Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Brännström, Andreas | Nieves, Juan Carlos

Article Type: Research Article

Abstract: This paper introduces an automated decision-making framework for providing controlled agent behavior in systems dealing with human behavior-change. Controlled behavior in such settings is important in order to reduce unexpected side-effects of a system’s actions. The general structure of the framework is based on a psychological theory, the Theory of Planned Behavior (TPB), capturing causes to human motivational states, which enables reasoning about dynamics of human motivation. The framework consists of two main components: 1) an ontological knowledge-base that models an individual’s behavioral challenges to infer motivation states and 2) a transition system that, in a given motivation state, decides …on motivational support, resulting in transitions between motivational states. The system generates plans (sequences of actions) for an agent to facilitate behavior change. A particular use-case is modeled regarding children with Autism Spectrum Conditions (ASC) who commonly experience difficulties in everyday social situations. An evaluation of a proof-of-concept prototype is performed that presents consistencies between ASC experts’ suggestions and plans generated by the system. Show more

Keywords: Interactive agents, strategic decision-making, behavior-change systems, theory of planned behavior, Autism

DOI: 10.3233/JIFS-219335

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024

Authors: López-López, Aurelio | Garcıa-Gorrostieta, Jesús Miguel | González-López, Samuel

Article Type: Research Article

Abstract: Emotion detection in educational dialogues, particularly within student-teacher interactions, has become a crucial research area for improving the learning experience. In this paper, we employ two models, one generic Bidirectional Encoder Representations from Transformers (BERT) and the Emotion detection model Robustly Optimized BERT Approach (EmoRoBERTa), to automatically classify emotions in a corpus of student-teacher chat interactions. Then subsequently, we validate these classifications using a scheme based on oracles, employing two generative large language models (ChatGPT and Bard). Experiments on emotion detection in dialogues between students and teachers revealed that EmoRoBERTa exhibited a reasonable level of agreement with the oracles, while …ChatGPT demonstrated the highest consistency with EmoRoBERTa’s predictions. Furthermore, we identified the impact of specific words on emotion classification, offering insights into the decision-making process of these models. The results not only highlight the prominent presence of emotions like approval, gratitude, curiosity, disapproval, amusement, confusion, remorse, joy , and surprise but also provide substantial support for the utilization of the proposed emotion detection model to enhance the student learning environment. Exploring the emotional aspects of educational dialogues holds the potential to enhance instruction methods, provide timely assistance to students in need, and create an improved learning atmosphere. Show more

Keywords: Emotion detection, learning interaction, transfer learning, large language models, active learning

DOI: 10.3233/JIFS-219340

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024

Authors: Shi, Xiaolong | Kosari, Saeed | Rangasamy, Parvathi | Nivedhaa, R.K. | Rashmanlou, Hossein

Article Type: Research Article

Abstract: Modern image processing techniques are improving beyond old methods, which include advanced approaches, for example deep learning. Convolutional Neural Networks (CNNs) are excellent at automatic feature extraction, whereas Generative Adversarial Networks (GANs) produce realistic images. Transfer learning uses pre-trained models, whereas semantic segmentation identifies pixels in images. Super-resolution, style transfer, and attention mechanisms can increase the quality of images and understanding. Adversarial defenses address purposeful manipulations, while 3D image processing handles three-dimensional data. These advancements make use of improved computational power and massive datasets to revolutionize image processing capabilities. Traditional image processing algorithms frequently fail to handle the complex and …multidimensional structure of color images, particularly when dealing with uncertainty and imprecision. In this study, the 3D-EIFIM frame work is extented and scaled aggregation operations 3D-EIFIM tailored for image data are proposed. By representing each pixel as an entry of 3D-EIFIM and applying aggregation techniques to enable more effective image analysis, manipulation, and enhancement. The practical implications of this research are significant, as it can lead to advancements in fields such as computer vision, medical imaging, and remote sensing. Show more

Keywords: IFP, conjunction, disjunction, IFIM, EIFIM, 3D-IFIM, 3D-EIFIM

DOI: 10.3233/JIFS-238252

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024

Authors: Vimala, S. | Valarmathi, K.

Article Type: Research Article

Abstract: This study proposes a novel method using hybrid CNN-LSTM networks to measure and predict the effectiveness of speech and vision therapy. Traditional methods for evaluating therapy often rely on subjective assessments, lacking precision and efficiency. By combining CNN for visual data and MFCC for speech, alongside LSTM for temporal dependencies, the system captures dynamic changes in patients’ conditions. Pre-processing of audio and visual data enhances accuracy, and the model’s performance outperforms existing methods. This approach exhibits the potential of deep learning in monitoring patient progress effectively in speech and vision therapy, offering valuable insights for improving treatment outcomes. The proposed …system’s effectiveness is assessed by various performance metrics. The suggested system’s results are compared with those of other methods already in use. The study’s findings indicate that the suggested approach is more accurate than other existing models. In conclusion, this study offers important new information on how deep learning methods are being used to track patients’ progress in speech and vision therapy. Show more

