This study proposes a convolutional neural network (CNN)-based steganalytic method that allows ternary classification to simultaneously identify WOW and UNIWARD, which are representative adaptive image steganographic algorithms. WOW and UNIWARD have...
A promoter is a small region within the DNA structure that has an important role in initiating transcription of a specific gene in the genome. Different types of promoters are recognized by their different functions. Due to the importance of promoter...
The Welded Bead Bending Test (WBBT), used in Germany to assess the crack-arrest capacity of structural steels, is conducted in accordance with ZTV-ING Part 4 or Deutsche Bahn Standard 918 002-02 and specified in Stahl-Eisen-Prüfblatt 1390. Three...
This study explores the potential of using convolutional neural networks (CNNs) to diagnose dementia early and manage it in an individualized way. Segmented brain magnetic resonance imaging (MRI) images from the Alzheimer’s Disease Neuroimaging...
With the rise of artificial intelligence, many advanced Synthetic Aperture Radar (SAR) ship classifiers based on convolutional neural networks (CNNs) have achieved better accuracies than traditional hand-crafted feature ones. However, most existing C...
With the rapid growth and diversification of malware, accurate multi-class detection remains challenging due to severe class imbalance and limited labeled data. This work presents an image-based malware classification framework that converts executab...
Promoter identification is a fundamental step in understanding bacterial gene regulation mechanisms. However, accurate and fast classification of bacterial promoters continues to be challenging. New methods based on deep convolutional networks have b...
The escalating prevalence of malware poses a significant threat to digital infrastructure, demanding robust yet efficient detection methods. In this study, we evaluate multiple Convolutional Neural Network (CNN) architectures, including basic CNN, Le...
This study explores the application of Convolutional Neural Networks (CNNs) for brain tumor segmentation, leveraging their ability to automatically extract hierarchical features from medical images. CNN architectures like U-Net, V-Net, and ResNet hav...
Sleep position recognition plays a crucial role in diagnosing and managing various health conditions, such as sleep apnea, pressure ulcers, and musculoskeletal disorders. Accurate monitoring of body posture during sleep can provide valuable insights...
This paper presents an automatic recognition system for classifying stones belonging to different Calabrian quarries (Southern Italy). The tool for stone recognition has been developed in the SILPI project (acronym of “Sistema per l’Ident...
Osteoarthritis (OA) is a prevalent joint disorder characterized by symptoms such as pain and stiffness, often leading to loss of function and disability. Knee osteoarthritis (KOA) represents the most prevalent type of osteoarthritis. KOA is usually d...
Brain tumor classification from magnetic resonance imaging, which is also known as MRI, plays a sensitive role in computer-assisted diagnosis systems. In recent years, deep learning models have achieved high classification accuracy. However, their se...
Automated fish species classification is essential for marine biodiversity monitoring, fisheries management, and ecological research. However, challenges such as environmental variability, class imbalance, and computational demands hinder the develop...
Convolutional Neural Networks (CNN) have brought spectacular improvements in several fields of machine vision including object, scene and face recognition. Nonetheless, the impact of this new paradigm on the classification of fine-grained images&mdas...
It is important for food recognition to separate each ingredient within a food image at the pixel level. Most existing research has trained a segmentation network on datasets with pixel-level annotations to achieve food ingredient segmentation. Howev...
There is a massive growth in malicious software (Malware) development, which causes substantial security threats to individuals and organizations. Cybersecurity researchers makes continuous efforts to defend against these malware risks. This research...
Parkinson’s disease (PD) is a progressive neurodegenerative disorder for which dopamine transporter single-photon emission computed tomography (DaT-SPECT) is widely used to support clinical diagnosis. Recent convolutional neural network (CNN)-b...
In regular convolutional neural networks (CNN), fully-connected layers act as classifiers to estimate the probabilities for each instance in classification tasks. The accuracy of CNNs can be improved by replacing fully connected layers with gradient...
Oil palm fresh fruit bunch (FFB) ripeness classification is essential for improving the oil extraction rate, oil quality, and mill processing efficiency. However, RGB-based classification is often limited by insufficient colour information from dark...
Conventional geographic object-based image analysis (GEOBIA) land cover classification methods by using very high resolution images are hardly applicable due to their complex ground truth and manually selected features, while convolutional neural net...
The main objective of the present study was to develop a real-time mineral classification system designed for multiple detection, which integrates classical computer vision techniques with advanced deep learning algorithms. The system employs three C...
One of the most promising research areas in the healthcare industry and the scientific community is focusing on the AI-based applications for real medical challenges such as the building of computer-aided diagnosis (CAD) systems for breast cancer. Tr...
Unobserved fruit crop illnesses are a major threat to agricultural productivity worldwide and frequently cause farmers to suffer large financial losses. Manual field inspection-based disease detection techniques are time-consuming, unreliable, and un...
Spectral confusion among land cover classes is quite common, let alone in a complex and heterogenous system like the semi-arid Mediterranean environment; thus, employing new developments in remote sensing, such as multispectral imagery (MSI) captured...
