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51 results for “learning technologies”
Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO2 Capture Technologies
<p>Dataset of process simulations results of the natural gas sweetening and flue gas treatment (first and second sheet, respectively as indicated by the sheet name in the .xlsx file). The dataset refers to the publication <em>Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO<sub>2</sub> Capture Technologies </em>by V. Negri, Vàzquey D., Sales-Pardo, Marta, Guimerà, R. and Guillén-Gosàlbez, G. The training and testing dataset are used to generate the figures in the main manuscript and supplementary information. </p> <p> </p>
Home-based measurements of dystonia and choreoathetosis in cerebral palsy using smartphone-coupled inertial sensor technology and machine learning: A proof-of-concept study - dataset
<p>Home-based measurements of dystonia in cerebral palsy using smartphone-coupled inertial sensor technology and machine learning: A proof-of-concept study</p> <p> </p> <p>This project contains:</p> <p>- 1 main MATLAB script: MODYSathome_main.m<br> - 12 MATLAB functions:<br> - function_calc_mean_recall_precision.m<br> - function_create_dataframes.m<br> - function_deep_learning.m<br> - function_determine_best_ML_model.m<br> - function_display_DL_results.m<br> - function_display_ML_results.m<br> - function_index_extremities.m<br> - function_machine_learning.m<br> - function_oversample.m<br> - function_partition_data.m<br> - function_pick_best_models.m<br> - function_prepare_DL_data.m</p> <p>Downloading the Matlab scripts</p> <p> - Create a folder named 'MODYS' and create a subfolder named 'results'<br> - Download the zip file via <a href="https://zenodo.org/record/6379348">RehabAUmc/modys-at-home: v1.0 | Zenodo</a><br> - Unzip the zip file in the path MODYS\</p> <p>STEPS<br> 1. Open MATLAB<br> 2. In MATLAB, go to the 'HOME' tab and click on 'Set Path'<br> 3. Click on 'Add Folder' and browse to MODYS/RehabAUmc-modys-at-home-86b14c3/functions<br> 4. Click on 'Select Folder' and click on 'Save'<br> 5. Click on 'Browse to folder' and browse to a patients' data in MODYS/data/PatientXXX, then click on 'Select Folder'<br> 6. In the 'HOME' tab click on 'Open' and open MODYSathome.m in MODYS/RehabAUmc-modys-at-home-86b14c3<br> 7. In the 'EDITOR' tab click on 'Run Section' to run the script<br> 8. When the code has been run, the results are displayed in the Command Window and saved in MODYS/results/PatientXXX</p>
Machine Learning Enabled Multi-Radio Access Technology Selection in 5G Networks
<p>In this paper, we present a machine learning algorithm for effective RAT selection in 5G networks by considering the geo-location (latitude and longitude) of the user as well as the received signal strength intensity (RSSI) from the base station as basic parameters, real live data from a 5G network base-station were collated, divided into training and testing data-sets, the training data-sets (input) were used to train models of supervised machine learning classification algorithm: Decision Tree (DT), Extra Tree (XTREE), Random Forest (RF), Gradient Boosting (GB), and eXtreme Gradient Boosting (XGBoost); these trained models are further tested with input test data-sets to predict/select the appropriate RAT (4G/5G) as labelled output. Evaluation of results showed a measure of accuracy of our chosen model of RAT selection; (XGBoost) at optimal level 93.86\%, which was further cross validated at 92.9\% when compared with other algorithms for its effectiveness on future data and mitigation ability on over-fitting and under-fitting issues, hence recommended for planning and optimization purposes in similar urban/dense-urban environment to assist in maintaining the rapidly increasing demand of network connections and devices.</p>
Datasets used in "Assesing the quality of random number generators through neural networks", Machine Learning: Science and Technology 5 (2024) 025072
<p>Datasets corresponding to the bits generated by different random number generators used in J. L. Crespo et al, Machine Learning: Science and Technology 5 (2024) 025072.</p> <p>VCSEL_QRNG_postprocessed_bits.txt: postprocessed bits from the random generator based on gain-switching of VCSELs </p> <p>EC_LCG_bits.txt: bits from the linear congruential generator on elliptic curves</p> <p>LCG_32_bits.txt:: bits from the linear congruential generator with 32 bits</p> <p>VCSEL_QRNG_raw_bits.txt: raw bits from the random generator based on gain-switching of VCSELs</p> <p> </p>
Data collected through a questionnaire regarding satisfaction in the use of technological simulation tools in learning, in a University of Peru
