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opencc-zeroJun 2024View details →

Dataset: Mobile-health Network Solutions (MNDR) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-zeroJun 2024View details →

Dataset: First Trust S-Network Global E-Commerce ETF (ISHP) Stock Performance

opencc-zeroJun 2024View details →

Dataset: Professional Diversity Network, Inc. (IPDN) Stock Performance

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 8 . The Comparision of Run Times

That is distinct that dynamic mutation rate or reduction idea for mutation operator is more better of fixed rate. In fact obtain to high accuracy is result of our idea for mutation operator. The number of hidden layer neurone is important problem for NN. The natural selection by GA help finding the number of hidden layer neurone and it progress on duration generations. The structured model of GANN finds better answer than NN but with much run time in simulation. The learning of GA is much better than NN with back propagation because BP is a method based on gradient descend and local optimum is a serious risk for that. We hope that the number of training samples is more accurate without error, the new algorithm is better. Tests show that the combination of genetic algorithms and neural networks to an acceptable level solves the problem of overfitting.

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 7 . Test Accuracy with prograess generation

In the training phase, the neural network weights errors are minimized and network design problem which the objective function to an acceptable level. In test step we have better results because weights of neural network are adjusted by genetic algorithm and back propagation method. Of course achievement to accuracy with 83.5% is reason using of good feature with minimum error.

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 6. Training Accuracy with prograess generation

There are many features will reduce the efficiency of the algorithm and its complexity. Among the methods for selecting the appropriate features, the algorithm is a GA. One of the important parameters for testing methods is accuracy rate on progress generation. In fact accuracy is reverse error in algorithm results. As reader can compare the results of our paper with another works. Figure 4 show that accuracy present for Training step. We achieve to best answers of 800 generation to after generation.

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 4. Structural Crossover

Guided crossover operator is based on the two point separation from parents are selected Left and right parts of them are related to each other by the condition to be meaningful With this new child of his parents is that. But a new generation of the random choice to have reached this stage. The crossover rate is fixed for our algorithm.

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 5. Insertion and Deletion Hidden Layer in NN

Change in NN structure is other method that we used to optimization of solution[18]. Insertion a hidden layer caused to mutation operator is much natural. As connection with father and mother nodes is easily[20],[21]. Weights of node and errors automatically calculated. For each stage of the implementation of the mutation operator in genetic algorithms, neural networks, only one of the nodes in the hidden layer is selected and inserted. These layers are inserted on condition that the definition does not harm the network structure and the action is meaningful. As an added layer can adjust the weights and the connection to the parent node of a network layer to be removed.

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 3. The Structure of Neural Network

A neural network (NN), in the case of artificial neurons called artificial neural network (ANN) or simulated neural network (SNN), is an interconnected group of natural or artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network[9]. In more practical terms neural networks are nonlinear statistical data modelling or decision making tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data. Two neurons neural network active in memory (ON or 1) or disable (Off or 0), and each edge (synapses or connections between nodes) is a weight. Edges with positive weight, stimulate or activate next active node, and edges with negative weight, disable or inhibit the next connected node (if it is active) ones.

opencc-by-4.0Jan 2012View details →

Figure 6. Performance plot for NASDAQ index (RNN)-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.

opencc-by-4.0Jan 2012View details →

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

opencc-by-4.0Nov 2023View details →

Agronomic performance of cultivar mixtures of winter wheat varieties, obtained from mixture field trials at 5 locations in Switzerland from 2019 to 2020, together with yield data from the varieties in pure stand obtained from the national variety testing trial network

