The proceedings contain 178 papers. The topics discussed include: construction andapplication of machinelearning model in network intrusion detection;design and implementation of personal medical assistant system ba...
The proceedings contain 178 papers. The topics discussed include: construction andapplication of machinelearning model in network intrusion detection;design and implementation of personal medical assistant system based on edge computing;research on the application of computer technology in the field of art under big data;ballistic target signal separation based on differential evolution algorithm;research on machinelearning algorithm based on contour matching modal matrix;identification of depression using support vector machine with different connectivity;scheduling optimization for batch processing machines using advanced genetic algorithm;facial expression recognition algorithm based on convolution neural network and multi-feature fusion;sequence labeling model based on hierarchical features and attention mechanism;a dynamic resource chain task unloading method based on improved greedy algorithm;evaluation method of pipe corrosion based on the natural frequency II: corrosion of internal surface of pipe;and research on NSGA-II flexible job shop scheduling based on variable length chromosomes.
A method of multi-sensor data fusion using deep learning is proposed for the autonomous driving system. First, the realtime road condition information is collected through the binocular camera mounted on the car model...
A method of multi-sensor data fusion using deep learning is proposed for the autonomous driving system. First, the realtime road condition information is collected through the binocular camera mounted on the car model, and the road and the obstacles on the road are identified with the semantic segmentation of the deep neural network image. At the same time, supplemented with other information collected by the laser sensor and ultrasonic sensor on the car model, as well as the road information obtained by Beidou navigation, the Kalman filter parameters are optimized with deep learning and passed via algorithm fusion to the car model actuator for automatic operation. The signals collected by multiple groups of the same type of sensors on the car model are optimized by an improved Kalman filter algorithm to achieve fast and accurate output of real-time results. The actual vehicle experiment results of the smart car model show that the solution can realize tasks such as autonomous navigation and driving, automatic obstacle avoidance, automatic route planning and prediction in a variety of complex road environments, with an operating speed equivalent to 80 km/h and a safety rate of 99.8%.
The aging of the population and the high incidence of hemiplegia have led to an increasing demand for easy-to-use rehabilitation training. The feedback sensing system which can measure and analyze the lower limb rehab...
The aging of the population and the high incidence of hemiplegia have led to an increasing demand for easy-to-use rehabilitation training. The feedback sensing system which can measure and analyze the lower limb rehabilitation motions is highly significant for improving the rehabilitation outcome. computer vision-based human motion angle measurement has attracted significant interest. This study aims to measure and analyze the lower limb motion angle in the sagittal plane with a single RGB camera. This paper proposes a method for extracting and monitoring of the lower limb marker points based on YOLOv3 and DarkNet-53 convolutional neural networks, and optimizes the pixel coordinates of the target point based on Kalman. The measurement accuracy of the proposed method is tested by JACO robotic arm, and the test shows that the standard deviation (SD) of the measurement is less than 0.5deg.
Air pollution is a threat that all urban municipalities across the globe are trying to tackle. In India, air pollution is the fifth major cause of death, leading to around2 million deaths per year, according to the W...
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HR technology leaders foresee AI's growing role in a variety of areas, such as aiding recruitment, improving compliance, augmenting training, streamlining onboarding and more. New artificial intelligence technolog...
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This article discusses the processing of video information using cloud technologies and foggy environments. Features of mobile devices and embedded computers are touched upon. Data processing processes for video analy...
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This contribution introduces HCD(3)A, a process model to guide and support the development of data-driven applications. HCD(3)A is a specialized human-centered design (HCD) process model derived from and based on the ...
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ISBN:
(纸本)9783030777715;9783030777722
This contribution introduces HCD(3)A, a process model to guide and support the development of data-driven applications. HCD(3)A is a specialized human-centered design (HCD) process model derived from and based on the ISO 9241-210 standard. In order to test the suitability of the HCD(3)A process model a prototype of a machinelearning (ML) application is developed along this process. This application is integrated in a learning management system and tailored to the needs of computer science students in an online learning context. The learningapplication uses an ML approach to support students in their learning behavior by helping them to avoid procrastination and motivating them for assignments and final exams. This is e.g. done by predicting the students exam success probability. The most important claim in regard to the ML components was explainability. Although the evaluation of the prototype in regard to the suitability of HCD(3)A has not been completed the first results show that it is promising in particular to make ML applications more transparent for the users.
Cultural diversity, history and natural charm make West Sumatra worthy of being a leading tourist destination. Culinary diversity is the main attraction in the tourism sector in West Sumatra Province. Along with these...
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Fake news, as a kind of public information, releases false information to deceive people for a specific purpose. Fake news not only causes serious damage to the credibility of the public media but also damages the rig...
Fake news, as a kind of public information, releases false information to deceive people for a specific purpose. Fake news not only causes serious damage to the credibility of the public media but also damages the rights of the parties. So, it is necessary to detect fake news from both online social media and traditional media. For decades, different approaches for fake news detection have been proposed. And these methods can be categorized into manual detection and automatic detection. In this paper, we make a comprehensive review of automatic fake news detection. We first introduce the definition of fake news and fake news detection. Then, some commonly used datasets and evaluation metrics are demonstrated. Besides, we also revise some deep learning approaches for automatic fake news detection. Finally, we discuss some potential challenges and problems for the future. The survey provides a good introduction to fake news detection for both NLP researchers and social media scientists.
With the acceleration of global carbon-neutralizing process, green chemical industry has attracted more and more attention. Finding appropriate parameters for experiments is important for green chemical reaction. This...
With the acceleration of global carbon-neutralizing process, green chemical industry has attracted more and more attention. Finding appropriate parameters for experiments is important for green chemical reaction. This work focuses on using experiment parameters to predict chemical reaction yield of products based on machinelearning methods, which takes C4 olefin preparation from ethanol as an example. To explore how experiment parameters influence the reaction yield of products, figures are plotted to show the correlations between parameters and reaction index. Then the conversion models and selectivity models are constructed based on polynomial regression, decision tree, random forest, Light GBM and neural networks. Different evaluations of the models all indicate that LightGBM performs best to predict the conversion of ethanol, while the selectivity of C4 olefin prefers neural network. Comprehensively we propose a hybrid method to predict yield of C4 olefin which significantly exceeded all single regression models. Furthermore, the sensitivity of optimized conversion/selectivity/yield models are shown in heatmaps that imply the robustness of our models.
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