Accurate and timely disaster classification is critical for effective disaster management and emergency response. This study proposes an improved ResNet-50-based deep learning model to classify seven types of natural disasters, including earthquake,...
Automaticidentification and prevention of leaf diseases and insect pests on fruit crops represent a key trend in the development of smart agriculture. In order to address the limitations of existing models with low identification rates of apple leaf...
Background/Objectives: Pneumonia is a critical lung infection that demands timely and precise diagnosis, particularly during the evaluation of chest X-ray images. Deep learning is widely used for pneumonia detection but faces challenges such as poor...
The present study proposes a Real-Time Pose Estimation technique using OpenPose based on ResNet-50 that enables rapid safety prevention and accident detection among field workers. Field workers perform tasks in high-risk environments, and accurate Po...
This study introduces Hybrid-DC, a hybrid deep-learning model integrating ResNet-50 and Vision Transformer (ViT) for high-accuracy steel surface defect classification. Hybrid-DC leverages ResNet-50 for efficient feature extraction at both low and hig...
The primary objective of this study is to develop an advanced, automated system for the early detection and classification of leaf diseases in potato plants, which are among the most cultivated vegetable crops worldwide. These diseases, notably early...
The vibration signal of a pump is often used for analysis in the study of hydraulic-pump fault diagnosis methods. In this study, for the analysis, sound signals were used, which can be used to acquire data in a non-contact manner to expand the use sc...
Pedestrians, as the most vulnerable road users in traffic crashes, prompt transportation researchers and urban planners to prioritize pedestrian safety due to the elevated risk and growing incidence of injuries and fatalities. Thorough pedestrian cra...
Early diagnosis and preventive measures are necessary to mitigate diseases’ impact on the yield of Colocasia esculenta (Taro). This study addresses the challenges of Taro disease identification by employing two key strategies: integrating expla...
Objective: Segmentation of liver and liver tumors from 3D medical images is a challenging and computationally expensive task. Organs that are in close proximity may have similar shape, texture, and intensity, which makes it difficult for accurate seg...
Climate change has exacerbated severe rainfall events, leading to rapid and unpredictable fluctuations in river water levels. This environment necessitates the development of real-time, automated systems for water level detection. Due to degradation,...
The lungs are two of the most crucial organs in the human body because they are connected to the respiratory and circulatory systems. Lung cancer, COVID-19, pneumonia, and other severe diseases are just a few of the many threats. The patient is subje...
Background. Alzheimer’s is a disease for which there is no cure. Diagnosing Alzheimer’s disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and...
This study utilizes the ResNet18 network to classify and recognize trajectories of planar mechanisms. This research begins by deriving formulas for trajectory points in various typical planar mechanisms, and the resulting trajectory images are employ...
In order to reduce the elemental species produced in the recycling and melting of aluminum scrap and to improve the quality of pure aluminum and aluminum alloys, it is necessary to classify the different grades of aluminum scrap before melting. For t...
Convolutional neural networks (CNNs) have achieved promising results in many tasks, and evaluating the model’s generalization ability based on the trained model and training data is paramount for practical applications. Although many measures f...
We aim to investigate the feasibility and evaluate the performance of a ResNet-50 convolutional neural network (CNN) based on magnetic resonance imaging (MRI) in predicting primary tumor sites in spinal metastases. Conventional sequences (T1-weighted...
In this paper, a deep learning-based device modeling framework for design-technology co-optimization (DTCO) is proposed. A ResNet surrogate model is utilized as an alternative to traditional compact models, demonstrating high accuracy in both single-...
With the increasing popularity of deep learning, enterprises are replacing traditional inefficient and non-robust defect detection methods with intelligent recognition technology. This paper utilizes TL (transfer learning) to enhance the model’...
Breast cancer is a significant cause of cancer-related mortality among women around the globe, underscoring the need for early and accurate diagnosis. Typically, histopathological analysis of biopsy slides is utilized for tumor classification. Howeve...
Background/Objectives: This paper presents a Residual Neural Network (ResNet) based framework tailored for structured traffic accident data, aiming to improve accident severity prediction. The proposed model leverages residual learning to effectively...
Fruit freshness monitoring represents one of the key research foci in the quality control of fruits and vegetables. Traditional manual inspection methods are characterized by subjectivity and inefficiency, which renders them unsuitable for large-scal...
Vision impairment affects an individual’s quality of life, posing challenges for visually impaired people (VIPs) in various aspects such as object recognition and daily tasks. Previous research has focused on developing visual navigation system...
The deep learning-based side-channel analysis gave some of the most prominent side-channel attacks against protected targets in the past few years. To this end, the research community’s focus has been on creating the following: (1) powerful mul...
Sign language recognition technology can help people with hearing impairments to communicate with non-hearing-impaired people. At present, with the rapid development of society, deep learning also provides certain technical support for sign language...
