Abstract Semantic web services increasingly rely on annotation and tools. To prepare and make the annotation tools, various benchmarks are currently employed. The use of use of randomly generated or resampled descriptions are restriced in such annotations. WSDL collections are in use, but their semantic annotation requires a certain level of automation, due to the number of operations to be processed.... Semantic web services increasingly rely on annotation and tools. To prepare and make the annotation tools, various benchmarks are currently employed. The use of use of randomly generated or resampled descriptions are restriced in such annotations. WSDL collections are in use, but their semantic annotation requires a certain level of automation, due to the number of operations to be processed. Through this study, we advocate a complete automatic method to semantically annotate such large WS collections. The latent sematics have potentials in names, type names and structures and hence they are applied in this study.The Wordnet mapping SIgma is used for Concept-to-word association to the SUMO ontology. First we narrate in detail the annotation method we have used followed by the application of it to the larger collection of realworld syntactic WS descriptions. Our testings show that their is efficiency in the use Read More Read Less

Abstract Accelerated Particle Swarm Optimization has potential i n many applications and the business sector is one in such wider scope. Coupled with the support vector machine and metaheuristics the particle swarm optimization is now widely used in solving tough optimization problems. By considering the potential features of these two, we have developed an integrated framework for solving business optimization problems.... Accelerated Particle Swarm Optimization has potential i n many applications and the business sector is one in such wider scope. Coupled with the support vector machine and metaheuristics the particle swarm optimization is now widely used in solving tough optimization problems. By considering the potential features of these two, we have developed an integrated framework for solving business optimization problems. Basically the proposed APSO-SVM to production optimization is used followed by the income prediction and project scheduling. Finally we tested the framework both by parametric studies as well as discussing the advantages of the proposed metaheuristic SVM. Read More Read Less

Abstract Plastics production is an important industrial sector in the world. However, producing the right plastic color with minimal reject is a challenge for plastic manufacturer. In this paper, Business intelligence is used for production data analysis in order to isolate the parameters the most susceptible of causing color mismatch. Consequently, the number of parameters to be further examined is significantly... Plastics production is an important industrial sector in the world. However, producing the right plastic color with minimal reject is a challenge for plastic manufacturer. In this paper, Business intelligence is used for production data analysis in order to isolate the parameters the most susceptible of causing color mismatch. Consequently, the number of parameters to be further examined is significantly reduced. And, the processing parameters causing rejects can be quickly identified and solved. Read More Read Less

Abstract Searching learning information from the web or from databases is a user’s need to learn or to teach. In order to satisfy these user’s needs, we proposed here a model which aims at automatically feeding texts with semantic metadata. These metadata would allow us to search and extract learning information from texts indexed in that way. This model is build... Searching learning information from the web or from databases is a user’s need to learn or to teach. In order to satisfy these user’s needs, we proposed here a model which aims at automatically feeding texts with semantic metadata. These metadata would allow us to search and extract learning information from texts indexed in that way. This model is build up from two parts: the first part consists on a semantic annotation of learning objects according to their semantic categories (definition, example, exercise, etc.). The second part uses automatic semantic annotation which is generated by the first part to create a semantic inverted index which is able to find relevant learning objects for queries associated with semantic categories. To sort the results according to their relevance, we apply the Rocchio’s classification technique on the learning objects. We have implemented a system called SRIDoP, on the basis of the proposed model and we have verified its effectiveness. Read More Read Less