Fuzzy Boundaries Research Articles

The representation of emotional experience from imagined scenarios.

One of the key unresolved issues in affective science is understanding how the subjective experience of emotion is structured. Semantic space theory has shed new light on this debate by applying computational methods to high-dimensional data sets containing self-report ratings of emotional responses to visual and auditory stimuli. We extend this approach here to the emotional experience induced by imagined scenarios. Participants chose at least one emotion category label among 34 options or provided ratings on 14 affective dimensions while imagining two-sentence hypothetical scenarios. A total of 883 scenarios were rated by at least 11 different raters on categorical or dimensional qualities, with a total of 796 participants contributing to the final normed stimulus set. Principal component analysis reduced the categorical data to 24 distinct varieties of reported experience, while cluster visualization indicated a blended, rather than discrete, distribution of the corresponding emotion space. Canonical correlation analysis between the categorical and dimensional data further indicated that category endorsement accounted for more variance in dimensional ratings than vice versa, with 10 canonical variates unifying change in category loadings with affective dimensions such as valence, arousal, safety, and commitment. These findings indicate that self-reported emotional responses to imaginative experiences exhibit a clustered structure, although clusters are separated by fuzzy boundaries, and variable dimensional properties associate with smooth gradients of change in categorical judgments. The resultant structure supports the tenets of semantic space theory and demonstrates some consistency with prior work using different emotional stimuli. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Weakly supervised bilayer convolutional network in segmentation of HER2 related cells to guide HER2 targeted therapies.

Overexpression of human epidermal growth factor receptor 2 (HER2/ERBB2) is identified as a prognostic marker in metastatic breast cancer and a predictor to determine the effects of ERBB2-targeted drugs. Accurate ERBB2 testing is essential in determining the optimal treatment for metastatic breast cancer patients. Brightfield dual in situ hybridization (DISH) was recently authorized by the United States Food and Drug Administration for the assessment of ERRB2 overexpression, which however is a challenging task due to a variety of reasons. Firstly, the presence of touching clustered and overlapping cells render it difficult for segmentation of individual HER2 related cells, which must contain both ERBB2 and CEN17 signals. Secondly, the fuzzy cell boundaries make the localization of each HER2 related cell challenging. Thirdly, variation in the appearance of HER2 related cells is large. Fourthly, as manual annotations are usually made on targets with high confidence, causing sparsely labeled data with some unlabeled HER2 related cells defined as background, this will seriously confuse fully supervised AI learning and cause poor model outcomes. To deal with all issues mentioned above, we propose a two-stage weakly supervised deep learning framework for accurate and robust assessment of ERBB2 overexpression. The effectiveness and robustness of the proposed deep learning framework is evaluated on two DISH datasets acquired at two different magnifications. The experimental results demonstrate that the proposed deep learning framework achieves an accuracy of 96.78 ± 1.25, precision of 97.77 ± 3.09, recall of 84.86 ± 5.83 and Dice Index of 90.77 ± 4.1 and an accuracy of 96.43 ± 2.67, precision of 97.82 ± 3.99, recall of 87.14 ± 10.17 and Dice Index of 91.87 ± 6.51 for segmentation of ERBB2 overexpression on the two experimental datasets, respectively. Furthermore, the proposed deep learning framework outperforms 15 state-of-the-art benchmarked methods by a significant margin (P<0.05) with respect to IoU on both datasets.

Finite-Time Fuzzy Boundary Control for 2-D Spatial Nonlinear Parabolic PDE Systems

Seldom existing studies directly focus on the control issues of two-dimensional (2-D) spatial PDE systems, although they have strong application backgrounds in production and life. Therefore, this article investigates the finite-time control problem of an 2-D spatial nonlinear parabolic PDE system via T–S fuzzy boundary control scheme. First, the overall fuzzy system model is constructed using T–S fuzzy rules to approximate the original nonlinear system. Second, based on the planar distributed measurement, boundary collocated measurement, and linear measurement methods, three novel kinds of fuzzy boundary controllers are designed. Then, by employing the variable substitution and integral inequality techniques, three criteria that ensure the finite-time boundness of the considered system are obtained. Finally, simulations of main results are provided to verify the effectiveness and practicability of the proposed measurement and control schemes.