Keywords: Monitor, speech and vision, deep learning, therapy patient, recording device, CNN-LSTM, categorization

DOI: 10.3233/JIFS-237363

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024

Authors: Ravi, Vinayakumar

Article Type: Research Article

Abstract:  Deep learning-based models are employed in computer-aided diagnosis (CAD) tools development for pediatric pneumonia (P-Pneumonia) detection. The accuracy of the model depends on the scaling of the deep learning model. A survey on deep learning shows that models with a greater number of layers achieve better performances for P-Pneumonia detection. However, the identification of the optimal models is considered to be important work for P-Pneumonia detection. This work presents a hybrid deep learning model for P-Pneumonia detection. The model leverages the EfficientNetV2 model that employs various advanced methodologies to maintain the balance between the model scaling and the performance of …the model in P-Pneumonia detection. The features of EfficientNetV2 models are passed into global weighted average pooling (GWAP) which acts like an attention layer. It helps to extract the important features that point to the infected regions of the radiography image and discard all the unimportant information. The features from GWAP are high in dimension and using kernel-based principal component analysis (K-PCA), the features were reduced. Next, the reduced features are combined together and passed into a stacked classifier. The stacked classifier is a two-stage approach in which the first stage employs a support vector machine (SVM) and random forest tree (RFT) for the prediction of P-Pneumonia using the fused features and logistic regression (LRegr) on values of prediction for classification. Detailed experiments were done for the proposed method in P-Pneumonia detection using publically available benchmark datasets. Various settings in the experimental analysis are done to identify the best model. The proposed model outperformed the other methods by improving the accuracy by 4% in P-Pneumonia detection. To show that the proposed model is robust, the model performances were shown on the completely unseen dataset of P-Pneumonia. The hybrid deep learning-based P-Pneumonia model showed good performance on completely unseen data samples of P-Pneumonia patients. The generalization of the proposed P-Pneumonia model is studied by evaluating the model on similar lung diseases such as COVID-19 (CV-19) and Tuberculosis (TBS). In all the experiments, the P-Pneumonia model has shown good performances on similar lung diseases. This indicates that the model is robust and generalizable on data samples of different patients with similar lung diseases. The P-Pneumonia models can be used in healthcare and clinical environments to assist doctors and healthcare professionals in improving the detection rate of P-Pneumonia. Show more

Keywords: Pediatric pneumonia, machine learning, deep learning, dimensionality reduction, feature fusion

DOI: 10.3233/JIFS-219397

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024

Authors: Vaikunta Pai, T. | Nethravathi, P.S. | Birau, Ramona | Popescu, Virgil | Karthik Pai, B.H. | Naik, Pramod Vishnu

Article Type: Research Article

Abstract: Multimodal conversational AI systems have gained significant attention due to their potential to enhance user experience and enable more interactive and engaging interactions. This vital and complex research field seeks to integrate diverse modalities, including text, images, and speech, to develop conversational AI systems capable of comprehending, perceiving, and generating responses within a multimodal framework. By seamlessly incorporating various modalities, these systems can provide a more comprehensive and immersive conversational experience, enabling users to communicate in a more natural and intuitively. This research presents a novel multimodal architecture empowered by Deep Neural Networks (DNNs) for simultaneous integration and processing of …diverse modalities. Multimodal data encompasses various sources like text, images, audio, video, or sensor data. The objective is to merge and harness information from these modalities to amplify learning and enhance performance across a spectrum of tasks. This research explores the extension of ChatGPT, a state-of-the-art conversational AI model, to handle multimodal inputs, including text and images or text and speech. We present a comprehensive analysis of the benefits and challenges of integrating various options into ChatGPT, examining their impact on understanding, interaction, and overall system performance. Through extensive experimentation and evaluation, we demonstrate the potential of multimodal ChatGPT to provide richer, more context-aware conversations, while also highlighting the existing limitations and open research questions in this evolving field. Multimodal ChatGPT outperform the current GPT-3.5 by 16.51% and it is clear that multimodal ChatGPTis capable of better performance and offer a pathway for further progress in the field of language models. Show more

Keywords: Large language model, generative pre-trained transformer, deep learning, State-Of-The-Art (SOTA), artificial intelligence (AI), reinforcement training from human feedback, natural language processing (NLP), convolutional neural networks (CNN), recurrent neural networks (RNN)

