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

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

Authors: Ahani, Zahra | Shahiki Tash, Moein | Ledo Mezquita, Yoel | Angel, Jason

Article Type: Research Article

Abstract: Super-enhancers are a category of active super-enhancers densely occupied by transcription factors and chromatin regulators, controlling the expression of disease-related genes and cellular identity. Recent studies have demonstrated the formation of complex structures by various factors and super-enhancers, particularly in various cancers. However, our current knowledge of super-enhancers, such as their genomic locations, interaction with factors, functions, and distinction from other super-enhancers regions, remains limited. This research aims to employ deep learning techniques to detect and differentiate between super-enhancers and enhancers based on genomic and epigenomic features and compare the accuracy of the results with other machine learning methods In …this study, in addition to evaluating algorithms, we trained a set of genomic and epigenomic features using a deep learning algorithm and the Python-based cross-platform software to detect super-enhancers in DNA sequences. We successfully predicted the presence of super-enhancers in the sequences with higher accuracy and precision. Show more

Keywords: Super-enhancer, enhancer, genomic, epigenomic, deep learning

DOI: 10.3233/JIFS-219356

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

Authors: Shahbazova, Shahnaz N. | Rzayev, Ab.G. | Asadova, R.Sh. | Jabiyev, K.M.

Article Type: Research Article

Abstract: The paper gives a systems analysis in the field of heat transfer and temperature distribution (TD) along the length of oil production wells (OPW). The analysis shows that the existing mathematical models are suitable only for determining TD along the length of casing string (CS) and are not suitable for determining TD along the length of the tubing run, since the existence of the interfacial (between the CS and the tubing) annulus of the fluid and gas layers with heat capacity and thermal conductivity that differ significantly from the heat capacity and thermal conductivity of rocks surrounding the CS. Given …the above, mathematical models taking into account the impact of the fluid and gas layers in the annulus on the heat transfer from the upward fluid flow to the tubing wall and from the wall to the interfacial annulus are developed. To ensure the technological effectiveness of the obtained model, formulas for quantitative estimation of the heat transfer of the fluid flow into the surrounding environment are given. Show more

Keywords: Heat exchange, heat transfer, heat dissipation, thermal conductivity, temperature distribution, oil production well.

DOI: 10.3233/JIFS-219366

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024

Authors: Chen, Meng | Wang, Xue-ping

Article Type: Research Article

Abstract: In this article, we characterize triangular norms that have not the limit property, which are applied for exploring the characterizations of function f  : [0, 1] → [0, 1] with f ( x ) = lim n → ∞ x T ( n ) for a triangular norm T when the function f is continuous. In particular, we prove that a continuous t-norm T satisfies that f  (x ) >0 for all x  ∈ (0, 1) if and only if 0 is an accumulation point of its non-trivial idempotent elements, and the function …f is continuous on [0,1] if and only if T  = T M . Show more

Keywords: Triangular norm, the limit property, the contrary limit property, Archimedean property, continuity

DOI: 10.3233/JIFS-237999

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024

Authors: Sharma, Amit | Naga Raju, M. | Hema, P. | Chaitanuya, Morsa | Jagannatha Reddy, M.V. | Vignesh, T. | Chandanan, Amit Kumar | Verma, Santhosh

Article Type: Research Article

Abstract: Wireless Sensor Networks (WSNs) have gained significant attention in recent years due to their wide range of applications, such as environmental monitoring, smart agriculture, and structural health monitoring. With the increasing volume of data generated by WSNs, efficient data analytics techniques are crucial for improving the overall performance and reducing energy consumption. This paper presents a novel distributed data analytics approach for WSNs using fuzzy logic-based machine learning. The proposed method combines the advantages of fuzzy logic for handling uncertainty and imprecision with the adaptability of machine learning techniques. It enables sensor nodes to process and analyze data locally, reducing …the need for data transmission and consequently saving energy. Furthermore, this approach enhances data accuracy and fault tolerance by incorporating the fusion of heterogeneous sensor data. The proposed technique is evaluated on a series of real-world and synthetic datasets, demonstrating its effectiveness in improving the overall network performance, energy efficiency, and fault tolerance. The results indicate the potential of our approach to be applied in various WSN applications that demand low-energy consumption and reliable data analysis. Show more

Keywords: Wireless sensor networks, distributed data analytics, fuzzy logic, machine learning, energy efficiency

DOI: 10.3233/JIFS-234007

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

Authors: Kumar, Manoj | Sharma, Sukhwinder | Mittal, Puneet | Singh, Harmandeep | Singh, Sukhwinder

Article Type: Research Article

Abstract: The rapid expansion of Internet of Things (IoT) applications and the increasing complexity of Wireless Sensor Networks (WSNs) have created a critical need for efficient load balancing strategies. This paper proposes a dynamic load balancing approach for IoT-enabled WSNs using a fuzzy logic-based control mechanism. The proposed method aims to optimize energy consumption, reduce latency, and enhance network lifetime by intelligently distributing the workload among sensor nodes. The fuzzy logic controller takes into account various parameters, such as energy levels, communication distances, and node density, to make adaptive load balancing decisions. The control mechanism allocates tasks to the most suitable …nodes, ensuring efficient utilization of resources and preventing overloading of individual nodes. Simulations are conducted in diverse network scenarios to validate the performance of the proposed approach. Results demonstrate significant improvements in energy efficiency, latency reduction, and overall network lifetime compared to traditional load balancing techniques. The fuzzy logic-based control mechanism proves to be a promising solution for addressing the dynamic and resource-constrained nature of IoT-enabled WSNs, paving the way for more robust and resilient networks in various IoT applications. Show more

Keywords: IoT, Wireless Sensor Networks, load balancing, fuzzy logic, network lifetime

DOI: 10.3233/JIFS-234075

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

Authors: Vinoth Kumar, M. | Supreeth, B.R. | Hariprabhu, M. | Shanmuga Priya, P. | Ahmed, Ahmed Najat | Nagrare, Trupti | Mathur, Shruti | Manikandan, G.

Article Type: Research Article

Abstract: Containerized data centers (CDCs) have experienced rapid growth in recent years, owing to their modular and scalable nature. However, ensuring reliability and early fault detection in these complex systems is critical. This paper presents a novel Fuzzy Logic-based Fault Detection (FLFD) framework for CDCs using Digital Twins (DTs). The proposed approach employs DTs to create accurate virtual representations of the CDCs, which enable real-time monitoring and analysis of the physical systems. This paper focuses on three main aspects: (1) the development of a comprehensive DT model for CDCs, (2) the design and implementation of a FLFD algorithm, and (3) the …validation of the proposed approach through extensive simulations and real-world case studies. The FLFD algorithm leverages fuzzy logic principles to identify and localize faults in the system, thereby enhancing the overall fault detection accuracy and reducing false alarms. Results demonstrate the effectiveness of the proposed framework, with significant improvements in fault detection performance and system reliability. The FLFD approach offers a promising solution for proactive maintenance and management in containerized data centers, paving the way for more efficient and resilient operations. Show more

Keywords: Fuzzy logic, fault detection, containerized data centers, digital twins, proactive maintenance

DOI: 10.3233/JIFS-233736

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2023

Authors: Sitharamulu, V. | Mahammad Rafi, D. | Naulegari, Janardhan | Battu, Hanumantha Rao

Article Type: Research Article

Abstract: In this study, we investigate the viability of applying fuzzy reinforcement learning (FRL) to Internet of Things-based robots for purposes of autonomous navigation and collision avoidance. The proposed approach utilises FRL, IoT, and a sensor network to give the robot the ability to learn from its environment and act in accordance with those policies. The authors used FRL to train a mobile robot with wheels to move around and avoid obstacles, and then they put the robot through its paces in a virtual world. Results showed that the FRL-based technique improved the robot’s navigation and collision avoidance performance compared to …traditional rule-based approaches. The results of this study indicate that FRL may be a viable technique for enabling autonomous navigation and obstacle avoidance in IoT-based robotics. Show more

Keywords: Fuzzy reinforcement learning, IoT-based robotics, autonomous navigation, collision avoidance, sensor network

DOI: 10.3233/JIFS-233860

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Rajesh Kanna, R. | Ulagamuthalvi, V.

Article Type: Research Article

Abstract: Diagnosis is given top priority in terms of farm resource allocation, because it directly affects the GDP of the country. Crop analysis at an early stage is important for verifying the efficient crop output. Computer vision has a number of intriguing and demanding concerns, including disease detection. After China, India is the world’s second-largest creator of wheat. However, there exist algorithms that can accurately identify the most prevalent illnesses of wheat leaves. To help farmers keep track on a large area of wheat plantation, leaf image and data processing techniques have recently been deployed extensively and in pricey systems. In …this study, a hybrid pre-processing practice is used to remove undesired distortions while simultaneously enhancing the images. Fuzzy C-Means (FCM) is used to segment the affected areas from the pre-processed images. The data is then incorporated into a disease classification model using a Convolutional Neural Network (CNN). It was tested using Kaggle data and several metrics to see how efficient the suggested approach was. This study demonstrates that the traditional Long-Short Term Memory (LSTM) technique achieved 91.94% accuracy on the input images, but the hybrid pre-processing model with CNN achieved 95.06 percent accuracy. Show more

Keywords: Plant leaves diseases, convolutional neural network, fuzzy c-means, wheat production, pre-processing techniques

DOI: 10.3233/JIFS-233672

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Prabu Shankar, K.C. | Shyry, S. Prayla

Article Type: Research Article

Abstract: Early detection of diseases in men and women can improve treatment and reduce the risk involved in human life. Nowadays techniques which are non-invasive in nature are popularly used to detect the various types of diseases. Histopathological analysis plays a major role in finding the nature of the disease through medical images. Manual interpretation of these medical imaging takes time, is tedious, subjective, and can have human errors. It has also been discovered that the interpretation of these images varies amongst diagnostic labs. As computer power and memory capacity have increased, methodologies and medical image processing techniques have been developed …to interpret and analyse these images as a substitute for human involvement. The challenge lies in devising an efficient pre-processing technique that helps in analysing, processing and preparing the medical image for further diagnostics. This research provides a hybrid technique that reduces noise in the NITFI medical image by using a 2D adaptive median filter at level 1. The edges of the filtered medical image are preserved using the modified CLAHE algorithm which preserves the local contrast of the image. Expectation Maximization (EM) algorithm extracts the ROI part of the image which helps in easy and accurate identification of the disease. All the three steps are run over the 3D image slices of a NIFTI image. The proposed method proves that it achieves close to ideal RMSE, PSNR and UQI values as well as achieves an average runtime of 37.193 seconds for EM per slice. Show more

Keywords: 2D adaptive, expectation maximization, NIFTI, UQI, edge preservation, 3D slice, computational intelligence

DOI: 10.3233/JIFS-233931

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2023

Authors: Rajendran, Aishwarya | Ganesan, Sumathi | Rathis Babu, T.K.S.

Article Type: Research Article

Abstract: Brain tumor is observed to be grown in irregular shape and presented deep inside the tissues that led to cancer. Human brain tumor identification and categorization are performed with high latency, but also an essential task for the medical experts. The assistance through the automated diagnosis is generally utilized for the advancement in the diagnosis ability in order to get superior accuracy in brain tumor detection. Although the researches are enhancing the brain tumor detection performance, the highly challenging is to segment the brain tumor since it has variability concerning the tumor type, contrast, image modality and also in other …factors. To meet up all the challenges, a novel classification method is introduced using segmentation and machine learning approaches. Initially, the required images are collected from benchmark data sources. The input images are undergone for pre-processing stage, where it is done via “Contrast Limited Adaptive Histogram Equalization (CLAHE) and filtering methods”. Further, the pre-processed imagesare given as input to two classifier models as “Residual Network (ResNet) and Gated Recurrent Unit (GRU)”, in which the model provide the result as normal and abnormal images. In the second part, obtained abnormal image acts an input for segmentation step. In segmentation, it is needed to extract the relevant features by texture and spatial features. The resultant features are subjected for optimizing, where the optimal features are acquired through Adaptive Coyote Optimization Algorithm (ACOA). Then, the extracted features are fed into machine learning model like “Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF)” to render the segmented image. Finally, the hybrid classification named Hybrid ResGRUis developed by integrating the ResNet and GRU, where the hyper parameters are tuned optimally using developed ACOA, thus it is used for classifying the abnormal image that belongs to benign stage or malignant stage. The experimental results are evaluated, and its performance is analyzed by various metrics. Hence, the proposed classification model ensures effective segmentation and classification performance. Show more

Keywords: Brain tumour segmentation and classification, adaptive coyote optimization algorithm, residual network, gated recurrent unit, ensemble machine learning-based tumor segmentation, deep learning-based classification

DOI: 10.3233/JIFS-233546

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Abdus Subhahan, D. | Vinoth Kumar, C.N.S.

