30,814 research outputs found
A two-step fusion process for multi-criteria decision applied to natural hazards in mountains
Mountain river torrents and snow avalanches generate human and material
damages with dramatic consequences. Knowledge about natural phenomenona is
often lacking and expertise is required for decision and risk management
purposes using multi-disciplinary quantitative or qualitative approaches.
Expertise is considered as a decision process based on imperfect information
coming from more or less reliable and conflicting sources. A methodology mixing
the Analytic Hierarchy Process (AHP), a multi-criteria aid-decision method, and
information fusion using Belief Function Theory is described. Fuzzy Sets and
Possibilities theories allow to transform quantitative and qualitative criteria
into a common frame of discernment for decision in Dempster-Shafer Theory (DST
) and Dezert-Smarandache Theory (DSmT) contexts. Main issues consist in basic
belief assignments elicitation, conflict identification and management, fusion
rule choices, results validation but also in specific needs to make a
difference between importance and reliability and uncertainty in the fusion
process
Fuzzy Interval-Valued Multi Criteria Based Decision Making for Ranking Features in Multi-Modal 3D Face Recognition
Soodamani Ramalingam, 'Fuzzy interval-valued multi criteria based decision making for ranking features in multi-modal 3D face recognition', Fuzzy Sets and Systems, In Press version available online 13 June 2017. This is an Open Access paper, made available under the Creative Commons license CC BY 4.0 https://creativecommons.org/licenses/by/4.0/This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking and deciding on the best choice of multi-modal features at the decision stage. It provides a formal mechanism of benchmarking their performances against a set of criteria. The technique demonstrates its ability in scaling up the multi-modal features.Peer reviewedProo
Data fusion strategy for precise vehicle location for intelligent self-aware maintenance systems
Abstract— Nowadays careful measurement applications are
handed over to Wired and Wireless Sensor Network. Taking
the scenario of train location as an example, this would lead to
an increase in uncertainty about position related to sensors
with long acquisition times like Balises, RFID and
Transponders along the track. We take into account the data
without any synchronization protocols, for increase the
accuracy and reduce the uncertainty after the data fusion
algorithms. The case studies, we have analysed, derived from
the needs of the project partners: train localization, head of an
auger in the drilling sector localization and the location of
containers of radioactive material waste in a reprocessing
nuclear plant. They have the necessity to plan the maintenance
operations of their infrastructure basing through architecture
that taking input from the sensors, which are localization and
diagnosis, maps and cost, to optimize the cost effectiveness and
reduce the time of operation
Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks
Information fusion is an essential part of numerous engineering systems and
biological functions, e.g., human cognition. Fusion occurs at many levels,
ranging from the low-level combination of signals to the high-level aggregation
of heterogeneous decision-making processes. While the last decade has witnessed
an explosion of research in deep learning, fusion in neural networks has not
observed the same revolution. Specifically, most neural fusion approaches are
ad hoc, are not understood, are distributed versus localized, and/or
explainability is low (if present at all). Herein, we prove that the fuzzy
Choquet integral (ChI), a powerful nonlinear aggregation function, can be
represented as a multi-layer network, referred to hereafter as ChIMP. We also
put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient
descent-based optimization in light of the exponential number of ChI inequality
constraints. An additional benefit of ChIMP/iChIMP is that it enables
eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP
is applied to the fusion of a set of heterogeneous architecture deep models in
remote sensing. We show an improvement in model accuracy and our previously
established XAI indices shed light on the quality of our data, model, and its
decisions.Comment: IEEE Transactions on Fuzzy System
Using the Pattern-of-Life in Networks to Improve the Effectiveness of Intrusion Detection Systems
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.As the complexity of cyber-attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of Intrusion Detection Systems (IDSs) should be able to adapt their detection characteristics based not only on the measureable network traffic, but also on the available high- level information related to the protected network to improve their detection results. We make use of the Pattern-of-Life (PoL) of a network as the main source of high-level information, which is correlated with the time of the day and the usage of the network resources. We propose the use of a Fuzzy Cognitive Map (FCM) to incorporate the PoL into the detection process. The main aim of this work is to evidence the improved the detection performance of an IDS using an FCM to leverage on network related contextual information. The results that we present verify that the proposed method improves the effectiveness of our IDS by reducing the total number of false alarms; providing an improvement of 9.68% when all the considered metrics are combined and a peak improvement of up to 35.64%, depending on particular metric combination
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