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6,467 results for “process”
BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 2. Processes and Interfaces of the SAH Human Brain Model
<p>The proposed SAH Human Brain Model starts assigning the main attributes to the “heavy pieces” (king, queen, rooks, bishops, knights) and assigning to pawns the interfaces as an advanced guard. The interface represents senses and processed human actions (equilibrium, movements, and speech) and it results from the brain activity (see Figure 2). </p>
Figure 1. Visual and synthetic representation of the modelling process in the software industry-The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts
<p>The experience that is accumulated regarding the modelling paradigms in the software engineering is impressive. Thus, the software engineering recognizes modelling paradigms like object orientation, aspect orientation, component orientation, service orientation, agent orientation. In one form or another, these paradigms prove their ex- cellence in certain types of IT projects. At the same time, these paradigms reveal their objective limits when they are used to engineer the real world software systems. Every modelling paradigm represents, in fact, a modality to represent the real world using a specific formal framework. The specificity of the formal framework is defined from both a syntactic and semantic perspective. The formal syntactic framework of a paradigm refers to the concepts that are used by the paradigm in order to represent the real world, but also to the recommended principles that allow for these concepts to interact in a correct and efficient manner. Both the concepts and the principles benefit from a formal representation that ultimately favours communication as a secondary modelling lever inside the IT projects. Every syntactic artefact of a paradigm can be associated with a certain real world semantics, which it abstracts. As a consequence, considering that the real world continuously enhances its semantic potential, the syntactic constructs that are favoured by the paradigm may become problematic.</p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 5. The process of MICALK for solving the TSP
<p>Moreover, in order to prevent the ICA from getting trapped in stagnation, we used a local searching algorithm when the algorithm attained a better solution compared to previous iterations. In fact, the probability of finding better solutions near a good solution is relatively high. There exist many algorithms for the local search and they have of course their pros and cons. Since LinKernighan algorithm is simple and it is one of the most successful methods for generating optimal or near optimal solutions for the TSP, we have used it in this study. The main steps of MICALK are summarized in the pseudo-code given in Figure 5. </p>
Fig. 2. Morphological characters used for the analysis and key. A–D. Pronotum. E–F. Prosternal process. G–H. Mesoventral cavity. I. Elytral striae. J. Aedeagus. K in The world fauna of Synchroidae Lacordaire, 1859 (Coleoptera, Tenebrionoidea, Synchroidae)
Fig. 2. Morphological characters used for the analysis and key. A–D. Pronotum. E–F. Prosternal process. G–H. Mesoventral cavity. I. Elytral striae. J. Aedeagus. K. Ovipositor.
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 3. Demonstrating the process of classification by applying the ANN, then the SVM
<p>Essentially, if the number of nodes in the hidden layers increases, processing time increases, and the resultant ANN will suffer from over-fitting. Conversely, too small of a number of hidden layers will cause under-fitting for the ANN. In our setting, the number of hidden layers and the number of nodes in each hidden layer were fixed at five. Concerning the learning rate and momentum settings, these impact sensitive training performances are set to optimal values obtained via a grid search technique. The number of nodes in the output layer was three because there are three different class labels (i.e., 2Dchart, bar, and pie) in our datasets. We used the ANN here because our datasets have nonlinear separation, and the ANN is also highly applicable to nonlinear modeling. Thus the ANN with multiple hidden layers was an optimal candidate; however, since the ANN is a black box learning approach, it is difficult to interpret implicit relationships between inputs and outputs.</p>
RAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 4. Processes of all experiments:
<p>In this study, accuracy values of each dataset showed the performance of each method. These values represent are the proportion of the total number of predictions that were correctly classified. Initially, we classified training instances into three classes, with approximately 300 images per class. The graphs had been selectively gathered from the Web. We manually normalized the collected images by eliminating unused areas, such as unnecessary text. Moreover, we evaluated the experiments with 10 folds cross-validation because such an approach can mitigate the problem of over-fitting. </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 2. Illustrating the core process of one-dimensional image construction by applying a DFT
