automatic test case generation is an approach to decrease cost and time in software testing. Although there have been lots of proposed methods for automatic test case generation of web applications, there still exists some challenges which needs more researches. The most important problem in this area is the lack of a complete descriptive model which indicates the whole behaviors of web application as guidance for the generation of test cases with high software coverage. In this paper, test cases are generated automatically to test web applications using a machine learning method. The proposed method called RTCGW (Rule-based Test Case Generator for Web Applications) generates test cases based on a set of fuzzy rules that try to indicate the whole software behaviors to reach to a high level of software coverage. For this purpose a novel machine learning approach based on fuzzy neural networks is proposed to extract fuzzy rules from a set of data and then used to generate a set of fuzzy rules representing software behaviors. The fuzzy rule set is then used to generate software test cases and the generated test cases are optimized using an optimization algorithm based on combination of genetic and simulated annealing algorithms. Two benchmark problems are tested using the optimized test cases. The results show a high level of coverage and performance for the proposed method in comparison with other methods.

Atmospheric conditions represent one of the most significant natural hazards for floating units. A critical issue related to these conditions is the insufficient attention given by watch officers to accurately collecting atmospheric data from the instruments on the bridge. For instance, air pressure readings are often taken without proper corrections for instrument and altitude variations and are rarely compared to long-term averages. To address this, there is a pressing need to develop and install a system on floating units that can automatically measure atmospheric parameters. This system would calculate essential parameters from the measured data, enabling informed decision-making regarding maneuvers to avoid rough seas. This research discusses the details of establishing an automatic meteorological station capable of collecting atmospheric data from the environment, storing it in a computer, and comparing the measurements with long-term averages to identify atmospheric and oceanic anomalies.

New Page 1 In this paper, a fast method for automatic generation and scientific design of Persian letters is proposed. Scientific typeface design is an approach in which fonts are described by mathematical curves with well defined parameters, where these parameters can be automatically tuned. META FONT is a language suitable for the type of design used in this work. This language is particularly useful in designing Persian fonts because it can be used to simulate the pen movements of a calligrapher through automatic conversion of the scanned bitmap image of a font into a META FONT program, which can in turn, produce the font at a high quality. A complete software has been implemented based on these algorithms that works interactively with the user to facilitate the font design.

Nowadays, automatic analysis of music signals has gained a considerable importance due to the growing amount of music data found on the Web. Music genre classification is one of the interesting research areas in music information retrieval systems. In this paper several techniques were implemented and evaluated for music genre classification including feature extraction, feature selection and music genre modeling on a database of 8 different music genres containing Celtic, Classic, Classic Piano, Jazz, Metal, Persian Classic, Relaxing and Dance music. This database was gathered from several albums composed by different musicians. Short, middle and long term features were studied and finally only short and middle term features were used in our experiments. The long term features were discarded due to their low performance in music genre classification. Two modeling types of the music genres were evaluated. In the first type, only distribution of the feature vectors was used and in the second type, the ordering of the feature vectors was taken into account. Some modeling techniques such as ANN, GMM, Decision Tree and SVM were used individually and in a hierarchical approach. We proposed a taxonomy which classifies the music genres in a hierarchy where there are a small number of classes in the root and large number of classes in leaves. In fact, each class at the root of taxonomy contains one or more music genres and each genre is represented as a leaf at the bottom of the taxonomy. In addition, several classifiers were used simultaneously, in a way that each of them classifies the music genres individually.The decision is finally made using a voting algorithm. Besides, several short-term feature extraction techniques which have successfully been applied in speech recognition, music instrument classification and also music genre classification were studied and after analysis of the experimental results using statistical measures and different combinations of features, a near optimal feature vector was selected.