The precise boundaries of the cadastral parcels from the Unmanned Aerial Vehicle (UAV) data are essential for any eGovernance application. The pix2pix, image-to-image translation using the conditional Generative Adver...
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The precise boundaries of the cadastral parcels from the Unmanned Aerial Vehicle (UAV) data are essential for any eGovernance application. The pix2pix, image-to-image translation using the conditional Generative Adversarial Network (cGAN) models, has emerged as an alternative to the traditional machine learning and imageprocessingalgorithms. It has been used and demonstrated for productive purposes in different domains without any change in the pix2pix network model and loss functions. The pix2pix model is implemented in this research for extracting the cadastral parcel boundaries using the existing UAV data set, and the corresponding digitised data. The input data set is prepared using the python modules. The model is also used to predict the synthetic UAV data from the map data. The predicted boundary of the model is very useful. The proposed model can reduce the manual labour and human interventions in outlining the parcel boundary from UAV data.
A pothole is a significant disadvantage in the roadways, resulting from depressions formed due to the improper quality of road materials and external conditions, such as extreme weather forces and the heavy vehicles p...
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To detect surface damage to buildings, it is necessary to involve workers who are at risk of industrial injuries when inspecting hard-to-reach areas of industrial premises. Attraction of special means, such as aerial ...
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Despite their success, the traditional deep neural networks are unable to rapidly generalize from a limited number of samples. To address the issue of large training data requirements with deep neural networks, one-sh...
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The application of CBCT systems in intraoperative environments has become increasingly common, but concurrent CBCT systems are unsuitable for situations that require a large longitudinal imaging FoV, such as orthopedi...
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This research work proposes an efficient framework to improve multispectral image classification through convolutional neural networks (CNNs) with optimized hyperspectral band selection. Hyperspectral images contain e...
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Objective image quality assessment measures were extensively used to evaluate the performance of different imageprocessing and analysis algorithms. However, they are application-driven. In contrast, subjective assess...
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The proceedings contain 87 papers. The topics discussed include: Auto-FP: an experimental study of automated feature preprocessing for tabular data;Data-CASE: grounding data regulations for compliant data processing s...
ISBN:
(纸本)9783893180950
The proceedings contain 87 papers. The topics discussed include: Auto-FP: an experimental study of automated feature preprocessing for tabular data;Data-CASE: grounding data regulations for compliant data processingsystems;data coverage for detecting representation bias in image datasets: a crowdsourcing approach;balancing utility and fairness in submodular maximization;stateful entities: object-oriented cloud applications as distributed dataflows;learning over sets for databases;a new PET for data collection via forms with data minimization, full accuracy and informed consent;adaptive compression for databases;analysis of open government datasets from a data design and integration perspective;fine-grained geo-obfuscation to protect workers’ location privacy in time-sensitive spatial crowdsourcing;and a framework to evaluate early time-series classification algorithms.
The proceedings contain 180 papers. The topics discussed include: analysis of medical data and machine-learning algorithms from the perspective of public-goods models of data-provision decision making;price competitio...
The proceedings contain 180 papers. The topics discussed include: analysis of medical data and machine-learning algorithms from the perspective of public-goods models of data-provision decision making;price competition for service provision with different bargaining abilities;survey on stakeholder cooperative behavior for designing voluntary medical data provision motivation mechanisms;analysis of excellent service systems from co-creation and emergent synthesis perspective;meta-heuristic scheduling auction applying distributed genetic algorithm;the impact of characteristic function of Shapley value mechanism in distributed machine learning environment for equipment diagnosis;automatic measurement of timber diameter using imageprocessing;intelligent scheduling based on discrete-time simulation using machine learning;and a fuzzy synthesis approach for hierarchical decision analysis to select optimum repair technique.
Visual pollution is a significant obstacle in the modern era, where the world is advancing towards increasingly diverse inventions. These inventions require a suitable environment to achieve accurate outcomes. Artific...
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ISBN:
(纸本)9783031664304;9783031664311
Visual pollution is a significant obstacle in the modern era, where the world is advancing towards increasingly diverse inventions. These inventions require a suitable environment to achieve accurate outcomes. Artificial intelligence has already permeated all fields and interests of life;similarly, visual pollution also needs to be addressed properly. Visual pollution often creates obstacles in performing various tasks. To mitigate these issues, an artificial intelligence-based model will play a vital role. This work deals with detecting visual pollution using an artificial intelligence-based algorithm to apply practical solutions that enhance urban public scenery. In the first step, a dataset is chosen from an authorized organization;specifically, the data is sourced from Mendeley, named the Saudi Arabia Public Roads Visual Pollution Dataset 2023. The second step involves data scaling and background removal from training images to facilitate learning in AI models. In the third step, the dataset is processed using Random Forest and support vector machine algorithms to visualize the model's accuracy results. The support vector machine demonstrates better performance compared to the Random Forest.
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