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Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 6. Purposes of using mobile devices for learning

Figure 6 shows the purposes of mobile and ubiquitous learning in the studies. A majority of the cases were multi-purpose (54%). Those with a single specific purpose were focused on “practice or revision” (34%) and “knowledge acquisition” (10%). There was a case where the application was to help new students to become familiar with the campus and teach them about the use of the facilities.

Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 6. Mutation process

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.

Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 6. The first implementation of the virtual campus (OpenSim import of the 3D model)

For our research, several iterations and methods were employed for the 3D design of an online campus.  Different virtual models of a faculty building (see Figures 4,5) were designed and finally a virtual model of a 3D campus comprising a simplified 3-story faculty building (Figure 6) was created. The objective was the optimization of the 3D model and the demonstration of the desired functionalities. For these purposes two 3D modeling and post-processing software were used, i.e. 3DSMax and Trimble Sketchup. The model of the building resulted in 5962 vertices and 4528 faces. The textures and illumination were applied using OpenSim’s in-world tools. Furniture objects (tables, chair, computer monitors) were taken from the Google 3D Warehouse, distributed and shared under Trimble General Model License.

Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 6. RSE and R2 of regression models of MapReduce applications

Figure 6 shows the fit quality of regression models. It is following.The left panel and the right panel of figure 6 show the residual standard error (RSE) distribution and R2 distribution of each application. The good fit quality corresponds to a taller R2 bar and a shorter RSE bar. The R2 almost 1 and small RSE show the best fit quality of the regression models on memory usage as the response. The overall higher RSE and lower R2 of regression models on CPU as the response show the worse quality of fitting goodness. The regression models on read rate as the response also show a moderate fitting quality. For the regression models on write rate as the response, Terasort application exhibits the best quality and Teragen application as well. Others show the worse fitting quality. The results show that the regression models on intensive usage parameters as response exhibit the good fitting quality.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 6. Drawing of auto correlation function and partial correlation for females primary stage students

The instability of the time series is noticed and to be more precise we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to affirm the stability according to the figure 6.

Classification of Phonocardiograms with Convolutional Neural Networks-Figure 6. Confusion matrix of multilayer feedforward network

Multilayer feedforward network was used for classification with ANN. In this application, the ANN structure and parameters were obtained after a review of previous studies and very much number of trial runs. There is a total of 10 neurons in the hidden layer in ANN. The Bayesian regularization backpropagation was used for learning algorithm and the mean square error function was also used the performance algorithm. 134 samples in the data set were used for training data, 29 samples were used for validation data, and 29 samples were used for testing data. The confusion matrix obtained at the end of the classification was given in Figure 6. As seen from the confusion matrix, the accuracy of classification 82.8% was achieved in the ANN classification. The ANN performed with a sensitivity of 92.40% and a specificity of 88.82%.

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 6. Proposed feed-forward neural network classifier

After the feature extraction stage, neural network is used for classifying the modes. In this study, the utilized expressions are normal, smiling, open mouth, rising the eyebrows, anger and pursing modes. In fact, they are some selective modes for face movements. It should be noted that the modes can be increased but in this case we work with these six modes. This paper used three layers feed-forward neural network (Figure 6). The proposed neural network includes 800 nodes for the input layer (400 nodes for U matrix and 400 nodes for V matrix), 100 nodes for the hidden layer and 6-nodes for output layer. From the collected data 70% are used for training, 15% for validation and the last 15% are used to evaluate the neural network.

A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 6. Methodology

Our method will first resize the image using normal image resizing option provided by operating system or standard library and attach the extra 2 array of data which contains no of 1 in original image in each row and column. Also, the total no of 1 in that image will be present too.

Artificial Intelligence and the Future of Smart Cities-Figure 6. Importance of AI for respondents business or industry by respondent's age

On question 8 respondents have to indicate on a scale of 1 to 5 (1 being “not important” and 5 being “critical for survival”), how important they think the AI will be for their business or industry (or for the one they are preparing for) in the next 10 years? No statistical interaction was found between age and gender F (3, 108) = .174, p>0.05). There were statistically significant differences by gender F (1, 108) = 50.261, p<0.05 and age, F (3, 108) = 9.298, p<0.05. Female participants scored significantly higher (M=4.60, SD=.49) than male participants (M=3.36, SD=.88) on question 8 about the importance of AI for the fields of activity of the participants (or for those they are preparing for) in the next 10 years. At the same question: the 26-30 age group scored the highest  followed by the 18-25 age group (M=3.80, SD=1.18); the 31-40 age group scored lower than the 18-25 age group (M=3.50, SD=.87); the 31-40 age group scored also lower than the 18-25 age group (M=3.50, SD=.51) (Figure 6).

opencc-by-4.0Dec 2018View details →

Figure 6 in A revision of the egg-eating snakes of the genus Dasypeltis Wagler (Squamata: Colubridae: Colubrinae) in north-eastern Africa and south-western Arabia, with descriptions of three new species