<p>First, we collect graph images as raw data, which contain different scales and sizes, and therefore need to be normalized. We clean the images by omitting irrelevant areas. For example, we omit unnecessary text that has nothing to do with our classification procedure. Moreover, to standardize the sizes and shapes of the images, we resize and reshape them to be 64 x 64 squares. Second, we examine each image pixel, each of which contains one color value. After each pixel is projected along the x- and y-axes, we count the number of projected pixels with a color value greater than zero to reduce image dimensionality. We, therefore, obtain two one-dimensional images from the x- and y-axes. </p>
Processed Hi-C contact matrices for "Three invariant Hi-C interaction patterns: applications to genome assembly"
<p>Processed Hi-C interaction matrices, saved in numpy npz format.</p> <p>Matrices were processed using Dekker lab cMapping pipeline.</p> <p>Raw sequence data was taken from:</p> <p>Hap1: Haarhuis et al 10.1016/j.cell.2017.04.013</p> <p>IMR90, H1ESC, MESC, MCORTEX: Dixon et al 10.1038/nature11082</p> <p>Worm: Crane et al 10.1038/nature14450</p> <p>Caulobacter: Le et al 10.1126/science.1242059</p> <p> </p>
Research data, sources and documents for thesis on Exploring Complexity Metrics for Artifact-Centric Business Process Models
<p>Research data, sources and documents for thesis on Exploring Complexity Metrics for Artifact-Centric Business Process Models This repository contains the supplemental material for the <a href="https://pqdtopen.proquest.com/pubnum/10759956.html">thesis "Exploring Complexity Metrics for Artifact-Centric Business Process Models" by Marin, Mike A., Ph.D., University of South Africa (South Africa), 2017.</a></p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 6. Mutation process
<p>The individuals obtained at the end of crossing over might not provide the desired level of variability. In that case, the produced individuals are mutated independently from another individual in such a way that their own gene sequence will change. The mutation process is performed in the event that the mutation possibility that is specified in the beginning comes true. The results obtained from mutation can enhance the outcome or make it worse. It is of utmost importance to specify the most suitable mutation possibility. This possibility should be high enough to prevent the method from becoming stuck at a local point, but at the same time, low enough to allow the best results produced by crossing over and multiplexing. In this study, the mutation possibility was selected as 10%, and the locations of two randomly selected bus stops were changed during the mutation process. As in the crossing over, also during this process, the limitations regarding producing a new individual (route) were adapted. Figure 6 shows an example to mutation process.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 5. Crossover process
<p>In the selection mechanism, the individuals passed down from the previous generation occasionally cannot produce a better individual. In that case, the compatibility of the individuals might worsen, while producing the exact opposite is what is expected. To avoid this, the elitism operator is used and it is ensured that the best individual of the previous generation is passed down to the next generation, even though the current population is diminishing on an overall basis as a result of the production operators (Goldberg, 1989). In the current study, the consecutive selection method and elitism selection were preferred. For this aim, after calculating the compatibility function, the population was ranked according to the population function values (total route length). In case the crossover possibility is realized, the number of individuals to select will be determined according to the parameter related to the crossover size. To ensure a high level of variability in the generation, it is suggested that this possibility is taken as 50% and 95% (Goldberg, 1989). The crossover process allows the production of a new individual using the genes taken from two individuals, based on the selected crossover method. In this study, a single point crossover method was selected. During the crossing over, limitations that were previously mentioned regarding the formation of a new initial population were taken into consideration. The identified initial or final point was fixed and kept out of the context of crossing over. An example of crossover can be seen in Figure 5.</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 1. Block diagram of classification process
<p>Heart and vascular diseases are the first cause of death in the world. The heartbeat, which is the result of the blood pumping of the heart, gives information about the functioning of the heart valves. Thus, features can be obtained that can differentiate between normal and abnormal heart sound signals. With the ECG signal, it is possible to visualize the parts of the heartbeat signal. However, ECG measurement is usually expensive and requires extra time. For this reason, studies for the detection of heart diseases generally focus on the analysis of heart sounds. In this context, it is aimed to make a successful classification using deep learning method in this study. The block diagram of the application is shown in Figure 1. The details of each block in the diagram are explained as follows. For all classification study, MATLAB r2017a software was used.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 3. Data compression and image reconstruction (55:148 Digital Image Processing, 2017)
