Machine Learning
The EegDoc data set collected using a Cerebus neural signal acquisition equipment involved 2 types of odors (smelling like roses and rotten odors), each with 5 concentrations. Five concentrations of the rose odor are expressed as A10-3.0 (A30), A10-3.5 (A35), A10-4.0 (A40), A10-4.5 (A45) and A10-5.0 (A50), and five concentrations of the rotten odor are expressed as C10-4.0 (C40), C10-4.5 (C45), C10-5.0 (C50), C10-5.5 (C55) and C10-6.0 (C60).
- Categories:
This dataset contains a total of 160 vibration time-frequency maps and 160 corresponding label. The original signals were collected from 1.5 MW fans by a DAQ system with a sampling frequency of 16384 Hz in April 2021. There are four health states in the dataset, and each of them contains 4 gearboxes.
- Categories:
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.
- Categories:
Currency recognition and classification is one essential task to do. Both paper and coin currency play important role in transactions in everyday life. But provided there are many datasets available of paper currency, and very less datasets are available of coin currency. Coin currency recognition becomes important because even though the amount for which people do coin transactions is small but inaccuracy in recognition can lead to huge loss. Following are the objectives to create this dataset:
- Categories:
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.
- Categories:
Two electric vehicles were used in this study, namely the Renault Zoe Q210 2016 and the Renault Kangoo ZE 2018. The EVs were equipped with data loggers connected to the CAN bus recording data on the HV battery current, voltage, SoC, and instantaneous speeds. We also used a GPS logger mobile application to record GPS tracks and altitudes. Data were collected from six drivers (four men and two women) with varying levels of driving experience (from less than two months to more than 10 years) on a variety of roads and driving conditions for nearly 200 kilometers
- Categories:
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.
- Categories:
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.
- Categories:
Task prioritization is one of the most researched areas in software development. Given the huge amount of papers written on the topic, it might be challenging for IT practitioners to find the most appropriate tools or methods developed to date to deal with this important issue. To overcome this problem, we conducted a systematic literature review. The main goal of this work is to review the current state of research and practice on task prioritization among IT practitioners and to individuate the most effective ranking tools and techniques used in the industry.
- Categories: