Classification Error Research Articles

Improved multiscale coded dispersion entropy: A novel quadratic-coded health indicator of rolling bearings

Abstract Dispersion entropy (DE) is widely used to quantify the complexity of nonlinear time series. In order to improve the ability to capture fault characteristics, a novel approach called coded dispersion entropy (CDE) has been introduced in recent years. CDE aims to expand the number of possible dispersion patterns and enhance the encoding of similar dispersion patterns. However, the coding method of CDE ignores the amplitude differences between dispersion elements and average elements, resulting in the inaccurate assignment of samples. Additionally, CDE is unable to extract effective information from other time scales. These limitations hinder the ability of CDE to effectively characterize faults. This paper proposes an improved multiscale coded dispersion entropy (IMCDE) to solve these limitations. The method introduces the interval scaling factor "R" and utilizes the sum and difference between the mean element and R as new coding boundaries. This approach addresses the sensitivity issue of the CDE coding mode towards smaller amplitude differences by the reasonable quadratic coding mode. Additionally, a composite coarse-graining process is introduced to rearrange the first coarse-grained point in sequence, resulting in multiple sets of sequences. The average probability of the same dispersion pattern at each scale is calculated to correct the entropy error. The experimental results from two sets of bearing faults demonstrate the effectiveness of this method in extracting critical features associated with faulty bearings. Furthermore, the method showed higher classification accuracy compared to multiscale dispersion entropy (MDE) and multiscale coded dispersion entropy (MCDE). Additionally, it exhibited smaller classification error than MDE and MCDE.

Rand-Index Target Projective Gradient Deep Belief Network for Software Fault Prediction

Predicting defective software modules before testing is a valuable operation that reduces time and cost of software testing. Source code fault prediction plays a vital role in improving software quality that effectively assists in optimization testing resource allocation. Several machine learning and ensemble learning techniques has been extensively evolved over the recent few years to predict defect at an early stage. These techniques made predictions based on historical defect data, the software metrics. Nevertheless, the time efficient and accurate fault predictions are the major challenging tasks that yet have to be addressed. In order to ensure accurate software fault prediction, a method called Rand-Index Target Projective Gradient Deep Belief Network (RTPGDBN) is designed. The proposed RTPGDBN method comprises of three processes namely acquiring JAVA packages, software metric selection and classification. First, the number of JAVA packages is used as input from the dataset. Second with the JAVA packages obtained as input, Rand similarity indexive target projection function is applied for selecting the most significant software metrics in order to minimize time complexity of fault prediction. Third, with the selected metrics, classification is performed using Tversky Gradient Deep Belief neural network. Also, gradient descent function is applied to minimize classification error, therefore ensuring accurate software fault classification results obtained at the output layer. Experimental setup of proposed RPGDDBN and existing methods are implemented in Java language and the dataset collected from smell prediction replication package. Performance analysis is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity and space complexity. Through extensive experiments on repository data, experimental results indicate that our RPGDDBN method outperforms two state-of-the-art defect detection methods in terms of different performance metrics.

Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning.

The effectiveness of active learning largely depends on the sampling efficiency of the acquisition function. Expected Loss Reduction (ELR) focuses on a Bayesian estimate of the reduction in classification error, and more general costs fit in the same framework. We propose Bayesian Estimate of Mean Proper Scores (BEMPS) to estimate the increase in strictly proper scores such as log probability or negative mean square error within this framework. We also prove convergence results for this general class of costs. To facilitate better experimentation with the new acquisition functions, we develop a complementary batch AL algorithm that encourages diversity in the vector of expected changes in scores for unlabeled data. To allow high-performance classifiers, we combine deep ensembles, and dynamic validation set construction on pretrained models, and further speed up the ensemble process with the idea of Monte Carlo Dropout. Extensive experiments on both texts and images show that the use of mean square error and log probability with BEMPS yields robust acquisition functions and well-calibrated classifiers, and consistently outperforms the others tested. The advantages of BEMPS over the others are further supported by a set of qualitative analyses, where we visualise their sampling behaviour using data maps and t-SNE plots.