Article Type: Research Article

Abstract: The worldwide deforestation rate worsens year after year, ultimately resulting in a variety of severe implications for both mankind and the environment. In order to track the success of forest preservation activities, it is crucial to establish a reliable forest monitoring system. Changes in forest status are extremely difficult to manually annotate due to the tiny size and subtlety of the borders involved, particularly in regions abutting residential areas. Previous forest monitoring systems failed because they relied on low-resolution satellite images and drone-based data, both of which have inherent limitations. Most government organizations still use manual annotation, which is a …slow, laborious, and costly way to keep tabs on data. The purpose of this research is to find a solution to these problems by building a poly-highway forest convolution network using deep learning to automatically detect forest borders so that changes over time may be monitored. Here initially the data was curated using the dynamic decomposed kalman filter. Then the data can be augmented. Afterward the augmented image features can be fused using the multimodal discriminant centroid feature clustering. Then the selected area can be segmented using the iterative initial seeded algorithm (IISA). Finally, the level and the driver of deforestation can be classified using the poly-highway forest convolution network (PHFCN). The whole experimentation was carried out in a dataset of 6048 Landsat-8 satellite sub-images under MATLAB environment. From the result obtained the suggested methodology express satisfied performance than other existing mechanisms. Show more

Keywords: Deforestation, dynamic decomposed kalman filter, multimodal discriminant centroid feature clustering, iterative initial seeded algorithm, poly-highway forest convolution network

DOI: 10.3233/JIFS-233534

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Agrawal, Monika | Moparthi, Nageswara Rao

Article Type: Research Article

Abstract: Sentiment analysis (SA)at the sentence, aspect, and document levels determines the sentiment of particular aspect phrases in a given sentence. Due to their capacity to extract sentiment information from text in aspect-level sentiment classification, neural networks (NNs) have achieved significant success. Generally speaking, sufficiently sizable training corpora are necessary for NNs to be effective. The performance of NN-based systems is reduced by the small size of the aspect-level corpora currently available. In this research, we suggest a gated bilateral recurrent neural network (G-Bi-RNN) as a foundation for multi-source data fusion, their system offers sentiment information that several sources. We develop …a uniform architecture specifically to include information from sentimental lexicons, including aspect- and sentence-level corpora. To further provide aspect-specific phrase representations for SA, we use G-Bi-RNN, a deep bilateral Transformer-based pre-trained language model. We assess our methods using SemEval 2014 datasets for laptops and restaurants. According to experimental findings, our method consistently outperforms cutting-edge techniques on all datasets. We use a number of well-known aspect-level SA datasets to assess the efficacy of our model. Experiments show that when compared to baseline models, the suggested model can produce state-of-the-art results. Show more

Keywords: Sentiment analysis (SA), gated bilateral recurrent neural network (G-Bi-RNN), language model

DOI: 10.3233/JIFS-234076

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Pughazendi, N. | Valarmathi, K. | Rajaraman, P.V. | Balaji, S.

Article Type: Research Article

Abstract: Internet of Things (IoT) devices installed in hospital direct data unceasingly; in this manner, energy usage augments with the number of broadcasts too. In this paper, Reliable Cluster based Data Collection Framework (RCDCF) for IoT-Big Data Healthcare Applications (HA) is developed. During clustering process, the connected IoT devices are grouped into clusters. In clustering technique, the available IoT devices are gathered into groups. The device with high battery capacity and processing ability is selected as a cluster head (CH). Each member of the cluster is allocated multiple slots by applying a general function pooled by the Fog node and the …entire devices. To perceive and eliminate outliers from the sensor data, Density-based spatial clustering of applications with noise (DBSCAN) method is utilized. To forecast the objective and subjective behaviours of the equipments, a Random Forest Deep Neural Network (RF-DNN) based classification model is utilized. By experimental results, it has been shown that RCDCF achieves 19% and 20% reduced energy consumption at Cloud and Fog centers, respectively. Moreover, RCDCF has 2.1% and 1.3% increased correctness of data at Cloud and Fog data centers, respectively, when compared to the existing framework. Show more

Keywords: Internet of Things (IoT), big data, cloud, clustering, health care solution, slot allocation, Random Forest Deep Neural Network (RF-DNN), categorization

DOI: 10.3233/JIFS-233505

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2023

Authors: Subburaj, S. | Murugavalli, S. | Muthusenthil, B.

