Short text classification is an important problem of natural language processing (NLP), and graph neural networks (GNNs) have been successfully used to solve different NLP problems. However, few studies employ GNN for short text classification, and m...
This study investigates how the learning order between segmentation and classification tasks influences performance and generalization in medical image analysis. We propose a Sequential Swin Transformer framework that reuses a shared Transformer back...
Background/Objectives: The ascending pharyngeal artery (APA) exhibits considerable variability in origin. Understanding its anatomy is essential for head and neck surgery, endovascular procedures, and skull base approaches. This study aimed to (1) sy...
Retinal optical coherence tomography (OCT) classification is often affected by substantial class imbalance and by evaluation bias when images from the same patient appear in both training and test sets. This study proposes MCSP-LightP3R-MoE, a patien...
Wireless Capsule Endoscopy (WCE) is a procedure to examine the human digestive system for potential mucosal polyps, tumours, or bleedings using an encapsulated camera. This work focuses on polyp detection within WCE videos through Machine Learning. W...
Currently, malware shows an explosive growth trend. Demand for classifying malware is also increasing. The problem is the low accuracy of both malware detection and classification. From the static features of malicious families, a new deep learning m...
Honeycomb structure composites are taking an increasing proportion in aircraft manufacturing because of their high strength-to-weight ratio, good fatigue resistance, and low manufacturing cost. However, the hollow structure is very prone to liquid in...
Sequential minimal optimization (SMO) method is an algorithm for solving optimization problems arising from the training process of support vector machines (SVM). The SMO algorithm is mainly used to solve the optimization problem of the objective fun...
The paper presents the comparative analysis of Long short-term memory (LSTM) and one-dimensional convolutional neural networks (1-D CNNs) for tool wear classification in chipboard milling processes. The complexity of sequence data in various fields m...
VIKOR uses the idea of overall utility maximization and individual regret minimization to afford a compromise result for multi-attribute decision-making problems with conflicting attributes. Many researchers have proposed corresponding improvements a...
Earth observation (EO) sensors deliver data at daily or weekly intervals. Most land use and land cover classification (LULC) approaches, however, are designed for cloud-free and mono-temporal observations. The increasing temporal capabilities of toda...
In parallel with the rapid adoption of transport layer security (TLS), malware has utilized the encrypted communication channel provided by TLS to hinder detection from network traffic. To this end, recent research efforts are directed toward malware...
In recent years, hyperspectral remote sensing (HRS) has become common practice for remote analyses of the physiognomy and composition of forests. Supervised classification is often used for this purpose, but demands intensive sampling and analyses, w...
We investigate Ricci–Bourguignon solitons on mixed doubly sequential warped product manifolds. Necessary and sufficient conditions for the existence of such solitons are established, and their implications for Einstein manifolds are analyzed. T...
Existing stream data learning models with limited labeling have many limitations, most importantly, algorithms that suffer from a limited capability to adapt to the evolving nature of data, which is called concept drift. Hence, the algorithm must ove...
Wafer map defect pattern classification supports quality monitoring in semiconductor manufacturing, but public benchmark datasets such as WM-811K exhibit extreme class imbalance, where majority classes can dominate standard metrics. This study aims t...
To improve the accuracy of classification with a small amount of training data, this paper presents a self-learning approach that defines class labels from sequential patterns using a series of past land-cover maps. By stacking past land-cover maps,...
The pivotal role of sleep has led to extensive research endeavors aimed at automatic sleep stage classification. However, existing methods perform poorly when classifying small groups or individuals, and these results are often considered outliers in...
Rice diseases and pest-related damage severely threaten agricultural productivity and global food security. While deep learning has advanced automated crop diagnostics, distinguishing visually similar disease symptoms and damage patterns remains chal...
Hyperspectral images have the characteristics of high spectral resolution and low spatial resolution, which will make the extracted features insufficient and lack detailed information about ground objects, thus affecting the accuracy of classificatio...
This paper proposes a novel extended object tracking (EOT) approach with embedded classification. Traditionally, for extended objects, only tracking is addressed without considering classification. This has serious defects: On the one hand, some prac...
American foulbrood is a dangerous bee disease that attacks the sealed brood. It quickly leads to the death of bee colonies. Efficient diagnosis of this disease is essential. As specific odours are produced when larvae rot, it was investigated whether...
By using computer-aided arrhythmia diagnosis tools, electrocardiogram (ECG) signal plays a vital role in lowering the fatality rate associated with cardiovascular diseases (CVDs) and providing information about the patient’s cardiac health to t...
The classification of shallow sea sediments based on airborne LiDAR bathymetry represents a significant advancement in marine science and engineering. Airborne LiDAR is a highly valuable tool for the classification of seabed sediments, offering high...