Multi-Label Text Classification model integrating Label Attention and Historical Attention

Multi-Label Text Classification (MLTC) is one of the most import research of natural language processing. Although Deep Learning (DL) models have been widely applied to MLTC, there still exist some drawbacks. First, traditional DL models utilize all the words in the document to construct the embedding vector, while there are many words that affect the classification results. Next, the labels in MLTC have specific semantics, while traditional DL models ignore fine-grained matching signals between words and labels. Then, traditional DL models have difficulty in handling the data imbalance issue in MLTC datasets. In addition, during the training process, small errors in a certain epoch may be amplified with the increase of the number of iterations, resulting in classification errors. To address the above problems, a MLTC model integrating Label Attention and Historical Attention (i.e. LAHA) is proposed. First, a word filter is set up to select important words based on the cosine similarity between words and labels. Next, Document Self Attention (DSA) and Label Attention (LA) are obtained, and DSA-attended LA co-attention (LA-co) and LA-attended DSA co-attention (DAS-co) networks are constructed. Then, the fine-grained matching signals between words and labels are integrated through the adaptive plus of LA-co and DAS-co. At last, Historical Attention is integrated to LAHA, which not only avoids mis-classification caused by minor errors of a certain epoch, but also reduces overfitting to high-frequency labels. Multiple comparative experiments on four benchmark datasets demonstrate that LAHA outperforms the state-of-the-art baseline models and can effectively solve the data imbalance issue in MLTC datasets. Our code is available at https://github.com/sgysgywaityou/LAHA.

Factor annealing decoupling compositional training method for imbalanced hyperspectral image classification

AbstractDue to differences in the quantity and size of observed targets, hyperspectral images are characterized by class imbalance. The standard deep learning classification model training scheme optimizes the overall classification error, which may lead to performance imbalance between classes in hyperspectral image classification frameworks. Therefore, a novel factor annealing decoupling compositional training method is proposed in this paper. Without requiring resampling or reweighting, it implicitly modulates the training process, so standard models can sufficiently learn the representation of the minority classes and further be trained as robust classifiers. Specifically, the label‐distribution‐aware margin loss is combined with the error‐rate‐based cross‐entropy loss via combination factor, which considers both imbalanced data representation learning and classifier overall performance. Then, a factor annealing optimization training scheme is designed to adjust the combination factor, which solves the stage division problem of two‐stage decoupling learning. Experimental results on two hyperspectral image datasets demonstrate that, as compared with other competing approaches, the proposed method can continuously and stably optimize the model parameters, achieving improvements in class average metrics and difficult classes without affecting overall classification performance.

Optimization Method Based on Machine Learning for College Students’ Psychological Control Source Propensity Classification

Abstract College students tend to have more locus of control, which is greatly affected by college students, resulting in higher classification error rate and longer classification time. An optimized method for classifying the tendency of college students’ locus of control tendency based on machine learning is proposed in this article. Collect the data of college students’ locus of control tendency, build an emotional dictionary based on it, and extract the emotional words and text features from it. According to the feature extraction results, the support vector machine is used to build a base classifier to obtain the preliminary classification results. The deep belief network is used to optimize the preliminary classification results of college students’ locus of control tendency, and the final optimization results of college students’ locus of control tendency classification are obtained. The experimental results show that the error rate of college students’ locus of control tendency classification is −1∼1 %, the average recall rate is 96.2 %, and the average classification time is 0.7 s.

A Deep Learning Framework for Start-End Frame Pair-Driven Motion Synthesis.

A start-end frame pair and a motion pattern-based motion synthesis scheme can provide more control to the synthesis process and produce content-various motion sequences. However, the data preparation for the motion training is intractable, and concatenating feature spaces of the start-end frame pair and the motion pattern lacks theoretical rationality in previous works. In this article, we propose a deep learning framework that completes automatic data preparation and learns the nonlinear mapping from start-end frame pairs to motion patterns. The proposed model consists of three modules: action detection, motion extraction, and motion synthesis networks. The action detection network extends the deep subspace learning framework to a supervised version, i.e., uses the local self-expression (LSE) of the motion data to supervise feature learning and complement the classification error. A long short-term memory (LSTM)-based network is used to efficiently extract the motion patterns to address the speed deficiency reflected in the previous optimization-based method. A motion synthesis network consists of a group of LSTM-based blocks, where each of them is to learn the nonlinear relation between the start-end frame pairs and the motion patterns of a certain joint. The superior performances in action detection accuracy, motion pattern extraction efficiency, and motion synthesis quality show the effectiveness of each module in the proposed framework.

