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opencc-by-4.0Dec 2023View details →

Fig. 2 in A new record of Ardisia walkeri, a hybrid of A. japonica and A. pusilla, (Primulaceae) from Jeju Island, Korea

Fig. 2. Young stems and petioles of Ardisia plants from Jeju Island. A. A. japonica (G. Kokubugata21530). B. Ardisia spp. (G. Kokubugata 21540). C. A. pusilla (G. Kokubugata21533). The bar indicates 3 mm. Refer to Table 1 for voucher specimen numbers.

Fig. 1 in Morphological characters and SNP markers suggest hybridization and introgression in sympatric populations of the pleurocarpous mosses Homalothecium lutescens and H. sericeum

Fig. 1 Measurements of branch leaf characteristics of Homalothecium spp. Leaf characters (L1-L18) are explained in Table 3

Fig. 4 in Morphological characters and SNP markers suggest hybridization and introgression in sympatric populations of the pleurocarpous mosses Homalothecium lutescens and H. sericeum

Fig. 4 Relationship between width and length of leaf lamina of Homalothecium leaf specimens (N = 240) collected from the allopatric populations of H. lutescens (N = 60) and H. sericeum (N = 59) and the

Fig. 6 in Morphological characters and SNP markers suggest hybridization and introgression in sympatric populations of the pleurocarpous mosses Homalothecium lutescens and H. sericeum

Fig. 6 The positions of putatively hybrid sporophytes from the sympatric populations of H. lutescens and H. sericeum superimposed in the PCA from Fig. 5, based on leaf morphology of the maternal gametophytes. The morphospace of leaves from allopatric populations of H. lutescens and H. sericeum are shown as encircled surfaces (blue circle = H. lutescens and yellow circle = H. sericeum). The red circle represents the morphospace of individuals from the sympatric populations. Each square represents a hybrid sporophyte specimen, collected on its

Fig. 5 in Morphological characters and SNP markers suggest hybridization and introgression in sympatric populations of the pleurocarpous mosses Homalothecium lutescens and H. sericeum

Fig. 5 Principal component analysis of 14 leaf characters from 240 specimens representing allopatric and sympatric populations of Homalothecium lutescens and H. sericeum. The first two axes (PC1 and PC2) representing together 36% of variation are shown. The colours and shapes of data points correspond to the population of the specimens. Leaf

Fig. 2 in Morphological characters and SNP markers suggest hybridization and introgression in sympatric populations of the pleurocarpous mosses Homalothecium lutescens and H. sericeum

Fig. 2 (a) Measurement of capsule orientation in relation to the seta in Homalothecium as the angle (in degrees) between the seta and spore capsule at the basis of the spore capsule. (b) Capsule inclinations of individuals from the allopatric populations fell into the black ranges of variation; in the sympatric populations individuals occurred with capsule inclinations ranging outside typical capsule inclinations of the pure species (red zone: 150°- 164°), indicating hybrid origin

opencc-by-4.0Jun 2012View details →

Figure 3. Different domain-Hybridization of Fuzzy Clustering and Hierarchical Method for Link Discovery

In this paper, we propose a new hybrid algorithm, which combines the features of fuzzy algorithm and hierarchical algorithm. Our algorithm decreases the number of comparisons on link discovery. Using hierarchical algorithm in the first level, the data is divided into two groups. In the second level the worst cluster is determined by matrix memberships and then it split. This stage is repeated until the optimal number of clusters is achieved.Creating typed links, between the entities of different datasets is one of the key challenges on web of data .We presented the clustering approach, which decreases the number of comparisons on link discovery. The results of linking the movies in LinkedMDB to corresponding movies in DBpedia and also linking the places in LinkedGeoData to the places of DBpedia show the it reduces the number of comparisons without loss of recall and precision. Hopefully in the future, we will be able to elevate the proposed method recall to 100 % using the membership matrix.

opencc-by-4.0Jun 2012View details →

Figure 1. Workflow of approach-Hybridization of Fuzzy Clustering and Hierarchical Method for Link Discovery

In this section, we present our model in more detail. Figure 1 gives an overview of the workflow. Our approach is organized in two phases: first, the division of data in two clusters; thenthe determination of the worst cluster and splitting. The number of clusters is unknown, but our algorithms can find this parameter based on the complexity of cluster structure.

