In recent years, the field of human action recognition has been the focus of computer vision, and human action recognition has a good prospect in many fields, such as security state monitoring, behavior characteristic...
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Digital transformation (DT) is an essential need for Higher Education Institution (HEI) in the era of modern technology to improve the quality of education and operational efficiency. this research develops a DT model...
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Withthe accelerated development of internet and information technology, the healthcare industry has undergone tremendous changes. Big data, cloud computing and intelligent algorithms are playing an important role in ...
Withthe accelerated development of internet and information technology, the healthcare industry has undergone tremendous changes. Big data, cloud computing and intelligent algorithms are playing an important role in the medical field. Due to unbalanced economic development, there is a large gap between medical care in urban and rural areas. However, most patients with chronic disease live in rural and remote areas. In order to enable low-income groups to have access to advanced medical resources, this paper designs an intelligent healthcare system for chronic disease auxiliary diagnosis. Taking heart disease, the most common disease in daily life, as the research object, an ensemble learning algorithm based on stacking is explored to predict the early diagnosis of heart disease for users. the results show that the ensemble learning algorithm is better than a single machine learning algorithm, and the accuracy of the prediction can be greatly improved. It is expected that this can be used to improve diagnosis in rural and remote areas.
According to the impact of marine pollution on mariculture, an intelligent monitoring system for mariculture water quality based on ZigBee is designed in this study. the system includes a data acquisition part, a data...
According to the impact of marine pollution on mariculture, an intelligent monitoring system for mariculture water quality based on ZigBee is designed in this study. the system includes a data acquisition part, a data transmission part and a user-side early warning part. It is composed of data terminals and multiple data acquisition points, which detect the temperature, PH value, oxygen solubility and other related parameters of water quality. the data of each collection point is transmitted to the ZigBee coordinator node through the ZigBee network. the coordinator node monitors the collected data value and makes a risk warning according to the early warning value. GPRS is used to upload the data to the server in real time, so that the staff can find and handle the abnormalities in time, and an intelligent monitoring system based on the Internet of things is utilized.
Flamingos are known for their vibrant pink and reddish hues, which are not merely aesthetic but indicative of their overall health and diet. these colors are derived from carotenoid pigments in their food sources, mak...
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In this paper, we aim to reduce the number of nodes from Graph Neural Networks (GNNs), thereby simplifying models and reducing computational costs. GNNs are highly effective for various tasks, such as prediction, clas...
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Genome-wide association studies (GWASs) enable simultaneous detection of several genetic variations. the variants that do not match GW ASs' significance threshold can be missed. We aimed at the identification of n...
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Withthe exponential growth of data, the demand for effective data analysis tools has increased significantly. R language, known for its statistical modeling and data analysis capabilities, has become one of the most ...
Withthe exponential growth of data, the demand for effective data analysis tools has increased significantly. R language, known for its statistical modeling and data analysis capabilities, has become one of the most popular programming languages among data scientists and researchers. As the importance of energy-aware software systems continues to rise, several studies investigate the impact of source code and different stages of machine learning model training on energy consumption. However, existing studies in this domain primarily focus on programming languages like Python and Java, resulting in a lack of energy measuring tools for other programming languages such as R. To address this gap, we propose “RJoules”, a tool designed to measure the energy consumption of R code snippets. We evaluate the correctness and performance of RJoules by applying it to four machine learning algorithms on three different systems. Our aim is to support developers and practitioners in building energy-aware systems in R. the demonstration of the tool is available at https://***/yMKFuvAM-DE and related artifacts at https://***/RJoules/.
the work presented in this paper compares the performance of different machine learning approaches, based on spectral and statistical features, in identifying coughs from accelerometry data sensed via a wearable attac...
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ISBN:
(纸本)9783031625015;9783031625022
the work presented in this paper compares the performance of different machine learning approaches, based on spectral and statistical features, in identifying coughs from accelerometry data sensed via a wearable attached to a shirt's collar. the extracted features are separately used to train and evaluate neural network models for cough detection - first as a multi-class problem, second as a binary problem. the models' performance was compared in terms of overall accuracy, cough sensitivity, specificity, and F1-score. It is concluded that the model using statistical features for a multi-class cough detection achieved the best cough sensitivity of 100% and F1-score of 0.95 with cough specificity of 97.3%. the four classification models were further evaluated for on-board performance as a TinyML cough system. they were all successfully deployed on Nordic thing:53 using Edge impulse, and their memory requirements and estimated time per inference are reported. In terms of memory and time, the statistical- multi-class model was the smallest model occupying 13.7 KB of Flash memory and 1.1 KB of RAM;it was also the fastest model, requiring an estimated 1 ms per inference.
Majority of widely used machine learning models are vulnerable to adversarial attacks, which are slight modifications of inputs that change the model's prediction with high confidence. the detection of such attack...
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ISBN:
(纸本)9781665426060
Majority of widely used machine learning models are vulnerable to adversarial attacks, which are slight modifications of inputs that change the model's prediction with high confidence. the detection of such attacks and adversarial samples is still an open problem. In this work, we continue exploration of a novel method for adversarial image detection by means of linear algebra approach. the method is built on a comparison of distances to the centroids for a given point and its neighbours. the method of adversarial examples detection is considered theoretically, and the numerical experiments are done to illustrate the approach. A number of methods of adversarial examples generation are considered. It is shown, that the method is prone to error in case of certain adversarial attacks and training sets.
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