Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin

Improving the Scalability of Reduct Determination in Rough Sets

Rough Set Data Analysis (RSDA) is a non-invasive data analysis approach that solely relies on the data to find patterns and decision rules. Despite its noninvasive approach and ability to generate human readable rules, classical RSDA has not been successfully used in commercial data mining and rule generating engines. The reason is its scalability. Classical RSDA slows down a great deal with the larger data sets and takes much longer times to generate the rules. This research is aimed to address the issue of scalability in rough sets by improving the performance of the attribute reduction step of the classical RSDA - which is the root cause of its slow performance. We propose to move the entire attribute reduction process into the database. We defined a new schema to store the initial data set. We then defined SOL queries on this new schema to find the attribute reducts correctly and faster than the traditional RSDA approach. We tested our technique on two typical data sets and compared our results with the traditional RSDA approach for attribute reduction. In the end we also highlighted some of the issues with our proposed approach which could lead to future research

Internet-based solutions to support distributed manufacturing

With the globalisation and constant changes in the marketplace, enterprises are adapting themselves to face new challenges. Therefore, strategic corporate alliances to share knowledge, expertise and resources represent an advantage in an increasing competitive world. This has led the integration of companies, customers, suppliers and partners using networked environments. This thesis presents three novel solutions in the tooling area, developed for Seco tools Ltd, UK. These approaches implement a proposed distributed computing architecture using Internet technologies to assist geographically dispersed tooling engineers in process planning tasks. The systems are summarised as follows. TTS is a Web-based system to support engineers and technical staff in the task of providing technical advice to clients. Seco sales engineers access the system from remote machining sites and submit/retrieve/update the required tooling data located in databases at the company headquarters. The communication platform used for this system provides an effective mechanism to share information nationwide. This system implements efficient methods, such as data relaxation techniques, confidence score and importance levels of attributes, to help the user in finding the closest solutions when specific requirements are not fully matched In the database. Cluster-F has been developed to assist engineers and clients in the assessment of cutting parameters for the tooling process. In this approach the Internet acts as a vehicle to transport the data between users and the database. Cluster-F is a KD approach that makes use of clustering and fuzzy set techniques. The novel proposal In this system is the implementation of fuzzy set concepts to obtain the proximity matrix that will lead the classification of the data. Then hierarchical clustering methods are applied on these data to link the closest objects. A general KD methodology applying rough set concepts Is proposed In this research. This covers aspects of data redundancy, Identification of relevant attributes, detection of data inconsistency, and generation of knowledge rules. R-sets, the third proposed solution, has been developed using this KD methodology. This system evaluates the variables of the tooling database to analyse known and unknown relationships in the data generated after the execution of technical trials. The aim is to discover cause-effect patterns from selected attributes contained In the database. A fourth system was also developed. It is called DBManager and was conceived to administrate the systems users accounts, sales engineers’ accounts and tool trial monitoring process of the data. This supports the implementation of the proposed distributed architecture and the maintenance of the users' accounts for the access restrictions to the system running under this architecture

Microarray-Based Cancer Prediction Using Soft Computing Approach

One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably

Can bank interaction during rating measurement of micro and very small enterprises ipso facto Determine the collapse of PD status?

This paper begins with an analysis of trends - over the period 2012-2018 - for total bank loans, non-performing loans, and the number of active, working enterprises. A review survey was done on national data from Italy with a comparison developed on a local subset from the Sardinia Region. Empirical evidence appears to support the hypothesis of the paper: can the rating class assigned by banks - using current IRB and A-IRB systems - to micro and very small enterprises, whose ability to replace financial resources using endogenous means is structurally impaired, ipso facto orient the results of performance in the same terms of PD assigned by the algorithm, thereby upending the principle of cause and effect? The thesis is developed through mathematical modeling that demonstrates the interaction of the measurement tool (the rating algorithm applied by banks) on the collapse of the loan status (default, performing, or some intermediate point) of the assessed micro-entity. Emphasis is given, in conclusion, to the phenomenon using evidence of the intrinsically mutualistic link of the two populations of banks and (micro) enterprises provided by a system of differential equation