opencc-by-4.0Jun 2012View details →

Figure 2. Decrease comparisons.-Hybridization of Fuzzy Clustering and Hierarchical Method for Link Discovery

Figure 4. Extracted bands diagramClassification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Based on that coefficient, different bands have to be extracted. The bands are alpha, beta, theta, gamma, and delta. Figure 4 shown in below which is represent the different extract band diagrams.

opencc-by-4.0Aug 2015View details →

Figure 3. Electrode placement diagram-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Reference electrode placed AFz placed in between AF1 and AF2 electrode and ground electrode Oz is placed between O1 and O2 electrodes. The impedance of the electrode range is 5KΩ. The sampling rate was fixed range between 256 samples per second for all the channels. Figure 3 shown in below which is represent the electrode placement diagram. The recorded EEG signal is used to recognize the different level of emotions.

opencc-by-4.0Aug 2015View details →

Figure 2. Emotion recognition using EEG-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

This section describes that collection of EEG signals for different emotion recognition experiments. The electroencephalography signals of 32 participants were recorded during one minute videos. Based on that participants are rated in terms of valence and arousal, like/dislike, familiarities and dominance. Emotion related ratings are given based on the online self assessment which is 120 one minute extracted music videos, which are rated by 14-16 volunteers based on arousal and valence. The EEG signals were recorded using 64 electrodes, first 62 electrodes are active electrode, one for reference and remaining one is ground electrode. All the electrodes are placed on the scalp which is made up of the Ag/Ag-Cl. Figure 2 shown in below which is represent the basic EEG signal recording methods and emotion analysis process.

opencc-by-4.0Aug 2015View details →

Figure 1. Brain Structure-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

EEG data have collected from desirable subjects. Each and every EEG signal has different kind of bands like Alpha, Beta, Gamma, Theta, and Delta. Each band stores the particular information about the emotions. Alpha band (8-13 Hz) which located in Frontal Occipital, Beta band (13-30 Hz) which located in Frontal Central, Gamma band (30-100 Hz), Theta band (4- 7 Hz) which located in Midline Temp, Delta band (0-4Hz) which located in Frontal Lobe. Before processing the EEG signal and extracting these bands, preprocess the signal and reduce the noise. The basic brain figure is shown in below.

Figure 8. Sample Emotion Classification Result-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

For all the 32 participants the EEG signal has to be sampled and process their emotions. The emotions are depends on the music and video clips. In this paper the video clips are changed from one person to other person. Here hybrid feed forward neural networks with radial basis function; probabilistic neural network classifier is used to classify the emotions from EEG. PNN is very fast and insensitive neural network which provides the optimized classification result. Compare to the multi layer perception neural network it provide accurate result. It classifies the emotion into two different groups like arousal and valence. Figure 8 shows that the model implemented result of emotion classification and person identification.

Figure 7. Neural Network model-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

In Probabilistic Neural Network the operations are organized into a multilayer feed forward neural network with four layers like input layer, hidden layer, pattern layer and output layer. PNN use the Euclidean distance measure the difference between one neuron to other neurons. The actual target values are stored in the hidden neuron and the optimized weighted values are fed into the same category hidden neuron. Then finally the output layer compared the weighted votes of each target values and the target votes are used to predict the emotions.

Figure 10. Sensitivity and specificity of different bands-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Figure10 has shown in sensitivity and specificity of different neural network which is used to explain how exactly the emotions are classified into groups and accuracy value shown in above table 3.

Figure 9. Mean Square Error for different bands-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Figure 9 has shown in different epochs using neural networks with mean square error performance and the Table 3 shows that different bands mean square error values while training the neural networks with particle swarm optimization.

Figure 6. Mean square error of alpha band-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Particle swarm optimization algorithm first optimizes the neural networks weight and bias and provides the minimum mean square error with nearer by zero. The following figure 6 has shown that minimum mean square error when training the particular band features.

Figure 5. Process flow of PSO-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

PSO is used to identify the best solution from collection of solution. It is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle's position and velocity. Each particle's movement is influenced by its local best known position but, is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions. PSO is a metaheuristic as it makes few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. However, metaheuristic such as PSO do not guarantee an optimal solution is ever found. The following Figure 5 explains the basic flow of PSO process.