundaries. The advantage of the method is that it does not require precisely labeled images for training the CNN. A comparison of the experimental results with models employing CNN alone and with other state-of-the art methods demonstrates the superior performance and consistency of the proposed method. Show more
Keywords: edema segmentation, convolutional neural network, active contour model
DOI: 10.3233/JIFS-219401
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Wu, Donghui | Wang, Jinfeng | Zhao, Wanwan | Geng, Xin | Liu, Guozhi | Qiu, Sen
Article Type: Research Article
Abstract: Gesture recognition based on wearable sensors has received extensive attention in recent years. This paper proposes a gesture recognition model (CGR_ATT) based on Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) fused attention mechanism to improve accuracy rate of wearable sensors. First, CNN serves as a feature extractor, learning features automatically from sensor data by performing multiple layers of convolution and pooling operations, capturing spatial features of gestures. Furthermore, a temporal modeling unit GRU is introduced to capture the temporal dynamics in gesture sequences. By controlling the information flow through gate mechanisms, it effectively handles the temporal relationships in …sensor data. Finally, an attention mechanism is introduced to assign different weights to the hidden state of the GRU. By calculating the attention weights for each time period, the model automatically selects key time periods related to gesture movements. The GR-dataset proposed in this paper involves 910 sets of training parameters. The model achieves an ultimate accuracy of 97.57% . In compare with CLA-net, CLT-net, CGR, GRU, LSTM and CNN, the experimental results demonstrate that the proposed method has superior accuracy. Show more
Keywords: Wearable gesture recognition system, CGR_ATT model, deep learning, wearable devices
DOI: 10.3233/JIFS-240427
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Hu, Junhua | Zhou, Yingling | Li, Huiyu | Liang, Pei
Article Type: Research Article
Abstract: To enhance infection diseases interval prediction, an improved model is proposed by integrating neighborhood fuzzy information granulation (NNIG) and spatial-temporal graph neural network (STGNN). Additionally, the NNIG model can efficiently extract the most representative features from the time series data and identifies the support upper and lower bounds. NNIG model transfers time series data from numerical level to granular level, and processes data feed it into STGNN for interval prediction. Finally, experiments are conducted for evaluation based on the COVID-19 data. The results demonstrate that the NNIG outperforms baseline models. Further, it proves beneficial in offering a valuable approach for …policy-making. Show more
Keywords: Time series, fuzzy information granulation, interval prediction, spatial-temporal graph neural network
DOI: 10.3233/JIFS-236766
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Visvanathan, P. | Durai Raj Vincent, P.M.
Article Type: Research Article
Abstract: A Stroke is a sudden loss of blood circulation in certain parts of the brain that results in a loss of neurological function. To save a patient from stroke, an immediate diagnosis and treatment plan must be implemented. Artificial intelligence-based machine learning algorithms play a major role in the prediction. To predict a person likely to have a stroke, stroke healthcare data records must be accessed, which is very sensitive. Data shared for machine learning training pose security risks and have concerns about privacy. To overcome this issue, Genetic Algorithm and Federated Learning (GA-FL) –based hybridization approach is proposed to …predict the risk of stroke in a person. Federated Learning was developed by Google, which can provide security to the data during the training process because every client participating in this training process needs to exchange only the training parameters without sharing the data. In addition to the security features, a genetic algorithm was used to optimize the parameters required to train a model using the perceptron neural network model. The experimental results show that our proposed research model (GA-FL) provides security and predicts the risk of stroke more accurately than any other existing algorithm. Show more
Keywords: Federated learning, genetic algorithm, stroke risk, perceptron neural network
DOI: 10.3233/JIFS-236354
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Hossain, AKM B. | Salam, Md. Sah Bin Hj. | Alam, Muhammad S. | Hossain, AKM Bellal
Article Type: Research Article
Abstract: Semantic segmentation is crucial for the treatment and prevention of brain cancers. Several neural network–based strategies were rapidly presented by research groups to enhance brain tumor thread segmentation. The tumor’s uneven form necessitates the usage of neural networks for its detection. Therefore, improved patient outcomes may be achieved with precise segmentation of brain tumor. Brain tumors can range widely in size, form, and position, making diagnosis difficult. Thus, this work offers a Multi-level U-Net (MU-Net) approach for analyzing the brain tumor data augmentation for improved segmentation. Therefore, a significant amount of data augmentation is employed to successfully train the recommended …system, removing the problem of a lack of data when using MR images for the diagnosis of multi-grade brain cancers. Here, we presented the “Multi-Level Pyramidal Pooling (MLPP)” component, where a new pyramidal pool will be employed to capture contextual data for augmentation. The “High-Grade Glioma” (HGG) datasets from the Kaggle and BraTs2021 were used to assess the proposed MU-Net. Overall Tumor (OT), Enhancing Core (EC), and Tumor Core (TC) were the three main designations to be segmented. The dice score was used to contrast the results empirically. The suggested MU-Net fared better than most existing methods. Researchers in the fields of bioinformatics and medicine might greatly benefit from the high-performance MU-Net. Show more
Keywords: Brain tumor, Data Augmentation (DA), Multi-level U-Net (MU-Net), Multi-Level Pyramidal Pooling (MLPP), Adaptive Curvelet Transform (ACT), wavelet threshold
DOI: 10.3233/JIFS-232782
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Wu, Jie | Hou, Mengshu
Article Type: Research Article
Abstract: Table-based fact verification (TFV) is a binary classification task that requires understanding and reasoning about both table and text. This task poses many challenges, such as table parsing, text comprehension, and numerical reasoning. However, existing methods tend to depend solely on pre-trained models for tables, treating all types of reasoning equally and disregarding the importance of identifying logic types in inference process. In this regard, we propose MoETFV, an efficient and explanatory approach to solving TFV, which is based on a Mixture-of-Experts (MoE) framework. This approach can detect the underlying logic types of statements and leverage multiple independent experts to …emulate diverse logical reasoning. It consists of one shared expert for general semantic understanding and several specific experts with distinct responsibilities for different logical inferences. Moreover, the practical applications of the MoE method in TFV are thoroughly investigated. This model doesn’t necessitate any table pre-trained models, and aligns closely with human cognitive processes in addressing such issues. Experimental results demonstrate the innovation and feasibility of the proposed approach. Show more
Keywords: Tabular data, fact verification, mixture-of-experts, logical reasoning, natural language processing
DOI: 10.3233/JIFS-238142
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Chen, Longkai | Huang, Jingjing
Article Type: Research Article
Abstract: Urban traffic accidents impose a significant threat to public safety because of its frequent occurrence and potential for severe injuries and fatalities. Hence, an effective analysis of accident patterns is crucial for designing accident prevention strategies. Recent advancement in data analytics have provided opportunities to improve the pattern of urban traffic accidents. However, the existing works face several challenges in adapting the complex dynamics, and heterogeneity of the accident data. To overcome these challenges, we proposed an innovative solution by combining the K-means clustering and Support Vector Machine to precisely predict the traffic accident patterns. By leveraging the efficiencies of …clustering technique and machine learning, this work intends to identify the intricate patterns within the traffic database. Initially, a traffic accident database was collected and fed into the system. The collected database was pre-processed to improve and standardize the raw dataset. Further, cluster analysis is employed to identify distinct patterns within the dataset and group similar accidents into clusters. This clustering enables the system to recognize common accident scenarios and identify recent accident trends. Subsequently, a Support Vector Machine is deployed to classify accidents into distinct categories through intensive training with identified clusters. The combination enables the system to understand the complex relationships among diverse accident variables, making it an effective framework for real-time pattern recognition. The proposed strategy is implemented in Python and validated using the publicly available traffic accident database. The experimental results manifest that the proposed method achieved 99.65% accuracy, 99.53% precision, 99.62% recall, and 99.57% f-measure. Finally, the comparison with the existing techniques shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones. shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones. Show more
Keywords: Support vector machine, traffic accident pattern recognition, cluster analysis, machine learning
DOI: 10.3233/JIFS-241018
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
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