DOI: 10.3233/JIFS-239465

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024

Authors: Li, Ye | Zhou, Jingkang

Article Type: Research Article

Abstract: Semi-supervised learning (SSL) aims to reduce reliance on labeled data. Achieving high performance often requires more complex algorithms, therefore, generic SSL algorithms are less effective when it comes to image classification tasks. In this study, we propose ComMatch, a simpler and more effective algorithm that combines negative learning, dynamic thresholding, and predictive stability discriminations into the consistency regularization approach. The introduction of negative learning is to help facilitate training by selecting negative pseudo-labels during stages when the network has low confidence. And ComMatch filters positive and negative pseudo-labels more accurately as training progresses by dynamic thresholds. Since high confidence does …not always mean high accuracy due to network calibration issues, we also introduce network predictive stability, which filters out samples by comparing the standard deviation of the network output with a set threshold, thus largely reducing the influence of noise in the training process. ComMatch significantly outperforms existing algorithms over several datasets, especially when there is less labeled data available. For example, ComMatch achieves 1.82% and 3.6% error rate reduction over FlexMatch and FixMatch on CIFAR-10 with 40 labels respectively. And with 4000 labeled samples, ComMatch achieves 0.54% and 2.65% lower error rates than FixMatch and MixMatch, respectively. Show more

Keywords: Semi-supervised learning, negative learning, dynamic threshold, predictive stability

DOI: 10.3233/JIFS-233940

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Sun, Haobin | Chen, Bingsan | Zhang, Wenshui | Wei, Songma | Lian, Changwei

Article Type: Research Article

Abstract: In the process of production, the label on the product provides the basic product information. Due to the complex text contained on the product labels, the high accuracy recognition for online production labels has always been a challenging problem. To address this issue, a more effective method for complex text detection by improving the convolutional recurrent neural network has been proposed to enhance the recognition accuracy of complex text. Firstly, the SE-DenseNet feature extraction network has been introduced for feature extraction, aiming to improve the model’s depth and feature extraction capacity. Then, the Bi-GRU network is utilized to learn and …model the hidden states and spatial features extracted by SE-DenseNet, anticipate preliminary sequence results, reduce model parameters, and improve the model’s calculation performance. Finally, the CTC network is employed for transcription to convert each feature sequence prediction output by Bi-GRU into a label sequence, achieving complex text recognition. Experimental results on the SVT, IIIT-5K, ICDAR2013 public dataset, and a self-built dataset demonstrate that the proposed model achieves superior outcomes on both public and self-built datasets. Remarkably, the model exhibits the highest recognition accuracy of 93.2% on the ICDAR2013 public dataset, demonstrating its potential to support complex text recognition for online production labels. Show more

Keywords: Online production labels, complex text recognition, SE-DenseNet, Bi-GRU

DOI: 10.3233/JIFS-234748

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Lv, Zhangwei

Article Type: Research Article

Abstract: In the context of China’s cultural and tourism industry, cultural equipment plays a critical role in cultural dissemination, especially in remote areas with harsh road conditions and unique environmental factors. However, the efficiency and stability of manual analysis are significantly challenged by these conditions and the vast yet sparsely collected monitoring data. This study aims to develop a method for extracting valuable information from monitoring data to assess the health status of cultural equipment. We introduce a deep learning-based algorithm that leverages convolutional neural networks (CNNs) to extract local features from multidimensional monitoring indicators and long short-term memory (LSTM) networks …to capture time series features, facilitating the classification of cultural equipment’s health status. The algorithm’s effectiveness is demonstrated through simulation results, highlighting its practicality and applicability in real-world scenarios. This research not only provides a novel approach for cultural equipment health assessment but also contributes significantly to the field by addressing the challenges of data analysis in complex environments, underscoring the importance of technological advancements in preserving cultural heritage. Show more

Keywords: Environmental evaluation, convolutional neural network, long short term memory, health status

DOI: 10.3233/JIFS-241607

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Shamma, Aashitha L. | Vekkot, Susmitha | Gupta, Deepa | Zakariah, Mohammed | Alotaibi, Yousef Ajami

Article Type: Research Article

Abstract:  This paper investigates the potential of COVID-19 detection using cough, breathing, and voice patterns. Speech-based features, such as MFCC, zero crossing rate, spectral centroid, spectral bandwidth, and chroma STFT are extracted from audio recordings and evaluated for their effectiveness in identifying COVID-19 cases from Coswara dataset. The explainable AI SHAP tool is employed which identified MFCC, zero crossing rate, and spectral bandwidth as the most influential features. Data augmentation techniques like random sampling, SMOTE, Tomek, and Edited Nearest Neighbours (ENN), are applied to improve the performance of various machine learning models used viz. Naive Bayes, K-nearest neighbours, support vector machines, …XGBoost, and Random Forest. Selecting the top 20 features achieves an accuracy of 73%, a precision of 74%, a recall of 94%, and an F1-score of 83% using the Random Forest model with the Tomek sampling technique. These findings demonstrate that a carefully selected subset of features can achieve comparable performance to the entire feature set while maintaining a high recall rate. The success of the Tomek undersampling technique highlights the ability of model to handle sparse clinical data and predict COVID-19 and associated diseases using speech-based features. Show more

