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Figure 2. Overall process of the system -An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

This paper mainly focuses on automated detection of White Matter Lesions of brain using fast and efficient clustering algorithms. The goal of clustering a medical image is to simplify the representation of an image into a meaningful image and makes it easier to analyze. As a first step, MRI brain image is pre-processed using Contrast Stretching technique which is one of the efficient image enhancement techniques. The pre-processed image is subjected to clustering. The clustering algorithms include Fuzzy c-means Clustering (FCM), Geostatistical Possibilistic Clustering (GPC) and Geostatistical Fuzzy Clustering Model (GFCM). However clustering techniques are sensitive to initialization and are easily trapped in local optima. In order to obtain an optimized result, the clustered images are undergone optimization. Particle swarm optimization (PSO) is a stochastic global optimization tool which is used in many optimization problems. Figure 2 represents overall process of automatic detection of WMLs of brain. Since MS lesions present different characteristics from lesions in elderly individuals there are many clustering models to determine the accuracy but those methods are not directly applicable to predict the accurate lesions because of the decreased contrast between White Matter and Grey Matter in elderly people. The proposed clustering models are derived by extending the objective functions of FCM and Possibilistic clustering with a Geostatistical (spatial) model. These algorithms are applied to real magnetic resonance images and is shown to be more robust to noise and other artifacts than competing approaches.

Figure 5. Sensory score and period of storage for processed cheese-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

R2 was found to be 96.5 percent of the total variation as explained by sensory scores. Period of storage (days) for which the processed cheese has been in the shelf can be determined based on sensory score (Fig. 5).

Figure 4. Comparison of ASS and PSS for multilayer model R-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

TDNN models with single and multi layers were developed taking soluble nitrogen, pH, standard plate count, yeast & mould count, spore count as input parameters, and sensory score as output parameter for predicting the shelf life of processed cheese stored at 30o C. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were used in order to compare the prediction ability of the developed TDNN models. Regression equations were developed for predicting the shelf life of processed cheese, which came out as 28.25 days. Since, predicted value is close to the experimentally determined shelf life of 30 days, hence from the study it can be concluded that TDNN artificial neural network models are quite efficient in predicting shelf life of processed cheese.

Figure 2. Training pattern of TDNN models-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

The Neural Network Toolbox under MATLAB software was used for developing the TDNN models. Training pattern of TDNN models is presented in Fig.2.

Figure 1. Inputs and output parameters for TDNN models-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

The data consisted of 36 samples, which were divided into two subsets, i.e., 30 used for training the network and 6 for testing the TDNN models. Soluble nitrogen, pH, standard plate count, yeast & mould count, and spore count were taken as input parameters, and sensory score as output parameter for developing TDNN single and multilayer models (Fig.1).

Figure 3. Comparison of ASS and PSS single layer model-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

Figure 3. Traditional Approach and Process Management System Approach – Effort Percentage Comparison-Business Process Management – A Traditional Approach versus a Knowledge Based Approach

Comparing the results obtained in using the two approaches (Figure 3), it is possible to note a significant reduction in terms of both effort and working hours in correspondence of design and development phases.

Figure 11. Cognitive architecture of the process of social signals perception-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

A possible cognitive architecture and formalization of the process of learning via multisensory integration is presented in figure 11. The formal description of the proposed cognitive architecture, capable of interpreting social-communication signals, signs and symbols, is based on multisensory integration at the level of perception, parallel processing at the level of interpretation and decision making followed by verbalization, as well as performing an action (eye contact, gesture, mimicking) at the level of behaviour.

Figure 9. The impossible figure (right) is not noticeable as such at first glance-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

In the lexical domain a similar effect of holistic word processing is described in (Anstis, 2005b). The viewers were presented with pairs of three-letter words in quick succession and asked to report if the upper halves of the successively presented words were identical. Surprisingly, even when the upper halves of the words were orthographically identical, the error rate was reliably higher than expected and in comparison with matching identical successive words. As the author of the study Stuart Anstis points out: “students were processing the words not as separable parts, but holistically as perceptual units that could not be perceptually split apart. These results show that in normal circumstances, the visual system cannot, or does not, divide words into upper and lower halves” (Anstis, 2005b, p. 239).The author relates the results of his study to studies of visual perception of faces as evidence that the mechanism of holistic processing in the visual and the lexical domains is essentially the same.

