Wavelet theory has been widely applied in imageprocessing, and machine learning techniques have permeated various fields, significant improvements in image denoising remain possible. This paper introduces a novel ima...
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The accurate identification of large and medium slag in power plant slag transport system has an important impact on the safety, efficiency, environmental protection and economic benefits of power plant. The timely pr...
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In this paper, we propose an adaptive multi-exposure image fusion (AMEF) framework using self-supervised learning. Our encoder-decoder network can adaptively increase the number of feature extraction branches to fuse ...
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Unmanned Aerial Vehicle (UAV) into Mobile Edge Computing (MEC) systems can effectively expand their flexibility and coverage. The current research trend in UAV-assisted MEC is mainly focused on optimizing flight contr...
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The development of conversational artificial intelligence (AI) is examined in this research paper, with a focus on how speech and image recognition technologies can be combined to transform and interact with systems. ...
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This electronic document is a "live"template and already defines the coThe purpose of this paper is to develop a secure storage system for financial data based on fuzzy recognition algorithm to improve the s...
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In response to the background penetration problem of unsupervised style transfer algorithms in most cases, a Transformer style transfer network DualGGAN based on dual generators and fusion of relative position encodin...
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In this paper, a blind watermarking algorithm based on redistributed invariant integer wavelet transform (RI-IWT) and BP network is proposed. The host image is processed by RI-DWT and QR decomposition, and the waterma...
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The most prevalent and dangerous kind of cancer in humans is skin cancer. Melanoma is a kind of skin cancer that is fatal. One of the deadliest cancers in the world, melanoma will spread to other body parts if it is n...
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ISBN:
(纸本)9798350375480;9798350375497
The most prevalent and dangerous kind of cancer in humans is skin cancer. Melanoma is a kind of skin cancer that is fatal. One of the deadliest cancers in the world, melanoma will spread to other body parts if it is not detected at an early stage. It is easily curable if detected in its early stages. The goal's highlights the issues of a global increase in skin cancer cases, excessive medical expenses, and an exponential rise in the effect of death from delayed diagnosis due to missed opportunities for early intervention. The biopsy procedure is the official way for melanoma diagnosis and detection. This approach can be extremely painful and time-consuming. In the modern years, deep learning techniques have shown promise in automating the detection process, offering the potential for more accurate and timely diagnoses. This system is a deep learning-based predictive algorithm that uses dermoscopic image analysis to dynamically forecast melanoma skin cancer. The main objectives of this research are to find skin cancer more accurately and quickly in its early stages. This work provides a computer-aided detection technique for melanoma early detection. Our research work suggests using InceptionV3 and Resnet50 algorithms, along with imageprocessing techniques, to create an effective diagnosis system in this study. An image of the impacted skin is captured and subjected to a few pre-processing methods to produce an improved and smoothed image. Next, the picture is subjected to morphological, thresholding, and grey scaling techniques during the data augmentation phase. It categorizes the provided image as either Stage 1 or Stage 2 melanoma. An impressive 84.5% accuracy is attained. Clinical diagnosis of many disorders is increasingly dependent on harmless clinical computer vision or medical imageprocessing. These methods offer an automatic evaluation of images tool for a quick and precise assessment. Overall, the suggested technique is a major improvement in the identif
Application of Artificial Intelligence(AI)in tunnel construction has the potential to transform the industry by improving efficiency,safety,and *** paper presents a comprehensive literature review and analysis of hots...
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Application of Artificial Intelligence(AI)in tunnel construction has the potential to transform the industry by improving efficiency,safety,and *** paper presents a comprehensive literature review and analysis of hotspots and frontier topics in artificial intelligence-related research in tunnel construction.A total of 554 articles published between 2011 and 2023 were collected from the Web of Science(WOS)core collection database and analyzed using CiteSpace *** analysis identified three main study areas:Tunnel Boring Machine(TBM)performance,construction optimization,and rock and soil *** review highlights the advancements made in each area,focusing on design and operation,performance prediction models,and fault detection in TBM performance;computer vision and imageprocessing,neural network algorithms,and optimization and decision-making in construction optimization;and geo-properties and behaviours,tunnel stability and excavation,and risk assessment and safety management in rock and soil *** paper concludes by discussing future research directions,emphasizing the integration of AI with other advanced technologies,realtime decision-making systems,and the management of environmental impacts in tunnel *** comprehensive review provides valuable insights into the current state of AI research in tunnel engineering and serves as a reference for future studies in this rapidly evolvingfield.
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