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Figure 5. Performance plot for NASDAQ index (MLP)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 5 we can see that the mse curve reaches the In performance goal but it does not decrease in that good manner,but in Figure 6 the mse is reduces widely. By analyzing all these results one can say that RNN is better choice than Feedforward MLP in prediction purpose.

Figure 3. Regression plot for NASDAQ index (MLP)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 3 depicts the regression plot for the feedforward MLP network, analyzing it we can say that Y=T regression is not so good.

Figure 4. Regression plot for NASDAQ index (RNN)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 4,depicts the regression plot for the Timedelay RNN network, analyzing it we can say that Y=T regression is totally fit. This paper also comprises of comparative study of performance(mse) plot of both network.

Figure 2.Flow Chart for Data preprocessing & Training-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Methodology This paper develops an ANN based comparative predictive model for NASDAQ stock prediction. The first ANN model is developed with Multi-Layer Feed forward Network Architecture & the second model is developed with Recurrent Neural Network Architecture. In this paper gradient descent based back propagation learning algorithm is used for the supervised learning of the predictive network.

opencc-by-4.0Dec 2008View details →

Fig. 3 in MAX WEBER AN ARTIFICIAL SOCIOLOGY. PROPOSAL FOR A NEURAL NETWORK WITH WEBERIAN REASONING

Fig. 3. The ornithopod dinosaur Gasparinisaura cincosaltensis Coria and Salgado, 1996 from the Late Cretaceous Anacleto Formation of Patagonia, MCSPv 111 (A) and MCSPv 112 (B). A1, nearly complete postcranial skeleton in left lateral view; A2, cluster of gastroliths in the abdominal cavity; B1, nearly complete skeleton in dorsal view; B2, the largest cluster of gastroliths below the last dorsal vertebra.

opencc-by-4.0Dec 2008View details →

Fig. 2 in MAX WEBER AN ARTIFICIAL SOCIOLOGY. PROPOSAL FOR A NEURAL NETWORK WITH WEBERIAN REASONING

Fig. 2. The ornithopod dinosaur Gasparinisaura cincosaltensis Coria and Salgado, 1996, MUCPv 213 from the Late Cretaceous Anacleto Formation of Patagonia. A. Forelimb bones and ribs. B. The longest gastrolith in contact with two right dorsal ribs. C. Cluster of gastroliths associated with the ribs. D. Scanning electron microphotograph of a gastrolith from metamorphic rock.

opencc-by-4.0Dec 2008View details →

Fig. 1 in MAX WEBER AN ARTIFICIAL SOCIOLOGY. PROPOSAL FOR A NEURAL NETWORK WITH WEBERIAN REASONING

Fig. 1. The surroundings of Cinco Saltos City where MCSPv 111, MCSPV 112 (Site 1) and MUCPv 213 (Site 2) were found (modified from Andreis et al. 1974).

opencc-by-4.0Dec 2022View details →

Dataset of "Near-real-time diagnosis of electron optical phase aberrations in scanning transmission electron microscopy using an artificial neural network"

Dataset containing the jupyter notebook used to construct the database of image, to model and train ANN and to analyze the experimental data. Furthermore there are also a reduced database of 100 images that can be utilized to test the ANN, the h5 file containing the ANN weigths and other supporting files.

opencc-zeroMay 2015View details →

Neutralization Data and Aligned ENV Sequences for Predicting Antibody Affinities using Artificial Neural Networks

Sample file with neutralization data (IC50) for different antibodies and viral strains, adapted from J. Huang, G. Ofek, L. Laub, M. K. Louder, N. A. Doria-Rose, N. S. Longo, H. Imamichi, R. T. Bailer, B. Chakrabarti, S. K. Sharma, S.  M. Alam, T. Wang, Y. Yang, B. Zhang, S. A. Migueles, R. Wyatt, B. F. Haynes, P. D. Kwong, J. R. Mascola, and M. Connors, “Broad and potent neutralization of HIV-1 by a gp41-specific human antibody.,” Nature, vol. 491, no. 7424, pp. 406–12, Nov. 2012.   Aligned ENV sequences downloaded from the HIV Sequence Database (www.hiv.lanl.gov/content/sequence/HIV/mainpage.html). There are 4907 sequences and the alignment length is 1369.

opencc-by-4.0Nov 2023View details →

Theory and implementation of inelastic Constitutive Artificial Neural Networks: Source code and data