Existence and Uniqueness of Solutions for Fuzzy Boundary Value Problems Under Granular Differentiability

Existence and Uniqueness of Solutions for Fuzzy Boundary Value Problems Under Granular Differentiability

Variational Iteration Approach for Solving Two-Points Fuzzy Boundary Value Problems

The main objective of this paper is to introduce interval two-point fuzzy boundary value problems, in which the fuzziness course when the coefficients of the governing ordinary differential equation and/or the boundary conditions include fuzzy numbers of either triangular or trapezoidal types. Such equations will be solved by introducing the concept of α – level sets, α  [0,1] to treat the fuzzy ordinary differential equation into two nonfuzzy ordinary differential equations, which correspond to the lower and upper solutions of the interval fuzzy solutions. The well-known variational iteration method has been used to solve two-point fuzzy boundary value problems and linear equations have been examined.

CTA-UNet: CNN-transformer architecture UNet for dental CBCT images segmentation

In view of the limitations of current deep learning models in segmenting dental cone-beam computed tomography (CBCT) images, specifically dealing with complex root morphological features, fuzzy boundaries between tooth roots and alveolar bone, and the need for costly annotation of dental CBCT images. We collected dental CBCT data from 200 patients and annotated 45 of them for network training, and proposed a CNN-Transformer Architecture UNet network, which combines the advantages of CNN and Transformer. The CNN component effectively extracts local features, while the Transformer captures long-range dependencies. Multiple spatial attention modules were included to enhance the network’s ability to extract and represent spatial information. Additionally, we introduced a novel Masked image modeling method to pre-train the CNN and Transformer modules simultaneously, mitigating limitations due to a smaller amount of labeled training data. Experimental results demonstrate that the proposed method achieved superior performance (DSC of 87.12%, IoU of 78.90%, HD95 of 0.525 mm, ASSD of 0.199 mm), and provides a more efficient and effective approach to automatically and accurately segment dental CBCT images, has real-world applicability in orthodontics and dental implants.

DMAGNet: Dual‐path multi‐scale attention guided network for medical image segmentation

AbstractIn recent years, convolutional neural networks (CNN)‐based automatic segmentation of medical images has become one of the hot topics in clinical disease diagnosis. It is still a challenging task to improve the segmentation accuracy of the network model with the large variation of pathological regions in different patients and the fuzzy boundary of pathological regions. A Dual‐path Multi‐scale Attention Guided network (DMAGNet) for medical image segmentation is proposed in this paper. First, the Dual‐path Multi‐scale Attention Fusion Module (DMAF) is proposed as a novel skip connection strategy, which is applied to encode semantic dependencies between high‐level and low‐level channels. Second, the Multi‐scale Normalized Channel Attention Module (MNCA) based on the atrous convolution, normalization channel attention mechanism, and the Depthwise Separable Convolutions (DSConv) is developed to strengthen dependencies between channels. Finally, the encoder–decoder backbone employs the DSConv, as well as the pretrained Resnet34 block is combined in the encoder part to further improve the backbone network performance. Comprehensive experiments on brain, lung, and liver segmentation tasks show that the proposed DMAGNet outperforms the original U‐Net method and other advanced methods.

Inshore Ship Detection Based on Multi-Modality Saliency for Synthetic Aperture Radar Images

Synthetic aperture radar (SAR) ship detection is of significant importance in military and commercial applications. However, a high similarity in intensity and spatial distribution of scattering characteristics between the ship target and harbor facilities, along with a fuzzy sea-land boundary due to the strong speckle noise, result in a low detection accuracy and high false alarm rate for SAR ship detection with complex inshore scenes. In this paper, a new inshore ship detection method based on multi-modality saliency is proposed to overcome these challenges. Four saliency maps are established from different perspectives: an ocean-buffer saliency map (OBSM) outlining more accurate coastline under speckle noises; a local stability saliency map (LSSM) addressing pixel spatial distribution; a super-pixel saliency map (SPSM) extracting critical region-based features for inshore ship detection; and an intensity saliency map (ISM) to highlight target pixels with intensity distribution. By combining these saliency maps, ship targets in complex inshore scenes can be successfully detected. The method provides a novel interdisciplinary perspective (surface metrology) for SAR image segmentation, discovers the difference in spatial characteristics of SAR image elements, and proposes a novel robust CFAR procedure for background clutter fitting. Experiments on a public SAR ship detection dataset (SSDD) shows that our method achieves excellent detection performance, with a low false alarm rate, in offshore scenes, inshore scenes, inshore scenes with confusing metallic port facilities, and large-scale scenes. The results outperform several widely used methods, such as CFAR-based methods and super-pixel methods.

