A Novel Image Classification Algorithm

The visual word dictionary is based on measuring metrics at the image level and does not follow a “decision-support tool” approach, where the system is used to aid diagnosis, as it is found in most of the current methods. The decision-support approach requires pinpointing the location of each lesion to allow the specialist to evaluate the image for diagnosis. In this case, metrics based on the accuracy of detecting each type of lesion is more relevant. Using a visual words dictionary as the basis for a classification algorithm was inspired by the computer vision, where visual dictionaries and PoIs are used as a basis for several applications such as image retrieval and classification. The visual word that combines feature information contained within the images in a framework easily extendible to different types of retinal lesions or pathologies and builds a specific projection space for each class of interest (e.g., white lesions such as exudates or normal regions) instead of a common dictionary for all classes. The visual words dictionary was applied to classifying bright and red lesions with classical cross validation and cross dataset validation to indicate the robustness of this approach. The curve (AUC) of 95.3% for white lesion detection and an AUC of 93.3% for red lesion detection using fivefold cross validation and data consisting of 687 images of normal retinae, 245 images with bright lesions, 191 with red lesions, and 109 with signs of both bright and red lesions. The image classification resulted in an AUC of 88.1% when classifying the RetiDB dataset and in an AUC of 89.3% when classifying the Messidor dataset, both cases for bright lesion detection. The results indicate the potential for training with different acquisition images under different setup conditions with a high accuracy of referral based on the presence of either red or bright lesions or both.

Improved Rapid Corner Detection Algorithm in Medical Image

Characterizing images by high-level concepts from a learned visual dictionary is extensively used in image classification and retrieval. This paper deals with inferring discriminative visual dictionaries for effective image retrieval and examines a non-negative visual dictionary learning scheme towards this direction. More specifically, a non-negative matrix factorization framework with \(\ell _0\)-sparseness constraint on the coefficient matrix for optimizing the dictionary is proposed. It is a two-step iterative process composed of sparse encoding and dictionary enhancement stages. An initial estimate of the visual dictionary is updated in each iteration with the proposed \(\ell _0\)-constraint gradient projection algorithm. A desirable attribute of this formulation is an adaptive sequential dictionary initialization procedure. This leads to a sharp drop down of the approximation error and a faster convergence. Finally, the proposed dictionary optimization scheme is used to derive a compact image representation for the retrieval task. A new image signature is obtained by projecting local descriptors on to the basis elements of the optimized visual dictionary and then aggregating the resulting sparse encodings in to a single feature vector. Experimental results on various benchmark datasets show that the proposed system can infer enhanced visual dictionaries and the derived image feature vector can achieve better retrieval results as compared to state-of-the-art techniques.

Image classification is critical in computer vision, with numerous applications ranging from e-commerce to medical imaging. This study provides a comprehensive evaluation of traditional machine learning algorithms for image classification, implementing and analysing novel fault tolerance mechanisms amongst these algorithms. The authors compared the performance of K-Nearest Neighbors (KNN), Decision Trees, Random Forest, and XGBoost on both Fashion MNIST and CIFAR-10 datasets. The comparison was extended to include Support Vector Machine (SVM), Logistic Regression, and Naive Bayes classifiers in order to expand the evaluation of these models on the indicated datasets. Key findings demonstrated the superiority of ensemble methods, particularly XGBoost, which achieved 89.31% of accuracy on Fashion MNIST and 54.93% on CIFAR-10, consistently outperforming other models across various configurations. Random Forest exhibited robust performance as the secondbest model, reaching 87.42% and 51.64% of accuracy on the respective datasets. The significant performance gap between datasets demonstrated the challenges that traditional machine learning models face with complex image data. Implementing the fault tolerance framework in this study has also shown a remarkable effectiveness, achieved a 94.6% recovery rate while maintaining model accuracy within 0.1% of standard implementations. This was achieved with minimal computational overhead (2.3% of training time and 1.8% of memory usage), making it highly practical for production deployments. The system significantly reduced operational failures, decreasing crashes from 5.2 to 0.3 per day and increasing average uptime from 4.3 to 12.0 hours. The study also reveals important insights regarding model scalability and resource requirements, with memory usage varying significantly across models (325MB to 8,923MB). These findings provide valuable guidance for practitioners in selecting and implementing machine learning models for image classification tasks, particularly in scenarios where both performance and system reliability are critical. This research contributes to the field by demonstrating the feasibility of implementing robust fault tolerance in machine learning systems without compromising accuracy while also providing comprehensive performance comparisons across different model architectures and dataset complexities. The developed framework serves as a foundation for building more reliable machine-learning systems for real-world applications.

Performance analysis of SAR filtering techniques using SVM and Wishart Classifier

Corner detection in color images through a multiscale combination of end-stopped cortical cells

Recognition and determination of fuzzy logical relationship in the system fault evolution process

Multicriteria decision-making (MCDM) models are valuable for handling complex problems with various criteria and choices. Conventional MCDM lacks a generic paradigm for many real-world situations, like group choices and ambiguous preferences, and only considers crisp decision problems. Therefore, it makes sense to consider most MCDM problems in the actual world to be fuzzy MCDM problems. Spherical fuzzy sets extend the concept of fuzzy sets by considering uncertainty in three ways rather than fuzzy sets' membership degrees only. Effectively identifying, characterizing, and comparing the many variables that may affect a decision is the multi-attribute utility theory (MAUT) approach. This research proposes a novel and expanded MAUT approach that deals with rough approximations under a spherical fuzzy environment. The entropy weighting technique, a key component of our proposed method, assigns weights to criteria based on their relative importance, thereby providing a robust and objective basis for decision-making. This method is applied when selecting medication service robots (MSR) for COVID-19 patients. MSRs are increasingly being used in healthcare to reduce global fatality and spread rates, particularly for highly infectious diseases like COVID-19. However, selecting the most suitable robot from various options can be complex, necessitating mathematical tools for proper selection. The proposed method's effectiveness is not just a claim but is backed by sensitivity and comparative analyses, where we compared the results of our method with those of other established methods, demonstrating its superiority in terms of accuracy and efficiency.

the air defense of ship formation cooperation decision-making is a multi-stage multi-objective system. Based on the concepts of the fuzzy weighted distance and membership degree, considering the collaborative relationship in different phase, put forward the concept of thermodynamic power allotment wreath of the cooperation degree,combined the fuzzy optimum selection theory with dynamic programming methodology,it provides a new method for solving the multi-stage multi-objective optimization based on collaboration to obtain the optimum balanced tactic. This method is applied to solve the collaborative decision-making optimization problem of colligation anti-missile by the air defense of ship formation cooperation and gives some instructive results. Ships formation cooperative anti-air is a multi-stage and multi-objective decision-making system based on collaborative. The anti-air combat effect and combat resource consumption is a pair of contradiction. Operational effectiveness use minimal resource consumption to gain the biggest operational effects. For different decision form different operational effectiveness, operational effectiveness on surface is different, have different resource allocation, it is a complicated problem. Comprehensive analysis of fleet anti-missile process, we can see that it is a multi-stage multi-objective system, and because there is synergy between the stages, so we need to search based on multi-stage multi-objective dynamic optimization method of synergy. Multi-stage system optimization objective is to seek to optimize the index of the whole process of the target or the decision strategy of each stage. Fuzzy distance and membership degree is presented in this paper on the basis of the concept of coordination degree on the basis of fire distribution ring measurement, the fuzzy optimization theory and dynamic programming theory organically, and based on coordination degree of fire distribution ring multi-stage multi-objective decision-making system fuzzy optimization dynamic programming model.

