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12 results for “Search algorithm”
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>
Data and code for "Search Algorithm, Repetitive Information, and Sales on Online Platform"
<p>Data and code for "Search Algorithm, Repetitive Information, and Sales on Online Platform".</p> <p>The R code containsboth code for simulation and code for estimation.</p>
Supporting datasets PubFig05 for: "Heterogeneous Ensemble Combination Search using Genetic Algorithm for Class Imbalanced Data Classification"
<p><strong>Faces Dataset: PubFig05</strong></p> <p>This is a subset of the ''PubFig83'' dataset [1] which provides 100 images each of 5 most difficult celebrities to recognise (referred as class in the classification problem). For each celebrity persons, we took 100 images and separated them into training and testing sets of 90 and 10 images, respectively:</p> <p><strong>Person: </strong>Jenifer Lopez; Katherine Heigl; Scarlett Johansson; Mariah Carey; Jessica Alba</p> <p> </p> <p><strong>Feature Extraction</strong></p> <p>To extract features from images, we have applied the HT-L3-model as described in [2] and obtained 25600 features.</p> <p><strong>Feature Selection</strong></p> <p>Details about feature selection followed in brief as follows:</p> <ol> <li> <p><strong>Entropy Filtering:</strong> First we apply an implementation of Fayyad and Irani's [3] entropy base heuristic to discretise the dataset and discarded features using the minimum description length (MDL) principle and only 4878 passed this entropy based filtering method.</p> </li> <li> <p><strong>Class-Distribution Balancing:</strong> Next, we have converted the dataset to binary-class problem by separating into 5 binary-class datasets using one-vs-all setup. Hence, these datasets became <em>imbalanced</em> at a ratio of 1:4. Then we converted them into <em>balanced binary-class</em> datasets using random sub-sampled method. Further processing of the dataset has been described in the paper.</p> </li> <li> <p><strong>(alpha,beta)-k Feature selection:</strong> To get a good feature set for training the classifier, we select the features using the approach based on the (alpha,beta)-k feature selection [4] problem. It selects a minimum subset of features that maximise both within class similarity and dissimilarity in different classes. We applied the entropy filtering and (alpha,beta)-k feature subset selection methods in three ways and obtained different numbers of features (in the Table below) after consolidating them into binary class dataset.</p> </li> </ol> <ul> <li> <p><strong>UAB:</strong> We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets and we took the <em>union</em> of selected features for each binary-class datasets. Finally, we applied the (alpha,beta)-k feature set selection method on each of the binary-class datasets and get a set of features.</p> </li> <li> <p><strong>IAB:</strong> We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets and we took the <em>intersection</em> of selected features for each binary-class datasets. Finally, we applied the (alpha,beta)-k feature set selection method on each of the binary-class datasets and get a set of features.</p> </li> <li> <p><strong>UEAB:</strong> We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets. Then, we applied the entropy filtering and (alpha,beta)-k feature set selection method on each of the balanced binary-class datasets. Finally, we took the <em>union</em> of selected features for each <em>balanced binary-class</em> datasets and get a set of features.</p> </li> </ul> <p>All of these datasets are inside the compressed folder. It also contains the document describing the process detail.</p> <p> </p> <p><strong>References</strong></p> <p>[1] Pinto, N., Stone, Z., Zickler, T., & Cox, D. (2011). Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on (pp. 35–42).</p> <p>[2] Cox, D., & Pinto, N. (2011). Beyond simple features: A large-scale feature search approach to unconstrained face recognition. In Automatic Face Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on (pp. 8–15).</p> <p>[3] Fayyad, U. M., & Irani, K. B. (1993). Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In International Joint Conference on Artificial Intelligence (pp. 1022–1029).</p> <p>[4] Berretta, R., Mendes, A., & Moscato, P. (2005). Integer programming models and algorithms for molecular classification of cancer from microarray data. In Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38 (pp. 361–370). 1082201: Australian Computer Society, Inc.</p> <p> </p>
Do expectation-maximization algorithms have to make use of 'background distribution' for de novo motif search?