This dataset contains agronomic parameters of 32 winter wheat variety mixtures tested during 2 growing seasons (2019-2020) at 5 locations in Switzerland, as well as yield data of these varieties in pure stands originating from the Swiss national variety testing network. The dataset has been used to investigate the links between asynchrony and yield stability, published in <a href="https://doi.org/10.1002/csc2.21151">https://doi.org/10.1002/csc2.21151</a>.   The field trials were performed under the Swiss Extenso (low input) conditions, conducted by Agroscope and DSP.  <h2>Methods </h2> Field trials  The experiment took place in five sites across Switzerland, in 2019 and 2020. The sites were located in Nyon (1260), Delley (1567), Utzenstorf (3428), Zurich (8046), and Ellighausen (8566). Experimental communities consisted of 32 different two-variety mixtures grown in 7.1-m2 plots (1.5 × 4.7 m). We replicated the mixture experiment three times per site with the exact same variety composition. We used a randomized block design, with plots being randomized at each site within each block. Density of sowing was 350 seeds/m2, and seeds were mixed beforehand at a 50:50 ratio in terms of mass. We used the 50:50 mass ratio as this is what is generally done in practice by farmers and seed suppliers. Plots were sown mechanically each autumn. The plots were mechanically fertilized according to the Principles of Agricultural Crop Fertilisation in Switzerland (Federal Office for Agriculture) with an average of 140 kg N/ha (ammonium nitrate), applied in three splits (40 at the tillering stage—60 at stem elongation stage—40 when the flag leaf is visible). The experimental trials were conducted following the extenso Swiss scheme, which means that there was no application of any fungicide, insecticide, or plant growth regulator.  The performances of single varieties were obtained by going through the trials of the national variety testing program. We gathered the data for the years 2018/2019 and 2019/2020. The data regarding single varieties could be obtained for three out of the five sites used for the mixtures: 1260, 1567, and 8566. Because there were no national variety trials at the two other sites (8046, 3428), we could not get any data for single varieties in these sites. Thus, all further analyses including single variety data were only done for the three sites mentioned above. At each of these sites, the variety trials were located on the same plot as the mixture trials, even though a little further apart. Therefore, soil parameters and crop precedents were the same between the mixture and variety testing trials. Furthermore, we only selected the national variety testing trials that respected the extenso conditions, that is, no fungicide, pesticide, or growth regulator application, and that received the same amount of fertilization as the mixture trials. In 8566 and 1567, sowing and harvesting dates were identical between the two trials; in 1260, sowing and harvesting dates could vary but remained within a week of each other.   Data collection  For each plot, heading dates were monitored, and average height at BBCH 59–75 was measured. The prevalence of diseases was scored twice in the growing season. Specifically, the severity of brown rust, yellow rust, powdery mildew, and Septoria tritici blotch was assessed. This was performed by grading each individual plot from 1 to 9 for each disease, with 1 representing no disease and 9 a complete infection. The scoring scale follows a logistic progression based on the symptoms of the top three leaves. We used the data from the final scoring for statistical analysis, as the disease severity was usually more important then. At maturity, we harvested each plot with a combine harvester. The harvested grains were dried when needed, weighed a first time, then sorted and cleaned by air and with a sieve cleaner, and subsequently weighted again. We measured specific weight and water content at the plot level using a Dickey-John machine (GAC 2100). Grain yield was subsequently standardized to 15% of humidity. Protein content was measured at the site level with a near-infrared instrument (ProxiMate; Büchi instruments).

opencc-by-4.0Apr 2023View details →

PREDICTING THE PERFORMANCE OF GREEN STORMWATER INFRASTRUCTURE USING MULTIVARIATE LONG SHORT-TERM MEMORY (LSTM) NEURAL NETWORK

The expected performance of Green Stormwater Infrastructure (GSI) is typically quantified through numerical models based on hydrologic parameters and physics-based equations. With numerical models, the choice of a spatio-temporal discretization scheme for the computational domain is a strenuous task that requires extensive calibration and potentially lab-based parameters and experimentation. The performance of GSI has high temporal dynamics due to natural, anthropogenic, and climatic processes that are not well represented by the traditional physics-based hydrologic models, which are calibrated against only a few historical observations and have a user-defined and constrained set of computational outcomes. Deep learning-based predictive models, such as Long Short-Term Memory (LSTM) neural networks, offer an exciting opportunity to quantify GSI performance, accounting for its highly dynamic and constantly evolving nature by leveraging advancements in observational data. A LSTM regression can overcome some of the limitations associated with traditional hydrological models to aid the development of a fully data-informed GSI performance predictor. To demonstrate the LSTM and traditional model outcomes, both methods were applied to a rain garden in Villanova, PA, USA. Specifically, a LSTM model was used to predict the recession of ponded water depth in the rain garden using five years of observed data.

opencc-by-4.0May 2023View details →

Deliverable D6.2 "TOOL FOR PERFORMANCE ASSESSMENT"- BN network and Bellman shortest path analysis_Module 3_Annex 6

This file will introduce the BN network and Bellman shortest path analysis Module 3 Annex 6 in deliverable D6.2 "TOOL FOR PERFORMANCE ASSESSMENT".

opencc-by-4.0Mar 2020View details →

Artifacts related to "Using Informed Access Network Selection to Improve HTTP Adaptive Streaming Performance"