Figure 6. Dasypeltis scabra. Close-up dorsal view (in alcohol) of MNHN 7227 (paralectotype of Rachiodon abyssinus, see p. 39) from the White Nile River (possibly near Khartoum, Sudan) showing a '5N' pattern (see Gans 1959) consisting of dark oval saddles and dark dorso-ventrally elongated lateral bars, with paler centres. (Photo: M.F. Bates)

opencc-by-4.0Dec 1990View details →

Figure 6 in A Partial Skeleton of the Tyrannosaurid Dinosaur Aublysodon from the Upper Cretaceous of New Mexico

Figure 6—Comparison of distal end of right tibia and astragalus. 1, 2, Aublysodon cf. A. mirandus (OMNH 10131). 3, A. libratus (AMNH 5432). 4, A. sarcophagus (NMC 5601). An interpretive outline of the astragalus is shown by the dotted line in 2, as is the extent of the tibia behind the astragalus in 4. Abbreviations: a, astragalar facet; 1, lateral malleolus; m, medial malleolus. Scale bar 10 cm.

opencc-by-4.0Jul 2019View details →

FIGURE 6. Amphiura sarsi Ljungman, 1872 in The Echinoderm Fauna of the Azores (NE Atlantic Ocean)

FIGURE 6. Amphiura sarsi Ljungman, 1872 (EMEPC G3D2 Ma001). Dorsal view (A); ventral view (B); detail of the disc, ventral view (C).

FIGURE 6 in Craniofacial ontogeny in Tyrannosauridae (Dinosauria, Coelurosauria)

FIGURE 6. Restored skull with occluded jaws of a Stage 1 Tyrannosaurus rex (CMNH 7541) in left lateral (A), dorsal (B), occipital (C), and palatal (D) views. Skull length is 572 mm. Rostral and transverse crushing of the suspensorium in A and B has not been corrected. Basisphenoid pneumatic foramina are restored in D; right squamosal is restored in B; occipital condyle is restored in C. Abbreviations: ang, os angulare; ar, os articulare; bo, os basioccipitale; bs, os basisphenoidale; bs rec cl, basisphenoid recess ceiling; co, os coronoideum; cr sag, crista sagittalis; dn, os dentale; ect, os ectopterygoideum; imf, fenestra intramandibulare; ipv, vacuita interpterygoideum; la, os lacrimale; mx, os maxillare; na,

FIGURE 6 in Craniofacial ontogeny in Tyrannosauridae (Dinosauria, Coelurosauria)

FIGURE 6. Continued os nasale; ot, os otoccipitale; pa, os parietale; pl, os palatinum; pmx, os premaxillare; pra, os prearticulare; prf, os prefrontale; pt, os pterygoideum; qj, os quadratojugale; qu, os quadratum; qu for, foramen quadratum; rec pnu bs, recessus pneumaticum basisphenoidale; rec pn pl, recessus pneumaticum palatinum; sq, os squamosum; spl, os spleniale; spl fen, fenestra spleniale; suborb fen, fenestra suborbitalis; sur, os surangulare; tp, tooth puncture; vo, vomer.

FIGURE 6 in Craniofacial ontogeny in Tyrannosauridae (Dinosauria, Coelurosauria)

FIGURE 6 in Craniofacial ontogeny in Tyrannosauridae (Dinosauria, Coelurosauria)

opencc-by-4.0Dec 1986View details →

FIGURE 6 in Tyrannosaurus rex from the McRae Formation (Lancian, Upper Cretaceous), Elephant Butte Resevoir, Sierra County, New Mexico

FIGURE 6—-Dorsal view of anterior extremity of left dentary NMMNH P ~ 1013~ l, 311161101 anterior' [to O right Broken tooth in alveolus 1' no 10 4 illustrates cross-sectional v shape of the tooth Interdental plates surround the alveoli. Bar scale 10 cm

opencc-by-4.0Dec 1986View details →

FIGURE 6 in Tyrannosaurus rex from the McRae Formation (Lancian, Upper Cretaceous), Elephant Butte Resevoir, Sierra County, New Mexico

FIGURE 6—-Dorsal view of anterior extremity of left dentary NMMNH P~ 1013~ l, 311161101 anterior' [to O right Broken tooth in alveolus 1' no 10 4 illustrates cross-sectional v shape of the tooth Interdental plates surround the alveoli. Bar scale 10 cm

opencc-by-4.0Dec 1986View details →

FIGURE 6 in Tyrannosaurus rex from the McRae Formation (Lancian, Upper Cretaceous), Elephant Butte Resevoir, Sierra County, New Mexico

FIGURE 6—-Dorsal view of anterior extremity of left dentary NMMNH P~ 1013~ l, 311161101 anterior' [to O right Broken tooth in alveolus 1' no 10 4 illustrates cross-sectional v shape of the tooth Interdental plates surround the alveoli. Bar scale 10 cm