Machine Learning
Several fields of study can benefit from a large, structured, and accurate dataset of historical figures. Due to a lack of such a dataset, in this paper, we aim to use machine learning and text mining models to collect, predict, and cleanse online data with a focus on age and gender. We developed a five-step method and inferred birth and death years, binary gender, and occupation from community-submitted data to all language versions of the Wikipedia project.
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This dataset includes the rotor geometrical parameters (*.csv) and motor parameters (*.csv) of interior permanent magnet synchronous motors. The rotor geometry covers three structures: 2D-, V-, and Nabla-structures. The motor parameters are generated by machine learning based on the finite element analysis results. The software JMAG Designer 19.1 was used for the finite element analysis.
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Nowadays, with the rapid increase in the number of applications and networks, the number of cyber multi-step attacks has been increasing exponentially. Thus, the need for a reliable and acceptable Intrusion Detection System (IDS) solution is becoming urgent to protect the networks and devices. However, implementing a robust IDS needs a reliable and up-to-date dataset in order to capture the behaviors of the new types of attacks, especially multi-step attacks. In this work, a new benchmark Multi-Step Cyber-Attack Dataset (MSCAD) is introduced.
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This repository contains code to apply the ESPER method to quasi-continuum models of biomolecules exhibiting multiple degrees of freedom, as described in Seitz et al. (2022, IEEE TCI). As inputs into ESPER, detailed instructions are also provided for generating custom synthetic datasets with increasing complexity to mirror known cryo-EM image attributes.
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This dataset is gathered by using Inertial Measurement Unit Sensor (IMU) (MPU-9250) Positioned on the seat of Vehicle (like Bus, car bike, cycle). This Dataset is record with the help of IMU Sensor which gather only accelerometer data. Currently, collecting plain and rutty surface data through IMU Sensor by travelling in Bus, car, bike and cycle in different places in Haryana.
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This dataset contains the data of three different positions of persons. The main focus of this dataset is on three positions those are Sit, Stand and Sleep. This dataset is collected by using a 3-axis accelerometer sensor value using the Inertial Measurement Unit (IMU) (MPU-9250) Sensor. We collected this data by positioning this instrument on the arm of the person.
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This dataset is gathered by using Inertial Measurement Unit Sensor (IMU) (MPU-9250) positioned on the seat of vehicle (Van). This dataset represents the real time sensory data collected with the help of vehicle i.e. School Van on a road at different places in Punjab. The objective of this dataset is to provide an accurate data for plain road and a road with pits.
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