<p>Data for the article "Do expectation-maximization algorithms have to make use of ‘background distribution’ for de novo motif search?". The preprint is live on Research Square (DOI: <a href="https://www.researchsquare.com/article/rs-1725972/v2">10.21203/rs.3.rs-1725972/v2</a>).</p>
The optimization of a jet turbojet engine by PSO and searching algorithms
<p>The turbojet engine operates on the ideal Brayton cycle (gas turbine) and consists of six main parts: diffusers, compressors, combustion chambers, turbines, afterburners and nozzles. Using computer code writing in MATLAB software environment, exergy analysis on all selected turbojet engine components, exergy analysis on J85-GE-21 turbojet engine for selective height of 10008000 meters above sea level at speeds of 200 m/s and temperatures of 10, 20 and 40 ° C have been provided and then, according to the system functions, the system is optimized based on the PSO method. For the purpose of optimization, variables of Mach number, efficiency of the compressor, turbine, nozzle and compressor pressure ratio are considered in the range of 0.6 to 1.4, 0.8 to 0.95, 0.8 to 0.95 and 7 to 10, respectively. The highest exergy efficiency of different parts of the engine at sea level with an inlet air velocity of 200 m/s corresponds to a diffuser with 73.1%. Then, the nozzle and combustion chamber are respectively 68.6% and 51.5%. The lowest exergy efficiency is related to compressor with 4%. After that, the afterburner is ranked second with 11.6%. Also, the values of entropy produced and the efficiency of the second law before optimization were 1176.99 and 479 w/k respectively and the same values after optimization were 1129 and 51.4 w/k respectively which is identified. After the optimization process, the amount of entropy produced is reduced and the efficiency of the second law of thermodynamics has increased.<br> </p>
A Novel Algorithm for Estimating Web Page Ranking in Search Engine Results Pages
<p><em><strong>Abstract:</strong> </em>Search engine optimization (SEO) can make a big improvement in the traffic to a web page. Because search engines keep their main rules of ranking undeclared, it’s important to develop models that can estimate the ranking of a web page in the search engine to be able to optimize web pages to rank higher in the search engine. The available research methodologies used machine learning algorithms to provide solutions for this target with the help of generated datasets by scraping the search engine results pages (SERP) and crawling web pages. Their proposed models suffered from the inability to be updated dynamically if the search engine updated its ranking algorithm, and their input data did not include the diversity of web pages and languages. This research will propose a novel original rank estimation algorithm that’s able to overcome other research challenges, with a set of comparative experiments and complexity analysis. Results will show that the proposed algorithm could achieve higher values of accuracy, precision, and recall.</p> <p><strong><em>Dataset: </em></strong></p> <p>For research purpose, the dataset will play two roles, first, it will act the role of search engine result pages (SERP), and second, it will be used to test algorithms and calculate performance measurements. Dataset is consisting of 9930 web pages, aimed to identify search results pages, focusing on the top 3 pages of SERP, with 31 extracted attributes that's related to search engine optimization (SEO). The distribution of examples between class labels was balanced, with changes due to scraping operation issues, but not significantly different, with fractions of 39.9%, 34.6%, and 25.5% for the class labels page1, page2, and page 3. Feature names are: 'Title 1 Length', 'Title 2 Length', 'Meta Description 1 Length', 'Meta Description 2 Length', 'Meta Keywords 1 Length', 'H1-1 Length', 'H1-2 Length', 'H2-1 Length', 'H2-2 Length', 'Size (bytes)', 'Word Count', 'Text Ratio', 'Inlinks', 'Unique Inlinks', 'Unique JS Inlinks', '% of Total', 'Outlinks', 'Unique Outlinks', 'Unique JS Outlinks', 'External Outlinks', 'Unique External Outlinks', 'Unique External JS Outlinks', 'Response Time', 'Status Code', 'Keyword in MetaDescription1', 'Keyword in Title1', 'Keyword in MetaKeywords1', 'Keyword in URL', 'Has LastModified', 'Keyword in Headers', and 'Keyword in Emphasized Text'.