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The stock market is a volatile and nonlinear environment, making it difficult to predict returns accurately. However,
machine learning and deep learning models have been able to
achieve some degree of accuracy in predicting financial time
series. The recurrent neural networks (RNN) are derived from
the feedforward neural networks, a deep learning algorithm.
The cases of gradient vanishing and explosion are commonly
associated with the traditional RNNs. The Long-Short Term
Memory (LSTM) model is capable of eliminating the problems
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We created a 5G dataset by measuring 5G traffic directly from a major mobile operator in South Korea. The model name of the mobile terminal used for traffic measurement is the Samsung Galaxy A90 5G, equipped with a Qualcomm Snapdragon X50 5G modem. We installed PCAPdroid, a packet sniffer software, on the terminal via Google Play. Traffic was measured sequentially per application on two stationary terminals (only one terminal is used for noninteractive services) with no background traffic.
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5910 Views
The real system in our experiment comprises four production stations: Pick and place, assembly, muscle compressing and sorting. These modular stations are controlled by Siemens PLC. This is the data gathered from a real manufacturing system and its Digital Twin data when under the denial of service attacks.
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792 Views
<p> The dataset is digital health data. It contains heart rate data extracted from Fitbit version 2 smartwatch worn by a healthy male Asian person of 48 years old. Data is of one-month duration. We have uploaded a zip file that contains data from different days. Data for each day has a separate file. The file name contains the date. Each file is in csv format. Each file has two columns – timestamp and heart rate. It is a continuous time-series heart rate data. Heart rate was recorded seamlessly at 5 sec interval. However, there may be missing datum.
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2714 Views
This dataset presents a comprehensive collection of measurements obtained from a self-made test bed called the NET Playground, focusing on the performance evaluation of an IPFS (InterPlanetary File System) consumer and multiple providers. The dataset comprises 45 sets of data, each representing a scenario involving the consumer requesting a 10MB file from a varying number of available providers ranging from 1 to 15. The network configuration involves all nodes (consumer and providers) being connected in a peer-to-peer network and utilizing IPFS as the communication protocol.
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80 Views
The Intel D435i camera is selected to collect the point cloud data of lower limbs,the point cloud data source was 10 subjects including 4 females and 6 males, subjects are informed and voluntary, aged between 23-26 years, with an average age of 24.3 (±1.03) years, height of 172.1 (±6.46) cm, and weight of 71.3 (±9.58 kg.). The subjects were not trained prior to the testThe experimental data from these ten individuals were divided into two parts, half for training the long and short term memory neural network and half for validating the real-time and accuracy of the training model.
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This dataset provides valuable insights into hand gestures and their associated measurements. Hand gestures play a significant role in human communication, and understanding their patterns and characteristics can be enabled various applications, such as gesture recognition systems, sign language interpretation, and human-computer interaction. This dataset was carefully collected by a specialist who captured snapshots of individuals making different hand gestures and measured specific distances between the fingers and the palm.
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996 Views
Grasp intention recognition is a vital problem for controlling assistive robots to help the elderly and infirm people restore arm and hand function. This dataset contains gaze data and scene image data of healthy individuals and hemiplegic patients while performing different grasping tasks. It can be used for gaze-based grasp intention recognition studies.
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186 Views
Currently, Internet applications running on mobile devices generate a massive amount of data that can be transmitted to a Cloud for processing. However, one fundamental limitation of a Cloud is the connectivity with end devices. Fog computing overcomes this limitation and supports the requirements of time-sensitive applications by distributing computation, communication, and storage services along the Cloud to Things (C2T) continuum, empowering potential new applications, such as smart cities, augmented reality (AR), and virtual reality (VR).
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1342 Views
Supplementary material to the article "Improving the teaching of real-world software practices by means of course integration"
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