Deep learning architectures like ResNet and Inception have produced accurate predictions for classifying benign and malignant tumors in the healthcare domain. This enables healthcare institutions to make data-driven decisions and potentially enable e...
This work aims to develop a novel convolutional neural network (CNN) named ResNet50* to detect various gastrointestinal diseases using a new ResNet50*-based deep feature engineering model with endoscopy images. The novelty of this work is the develop...
A high-strength bolt connection is the key component of large-scale steel structures. Bolt loosening and preload loss during operation can reduce the load-carrying capacity, safety, and durability of the structures. In order to detect loosening damag...
Colorectal cancer is the third most prevalent type of cancer globally, and it typically progresses unnoticed, making early detection via effective screening methods crucial. This study presents the TASPP-UNet, an advanced deep learning model that int...
Rice production has faced numerous challenges in recent years, and traditional methods are still being used to detect rice diseases. This research project developed an automated rice blast disease diagnosis technique based on deep learning, image pro...
Borehole hydraulic fracturing in coal mines can effectively prevent coal rock dynamic disasters. Accurately recognizing weak microseismic events is an essential prerequisite for the micro-seismic monitoring of hydraulic fracturing in coal seams. This...
Aiming at the non-uniform blurring of image caused by optical system defects or external interference factors, such as camera shake, out-of-focus, and fast movement of object, a multi-scale cyclic image deblurring model based on a parallel void convo...
Background: The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. Objective:...
Medicinal plants have incredibly high economic value, and a practical evaluation of their quality is the key to promoting industry development. The deep learning model based on residual convolutional neural network (ResNet) has the advantage of autom...
The elevated precision of data regarding the Earth’s surface, facilitated by the enhanced interoperability among various GNSSs (Global Navigation Satellite Systems), enables the classification of land use and land cover (LULC) via satellites eq...
Point feature cartographic label placement is a key problem in the automatic configuration of map labeling. Prior research on it only addresses label conflict or overlap issues; it does not fully take into account and resolve both types of issues. In...
Detection of human lower body provides an implementation idea for the automatic tracking and accurate relocation of automatic vehicles. Based on traditional SSD and ResNet, this paper proposes an improved detection algorithm R-SSD for human lower bod...
The wear particle classification algorithm proposed is based on an integrated ResNet50 and Vision Transformer, aiming to address the problems of a complex background, overlapping and similar characteristics of wear particles, low classification accur...
Most network attacks occur at the application layer, where many application layer protocols exist. These protocols have different structures and functionalities, posing feature extraction challenges and resulting in low identification accuracy. This...
A landslide is one of the most common geological disasters, which is associated with great destructive power and harm. In recent years, semantic segmentation models have been applied to landslide recognition research and have made some achievements....
Background: Accurate classification of brain tumors in medical images is vital for effective diagnosis and treatment planning, which improves the patient’s survival rate. In this paper, we investigate the application of Convolutional Neural Net...
In order to address the issue of the accuracy of partial discharge pattern recognition being constrained by unbalanced samples and the deep structure of the deep learning network, a method for partial discharge data enhancement and pattern recognitio...
SARS-CoV-19 is one of the deadliest pandemics the world has witnessed, taking around 5,049,374 lives till now across worldwide and 459,873 in India. To limit its spread numerous countries have issued many safety measures. Though vaccines are availabl...
As artificial intelligence develops, deep learning algorithms are increasingly being used in the field of dorsal hand vein (DHV) recognition. However, deep learning has high requirements regarding the number of samples, and current DHV datasets have...
Addressing the technological requirement for real-time monitoring of fish density in dim aquaculture environments, this study proposes a near-infrared (NIR) image classification method using a modified ResNet18 architecture. Initially, an NIR-Fish da...
Dragonfly classification is crucial for biodiversity conservation. Traditional taxonomic approaches require extensive training and experience, limiting their efficiency. Computer vision offers promising solutions for dragonfly taxonomy. In this study...
Tool wear is a key factor in the machining process, which affects the tool life and quality of the machined work piece. Therefore, it is crucial to monitor and diagnose the tool condition. An improved CaAt-ResNet-1d model for multi-sensor tool wear d...
Although deep learning-based diabetic retinopathy (DR) classification methods typically benefit from well-designed architectures of convolutional neural networks, the training setting also has a non-negligible impact on prediction performance. The tr...
Bone age is the chronological age of human bones, which serves as a key indicator of the maturity of bone development and can more objectively reflect the extent of human growth and development. The prevalent viewpoint and research development direct...
Multilayer composite materials often develop internal defects at varying depths due to manufacturing and environmental factors. Traditional planar scanning methods lack the ability to pinpoint defect locations in depth. This study proposes a terahert...
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