노인의 죽음 준비 잠재유형과 웰다잉 인식

Although social awareness of the importance of well-dying has increased, empirical evidence for well-dying policies and programs is very scarce. Additionally, there is limited research examining whether perceptions about dying well vary by the types of end-of-life preparation. Accordingly, this study sought to classify the end-of-life preparation among older Korean adults and verify differences in perceptions about well-dying according to subgroups. For this, older Korean adults aged 65 and above who participated in ‘2020 National Survey of Older Koreans’ were analyzed by dividing them into three groups: 5,977 young-old adults (65-74 years old), 3,333 old-old adults (75-84 years old), and 610 oldest-old adults (85 years old and above). End-of-life preparation was measured by four questions in the ritual aspect (e.g., preparing shrouds or funeral portraits), two questions in the psycho-social aspect (e.g., attending educational programs and writing a will), and two questions in the medical aspect (e.g., writing advance directives and pledging to donate organs). Perceptions about well-dying were measured using four items, assessing the importance of dying autonomously, without pain, in the company of family and acquaintances, and without burdening those around them. For statistical analysis, a three-stage latent class analysis and difference test which accounted for classification errors were conducted. As a result of the analysis, the young-old adults were classified into the ‘active preparation’ group, which showed a relatively active level of preparation, and the ‘no preparation’ group, which lacked preparation for end-of-life in all dimen sion s. The old-old and oldest-old adults were classified into the ‘active preparation’ group and the ‘passive preparation’ group, which was limited to end-of-life preparation only in the ritual aspect. In particular, the proportion of the ‘active preparation’ group in all generations was very low, ranging from 3 to 6%. The results of the difference test on perceptions of well-dying showed that among the young-old an d old-old adults classified into the ‘active preparation’ group, the scores for the importance of each item were significantly lower compared to those of the ‘no preparation’ or the ‘passive preparation’ groups. Among the oldest-old adults, the ‘active preparation’ group regarded end-of-life companionship with family and acquaintances as less important than the ‘passive preparation’ group. The results of this study suggest that the level of end-of-life preparation among older adults in Korea, from young-old to oldest-old, remains low and is predominantly focused on the ritual aspect. Furthermore, perceptions about well-dying vary according to the types of end-of-life preparation. The findings of this study underscore the need for tailored approaches in well-dying policies and programs, such as end-of-life preparation education, that consider the heterogeneity within the older population.

Reducing the volume of computations when building analogs of neural networks for the first stage of an ensemble classifier with stacking

The object of research in this work is ensemble classifiers with stacking, intended for the classification of objects in images with the presence of small sets of labeled data for training. To improve the quality of classification at the first stage of such a classifier, it is necessary to place more primary classifiers that differ in heterogeneous structured processing. However, the number of known neural networks with appropriate characteristics is limited. One approach to solving this problem is to build analogs of known neural networks that make classification errors on other images compared to the base network. The disadvantage of the known methods for constructing such analogs is the need to perform additional floating-point operations. The current paper proposes and investigates a new method to form analogs through random cyclic shifts of rows or columns of input images. This has made it possible to completely eliminate additional floating-point operations. The effectiveness of using this method is explained by the structured processing of input images in basic neural networks. The use of analogs obtained by the proposed method does not impose additional restrictions in practice. This is because the heterogeneity of structured processing in basic neural networks is a typical requirement for them in an ensemble classifier with stacking. The simulation for the CIFAR-10 data set demonstrated that the proposed technique for constructing analogs allows for a comparative quality of classification by the ensemble classifier. Using MLP-Mixer analogs provided an improvement of 4.6 %, and CCT analogs – 5.9 %

LBCapsNet: a lightweight balanced capsule framework for image classification of porcelain fragments

The image classification task of porcelain fragments is of great significance for the digital preservation of cultural heritage. However, common issues are encountered in the image processing of porcelain fragments, including the low computation speed, decreased accuracy due to the uneven distribution of sample categories, and model instability. This study proposes a novel Capsule Network model, referred to as LBCapsNet, which is suitable for the extraction of features from images of porcelain artifacts fragments. A bottleneck-like channel transformation module denoted by ChannelTrans, which resides between the convolutional layer and the PrimaryCaps layer, was first designed. This module is used to reduce the computational complexity and enhance the processing speed when dealing with intricate porcelain images. The MF-R loss function was then proposed by incorporating focal loss into the original loss function. This allows to address the issue of imbalanced distribution of ceramic shard samples and reduce the classification errors, which leads to faster convergence with smoother trend. Finally, an adaptive dynamic routing mechanism is designed with a dynamic learning rate to enhance the overall stability of the classification process. The experimental results obtained on public datasets, such as MNIST, Fashion- MNIST, CIFAR10, FMD and DTD as well as porcelain fragments dataset, demonstrate that LBCapsNet achieves high classification accuracy with faster and more stable computation compared with existing methods. Furthermore, the ability of LBCapsNet to process special textures can provide technical support for the digital preservation and restoration of cultural heritage.

Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset

Gastrointestinal (GI) disorders, encompassing conditions like cancer and Crohn’s disease, pose a significant threat to public health. Endoscopic examinations have become crucial for diagnosing and treating these disorders efficiently. However, the subjective nature of manual evaluations by gastroenterologists can lead to potential errors in disease classification. In addition, the difficulty of diagnosing diseased tissues in GI and the high similarity between classes made the subject a difficult area. Automated classification systems that use artificial intelligence to solve these problems have gained traction. Automatic detection of diseases in medical images greatly benefits in the diagnosis of diseases and reduces the time of disease detection. In this study, we suggested a new architecture to enable research on computer-assisted diagnosis and automated disease detection in GI diseases. This architecture, called Spatial-Attention ConvMixer (SAC), further developed the patch extraction technique used as the basis of the ConvMixer architecture with a spatial attention mechanism (SAM). The SAM enables the network to concentrate selectively on the most informative areas, assigning importance to each spatial location within the feature maps. We employ the Kvasir dataset to assess the accuracy of classifying GI illnesses using the SAC architecture. We compare our architecture’s results with Vanilla ViT, Swin Transformer, ConvMixer, MLPMixer, ResNet50, and SqueezeNet models. Our SAC method gets 93.37% accuracy, while the other architectures get respectively 79.52%, 74.52%, 92.48%, 63.04%, 87.44%, and 85.59%. The proposed spatial attention block improves the accuracy of the ConvMixer architecture on the Kvasir, outperforming the state-of-the-art methods with an accuracy rate of 93.37%.

Deep learning method for optical fiber curvature measurements based on time series data

Curvature detection is an essential technique for monitoring landslides, which are frequent and destructive disasters. Existing methods for curvature detection using fiber-optic sensors have limitations such as complex fabrication or large data size. We propose a data processing method for high-accuracy curvature detection that employs deep learning. We experimented using different levels of curvature and compared our method with other methods. Our method achieves 99.82% accuracy for classification and root mean square error of 0.042m−1 for regression with a simpler structure and smaller data size. Our approach demonstrates its potential for landslide detection and integration with communication systems.

Tracking annual changes of coastal aquaculture ponds in China during 1986–2021

The rapid expansion of coastal aquaculture ponds has made outstanding contributions to global food security and economic development in coastal areas, but this expansion inevitably has a significant impact on coastal ecosystems. Strengthening the spatiotemporal monitoring of aquaculture ponds is crucial for optimizing coastal land use and efficiently managing aquaculture ponds. However, aquaculture pond data on national scale and interannual variability are lacking. Here, we utilized Landsat times series images from 1986 to 2021 to generate annual maps of aquaculture ponds in China's coastal areas and investigated its spatiotemporal characteristics. We find that the overall accuracy of our aquaculture ponds classification was over 90% each year. Aquaculture ponds were concentrated in Bohai Bay, the Jiangsu Coastal Plain, and the Pearl River Delta in China's coastal areas. The time series of aquaculture ponds change could be divided into three stages: rapid growth (1986–2000), stable growth (2001–2016), and a slow decline (2017–2021). The spatial evolution of aquaculture ponds was characterized by (1) continuous seaward expansion, (2) continuous inland and seaward expansion, (3) continuous inland expansion, (4) continuous expansion and then shrinkage, and (5) rapid shrinkage. Moreover, the main aquaculture ponds classification errors were salt pans, reservoirs, abandoned ponds, port facilities, lakes, and pit ponds. Our study demonstrated a new method for applying satellite remote sensing for coastal ecosystem management, and our maps and findings can be used to optimize the development and sustainability of coastal aquaculture ponds.