This study applies deep learning to classify façade features in Tel Aviv’s market-developed apartment housing (1980s–1990s), a vast landscape typically excluded from architectural history due to its non-iconic character. We constru...
Traditional and convolutional neural network (CNN)-based geographic object-based image analysis (GeOBIA) land-cover classification methods prosper in remote sensing and generate numerous distinguished achievements. However, a bottleneck emerges and h...
ECG pattern classification for identifying the progress status of various heart diseases is a typical nonlinear problem. Therefore, deep learning-based automatic ECG diagnosis is being widely studied, and for this purpose, the CNN is mainly used to c...
The physical features of fruit and vegetables make the task of vision-based classification of fruit and vegetables challenging. The classification of fruit and vegetables at a supermarket self-checkout poses even more challenges due to variable light...
To address the high computational cost and significant resource consumption of radar Doppler-based target recognition, which limits its application in real-time embedded systems, this paper proposes a lightweight CNN (Convolutional Neural Network) ap...
Every pixel in a hyperspectral image contains detailed spectral information in hundreds of narrow bands captured by hyperspectral sensors. Pixel-wise classification of a hyperspectral image is the cornerstone of various hyperspectral applications. No...
Data on the distribution of tree species are often requested by forest managers, inventory agencies, foresters as well as private and municipal forest owners. However, the automated detection of tree species based on passive remote sensing data from...
Recent advances in deep convolutional neural networks (D-CNNs) have improved automated welding defect inspection. This study presents an explainable comparative framework for multi-class classification of defects in Aluminium 5083 TIG weld joints usi...
Arrhythmia is the main cause of sudden cardiac death, and ECG signal analysis is a common method for the noninvasive diagnosis of arrhythmia. In this paper, we propose an arrhythmia classification model based on the combination of a channel attention...
Motor Current Signature Analysis (MCSA) is a commonly used non-invasive method for diagnosing faults in electric motors. Although MCSA provides significant advantages—current signals are easy to acquire and inherently robust against noise&mdash...
The United States leads in corn production and consumption in the world with an estimated USD 50 billion per year. There is a pressing need for the development of novel and efficient techniques aimed at enhancing the identification and eradication of...
The widespread adoption of modern cryptographic protocols has rendered traditional packet payload inspection obsolete, pushing network management toward deep learning-based behavioral profiling. While Convolutional Neural Networks (CNNs) have emerged...
Accurate and resource-efficient automated diagnosis is a cornerstone of modern agricultural expert systems. While Convolutional Neural Networks (CNNs) have established benchmarks in plant pathology, their ability to capture long-range spatial depende...
The further exploration of the capacitive ECG (cECG) is hindered by frequent fluctuations in signal quality from body movement and changes in sleep position. The processing framework must be fundamentally adapted to make full use of this signal. Ther...
The BeiDou-3 Satellite Navigation System (BDS-3) serves as a critical national space-based information infrastructure. However, the signals received at user terminals are characterized by power levels that lie significantly below the noise floor, ren...
This study aims to autonomously detect and classify Unmanned Aerial Vehicles (UAVs), which are becoming increasingly widespread, based on their emitted radio frequency (RF) signals in highly noisy and interference-prone RF environments where traditio...
Sustainability has increasingly emphasized the importance of recycling and repairing materials. Cutting tools, such as milling cutters and drills, play a crucial role due to the high demands placed on products used in CNC machining. As a result, the...
In recent years, convolutional neural networks (CNNs) have achieved competitive performance in the field of ground-based cloud image (GCI) classification. Proposed CNN-based methods can fully extract the local features of images. However, due to the...
For real-time target classification, a study was conducted to improve the AI-based target classification performance using RCS measurements that are vulnerable to noise, but can be obtained quickly. To compensate for the shortcomings of the RCS, a 1D...
Strip steel surface defects occur frequently during the manufacturing process, and these defects cause hidden risks in the use of subsequent strip products. Therefore, it is crucial to classify the strip steel’s surface defects accurately and efficie...
Acoustic scene classification (ASC) categorizes an audio file based on the environment in which it has been recorded. This has long been studied in the detection and classification of acoustic scenes and events (DCASE). This presents the solution to...
Hyperspectral images (HSIs) are characterized by hundreds of spectral bands. The goal of HSI is to associate the pixel with a corresponding category label by analyzing subtle differences in the spectrum. Due to their excellent local context modeling...
Unmanned Aerial Vehicles (UAVs) or drones have found a wide range of useful applications in society over the past few years, but there has also been a growth in the use of UAVs for malicious purposes. One way to manage this issue is to allow reportin...
Deep learning techniques have brought substantial performance gains to remote sensing image classification. Among them, convolutional neural networks (CNN) can extract rich spatial and spectral features from hyperspectral images in a short-range regi...
Deep learning artificial intelligence technology, which has the advantages of nonlinear mapping ability, massive information extraction ability, spatial-temporal modeling ability, and so on, provides new ideas and methods for further improving the ac...
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