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35 results for “Flowchart”
Flowchart on the methodology used to conduct the literature search on provenance representation models and change-tracking in RDF
<p>The image contains a flowchart on the methodology used to conduct the literature search on provenance representation models and change-tracking in RDF. This methodology was used in the Abstract submitted to the DH2023 conference in Graz.</p>
Figure 4. CBR Implementation-Intelligent Flowcharting Developmental Approach to Legal Knowledge Based System
<p>The development of the case based reasoning module is in done in java net-beans. Proper<br> verification and validation of this module was done by the legal experts. The cases related to<br> Transfer of property act were collected from different legal databases and compiled. The necessary<br> keywords were framed, which were used in searching for the related cases. The following Fig 2.0<br> gives the screen shot of the CBR module develoed in Java Net beans.</p>
Fig 3 : VisiRule Implementation Module-1-Intelligent Flowcharting Developmental Approach to Legal Knowledge Based System
<p>The code of this flowchart is developed by the VisiRule in FLEX/ Prolog. As the source<br> code very huge it has not be incorporated in the paper.</p>
Figure 2. Simple Chart-Intelligent Flowcharting Developmental App
<p>The simplest VisiRule charts consist of a start box, one or more question boxes, some<br> expression boxes and some end boxes which are the conclusions drawn from the answers to the<br> questions.</p>
Figure 1 - VisiRule architecture-Intelligent Flowcharting Developmental Approach to Legal Knowledge Based System
<p>In the development of RBR we used the intelligent flowcharting approach. VisiRule is a tool<br> for creating decision support software purely by drawing flowcharts. The end result is Flex or<br> Prolog code which is automatically generated, compiled and ready to run, but which can also be<br> copied and used in a separate program. Not only can VisiRule be used by people with minimal<br> programming skills. VisiRule also enhances productivity by considerably reducing the time it takes<br> to produce a decision support system. VisiRule is an intelligent flowcharting tool in two senses.<br> Firstly, it is used to create knowledge-based systems and, secondly, it intelligently guides the<br> construction process by constraining what you can and can't do on the basis of the semantic content<br> of the emerging program. VisiRule provides the automatic construction of menu dialogues from<br> questions. These are populated by items inferred from expression boxes throughout the flowchart<br> tree which have a path to the question.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 8. Flowchart for movements of creature
<p>Window time has a fixed length between and and is an appropriate time interval [5]. So in each time window the total number of spikes in each three neurons is compared with other three neurons and artificial creature moves toward direction that the respective neurons fired maximum number of spikes. These fixed time<br> windows consist of 600 time-steps. Each time step is 0.5 ms. Flowchart in Figure 8 shows details.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 12. Genetic Algorithm flowchart
<p>Then next generation are produced by combination of the elites (15%), crossover (55%) and mutation (30%) of the initial population. Elites are the best chromosomes which are directly transferred to the next generation. Because of long chromosome length, for crossover, five points are randomly chosen in each parent as cut points. Figure 11 shows a typical crossover with two cutpoints and Figure 12 illustrates a flowchart for the proposed evolutionary model. Selections are based on Roulette Wheel selection, more detailed information can be found in.</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 3. (3.a) – The flowchart of Graph cuts method; (3.b)- the result of Graph cuts image segmentation.
<p>Figure 3 describes the steps implemented Graph cuts algorithm for the segmentation of human body parts. The results obtained are 5 main sections that include the hands, the legs, the center of the body (chest, waist, hips), and the head. The result of the display image is taken from the human image database, which was collected by us (Нгуен, 2016). </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 1. Flowchart of anthropometric system
<p> Our purpose is to develop an automatic measurement and modeling system based on 2D images (front and side images). This system used to image processing methods and machine learning algorithms. Our system has 3 main parts; there are human body feature extraction, training and testing processes, and the classification for new data. The novelty of our approach: - Classification of anthropometric features based on machine learning algorithms. - Development a non-contact anthropometric program for the smartphones on operation system Android. - Construction of a 3D-model of the human body based on the results of anthropometric features extraction. Our system can also be used to integrate to different environments, such as online shopping websites to support users fitting their clothes sizes and medical applications. The flowchart of our anthropometric system is described in figure 1. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 5. Flowchart of data classification
<p>The Random Forest is a powerful classification method because of the following. First, errors are minimized as a result of a random forest, synthesizing through training (learner). The second, random choice at every stage in the Random Forest will reduce the correlation between the learners in the synthesis of the results. In addition, we also found that the total error of layered forest trees depends on their individual errors in forest trees, as well as the correlation between the trees. The article uses the wrapper model (Christopher Tong, 2000) with the objective function for the evaluation, Random Forest algorithm is shown in figure 5. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 4. Flowchart and results of ICP algorithm
