Search results for "Bayesian network"

The Algorithm of a Game-Based System in the Relation between an Operator and a Technical Object in Management of E-Commerce Logistics Processes with …

2021

Machine learning (ML) is applied in various logistic processes utilizing innovative techniques (e.g., the use of drones for automated delivery in e-commerce). Early challenges showed the insufficient drones’ steering capacity and cognitive gap related to the lack of theoretical foundation for controlling algorithms. The aim of this paper is to present a game-based algorithm of controlling behaviours in the relation between an operator (OP) and a technical object (TO), based on the assumption that the game is logistics-oriented and the algorithm is to support ML applied in e-commerce optimization management. Algebraic methods, including matrices, Lagrange functions, systems of differential e…

Self-Perceived Health, Objective Health, and Quality of Life among People Aged 50 and Over: Interrelationship among Health Indicators in Italy, Spain…

2020

It is well known that self-perceived health (SPH), even if it is a subjective health indicator, is significantly associated with objective health and quality of life (QoL) in the general population. Whether it can be considered an indicator of cognitive functioning and quality of life in the elderly is still an open issue. This study used a data-driven approach to investigate the interrelationship among SPH, non-communicable diseases (NCDs), QoL, and cognitive functioning to answer this question. The study sample included information about 12,831 people living in Italy, Spain, and Greece, extracted from the Survey on Health, Aging, and Retirement in Europe, in the year 2015. The additive Ba…

Integrating the human factor in FMECA-based risk evaluation through Bayesian networks

2020

The contribution of the present paper aims to develop the traditional Failure Modes, Effects and Criticality Analysis (FMECA) for quantitative risk analysis from a Bayesian Network (BN)-based perspective. The main purpose of research consists in providing a framework for analysing causal relationships for risk evaluation and deriving probabilistic inference among significant risk factors. These parameters will be represented by linguistic variables and will include the human factor as a key element of analysis. Traditional approaches for risk evaluation and management performed by FMECA [1] represent helpful tools to globally enhance systems and processes conditions [2]. However, such appro…

Hub-Centered Gene Network Reconstruction Using Automatic Relevance Determination

2012

Network inference deals with the reconstruction of biological networks from experimental data. A variety of different reverse engineering techniques are available; they differ in the underlying assumptions and mathematical models used. One common problem for all approaches stems from the complexity of the task, due to the combinatorial explosion of different network topologies for increasing network size. To handle this problem, constraints are frequently used, for example on the node degree, number of edges, or constraints on regulation functions between network components. We propose to exploit topological considerations in the inference of gene regulatory networks. Such systems are often…

Hidden connections: Network effects on editorial decisions in four computer science journals

2018

Abstract This paper aims to examine the influence of authors’ reputation on editorial bias in scholarly journals. By looking at eight years of editorial decisions in four computer science journals, including 7179 observations on 2913 submissions, we reconstructed author/referee-submission networks. For each submission, we looked at reviewer scores and estimated the reputation of submission authors by means of their network degree. By training a Bayesian network, we estimated the potential effect of scientist reputation on editorial decisions. Results showed that more reputed authors were less likely to be rejected by editors when they submitted papers receiving negative reviews. Although th…

Predictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in Manufacturing

2018

International audience; Bayesian networks (BNs) represent a promising approach for the aggregation of multiple uncertainty sources in manufacturing networks and other engineering systems for the purposes of uncertainty quantification, risk analysis, and quality control. A standardized representation for BN models will aid in their communication and exchange across the web. This article presents an extension to the predictive model markup language (PMML) standard for the representation of a BN, which may consist of discrete variables, continuous variables, or their combination. The PMML standard is based on extensible markup language (XML) and used for the representation of analytical models…

An adaptive probabilistic graphical model for representing skills in PbD settings

2010

Bayesian System for Differential Cryptanalysis of DES

2014

AbstractThis paper proposes a new formalization for the differential cryptanalysis of DES (Data Encryption Standard) based on Bayesian Networks (BN), an artificial intelligence framework used for reasoning on data affected by uncertainty. Through the proposed approach it is possible to analyze DES from a novel point of view, thus paving the way for the development of a new class of cryptanalysis methods.

An adaptive probabilistic approach to goal-level imitation learning

2010

Imitation learning has been recognized as a promising technique to teach robots advanced skills. It is based on the idea that robots could learn new behaviors by observing and imitating the behaviors of other skilled actors. We propose an adaptive probabilistic graphical model which copes with three core issues of any imitative behavior: observation, representation and reproduction of skills. Our model, Growing Hierarchical Dynamic Bayesian Network (GHDBN), is hierarchical (i.e. able to characterize structured behaviors at different levels of abstraction), and growing (i.e. skills are learned or updated incrementally - and at each level of abstraction - every time a new observation sequence…

Classification and retrieval on macroinvertebrate image databases

2011

Aquatic ecosystems are continuously threatened by a growing number of human induced changes. Macroinvertebrate biomonitoring is particularly efficient in pinpointing the cause-effect structure between slow and subtle changes and their detrimental consequences in aquatic ecosystems. The greatest obstacle to implementing efficient biomonitoring is currently the cost-intensive human expert taxonomic identification of samples. While there is evidence that automated recognition techniques can match human taxa identification accuracy at greatly reduced costs, so far the development of automated identification techniques for aquatic organisms has been minimal. In this paper, we focus on advancing …