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1,063 results for “Search”
Figure 1. Brain Structure-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>EEG data have collected from<br> desirable subjects. Each and every EEG signal has different kind of bands like Alpha, Beta,<br> Gamma, Theta, and Delta. Each band stores the particular information about the emotions. Alpha<br> band (8-13 Hz) which located in Frontal Occipital, Beta band (13-30 Hz) which located in Frontal<br> Central, Gamma band (30-100 Hz), Theta band (4- 7 Hz) which located in Midline Temp, Delta<br> band (0-4Hz) which located in Frontal Lobe. Before processing the EEG signal and extracting these<br> bands, preprocess the signal and reduce the noise. The basic brain figure is shown in below.</p>
Figure 8. Sample Emotion Classification Result-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>For all the 32 participants the EEG signal has to be sampled and process their emotions. The<br> emotions are depends on the music and video clips. In this paper the video clips are changed from<br> one person to other person. Here hybrid feed forward neural networks with radial basis function;<br> probabilistic neural network classifier is used to classify the emotions from EEG. PNN is very fast<br> and insensitive neural network which provides the optimized classification result. Compare to the<br> multi layer perception neural network it provide accurate result. It classifies the emotion into two<br> different groups like arousal and valence. Figure 8 shows that the model implemented result of<br> emotion classification and person identification.</p>
Figure 7. Neural Network model-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>In Probabilistic Neural Network the operations are organized into a multilayer feed forward<br> neural network with four layers like input layer, hidden layer, pattern layer and output layer. PNN<br> use the Euclidean distance measure the difference between one neuron to other neurons. The actual<br> target values are stored in the hidden neuron and the optimized weighted values are fed into the<br> same category hidden neuron. Then finally the output layer compared the weighted votes of each<br> target values and the target votes are used to predict the emotions.</p>
Figure 10. Sensitivity and specificity of different bands-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>Figure10 has shown in sensitivity and specificity of different neural network which is used<br> to explain how exactly the emotions are classified into groups and accuracy value shown in above<br> table 3.</p>
Figure 9. Mean Square Error for different bands-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>Figure 9 has shown in different epochs using neural networks with mean square error<br> performance and the Table 3 shows that different bands mean square error values while training the<br> neural networks with particle swarm optimization.</p>
Figure 6. Mean square error of alpha band-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>Particle swarm optimization algorithm first optimizes the neural networks weight and bias<br> and provides the minimum mean square error with nearer by zero. The following figure 6 has<br> shown that minimum mean square error when training the particular band features.</p>
Figure 5. Process flow of PSO-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>PSO is used to identify the best solution from collection of solution. It is a computational<br> method that optimizes a problem by iteratively trying to improve a candidate solution with regard to<br> a given measure of quality. PSO optimizes a problem by having a population of candidate solutions,<br> here dubbed particles, and moving these particles around in the search-space according to simple<br> mathematical formulae over the particle's position and velocity. Each particle's movement is<br> influenced by its local best known position but, is also guided toward the best known positions in<br> the search-space, which are updated as better positions are found by other particles. This is expected<br> to move the swarm toward the best solutions. PSO is a metaheuristic as it makes few or no<br> assumptions about the problem being optimized and can search very large spaces of candidate<br> solutions. However, metaheuristic such as PSO do not guarantee an optimal solution is ever found.<br> The following Figure 5 explains the basic flow of PSO process.</p>
Cybercrime search index – Use Case n.3
<p>Test data created for SUNFISH project UK Use Case testing and evaluation </p>
A survey of current practice of data search services
<p>Relevancy ranking is an important component of making a data repository's search system<br> responsive to data seekers’ needs. The <a href="https://www.rd-alliance.org/groups/data-discovery-paradigms-ig">Research Data Alliance (RDA) Data Discovery Paradigms<br> Interest Group</a> is a collaborative activity within our data community which aims to improve data<br> searchability. This survey is intended to gather information about the current practices and lessons<br> learnt by data repositories in implementing relevancy ranking in search systems. We expect that<br> analysis of the survey results will:</p> <ul> <li>Help data repositories choose appropriate technologies when implementing or improving their search functionality;</li> <li>Provide a means for sharing experiences in improving relevancy ranking;</li> <li>Capture the aspirations, successes and challenges encountered from research data repository managers;</li> <li>Help the Data Discovery Paradigms Interest group align future activities on data search improvement with the interests of data search service providers.