Find research datasets worth reusing

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 4. Table structure and relationships

The structure of the tables, primary keys and foreign keys are shown in figure 4.The names of the fields in the database tables are relevant for their content. Only the SPRAS field in the translation-tables TABT and ARET must be explained: SPRAS is a system-field which stands for the language and is used in order to maintain the languages in which the tab/area is translated into.

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 1. Popup layout for the Material Master Data Application

The Material Master Data Application provides an update popup layout, including tabs, areas and fields (also customer-specific fields). Each tab consists of one or more areas and each area of one or more fields, similar to the example below (figure 1). The application is called flexible because the user must have the possibility to add, delete, reorder or rename tabs, areas and fields.

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 3. The logical data model of tables and views

The five tables, named TAB, TABT, AREA, ARET and FLD, are combined within three views (TABV, AREV and FLDV) which build a cluster view, TAFC (figure 3).

opencc-by-4.0Aug 2016View details →

BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 3.The chart of emotional/unemotional detection on the face in negative, positive and surprise states (Regular and magnified data)

To evaluate the emotional/unemotional detection on the face, 328 tests were performed: 164 tests on the magnified data and 164 tests on the regular data. For this purpose, the train set includes the neutral state and only one of the emotional states (negativism, positivism and surprise) according to the test set. So that the train set includes regular data in 328 experiments. The experimental results are shown in Figure 3.

opencc-by-4.0Aug 2016View details →

BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 5. Flowchart of data classification

The Random Forest is a powerful classification method because of the following. First, errors are minimized as a result of a random forest, synthesizing through training (learner). The second, random choice at every stage in the Random Forest will reduce the correlation between the learners in the synthesis of the results. In addition, we also found that the total error of layered forest trees depends on their individual errors in forest trees, as well as the correlation between the trees. The article uses the wrapper model (Christopher Tong, 2000) with the objective function for the evaluation, Random Forest algorithm is shown in figure 5.

BRAIN Journal-Prediction of Thyroid Disease Using Data Mining Techniques-Figure 2. Attributes of the classification models used in the experiments

The authors used for their experiments a data set (UCI, 2016) containing 756 records about persons with thyroid dysfunctions. The classification model has 22 attributes; the class attribute is the target and it has three possible values: hypothyroidism, hyperthyroidism and normal. The current data set was extracted and preprocessed from the original file. A description of the attributes used in the experiments is given in Figure 2 (an extract from thyroid.arff test file).

BRAIN Journal-Prediction of Thyroid Disease Using Data Mining Techniques-Figure 3. KNIME Diagram

The proposed KNIME diagram representing the data mining models is given in Figure 3. The nodes that constitute the model diagram are: ARFF Reader – the input node used to load the data set in arff format, Partitioning – the node with the role of data set partition (for training and for the validation of the classification model), Naive Bayes Learner and Decision Tree Learner – the nodes used to build the classification model, Naive Bayes Predictor and Decision Tree Predictor – the nodes used to validate the model, Scorer – the node reports a confusion matrix and the accompanying quality measures in its view, Normalizer – the data set are normalized to be able to apply the neural network models, Multilayer Perceptron and RBFNetwork – the nodes corresponding to the neural network classification models, Weka Predictor – a node implemented in Weka to validate the models.

BRAIN Journal-Prediction of Thyroid Disease Using Data Mining Techniques-Figure 1. Factors that Affect Thyroid Function (The Institute for Functional Medicine, 2014)

In Figure 1 are presented the main factors that affect the thyroid function. It is obvious that factors such as stress, infection, toxins, trauma and certain medication are directly responsible for the improper production of thyroid hormones. Symptoms identification and the early detection of abnormal values of thyroid hormones after clinical investigation will help in establishing the proper diagnostic and to prescribe the right medication. The patient must periodically evaluate his clinical state in order to receive the treatment as long as he needs it.

opencc-by-4.0Apr 2017View details →

BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 4. Mobile Application to collect Sketch data

The drawing of a tree is shown on the drawing canvas in Figure 4. It is clear from the figure that there is some empty area on top, right, left and bottom of the drawing sketch, which can cause problems while using this image for training the neural network.

opencc-by-4.0Sep 2017View details →

BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 3. Symbols definition for a graph-based test

In this scenario, we intend to generate a random graph and compute a deep first-search node list. The first defined random symbol is n, namely the number of nodes in the graph as an integer from 5 to 9. The next symbol is named g and denotes the graph object created randomly using 3 parameters: the number of nodes, the minimum, and the maximum value for the weight. For the number of nodes, we used the previously computed value of n, whereas for the weights, we used two constants 0 and 1 since the graph is not weighted

opencc-by-4.0Apr 2017View details →

BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 2. Auto-generative Learning Object Model Definition

In this section, we will present the structure of AGLOs in the context of our approach. The AGLO meta-model is structured in XML as in Figure 2,a refinement from Chirila, Ciocarlie, and Stoicu (2015). The AGLO definition contains several sections like name, scenario, theory, question, answers, and feedback (line 01). The name element contains the name of the AGLO, possibly a small description in the human language (line 02). The section of the scenario (line 03) contains a comment (line 04) followed by a set of symbol definitions. The comment should describe the imagined scenario in details and it has the same role as code comments. The symbol is the central element of the AGLO model. The symbol has a name and is very similar to programming language variables. Symbols may be called also parameters since they control the content of the AGLO content in the process of instantiation.