</p> <p>The process of dataset generation involved scraping the search engine, extracting URLs for selected keywords, focusing on feature extraction, cleaning and preprocessing, and generating new attributes related to keywords in web pages. It involved also removing missing values, duplicates, and data type conversions to obtain a comprehensive dataset.<br> Keyword selection involves selecting keywords from various categories and considering diversity, including high and low traffic, long-term and short-term keywords, and generic and branded keywords. Apify online tool was used for search engine scraping with default language and US country, resulting in 388 selected keywords with 30 results per keyword. Dataset included extracted SEO features from 9991 web pages using screamingFrog desktop software and Rapidminer desktop software, determining page SEO-friendliness and comparing it to SERP rankings. Dataset cleaning involved removing redundant attributes, removing paid SERP results, replacing missing values, and converting data types. Rapidminer was used for data cleaning and preprocessing, generating new attributes related to keyword usage in web pages.<br> </p>
Screener and Enumerator with Force-Field Optimization (SEFFO): algorithm for searching adsorption sites and configurations on 2D materials
<p>See ref:</p> <p> </p> <p>(submission stage)</p>
Applicability of Search-based Algorithms for Model-based Game Play Testing - an Empirical Study
<p>Experimental data used for the study on EvoMBT approach for testing of games.</p>
Validation of SEARCH, a Novel Hierarchical Algorithm to Define Long-term Outcomes After Pulmonary Embolism
ClinicalTrials.gov study NCT05568927. IPD Sharing: NO. Countries: 1. Publications: 46.
In Search of Best Practices for Patients With Heart Failure and Secondary Mitral Regurgitation: An Evaluation of the Inova Heart Failure Treatment Algorithm
ClinicalTrials.gov study NCT06675448. IPD Sharing: NO. Countries: 1. Publications: 16.
Searching for evidence of algorithmic randomness and incomputability in the output of quantum random number generators
<p>This upload contains the data and code used in the following paper:</p> <p>J. T. Kavulich, B. P. Van Deren, and M. Schlosshauer, “Searching for evidence of algorithmic randomness and incomputability in the output of quantum random number generators,” <em>Phys. Lett. A</em> 388, 127032 (2021), <a href="https://doi.org/10.1016/j.physleta.2020.127032">doi.org/10.1016/j.physleta.2020.127032</a></p> <p>The contents of the data set are as follows:</p> <p>1) Random strings for two QRNGs and four PRNGs. For each RNG, a zip archive provides 100 strings containing 25 x 2<sup>26</sup> = 1,677,721,600 bits each.</p> <p>2) C++ code for the Chaitin–Schwartz–Solovay–Strassen (CSSS) and Borel-normality tests (code.zip, 13 KB).</p> <p>The bulk of this code is not ours, but was written and made publicly available at <a href="https://www.cs.auckland.ac.nz/research/groups/CDMTCS/export/80_random_seqs">this link</a> by the authors of the following paper:</p> <p>A. A. Abbott, C. S. Calude, M. J. Dinneen, and N. Huang, <em>Phys. Scri.</em> 94 (2019) 045103, <a href="https://doi.org/10.1088/1402-4896/aaf36a">doi:10.1088/1402-4896/aaf36a</a></p> <p>We have made just a few small modifications to their original code:</p> <ul> <li>For the CSSS tests, a text file containing the Carmichael numbers (for tests 1–3) and odd composites up to 100 (for test 4) is read in and used to perform the tests. (Note: The set of Carmichael numbers used in the tests was generously provided to us by R. G. E. Pinch. Reference: R. G. E. Pinch, The Carmichael numbers up to 10<sup>21</sup>, in: A.-M. Ernvall-Hytönen (Ed.), Proceedings of Conference on Algorithmic Number Theory, Vol. 46, Turku Centre for Computer Science, Turku, Finland, 2007, pp. 129–131.)</li> <li>We combined the first and second CSSS tests into a single program.</li> <li>We reformatted the display of the output, and included a VERBOSE flag for additional status output.</li> </ul> <p>3) Results from CSSS and Borel-normality tests in Python format (results.zip, 22 KB). This archive also contains a Python script (analyze.py) that reads the result files, carries out the statistical analysis, and displays the plots.</p>
Self-adaptive Search Equation-Based Artificial Bee Colony Algorithm with CMA-ES on the Noiseless BBOB Testbed
<p>This file contains the data for results of SSEABC algorithm on BBOB functions testbed.</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.