WebGL has long been the prevalent API for GPU-accelerated graphics in web browsers, boosting 2D/3D graphical web applications. Despite widespread adoption, WebGL’s programming model hinders its rendering performance ...
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作者：
Ahmed HamziJazan University
College of Engineering and Computer Science Department of Industrial Engineering Department of Industrial Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia
Preparing machines for production and set them up in the manufacturing industry is one of the most important steps that organization always trying to minimize its time. This paper document the implementation of lean m...
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ISBN:
(纸本)9798400710742
Preparing machines for production and set them up in the manufacturing industry is one of the most important steps that organization always trying to minimize its time. This paper document the implementation of lean manufacturing tool project in a manufacturing industry to reduce the time-wasting step and improving its productivity with the focus on the setup process. In this project, applying lean manufacturing tools helps to determine which type of machine has the highest setup time. Machines type 1 has the highest setup time with an average of 1.27 hour per setup with 24% of that time consumed only by the first three steps of the setup process. As result of applying lean manufacturing tools a reduction of 8% of the setup time were achieved.
Low-dose computed tomography (LDCT) is important for reducing radiation exposure but often results in noisy images that can affect diagnostic accuracy. To address this, we propose a denoising method called OTID (Optim...
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Respiratory diseases present significant health challenges globally, prompting research into non-pharmaceutical alternative treatments. Medicinal plants have been extensively utilized in traditional medicine due to th...
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Respiratory diseases present significant health challenges globally, prompting research into non-pharmaceutical alternative treatments. Medicinal plants have been extensively utilized in traditional medicine due to their potential therapeutic benefits. These plants contain bioactive compounds with expectorant, antimicrobial, and anti-inflammatory properties that can help alleviate throat irritation, reduce congestion, and relieve coughing. Phytopharmaceuticals, generally low in toxicity and cost-effective, are often derived from medicinal plants undergoing scientific research and standardization. While numerous studies demonstrate the effectiveness of medicinal plants in treating respiratory conditions, factors like small sample sizes, variations in herbal preparations, and a lack of standardization hinder the generalizability of these findings. As interest in naturopathy grows as an alternative to allopathic medicine, many individuals turn to herbal remedies. However, caution is warranted, as herbal remedies may pose potential side effects, safety concerns, and interactions with prescription medications. This research aims to investigate the efficacy of medicinal plants in treating respiratory ailments based on scientific literature. Additionally, the study explores the classification of medicinal plants using deep learning models such as DenseNet 121, Inception V3, and EfficientNet B4 applied to a dataset fetched from Mendeley, which includes images of 80 different leaf classes of medicinal plants. The proposed enhanced EfficientNet B4 model outperformed the other models, achieving a testing accuracy of 91.37%, precision of 91.97%, recall of 91.37%, and an F1 score of 91.40%. The results suggest that our given model performs effectively on the provided dataset.
Benefiting from the advantages of low storage cost and high retrieval efficiency, hash learning could significantly speed up large-scale cross-modal retrieval. Based on the prior annotations, most of the available cro...
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The brain is one of the most unexplored parts of the human body and its complex and delicate structure has scientists worldwide looking for answers about its intricacies. Also, since the advent of deep learning techni...
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The brain is one of the most unexplored parts of the human body and its complex and delicate structure has scientists worldwide looking for answers about its intricacies. Also, since the advent of deep learning techniques as well as imaging techniques like computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), analysis of the brain has become the most intriguing and researched area in healthcare, as well as deep learning sectors of artificial intelligence. The extraction of the brain from the skull forms the basis and source of study for the prediction of age-related diseases like Alzheimer’s disease. Nowadays With the increase in life expectancy and the extravagant use of technology, it is evident that neurological diseases are on the rise. Therefore, it becomes essential that such diseases can be diagnosed at an early stage of their occurrence. The proposed work presents brain extraction from the skull with the help of three basic steps, data acquisition, pre-processing, and largest connected component extraction using contours. The data acquired is using the ADNI data repository. The preprocessing step involves contrast enhancement using CLAHE, binarization of the scan using Otsu thresholding, and de-blurring so that the noise that might be there in the scans can be removed and a clear image of the brain is available for further processing and classification of Alzheimer’s disease.
With the proliferation of mobile sensing techniques, huge amounts of time series data are continuously generated and accumulated in various domains, fueling considerable real-world mobile computing applications. In th...
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Real-time, interactive 4D traffic scene generation enables rapid digital twinning of traffic scenarios, improving management and decision-making in intelligent transportation systems. However, current text-to-video mo...
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This research analyzes groundwater levels across multiple districts using data from over 100 observation wells in each district. To capture seasonal variations and predict groundwater behavior, this research has devel...
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ISBN:
(数字)9798331522667
ISBN:
(纸本)9798331522674
This research analyzes groundwater levels across multiple districts using data from over 100 observation wells in each district. To capture seasonal variations and predict groundwater behavior, this research has developed three models: periodic, polynomial, and rainfall-based. The periodic and polynomial models describe groundwater level fluctuations based on historical well data, while the rainfall model assesses the influence of precipitation on water levels in the wells. In addition, this research explores advanced predictive techniques by incorporating Spatio-temporal Graph Convolutional Networks (GCNs) and XGBoost. These methods enable a more nuanced understanding of spatio-temporal dependencies and improve predictive accuracy by leveraging both spatial relationships among wells and the temporal evolution of groundwater levels. The integration of traditional models with machine learning techniques aims to enhance groundwater management and inform decision-making.
Diabetes, influenced by factors like high blood pressure, aging, obesity, and poor lifestyle choices, has become a significant health issue, increasing the risk of heart disease, kidney disease, stroke, and other seri...
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ISBN:
(数字)9798331530952
ISBN:
(纸本)9798331530969
Diabetes, influenced by factors like high blood pressure, aging, obesity, and poor lifestyle choices, has become a significant health issue, increasing the risk of heart disease, kidney disease, stroke, and other serious conditions. Traditional diagnostic methods in hospitals can sometimes be misleading, underscoring the need for big data analysis in healthcare. This approach can uncover hidden patterns in diabetes-related data, enabling more accurate predictions of outcomes. This paper aims to classify large diabetes datasets to predict the disease's behavior using various attributes. A machine learning model with sixteen classifiers, fine-tuned through k-fold cross-validation, was developed to ensure optimal results, with accuracy measured by F1 score, precision, and recall.
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