</li> </ul> <p>For the above the purpose, we designed a survey instrument to answer the following topics (the numbers in brackets indicate the number of questions asked per topic):</p> <ol> <li>What are characteristics of each repositories (5)?</li> <li>What are system configurations (e.g., ranking model, index methods, query methods) (7)?</li> <li>Evaluation methods and benchmark (10) <ul> <li>What has been evaluated?</li> <li>What evaluation methods have been applied?</li> <li>How was the evaluation collection built?</li> <li>What is approximate performance range of search systems with certain configuration?</li> </ul> </li> <li>What methods have been used to boost searchability to web search engines (e.g., Google, Bing) (2)</li> <li>What other technologies or system configurations have been employed (5)?</li> <li>Wish list for future activities for the RDA relevance task force (2)?</li> </ol> <p>Notes: Survey instruments from Version 1 and Version 2 have same questions, but order questions slightly different. Version 2 has the one as instrumented to participants.</p>
Data to accompany the paper "Improved fragment-based protein structure prediction by redesign of search heuristics"
<p>This repository contains the older and newer input fragment sets and other data used for the analyses in our paper. The filenames for each tarball contain the PDB identifier of each protein along with a chain ID if applicable, followed by 'old' or 'new' for old and new fragments, respectively. Each tarball contains: a .fasta file of the input sequence, a matching PDB structure file, the relevant PSIPRED secondary structure prediction file, and the 9mer and 3mer fragment files. <br> <br> An additional tarball, ScoreRMSDplots_3protocols.tgz, contains extended versions of Figure 3 which show score and RMSD distributions clearly. Additionally, the same data is shown for equivalent experiments using the older fragment set.</p>
Hit Expansion using Substructure Search, Virtual Screening & Free Energy Perturbation
<p>Identification of commercially available chemical analogs of primary hits previously crystallized in complex with the zinc finger ubiquitin binding domain (Zf-UBD) of USP5 and prioritization of chemical analogues by free energy perturbation (FEP). </p>
What does Google recommend when you want to compare insurance offerings? – A method and empirical study considering Google's top search results
<p>This dataset is part of a publication and shows Google's search results for German search queries on insurance comparison offerings.</p> <p>Relevant search queries were extracted from a commercial search engine log file consisting of more than 640,000 different search queries. From the log, we extracted a variety of query formulations for the same topic, i.e., queries containing the same word or phrase. The selection was based on pre-defined keywords in the context of insurance comparisons. The queries from the log file were automatically selected by combining the terms "*insurance*" and "*comparison*" (including left as well as right truncation). Examples of such inquiries are "car insurance comparison", "occupational disability insurance comparison", "liability insurance in comparison". This procedure identified a total of 121 different search queries. Scraping of the results took place between 08.05. - 09.05.2018.The adress data were extracted by using a text classification algorithm and a crawler to find the contact data on a website.</p> <p>It is a tab-separated file with the following attributes:</p> <p>ID: Unique row identifier</p> <p>ID Query: Unique search query identifier</p> <p>Query: German search query </p> <p>Position: Result position to the search query </p> <p>URL: URL of the search result </p> <p>Host: Host of the search result </p> <p>Company: Name of the company on the website</p> <p>Street: Street in the address on the website </p> <p>Zipcode: Street in the address on the website </p> <p>Location: Location in the address on the website </p> <p>District: District in the address on the website </p> <p>State: State in the address on the website </p> <p>Country: Country in the address on the website </p>
First Results from ABRACADABRA-10 cm: A Search for Sub-$\mu$eV Axion Dark Matter — 2018 Data
<p>Extracted limits from the Axion Dark Matter search associated with the article: <em>First Results from ABRACADABRA-10 cm: A Search for Sub-μeV Axion Dark Matter </em>(To be published in Phys. Rev. Lett. 2019)</p>
Webis Search Mission Corpus 2012 (Webis-SMC-12)
<p>The Webis Search Mission Corpus 2012 (Webis-SMC-12) contains 8840 search engine interactions of 127 users. Two human annotators divided these interactions into 2881 logical sessions and 1378 missions. Cases where the annotators did not agree initially were discussed to reach a consensus.</p>
Confirmation Bias in Web-Based Search: A Randomized Online Study on the Effects of Expert Information and Social Tags on Information Search and Evaluation