Article Type: Research Article

Abstract: SLR, which assists hearing-impaired people to communicate with other persons by sign language, is considered as a promising method. However, as the features of some of the static SL could be the same as the feature in a single frame of dynamic Isolated Sign Language (ISL), the generation of accurate text corresponding to the SL is necessary during the SLR. Therefore, Edge-directed Interpolation-based Recurrent Neural Network (EI-RNN)-centered text generation with varied features of the static and dynamic Isolated SL is proposed in this article. Primarily, ISL videos are converted to frames and pre-processed with key frame extraction and illumination control. …After that, the foreground is separated with the Symmetric Normalised Laplacian-centered Otsu Thresholding (SLOT) technique for finding accurate key points in the human pose. The human pose’s key points are extracted with the Media Pipeline Holistic (MPH) pipeline approach and to improve the features of the face and hand sign, the resultant frame is fused with the depth image. After that, to differentiate the static and dynamic actions, the action change in the fused frames is determined with a correlation matrix. After that, to engender the output text for the respective SL, features are extracted individually as of the static and dynamic frames. It is obtained from the analysis that when analogized to the prevailing models, the proposed EI-RNN’s translation accuracy is elevated by 2.05% in INCLUDE 50 Indian SL based Dataset and Top 1 Accuracy 2.44% and Top 10 accuracy, 1.71% improved in WLASL 100 American SL. Show more

Keywords: Isolated Sign Language (ISL), Sign Language Recognition (SLR), Edge directed Interpolation based Recurrent Neural Network (EIRNN), text generation, word level sign language, Media Pipeline Holistic (MPH)

DOI: 10.3233/JIFS-233610

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Prasath, N. | Arun, A. | Saravanan, B. | Kamaraj, Kanagaraj

Article Type: Research Article

Abstract: Intelligent Fuzzy Edge Computing (IFEC) has emerged as an innovative technology to enable real-time decision-making in Internet of Things (IoT)-based Digital Twin environments. Digital Twins provide virtual models of physical systems, facilitating predictive maintenance and optimization. However, implementing real-time decision-making in these environments is challenging due to massive data volumes and need for quick response times. IFEC addresses this by offering a flexible, scalable and efficient platform for real-time decision-making. This paper presents an overview of key aspects of IFEC including fuzzy logic, edge computing and Digital Twins. The use of fuzzy logic in IFEC provides an adaptive framework for …handling uncertainties in data. Edge computing enables localized processing, reducing latency. The integration of Digital Twins allows system monitoring, analysis and optimization. Potential applications of IFEC are highlighted in domains such as manufacturing, healthcare, energy management and transportation. Recent advancements in IFEC are also discussed, covering new fuzzy inference systems, edge computing architectures, Digital Twin modeling techniques and security mechanisms. Overall, IFEC shows great promise in enabling real-time decision-making in complex IoT-based Digital Twin environments across various industries. Further research on IFEC will facilitate the ongoing digital transformation of industrial systems. Show more

Keywords: Intelligent fuzzy edge computing, real-time decision making, IoT-based digital twins, predictive maintenance, fuzzy logic, edge computing

DOI: 10.3233/JIFS-233495

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Vishnukumar, Ravula | Ramaiah, Mangayarkarasi

Article Type: Research Article

Abstract: The Internet’s evolution resulted in a massive amount of data. As a result, the internet has become more sophisticated and vulnerable to massive attacks. The attack detection system is a key feature for system security in modern networks. The IDS might be signature-based or detect anomalous behavior. Researchers recently created several detection algorithms for identifying network intrusions in vehicular network security, but they failed to detect intrusions effectively. For this reason, the optimal Deep Learning approach, namely Political Fractional Dingo Optimizer (PFDOX)-based Deep belief network is introduced for attack detection in network security for vehicles. The Internet of Vehicle simulation …is done initially, and then the input data is passed into the pre-processing phase, which removes noise present in the data. Then, the feature extraction module receives the pre-processed data. The Deep Maxout Network is trained using the Fractional Dingo optimizer (FDOX)is utilized to detect normal and abnormal behavior. Fractional calculus and Dingo optimizer (DOX) are combined to create the proposed FDOX. Finally, intruder/attack types are classified using the Deep Belief Network, which is tuned using the PFDOX. The PFDOX is created by the assimilation of the DOX, Fractional Calculus, and Political Optimizer (PO). The experimental result shows that the designed PFDOX_DBN for attack type classification offers a better result based on f-measure, precision, and recall with the values of 0.924, 0.916, and 0.932, for the CIC-IDS2017 dataset. Show more