Figure 8. Machine faces, perceived as more figure-like(left) and less figure-like (right)-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

In an experimental study on visual perception, addressing directly Gestalt phenomena, a new Gestalt cue for figure-ground assignment was introduced (Vechera et al., 2002). The foreground versus the background organization is a strong determinant for decisions on objects seen among image elements. A well-known set of perceptual cues that are often called Gestalt cues are the size or area, the symmetry and the convexity vs. concavity judgments. It is generally assumed that figures are ‘small, symmetrical and convex’. The authors asked the question whether these cues are all that are necessary for a region of the image to be judged as a figure. The main result of this study is that regions in the lower portion of a stimulus array appear more figure-like than regions in the upper portion of the display.

Figure 6. Noticeable subjective response to the distorted face to the right-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

Quite surprisingly, if the distortion is viewed in the normal upward position, it evokes strong emotional response to the distorted face to the right in figure 6.

Figure 5. The distortion is barely noticeable if the faces are viewed in the reversed position-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

According to the feature-based processing theories of human faces the main elements, noticed and remembered in a face, are the eyes, the nose and the mouth (Thompson, 1980; Anstis, 2005a). If, however, we distort some of the elements of a face, these should influence perception,regardless of the position of the image – upright or reversed – from the observer viewpoint. Figure 5 presents the reversed image of the face on the left and the reversed distorted face on the right. The distortion was achieved by rotating the eyes of the image in the vertical direction. Figure

Figure 4. Main elements of a face, according to the feature-based processing theories-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

In 1980 Peter Thompson proposed a new experimental paradigm for investigation of perception, called “face thatcherization” (also named “Thomson illusion”) (Thompson, 1980). Imagine that the following face, depicted in figure 4, is a photo of the then UK Prime Minister Margaret Thatcher.

Figure 3. Robotic faces, similar to smiley emoticons-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

The smiley Gestalt is the result from a dynamic (evolved in time) cognitive process, it is immediately given in cognition, memorable, emotionally rich and socially relevant and reflects the special kind of Gestalt complexity as defined by Edwin Rausch (1988). Conventional representations of holistic entities like smileys or novel robotic faces come to life because they capture essential Gestalt qualities of the perceived image. For example, in figure 3 the robotic faces resemble the smileys in terms of the evoked internal/emotional reactions.

Figure 2. Taxonomy of the educational technologies for children with ASC-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

learner (Perlin, 1997). MOSOCO is an emergent technology implemented in a smartphone called “Mobile Social Compass” (Escobedo et al., 2012). Six basic social skills are being encouraged by prompting the user to initiate social contact. The menu displays symbols for the basic social skills – eye contact, space and proximity, start an interaction, asking questions, sharing interests and finish an interaction. The MOSOCO application has turned out to be an extremely useful tool as an online prompt in starting, maintaining and finishing social interaction for both typical and autistic students, as well as to anyone that feels need for improving their social competence.

Figure 1. Necker cube depth illusion (Adapted from [http://en.wikipedia.org/wiki/Necker_cube])-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

Often the term Gestalt is used interchangeably with the term “emergent whole” (Johansson, 1998). The emergence of a cognitive Gestalt structure adds dynamical and psychophysical forces, which are different from the static notion of the “emergent whole”. An eminent example for the dynamic nature of the emergent process is the Necker cube, which cannot be perceived as static, but rotates in front of our eyes to the complete exhaustion of the eye gazing process (figure 1).

Figure 7. Kanizsa square makes us see a non-existing figure – white square (Adapted from [http://en.wikipedia.org/wiki/Optical_illusion]-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

A special case of Gestalt processing is the perceiving of illusions. Illusions make us see things or processes that are not there – for example the Kanizsa square like the one depicted in figure 7.

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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.

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Figure 4. A graphic on values of TP, TN, FP, and FN for each different application process.-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes

This study has proposed a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM) and Cognitive Development Optimization Algorithm (CoDOA). In this approach, the training process of the SVM has been supported with the CoDOA and after determining the most optimum sigma (σ) parameter of the Gauss (RBF) kernel function (so the most optimum SVM), a better classification formation has been tried to be achieved. In the context of the study, diabetes data set, which is related to Pima Indians, has been used for evaluating effectiveness of the proposed approach and after six different application processes, it was seen that the approach is well-enough on classification, which means being capable of determining diabetes. There are also some future works regarding the developed CoDOA-SVM based approach. In this context, there will be some more works for improving classification accuracy and also setting different optimization plans on i.e. different parameters of the kernel function. Additionally, it is aimed to evaluate the approach with datasets belonging to different diseases.