One-class support vector machine (OCSVM) is one of the most popular algorithms in the one-class classification problem, but it has one obvious disadvantage: it is sensitive to noise. In order to solve this problem, the fuzzy membership degree is introduced into OCSVM, which makes the samples with different importance have different influences on the determination of classification hyperplane and enhances the robustness. In this paper, a new calculation method of membership degree is proposed and introduced into the fuzzy multiple kernel OCSVM (FMKOCSVM). The combined kernel is used to measure the local similarity between samples, and then, the importance of samples is determined based on the local similarity between training samples, so as to determine the membership degree and reduce the impact of noise. The proposed membership requires only positive data in the calculation process, which is consistent with the training set of OCSVM. In this method, the noise has a smaller membership value, which can reduce the negative impact of noise on the classification boundary. Simultaneously, this method of calculating membership has a higher efficiency. The experimental results show that FMKOCSVM based on proposed local similarity membership is efficient and more robust to outliers than the ordinary multiple kernel OCSVMs.

Ecotones zones lie between homogeneous ecological systems. They are characterised on images by heterogeneous pixels in a specific neighbourhood. Fuzzy classifiers output membership degrees that better represent the heterogeneity within a pixel and can be further processed within the context of a local neighbourhood. This Letter formalises these notations. Two coral reef systems examples are presented. They illustrate the use of possibility measurement to characterise ecotones, and the use of information on well-known ecotones to increase the accuracy of image classification.

The conventional method used in forest inventory is time-consuming, especially in field surveying and requires an expert’s knowledge and observation to do species identification. This standard method can be facilitated by advances in remote sensing technology. An airborne hyperspectral image mosaic was utilised in the research to evaluate the ability of this high spectral fidelity dataset that was incorporated into a feature extraction Support Vector Machine (SVM) classifier to successfully detect and map a tree species of interest (Shorea kunstleri King) within the mixed dipterocarp forest stand of the Lambir Hills National Park. In this study, two types of feature extraction Principal Component Analysis (PCA) and Spectral Derivative (SD) were applied to classifiers SVM. To demonstrate the effect, classification of the hyperspectral image with no feature extraction was done. The accuracies achieved from different feature extraction were compared statistically. Based on the results, Shorea kunstleri King was able to be discriminated against within 53 species of Shorea. SVM classifier is known for its ability to generalise well even with limited training samples and is commonly used in image classification. The Kernel parameter in SVM classifier alongside feature extraction significantly affects the classification accuracy. Therefore, feature extraction does affect the classification accuracy, but it depends on the morphology of the study area. It is concluded that the SVM method with SD feature extraction could detect tree crowns of Shorea kunstleri with a Kappa coefficient of 0.8126 which showed a great agreement with the observed samples and overall accuracy of 89.89% that shows the accuracy of the final map.

Investigation of the machinability of commercially pure titanium in ultrasonic machining using graph theory and matrix method

This paper presents a robust, two-step method for edge and corner detection in noisy images. First it identifies the type of noise using a new pattern classification approach and then restores the image using a good restoration technique suitable for the type of noise identified. The types of noise considered here include uniform white, Gaussian white, speckle, and salt-and-pepper noise. From the restored image, edge and corner strengths are determined using gradient based techniques, and finally, a fuzzy k-means clustering algorithm is used to find adaptive thresholds for detecting the edge and corner points. Results of some simulation studies are presented here and they seem to indicate that the proposed algorithm works very well.

In order to solve the problem of multisolution and ill-formedness of the 3D reconstruction method of a single image (purpose), the author proposes a microscope image segmentation algorithm based on the Harris multiscale corner detection. Separating complex engineering images into several simple basic geometric shapes, rebuild them separately to avoid the ill-conditioned solution problem of directly recovering depth information. In order to improve the registration accuracy of the corner-based image registration algorithm, the idea of multiresolution analysis was introduced into the classic Harris corner detection, and a gray intensity variation formula based on the wavelet transform was constructed, and a scale transformation characteristic was obtained so that the improved Harris corner detection algorithm is invariant to rotation, translation, and scale. Experimental results show that after reconstruction, the error between the length of the object measured based on the point cloud data and the actual length of the object is small, and both remain within the error range of 3 mm. The experiment verifies the fast, accurate, and stable characteristics of the improved algorithm.