This dataset contains the source code of the inelastic Constitutive Artificial Neural Network (iCANN) as well as the data for the examples from the publication: Holthusen, H., Lamm, L., Brepols, T., Reese, S., & E. Kuhl. Theory and implementation of inelastic Constitutive Artificial Neural Networks. arXiv: <a href="https://doi.org/10.48550/arXiv.2311.06380">https://doi.org/10.48550/arXiv.2311.06380</a> Computer Methods in Applied Mechanics and Engineering: <a href="https://doi.org/10.1016/j.cma.2024.117063">https://doi.org/10.1016/j.cma.2024.117063</a>   01_Example01:  Artificially generated data This example investigates whether the iCANN is able to discover a model for the data generated by a continuum mechanical model.   02_Example02: Discovering a model for the polymer VHB 4910 subjected to cyclic loading Here, we investigate the ability of iCANN to discover and learn a model for the material response of  VHB 4910 polymer subjected to cyclic loading at different stretch rates. The experimental data are taken from the literature: Hossain, M., Vu, D. K., & Steinmann, P. (2012). Experimental study and numerical modelling of VHB 4910 polymer. Computational Materials Science, 59, 65-74. <a href="https://doi.org/10.1016/j.commatsci.2012.02.027">https://doi.org/10.1016/j.commatsci.2012.02.027</a>   03_Example03: Discovering a model for passive skeletal muscle subjected to relaxation In this example, we investigate whether the iCANN is able to discover a model for the material behavior of passive skeletal muscles. A total of five independent experiments are carried out in which the maximum applied compression stretch and the stretch rate are varied. In addition, the learning performance of the iCANN is investigated. Training is first carried out in each of the five experiments and then in each of four of the five experiments. The experimental data are taken from the literature: Van Loocke, M., Lyons, C. G., & Simms, C. K. (2008). Viscoelastic properties of passive skeletal muscle in compression: stress-relaxation behaviour and constitutive modelling. Journal of biomechanics, 41(7), 1555-1566. <a href="https://doi.org/10.1016/j.jbiomech.2008.02.007">https://doi.org/10.1016/j.jbiomech.2008.02.007</a>   python_requirements.txt: File containing a list of installed Python modules used to implement the iCANN

opencc-by-4.0Nov 2023View details →

Application of artificial neural network to forecast indoor air temperature in a building with artificial ventilation: impact of early stopping.

Indoor air temperature prediction can facilitate energy-saving actions without compromising the indoor thermal comfort of occupants. The aim of this study was to analyse the performance of various artificial neural networks with a view to proposing an optimal approach for predicting the indoor temperature of a tertiary building with artificial ventilation. The MLP, CNN, LSTM models and the CNN-LSTM combination (long short-term memory network) were used and coupled with the optimisation algorithms (Adam, SGD) and the independent hyper-parameters early stopping and dropout. The parameters used are outdoor ambient temperature, outdoor relative humidity, indoor relative humidity, wet bulb temperature, black globe temperature and mean radiant temperature. The data is collected in an artificially ventilated building in Yaoundé, Cameroon. A numerical code was developed in Python to run the simulations. In order to study the impact of the parameters on the prediction, two scenarios were distinguished in this work: (1) all the parameters are input to the network, (2) only the parameters whose absolute value of the correlation coefficient was greater than or equal to 0.5 were used. The impact of early stopping is assessed by distinguishing two case studies: the first without early stopping, the second with early stopping. The results showed that without early stopping, the MLP, CNN, LSTM and CNN-LSTM networks are adequate for predicting the temperature with the second scenario, mainly with both the SGD and Adam algorithms, and CNN-LSTM is the most appropriate model because the MSE and MAE values obtained in this case were closer to 0. With early stopping, the learning time is reduced and the learning curves are improved; the models optimised better with the SGD algorithm in general, but the best neural network model was obtained with the Adam algorithm and the LSTM network for the performances MSE=0.0005, MAE=0.0130 with the second scenario.Keywords: prediction, indoor temperature, artificial neural network, early stopping, artificially ventilated building.

opencc-by-4.0Dec 2023View details →

Data set for the article: An artificial neural network approach to finding the key length of the Vigenere cipher

Data supporting the work in the article: An artificial neural network approach to finding the key length of the Vigen\`{e}re cipher.

opencc-by-4.0Mar 2024View details →

Prediction model of the temporal dynamics of severe pest cashew Anacampsis phytomiella using artificial neural networks

Open the record for dataset details and reuse information.

opencc-by-4.0Feb 2022View details →

Training data for benchtop NMR and UV/vis spectroscopy for Artificial Neural Networks

Data set of low-field NMR spectra and UV/vis spectra for the synthesis of mesalazine intermediates, which were used as training or validation data for data processing with artificial neural networks development Low-field NMR spectra for the nitration step: The pure component spectrum of 2ClBA, 3N-2ClBA, and 5N-2ClBA are marked as NMR_pure_spectrum. The concentration levels for 2ClBA, 3N-2ClBA and 5N-2ClBA are in row 1, 2, and 3, respectively. The data sets marked as NMR_ represents low-field NMR-spectra recorded. The reference values for 2ClBA, 3N-2ClBA and 5N-2ClBA are in column 1, 2, and 3, respectively. Datafusion data sets for the hydrolysis and nitration step The NMR data are either recorded or simulated from the pure NMR spectrum of each individual component. The reference values for 2ClBA, 3N-2ClBA, 5N-2ClBA, 3-NSA and 5-NSA are either assigned with UHPLC measurements or calculated from the prepared solutions. The NMR spectra are depicted in datafusion_NMR_training. The reference values for 2ClBA, 3N-2ClBA and 5N-2ClBA are in column 1, 2, and 3, respectively. The UV/vis spectra are depicted in datafusion_UVvis_training. The reference values for 2ClBA, 3N-2ClBA, 5N-2ClBA, 3-NSA and 5-NSA are in column 1, 2, 3, 4, and 5, respectively. Process data The NMR spectra for the stability run and the run with dynamic changes are depicted in process_NMR_. The first column is the time stamp. The UV/vis spectra for the stability run and the run with dynamic changes are depicted in process_UV_. The first column is the time stamp.