Reinforcing optimization enabled interactive approach for liver tumor extraction in computed tomography images

&lt;p&gt;Detecting liver abnormalities is a difficult task in radiation planning and treatment. The modern development integrates medical imaging into computer techniques. This advancement has monumental effect on how medical images are interpreted and analyzed. In many circumstances, manual segmentation of liver from computerized tomography (CT) imaging is imperative, and cannot provide satisfactory results. However, there are some difficulties in segmenting the liver due to its uneven shape, fuzzy boundary and complicated structure. This leads to necessity of enabling optimization in interactive segmentation approach. The main objective of reinforcing optimization is to search the optimal threshold and reduce the chance of falling into local optimum with survival of the fittest (SOF) technique. The proposed methodology makes use of pre-processing stage and reinforcing meta heuristics optimization based fuzzy c-means (FCM) for obtaining detailed information about the image. This information gives the optimal threshold value that is used for segmenting the region of interest with minimum user input. Suspicious areas are recognized from the segmented output. Both public and simulated dataset have been taken for experimental purposes. To validate the effectiveness of the proposed strategy, performance criteria such as dice coefficient, mode and user interaction level are taken and compared with state-of-the-art algorithms.&lt;/p&gt;

Giant liver cyst in a newborn with pseudoprune-belly syndrome - A case report

Pseudoprune-belly syndrome is a rare anomaly, an incomplete form of Prune-belly syndrome. The etiology is not clear, and though this syndrome may present associated with other malformations, the association with a liver cyst is extremely rare. We report the case of a male newborn who was referred to our hospital with an antenatal ultrasound that suggested a gastric duplication. Clinical examination revealed lax abdominal wall, unilateral undescended testis and a palpable mass on the right upper quadrant with fuzzy boundaries. On laparotomy we found a giant hepatic cyst displacing the bowel to the left. The baby underwent a partial ressection and marsupialization of the cyst as total resection was infeasible. The patient's recovery was uneventful. Although rarely, the Pseudoprune-belly syndrome can present with a liver cyst. Large liver cysts should be partially or totally ressected as soon as possible.

Spatial Attention Mechanism and Cascade Feature Extraction in a U-Net Model for Enhancing Breast Tumor Segmentation

In the field of medical imaging, the accurate segmentation of breast tumors is a critical task for the diagnosis and treatment of breast cancer. To address the challenges posed by fuzzy boundaries, vague tumor shapes, variation in tumor size, and illumination variation, we propose a new approach that combines a U-Net model with a spatial attention mechanism. Our method utilizes a cascade feature extraction technique to enhance the subtle features of breast tumors, thereby improving segmentation accuracy. In addition, our model incorporates a spatial attention mechanism to enable the network to focus on important regions of the image while suppressing irrelevant areas. This combination of techniques leads to significant improvements in segmentation accuracy, particularly in challenging cases where tumors have fuzzy boundaries or vague shapes. We evaluate our suggested technique on the Mini-MIAS dataset and demonstrate state-of-the-art performance, surpassing existing methods in terms of accuracy, sensitivity, and specificity. Specifically, our method achieves an overall accuracy of 91%, a sensitivity of 91%, and a specificity of 93%, demonstrating its effectiveness in accurately identifying breast tumors.

Class Divisions in Use: The Swedish Social Group Taxonomy as Difference Technology, 1911–1970

This article investigates an important but understudied phenomenon: the bureaucratic class division, which is analysed as a difference technology for envisioning, studying and managing the population. I examine a long-lived and widely spread taxonomy of the Swedish population into three social groups (Socialgrupper). Specifically, I look at how it influenced the production of statistics and knowledge about the voter during the first half of the twentieth century and higher education in the post-war welfare state era. The article understands the effects of the taxonomy as a ‘scientisation of the social’, using Lutz Raphael's term, in which fuzzy conceptual class boundaries were turned into exact classification, making it possible for different actors to act and calculate through them. The division was at the same time contested among social scientists and politicians. However, because of lack of alternatives and because it was well established, actors continued using it.