<p>ABSTRACT</p> <p>Background: The public typically believes psychotherapy to be more effective than pharmacotherapy for depression treatments. This is not consistent with current scientific evidence, which shows that both types of treatment are about equally effective.</p> <p>Objective: The study investigates whether this bias towards psychotherapy guides online information search and whether the bias can be reduced by explicitly providing expert information (in a blog entry) and by providing tag clouds that implicitly reveal experts’ evaluations.</p> <p>Methods: A total of 174 participants completed a fully automated Web-based study after we invited them via mailing lists. First, participants read two blog posts by experts that either challenged or supported the bias towards psychotherapy. Subsequently, participants searched for information about depression treatment in an online environment that provided more experts’ blog posts about the effectiveness of treatments based on alleged research findings. These blogs were organized in a tag cloud; both psychotherapy tags and pharmacotherapy tags were popular. We measured tag and blog post selection, efficacy ratings of the presented treatments, and participants’ treatment recommendation after information search.</p> <p>Results: Participants demonstrated a clear bias towards psychotherapy (mean 4.53, SD 1.99) compared to pharmacotherapy (mean 2.73, SD 2.41; <em>t</em><sub>173</sub>=7.67, <em>P</em><.001, <em>d</em>=0.81) when rating treatment efficacy prior to the experiment. Accordingly, participants exhibited biased information search and evaluation. This bias was significantly reduced, however, when participants were exposed to tag clouds with challenging popular tags. Participants facing popular tags challenging their bias (n=61) showed significantly less biased tag selection (<em>F</em><sub>2,168</sub>=10.61, <em>P</em><.001, partial eta squared=0.112), blog post selection (<em>F</em><sub>2,168</sub>=6.55, <em>P</em>=.002, partial eta squared=0.072), and treatment efficacy ratings (<em>F</em><sub>2,168</sub>=8.48, <em>P</em><.001, partial eta squared=0.092), compared to bias-supporting tag clouds (n=56) and balanced tag clouds (n=57). Challenging (n=93) explicit expert information as presented in blog posts, compared to supporting expert information (n=81), decreased the bias in information search with regard to blog post selection (<em>F</em><sub>1,168</sub>=4.32, <em>P</em>=.04, partial eta squared=0.025). No significant effects were found for treatment recommendation (<em>P</em>s>.33).</p> <p>Conclusions: We conclude that the psychotherapy bias is most effectively attenuated—and even eliminated—when popular tags implicitly point to blog posts that challenge the widespread view. Explicit expert information (in a blog entry) was less successful in reducing biased information search and evaluation. Since tag clouds have the potential to counter biased information processing, we recommend their insertion.</p>
Source data belonging to "Visualisation of dCas9 target search in vivo using an open-microscopy framework"
<p>Source data corresponding to "Visualisation of dCas9 target search <em>in vivo</em> using an open-microscopy framework". Contains pTarget and pNonTarget raw datasets, as well as all localization data, cell UV intensity data, cell outline data, and analysed diffusion coefficient lists.</p>
The Locus Algorithm Exoplanet-Search Pointings Catalogue
<p>Presented here is a Catalogue of Pointings for Optimal Differential Photometry for 61,662,376 stars presented in the form of a CSV file. A total of 67,043,579 stars were analysed using the Locus Algorithm (Creaner et al, 2019), and the results of that analysis are presented here. A paper detailing this work is currently in writing. This catalogue is invisaged for use in the search for extrasolar planets by using the pointings presented here to allow for a maximum differential photometry precision and thus aid ground based searches for extrasolar planets.</p> <p>Instructions on how to use the data are contained in readme.md. An SQL script to identify the reference stars is also presented.</p> <p>Also given here are source files containing the fits and csv files generated by the grid jobs in a .zip archive.</p>
Webis-Voice-based-and-Conversational-Argument-Search-20
<p>Interface, questionnaires, and collected data for the paper "<a href="https://webis.de/publications.html#?q=stein2020b">Investigating Expectations for Voice-based and Conversational Argument Search on the Web</a>".</p> <p>Data is mostly in tab-separated values format (like csv, just with tabs). For the transcripts of the user study, only the sequence of action labels (in xml) is released for privacy reasons. The labels are described in user-study-tags-participant.txt and user-study-tags-system.txt.</p>
Data for study "Direct Answers in Google Search Results"
<p>The goal of this research is to examine <strong>direct answers</strong> in Google web search engine. Dataset was collected using Senuto (<a href="https://www.senuto.com/">https://www.senuto.com/</a>). Senuto is as an online tool, that extracts data on websites visibility from Google search engine.</p> <p>Dataset contains the following elements:</p> <ol> <li>keyword,</li> <li>number of monthly searches,</li> <li>featured domain,</li> <li>featured main domain,</li> <li>featured position,</li> <li>featured type,</li> <li>featured url,</li> <li>content,</li> <li>content length.</li> </ol> <p>Dataset with visibility structure has <strong>743 798 keywords</strong> that were resulting in SERPs with direct answer.</p>
Fig. 3 in Relationship between host searching and wind direction in Ophraella communa (Coleoptera: Chrysomelidae)
Fig. 3. Mean numbers (± SE) of Ophraella communa eggs and adults at various cardinal directions from the center of each plot on common ragweed plants in the concentric bands. Data represented by columns bearing the same letters were not significantly different (LSD, a = 0.05). The dominant direction of the wind during the period of the entire experiment was from the south.
ScienceDex guides
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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.