dryad36/100

Data from: Applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events

The data provided and the associated MATLAB code were used to build an Artificial Neural Network Model to capture the reconstruction (recovery) of various buildings subjected to tornado events in the State of Missouri. The ANN model utilizes relevant tornado, societal demographic, and structural data to determine a building's resulting damage state from an extreme wind event and the subsequent recovery time. Abstract for the publication is as follows: In a companion article, previously published in Royal Society Open Science, the authors used Graph Theory to evaluate artificial neural network models for potential social and building variables interactions contributing to building wind damage. The results promisingly highlighted the importance of social variables in modeling damage as opposed to the traditional approach of solely considering physical characteristics of a building. Within this update article, the same methods are used to evaluate two different artificial neural networks for modelling building repair and/or rebuild (recovery) time. In contrast to the damage models, the recovery models consider (A) primarily social variables and then (B) introduce structural variables. These two models are then evaluated using centrality and shortest path concepts of Graph Theory as well as validated against data from the 2011 Joplin Tornado. The results of this analysis do not show the same distinctions as were found in the analysis of the damage models from the companion article. The overarching lack of discernible and consistent differences in the recovery models suggests that social variables that drive damage are not necessarily contributions to recovery. The differences also serve to reinforce that machine learning methods are best used when the contributing variables are already well understood.

opencc-zeroMar 2022View details →

opencc-by-4.0Jul 2022View details →

Training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series

This dataset contains training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series. The data have been derived from Sentinel-5P total column carbon monoxide observations, using the offline processing stream. Preprocessing The following operations have been applied on the original S5P imagery: <ol> <li>Images have been resampled to 0.1 by 0.1 degree spatial resolution</li> <li>Pixels with quality assessment value less than or equal to 0.5 have been set to NA</li> <li>Images have been aggregated by day of observation</li> <li>Images have been cropped to -60 to 60 degrees latitude</li> <li>Images have been devided into spatiotemporal blocks of size 128 x 128 pixels and 16 days</li> </ol> Imagery has been recorded between 2021-01-01 and 2021-11-25. Notice that both the training and the validation blocks have been randomly sampled from all available blocks. Data Format and Naming Conventions Input and output data blocks are stored as GeoTIFF files, where bands represent time. Notice the following file naming conventions: <ul> <li>Files starting with X represent input measurements for training, where artificial gaps have been added.</li> <li>Files starting with Y represent true measurements without artificially added gaps (but still containing gaps in many cases).</li> <li>Binary masks of input data where all pixels with valid measurements are 1 and others 0 are stored in files whose name starts with MASK</li> <li>Files starting with VALMASK contain a binary mask where only pixels that are available in Y but not in X are 1. The latter is used for validation on artificially removed pixels only.</li> </ul> Numbers in filenames encode spatial and temporal block indexes. In addition, the dataset contains prediction of the validation blocks from different models in the `predictions` directory. The subfolders contain output from different models: <ul> <li>mean refers to simple block-wise mean predictions.</li> <li>timeseries refers to simple linear time series interpolation.</li> <li>gapfill refers to the method proposed in [1].</li> <li>stmra refers to the method proposed in [2].</li> <li>STpconv refers to predictions passed on an artificial neural netowork with three-dimensional partial convolutions.</li> </ul> References [1] Gerber, F., de Jong, R., Schaepman, M. E., Schaepman-Strub, G., & Furrer, R. (2018). Predicting missing values in spatio-temporal remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 56(5), 2841-2853. [2] Appel, M., & Pebesma, E. (2020). Spatiotemporal multi-resolution approximations for analyzing global environmental data. Spatial Statistics, 38, 100465.

opencc-by-4.0Sep 2022View details →

Replication Data for: ``Impact of Parameterized Isopycnal Diffusivity on Shelf-Ocean Exchanges under Upwelling-Favorable Winds: Offline Tracer Simulations Augmented by Artificial Neural Network''

This dataset contains the modified MAMEBUS source code, configuration files for the  MITgcm and MAMEBUS simulations, model diagnostics used in the paper, and scripts to train the Artificial Neural Networks.

opencc-by-4.0Oct 2017View details →

Figure 6 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran

Figure 6. Motion of colonies toward their relevant imperialist (AtashpazGargari 2009).

opencc-by-4.0Oct 2017View details →

Figure 4 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran

Figure 4. Flowchart of Imperialist Competitive Algorithm (AtashpazGargari 2009).