Security cameras have been proven to be particularly useful in preventing and combating crime through identification tasks. Here, two areas can be mainly distinguished: person and vehicle identification. Automatic lic...
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Security cameras have been proven to be particularly useful in preventing and combating crime through identification tasks. Here, two areas can be mainly distinguished: person and vehicle identification. Automatic license plate readers are the most widely used tool for vehicle identification. Although these systems are very effective, they are not reliable enough in certain circumstances. For example, due to traffic jams, vehicle position or weather conditions, the sensors cannot capture an image of the entire license plate. However, there is still a lot of additional information in the image which may also be of interest, and that needs to be analysed quickly and accurately. The correct use of the processing mechanisms can significantly reduce analysis time, increasing the efficiency of video cameras significantly. To solve this problem, we have designed a solution based on two technologies: license plate recognition and vehicle re-identification. For its development and testing, we have also created several datasets recreating a real environment. In addition, during this article, it is also possible to read about some of the main artificial intelligence techniques for these technologies, as they have served as the starting point for this research.
Urban vitality has significant practical implications for urban management and planning. In this study, we propose a comprehensive research framework that combines street scene images, point of interest (POI) data, ro...
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Urban vitality has significant practical implications for urban management and planning. In this study, we propose a comprehensive research framework that combines street scene images, point of interest (POI) data, road network data, and residential land data, and employs deep learning algorithms to explore the characteristics and influencing factors of urban vitality from a social perception perspective. By designing multi-scale semantic segmentation models, emotion perception models, and street perception models, we deeply explore the street features of the city. At the block level, we use weighted calculation methods to quantify urban vitality by combining POI data and residential land data, accurately characterizing the city. Finally, we analyze the driving factors of urban vitality using random forest and SHAP methods. The research results show that Chengyang District and Laoshan District have advantages in visual perception, while Shibei District and Shinan District exhibit advantages in urban vitality. The overall urban vitality in the main urban area of Qingdao City is low, with high scores in emotion perception and visual perception, but low scores in transportation accessibility and facility convenience. Visual perception factors play a significant role in urban vitality, highlighting the importance of urban street beautification and humanized design in economic development and environmental construction. The analytical results of this study contribute to optimizing urban spatial features and provide references for urban planning and construction.
This research endeavors to prognosticate gender by harnessing the potential of skull computed tomography (CT) images, given the seminal role of gender identification in the realm of identification. The study encompass...
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This research endeavors to prognosticate gender by harnessing the potential of skull computed tomography (CT) images, given the seminal role of gender identification in the realm of identification. The study encompasses a corpus of CT images of cranial structures derived from 218 male and 203 female subjects, constituting a total cohort of 421 individuals within the age bracket of 25 to 65 years. Employing deeplearning, a prominent subset of machine learningalgorithms, the study deploys convolutional neural network (CNN) models to excavate profound attributes inherent in the skull CT images. In pursuit of the research objective, the focal methodology involves the exclusive application of deep learning algorithms to image datasets, culminating in an accuracy rate of 96.4%. The gender estimation process exhibits a precision of 96.1% for male individuals and 96.8% for female individuals. The precision performance varies across different selections of feature numbers, namely 100, 300, and 500, alongside 1000 features without feature selection. The respective precision rates for these selections are recorded as 95.0%, 95.5%, 96.2%, and 96.4%. It is notable that gender estimation via visual radiography mitigates the discrepancy in measurements between experts, concurrently yielding an expedited estimation rate. Predicated on the empirical findings of this investigation, it is inferred that the efficacy of the CNN model, the configurational intricacies of the classifier, and the judicious selection of features collectively constitute pivotal determinants in shaping the performance attributes of the proposed methodology.
Cloud computing revolutionizes fast-changing technology. Companies' computational resource use is changing. Businesses can quickly adapt to changing market conditions and operational needs with cloud-based solutio...