Keywords: Covid-19, MFCC, spectral bandwidth, zero crossing rate, SHAP tool, Tomek

DOI: 10.3233/JIFS-219387

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Zou, Chao | Zhu, Jiwei | Cao, Jiawei | Wang, Xin | Mei, Zhenyu | Zhou, Kui

Article Type: Research Article

Abstract: Prefabricated buildings (PBs) are a new type of building construction, which are less time-consuming and cause low environmental pollution and resource consumption. They play an important role in industrialized construction and clean production and have gained worldwide attention. However, the high construction costs have become a major obstacle to their popularity and application. This study investigates the factors influencing construction costs of PBs in China using a systematic literature review (SLR), fuzzy interpretive structure modeling (fuzzy ISM), and the Matrice d’Impacts croises-multiplication appliqué an classment (MICMAC) technique. First, 32 influencing factors were identified from the SLR. Second, out of which …16 critical factors were selected and mapped in a hierarchical model through semi-structured interview screening, and the MICMAC technique was used to classify the cost-influencing factors of PBs into different categories. The results revealed that all identified factors played pivotal roles in various capacities and influenced the cost of PB construction. This study may assist administrators and policymakers in better understanding the factors that influence the costs of PBs construction to manage and reduce them. Show more

Keywords: Prefabricated buildings, construction costs, critical factors, fuzzy ISM, MICMAC technique

DOI: 10.3233/JIFS-240206

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024

Authors: Ding, Zongchao

Article Type: Research Article

Abstract: The networks have achieved good results by using sparse connections, weight sharing, pooling, and establishing their own localized receptive fields. This work aims to improve the Space Invariant Artificial Neural Network approach and raise its recognition accuracy and convergence rate. Incorporating the continuous neural architecture into the Space Invariant Artificial Neural Network is the first step toward simultaneously learning the deep features of an image. Second, the skip convolution layer of ResNet serves as the foundation for developing a new residual module named QuickCut3-ResNet. A dual evaluation model is then developed to achieve the combined evaluation of the convolutional and …complete connection process. Ultimately, the best network parameters of the Space Invariant Artificial Neural Network are determined after simulation experiments are used to examine the impact of various network parameters on the network performance. Results from experiments demonstrate that the Space Invariant Artificial Neural Network technique described in this research can learn the image’s varied characteristics, which enhances the Space Invariant Artificial Neural Network’s capacity to recognize images and extract features accurately. Show more

Keywords: Artificial intelligence, big data, space invariant artificial neural network, image recognition, QuickCut3-ResNet

DOI: 10.3233/JIFS-239538

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Wang, Zhimin | Li, Boquan

Article Type: Research Article

Abstract: This paper introduces an expert system to decision-making. The expert system is linguistic summarization combined with prioritized operators. In the practical decision-making problems, the information of attributes is linguistic type and needs to be converted into numerical type. The validity of the linguistic summarization is recorded as the attribute value. We discuss how to calculate the validity of the linguistic summarization, and present three prioritized operators. Then the three prioritized operators are used to aggregate the attribute values. Finally, a practical example is given. In addition, we conduct a comparative analysis between the expert system method and another multi-attribute decision-making …method by using a measure of specificity, and conclude that the expert system method is better. Show more

Keywords: Expert system, decision-making, linguistic summarization, prioritized operators, comparative analysis

DOI: 10.3233/JIFS-238556

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Yang, Fan | Zhou, Qing | Su, Renbin | Xiong, Weihong

Article Type: Research Article

Abstract: Molecular graph representation learning has been widely applied in various domains such as drug design. It leverages deep learning techniques to transform molecular graphs into numerical vectors. Graph Transformer architecture is commonly used for molecular graph representation learning. Nevertheless, existing methods based on the Graph Transformer fail to fully exploit the topological structural information of the molecular graphs, leading to information loss for molecular representation. To solve this problem, we propose a novel molecular graph representation learning method called MTS-Net (Molecular Topological Structure-Network), which combines both global and local topological structure of a molecule. In global topological representation, the molecule …graph is first transformed into a tree structure and then encoded by employing a hash algorithm for tree. In local topological representation, paths between atom pairs are transcoded and incorporated into the calculation of the Transformer attention coefficients. Moreover, MTS-Net has intuitive interpretability for identifying key structures within molecules. Experiments on eight molecular property prediction datasets show that MTS-Net achieves optimal results in three out of five classification tasks, the average accuracy is 0.85, and all three regression tasks. Show more

Keywords: Molecular representation, graph structure, graph transformer, property prediction

DOI: 10.3233/JIFS-236788

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Veeraiah, D. | Sai Kumar, S. | Ganiya, Rajendra Kumar | Rao, Katta Subba | Nageswara Rao, J. | Manjith, Ramaswamy | Rajaram, A.

