In the era of smart cities and AIoT infrastructure, deploying efficient machine learning models on resource-constrained edge devices has become critical for urban utility management. This paper evaluates the effectiveness of various data augmentation techniques in enhancing the performance of machine learning models on these devices despite their limited computational resources. Our study utilizes three datasets of digit images: one from Kaggle, one from SCUT, and a proprietary dataset. We tested ranges of parameters for data augmentation, including noise, brightness, contrast, and geometric transformations, to assess their impact on model accuracy. The findings indicate that while augmentation generally improves model performance, an optimal range exists beyond which accuracy may decline due to overfitting. This paper describes this standardized approach to parameter testing that contributes to developing more efficient and accurate edge-based machine learning applications.
Texture classification is a critical task with applications spanning various domains, from facial recognition to cancer detection in medical images. In traditional approaches, the application’s success heavily depends on the feature extraction and classification stages. Over the years, numerous feature extraction methods have been proposed, with non-handcrafted approaches consistently outperforming handcrafted ones. This paper investigates static and dynamic selection techniques of classifiers trained on features extracted from non-handcrafted architectures. The experiments were conducted on two challenging benchmarks widely used for texture classification evaluation: the FMD dataset and the Describable Texture Dataset (DTD). We first evaluated individual features and found that Visual Transformers (ViT) performed exceptionally well compared to other architectures. However, the significantly higher Oracle accuracy for the ensemble of classifiers suggests room for improvement in investigations concerning classifier combination and selection techniques. We observed enhanced performance when combining the top-performing individual classifiers by applying static combination methods such as sum, product, and max rules. Dynamic classifier selection techniques have not yielded improvements in the rates. The best performance was achieved using a static combination of classifiers through sum and product rules. In the FMD database, the F1-score was 93.2
In this paper, we propose a novel method for rotation invariant pattern recognition. We perform adaptive denoising to the input pattern images. If the noise level is above a threshold, we perform block matching and 3D filtering (BM3D) to reduce noise from the noisy pattern images. We do not conduct denoising otherwise. We extract ridgelet-Fourier features from the denoised pattern and classify the unknown pattern to one of the known classes with the nearest neighbor classifier. Experiments demonstrate that our new method achieves perfect classification rate (100
Classifying gravitational wave signals is an essential task in analyzing data from space collected by advanced tools such as an interferometer. In this paper, we present a new architecture of a convolutional neural network that classifies gravitational wave spectrograms into a selected class. For this purpose, a novel attention module based on the fuzzy controller architecture was proposed. The mechanism is based on the generation of matrices: query Q, keys K, and values V, where the first two are fuzzified by a Gaussian function and subjected to fuzzy inference. The inference results are sharpened and multiplied by values in V. This solution allows the use of the idea of a fuzzy controller to analyze features in neural networks. The model was tested and analyzed in terms of different evaluation metrics that show that this model can reach higher results than the state-of-the-art.
In this article, the authors address classification as a process that can be automated. The primary focus is the analysis of dermatological skin changes by leveraging expert knowledge and factor analysis based on structural geometric patterns associated with skin diseases. Furthermore, the study explores the feasibility of remote classification using a hierarchical model designed to optimize computational efficiency. Additionally, the classification process incorporates rejected principal components to identify relevant factors accurately.
