Find research datasets worth reusing

opencc-by-4.0Sep 2022View details →

Figure 6. Precision–recall curve for YOLOv5. Amoronthus polmeri achieved a in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean

Figure 6. Precision–recall curve for YOLOv5. Amoronthus polmeri achieved a slightly higher average precision (AP) (0.788) than soybean. Solid blue line represents mean average precision (mAP) computed on the test data set. The AP for each class and the mAP for the overall algorithm were representative of the area of the graph under each respective curve.

opencc-by-4.0Sep 2022View details →

Figure 5 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean

Figure 5. Change in mean average precision (mAP) @ 0.5 over each epoch during training. mAP was reported after the completion of each epoch. Training was terminated after visual inspection of curve and when mAP @ 0.5 curve was seen to "plateau."

opencc-by-4.0Jun 2024View details →

A small body open-source dataset for image processing algorithms

Crater-analog dataset acquired with a drone setup at the RIC-DFKI center. The dataset can be used to bridge the domain gap for image processing applications for lunar and small-body missions.

opencc-by-4.0Jun 2024View details →

Data for Robust Algorithms for the analysis of the FFC-NMRD dispersion curves

Data for article

opencc-by-4.0Mar 2017View details →

Fig. 1 in Actual and potential distribution of five regulated avocado pests across Mexico, using the maximum entropy algorithm

Fig. 1. Potential distribution of 5 insect pests of quarantine importance in Mexican avocados, based on ecological niche modeling. A) Conotrachelus aguacatae; B) Conotrachelus perseae; C) Copturus aguacatae; D) Heilipus lauri; and E) Stenoma catenifer. Current and potential distribution in Mexico was projected according to biogeographic provinces (Morrone 2005, 2014a), 1 = Baja California, 2 = California, 3 = Sonora, 4 = Sierra Madre Occidental, 5 = Mexican Plateau, 6 = Tamaulipeca, 7 = Mexican Pacific Coast, 8 = Trans-Mexican Volcanic Belt, 9 = Sierra Madre Oriental, 10 = Veracruzana, 11 = Balsas Basin, 12 = Sierra Madre del Sur, 13 = Chiapas, 14 = Yucatan. Scale values: 0 = absence, 1 = presence.

opencc-by-4.0Mar 2017View details →

Fig. 2 in Actual and potential distribution of five regulated avocado pests across Mexico, using the maximum entropy algorithm

Fig. 2. Geographic areas in Mexico where both the insect pest and avocados are found. Shading indicates hectares affected. A) Conotrachelus aguacatae; B) Conotrachelus perseae; C) Copturus aguacatae; D) Heilipus lauri; and E) Stenoma catenifer.

opencc-by-4.0Jan 2018View details →

Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the all-sky infrared radiative transfer algorithm

Dataset for AMT-2017-261 by DeSouza-Machado et. al. 1D-variational retrievals of temperature and moisture fields from hyperspectral infrared satellite sounders use cloud-cleared radiances as their observation. These derived observations allow the use of clear-sky only radiative transfer in the inversion for geophysical variables but at reduced spatial resolution compared to the native sounder observations. Cloud-clearing can introduce various errors, although scenes with large errors can be identified and ignored. Information content studies show that when using multi-layer cloud liquid and ice profiles in infrared hyperspectral radiative transfer codes, there are typically only 2-4 degrees of freedom of cloud signal. This implies a simplified cloud representation is sufficient for some applications which need accurate radiative transfer. Here we describe a single-footprint retrieval approach for clear and cloudy conditions, which uses the thermodynamic and cloud fields from Numerical Weather Prediction (NWP) models as a first guess, together with a simple cloud representation model coupled to a fast scattering radiative transfer algorithm (RTA). The NWP model thermodynamic and cloud profiles are first co-located to the observations, after which the N-level cloud profiles are converted to two slab clouds (typically one for ice and one for water clouds). From these, one run of our fast cloud representation model allows an improvement of the \emph{a-priori} cloud state by comparing the observed and model simulated radiances in the thermal window channels. The retrieval yield is over 90\%, while the degrees of freedom correlate with the observed window channel brightness temperature which itself depends on the cloud optical depth. The cloud representation/scattering package is bench-marked against radiances computed using a Maximum Random Overlap cloud scheme. All-sky infrared radiances measured by NASA’s Atmospheric Infrared Sounder (AIRS) and NWP thermodynamic and cloud profiles from the European Center for Medium Range Weather Forecasting (ECMWF) forecast model are used in this paper.

opencc-by-4.0Dec 2014View details →

BRAIN Journal-On the Idea of a New Artificial Intelligence Based Optimization Algorithm Inspired From the Nature of Vortex-Figure 1. Working mechanism of the VOA.