This archive contains data related to in the following paper: "Using Informed Access Network Selection to Improve HTTP Adaptive Streaming Performance" (published at the ACM MMSys 2020 conference) Copyright (c) 2020, Theresa Enghardt <theresa@tenghardt.net>, Fachgebiet INET - TU Berlin. See https://github.com/fg-inet/MMSys2020_Informed-Access-Network-Selection for more information. This data is released under the Creative Commons Attribution 4.0 International license.

dryad36/100

Data from: Reconfiguration of functional brain networks and metabolic cost converge during task performance

The ability to solve cognitive tasks depends upon adaptive changes in the organization of whole-brain functional networks. However, the link between task-induced network reconfigurations and their underlying energy demands is poorly understood. We address this by multimodal network analyses integrating functional and molecular neuroimaging acquired concurrently during a complex cognitive task. Task engagement elicited a marked increase in the association between glucose consumption and functional brain network reorganization. This convergence between metabolic and neural processes was specific to feedforward connections linking the visual and dorsal attention networks, in accordance with task requirements of visuo-spatial reasoning. Further increases in cognitive load above initial task engagement did not affect the relationship between metabolism and network reorganization but only modulated existing interactions. Our findings show how the upregulation of key computational mechanisms to support cognitive performance unveils the complex, interdependent changes in neural metabolism and neuro-vascular responses.

opencc-zeroApr 2020View details →

dryad36/100

Effect of green infrastructure on restoration of pollination networks and plant performance in semi-natural dry grasslands across Europe

Agricultural intensification, afforestation and land abandonment are major drivers of biodiversity loss in semi-natural grasslands across Europe. Reversing these losses requires the reinstatement of plant-animal interactions such as pollination. Here we assessed the differences in species composition and patterns of plant-pollinator interactions in ancient and restored grasslands and how these patterns are influenced by landscape connectivity, across three European regions (Belgium, Germany and Sweden). We evaluated the differences in pollinator community assemblage, abundance, and interaction network structure between 24 ancient and restored grasslands. We then assessed the effect of surrounding landscape functional connectivity (i.e. green infrastructure, GI) on these variables and tested possible consequences on the reproduction of two model plants, Lotus corniculatus and Salvia pratensis. Neither pollinator richness nor species composition differed between ancient and restored grasslands. A high turnover of interactions across grasslands was detected but was mainly due to replacement of pollinator and plant species. The impact of grassland restoration was consistent across various pollinator functional groups, whereas the surrounding GI had differential effects. Notably, bees, butterflies, beetles, and dipterans (excluding hoverflies) exhibited the most significant responses to GI variations. Interestingly, networks in restored grasslands were more specialised (i.e. less functionally redundant) than in ancient ones and also showed a higher number of insect visits to habitat-generalist plant species. Landscape connectivity had a similar effect, with habitat-specialist plant species receiving fewer visits at higher GI values. Fruit set in S. pratensis and L. corniculatus was unaffected by grassland type or GI. However, the fruit set in the specialist S. pratensis increased with the number of pollinator visits, indicating a positive correlation between pollinator activity and reproductive success in this particular species. Synthesis and applications. Our findings provide evidence of the necessity to enhance ecosystem functions while avoiding biotic homogenization. Restoration programs should aim at increasing landscape connectivity which influences plant communities, pollinator assemblages, and their interaction patterns. To avoid generalist species taking over from specialists in restored grasslands, we suggest reinforcing the presence of specialist species in the latter, for instance by means of introductions, as well as increasing the connectivity to source populations.

opencc-zeroJan 2024View details →

opencc-by-4.0Nov 2024View details →

Extra-P Version Used for Noise-Resilient Empirical Performance Modeling with Deep Neural Networks

This is the Extra-P source code that was used for the analysis and evaluation of the IPDPS 2021 paper "Noise-Resilient Empirical Performance Modeling with Deep Neural Networks". It also contains the checkpoints and saved models for the DNN part of the adaptive modeler as well as the gathered synthetic evaluation data.

opencc-by-4.0Nov 2024View details →

Supplemental Information on the Weighted Gene Co-expression Network Analysis performed for the work "Time-resolved oxidative signal convergence across the algae–embryophyte divide"

Supplemental Information on the Weighted Gene Co-expression Network Analysis (WGNCA) performed for the work "Time-resolved oxidative signal convergence across the algae–embryophyte divide" The results are sorted by the three species analysed: the two algae Zygnema circumcarinatum SAG 698-1b (Zygnema) and Mesotaenium endlicherianum SAG 12.97 (Mesotaenium); and the bryophyte Physcomitrium patens strain Gransden 2004 (Physcomitrium).