The heart beat is usually synchronized with the electrical activity of the heart, and the ECG records the representation of the heart's electrical signals on the body surface. In this study, we developed a classif...
The heart beat is usually synchronized with the electrical activity of the heart, and the ECG records the representation of the heart's electrical signals on the body surface. In this study, we developed a classification algorithm model for cardiac arrhythmias by analyzing ECG data. We first constructed a classification model to determine the danger level of ECG data using a decision tree approach to achieve real-time judgment and alarm of arrhythmias. The model was then post-pruned and optimized to further classify and predict specific categories for each risk level in conjunction with the actual situation, resulting in an algorithmic model with relatively high prediction accuracy. This will provide an important monitoring tool for cardiac patients and promote timely treatment.
The advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. In response to the urgent ...
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Eye detection represents an essential facial feature to be detected in driver monitoring systems representing the basis for further processing for attention or drowsiness detection. The machine learning approach for i...
Eye detection represents an essential facial feature to be detected in driver monitoring systems representing the basis for further processing for attention or drowsiness detection. The machine learning approach for infrared image vision problems comes with multiple obstacles like creating a recordings data set with drivers and labeling it to be able to train a model. In very strict areas like automotive or medical imaging machine learning approaches are still a big debate especially because of the black box that is represented by the model, meaning that a wrong detection is impossible to be predicted or explained. That’s why the entire focus is shifted to control the data set and the labeling process with very hard manual effort. This paper presents the experimental results of training convolutional neural networks for eye detection in automotive industry using ground truth data obtained from an automatic labeling module. These results are compared with neural networks that were trained using the same data set but with labels created by manual human effort. This experiment shows that similar accuracy of convolutional neural network can be obtained in image vision problems without manual work for image labeling.
The widespread deployment of Deep Learning Inference Services (DLISes) has raised people’s concerns about their data privacy being breached. Although data privacy enhancement has recently attracted a lot of attention...
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The widespread deployment of Deep Learning Inference Services (DLISes) has raised people’s concerns about their data privacy being breached. Although data privacy enhancement has recently attracted a lot of attention, existing solutions all require the cooperation of service providers. Users lose control of their data when making data privacy enhancement decisions. However, it is difficult to enable the user-side control of data abuse prevention because users do not have any programming skills, deep learning knowledge, or rich computing resources. In this work, we propose a fully-automatic userside data privacy enhancement solution, GAPter, for DLISes. Given such a DLIS, GAPter can adaptively fuzz the service for a suitable enhancement strategy, with no cooperation between the DLIS provider and the user. We have implemented and comprehensively evaluated GAPter. The experimental results show that GAPter can find good balance points between privacy enhancement and user data utility.
AIS (Automatic Identification System) data is an important data source in the shipping and logistics industry to track and monitor ships in real-time. Extraction of transshipment features from AIS data is essential in...
AIS (Automatic Identification System) data is an important data source in the shipping and logistics industry to track and monitor ships in real-time. Extraction of transshipment features from AIS data is essential in understanding the pattern and characteristics of transshipment activity at a particular port or place. This study discusses an efficient AIS data preprocessing framework to prepare the transshipment feature extraction. The preprocessing steps include data collection, data cleansing, trajectory extraction, trajectory cleaning, and overlapping trajectory extraction. One of the crucial approaches used in this study is the application of parallel computing. Parallel computing allows us to execute independent dataprocessing tasks at the same time simultaneously. In AIS data preprocessing, the parallel approach allows us to clean, populate, and process data from many vessels in less time. To test the applicability, we created a final comparison for different types of ships based on its AIS data for Indonesia from November 2022 to April 2023. Using parallel computing to process AIS data makes dataprocessing more efficient, especially for large and complex AIS data. This efficiency accelerates data preparation before transshipment feature extraction. The results show that the framework can accelerate runtime and improve trajectory quality.
With the advent of the era of big data, the image processing of big data based on Computer Vision (CV) has become a research field of great concern. The purpose of this study is to explore how to improve image process...
With the advent of the era of big data, the image processing of big data based on Computer Vision (CV) has become a research field of great concern. The purpose of this study is to explore how to improve image processing methods through deep learning technology to better adapt to large-scale and high-dimensional image data. We propose an innovative image processing framework, based on the improved ResNet50, and introduce multi-scale feature fusion, attention mechanism and width adaptation strategies. In the task of image classification, our method has achieved remarkable improvement in accuracy, precision, recall and FI value. Through the effective fusion of multi-scale features, the model better captures the abstract information in the image and improves the classification accuracy. In the target detection task, our method shows excellent performance in mAP, false alarm rate and so on. The improved ResNet50, which introduces attention mechanism, is more reliable in identifying the target position and category. The experimental results further prove the innovation and practicability of our proposed method. Compared with the traditional model and the latest image processing methods, our method shows significant advantages in big data image processing. This provides new ideas and technical support for solving large-scale and diversified image dataprocessing problems. The contribution of this study is to provide a new solution for big data image processing based on CV. The improved ResNet50 model has made remarkable progress in performance, which opens up a new research direction for deep learning research in the field of big data image processing.
