Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency.
However, it is generally agreed that maps which integrate several datasets (in this case, HAPPY data, BAC-end data and contig data) are more robust than those which depend on a single method.
To validate our approach, we have implemented a prototype, MenT2, which integrates several schemas via mediator interactions in a simulated network.
This framework is based on a general, distributed architecture which integrates several components; (i) coordinator, (ii) navigator, (iii) perception, and (iv) human-robot interface.
In this study, we propose a multiperiod mixed-integer linear programming model, which integrates several decisions related to multistage multiproduct batch plants.
A multi-layered, Service Oriented Architecture (SOA) based system, which integrates several sub-systems, such as sensor data fusion, context modelling and contextual data information provision has been developed.
We propose the adaptive scene dependent filter (ASDF) hierarchy for unsupervised learning of image segmentation, which integrates several processing pathways into a flexible, highly dynamic, and real-time capable vision architecture.
Despite the fact that the 3D feature descriptors adopted in this paper are commonly used in the domain of computer vision, from a general perspective, a framework which integrates several stages of pre-processing feature extraction, multiregion repre-processing featuresification—has not bextractioned in the literature.
We first introduce three challenges in the procedural modeling of digital architectural heritages and then present a general framework, which integrates several machine intelligence and semantic techniques, e.g., the ontology design approach, pattern mining, auto-annotating and rule reduction, to improve the procedural methods in architectural modeling.
It consists of a multi-objective mixed integer linear programming model which integrates several interconnected decisions: facility location and capacity determination; supplier selection and purchase levels definition; technology selection and allocation; transportation network definition including both unimodal and intermodal options; supply planning; product recovery and remanufacturing.
Each signature was also functionally profiled using g:PROFILER [163], which integrates several profiling resources, including Gene Ontology, KEGG and Reactome pathways, and TRANSFAC sequence motifs.
