Classification Method Research Articles

A Clinical and Radiographical Study on Augmentation of Fracture Healing in Long Bones of Dogs Using Synthetic Nanohydroxyapatite

Long bone fractures in six dogs were stabilized with intramedullary pinning and synthetic nanohydroxyapatite (Nha) graft material was deposited at the fracture site intra-operatively. The fractures were classified according to AO ASIF method of classification and the outcome of open reduction and internal fixation was evaluated based on clinical and radiological studies, on 7th, 15th, 30th and 45th post- operative days. Post-operative weight bearing and angulation and lameness grading, radiographic evaluation for alignment and implant stability were also evaluated. The stabilization technique proved to be satisfactory in all the cases. The radiographic score indicated better long bone fracture healing with Nha alone as a graft.

Efficient federated learning for pediatric pneumonia on chest X-ray classification

According to the World Health Organization (WHO), pneumonia kills about 2 million children under the age of 5 every year. Traditional machine learning methods can be used to diagnose chest X-rays of pneumonia in children, but there is a privacy and security issue in centralizing the data for training. Federated learning prevents data privacy leakage by sharing only the model and not the data, and it has a wide range of application in the medical field. We use federated learning method for classification, which effectively protects data security. And for the data heterogeneity phenomenon existing in the actual scenario, which will seriously affect the classification effect, we propose a method based on two-end control variables. Specifically, based on the classical federated learning FedAvg algorithm, we modify the loss function on the client side by adding a regular term or a penalty term, and add momentum after the average aggregation on the server side. The federated learning approach prevents the data privacy leakage problem compared to the traditional machine learning approach. In order to solve the problem of low classification accuracy due to data heterogeneity, our proposed method based on two-end control variables achieves an average improvement of 2% and an accuracy of 98% on average, and 99% individually, compared to the previous federated learning algorithms and the latest diffusion model-based method. The classification results and methodology of this study can be utilized by clinicians worldwide to improve the overall detection of pediatric pneumonia.

Graph node classification algorithm based on similarity random walk aggregation

Aiming at the relatively low accuracy of methods such as MLP and GCN in heterogeneous graph node classification tasks, this paper proposes a graph neural network based on similarity random walk aggregation (SRW-GNN). Most existing node classification methods usually take neighbor nodes as neighborhoods, but the target node and its neighbors in heterogeneous graphs usually belong to different categories. To reduce the impact of heterogeneity on node embedding, SRW-GNN uses the similarity between nodes as probability to perform random walks and takes the sampled paths as neighborhoods to obtain more homogeneous information. The order in which nodes appear in the path is particularly critical for capturing neighborhood information. However, most existing GNN aggregators are insensitive to node order. This paper introduces a path aggregator based on recurrent neural network (RNN) to simultaneously extract the features and order information of nodes in the path. In addition, nodes have different preferences for different paths. In order to adaptively learn the importance of different paths in node encoding, an attention mechanism is used to dynamically adjust the contribution of each path to the final embedding. Experimental results on multiple commonly used heterogeneous graph datasets show that the accuracy of this method is significantly better than that of MLP, GCN, H2GCN, HOG-GCN and other methods, verifying its effectiveness in heterogeneous graph node classification tasks.

Social Event Classification Based on Multimodal Masked Transformer Network

The key to multimodal social event classification is to fully and accurately utilize the features of both image and text modalities. However, most existing methods have the following limitations: (1) they simply concatenate the image features and text features of the event, and (2) there is irrelevant contextual information between different modalities, which leads to mutual interference. Therefore, it is not enough to only consider the relationship between the modalities of multimodal data, but also the irrelevant contextual information (i.e., regions or words) between the modalities. To overcome these limitations, a novel social event classification method based on multimodal masked transformer network (MMTN) is proposed. A better representation of text and image is learned through an image-text encoding network. Then, the obtained image and text representations are input into the multimodal masked transformer network to fuse the multimodal information, and the relationship between the modalities of multimodal information is modeled by calculating the similarity between the multimodal information, masking the irrelevant context between the modalities. Extensive experiments on two benchmark datasets show that the proposed multimodal masked transformer network model achieves state-of-the-art performance.

Imbalanced image classification algorithm based on fine-grained analysis

Fine-grained attribute analysis and data imbalance have always been research hotspots in the field of computer vision. Due to the complexity and diversity of fine-grained attribute images, traditional image classification methods have shortcomings in paying attention to fine-grained attributes of images and perform poorly when dealing with imbalanced data sets. To overcome these problems, this study proposes a fine-grained image threshold classification algorithm based on deep metric learning. By introducing a metric learning method, the focus on fine-grained attributes of images is enhanced. At the same time, by applying pairwise loss and proxy loss, the classification accuracy of the model is improved and the model convergence speed is accelerated. In order to deal with the problem of data imbalance, a classifier based on threshold analysis is designed. The classifier uses threshold analysis technology to achieve multi-level classification of fine-grained images, thereby improving the problem of low classification accuracy of a few categories in imbalanced data sets. Experimental results show that the proposed fine-grained image threshold classification algorithm based on deep metric learning is significantly superior to other methods in terms of classification accuracy.

Semi-Supervised Subcategory Centroid Alignment-Based Scene Classification for High-Resolution Remote Sensing Images

It is usually hard to obtain adequate annotated data for delivering satisfactory scene classification results. Semi-supervised scene classification approaches can transfer the knowledge learned from previously annotated data to remote sensing images with scarce samples for satisfactory classification results. However, due to the differences between sensors, environments, seasons, and geographical locations, cross-domain remote sensing images exhibit feature distribution deviations. Therefore, semi-supervised scene classification methods may not achieve satisfactory classification accuracy. To address this problem, a novel semi-supervised subcategory centroid alignment (SSCA)-based scene classification approach is proposed. The SSCA framework is made up of two components, namely the rotation-robust convolutional feature extractor (RCFE) and the neighbor-based subcategory centroid alignment (NSCA). The RCFE aims to suppress the impact of rotation changes on remote sensing image representation, while the NSCA aims to decrease the impact of intra-category variety across domains on cross-domain scene classification. The SSCA algorithm and several competitive approaches are validated on two datasets to demonstrate its effectiveness. The results prove that the proposed SSCA approach performs better than most competitive approaches by no less than 2% overall accuracy.

Mental illness detection through harvesting social media: a comprehensive literature review

Mental illness is a common disease that at its extremes leads to personal and societal suffering. A complicated multi-factorial disease, mental illness is influenced by a number of socioeconomic and clinical factors, including individual risk factors. Traditionally, approaches relying on personal interviews and filling out questionnaires have been employed to diagnose mental illness; however, these manual procedures have been found to be frequently prone to errors and unable to reliably identify individuals with mental illness. Fortunately, people with mental illnesses frequently express their ailments on social media, making it possible to more precisely identify mental disease by harvesting their social media posts. This study offers a thorough analysis of how to identify mental illnesses (more specifically, depression) from users’ social media data. Along with the explanation of data acquisition, preprocessing, feature extraction, and classification techniques, the most recent published literature is presented to give the readers a thorough understanding of the subject. Since, in the recent past, the majority of the relevant scientific community has focused on using machine learning (ML) and deep learning (DL) models to identify mental illness, so the review also focuses on these techniques and along with their detail, their critical analysis is presented. More than 100 DL, ML, and natural language processing (NLP) based models developed for mental illness in the recent past have been reviewed, and their technical contributions and strengths are discussed. There exist multiple review studies, however, discussing extensive recent literature along with the complete road map on how to design a mental illness detection system using social media data and ML and DL classification methods is limited. The review also includes detail on how a dataset may be acquired from social media platforms, how it is preprocessed, and features are extracted from it to employ for mental illness detection. Hence, we anticipate that this review will help readers learn more and give them a comprehensive road map for identifying mental illnesses using users’ social media data.

Text-aware small sample remote sensing image classification based on contrastive learning

In view of the fact that existing methods mainly use a single modality of remote sensing images to solve the problem of low similarity of the same category, a remote sensing image classification method based on multimodal learning is proposed. Firstly, the spatial features of the image are corrected and the image encoder is pre-trained using contrastive learning to generate image features, and the text encoder is used to generate text features. Secondly, a feature decoder is introduced to obtain text-aware visual features, and a new attention mechanism method is proposed in the feature fusion stage. Then, a new image encoder is designed to improve the classification accuracy. Finally, the similarity between the support set and the query set is calculated to further predict the category. Experiments are conducted on the NWPU-RESISC45, AID, and UC Merced datasets. Its 5-way 5-shot accuracy reaches 86.46%、85.89%and 80.32%outperforms the existing small-sample remote sensing image classification methods, respectively.

Unsupervised Change Detection Methods Applied to Landslide Mapping: Case Study in São Sebastião, Brazil

ABSTRACTLandslides represent a growing global geological hazard, further intensified by climate‐induced changes. Remote sensing data, through its capacity for repetitive collection and change detection techniques, that compare and quantify the spatio‐temporal alterations over time, plays a critical role in landslide detection. Considering the February 2023 São Sebastião event and Sentinel‐2 imagery, we assessed diverse unsupervised change detection techniques, encompassing both traditional and recent machine learning‐based approaches. Notably, the Floating References (FR) and Homogeneous Blocks Single‐class Classification (HBSC) methods outperform classic approaches and deliver the most accurate results with F1‐Score and kappa coefficient exceeding 0.96 and 0.92, respectively. These outcomes demonstrate the efficacy of machine learning in automating landslide delineation and underscore the necessity of meticulous data and parameter selection in achieving high‐accuracy automatic landslide mapping. Lastly, this study fills a significant gap in the existing literature by evaluating unsupervised change detection methods for landslide mapping within the Brazilian context.

Application of Label Correlation in Multi-Label Classification: A Survey

Multi-Label Classification refers to the classification task where a data sample is associated with multiple labels simultaneously, which is widely used in text classification, image classification, and other fields. Different from the traditional single-label classification, each instance in Multi-Label Classification corresponds to multiple labels, and there is a correlation between these labels, which contains a wealth of information. Therefore, the ability to effectively mine and utilize the complex correlations between labels has become a key factor in Multi-Label Classification methods. In recent years, research on label correlations has shown a significant growth trend internationally, reflecting its importance. Given that, this paper presents a survey on the label correlations in Multi-Label Classification to provide valuable references and insights for future researchers. The paper introduces multi-label datasets across various fields, elucidates and categorizes the concept of label correlations, emphasizes their utilization in Multi-Label Classification and associated subproblems, and provides a prospect for future work on label correlations.

Evaluating Methods for Violence Classification and Firearm Detection in Indoor CCTV Environment

The adoption of security systems based on computer vision for violence detection has the potential to significantly improve safety in various public and private properties. However, developing these systems can be extremely challenging.We can choose to use classification models to identify violence in images or also use object detection models to identify firearms, which may indicate robbery. Additionally, when developing such systems focused on private environments, we encounter specific challenges, such as obtaining appropriate datasets to train the algorithms. Many publicly available datasets for violence detection consist of outdoor images, with elements such as streets and cars, which do not adequately reflect the nuances and unique characteristics of private properties. In this work, we evaluate both learned and handcrafted features to classify videos as 'violence' or 'non-violence' across a variety of datasets, including a new dataset composed exclusively of closed-circuit television (CCTV) images. Additionally, we propose a new dataset for firearm detection in CCTV images and conduct some experiments using YoloV8. In this way, we hope to provide a clearer insight into the possible decisions when developing a security system for indoor environments.

Multi-granularity self-attention mechanisms for few-shot Learning

Few-shot learning aims to classify novel data categories with limited labeled samples. Although metric-based meta-learning has shown better generalization ability as a few-shot classification method, it still faces challenges in handling data noise and maintaining inter-sample distance stability. To address these issues, our study proposes an innovative few-shot learning approach to enhance image features' global and local semantic representation. Initially, our method employs a multiscale residual module to facilitate extracting multi-granularity features within images. Subsequently, it optimizes the fusion of local and global features using the self- attention mechanism inherent in the Transformer module. Additionally, a weighted metric module is integrated to improve the model's resilience against noise interference. Empirical evaluations on CIFAR-FS and MiniImageNet few-shot datasets using 5-way 1-shot and 5-way 5-shot scenarios demonstrate the effectiveness of our approach in capturing multi-level and multi-granularity image representations. Compared to other methods, our method improves accuracy by 2.63% and 1.27% for 5-shot scenes on these two datasets. The experimental results validate the efficacy of our model in significantly enhancing few-shot image classification performance.

A cardiac audio classification method based on image expression of multidimensional features.

Heart sound auscultation plays a crucial role in the early diagnosis of cardiovascular diseases. In recent years, great achievements have been made in the automatic classification of heart sounds, but most methods are based on segmentation features and traditional classifiers and do not fully exploit existing deep networks. This paper proposes a cardiac audio classification method based on image expression of multidimensional features (CACIEMDF). First, a 102-dimensional feature vector is designed by combining the characteristics of heart sound data in the time domain, frequency domain and statistical domain. Based on the feature vector, a two-dimensional feature projection space is constructed by PCA dimensionality reduction and the convex hull algorithm, and 102 pairs of coordinate representations of the feature vector in the two-dimensional space are calculated. Each one-dimensional component of the feature vector corresponds to a pair of 2D coordinate representations. Finally, the one-dimensional feature component value and its divergence into categories are used to fill the three channels of a color image, and a Gaussian model is used to dye the image to enrich its content. The color image is sent to a deep network such as ResNet50 for classification. In this paper, three public heart sound datasets are fused, and experiments are conducted using the above methods. The results show that for the two-classification/five-classification task of heart sounds, the method in this paper can achieve a classification accuracy of 95.68%/94.53% when combined with the current deep network.

The Functional Features of Chunks in Journal Articles of Aquaculture

This study investigates the functional similarities and differences of four-word chunks in the academic discourse of aquaculture by Chinese and international scholars based on Hyland’s functional classification method within a corpus-driven approach. The findings reveal that, compared to their international counterparts, Chinese scholars significantly utilize more four-word chunks. Functionally, Chinese scholars frequently employ quantification, structure, framing, and engagement chunks, underscoring the importance they assign to the logic of discourse and the interaction between authors and readers. The infrequent use of description chunks suggests that it is essential for Chinese scholars to fully appreciate the significance of describing research objects, methods, and results in order to convey the foundational and experimental nature of hard science research. Furthermore, the structures of chunks used by Chinese and international scholars to express the same discourse functions differ. The expression of data indication among Chinese scholars appears more solidified. These research results can offer valuable references for academic writing.

CTCTime: A New Model for Unidimensional Time Series Classification

One-dimensional time series classification has always been an important research direction, playing an irreplaceable role in various fields. With the rapid development of deep learning, most traditional time series classification methods have gradually been replaced by neural network-based classification methods. At present, in the field of time series classification, models that perform well are mostly based on Convolutional Neural Networks (CNNs), while models with the Transformer architecture, which have shown outstanding performance in natural language processing and computer vision, do not stand out. In order to change this situation, We have investigated the feasibility of training models based on a single Transformer architecture directly on small datasets without additional data processing. Furthermore, we propose a new model called CTCTime, which combines the Transformer architecture with CNNs to address one-dimensional time series classification problems. We compared CTCTime with 13 traditional algorithms on 44 datasets from the UCR archive and with 7 advanced methods on 85 datasets. The UCR datasets are classic datasets for one-dimensional time series classification tasks. The experimental results demonstrate its feasibility, accuracy, and scalability.

Acoustic detection of Unmanned Aerial Vehicles in flight with respect to the signal-to-noise ratio using machine learning

Nowadays, unmanned Aerial vehicles (UAV) are more and more commonly found in the world for a variety of activities. It has become an important task to detect and classify them in order to manage their potential malicious intentions. Current detection and classification methods mostly rely on feature extraction and the use of artificial neural networks that can be heavily impacted by the signal-to-noise ratio (SNR). The approach proposed aims to solve a two-class problem (presence or absence of an UAV) by extracting information on the content of the acoustic source, and then presenting it as input to a neural network. The features used are strongly inspired by the harmonic sound emitted by UAV. Attention is also given on the SNR of the scene. Indeed, various disturbing sources such as trains, helicopters or footsteps are involved from an open library. These signals are added to our recordings of UAVs in flight by tuning their amplitude to obtain different SNRs. The aim is to establish the performance of the detection system as a function of the SNR of the scene.

Generating comprehensive lithium battery charging data with generative AI

In optimizing the performance and extending the lifespan of lithium batteries, accurate state prediction is crucial. Traditional regression and classification methods have achieved some success in predicting battery states. However, the effectiveness of these data-driven approaches largely depends on the availability and quality of public datasets. Additionally, generating electrochemical data primarily through battery experiments is a lengthy and costly process, making it extremely difficult to obtain high-quality data. This difficulty, combined with data incompleteness, significantly affects prediction accuracy. To address these challenges, this study introduces End of Life (EOL) and Equivalent Cycle Life (ECL) as supervised conditions for generative AI models. By integrating an embedding layer into the CVAE model, we developed the Refined Conditional Variational Autoencoder (RCVAE). Through preprocessing data into a quasi-video format, coupled with customized training and inference algorithms, the RCVAE generates the required charging data in real time based on the battery’s ECL and EOL. This avoids storing irrelevant information, saving space and resources. RCVAE uses a lightweight architecture, enabling fast generation of necessary voltage, current, temperature, and charging capacity data. This approach provides users with a comprehensive electrochemical dataset, pioneering a new research domain for the artificial synthesis of lithium battery data. Furthermore, based on the detailed synthetic data, various battery state indicators can be calculated, offering new perspectives and possibilities for lithium battery performance prediction.

Merging galaxies in isolated environments. II. Evolution of star formation and accretion activity during the merging process

It is now well known that certain massive galaxies undergo enormous enhancements in their star formation rate (SFR) when they undergo major mergers. These enhancements can be as high as 100 times the SFR of unperturbed galaxies of the same stellar mass. Previous works have found that the size of this boost in star formation (SF) is related to the morphology of and the proximity to the companion. The same trend has also been observed for the fraction of active galactic nuclei (AGN), where galaxies that are closer together tend to have higher AGN fractions. We aim to analyse the SF enhancement and AGN fraction evolution during the merger process by using a more timeline-like merger sequence. Additionally, we aim to determine the relation between the SF enhancement in mergers and the morphology of the galaxies involved. Taking advantage of the stellar masses (M$_*$) and SFRs of the sim 600 nearby isolated mergers obtained in our previous study, we calculated the distance of each of our galaxies from the star-forming main sequence (MS; specific SFR (sSFR)/sSFR$_ MS $), which werefer to as the SF mode. We then analysed how the SF mode varies during the merger process as a function of morphology and M$_*$. Additionally, we analysed the AGN content of our mergers, using multiple diagnostics based on emission line ratios and WISE colours. We observed that, overall, merging galaxies show an SF mode that is governed by their morphology. Spirals typically show high SF mode values, while highly disturbed (HD) galaxies are generally even more enhanced (median values of +0.8 dex and +1.0 dex above the MS, respectively). In contrast, elliptical and lenticular galaxies show the lowest SF modes, as expected. However, even they show SF enhancement compared to their unperturbed counterparts. For example, their median SF mode is just within the 1-sigma scatter of the MS, and this can occur even before the galaxies have coalesced. We observed a trend for the SF mode to gradually increase with increasing merger stage. We did not find a clear dependency of the observed AGN fraction on the merger stage for the majority of our classification methods. We find mergers can significantly enhance SF in galaxies of all morphologies. For early-type galaxies, this could suggest that some gas was present prior to the merger, which may be triggered to form stars by the tidal interaction. As the SF enhancement continues throughout the merger process, this suggests that the enhancement may be a long-lived event, contrary to the short starbursts seen in some models.

Local-global feature fusion network for hyperspectral image classification

ABSTRACT Hyperspectral images (HSIs) are rich in spatial and spectral information, making them crucial for accurate classification. While existing convolutional neural network (CNN) methods have shown promise in HSI classification, they primarily focus on local features and struggle with establishing long-range dependencies. To address these limitations, we propose a Local-Global Feature Fusion Network (LGFFN). LGFFN effectively learns joint local-global information from HSIs. Firstly, we employ a group-general convolution parallel module (GGCPM) to extract both local and global spectral features. Next, we utilize parallel branches: a CNN branch for capturing fine-grained spatial information and an improved transformer encoder (ITE) branch for capturing long-distance spatial-spectral features and exploiting semantic information. Finally, we elegantly integrate the diverse features obtained from these branches to obtain classification results. We validate our method on four datasets and compare it with six state-of-the-art hyperspectral classification methods. Our experimental results demonstrate that LGFFN achieves high classification accuracy, outperforming existing methods.

Laser-Induced Breakdown Spectroscopy as an Accurate Forensic Tool for Bone Classification and Individual Reassignment.

This article provides a detailed discussion of the evidence available to date on the application of laser-induced breakdown spectroscopy (LIBS) and supervised classification methods for the individual reassignment of commingled bone remains. Specialized bone chemistry studies have demonstrated the suitability of bone elemental composition as a distinct individual identifier. Given the widely documented ability of the LIBS technique to provide elemental emission spectra that are considered elemental fingerprints of the samples analyzed, the analytical potential of this technique has been assessed for the investigation of the contexts of commingled bone remains for their individual reassignment. The LIBS bone analysis consists of the direct ablation of micrometric portions of bone samples, either on their surface or within their internal structure. To produce reliable, accurate, and robust bone classifications, however, the available evidence suggests that LIBS spectral information must be processed by appropriate methods. When comparing the performance of seven different supervised classification methods using spectrochemical LIBS data for individual reassociation, those employing artificial intelligence-based algorithms produce analytically conclusive results, concretely individual reassociations with 100% accuracy, sensitivity, and robustness. Compared to LIBS, other techniques used for the purpose of interest exhibit limited performance in terms of robustness, sensitivity, and accuracy, as well as variations in these results depending on the type of bones used in the classification. The available literature supports the suitability of the LIBS technique for reliable individual reassociation of bone remains in a fast, simple, and cost-effective manner without the need for complicated sample processing.