With the constantly growing volume of facts being produced, conventional data evaluation structures are regularly insufficient in terms of presenting an correct overview of the most modern-day data. The utilization of...
With the constantly growing volume of facts being produced, conventional data evaluation structures are regularly insufficient in terms of presenting an correct overview of the most modern-day data. The utilization of system mastering provides the potential to create an improved real-time records evaluation device this is more able to making experience of and knowledge the records. The development of an progressed actual-time statistics evaluation device utilizing system studying can hugely enhance statistics analysis techniques by means of integrating relevant data and structured analyses of the identical. this will include strategies inclusive of natural language processing and clustering algorithms to perceive trends, sentiment evaluation to understand customer feedback, and predictive analytics to better perceive areas for improvement. Moreover, the realtime statistics analysis system can be further enhanced by means of incorporating AI to make extra correct predictions, as well as reinforcement studying to evolve present models as new statistics is integrated. this would allow the system to learn more efficaciously and modify its algorithms to continually enhance its overall performance. Universal, by using machine studying to develop an advanced actual-time statistics evaluation system, extra correct and comprehensive insights can be generated from information, and groups can use this facts to better tell their choice making. moreover, temporal records evaluation can be advanced with faster response times as a result of the device being able to process records
High-dimensional data often presents a challenge in machine learning tasks due to the curse of dimensionality. Feature selection is a common technique used to overcome this problem by reducing the number of features a...
High-dimensional data often presents a challenge in machine learning tasks due to the curse of dimensionality. Feature selection is a common technique used to overcome this problem by reducing the number of features and selecting the most relevant ones. In this research, we propose the use of the firefly algorithm for feature selection in high-dimensional data. The aim of this study is to evaluate the performance of the firefly algorithm in selecting informative features and compare it to other feature selection algorithms. We used several datasets with different numbers of features and instances to test the performance of the firefly algorithm. The results demonstrated the capability of the firefly algorithm to enhance accuracy after feature selection process: in the Breast Cancer dataset, it improved from 0.959 to 0.969; in the Lymphoma dataset, it improved from 0.479 to 0.575; in the arrhythmia dataset, it improved from 0.622 to 0.714; and in the leukemia dataset, it improved from 0.621 to 0.733. However, for the Central Nervous System (CNS) dataset, the accuracy remained the same before and after at 0.633. We also compared the results of the firefly algorithm with three other well-known feature selection algorithms: the genetic algorithm, particle swarm optimization, and ant-bee colony algorithm. Using performance metrics such as accuracy, precision, and recall, the firefly algorithm proved to be efficient in handling high-dimensional datasets with satisfactory results in several cases. It outperformed the other algorithms, particularly the ant-bee colony algorithm, which also surpassed the others in certain cases.
Records mining is a powerful analytical device used to find out styles, correlations, and traits in large facts sets. Smart application of data mining methods within the detection of fraudulent transactions has finish...
Records mining is a powerful analytical device used to find out styles, correlations, and traits in large facts sets. Smart application of data mining methods within the detection of fraudulent transactions has finished an extensive quantity of success. It normally entails statistical algorithms, facts evaluation, and machine getting to know techniques to stumble on suspicious sports associated with fraud. Using statistics mining techniques, it's far possible to music the behaviour of clients, discover anomalous transaction styles and developments in huge datasets, come across frauds and generate signals on suspicious activities. It also allows to identify which forms of merchandise are frequently centered, which price methods are used, which places are problem to danger, and which traders have capacity credit card fraud. The utility of facts mining within the detection of fraudulent sports entails principal techniques. The first method makes use of conventional techniques used by banks, credit groups and other retailers, which includes account reconciliation, annualized billing and complex question processing. Those methods are used to generate reviews that are used by investigators to perceive probably suspicious activities. The second technique entails the usage of superior analytical techniques such as neural networks, decision timber, and clustering algorithms. These strategies have the functionality to stumble on facts irregularities in several conditions that were formerly unseen or unknown.
In order to explore the research of computer network data security encryption technology. The author proposes a data encryption scheme that combines symmetric encryption algorithms with asymmetric encryption algorithm...
In order to explore the research of computer network data security encryption technology. The author proposes a data encryption scheme that combines symmetric encryption algorithms with asymmetric encryption algorithms, by combining AES algorithm with RSA algorithm and ECC algorithm, improved hybrid encryption algorithms AES-RSA and AES-ECC are obtained. In order to verify the correctness of the scheme, a Hadoop experimental platform was built and tested using Hadoop Eclipse, the time required for different algorithms to encrypt the same size of data was measured. Through the analysis of the obtained experimental data and the safety of the experimental plan, the experimental results indicate that, the AES-RSA algorithm takes slightly longer to encrypt the same data than the AES-ECC algorithm, this is because in these two hybrid encryption algorithms, the RSA algorithm defaults to using a 1024 bit key, while the ECC algorithm selects a key length of 160 bits. A 160 bit ECC key can provide security equivalent to a 1024 bit RSA key, so based on the advantage of small key bits, under the same security strength, the AES-ECC algorithm has higher encryption efficiency than the AES-RSA algorithm. It has been proven that the improved encryption algorithm has the advantages of efficiency and high security, and can achieve secure storage of data in cloud computing environments.
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