Article Type: Research Article

Abstract: Medical image fusion plays a crucial role in accurate medical diagnostics by combining images from various modalities. To address this need, we propose an AI model for efficient medical image fusion using multiple modalities. Our approach utilizes a Siamese convolutional neural network to construct a weight map based on pixel movement information extracted from multimodality medical images. We leverage medical picture pyramids to incorporate multiscale techniques, enhancing reliability beyond human visual intuition. Additionally, we dynamically adjust the fusion mode based on local comparisons of deconstructed coefficients. Evaluation metrics including F1-score, recall, accuracy, and precision are computed to assess performance, yielding …impressive results: an F1-score of 0.8551 and a mutual information (MI) value of 2.8059. Experimental results demonstrate the superiority of our method, achieving a remarkable 99.61% accuracy in targeted experiments. Moreover, the Structural Similarity Index (SSIM) of our approach is 0.8551. Compared to state-of-the-art approaches, our model excels in medical picture classification, providing accurate diagnosis through high-quality fused images. This research advances medical image fusion techniques, offering a robust solution for precise medical diagnostics across various modalities. Show more

Keywords: Multimodal medical image fusion, image classification, siamese CNN, LSTM, genetic algorithm

DOI: 10.3233/JIFS-240018

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Huang, Rongbing | Hanif, Muhammad Farhan | Aleem, Aqsa | Siddiqui, Muhammad Kamran | Hanif, Muhammad Faisal | Hussain, Mazhar

Article Type: Research Article

Abstract: The triangular γ-graphyne structure is highlighted in particular, as it is a new configuration with possible applications in medicine. We shed light on this structure’s special qualities and potential uses in healthcare by computing several topological indices linked to it through computational research. Furthermore, we use Shannon’s entropy measure to express the information content of the connection-based topological indices in tandem. This method offers a thorough comprehension of the intricate features and structural properties of the triangular γ-graphyne structure. A logarithmic regression model is built to establish a quantifiable relationship between the computed indices and entropy. The SPSS program was …used in the development of this model, allowing for a thorough examination of the relationship between structural features and informational entropy. A regression model based on triangular graphyne topological indices is used as a predictive tool for entropy estimation. Show more

Keywords: Connection number (CN), triangular γ-graphyne, line graph, logarithmic regression model, Shannon entropy

DOI: 10.3233/JIFS-240356

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Wang, Ke | Gu, Tianrui | Du, Xiaoye

Article Type: Research Article

Abstract: With the rapid economic development and increasingly serious environmental problems, many regions have launched green credit policies. Green credit can reduce the loan interest rate of the environmental protection industry and lower the financing threshold. Traditional risk prediction methods cannot comprehensively evaluate the green credit risk of the enterprise based on the degree of green environmental protection and the industry environment in which the enterprise is located, resulting in the inconsistency between the credit financial risk prediction and the actual results, which increases the bank credit risk. In order to strengthen the management level of green credit and reduce the …probability of non-performing loans, a scientific risk assessment method was constructed by using a combination of automatic encoding network and bidirectional long short-term memory neural network model to predict the financial risks of green credit, driven by multi-modal data. Through the study of multimodal data, this paper took green credit financial risk as the research object, aggregated the information of various enterprises to improve the bank’s capital utilization rate, and also promoted enterprises to take the initiative to transform into the direction of green environmental protection. Finally, the experiment proved that multimodal data fusion model was more superior than random forest in risk prediction, reducing the bank’s non-performing loan rate by 3.1% and improving the bank’s risk control level. Show more

Keywords: Financial risk, green credit, risk prediction, multimodal data

DOI: 10.3233/JIFS-237691

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Wang, Hengyou | Ke, Rongji | Jiang, Xiang

Article Type: Research Article

Abstract: Due to its remarkable performance, the convolutional neural network (CNN) has gained widespread usage in image inpainting challenges. However, most of these CNN-based methods reconstruct images only in the spatial domain, which produces satisfactory outcomes for small-region inpainting tasks, but blurs the details and generates incomplete structures for large-region inpainting tasks with complex backgrounds. In this paper, we address the issue of large-region inpainting tasks by our novel Adaptive Fourier Neural Network . Specifically, in our network, a Fourier-based global receptive field module is introduced to incorporate frequency information and expand the receptive field by transforming local convolutions into …global convolutions, enabling the proposed network to transmit global information to the missing region. Furthermore, to better fuse spatial and frequency features, an attention-based joint space-frequency module is proposed to combine spatial and frequency information. Finally, to validate the effectiveness and robustness of our proposed method, we conduct qualitative and quantitative experiments on two popular datasets Paris StreetView and Places. The experimental results demonstrate that our proposed method outperforms state-of-the-art methods by generating sharper, more coherent, and visually plausible inpainting results. Code will be released after this work published: https://github.com/langka9/AFNN.git . Show more

Keywords: Large-region image inpainting, Fourier-based global receptive field, frequency domain, Fourier Neural Network

DOI: 10.3233/JIFS-239513

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Ruby Elizabeth, J. | Kesavaraja, D. | Ebenezer Juliet, S.