Intelligent UAV fire monitoring method based on improved YOLOv5 algorithm

Aiming at the existing UAV fire detection system with low small target detection accuracy, a high leakage rate, and a slow rate, an improved YOLOv5 UAV flame detection algorithm is proposed. First, the anchor box clustering is optimized using the K-mean++algorithm to reduce the classification error rate. Second, the original backbone network is enhanced with the CBAM attention mechanism, which scans the whole globe to obtain the target area with a high weighting proportion and needs to be focused on. Replace the PANet network with the BiFPN network in the neck and introduce jump connections when performing feature fusion, which can better retain the semantic information of high-level and low-level features. Finally, the α-IoU loss function is added to achieve the regression accuracy of different levels of the bounding box by modulating α, which improves the detection accuracy of small datasets and the robustness to noise. According to the experimental results, using a randomly segmented dataset, the modified YOLOv5 algorithm obtains a mAP value of 80.2%, which is 6.7% higher than the original YOLOv5 method, while maintaining an FPS of 64 frames per second. The method helps to improve the accuracy of UAVs for fire monitoring, and the performance is better than the existing flame detection algorithms, which meet the requirements of practical applications.

Object-Based Semi-Supervised Spatial Attention Residual UNet for Urban High-Resolution Remote Sensing Image Classification

Accurate urban land cover information is crucial for effective urban planning and management. While convolutional neural networks (CNNs) demonstrate superior feature learning and prediction capabilities using image-level annotations, the inherent mixed-category nature of input image patches leads to classification errors along object boundaries. Fully convolutional neural networks (FCNs) excel at pixel-wise fine segmentation, making them less susceptible to heterogeneous content, but they require fully annotated dense image patches, which may not be readily available in real-world scenarios. This paper proposes an object-based semi-supervised spatial attention residual UNet (OS-ARU) model. First, multiscale segmentation is performed to obtain segments from a remote sensing image, and segments containing sample points are assigned the categories of the corresponding points, which are used to train the model. Then, the trained model predicts class probabilities for all segments. Each unlabeled segment’s probability distribution is compared against those of labeled segments for similarity matching under a threshold constraint. Through label propagation, pseudo-labels are assigned to unlabeled segments exhibiting high similarity to labeled ones. Finally, the model is retrained using the augmented training set incorporating the pseudo-labeled segments. Comprehensive experiments on aerial image benchmarks for Vaihingen and Potsdam demonstrate that the proposed OS-ARU achieves higher classification accuracy than state-of-the-art models, including OCNN, 2OCNN, and standard OS-U, reaching an overall accuracy (OA) of 87.83% and 86.71%, respectively. The performance improvements over the baseline methods are statistically significant according to the Wilcoxon Signed-Rank Test. Despite using significantly fewer sparse annotations, this semi-supervised approach still achieves comparable accuracy to the same model under full supervision. The proposed method thus makes a step forward in substantially alleviating the heavy sampling burden of FCNs (densely sampled deep learning models) to effectively handle the complex issue of land cover information identification and classification.

A Multi-Task Decomposition-Based Evolutionary Algorithm for Tackling High-Dimensional Bi-Objective Feature Selection

Evolutionary algorithms have been widely applied for solving multi-objective optimization problems, while the feature selection in classification can also be treated as a discrete bi-objective optimization problem if attempting to minimize both the classification error and the ratio of selected features. However, traditional multi-objective evolutionary algorithms (MOEAs) may have drawbacks for tackling large-scale feature selection, due to the curse of dimensionality in the decision space. Therefore, in this paper, we concentrated on designing an multi-task decomposition-based evolutionary algorithm (abbreviated as MTDEA), especially for handling high-dimensional bi-objective feature selection in classification. To be more specific, multiple subpopulations related to different evolutionary tasks are separately initialized and then adaptively merged into a single integrated population during the evolution. Moreover, the ideal points for these multi-task subpopulations are dynamically adjusted every generation, in order to achieve different search preferences and evolutionary directions. In the experiments, the proposed MTDEA was compared with seven state-of-the-art MOEAs on 20 high-dimensional classification datasets in terms of three performance indicators, along with using comprehensive Wilcoxon and Friedman tests. It was found that the MTDEA performed the best on most datasets, with a significantly better search ability and promising efficiency.

Detection of sickle cell disease using deep neural networks and explainable artificial intelligence

Abstract Sickle cell disease (SCD), a blood disorder that transforms the shape of red blood cells into a distinctive sickle form, is a major concern as it not only compromises the blood’s oxygen-carrying capacity but also poses significant health risks, ranging from weakness to paralysis and, in severe cases, even fatality. This condition not only underscores the pressing need for innovative solutions but also encapsulates the broader challenges faced by medical professionals, including delayed treatment, protracted processes, and the potential for subjective errors in diagnosis and classification. Consequently, the application of artificial intelligence (AI) in healthcare has emerged as a transformative force, inspiring multidisciplinary efforts to overcome the complexities associated with SCD and enhance diagnostic accuracy and treatment outcomes. The use of transfer learning helps to extract features from the input dataset and give an accurate prediction. We analyse and compare the performance parameters of three distinct models for this purpose: GoogLeNet, ResNet18, and ResNet50. The best results were shown by the ResNet50 model, with an accuracy of 94.90%. Explainable AI is the best approach for transparency and confirmation of the predictions made by the classifiers. This research utilizes Grad-CAM to interpret and make the models more reliable. Therefore, this specific approach benefits pathologists through its speed, precision, and accuracy of classification of sickle cells.