<p>The key concept of the standard ICP algorithm can be summarized in two steps: - Compute correspondences between the two scans. - Compute a transformation which minimizes the distance between corresponding points. It is forced to add a maximum matching threshold dmax. In most implementations of ICP, the choice of dmax represents a tradeoff between convergence and accuracy. A low-value result in bad convergence, a large value causes incorrect correspondences to pull the final alignment away from the correct value. Figure 4 describes the steps of the algorithm which determines the point features closest to object boundary. The result of the algorithm is described by images cut from the program (Нгуен, 2016)</p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 3. Flowchart of the ICA
<p>At last, the most powerful empire will take the possession of other empires and will win the competition. In other words, imperialistic competition hopefully converges to a state in which there exists only one empire and its colonies are in the same position and have the same cost as the imperialist. Figure 3 shows the flowchart of the basic ICA.</p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 3. The methodology flowchart
<p>The methodology of our experimental method is described in Figure 3 below.</p> <p>It helps to write our code in C# and to make an application in dot net framework, which collects facial images using a webcam/or other video grabbing tools. Then it implements Haar detection to extract facial features and to draw image pattern for matching both images.</p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 4: Methodology flowchart
<p>The detailed methodology approach is given in Figure 4:</p> <p>a) Generate a random number of robots to have a swarm.<br> b) Organize the robots in circular formations, where every robot will be in a group related to<br> circular formation. This means every circular formation is a cycle that has cyclic groups of robots<br> on its circumference as shown in Figure 2 and Figure 3. We might have more than one circular<br> formation.<br> c) Move the robots forward in a steady state.<br> d) Avoiding obstacles in case of facing an obstacle, and the swarm must adapt itself based on<br> the type of the obstacle. Various types of obstacles will be considered.<br> e) The swarm reorganizes itself after avoiding the obstacle in the same way as it was before<br> facing the obstacle. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 3. The methodology flowchart
<p>The methodology of our experimental method is described in Figure 3 below.</p> <p>It helps to write our code in C# and to make an application in dot net framework, which collects facial images using a webcam/or other video grabbing tools. Then it implements Haar detection to extract facial features and to draw image pattern for matching both images. </p>
FIGURE 2. A flowchart showing the steps for sediment processing for efficient and effective ichthyolith isolation from a in Methods for isolation and quantification of microfossil fish teeth and elasmobranch dermal denticles (ichthyoliths) from marine sediments
FIGURE 2. A flowchart showing the steps for sediment processing for efficient and effective ichthyolith isolation from a variety of sediment types. Sediment types are in boxes, while processing steps are shown in ovals.
VOLCON - overview / flowchart
<p>Flowchart of the studies included in the PhD thesis of Rikke Thorninger, MD</p> <table> <tbody> <tr> <td><strong>Publication / manuscript</strong></td> <td><strong>Color of boxes</strong></td> <td><strong>Type of study</strong></td> <td><strong>DOI</strong></td> </tr> <tr> <td><strong>I</strong></td> <td> </td> <td>protocol</td> <td><a href="http://doi.org/10.1186/s12891-019-2677-y">doi.org/10.1186/s12891-019-2677-y</a></td> </tr> <tr> <td><strong>II</strong></td> <td>grey</td> <td>prospective case series</td> <td><a href="http://doi.org/10.3390/jcm10091831">doi.org/10.3390/jcm10091831</a></td> </tr> <tr> <td><strong>III</strong></td> <td>green -- blue</td> <td>randomized controlled trial</td> <td><a href="http://doi.org/10.1186/s10195-022-00673-4">doi.org/10.1186/s10195-022-00673-4</a></td> </tr> <tr> <td><strong>IV</strong></td> <td>grey -- green</td> <td>prospective cohort study</td> <td><a href="https://doi.org/10.3390/jcm12052076">doi.org/10.3390/jcm12052076</a></td> </tr> <tr> <td><strong>V</strong></td> <td>grey + white</td> <td>prospective case series</td> <td>under review</td> </tr> </tbody> </table> <p> </p>
Generalist Repository Selection Flowchart
<p>The repository selection flow chart is a product of the <a href="https://datascience.nih.gov/data-ecosystem/generalist-repository-ecosystem-initiative">Generalist Repository Ecosystem Initiative</a> (GREI) and is designed to guide users through a series of considerations for selecting the right repository for sharing data. Developed with researchers and librarians in mind, this tool is intended to provide clear guidance in self-service or consultation-based situations.</p>
Astropy Infrastructure Flowchart
<p>This flowchart represents the relationships between different packages or projects involved in supporting the infrastructure in the Astropy Project. The relationships were as of the publication date and may have been outdated since. The packages or projects in blue/red bold font are the ones that the Astropy Project maintains directly.</p> <p>For more information and further discussions related to this flowchart, please see https://github.com/astropy/astropy-project/pull/122 and https://github.com/astropy/astropy-project/issues/118.</p>
CONSORT Flowchart
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.