ETACM: an encoded-texture active contour model for image segmentation with fuzzy boundaries

ETACM: an encoded-texture active contour model for image segmentation with fuzzy boundaries

Context fusion network with multi-scale-aware skip connection and twin-split attention for liver tumor segmentation.

Manually annotating liver tumor contours is a time-consuming and labor-intensive task for clinicians. Therefore, automated segmentation is urgently needed in clinical diagnosis. However, automatic segmentation methods face certain challenges due to heterogeneity, fuzzy boundaries, and irregularity of tumor tissue. In this paper, a novel deep learning-based approach with multi-scale-aware (MSA) module and twin-split attention (TSA) module is proposed for tumor segmentation. The MSA module can bridge the semantic gap and reduce the loss of detailed information. The TSA module can recalibrate the channel response of the feature map. Eventually, we can count tumors based on the segmentation results from a 3D perspective for cancer grading. Extensive experiments conducted on the LiTS2017 dataset show the effectiveness of the proposed method by achieving a Dice index of 85.97% and a Jaccard index of 81.56% over the state of the art. In addition, the proposed method also achieved a Dice index of 83.67% and a Jaccard index of 80.11% in 3Dircadb dataset verification, which further reflects its robustness and generalization ability.

A fatty liver diseases classification network based on adaptive coordination attention with label smoothing

Accurate classification of fatty liver disease (FLD) from fuzzy boundary areas and lesion areas in ultrasound imaging is crucial for clinical diagnosis and treatment. This paper presents an efficient convolutional neural network based on adaptive coordinated attention (ACA), which combines label smoothing and EfficientNet for FLD classification. We only uses pixels and labels of original brightness mode ultrasound images as inputs, and achieves four classification of FLD. It avoids the decoupling of feature extraction and classification, and achieves end-to-end automatic classification identification. Based on ACA, the model can adaptively encode lateral and vertical positional information adaptively into channel attention that adaptively learns information in both spatial directions to enhance the representation of the objects of concern. Furthermore, the interpretability of the model is demonstrated through feature heatmap and hybrid heatmap visualization techniques. The experimental results reveal that the proposed approach attains an accuracy of 94.96% and an AUC of 0.97 on the private dataset. The F1 scores for the four categories are 0.963, 0.942, 0.932, and 0.955, respectively, contributing to an overall F1 score of 0.955, surpassing other baseline models and achieving a new state-of-the-art (SOTA). Meanwhile, this approach also achieves SOTA accuracy of 97.82% on public datasets. The proposed approach has surpassed the manual recognition of fatty liver brightness mode ultrasound images in terms of its cognitive ability, which helps to aid the expert diagnosis. It has better performance and achieves end-to-end classification compared to other methods.

Approximate Series Solution For Two-Point Fuzzy Boundary Value Problems

In this work, we have used double decomposition method to find approximate analytical solutions for the two-point fuzzy boundary value problems. This method is based on the standard Adomian decomposition method, which is a highly efficient method for solving fuzzy and non-fuzzy differential equations. This method allows for the solution to be calculated in the form of convergent series, in which the obtained are accurate solutions and very close to the exact analytical solutions. Some numerical results have been given to illustrate the efficiency of the used method.

Increasing the accuracy of electricity accounting by digitalinformation-measuring systems

Significant non-technological losses of electricity are observed in 0.38 kV distribution networks, which lead to financial dam-ages of energy supply companies. The reason for their occurrence is the deficiencies in the accountingunits functioning inthe re-duced load mode, which occurs during the downtime of the main technological equipment. The purpose of the study is to increase the accuracy of electricity measurement by a commercial accountingunit in the reduced load mode based on mathematical modeling of measurement uncertainty. Evaluation of regression parameters for the static characteristics of measuring current transformersin the reduced load mode is carried out using the covariance analysis methods and analysis of regression residuals. Estimation of the non-random uncertainty of electricity measurementby one measuring channel of theaccountingunit was carried out using the fuzzy set theory. The polynomial approximation of the experimental values of the membership function for the measured quantity was carried out according to the maximum norm method. The least square method was used to approximate the boundaries of fuzzy functions. As a result of research, a universal static characteristic of a measuring current transformer of a certain accuracy class was obtained at a reduced primary current. It was established that the sample estimates uncertainty of the current transformer error of the 0.5 S accura-cy class changes from ±11.7% to ±1.7%. The uncertainty of electricity measurement by the commercial accountingunit in the re-duced load mode is proposed to be estimated by a fuzzy function. The developed mathematical model takes into account the depend-ence of the fuzzy interval boundaries, which characterizes the measurement result, on the phase currents asymmetric values. Compar-ison of the analytically obtained membership function for the relative deviations of the readings of the accountingunits with the em-pirically obtained value of such a deviation made it possible to establish the limitvalue of the confidence level, which was not less than 0.54 at the minimum permissible value of 0.4 of the adequacy criterion. This confirms the adequacy of the results of mathemati-cal modeling with experimental data. Estimating the electricity metering uncertainty with a fuzzy interval increases the accuracy of the measurement, as it allows clarifyingthe monthly electricity consumption by taking into account the energy that was consumed during the reduced load mode

