Formation of Microstructure Image Features Based on Topological Decomposition

Subtilisin Carlsberg (SK) was shown to catalyze the solid phase segment coupling of peptides in complex with sodium dodecyl sulfate (SDS) in an organic medium on Aminosilochrom and polyvinyl alcohol (PVA) cryogel activated with glutaraldehyde or divinylsulfone. Diamines of different lengths with a general formula NH2-(CH2)n-NH2 (n = 2, 4, and 6) were used as spacers between the PVA cryogel and the peptide. A model reaction of enzymatic attachment of the Dnp-Ala-Ala-Leu-OMe tripeptide to the PVA cryogel was carried out by treatment with the SDS-SK complex in a mixture of anhydrous ethanol and DMSO (7: 3, v/v) using a tenfold excess of the carboxyl component. The molar enzyme-substrate ratio was 1 : 88. The effect of the method of matrix activation, length of a spacer, and reaction time on the coupling efficiency was studied. Hexamethylenediamine was found to be the most effective spacer for the enzymatic coupling on the PVA cryogel activated with glutaraldehyde (the reaction proceeded with the highest yield of 60%). The reaction efficiency was considerably lower in the case of ethylenediamine and tetramethylenediamine (10 and 15%, respectively). The best results were obtained on the PVA cryogel activated by divinylsulfone with hexamethylenediamine as a spacer. A two-step condensation of tripeptides was carried out on this supportsupport. The second step of condensation was shown to proceed better (in 85% yield) in comparison with the first step (37% yield).

Noise robust audio surveillance using reduced spectrogram image feature and one-against-all SVM

Religious, ethnicity, gender, and occupation are some examples of social characteristics that can accurately define and explain human social behavior. Being able to determine people's jobs based on their visual information in photographs can assist with better identifying people, determining social roles, offering personalized recommendations, and conducting security investi-gations. In this paper, our goal is to extract occupational data from human clothing in images. We collected a dataset called TH-UniformDB, which comprises 10,000 photos of a single individual wearing Thai uniforms from nine occupation classes and the other class; each class has 1,000 images. The dataset exhibits a significant level of intra-class variety as well as inter-class similarities, which pose challenges in occupation classification. To address these issues and improve classification performance, we propose an approach that performs visual occupation recognition by combining the strength of processing the color images along with that of the grayscale features of the same images. According to our experimental results, the combination of grayscale and RGB features of images can effectively improve the recognition accuracy of the traditional deep neural network model between 3.15 to 10.15 percent, resulting in less impact of the inter-class similarity and intra-class variance.

Poly vinyl alcohol cryogel as a selective test kit for pre and post blast trinitrotoluene

The study addresses the problem of the deteriorating quality of the raw material base in the Ukrainian coking industry, which is primarily caused by the increasing proportion of low-caking, high-ash, and high-sulfur coal grades in the charge. This transformation in the composition of the feedstock adversely affects the thermotechnical parameters of the coking process, leading to uneven thermal decomposition of coal components and deteriorating the physico-mechanical properties of the resulting coke, thereby limiting its applicability in blast furnace operations. One of the promising approaches to correcting the technological characteristics of the coal charge is the application of polyvinyl alcohol (PVA) cryogels as a binding and structure-forming component during the partial briquetting of coal. The mechanisms of three-dimensional porous structure formation in PVA cryogels under cyclic freeze-thaw conditions are described, highlighting their water solubility, non-toxicity, excellent film-forming ability, and efficiency in stabilizing dispersed coal particles. Experimental studies are presented to determine the optimal PVA content, freezing regimes, and drying parameters of the briquettes. It has been established that the incorporation of 5–10% PVA ensures the formation of a robust cryogel matrix, which provides the briquettes with enhanced mechanical stability, reduces their brittleness, and prevents structural failure during transportation and charging into coking chambers. The study demonstrates that the use of PVA cryogels compensates for the deficit of high-caking coal grades, improves the homogeneity of the coal charge, intensifies the initial stages of carbonization, and ensures the production of coke with the required operational properties. The obtained results confirm the high efficiency of using PVA as an innovative tool for raw material modification under contemporary industrial and resource constraints, providing a viable strategy to optimize coke quality and overall process performance in modern coking operations.

Discriminative dictionary learning (DDL) framework has been widely used in image classification which aims to learn some class-specific feature vectors as well as a representative dictionary according to a set of labeled training samples. However, interclass similarities and intraclass variances among input samples and learned features will generally weaken the representability of dictionary and the discrimination of feature vectors so as to degrade the classification performance. Therefore, how to explicitly represent them becomes an important issue. In this paper, we present a novel DDL framework with two-level low rank and group sparse decomposition model. In the first level, we learn a class-shared and several class-specific dictionaries, where a low rank and a group sparse regularization are, respectively, imposed on the corresponding feature matrices. In the second level, the class-specific feature matrix will be further decomposed into a low rank and a sparse matrix so that intraclass variances can be separated to concentrate the corresponding feature vectors. Extensive experimental results demonstrate the effectiveness of our model. Compared with the other state-of-the-arts on several popular image databases, our model can achieve a competitive or better performance in terms of the classification accuracy.

Despite being treatable and preventable, tuberculosis (TB) affected one-fourth of the world population in 2019, and it took the lives of 1.4 million people in 2019. It affected 1.2 million children around the world in the same year. As it is an infectious bacterial disease, the early diagnosis of TB prevents further transmission and increases the survival rate of the affected person. One of the standard diagnosis methods is the sputum culture test. Diagnosing and rapid sputum test results usually take one to eight weeks in 24 h. Using posterior-anterior chest radiographs (CXR) facilitates a rapid and more cost-effective early diagnosis of tuberculosis. Due to intraclass variations and interclass similarities in the images, TB prognosis from CXR is difficult. We proposed an early TB diagnosis system (tbXpert) based on deep learning methods. Deep Fused Linear Triangulation (FLT) is considered for CXR images to reconcile intraclass variation and interclass similarities. To improve the robustness of the prognosis approach, deep information must be obtained from the minimal radiation and uneven quality CXR images. The advanced FLT method accurately visualizes the infected region in the CXR without segmentation. Deep fused images are trained by the Deep learning network (DLN) with residual connections. The largest standard database, comprised of 3500 TB CXR images and 3500 normal CXR images, is utilized for training and validating the recommended model. Specificity, sensitivity, Accuracy, and AUC are estimated to determine the performance of the proposed systems. The proposed system demonstrates a maximum testing accuracy of 99.2%, a sensitivity of 98.9%, a specificity of 99.6%, a precision of 99.6%, and an AUC of 99.4%, all of which are pretty high when compared to current state-of-the-art deep learning approaches for the prognosis of tuberculosis. To lessen the radiologist’s time, effort, and reliance on the level of competence of the specialist, the suggested system named tbXpert can be deployed as a computer-aided diagnosis technique for tuberculosis.

Fine-grained visual classification is a challenging task because of intra-class variation and inter-class similarity. Most fine-grained models predominantly focus on discriminative region localization which can effectively solve the intra-class variation, but ignore global information and the problem of inter-class similarity which easily leads to overfitting on specific samples. To address these issues, we develop an end-to-end model based on selecting discriminative features for fine-grained visual classification without the help of part or bounding box annotations. In order to accurately select discriminative features, we integrate effective information from different receptive fields to enhance the quality of features, then the features of discriminative regions detected by anchors and the whole image’s feature are jointly processed for classification. Besides, we propose a new loss function to optimize the model to find discriminative regions and prevent overfitting in the particular sample, which can simultaneously solve the problems of intra-class variation and inter-class similarity. Comprehensive experiments show that the proposed approach is superior to the state-of-the-art methods on CUB-200-2011, Stanford Cars and FGVC-Aircraft datasets.

Pathologists often deal with high complexity and sometimes disagreement over osteosarcoma tumor classification due to cellular heterogeneity in the dataset. Segmentation and classification of histology tissue in H&E stained tumor image datasets is a challenging task because of intra-class variations, inter-class similarity, crowded context, and noisy data. In recent years, deep learning approaches have led to encouraging results in breast cancer and prostate cancer analysis. In this article, we propose convolutional neural network (CNN) as a tool to improve efficiency and accuracy of osteosarcoma tumor classification into tumor classes (viable tumor, necrosis) versus nontumor. The proposed CNN architecture contains eight learned layers: three sets of stacked two convolutional layers interspersed with max pooling layers for feature extraction and two fully connected layers with data augmentation strategies to boost performance. The use of a neural network results in higher accuracy of average 92% for the classification. We compare the proposed architecture with three existing and proven CNN architectures for image classification: AlexNet, LeNet, and VGGNet. We also provide a pipeline to calculate percentage necrosis in a given whole slide image. We conclude that the use of neural networks can assure both high accuracy and efficiency in osteosarcoma classification.

Machine learning algorithms are designed to identify efficiently and to predict accurately patterns within multivariate data. They provide analysts computational tools to aid predictive modelling and the interpretation of interactions between data and the phenomena under investigation. The analysis of large volumes of disparate multivariate geospatial data using machine learning algorithms therefore offers great promise to industry and research in the geosciences. Geoscience data are frequently characterised by a restriction in the number and distribution of direct observations, irreducible noise in these data and a high degree of intraclass variability and interclass similarity. The choice of machine learning algorithm, or algorithms and the details of how algorithms are applied must therefore be appropriate to the context of geoscience data. With this knowledge, I aim to employ machine learning as a means of understanding the spatial distribution of complex geological phenomena. I conduct a rigorous and comprehensive comparison of machine learning algorithms, representing the five general machine learning strategies, for supervised lithology classification applications. I also develop and test a novel method for obtaining robust estimates of the uncertainty associated with machine learning algorithm categorical predictions. The insights gained from these experiments leads to the further development and comparison of new methods for the incorporation of spatial-contextual information into machine learning supervised classifiers. In using machine learning algorithms for geoscience applications, I have developed bestpractice methodologies that address the challenges facing geoscientists for geospatial supervised classification. Guidelines are established that detail the preparation and integration of disparate spatial data, the optimisation of trained classifiers for a given application and the robust statistical and spatial evaluation of outputs. I demonstrate, through a case study in a region that is prospective for economic mineralisation, the combination of supervised and unsupervised machine learning algorithms for the critical appraisal of pre-existing geological maps and formulation of meaningful interpretations of geological phenomena. The experiments conducted as part of my research confirm the efficacy of machine learning algorithms to generate accurate geological maps representing a variety of terranes. I identify and explore key aspects of the spatial and statistical istributions of geoscience data that affect machine learning algorithm performance. My research clearly identifies Random Forests‚Äöv묢 as a good first-choice algorithm for the prediction of classes representing lithologies using commonly available multivariate geological and geophysical data. Furthermore, Random Forests prediction uncertainty is shown to be closely related to ambiguous and/or erroneous classifications and, thus provides a practical means of indicating variable levels of confidence. Spatial-contextual information is best incorporated into machine learning supervised classifiers via the pre-processing of input variables and/or the post-regularisation of classifications. My findings indicate that a trade-off between optimal predictive models and interpretable explanatory models exists, whereby, intuitively interpretable models are not necessarily the most accurate. The practical application of machine learning algorithms requires the implementation of three key stages: (1) data pre-processing; (2) algorithm training; and (3) prediction evaluation. This methodology provides the foundation for generating accurate and geologically meaningful predictions with minimal user intervention and assists in the formulation of robust interpretations of complex geological phenomena. For example, classifications obtained by Random Forests are useful for critically appraising interpreted geological maps. Clusters produced by Self-Organising Maps indicate the presence of discrete, spatially contiguous and geologically significant sub-classes within individual lithological units, which represent regions of contrasting primary composition and alteration styles. My results may be widely applied to a broad range of practical geoscience challenges such as ore deposit targeting, geo-hazard risk assessment, engineering and construction projects, hydrological and environmental modelling and ecological studies. The applications of machine learning algorithms detailed in this thesis align well with state-of-the-art Big Data online infrastructure and virtual laboratories currently emerging in Australia.

Due to melanoma is one of the skin cancers with the highest mortality rate and have a large amount of data during the collection and diagnosis, there is an urgent need to improve the diagnostic efficiency and accuracy. However, there remain problems in analyzing medical big data for skin lesion application, such as the intra-class variation and inter-class similarity in skin lesion images and the lacks of ability to focus on the lesion area affecting the classification results of the model. To address these dilemmas, in this paper, we proposed a novel machine learning-based approach that builds on top of DenseNet. It combines the attention mechanism and large margin loss to enhance the classification accuracy in terms of intra-class compactness and inter-class separability. We evaluated our model on ISIC 2017 (International Skin Imaging Collaboration) dataset, which has achieved 92% of Mean AUC. The experimental results show the effectiveness of our solution outperforms the state-of-the-art significantly in classify skin lesion and can accurately classify malignant melanoma on medical images.