<p>Data collected through a questionnaire regarding satisfaction in the use of technological simulation tools in learning, in a University of Peru</p>
Professional Technology Enhanced Learning: Systematic Literature Review Dataset
<p>Professional Technology-Enhanced Learning is concerned with technology-enhanced learning for professionals, i.e. people in employed jobs. Other terms used instead of professional learning are sometimes workplace learning or work-related learning; and there is a large overlap with research that focusses on informal, situated, or lifelong learning. <br> In this spreadsheet, we publish data collected for a systematic mapping study. The goal of the study was to map existing literature on professional technology-enhanced learning. The data collection followed Kitchenham and Charter's (2007) method for systematic literature reviews. This spreadsheet shows 58 papers, for which we excerpted authors (one, two or three author surnames fully shown, for more authors, only the first author name is shown with the extension "et al."), title, DOI or permanent link, the citation as given by the source, and the abstract.</p> <p>We hope that others find this data useful, and if they do so reference this data collection, referring to it as follows: Cicchinelli and Pammer-Schindler (2023). Professional Technology Enhanced Learning: A Systematically Collected Dataset of Academic Publications in the Field. DOI: https://doi.org/10.5281/zenodo.7957206</p>
Data for "ChickenSense: A low-cost deep learning-based solution for real-time poultry feed intake monitoring using sound technology"
<p>The dataset includes two types of data: scale and audio. The scale data is recorded using a hanging scale and measures the feeders' weight with a 5 Hz frequency. The audio data contains 1824 hours of recorded audio using a piezo-electric sensor attached to a bucket feeder to capture vibrations as audio signals with a sampling rate of 48 kHz. Each .wav file contains 15 minutes of audio recording.<br> Along with the raw data, a part of the dataset containing 36 30-second audio recordings was sampled and labeled in four classes, including pecking, singing, anomaly, and silence, resulting in 3319 labeled events. </p>
Availability and use of technology for e-learning in Bangladesh
<p>There are 1162 responses in the dataset. Respondents’ gender, division, and residence were obtained. Here, 6 items stand (“availability_item_1” to “availability_item_6”) for availability and 11 items (“usability_item_1” to “usability_item_11”) for use of technology measure, where the initial responses were in 5 points Likert scale (1 for “strongly disagree” and 5 for “strongly agree”). On the other hand, 14 items’ (“pss_item_1” to “pss_item_14”) PSS scale was used for measuring stress, where responses were also in five-point Likert scale (0 for “Never” and 4 for “Very often”). However, reverse scoring for items 4, 5, 7, & 8 are given in this data set, according to the instruction of scale scoring. In the dataset, “availability_item_1_binary” to “availability_item_6_binary” and “usability_item_1_binary” to “usability_item_11_binary” represent the categories (1 for “sub-optimum” and 2 for “optimum”) of the availability and usability of technology measure.</p>
Finger gesture recognition with smart skin technology and deep learning
<p class="p1">Finger gesture recognition was extensively studied in recent years for a wide range of human-machine interface applications. Surface electromyography (sEMG), in particular, is an attractive, enabling technique in the realm of finger gesture recognition, and both low and high-density sEMG were previously studied. Despite the clear potential, cumbersome electrode wiring and electronic instrumentation render contemporary sEMG-based finger gestures recognition to be performed under unnatural conditions. Recent developments in smart skin technology provide an opportunity to collect sEMG data in more natural conditions. Here we report on a novel approach based on a soft 16-electrode array, a miniature and wireless data acquisition unit and neural network analysis, in order to achieve gesture recognition under natural conditions. Finger gesture recognition accuracy values, as high as 93.1%, were achieved for 8 gestures when the training and test data were from the same session. For the first time, high accuracy values are also reported for training and test data from different sessions for three different hand positions. These results demonstrate an important step towards sEMG-based gesture recognition in non-laboratory settings, such as in gaming or Metaverse.</p>
Learning and Improving Alzheimer's Patient-Caregiver Relationships Via Smart Healthcare Technology
ClinicalTrials.gov study NCT04536701. IPD Sharing: NO. Countries: 1. Publications: 2.
