In the paper, the hybrid clustering-classification neural network is proposed. This network allows to increase a quality of information processing under the condition of overlapping classes due to the rational choice of learning rate parameter and introducing special procedure of fuzzy reasoning in the clustering-classification process, which occurs both with external learning signal (“supervised”), and without one (“unsupervised”). As similarity measure neighborhood function or membership one, cosine structures are used, which allow to provide a high flexibility due to self-learning-learning process and to provide some new useful properties. Many realized experiments have confirmed the efficiency of proposed hybrid clustering-classification neural network; also, this network was used for solving diagnostics task of reactive arthritis.

An inclement dusty weather can significantly reduce the visual quality of captured images and this consequently leads to hamper the observation of meaningful image details. Capturing images in such weather often leads to undesirable artifacts such as poor contrast, deficient colors or color cast. Hence, various methods have been proposed to process such unwanted event and recover vivid results with acceptable colors. These methods vary from simple to complex due to the variation of the used processing concepts. In this article, an innovative technique that utilizes tuned fuzzy intensification operators is introduced to expeditiously process poor quality images captured in an inclement dusty weather. Intensive experiments were carried out to check the processing ability of the proposed technique, wherein the obtained results exhibited its competence in filtering various degraded images. Specifically, it performed well in providing acceptable colors and unveiling fine details for the processed images.

Solar cells, as an alternate means of electricity supply, is rapidly advancing. Generally, output of solar cells depends largely on intensity of sun and angle of incidence on the cells. This means that to get maximum efficiency from these cells, they must remain directly pointed at the sun from sun rise to sun set. However, the position of sun’s highest intensity with respect to a given spot changes with time of the day. It is therefore necessary to automatically control position of solar cells to always align with the highest intensity of sun. In this paper, we present a prototype automatic solar panel controller, with night time hibernation. The proposed system consists of both software and hardware parts, and it automatically provides best alignment of solar panel with sun to get maximum intensity. The solar panel controller system detects the presence of sun rays using light dependent resistors (LDR). At the heart of the control mechanism is an AT89C52 microcontroller. It is programmed to constantly monitor the output of an LDR, actuate a stepper motor to reposition the solar panel to a direction with the highest intensity. The proposed system also has an option of manual control of the panel via a computer interface or a keypad unit for easy of user interactivity during maintenance. Testing the proposed system, results shows that it can successfully track the sun and enter idle mode in the absence of sun rays, hence, conserving over 50% of energy required to operate the system.

By the inherent nature of solving enormous number of problems with the concurrent execution, parallel process methods grow to be a popular technique. The challenges of parallel computing are dealing with the computing resources for the number of tasks and complexity, dependency, resource starvation, load balancing and efficiency. In this paper, the brief discussion about the parallel computation is carried out, and numerous performance issues are also discovered as an open issue. The risk encountered in parallel computing is the motivation to analyze different optimization techniques to accomplish the tasks without risky environment. Genetic Algorithm (GA) is another approach to make the concept of scheduling easy and fast. Here the paper presents a Task Duplication based Genetic Algorithm with Load Balance (TD-GA) approach on parallel processing for effective scheduling of multiple tasks with less schedule length and load balance. TD-GA algorithm truly handles the issues very well and the results show that complexity, load balance and resource utilization are finely managed when compared to the other optimization approaches.

The successive loss of the outermost pixel values or frames in the digital representation of handwritten digits is postulated to have an increasing impact on the degree of accuracy of categorizations of these digits. This removal of frames is referred to as trimming. The first few frames do not contain significant amounts of information and the impact on accuracy should be negligible. As more frames are trimmed, the impact becomes more significant on the ability of each classification model to correctly identify digits.
This study focuses on the effects of the trimming of frames of pixels, on the ability of the Recursive Partitioning and Classification Trees method, the Naive Bayes method, the k-Nearest Neighbor method and the Support Vector Machine method in the categorization of handwritten digits.
The results from the application of the k-Nearest Neighbour and Recursive Partitioning and Classification Trees methods exemplified the white noise effect in the trimming of the first few frames whilst the Naive Bayes and the Support Vector Machine did not. With respect to time all models saw a relative decrease in time from the initial dataset. The k-Nearest Neighbour method had the greatest decreases whilst the Support Vector Machine had significantly fluctuating times.

The purpose of this article is to consider the notions of intuitionistic fuzzy soft matrices and some basic results. This work deals particularly with the definition of transpose of intuitionistic fuzzy soft matrices and then some properties of transpose of intuitionistic fuzzy soft matrices are studied. After that symmetric intuitionistic fuzzy matrices are also defined and some properties are discussed. Numerical examples are provided to make the concept clear.

The economic development and promotion of a country or region is depends on several facts such as- tourism, industries, transport, technology, GDP etc. The Government of the country is responsible to facilitate the opportunities to develop tourism, technology, transport etc. In view of this, we look into the Department of Tourism to predict and classify the number of tourists visiting historical Indian monuments such as Taj- Mahal, Agra, and Ajanta etc.. The data set is obtained from the Indian Tourist Statistics which contains year wise statistics of visitors to historical monuments places. A survey undertaken every year by the government is preprocessed to fill out the possible missing values, and normalize inconsistent data. Various classification techniques under Decision Tree approach such as- Random Tree, REPTree, Random Forest and J48 algorithms are applied to classify the historical monuments places. Performance evaluation measures of the classification models are analyzed and compared as a step in the process of knowledge discovery.

Social Networks such as Facebook, Twitter, Linked In etc… are rich in opinion data and thus Sentiment Analysis has gained a great attention due to the abundance of this ever growing opinion data. In this research paper our target set is movie reviews. There are diverge range of mechanisms to express their data which may be either subjective, objective or a mixture of both. Besides the data collected from World Wide Web consists of lot of noisy data. It is very much true that we are going to apply some pre-processing techniques and compare the accuracy using Machine Learning algorithm Naïve Bayes Classifier. With ever growing demand to mine the Big Data the open source software technologies such as Hadoop using map reducing paradigm has gained a lot of pragmatic importance. This paper illustrates a comparitive study of sentiment analysis of movie reviews using Naïve Bayes Classifier and Apache Hadoop in order to calculate the performance of the algorithms and show that Map Reduce paradigm of Apache Hadoop performed better than Naïve Bayes Classifier.