Abstract Breast cancer (B-cancer) detection is still complex and challenging problem, and in that case, we propose and evaluate a four-step approach to segmentation and detection of B-cancer disease. Studies show that relying on pure nakedeye observation of experts to detect such diseases can be prohibitively slow and inaccurate in some cases. Providing automatic, fast, and accurate image-processing-and artificial intelligence-based solutions... Breast cancer (B-cancer) detection is still complex and challenging problem, and in that case, we propose and evaluate a four-step approach to segmentation and detection of B-cancer disease. Studies show that relying on pure nakedeye observation of experts to detect such diseases can be prohibitively slow and inaccurate in some cases. Providing automatic, fast, and accurate image-processing-and artificial intelligence-based solutions for that task can be of great realistic significance. As a testbed we use a set of mammogram images taken from the Medical Hussein City in Jordan. This study utilizes morphological operations as a segmentation approach with Artificial Neural Network (ANN) as a classifier tool. The presented approach itself scans the whole mammogram and performs filtering, segmentation, features extraction, and detection in a succession mode. The feasibility of the proposed approach was explored on 32 commonly virulent images, and the recognition rate achieved in the detection step is 100%; moreover, the overall accuracy is convinced and satisfied in all cases. Further, the approach is able to give reliable results on distorted medical images, since the approach is subjected to a rectification step. Finally, this study is very effectual in decreasing mortality and increasing the quality of treatment of early onset of B-cancer. Read More Read Less

Abstract Automatic Counting of the packed bar steel is unsolved. A new segmentation method based on quasi-circular assumption and a new object recognition method based on scanning are present. Owing to the simple addition, subtraction and logic operation, these two methods have real time property. The total CPU time is less than 1 second for an 640*480 pixels image, The experiments... Automatic Counting of the packed bar steel is unsolved. A new segmentation method based on quasi-circular assumption and a new object recognition method based on scanning are present. Owing to the simple addition, subtraction and logic operation, these two methods have real time property. The total CPU time is less than 1 second for an 640*480 pixels image, The experiments show that the new method can segment the aggregated bars effectively, and it can be used to improve the accuracy and the speed of recognizing and countering of irregular and aggregated objects. Read More Read Less

Abstract In this paper, we present a new model for semantic description of Web services called CYASA4WSDL (stands for Capacity-driven Yet Another Semantic Annotation for Web Services Description Language, CYASA for short). CYASA is strongly inspired from Capacity-driven Web Services Description Language (CWSDL for short) and Yet Another Semantic Annotation (YASA for short) description models. We also present a discovery algorithm... In this paper, we present a new model for semantic description of Web services called CYASA4WSDL (stands for Capacity-driven Yet Another Semantic Annotation for Web Services Description Language, CYASA for short). CYASA is strongly inspired from Capacity-driven Web Services Description Language (CWSDL for short) and Yet Another Semantic Annotation (YASA for short) description models. We also present a discovery algorithm for such Web services. We show throughout this paper how YASA4WSDL Web services are matched basing on with reference to the specificities of YASA4WSDL descriptions. This algorithm was implemented in CYASA-M, a new Web service matchmaker. CYASA-M is evaluated and compared versus YASA matchmaker to highlight its contribution. Read More Read Less

Abstract Prediction of humidity is one important and challenging task that needs lot of attention and study for analyzing atmospheric onditions, specially the warm weather. Advent of digital computers and development of data driven artificial intelligence approaches like Artificial Neural Networks (ANN) have helped in numerical prediction of humidity. However, very few works have been done till now in this area.... Prediction of humidity is one important and challenging task that needs lot of attention and study for analyzing atmospheric onditions, specially the warm weather. Advent of digital computers and development of data driven artificial intelligence approaches like Artificial Neural Networks (ANN) have helped in numerical prediction of humidity. However, very few works have been done till now in this area. The present study developed an ANN model based on the past observations of several meteorological parameters like temperature, humidity, atmospheric pressure and vapour pressure as an input for training the model. The novel architecture of the proposed model contains several multilayer perceptron network (MLP) to realize better performance. The model is enriched by analysis of several alternative models like online feature selection MLP (FSMLP) and self organizing feature map MLP (SOFM-MLP).The improvement of the performance in the prediction accuracy has been demonstrated by the selection of the appropriate features. The FSMLP is used as preprocessor to select good features. The results obtained after applying the model indicate that it is well suitable for humidity as well as warm weather prediction over large geographical areas. Read More Read Less