Abstract-Temperature warnings are important forecasts because they are used to protect life and property. Temperature forecasting is the application of science and technology to predict the state of the temperature for a future time and a given location. Temperature forecasts are made by collecting quantitative data about the current state of the atmosphere. In this paper, a neural network-based algorithm for predicting the temperature is presented. The Neural Networks package supports different types of training or learning algorithms. One such algorithm is Back Propagation Neural Network (BPN) technique. The main advantage of the BPN neural network method is that it can fairly approximate a large class of functions. This method is more efficient than numerical differentiation. The simple meaning of this term is that our model has potential to capture the complex relationships between many factors that contribute to certain temperature. The proposed idea is tested using the real time dataset. The results are compared with practical working of meteorological department and these results confirm that our model have the potential for successful application to temperature forecasting. Real time processing of weather data indicate that the BPN based weather forecast have shown improvement not only over guidance forecasts from numerical models, but over official local weather service forecasts as well.

Content based image retrieval is the technique to retrieve similar images from a database that are visually similar to a given query image. It is an active and emerging research field in computer vision. In our proposed system, the Interest points based Histogram of Oriented Gradients (HOG) feature descriptor is used to retrieve the relevant images from the database. The dimensionality of the HOG feature vector is reduced by Principle Component analysis (PCA). To improve the retrieval accuracy of the system the Colour Moments along with HOG feature descriptor are used in this system. The Interest points are detected using the Harris-corner detector in order to extract the image features. The KD-tree is used for matching and indexing the features of the query image with the database images.

No abstract

Fracture detection based on image classification is an area of research which has proved to be challenging for the past several decades. This field has gained more attention due to the new challenges posed by voluminous image databases. In this research work, fusion-based classifiers are constructed, which extracts features from the images, use these features to train and test the classifiers for the purpose of detecting fractures in X-Ray images. The various features extracted are Contrast, Homogeneity, Energy, Entropy, Mean, Variance, Standard Deviation, Correlation, Gabor orientation (GO), Markov Random Field (MRF), and intensity gradient direction (IGD). Three classifiers, BPNN, SVM and NB classifiers are used. Using these features and classifiers, three single classifiers and four multiple classifiers were developed. All the classifiers were tested vigorously with the test dataset for evaluating the winner combination of classifiers and features that correctly identifies fractures in a bone image. The performance metrics used are sensitivity, specificity, positive predictive value, negative predictive value, accuracy and execution time. The experimental results showed that usage of fusion classifiers enhances the detection capacity and the combination SVM and BPNN produces the best result.

No abstract