As it can be seen from the algorithm steps, the VOA employs simple equations. It is an advantage that the algorithm can be formed and applied within optimization problems whereas alternative algorithms may contain some complex solution steps (This situation may be also an disadvantage for the VOA when it is applied in more difficult optimization problems but while the world is transformed into a ‘strong simplicity’, the VOA may be a practical solution approach). The working mechanism of the VOA can be visualized briefly as like in Figure 1.

opencc-by-4.0Jun 2012View details →

Figure 1. General Workflow of Algorithm-.Data Conflict Resolution among Same Entities in Web of Data

The Page Rank algorithm which is widely used in most search engines such as Google could be easily used to rank linked data. By starting from a point and random surfing, this algorithm evaluates the probability of finding any given page. The algorithm assumes a link between a page i to a page j demonstrates the importance of page j. In addition, the importance of page j is associated to the importance of page i itself and inversely proportional to the number of pages i point to. To adapt this algorithm to web of data, any page considered as a dataset and links between pages considered as links between datasets.

Fig. 8. Algorithmic Page Rank-Study of a Random Navigation on the Web Using Software Simulation

Another diagram shows that for each site there is only one constant value independent of the length of Markov Chain. Algorithmic Page Rank only depends on the number of inlinks and outlinks.Table 4 contains the values for Experimental Page Rank with balanced distribution. In the following figure it is shown the diagram for the values obtained for Experimental Page Rank. The Experimental Page Rank values oscillate between the same limits independently of the change of Markov Chain length (N).

Figure 3. Distribution of Q3 values-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm

The estimated accuracy for the α- helices (QH), β- strands (QE), C-coil states (QC), and three state together (Q3) for the system is shown in Figure 3.

Figure 2. PAM250 matrix for the encoded sequence-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm

The PAM matrix (Dayhoff et al., 1978) describes the probability that original amino acid will be replaced by another amino acid over a defined evolutionary interval. The unit of evolutionary divergence is defined as the interval in which 1% of the amino acids have been changed between two sequences. The work uses PAM250, which assumes the occurrence of 250- point mutations per 100 amino acids. So, for the given the protein sequence GIVEQCCASVCSLYQLENYCN, A will be replaced by 1 -3 0 1 -3 -1 0 5 -2 -3 -4 -2 -3 -5 0 1 0 -7 -5 -1 as shown in Figure 2.

Figure 1: Snapshot of the CB396 dataset-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning AlgorithmSecondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm

The dataset used for this work is CB396. This dataset contains 396 non-redundant sequences derived from the 3Dee database created by Cuff and Barton (Cuff & Barton, 1999). It contains 396 proteins with their respective secondary structure as shown in Figure 1.

Figure 17. Algorithm A9pseudocode-Efficient Filtering of Noisy Fingerprint Images

The proposed algorithms refer to two directions: suppressing the noise resulted from acquisition of the fingerprints, the Gaussian noise in filters: A1 to A8 and to “salt and pepper” reduction noise in A9. The set of 9 filters make use of some methods like: &thresholds for segmentation of the digital images and adapted for filters A4, A5 and A6; &quartiles that dive ranked data set in four equal groups and they are applied on the filters: A1, A2, A4, A6, A7, A8, A9; &selections of parameters, like in: A7, A8 and the dimension of the local neighborhood (in our case 10x10 pixels);

Figure 2. Algorithm A1pseudocode-Efficient Filtering of Noisy Fingerprint Images

A2&uses the principle of quartiles over the entire image in conjunction with applying the quartiles calculated on small areas "locally"(Figure 2.);

Figure 3. Algorithm A3pseudocode-Efficient Filtering of Noisy Fingerprint Images

A3&is achieved by the extrapolation of the values that are "in the immediate" neighborhood of extremes (0 and 255), made in a "local" manner (Figure 3.);

Figure 4. Algorithm A4pseudocode-Efficient Filtering of Noisy Fingerprint Images

A4& uses quartiles principle applied globally, in conjunction with the thresholds referred to A3 (Figure 4.);

Figure 19. Dynamics of the filtered images with the 9 algorithms and the 2 methods of classification-Efficient Filtering of Noisy Fingerprint Images

The classification (Malik, Gautam, Sahai, Jha & Singh, 2013) and ranking stage can be visualized in the Figure 19, the summary of the filtered images is shown in Table 3 and the pseudocode of the current step can be visualized in Figure 18. The overall results show that the two selection criterion: fuzzy and aggregation indicate that the most efficient algorithm is A6 and according to each criterion there can be made certain decisions to choose the best filters for each situation. Also, the results are influenced by the parameters set to calibrate the filtering, the fuzzy profiles, the weighted sum or the vicinity approach.

Figure 6. Algorithm A6pseudocode-Efficient Filtering of Noisy Fingerprint Images

A6&the principle of quartiles applied to the entire image, using the quartiles q1 and q3 as thresholds, but the update refers only for the pixel values only if Q1 < q1 or Q3 > q3, where q1 and q3 are the quartiles calculated in the 10x10 pixels area (Figure 6.);

Figure 7. Algorithm A7pseudocode-Efficient Filtering of Noisy Fingerprint Images

A7&applied to the entire surface using quartiles(Q1 and Q3) in conjunction with an algorithm for the processing of gray tones between Q1 and Q3 is as follows: if in addition is full field the condition that (q2 >Q2), when the initial value of the pixel is adjusted at (initial_value*(1&(q2/q1))), and on the other hand if (q2<Q2) then the value is adjusted to (initial_value*(1+q3/q2 )), please see Figure 7.;