opencc-by-4.0Apr 2017View details →

BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 1. The AGLO online assessment approach

In Figure 1, we present the lifetime of AGLOs in the context of online student assessment following a set of steps. In the backend, the tutor develops an AGLO model respecting a predefined meta-model. The model is intuitive, it has a few sections where symbols are defined using formulas and random numbers and then used in a section of a presentation for the student. When such models are created they are stored in a storage facility like a database to be selected by the student through the web application frontend. In the frontend, the students access the web application using a web browser from a workstation, tablet or smartphone. In the assessment process, the student will access several AGLOs. At this step, the accessed AGLOs are instantiated with random numbers, formulas are evaluated to fulfill the designed learning or testing scenario and to create the presentation content for the student. Nevertheless, the instantiated symbols will be used for the automatic assessment of the answers correctness

BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 5. Sample Image data 2

It helps to write our code in C# and to make an application in dot net framework, which collects facial images using a webcam/or other video grabbing tools. Then it implements Haar detection to extract facial features and to draw image pattern for matching both images.   After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation.

opencc-by-4.0Sep 2017View details →

BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 4. Online test assessment example

Thus, applying these restrictions the computed solution is C, E, G, J, L, H, I and is unique. Node C is the starting node since it is the first from the lexicographical point of view. The first step CE is the only choice coping with the restrictions from the [CE, CG, and CJ] edges. Next, EG is the first edge in the list of [EG, EJ]. The next step is GJ which is the only choice. Edge JL is another unique choice. Edge LH is the next step from the list [LH, LI]. Finally, the last edge is obtained by backtracking to node L and then taking edge LI. These restrictions allow us to drive the student to build only one solution from the possible set of solutions. This will determine an easier way of comparing the student’s answer with the answer of the computer. Another more general solution is to use validation functions which require implementation in domain libraries written in JavaScript.

BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 4. Sample image data 1

After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation.  After matching the images with the reference image, it gets the nearest orientation matches and they could be Font left, Font right, Down-left, Down Right, Up left, Upright, Font Straight, Up Straight, Down Straight. Initially, some constraints must be satisfied to realize a successful correct matching. The facial regions, concerned on eyes and nose points, have the following characteristic: if there is almost one missing point for the region of the same type then the comparison will be performed. There must be the same number of feature points for both eyes and nose separately. If this condition is satisfied then a new comparison will be performed.

BRAIN Journal-A New Challenge for Information Mining-Figure 5: New Approach Rich Data Exploration

In the following figure (see Figure 5) it has represented a scheme of the new approach proposed to Rich Data Set's Exploration. Other experiments are running in order to validate our idea, both in order to optimize this clustering model by applying new algorithms and distance measures to the datasets presented here, and both applying these techniques to a different domain from the didactic one. Other experiments are also conducted to improve user exploration by skillfully combining multiple methods and exploration techniques through the application of a variety of models such as the Association Rule to extract hidden relationships and association rules between data and Artificial Neural Network mechanisms of learning applicable to classification and forecasting problems.

BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 5. Hierarchical clustering by scores across the EPQ–R scales for data about all the participants

The clusters were generated using an implementation of a hierarchical clustering algorithm available in the R environment (R, n.d.). The top three clusters were extracted from a hierarchical cluster tree shown in Figure 5, while the color of data points in the visualization shown in figure 4 was determined based on cluster labels. Hierarchical clusters could be used when investigating which students in the analyzed sample share similar personality traits. This could be especially useful for smaller student groups as the teacher may manually inspect the cluster tree and its leaves, which designate individual students. For instance, there are three students in cluster 3, who are represented within the tree in Figure 5 by identifiers 14, 22, and 24. The students with identifiers 14 and 22 are more closely linked and more similar to each other than to the student with identifier 24.

BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 4. Radial visualization of scores across the EPQ–R scales for clustered data about all the participants

On the other hand, the division of data points by gender might not be the only useful strategy when visually inspecting the analyzed sample in a coordinate system. Numerous clustering algorithms may be used to determine which data points share similar scores across the EPQ–R scales, i.e., which data points belong to the same cluster of similar entities based on their corresponding EPQ–R scores. A radial visualization in which data points were organized into three clusters is given in Figure 4. Each cluster is marked by a different color: cluster 1 by red, cluster 2 by green, and cluster 3 by blue.

BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 5. Sample Image data 2

After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation.  Face orientation recognition is an important topic in computer vision and pattern recognition. Due to the non-rigid properties of faces, it is computationally expensive and difficult to achieve good recognition accuracy and robustness in face orientation recognition. In this paper, we propose an image mapping technique for face analysis in smart camera networks with a feature extraction and data from the facial feature. We estimate the face orientation angles in all camera views, based on the matched imaged data. Our objective is to obtain a set of facial structures which can work as landmarks for tracking and recognition of facial expressions.

BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 4. Sample image data 1

After matching the images with the reference image, it gets the nearest orientation matches and they could be Font left, Font right, Down-left, Down Right, Up left, Upright, Font Straight, Up Straight, Down Straight. Initially, some constraints must be satisfied to realize a successful correct matching. The facial regions, concerned on eyes and nose points, have the following characteristic: if there is almost one missing point for the region of the same type then the comparison will be performed. There must be the same number of feature points for both eyes and nose separately. If this condition is satisfied then a new comparison will be performed.  After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation.