The detection and identification of non-random patterns is an important task in statistical process control (SPC). When a non-random pattern appears on a control chart, it means that there are assignable causes which will gradually deteriorate the pr...
As one of the fundamental tasks in remote sensing (RS) image understanding, multi-label remote sensing image scene classification (MLRSSC) is attracting increasing research interest. Human beings can easily perform MLRSSC by examining the visual elem...
Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading cause of global mortality. While TB detection can be performed through chest X-ray (CXR) analysis, numerous studies have leveraged AI to automate and enhance the diagnostic pr...
Chest X-ray evaluation is challenging due to its high demand and the complexity of diagnoses. In this study, we propose an optimized deep learning model for the multi-label classification of chest X-ray images. We leverage pretrained convolutional ne...
The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NI...
Information regarding the conditions of roads is a safety concern when driving. In Bangkok, public weather sensors such as weather stations and rain sensors are insufficiently available to provide such information. On the other hand, a number of exis...
Purpose: Tongue image analysis for disease diagnosis is an ancient, traditional, non-invasive diagnostic technique widely used by traditional medicine practitioners. Deep learning-based multi-label disease detection models have tremendous potential f...
In this article, we propose a multi-label convolution neural network (MLCNN)-aided transmit antenna selection (AS) scheme for end-to-end multiple-input multiple-output (MIMO) Internet of Things (IoT) communication systems in correlated channel condit...
In the modern electromagnetic environment, the intra-pulse modulations of radar emitter signals have become more complex. Except for the single-component radar signals, dual-component radar signals have been widely used in the current radar systems....
News media always pursue informing the public at large. It is impossible to overestimate the significance of understanding the semantics of news coverage. Traditionally, a news text is assigned to a single category; however, a piece of news may conta...
In the complex battlefield electromagnetic environment, multiple jamming signals can enter the radar receiver simultaneously due to the development of jammers and modulation technology. The received compound jamming signals aggravate the difficulty o...
Convolutional Neural Networks (CNNs) have proven to be very effective in image classification due to their status as a powerful feature learning algorithm. Traditional approaches have considered the problem of multiclass classification, where the goa...
Supervised deep learning methods have been widely utilized in hyperspectral image (HSI) classification tasks. However, acquiring a large number of reliably labeled samples to train deep networks is not always possible in practical HSI applications du...
Proper and accurate mix proportion is deemed to be crucial for the concrete in service to implement its structural functions in a specific environment and structure. Neither existing testing methods nor previous studies have, to date, addressed the p...
With the rapid developments of Internet technology, a mass of law cases is constantly occurring and needs to be dealt with in time. Automatic classification of law text is the most basic and critical process in the online law advice platform. Deep ne...
The increasing density and complexity of electromagnetic signals have brought new challenges to multi-component radar signal recognition. To address the problem of low recognition accuracy under low signal-to-noise ratios (SNR) in adapting the common...
Data in the form of images are now generated at an unprecedented rate. A case in point is remote sensing images (RSI), now available in large-scale RSI archives, which have attracted a considerable amount of research on image classification within th...
Background/Objectives: Chest radiography remains a fundamental diagnostic tool for evaluating thoracic disease, yet its interpretation requires considerable time and specialized expertise. Worldwide shortages of trained radiologists can lead to lengt...
Mixtures analysis can provide more information than individual components. It is important to detect the different compounds in the real complex samples. However, mixtures are often disturbed by impurities and noise to influence the accuracy. Purific...
The recent Coronavirus Disease 2019 (COVID-19) pandemic has put a tremendous burden on global health systems. Medical practitioners are under great pressure for reliable screening of suspected cases employing adjunct diagnostic tools to standard poin...
We examine a classification task in which signals of naturally occurring earthquakes are categorized ranging from minor to major, based on their magnitude. Generalized to a single-label classification task, most prior investigations have focused on a...
Multilabel learning goes beyond standard supervised learning models by associating a sample with more than one class label. Among the many techniques developed in the last decade to handle multilabel learning best approaches are those harnessing the...
Supervised learning for Unmanned Aerial Vehicle (UAVs) visual-based navigation raises the need for reliable datasets with multi-task labels (e.g., classification and regression labels). However, current public datasets have limitations: (a) Outdoor d...
The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of...
Three frequently encountered problems—a variety of fault types, data with insufficient labels, and missing fault types—are the common challenges in the early fault diagnosis of space flywheel rotor systems. Focusing on the above issues, t...
Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representi...
Advances in deep learning are impressive in various fields and have achieved performance beyond human capabilities in tasks such as image classification, as demonstrated in competitions such as the ImageNet Large Scale Visual Recognition Challenge. N...
Automatic computer security inspection of X-ray scanned images has an irresistible trend in modern life. Aiming to address the inconvenience of recognizing small-sized prohibited item objects, and the potential class imbalance within multi-label obje...
Non-intrusive load monitoring (NILM) is the main method used to monitor the energy footprint of a residential building and disaggregate total electrical usage into appliance-related signals. The most common disaggregation algorithms are based on the...
Bioactive peptides are typically small functional peptides with 2–20 amino acid residues and play versatile roles in metabolic and biological processes. Bioactive peptides are multi-functional, so it is vastly challenging to accurately detect a...
10 September 2024
Background: Evaluating chest X-rays is a complex and high-demand task due to the intrinsic challenges associated with diagnosing a wide range of pulmonary conditions. Therefore, advanced methodologies are required to categorize multiple conditions fr...
This article addresses the non-trivial problem of classifying thoracic diseases in chest X-ray (CXR) images. A single CXR image may exhibit multiple diseases, making this a multi-label classification problem. Additionally, the inherent class imbalanc...
The identification of minerals is indispensable in geological analysis. Traditional mineral identification methods are highly dependent on professional knowledge and specialized equipment which often consume a lot of labor. To solve this problem, som...
Featured Application: The method presented in this paper can be applied in medical computer systems for supporting medical diagnosis.
Abstract: Thoracic radiography (chest X-ray) is an inexpensive but effective and widely used medical imaging procedur...
This work presents a deep learning method for scene description. (1) Background: This method is part of a larger system, called BlindSys, that assists the visually impaired in an indoor environment. The method detects the presence of certain objects,...
Detecting objects such as aircraft and ships is a fundamental research area in remote sensing analytics. Owing to the prosperity and development of CNNs, many previous methodologies have been proposed for object detection within remote sensing images...
MicroRNAs (miRNA) are small noncoding RNA sequences consisting of about 22 nucleotides that are involved in the regulation of almost 60% of mammalian genes. Presently, there are very limited approaches for the visualization of miRNA locations present...
Chest X-rays are vital in healthcare for diagnosing various conditions due to their low Radiation exposure, widespread availability, and rapid interpretation. However, their interpretation requires specialized expertise, which can limit scalability a...
Breast cancer profoundly affects women’s lives; its early diagnosis and treatment increase patient survival chances. Mammography is a common screening method for breast cancer, and many methods have been proposed for automatic diagnosis. Howeve...
Plant diseases pose a significant threat to global food security, affecting crop yield, quality, and overall agricultural productivity. Traditionally, diagnosing plant diseases has relied on time-consuming visual inspections by experts, which can oft...
The integration of Context-Aware Systems (CASs) in Future Operating Rooms (FORs) aims to enhance surgical workflows and outcomes through real-time data analysis. CASs require accurate classification of surgical tools, enabling the understanding of su...
Conventional remote sensing image retrieval (RSIR) systems perform single-label retrieval with a single label to represent the most dominant semantic content for an image. Improved spatial resolution dramatically boosts the remote sensing image scene...
In this paper, we present a portable camera-based method for helping visually impaired (VI) people to recognize multiple objects in images. This method relies on a novel multi-label convolutional support vector machine (CSVM) network for coarse descr...
This work discusses the challenges of multi-label image classification and presents a novel Efficient Shuffle Net (EffShuffNet) based on a convolutional neural network (CNN) architecture to address these challenges. Multi-label classification is diff...
Multi-teacher knowledge distillation is a powerful technique that leverages diverse information sources from multiple pre-trained teachers to enhance student model performance. However, existing methods often overlook the challenge of effectively tra...
The 12-lead resting electrocardiogram (ECG) is commonly used in hospitals to assess heart health. The ECG can reflect a variety of cardiac abnormalities, requiring multi-label classification. However, the diagnosis results in previous studies have be...
The primary objective of this study is to explore how machine learning techniques can be incorporated into the analysis of material deformation. Neural network algorithms are applied to the study of mechanical properties of wire rods subjected to col...
Customer complaints play an important role in the adjustment of business operations and improvement of services, particularly in the aviation industry. However, extracting adequate textual features to perform a multi-label classification of complaint...
Microscopic tissue analysis is the key diagnostic method needed for disease identification and choosing the best treatment regimen. According to the Global Cancer Observatory, approximately two million people are diagnosed with colorectal cancer each...
The quality of disposable bamboo chopsticks directly affects consumers’ usage experience and health safety. Therefore, quality inspection is particularly important, and multi-label classification of defects can better meet the refined demands o...
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