Article Type: Research Article

Abstract:  The retinal illness that causes vision loss frequently on the globe is glaucoma. Hence, the earlier detection of Glaucoma is important. In this article, modified AlexNet deep leaning model is proposed to category the source retinal images into either healthy or Glaucoma through the detection and segmentations of optic disc (OD) and optic cup (OC) regions in retinal pictures. The retinal images are preprocessed and OD region is detected and segmented using circulatory filter. Further, OC regions are detected and segmented using K-means classification algorithm. Then, the segmented OD and OC region are classified and trained by the suggested AlexNet …deep leaning model. This model classifies the source retinal image into either healthy or Glaucoma. Finally, performance measures have been estimated in relation to ground truth pictures in regards to accuracy, specificity and sensitivity. These performance measures are contrasted with the other previous Glaucoma detection techniques on publicly accessible retinal image datasets HRF and RIGA. The suggested technique as described in this work achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achieves 100% GDR for severe case on RIGA dataset. AIM: Segmenting the OD and OC areas and classifying the source retinal picture as either healthy or glaucoma-affected. METHODS: The retinal images are preprocessed and OD region is detected and segmented using circulatory filter. Further, OC region is detected and segmented using K-means classification algorithm. Then, the segmented OD and OC region classified are and trained by the suggested AlexNet deep leaning model. RESULTS: The suggested method as described in this work achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achieves 100% GDR for severe case on RIGA dataset. CONCLUSION: This article proposes the modified AlexNet deep learning models for the detections of Glaucoma utilizing retinal images. The OD region is detected using circulatory filter and OC region is detected using k-means classification algorithm. The detected OD and OC regions are utilized to classify the retinal images into either healthy or Glaucoma using the suggested AlexNet model. The proposed method obtains 100% Sey, 93.7% Spy and 96.6% CA on HRF dataset retinal images. The proposed AlexNet method obtains 97.7% Sey, 98% Spy and 97.8% CA on RIGA dataset retinal images. The proposed method stated in this article achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achieves 100% GDR for severe case on RIGA dataset. Show more

Keywords: Retina, deep learning, OD, OC, AlexNet

DOI: 10.3233/JIFS-234131

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Wang, Lu

Article Type: Research Article

Abstract: In this technology world, education is also becoming one of the basic necessities of human life like food, shelter, and clothes. Even in day-to-day daily activities, the world is moving toward an automated process using technology developments. Some of the technology developments in day-to-day life activities are smartphone, internet activities, and home and office appliances. To cope with these advanced technologies, the persons must have basic educational qualification to understand and operate those appliances easily. Apart from this, the education helps the person to develop their personal growth in both knowledge and wealth. With the development of technologies, different Artificial …Intelligence techniques have been applied on the datasets to analyze these factors and enhance the teaching method. But the current techniques were applied to one or two data models that analyze either their educational performance or demographic variable. But these models were not sufficient for analyzing all the factors that affects the education. To overcome this, a single optimized machine-learning approach is proposed in this paper to analyze the factors that affect the education. This analysis helps the faculty to enhance their teaching methodology and understand the student’s mentality toward education. The proposed Hybrid Cuckoo search-particle swarm optimization was implemented on three datasets to determine the factors that affect the education. These optimal factors are determined by identifying their relations to the final results of an individual person. All these optimal factors are combined and grades are grouped to analyze the proposed optimization process performance using regression neural network. The proposed optimization-based neural network was tested on three data models and its performance analysis showed that the proposed model can achieve higher accuracy of 99% that affects the individual education. This shows that the proposed model can help the faculty to enhance their attention to the students individually. Show more

Keywords: Education, demographic factors, optimization, hybrid, cuckoo search optimization, particle swarm, regression neural network

DOI: 10.3233/JIFS-234021

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Ramasamy, Uma | Santhoshkumar, Sundar

Article Type: Research Article

Abstract: In the expansive domain of data-driven research, the curse of dimensionality poses challenges such as increased computational complexity, noise sensitivity, and the risk of overfitting models. Dimensionality reduction is vital to handle high-dimensional datasets effectively. The pilot study disease dataset (PSD) with 53 features contains patients with Rheumatoid Arthritis (RA) and Osteoarthritis (OA). Our work aims to reduce the dimension of the features in the PSD dataset, identify a suitable feature selection technique for the reduced-dimensional dataset, analyze an appropriate Machine Learning (ML) model, select significant features to predict the RA and OA disease and reveal significant features that predict …the arthritis disease. The proposed study, Progressive Feature Reduction with Varied Missing Data (PFRVMD), was employed to reduce the dimension of features by using PCA loading scores in the random value imputed PSD dataset. Subsequently, notable feature selection methods, such as backward feature selection, the Boruta algorithm, the extra tree classifier, and forward feature selection, were implemented on the reduced-dimensional feature set. The significant features/biomarkers are obtained from the best feature selection technique. ML models such as the K-Nearest Neighbour Classifier (KNNC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Naïve Bayes Classifier (NBC), Random Forest Classifier (RFC) and Support Vector Classifier (SVC) are used to determine the best feature selection method. The results indicated that the Extra Tree Classifier (ETC) is the promising feature selection method for the PSD dataset because the significant features obtained from ETC depicted the highest accuracy on SVC. Show more