Due to the limited availability of training data, the diverse shapes of brain tumors among different patients, inter-class similarity, and intra-class variation, achieving high recognition accuracy and speed in deep learning-based brain tumor recognition remains challenging. To address these issues, we propose a Dual-Branch Ensemble and Gated Global-Local Attention network based on EfficientNetV2S and ConvNeXt (GGLA-NeXtE2NET) to improve identification accuracy and model interpretability. For inter-class and intra-class problems, we designed a Gated Global-Local Attention (GGLA) mechanism that captures dependency information of query points in both horizontal and vertical directions, thereby obtaining global information indirectly. Simultaneously, local information is captured through multiple convolutions with a gating layer. The gating mechanism within the GGLA dynamically balances the contributions of global and local information, enabling the model to adaptively focus on the most relevant features for accurate classification. Furthermore, we introduce a dual-branch ensemble network to address the issue of image variety. This network uses two branches to extract image features at different resolutions for fusion, thereby expanding the network receptive field. Additionally, we utilized an Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to generate images that balance MRI data and implemented multiple preprocessing techniques to tackle inherent noise in MRI images. These techniques enhance the clarity of MRI images while preserving essential details. This results in a clear improvement in the identification of tumor boundaries, crucial for accurate surgical planning and treatment strategies. We evaluated GGLA-NeXtE2NET on 3-class and 4-class brain tumor datasets and achieved 99.06%, and 99.62% overall accuracy on both datasets respectively.

Skin lesion classification using CNNs with grouping of multi-scale attention and class-specific loss weighting

As a pivotal area of research in the field of computer vision, the technology for food identification has become indispensable across diverse domains including dietary nutrition monitoring, intelligent service provision in restaurants, and ensuring quality control within the food industry. However, recognizing food images falls within the domain of Fine-Grained Visual Classification (FGVC), which presents challenges such as inter-class similarity, intra-class variability, and the complexity of capturing intricate local features. Researchers have primarily focused on deep information in deep convolutional neural networks for fine-grained visual classification, often neglecting shallow and detailed information. Taking these factors into account, we propose a Multi-level Attention Feature Fusion Network (MAF-Net). Specifically, we use feature maps generated by the Convolutional Neural Networks (CNNs) backbone network at different stages as inputs. We apply a self-attention mechanism to identify local features on these feature maps and then stack them together. The feature vectors obtained through the attention mechanism are then integrated with the original input to enhance data augmentation. Simultaneously, to capture as many local features as possible, we encourage multi-scale features to concentrate on distinct local regions at each stage by maximizing the Kullback-Leibler Divergence (KL-divergence) between the different stages. Additionally, we present a novel approach called subclass center loss (SCloss) to implement label smoothing, minimize intra-class feature distribution differences, and enhance the model's generalization capability. Experiments conducted on three food image datasets-CETH Food-101, Vireo Food-172, and UEC Food-100-demonstrated the superiority of the proposed model. The model achieved Top-1 accuracies of 90.22%, 89.86%, and 90.61% on CETH Food-101, Vireo Food-172, and UEC Food-100, respectively. Notably, our method not only outperformed other methods in terms of the Top-5 accuracy of Vireo Food-172 but also achieved the highest performance in the Top-1 accuracies of UEC Food-100.

Remote sensing scene classification (RSSC) is a fundamental but challenging task in the discipline of remote sensing (RS). Significant recent advancements have been achieved in RSSC utilizing convolution neural network. The increasing amount of RS images has resulted in two tough problems: intra-class diversity and inter-class similarity. Existing approaches cannot effectively concentrate on both global and local key features simultaneously. Therefore, in this letter, we propose a dual-branch global-local attention network (DBGA-Net) to solve this problem. Specifically, effective complementary features are first extracted from two distinct models. Following that, it was designed that global-local attention module, which concurrently focuses on both the entire image global features and local pertinent parts within it, would be necessary to successfully extract valid information from RS images. In the end, the fusion module efficiently combines the extracted complementary features to yield more comprehensive scene information. To verify the effectiveness of DBGA-Net, experiments are conducted on three scene classification datasets, 99.86%, 97.60% and 96.06% experimental results are obtained, respectively, with significant classification performance of the proposed method compared with the state-of-the-art (SOTA) method. Our code and images are available at https://github.com/ZhouYao1020/DBGANet.