Keywords: Deep maxout network, intrusion detection, deep belief network, dingo optimizer, fractional calculus, political optimizer

DOI: 10.3233/JIFS-233581

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2023

Authors: Nandipati, Bhagya Lakshmi | Devarakonda, Nagaraju

Article Type: Research Article

Abstract: Lung cancer incidence and mortality continue to rise rapidly around the world. According to the American Cancer Society, the five-year survivability for individuals in the metastasis phases is significantly lower, highlighting the importance of early lung cancer diagnosis for effective therapy and improved quality of life. To achieve this, it is crucial to combine PET’s sensitivity for recognizing abnormal regions with CT’s anatomical localization for evaluating PET-CT images in computer-assisted detection implementations. Current PET-CT image evaluation methods either run each modality independently or aggregate the data from both, but they often overlook the fact that different visual features encode different …types of data from different modalities. For instance, high atypical PET uptake within the lungs is more crucial for identifying tumors compared to physical PET uptake in the heart. To address the challenges of fine-grained issues during feature extraction and fusion, we propose an interpretable deep learning-based solution for lung cancer diagnosis using CT and PET images. This involves building an Optimal Adversarial Network for merging and an Optimal Attention-based Generative Adversarial Network with Classifier (Opt_att-GANC) to augment the classification of the existence and nonexistence of lung cancer based on extracted features. The performance of the Opt_att-GANC is compared with existing methodologies like global-feature encoding U-Net (GEU-Net), 3D Dense-Net, and 3D Convolutional Neural Network Technique (3D-CNN). Results show that the proposed Opt_att-GANC achieves an F1-score of 67.08%, 93.74% accuracy, 92% precision, 92.1% recall, and 93.74% recall. The prospective study aims to enhance the precision degree with reduced duration by incorporating an ensemble neural network paradigm for feature extraction. Show more

Keywords: Lung cancer, fuzzy fusion, feature extraction, classification, neural networks, Adversarial network, PET

DOI: 10.3233/JIFS-233491

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Arulmurugan, A. | Jose Moses, G. | Gandhi, Ongole | Sheshikala, M. | Arthie, A.

Article Type: Research Article

Abstract: In the current scenario, feature selection (FS) remains one of the very important functions in machine learning. Decreasing the feature set (FSt) assists in enhancing the classifier’s accuracy. Because of the existence of a huge quantity of data within the dataset (DS), it remains a colossal procedure for choosing the requisite features out of the DS. Hence, for resolving this issue, a new Chaos Quasi-Oppositional-based Flamingo Search Algorithm with Simulated Annealing Algorithm (CQOFSASAA) has been proffered for FS and for choosing the optimum FSt out of the DSs, and, hence, this lessens the DS’ dimension. The FSA technique can be …employed for selecting the optimal feature subset out of the DS. Generalized Ring Crossover has been as well embraced for selecting the very pertinent features out of the DS. Lastly, the Kernel Extreme Learning Machine (KELM) classifier authenticates the chosen features. This proffered paradigm’s execution has been tested by standard DSs and the results have been correlated with the rest of the paradigms. From the experimental results, it has been confirmed that this proffered CQOFSASAA attains 93.74% of accuracy, 92% of sensitivity, and 92.1% of specificity. Show more

Keywords: Quasi-oppositional, feature selection, Flamingo Search Algorithm, Simulated Annealing, convergence rate

DOI: 10.3233/JIFS-233557

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Harikumar, Yedhu | Muthumeenakshi, M.