Support Via Technology: Living and Learning With Advancing FTD
ClinicalTrials.gov study NCT05338710. IPD Sharing: YES. Countries: 1. Publications: 1.
Clinical Performance Evaluation of the Artificial Intelligence (AI)/ Machine Learning (ML) Technologies Utilized by the Origin Medical EXAM ASSISTANT
ClinicalTrials.gov study NCT06952439. IPD Sharing: NO. Countries: 1. Publications: 15.
Finger gesture recognition with smart skin technology and deep learning
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Students responses per School for the research project "Perspectives on Learning Technologies"
<p>A Microsoft Excel dataset that includes students' responses for a research project entitled "Perspectives on learning Technologies" conducted in year 2014. Students from three Schools (Psychology, Electrical Engineering Electronics and Computer Science-EEE & CS and Law) of the University of Liverpool participated in it.</p>
Teachers' responses per School for the research project "Perspectives on Learning Technologies"
<p>A Microsoft Excel dataset that includes teachers' responses for a research project entitled "Perspectives on learning Technologies" conducted in year 2014. Teachers from three Schools (Psychology, Electrical Engineering Electronics and Computer Science-EEE & CS and Law) of the University of Liverpool participated in it.</p>
An Assessment of University Students' Perception Towards the Adoption and Use of Mobile Learning Technologies for Learning
<p>Mobile learning technologies serve has as a transformative tool in the educational sector which gives room for accessibility, flexibility and scalability. It also improves student learning outcomes. This study aims to review research on students' attitude and perception towards the adoption and implementation of mobile learning technologies using the Unified Theory and Acceptance and Use of Technology (UTAUT) model. From the studies, results showed that students' perception has a major impact on the adoption and use of mobile technologies. However, based on the various literature reviewed, students' perception is influenced by performance expectancy, effort expectancy (ease of use of the technology), social influence, perceived enjoyment and satisfaction. All these must be put into consideration before design and implementation for the effectiveness. Nevertheless, if all or some of these constructs were not incorporated in the development of mobile learning technology, obstructs the adoption and implementation of mobile learning.</p>
An Assessment of University Students' Perception Towards the Adoption and Use of Mobile Learning Technologies for Learning
<p>Mobile learning technologies serve has as a transformative tool in the educational sector which gives room for accessibility, flexibility and scalability. It also improves student learning outcomes. This study aims to review research on students' attitude and perception towards the adoption and implementation of mobile learning technologies using the Unified Theory and Acceptance and Use of Technology (UTAUT) model. From the studies, results showed that students' perception has a major impact on the adoption and use of mobile technologies. However, based on the various literature reviewed, students' perception is influenced by performance expectancy, effort expectancy (ease of use of the technology), social influence, perceived enjoyment and satisfaction. All these must be put into consideration before design and implementation for the effectiveness. Nevertheless, if all or some of these constructs were not incorporated in the development of mobile learning technology, obstructs the adoption and implementation of mobile learning.</p>
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH. in Deep learning brings speed, accuracy to the life sciences.
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH.
Technology Acquisition Plans to Foster Supply Chain Risk Management Learning Outcomes in Project-Based Software Development Courses: Raw Data and TAP Template
<p>This is a data set and TAP template to accompany the paper</p> <p>"Technology Acquisition Plans to Foster<span> </span>Supply Chain Risk Management Learning Outcomes in Project-Based Software Development Courses" by Tenbergen and Mead published at the 36th International Conference on Software Engineering Education and Training.</p>
DYNAMIC PRICING IN FINANCIAL TECHNOLOGY: EVALUATING MACHINE LEARNING SOLUTIONS FOR MARKET ADAPTABILITY
<p>The rapid advancement of technology has transformed the financial services sector, leading to the rise of fintech companies that leverage cutting-edge tools such as artificial intelligence (AI) and machine learning (ML) to offer innovative solutions. One area where fintech is particularly impactful is dynamic pricing, which involves adjusting prices in real-time based on market conditions, user behavior, and external factors. The ability to optimize pricing in response to fluctuating conditions is critical for maximizing profitability, improving customer satisfaction, and maintaining competitiveness. In this context, machine learning algorithms provide a powerful framework for making data-driven pricing decisions by learning from historical data and predicting future trends.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.