Semi-Supervised Modified-UNet for Lung Infection Image Segmentation

Automatic lung infection segmentation in computed tomography (CT) scans can offer great assistance in radiological diagnosis by improving accuracy and reducing time required for diagnosis. The biggest challenges for deep learning (DL) models in segmenting infection region are the high variances in infection characteristics, fuzzy boundaries between infected and normal tissues, and the troubles in getting large number of annotated data for training. To resolve such issues, we propose a Modified U-Net (Mod-UNet) model with minor architectural changes and significant modifications in the training process of vanilla 2D UNet. As part of these modifications, we updated the loss function, optimization function, and regularization methods, added a learning rate scheduler and applied advanced data augmentation techniques. Segmentation results on two Covid-19 Lung CT segmentation datasets show that the performance of Mod-UNet is considerably better than the baseline U-Net. Furthermore, to mitigate the issue of lack of annotated data, the Mod-UNet is used in a semi-supervised framework (Semi-Mod-UNet) which works on a random sampling approach to progressively enlarge the training dataset from a large pool of unannotated CT slices. Exhaustive experiments on the two Covid-19 CT segmentation datasets and on a real lung CT volume show that the Mod-UNet and Semi-Mod-UNet significantly outperform other state-of-theart approaches in automated lung infection segmentation.

Salient object detection algorithm based on diversity features and global guidance information

Aiming at the problems of traditional salient object detection methods such as fuzzy boundary and insufficient information integrity, a salient object detection network composed of feature diversity enhancement module, global information guidance module and feature fusion module is proposed. Firstly, asymmetric convolution, cavity convolution and common convolution are spliced to form a feature diversity enhancement module to extract different types of spatial features corresponding to each feature layer. Secondly, the global information guidance module transmits the information captured by the coordinate attention mechanism to each feature layer through the global guidance stream, so as to learn the semantic relationship between different feature layers and alleviate the dilution effect; Finally, the feature fusion module receives the high-level features output from the previous layer, the low-level features of the corresponding layer and the global context information generated by the global information guidance module, and the cascade feature diversity enhancement module gradually integrates the multi-level features to refine the saliency feature map. Comparative experiments on five public data sets show that this method can achieve the highest values of 0.959 and 0.030 in F-measure and MAE. Compared with other seven advanced algorithms, it has better detection performance.

MineSDS: A Unified Framework for Small Object Detection and Drivable Area Segmentation for Open-Pit Mining Scenario.

To tackle the challenges posed by dense small objects and fuzzy boundaries on unstructured roads in the mining scenario, we proposed an end-to-end small object detection and drivable area segmentation framework for open-pit mining. We employed a convolutional network backbone as a feature extractor for both two tasks, as multi-task learning yielded promising results in autonomous driving perception. To address small object detection, we introduced a lightweight attention module that allowed our network to focus more on the spatial and channel dimensions of small objects without impeding inference time. We also used a convolutional block attention module in the drivable area segmentation subnetwork, which assigned more weight to road boundaries to improve feature mapping capabilities. Furthermore, to improve our network perception accuracy of both tasks, we used weighted summation when designing the loss function. We validated the effectiveness of our approach by testing it on pre-collected mining data which were called Minescape. Our detection results on the Minescape dataset showed 87.8% mAP index, which was 9.3% higher than state-of-the-art algorithms. Our segmentation results surpassed the comparison algorithm by 1 percent in MIoU index. Our experimental results demonstrated that our approach achieves competitive performance.