Article Type: Research Article

Abstract: The Indian stock market is a dynamic, complicated system that is impacted by many different variables, making it difficult to anticipate its future. The utilization of deep learning and optimization techniques to forecast stock market movements has gained popularity in recent years. To foresee the Indian stock market, an innovative approach is presented in this study that combines the Grey Wolf Optimization algorithm with a hybrid Convolutional Neural Network (CNN) and Bi-Directional Long-Short Term Memory (Bi-LSTM) framework. The stock market information is first pre-processed utilizing a CNN to extract pertinent features. The Bi-LSTM system, that is intended to capture the …long-term dependencies and temporal correlations of the stock market statistics, is then fed the CNN’s outcome. The model parameters are then optimized utilizing the Grey Wolf Optimization (GWO) technique, which also increases forecasting accuracy. The findings demonstrate that, in terms of forecasting accuracy, the suggested method outperforms a number of contemporary methods, including conventional time series models, neural networks, and evolutionary algorithms. Thus, the suggested methodology provides an effective way to forecast the Indian stock market by combining deep learning and optimization approaches. Show more

Keywords: Indian stock market, grey wolf optimization, deep learning approach, bi-directional long-short term memory, convolutional neural network

DOI: 10.3233/JIFS-233716

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Vallabhaneni, Nagalakshmi | Prabhavathy, Panneer

Article Type: Research Article

Abstract: Numerous people are interested in learning yoga due to the increased tension levels in the modern lifestyle, and there are a variety of techniques or resources available. Yoga is practiced in yoga centers, by personal instructors, and through books, the Internet, recorded videos, etc. As the aforementioned resources may not always be available, a large number of people will opt for self-study in fast-paced lifestyles. Self-learning makes it impossible to recognize an incorrect posture. Incorrect poses will have a negative effect on the patient’s health, causing severe agony and long-term chronic issues. Computer vision (CV)-related techniques derive pose features and …conduct pose analysis using non-invasive CV methods. The application of machine learning (ML) and artificial intelligence (AI) techniques to an inter-disciplinary field like yoga becomes quite difficult. Due to its potent feature learning ability, deep learning (DL) has recently achieved an impressive level of performance in classifying yoga poses. In this paper, an artificial algae optimizer with hybrid deep learning-based yoga pose estimation (AAOHDL-YPE) model is presented. The presented AAOHDL-YPE model analyzes yoga video clips to estimate pose. Utilizing Part Confidence Map and Part Affinity Field with bipartite equivalent and parsing, OpenPose can be employed to determine the joint location. The deep belief network (DBN) model is then used for Yoga recognition. Finally, the AAO algorithm is utilized to enhance the EfficientNet model’s recognition performance. The results of a comprehensive experimentation analysis reveal that the AAOHDL-YPE technique produces superior results in comparison to existing methods. Show more

Keywords: Yoga posture, activity recognition, deep learning, metaheuristics, computer vision

DOI: 10.3233/JIFS-233583

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Sendhil, R. | Arulmurugan, A. | Jose Moses, G. | Kaviarasan, R. | Ramadoss, P.

Article Type: Research Article

Abstract: Occult peritoneal metastasis often emerges in sick persons having matured gastric cancer (GC) and is inexpertly detected with presently feasible instruments. Due to the existence of peritoneal metastasis that prevents the probability of healing crucial operation, there relies upon a discontented requirement for an initial diagnosis to accurately recognize sick persons having occult peritoneal metastasis. The proffered paradigm of this chapter identifies the initial phases of occult peritoneal metastasis in GC. The initial phase accompanies metabolomics for inspecting biomarkers. If the sick person undergoes the initial signs of occult peritoneal metastasis in GC, early detection is conducted. Yet, the physical …prognosis of this cancer cannot diagnose it, and so, automated detection of the images by dissecting the preoperational Computed Tomography (CT) images by conditional random fields accompanying Pro-DAE (Post-processing Denoising Autoencoders) and the labeling in the images is rid by denoising strainers; later, the ensued images and the segmented images experience the Graph Convolutional Networks (GCN), and the outcome feature graph information experience the enhanced categorizer (Greywold and Cuckoo Search Naïve Bayes categorizer) procedure that is employed for initial diagnosis of cancer. Diagnosis of cancer at the initial phase certainly lessens the matured phases of cancer. Hence, this medical information is gathered and treated for diagnosing the sickness. Show more

Keywords: Gastric Cancer, MIoT, Greywold and Cuckoo Search Naïve Bayes categorizer, Cuckoo-Grey Wolf search Correlative Naïve Bayes categorizer

DOI: 10.3233/JIFS-233510

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023