Towards a shared method to classify contaminated territories in the case of an accidental nuclear event: the PRIME project

The analysis of the management of the accidentally radioactive contaminated areas such as those around Chernobyl nuclear power plant highlights the fact that the current spatial classification methods hardly help in recovering proper use of the contaminated territory. The cause is mainly to be searched for in the traditional construction of risks assessment methods; these methods rest on criteria defined by institutional experts, which are not applicable in practise because they are not shared by all the stakeholders involved in the management of the contaminated territories. Opposite such top-down tentative management, local efforts supported by Non-Governmental Organizations to restore life in the contaminated area seem to be more fruitful but very time and resources consuming and limited to the specific areas where they are experimented. The aim of the PRIME project, in progress at the French Institute for Radioprotection and Nuclear Safety, is to mix the advantages of both approaches in building a multicriteria decision tool based on the territorial specificities. The criteria of the method are chosen and weighted with representatives of the territory’s stakeholders (decision makers, local actors and experts) to warrant that all the points of view are taken into account and to enable the risk managers to choose the appropriate strategy in case of an accident involving radioactive substances. The area chosen for the pilot study is a 50 km radius territory around the nuclear sites of Tricastin-Pierrelatte in the lower valley of Rhône (France). One of the exploration questions of the PRIME project is whether a multicriteria method may be an appropriate tool to treat the data and make them visible and accessible for all the stakeholders

Spare parts classification in industrial manufacturing using the dominance-based rough set approach

Classification is one of the critical issues in the operations management of spare parts. The issue of managing spare parts involves multiple criteria to be taken into consideration, and therefore, a number of approaches exists that consider criteria such as criticality, price, demand, lead time, and obsolescence, to name a few. In this paper, we first review proposals to deal with inventory control. We then propose a three-phase multicriteria classification framework for spare parts management using the dominance-based rough set approach (DRSA). In the first phase, a set of ‘if–then’ decision rules is generated from historical data using the DRSA. The generated rules are then validated in the second phase by using both the automated and manual approaches, including cross-validation and feedback assessments by the decision maker. The third and final phase is to classify an unseen set of spare parts in a real setting. The proposed approach has been successfully applied to data collected from a manufacturing company in China. The proposed framework was practically tested on different spare parts and, based on the feedback received from the industry experts, 96% of the spare parts were correctly classified. Furthermore, the cross-validation results show that the proposed approach significantly outperforms other well-known classification methods. The proposed approach has several important characteristics that distinguish it from existing ones: (i) it is a learning-set based analysis approach; (ii) it uses a powerful multicriteria classification method, namely the DRSA; (iii) it validates the generated decision rules with multiple strategies; and (iv) it actively involves the decision maker during all the steps of the decision making process

Qualitative information-based heuristic for districting problems

Districting problems are of high importance in many real-world applications. When multiple criteria are considered, enumerating all the efficient solutions in these problems is known as being Non-Polynomial (NP)-hard. The objective of this paper is thus to introduce a heuristic to solve this type of problem. The proposed heuristic relies on a tree data structure, previously constructed based on a qualitative evaluation of the study area. This evaluation is grounded on several criteria and takes the form of a qualitative scale with a finite set of evaluation levels. The paper introduces the qualitative assessment approach, the mathematical formulation and the resolution heuristic

Decision Making by Applying Machine Learning Techniques to Mitigate Spam SMS Attacks

Due to exponential developments in communication networks and computer technologies, spammers have more options and tools to deliver their spam SMS attacks. This makes spam mitigation seen as one of the most active research areas in recent years. Spams also affect people’s privacy and cause revenue loss. Thus, tools for making accurate decisions about whether spam or not are needed. In this paper, a spam mitigation model is proposed to find spam from non-spam and the different processes used to mitigate spam SMS attacks. Also, anti-spam measures are applied to classify spam with the aim to have high classification accuracy performance using different classification methods. This paper seeks to apply the most appropriate machine learning (ML) techniques using decision-making paradigms to produce a ML model for mitigating spam attacks. The proposed model combines ML techniques and the Delphi method along with Agile to formulate the solution model. Also, three ML classifiers were used to cluster the dataset, which are Naive Bayes, Random Forests, and Support Vector Machine. These ML techniques are renowned as easy to apply, efficient and more accurate in comparison with other classifiers. The findings indicated that the number of clusters combined with the number of attributes has revealed a significant influence on the classification accuracy performance

Postural and event behaviours which differed in frequency within a single day in 21 working donkeys.

<p>Ear orientation: FF/SS  =  one ear forwards, one sideways; SS/BB  =  one ear sideways, one backwards; SD/SD  =  both ears sideways and facing down.</p

Suitability maps based on the LSP method

In this paper we propose the concept of logically aggregated geographic suitability maps (S-maps). The goal of S-maps is to provide specialized maps of the suitability degree of a selected geographic region for a specific purpose. There is a wide spectrum of purposes which include suitability for industrial development, agriculture, housing, education, recreation, etc. Our goals are to specify main concepts of S-maps development, and to identify some of the potential application areas. Our approach is based on soft computing with partial truth and graded logic functions within the framework of the LSP method