Automatic design of W-operators using membership functions: a case study in brain MRI segmentation

A W-operator is an image transformation that is locally defined inside a window W, invariant to translations. The automatic design of the W-operators consists of the design of functions, whose domain is a set of patterns or vectors obtained by translating a window through training images and the output of each vector is a class or label. The main difficulty to consider when designing W-operators is the generalization problem that occurs due to lack of training images. In this work, we propose the use of membership functions to solve the generalization problem in gray level images. Membership functions are defined from the training images to model regions that are often inaccurate due to ambiguous gray levels in the images. This proposal was applied to brain magnetic resonance image segmentation to test its performance in a field of interest in biomedical images. The experiments were carried out with different numbers of training and test images, windows sizes of 3×3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3\ imes 3$$\\end{document}, 5×5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5\ imes 5$$\\end{document}, 7×7\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$7\ imes 7$$\\end{document}, 11×11\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$11\ imes 11$$\\end{document}, and 15×15\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$15\ imes 15$$\\end{document}, and images with noise levels at 0, 1, 3, 5, 7, and 9%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}. To calculate the performance of each designed W-operator, the classification error, sensitivity, and specificity were used. From the experimental results, it was concluded that the best performance is achieved with a window of size 3×3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3\ imes 3$$\\end{document}. In images with noise levels from 1 to 5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}, the classification error is less than 4%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} and the sensitivity and specificity are greater than 94 and 98%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}, respectively.

Modified interactive multiple model particle filter for terrain referenced navigation with classification error minimisation strategy

AbstractThe authors propose an innovative solution to address challenges in terrain‐referenced navigation (TRN). The suggested solution is the interactive multiple‐model particle filter with a classification error minimisation strategy (IMM‐CPF) based on decision theory. TRN is a technique that estimates position by comparing measured terrain altitude to the digital elevation model and critically depends on obtaining accurate altitude measurements. However, these measurements can be easily contaminated to not only from sensor errors but also from vegetation effects. The TRN measurement noise model is characterised as a multi‐modal density, and it reveals an overlap between two density functions, with the mixture weight parameter varying based on surface environmental conditions. This variability can potentially degrade estimation accuracy. The proposed approach integrates truncated likelihoods into the mode estimation process to enhance mode estimation capability using a classification error minimisation strategy. The proposed strategy is based on decision theory and has been modified to be suited in the IMMPF form. The effectiveness of the proposed IMM‐CPF method is verified through simulations conducted under diverse surface conditions, demonstrating significant improvements in estimation accuracy compared to conventional algorithms. Furthermore, the significance of this method is presented in terms of computational cost and robustness.

Fabric surface defect classification and systematic analysis using a cuckoo search optimized deep residual network

Fabric defects can significantly impact the quality of a textile product. By analyzing the types and frequencies of defects, manufacturers can identify process inefficiencies, equipment malfunctions, or operator errors. Although deep learning networks are accurate in classification applications, some defects may be subtle and difficult to detect, while others may have complex patterns or occlusions. CNNs may struggle to capture a wide range of defect variations and generalize well to unseen defects. Discriminating between genuine defects and benign variations requires sophisticated feature extraction and modeling techniques. This paper proposes a residual network-based CNN model to enhance the classification of fabric defects. A pretrained residual network, ResNet50, is fine-tuned to classify fabric defects into four categories: holes, objects, oil spots, and thread errors on the fabric surface. The fine-tuned network is further optimized via cuckoo search optimization using classification error as a fitness function. The network is systematically analyzed at different layers, and the investigation of classification results are reported using a confusion matrix and classification accuracy for each class. The experimental results confirm that the proposed model achieved superior performance with 95.36% accuracy and a 95.35% F1 score for multiclass classification. In addition, the proposed model achieved higher accuracy with similar or fewer trainable parameters than traditional deep CNN networks.