Towards a Comprehensible and Accurate Credit Management Model: Application of four Computational Intelligence Methodologies

The paper presents methods for classification of applicants into different categories of credit risk using four different computational intelligence techniques. The selected methodologies involved in the rule-based categorization task are (1) feedforward neural networks trained with second order methods (2) inductive machine learning, (3) hierarchical decision trees produced by grammar-guided genetic programming and (4) fuzzy rule based systems produced by grammar-guided genetic programming. The data used are both numerical and linguistic in nature and they represent a real-world problem, that of deciding whether a loan should be granted or not, in respect to financial details of customers applying for that loan, to a specific private EU bank. We examine the proposed classification models with a sample of enterprises that applied for a loan, each of which is described by financial decision variables (ratios), and classified to one of the four predetermined classes. Attention is given to the comprehensibility and the ease of use for the acquired decision models. Results show that the application of the proposed methods can make the classification task easier and - in some cases - may minimize significantly the amount of required credit data. We consider that these methodologies may also give the chance for the extraction of a comprehensible credit management model or even the incorporation of a related decision support system in bankin

Experience in Social Affective Applications: Methodologies and Case Study

New forms of social affective applications are emerging, bringing with them challenges in design and evaluation. We report on one such application, conveying well-being for both personal and group benefit, and consider why existing methodologies may not be suitable, before explaining and analyzing our proposed approach. We discuss our experience of using and writing about the methodology, in order to invite discussion about its suitability in particular, as well as the more general need for methodologies to examine experience and affect in social, connected situations. As these fields continue to interact, we hope that these discussions serve to aid in studying and learning from these types of application

Hybrid Search: Effectively Combining Keywords and Semantic Searches

This paper describes hybrid search, a search method supporting both document and knowledge retrieval via the flexible combination of ontologybased search and keyword-based matching. Hybrid search smoothly copes with lack of semantic coverage of document content, which is one of the main limitations of current semantic search methods. In this paper we define hybrid search formally, discuss its compatibility with the current semantic trends and present a reference implementation: K-Search. We then show how the method outperforms both keyword-based search and pure semantic search in terms of precision and recall in a set of experiments performed on a collection of about 18.000 technical documents. Experiments carried out with professional users show that users understand the paradigm and consider it very powerful and reliable. K-Search has been ported to two applications released at Rolls-Royce plc for searching technical documentation about jet engines

Assembly and Disassembly Planning by using Fuzzy Logic & Genetic Algorithms

The authors propose the implementation of hybrid Fuzzy Logic-Genetic Algorithm (FL-GA) methodology to plan the automatic assembly and disassembly sequence of products. The GA-Fuzzy Logic approach is implemented onto two levels. The first level of hybridization consists of the development of a Fuzzy controller for the parameters of an assembly or disassembly planner based on GAs. This controller acts on mutation probability and crossover rate in order to adapt their values dynamically while the algorithm runs. The second level consists of the identification of theoptimal assembly or disassembly sequence by a Fuzzy function, in order to obtain a closer control of the technological knowledge of the assembly/disassembly process. Two case studies were analyzed in order to test the efficiency of the Fuzzy-GA methodologies

An application of hybrid life cycle assessment as a decision support framework for green supply chains

In an effort to achieve sustainable operations, green supply chain management has become an important area for firms to concentrate on due to its inherent involvement with all the processes that provide foundations to successful business. Modelling methodologies of product supply chain environmental assessment are usually guided by the principles of life cycle assessment (LCA). However, a review of the extant literature suggests that LCA techniques suffer from a wide range of limitations that prevent a wider application in real-world contexts; hence, they need to be incorporated within decision support frameworks to aid environmental sustainability strategies. Thus, this paper contributes in understanding and overcoming the dichotomy between LCA model development and the emerging practical implementation to inform carbon emissions mitigation strategies within supply chains. Therefore, the paper provides both theoretical insights and a practical application to inform the process of adopting a decision support framework based on a LCA methodology in a real-world scenario. The supply chain of a product from the steel industry is considered to evaluate its environmental impact and carbon ‘hotspots’. The study helps understanding how operational strategies geared towards environmental sustainability can be informed using knowledge and information generated from supply chain environmental assessments, and for highlighting inherent challenges in this process

From Science to Design: the Design4Materials virtuous cycle

Despite the large number of innovative materials developed in laboratories worldwide, their application in new mass-produced products is complicated. Design can reduce the risk that the research developed in scientific laboratories could fail to be properly exploited and triggering a beneficial cycle linking Science to Design. This paper present the Design4Materials, an italian network founded by the laboratories of leading schools of design: MaterialdesignLab|Sapienza Rome, Madec|Politecnico di Milano, HybridesignLab|SUN Naples, Soft Surfaces and Polisensoriality|Poliba Bari. After presenting the different skills of the network members, the authors describe the capabilities and the goals of the network and the main results developed like the project that define characteristics and identities for an open material, starting from a research of the IIT of Genoa. The Design4Materials aim is to play a leading role on design-driven innovation process, responding to society’s changing needs and developing a ‘circular’ methodology of innovation from a design standpoint