Keywords: Autoimmune disease, rheumatoid arthritis, osteoarthritis, feature reduction, feature selection, machine learning algorithms

DOI: 10.3233/JIFS-231537

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Sun, Yilin | Li, Shufan

Article Type: Research Article

Abstract: Contemporary art design not only pursues the quality of the work itself, but also pays attention to the sensory aspects of people’s needs for art design. Traditional art design methods can be limited by time, space and other objective conditions, and often fail to achieve the designer’s expected effect, and visitors’ experience is not strong. The usage of multimedia technology in art and design can enrich its expression and enhance visitors’ experience. In order to increase the sense of interaction between the platform and users, multimedia technology is incorporated into the interactive art design platform generated by VR technology in …this paper. This article combines multimedia technology with interactive technology to construct an interactive platform for art and design, and applies it to the display of Dunhuang murals. Through the analysis of user experience feedback, the effectiveness of art and design display and interaction is verified. Display and interact with Dunhuang murals as interactive platform applications. This test is to extract women’s clothing colors from the same tradition in different times in the color extraction exploration module of the interactive platform, so as to provide accurate information for displaying women’s clothing color changes and comparing interactions. The findings show that the platform is capable of extracting and recognizing the color characteristics of the murals, accurately identifying user signals, and noticing 3D modeling of images via VR technology. This capability provides solid technical and data support for the platform’s interaction module. The interaction design, platform functionality, and layout can support the majority of users in terms of cognition, perception, and interaction, pique their interest, and enhance their experience, according to evaluation of trial user information. The interaction ends abruptly, according to a small percentage of users, and they had a bad experience overall. Show more

Keywords: Multimedia technology, art and design, interactive, platform building

DOI: 10.3233/JIFS-238001

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Sheik Faritha Begum, S. | Suresh Anand, M. | Pramila, P.V. | Indra, J. | Samson Isaac, J. | Alagappan, Chockalingam | Gopala Gupta, Amara S.A.L.G. | Srivastava, Suraj | Vidhya, R.G.

Article Type: Research Article

Abstract: Thyroid tumours are a common form of cancer, and accurate classification of their type is crucial for effective treatment planning. This research presents a hybrid approach for the classification of thyroid tumours based on their type. The proposed approach combines the use of advanced machine learning techniques with a comprehensive database of thyroid tumour samples. The database includes various features such as tumour size, shape, and texture, as well as patient-specific information. The hybrid approach aims to optimize the classification process by leveraging the diverse set of features and utilizing the power of machine learning algorithms. By harnessing the power …of machine learning algorithms, this approach has the potential to revolutionize the field of thyroid tumour classification and significantly improve patient outcomes. The optimization strategy is Particle Swarm Optimization, refining the classification performance and ensuring optimal accuracy in identifying and categorizing four types of thyroid tumours. The utilization of advanced diagnostic tools and state-of-the-art Random forest classifier techniques in this approach marks a significant advancement in the field of thyroid tumour classification. Through the augmentation of the dataset and the pre-processing techniques employed, the hybrid classification system demonstrates enhanced accuracy and reliability in distinguishing between different types of thyroid tumours. This innovative approach not only provides a more comprehensive understanding of thyroid tumours but also paves the way for personalized and effective treatment strategies, ultimately improving patient care and outcomes. Show more

Keywords: Machine learning, thyroid tumours, Particle Swarm Optimization, Random Forest classifier, innovative approach

DOI: 10.3233/JIFS-239804

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Zhang, Jianwei | Chen, Lei | Hou, Ge | Huang, Jinlin | Wang, Yong

Article Type: Research Article

Abstract: Health assessment is one of the important theoretical bases for deciding whether the diversion tunnel can operate safely and stably. A project of the TBM diversion tunnel is taken as the research object to ensure the normal operation of the diversion tunnel. Based on measured data and considering multiple safety aspects such as structural response, durability, and external factors of the diversion tunnel, a TBM diversion tunnel structural health evaluation index system is established. A new method for the TBM diversion tunnel structural health comprehensive evaluation based on Analytic Hierarchy Process-Matter Element Extension-Variable Weight Theory (AMV) is proposed to explore …the impact of AMV fluctuation with the measured results of the indicators on the weight, closeness, and health grade of each evaluation index. The high sensitivity and high-risk evaluation indicators for the structural health of the diversion tunnels are identified. It is found that the variable weight varies with the changes in various indicator values, which can accurately evaluate the health status of tunnels in real-time. The characteristic values of the tunnel grade calculated by the AHP and the AMV are 1.589 and 1.695, respectively. The results of the corresponding interval diversion tunnel are the basic safety state of grade B. Except for the two evaluation indicators of concrete strength and slurry properties, the variable weight values and grade characteristic values of other evaluation indicators increase with the increase of indicator values. The four indicators of segment settlement, segment opening, segment misalignment, and segment cracks are more sensitive to the health of the TBM diversion tunnel. This AMV can accurately evaluate the health status of the diversion tunnel structure. The research results can provide references for later maintenance work and similar projects. Show more