This paper develops a new approach to band subset selection (BSS) for hyperspectral image classification (HSIC) which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS), rather than one...
Methods dealing with the problem of Joint Tracking and Classification (JTC) are abundant, among which Simultaneous Tracking and Classification (STC) provides a modularized scheme solving tracking and classification subproblems simultaneously. However...
Polymer based textile composites have gained much attention in recent years and gradually transformed the growth of industries especially automobiles, construction, aerospace and composites. The inclusion of natural polymeric fibres as reinforcement...
Polarimetric Synthetic Aperture Radar (PolSAR) has significant advantages for land-cover classification for its all-weather, day-and-night, and multi-polarization observation capability. Traditional methods often exhibit limited classification accura...
Temporal information plays a very important role in many analysis tasks, and can be encoded in at least two different ways. It can be modeled by discrete sequences of events as, for example, in the business intelligence domain, with the aim of tracki...
This study illustrates the application of conditional simulations to calculate the uncertainty associated with the thickness of bauxite ores. The bauxite deposit of Rondon do Pará in northern Pará State, Brazil, is characterized by a we...
This paper deals with joint tracking and classification (JTC) of multiple targets based on scattering center model (SCM) and wideband radar observations. We first introduce an SCM-based JTC method, where the SCM is used to generate the predicted high...
Applying deep learning models requires design and optimization when solving multifaceted artificial intelligence tasks. Optimization relies on human expertise and is achieved only with great exertion. The current literature concentrates on automating...
The classification of different age groups, such as adult and child, based on handwriting is very important due to its various applications in many different fields. In forensics, handwriting classification helps investigators focus on a certain cate...
The fatty acid (FA) profiles of 240 samples of ricotta whey cheese made from sheep, goat, cow, or water buffalo milk were analyzed by gas-chromatography (GC). Then, sequential preprocessing through orthogonalization (SPORT) was used in order to class...
In recent years, thanks to their numerous nutritional benefits, legumes have been rediscovered and have attracted interest from many consumers. However, these products, the most valuable ones traditionally produced in smaller communities in particula...
The early detection of defective lithium-ion batteries in cellular phones is critical due to the rapid increase in popularity and mass production of cellular phones. It is essential for manufacturers to design an optimal burn-in policy to differentia...
Understanding the response of terrestrial ecosystems to future climate changes would substantially contribute to the scientific assessment of vegetation–climate interactions. Here, the spatiotemporal distribution and dynamics of vegetation in China w...
Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the pr...
Small-scale (e.g., classroom) assessment represents the most common and needed scenario for cognitive diagnostic testing. In such settings, polytomously scored items (e.g., constructed-response tasks) are widely used, as they provide more fine-graine...
In this study, novel deep learning models based on high-speed flame images are proposed to diagnose the combustion instability of a gas turbine. Two different network layers that can be combined with any existing backbone network are established&mdas...
In dermatological research, accurately identifying different types of skin lesions, such as nodules, is essential for early diagnosis and effective treatment. This study introduces a novel method for classifying skin lesions, including nodules, by co...
This paper discusses using computer-aided diagnosis (CAD) to distinguish between hepatocellular carcinoma (HCC), i.e., the most common type of primary liver malignancy and a leading cause of death in people with cirrhosis worldwide, and liver abscess...
Walnuts have been widely investigated because of their chemical composition, which is particularly rich in unsaturated fatty acids, responsible for different benefits in the human body. Some of these fruits, depending on the harvesting area, are cons...
Catastrophic forgetting is a major challenge for deep learning models when they are incrementally trained on a sequence of new data. Reducing this forgetting in image and video data has been the primary research focus, but less attention has been giv...
Fiber-reinforced composites, such as glass fiber-reinforced polymer (GFRP), are widely used across industries but are susceptible to inclusion defects during manufacturing. Detecting and classifying these defects is crucial for ensuring material inte...
Autism disorder, generally known as Autism Spectrum Disorder (ASD) is a brain disorder characterized by lack of communication skills, social aloofness and repetitions in the actions in the patients, which is affecting millions of the people across th...
We address multimodal product attribute prediction of fashion items based on product images and titles. The product attributes, such as type, sub-type, cut or fit, are in a chain format, with previous attribute values constraining the values of the n...
This study’s aim is to examine the environmental performance of the South Korean manufacturing industry and suggest performance-oriented policies. The manufacturing industry is classified into seven sub-sectors based on individual sectoral diff...
This paper presents a nonparametric regression model of categorical time series in the setting of conditional tensor factorization and Bayes network. The underlying algorithms are developed to provide a flexible and parsimonious representation for fu...
Non-charge-based logic devices are promising candidates for the replacement of conventional complementary metal-oxide semiconductors (CMOS) devices. These devices utilize magnetic properties to store or process information making them power efficient...
of 19