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Cloud computing revolutionizes fast-changing technology. Companies' computational resource use is changing. Businesses can quickly adapt to changing market conditions and operational needs with cloud-based solutions' adaptability, scalability, and cost-efficiency. IT operations and service delivery have changed due to widespread computational resource access. Cloud computing efficiently allocates resources in cloud environments, making it crucial to this transformation. Resource allocation impacts efficiency, cost, performance, and SLAs. Users and providers can allocate cloud resources based on workloads using elasticity, scalability, and on-demand provisioning. IT economics and operational effectiveness have changed due to rapid and flexible resource allocation. Proactive versus reactive resource allocation is key to understanding cloud resource management challenges and opportunities. Reactive strategies allocate resources only when shortages or surpluses occur at demand. This responsive strategy often leads to inefficiencies like over- or under-allocation, which raises costs and lowers performance. Predictive analysis and workload forecasting predict resource needs in proactive resource allocation. Optimize resource use to avoid shortages and over-provisioning. Attention has been drawn to proactive predictive resource allocation. These methods predict resource needs using historical data, machine learning, and predictive analytics. Predictive strategies optimize resource allocation by considering future decisions. Reduced bottlenecks boost user satisfaction and lower operational costs. Matching resource distribution to workloads optimizes cloud resource management. Resource allocation prediction improves with deeplearning. CNN, LSTM, and Transformer cloud resource forecasting algorithms are promising. New tools for accurate and flexible workload predictions have come from their ability to spot intricate patterns in historical data. This paper compares CNN, LSTM,
Wildlife conservation, especially in Africa, faces significant challenges from hunting and poaching, endangering species like rhinos and elephants. Despite concerted efforts, the lack of annotated wild animal datasets...
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ISBN:
(纸本)9798400708275
Wildlife conservation, especially in Africa, faces significant challenges from hunting and poaching, endangering species like rhinos and elephants. Despite concerted efforts, the lack of annotated wild animal datasets has hampered the development of effective monitoring algorithms for reserves and parks. Although advances in technology have enabled institutions to upload images for monitoring purposes, manual annotation remains time-consuming and inefficient. To address this issue, the paper proposes a semi-automatic annotation framework for wild animal recognition dataset construction, focusing on six classes of common animals in danger in Africa (including rhino, elephant, lion, giraffe, cheetah, and zebra). The framework involves training a YOLOv5 model on a small manually annotated dataset of 10%, and using this model to semi-automatically label the remaining large-scale dataset. The framework results are convincing when compared to a semi-auto annotation tool (Bylabel) with an 86.16% improvement in terms of time consumption in seconds, which enhances the time efficiency for annotation. This is the first wild animal dataset constructed with boundary box labels based on semi-automatic annotation. The proposed framework has the potential to expedite research on wildlife protection, serve as a guide for semi-automatic annotation dataset construction, and have a significant impact on conservation communities. The dataset is publicly available at http://***/datasets/vhmvfbgvxj.
Sudden cardiac death (SCD) is one of the most frequent causes of death in adult patients with congenital heart disease (ACHD). Despite the rare frequency of its occurrence, the incident appears often when unexpected, ...
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Sudden cardiac death (SCD) is one of the most frequent causes of death in adult patients with congenital heart disease (ACHD). Despite the rare frequency of its occurrence, the incident appears often when unexpected, and many affected patients had not been identified priorly. Data on predictors for SCD are limited since the total number of ACHD is low. As the cohort is heterogeneous, it is difficult to define uniform risk factors that apply to all ACHD. Complexity of the congenital heart disease appears to play a role, but other factors may also be relevant and have not been sufficiently identified yet. In current guidelines, recommendations are primarily based on data of patients without congenital heart *** the ATROPOS registry, we are aiming to identify reliable risk factors for SCD. The registry enables physicians globally to include patients with congenital heart disease who died of or survived SCD. After acquisition, the data will be compared to an age and complexity of disease matched cohort to perform a case control analysis. Subsequently, a further analysis will be performed using deep learning algorithms with artificial intelligence to amplify the gathered information and find reliable risk factors.
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