Keywords: Diversion tunnel, Health evaluation, AMV, AHP, susceptibility

DOI: 10.3233/JIFS-239155

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Li, Yuerong | Zhang, Yuhua | Che, Jinxing

Article Type: Research Article

Abstract: Accurate prediction of short-term electricity price is the key to obtain economic benefit and also an important index of power system planning and management. Support vector regression (SVR) based ensemble works have gained remarkable achievements in terms of high accuracy and steady performance, but they are highly dependent on data representativeness and have a high computational complexity O  (k  * N 3 ) of data samples and parameter selection. To further improve the data representativeness and reduce its computational complexity, this paper develops a new approach to forecast electricity price via optimal weighted ensemble. In the model, the cluster-based subsampling …algorithm is proposed to categorize the inputs being seasonally decomposed into several groups, and representative data are drawn from each group in a certain proportion to ensure that each subset trained with SVR has the same representativeness and features. Moreover, the optimal weighted combination method is presented to assign weights to the sub-SVRs to obtain the optimal support vector regression ensemble model (OWSSVRE). The experimental results show that the improved support vector regression ensemble model with the same features and representativeness of the subset has better performance in electricity price forecasting. As a result, it is suitable to support decision making in the energy and other sectors. Show more

Keywords: Electricity price forecasting, support vector regression, K-means clustering, optimal weight, subsampling

DOI: 10.3233/JIFS-236239

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024

Authors: Thenmozhi, R. | Sakthivel, P. | Kulothungan, K.

Article Type: Research Article

Abstract: The Internet of Things and Quantum Computing raise concerns, as Quantum IoT defines security that exploits quantum security management in IoT. The security of IoT is a significant concern for ensuring secure communications that must be appropriately protected to address key distribution challenges and ensure high security during data transmission. Therefore, in the critical context of IoT environments, secure data aggregation can provide access privileges for accessing network services. "Most data aggregation schemes achieve high computational efficiency; however, the cryptography mechanism faces challenges in finding a solution for the expected security desecration, especially with the advent of quantum computers utilizing …public-key cryptosystems despite these limitations. In this paper, the Secure Data Aggregation using Quantum Key Management scheme, named SDA-QKM, employs public-key encryption to enhance the security level of data aggregation. The proposed system introduces traceability and stability checks for the keys to detect adversaries during the data aggregation process, providing efficient security and reducing authentication costs. Here the performance has been evaluated by comparing it with existing competing schemes in terms of data aggregation. The results demonstrate that SDA-QKM offers a robust security analysis against various threats, protecting privacy, authentication, and computation efficiency at a lower computational cost and communication overhead than existing systems. Show more

Keywords: Internet of things, security, data aggregation, access control, quantum cryptography

DOI: 10.3233/JIFS-223619

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024

Authors: Li, Chen | Liu, Na | Xu, Zhenshun | Zheng, Guofeng | Yang, Jie | Dao, Lu

Article Type: Research Article

Abstract: Medical short text classification is of great significance to medical information extraction and medical auxiliary diagnosis. However, medical short texts face challenges such as sparse features, semantic ambiguity, and the specialized nature of the medical field, resulting in relatively low accuracy in short text classification. Taking into consideration the characteristics of medical short texts, this paper proposes a Chinese medical short text classification model based on DPECNN. First, ERNIE is utilized to learn text knowledge and information in order to enhance the model’s semantic representation capabilities. Then, the DPECNN model is employed to extract rich feature information, and the classification …results are generated through a fully connected layer. In the case of DPCNN, it only considers deep-level contextual semantic information, overlooking the correlation of adjacent semantic information between channels. To address this, ECA channel attention is introduced to account for adjacent semantic information. The use of a self-normalizing activation function helps avoid the problem of vanishing gradients. To enhance the model’s robustness and generalization ability, the FGM adversarial training algorithm is employed to perturb the data. The F1 values achieved on the THUCNews, KUAKE-QIC, and CHIP-CTC datasets are 95.00%, 79.45%, and 82.81%, respectively. Show more

Keywords: Medical text mining, Chinese short text classification, ERNIE, DPECNN, confrontation training

DOI: 10.3233/JIFS-239006

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

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: 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: 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: 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: 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: 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