<p>There are several techniques which are normally divided into two categories lossy and lossless image compressions. In lossy compression, after recovery there are negligible difference present where lossless gives accurate image. Huffman encoding is very well known, which can provide optimal compression and decompression without error (55:148 Digital Image Processing, 2017). The basic idea of Huffman coding is to represent data by number of variable size, where more frequent info being represented by shorter number (55:148 Digital Image Processing, 2017). Currently the Lempel-Ziv (or Lempel-Ziv-Welch, LZW) algorithm for dictionary-based coding has got attention as a better compression algorithm (55:148 Digital Image Processing, 2017).</p>
Supplementary material for "Business Process Simulation: A Systematic Literature Review"
<p>This material containing a list of the bibliographical data of the final sample and the sample of 300 publications before excluding publications outside of our focus supplements the following literature review:</p> <p>Rosenthal, Kristina; Ternes, Benjamin; Strecker, Stefan, (2018). “Business Process Simulation: A Systematic Literature Review”. In: Proceedings of the 26th European Conference on Information Systems (ECIS), Portsmouth, UK, June 23–28, 2018.</p>
Scalasca analysis report for SPEC MPI.2007 benchmark 132.zeump2 on 512 processes in virtual-node mode on Blue Gene/P
<p>A Cube3 performance analysis report written by the Scalasca 1.x parallel analyzer of a measurement of the SPEC MPI 2007 benchmark 132.zeusmp2 (ZeusMP/2) executed in virtual-node mode on 512 processes of the IBM Blue Gene/P system JUQUEEN, operated by Forschungszentrum Jülich GmbH.</p>
Process Flow Diagram of Cardiac Surgery Pathway
<p><em><strong>This image is supplementary material for:</strong></em></p> <p>Currie, C. S.M. Monks, T. (2018) <em>Modelling Diseases: Prevention, Cure, and Management.</em> In M. Rabe, A. Juan, N. Mustafee, A. Skoogh, & B. Johansson (Eds.). Proceedings of the Winter Simulation Conference 2018. Gothenburg, Sweden.</p> <p><em>Relevant to:</em></p> <p><strong>Section 4.1.4. Spend some Time Observing How the Operations of the Disease Pathway are Managed</strong></p> <p>The description of the disease pathway obtained in the initial meeting may be idealized. It is advised that modelers spend at least a few hours in the one or more services in the real pathway to observe how it operates and to interview on-the-ground workforce. Observations of the system under pressure are useful to understand the complexities of the pathway and will reveal aspects that have been missed or that operate differently from described. This Figure illustrates a cardiac surgery pathway at a large hospital that operated on patients from a network of smaller hospitals in the UK. The diagram itself was primarily constructed in an initial meeting with a group of cardiac surgeons. Observation of the system and shadowing of specialist cardiac nurses revealed two differences from the idealized model. Firstly, patients could deteriorate and ‘bounce back’ from post-operative care to higher levels of care. Secondly, in times of pressure, the post operative beds were used flexibly to accommodate patients waiting for surgery and those who had already undergone surgery.</p>
Scalasca analysis report of the ASCI Sweep3D benchmark on 294,912 processes in virtual-node mode on IBM Blue Gene/P with manually annotated iterations
<p>A Cube3 performance analysis report written by the Scalasca parallel analyzer of a measurement of the ASCI benchmark Sweep3D, executed in virtual-node mode on 294,912 processes of the IBM Blue Gene/P system JUQUEEN, operated by Forschungszentrum Jülich GmbH. The measurement includes system topology information and manual annotations of the twelve iterations.</p>
Dataset of Paper "Novel procedure for the numerical simulation of solar water disinfection processes in flow reactors" (DOI: 10.1016/j.cej.2018.10.131)
<p>Datasets of Paper "Novel procedure for the numerical simulation of solar water disinfection processes in flow reactors".</p> <p>DOI: 10.1016/j.cej.2018.10.131</p> <p>Data of the velocity profiles at different distances from the inlet of a solar rainwater reactor.</p> <p>Data of the simulated radiation field inside of a solar rainwater reactor as a function of the location, date, time and CPC inclination.</p> <p>Data of the disinfection efficiency versus illumination time in a solar reactor under simulated and natural sunlight.</p>
The TRIple-frequency and Polarimetric radar Experiment for improving process observation of winter precipitation dataset
<p>The combined TRIple-frequency and Polarimetric radar Experiment for improving process observation of winter precipitation (TRIPEx) was a joint field experiment of the University of Cologne, the University of Bonn, the Karlsruhe Institute of Technology (KIT), and the Research Center Jülich. TRIPEx took place at the Jülich Observatory for Cloud Evolution (JOYCE) from 11 November 2015 until 04 January 2016. During this experiment, the X-, Ka- and W-band ground-based Doppler radars were used vertically pointing to observe the clouds. Here we provide the level 2 of the dataset collected during this campaign. Several step processing were applied to minimize the radar offset and attenuation. In addition, we provide the attenuation correction and the quality flags for each processing steps to allow the user to retrieve the data without our correction. The raw and the level 1 of the dataset are available for the users on request from the corresponding author.<br> </p>
Rotation Electron Diffraction Processing (REDp) Dataset
<p>RED data collected on garnet crystal</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.