Probabilistic reasoning for repeatability detection from urban facade image

Probabilistic conflicts in a search algorithm for estimating posterior probabilities in Bayesian networks

System reliability-based seismic collapse assessment of steel moment frames using incremental dynamic analysis and Bayesian probability network

Purpose This study aims to develop an assessment methodology using a Bayesian network (BN) to predict the failure probability of oil tanker shipping firms. Design/methodology/approach This paper proposes a bankruptcy prediction model by applying the hybrid of logistic regression and Bayesian probabilistic networks. Findings The proposed model shows its potential of contributing to a powerful tool to predict financial bankruptcy of shipping operators, and provides important insights to the maritime community as to what performance measures should be taken to ensure the shipping companies’ financial soundness under dynamic environments. Research limitations/implications The model and its associated variables can be expanded to include more factors for an in-depth analysis in future when the detailed information at firm level becomes available. Practical implications The results of this study can be implemented to oil tanker shipping firms as a prediction tool for bankruptcy rate. Originality/value Incorporating quantitative statistical measurement, the application of BN in financial risk management provides advantages to develop a powerful early warning system in shipping, which has unique characteristics such as capital intensive and mobile assets, possibly leading to catastrophic consequences.

Probabilistic Reasoning and Bayesian Belief Networks.

The increasing digitalization of our society has brought about numerous benefits, enabling seamless communication, convenient transactions, and efficient operations. However, with this growing reliance on interconnected systems and information technology, the risk of cyber-attacks has also surged. Cyber threats, such as data breaches, ransomware, and sophisticated malware, have become more prevalent, threatening the confidentiality, integrity, and availability of critical data and services. Organizations across industries face the daunting challenge of defending against a wide array of cyber-attacks that continue to evolve in complexity and stealth. In response to this ever-changing cyber threat landscape, Cyber Security Risk Management (CSRM) and attack detection have become critical components of any comprehensive cybersecurity strategy. The ability to identify and mitigate cyber risks and swiftly detect malicious activities is paramount for safeguarding sensitive information, preserving business continuity, and maintaining the trust of customers and stakeholders. A novel approach to Cyber Security Risk Management through an Attack Detection Model that utilizes Semi-Supervised Learning Auto-Encoders in conjunction with Probabilistic Bayesian Networks. The study compares the performance of Multi Connect Variational Auto-Encoder (MC-VAE), Probabilistic Bayesian Networks (PBN), and a combined model of MC-VAE and PBN. The study employs the NUSW-NB15_GT dataset for training and evaluation purposes. Notably, the Semi-Supervised Learning with Probabilistic Bayesian Networks (SSL-PBN) model demonstrates exceptional results, achieving a precision rate of 94% and a recall rate of 90%. The F1 score of 0.9191 highlights the SSL-PBN model's efficacy in achieving a balanced trade-off between precision and recall, critical for minimizing false positives and false negatives...

Gibbs samplings is a Markov Chain Monte Carlo technique for estimating conditional probabilities in Bayesian networks. A major problem of Gibbs sampling is the dependency of the generated chain of samples. Thus the estimates are biased unless the initial value of the chain is drawn from the target distribution. One elegant method to overcome the initial bias is regenerative samplings. We reported elsewhere the “stationary minorization condition” that makes any Markov Chain Monte Carlo technique regenerative. In this paper, we show how this condition can be easily met in the simulations of any Bayesian network.

Technologies that enable physical objects to communicate with each other are attracting a lot of interest day by day. Internet of Things (IoT) is a communication network that is used to connect with technological devices and systems. The growth of IoT networks has led to malicious targeting of these systems by third parties. To prevent such malicious purposes, network security should be at the forefront. Recently, artificial intelligence‐based approaches have been used in security domains. Two accessible datasets were used in this study. The datasets consist of simulated bot IoT network traffic data containing distributed denial‐of‐service (DDoS) attack types. In the proposed approach, probabilistic Bayesian neural networks and normal Bayesian neural networks are used as the deep learning models. All features of the dataset were trained using two models. Then, the datasets were optimized using hunger games search (HGS) method and the most efficient parameters of the datasets were selected and retrained using deep learning models. In the analysis performed using the proposed approach, an overall accuracy of 100% was obtained for the dataset used in the first experiment and 99.99% for the dataset used in the second experiment. It was also observed that metaheuristic optimization helps the proposed approach to save time in detecting DDoS attacks.

Given a fixed budget and an arbitrary cost for selecting each node, the budgeted influence maximization (BIM) problem concerns selecting a set of seed nodes to disseminate some information that maximizes the total number of nodes influenced (termed as influence spread) in social networks at a total cost no more than the budget. Our proposed seed selection algorithm for the BIM problem guarantees an approximation ratio of (1-1/√e). The seed selection algorithm needs to calculate the influence spread of candidate seed sets, which is known to be #P-complex. Identifying the linkage between the computation of marginal probabilities in Bayesian networks and the influence spread, we devise efficient heuristic algorithms for the latter problem. Experiments using both large-scale social networks and synthetically generated networks demonstrate superior performance of the proposed algorithm with moderate computation costs. Moreover, synthetic datasets allow us to vary the network parameters and gain important insights on the impact of graph structures on the performance of different algorithms.

Automatic detection of the lesion/tumor region is always of interest in medical imaging system. We address this issue in this paper for improving the accuracy and robustness as compared to the conventional methods. Active contour has been commonly used for the detection of irregular shape of region, however, it suffers the problem of the false attraction given the noisy image, and requires the correct estimation of the initial location of the object to be detected. In this paper, we present a novel method for robustly locating the object area by using a so-called intelligent mesh. With the accurate location and shape approximation in the initial stage, the object of interest is correctly detected by using a mesh-based active contour model. The correctness and robustness of the proposed algorithm are demonstrated on extracting the lesion/tumor regions of CT and Mammography images as an intensive test.

BackgroundSustainable energy transition of a country is complex and long-term process, which requires decision-making in all stages and at all levels, including a large number of different factors, with different causality. The main objective of this paper is the development of a probabilistic model for decision-making in sustainable energy transition in developing countries of SE Europe. The model will be developed according to the specificities of the countries for which it is intended—SE Europe. These are countries where energy transition is slower and more difficult due to many factors: high degree of uncertainty, low transparency, corruption, investment problems, insufficiently reliable data, lower level of economic development, high level of corruption and untrained human resources. All these factors are making decision-making more challenging and demanding.MethodsResearch was done by using content analysis, artificial intelligence methods, software development method and testing. The model was developed by using MSBNx—Microsoft Research’s Bayesian Network Authoring and Evaluation Tool.ResultsDue to the large number of insufficiently clear, but interdependent factors, the model is developed on the principle of probabilistic (Bayesian) networks of factors of interest. The paper presents the first model for supporting decision-making in the field of energy sustainability for the region of Southeastern Europe, which is based on the application of Bayesian Networks.ConclusionTesting of the developed model showed certain characteristics, discussed in paper. The application of developed model will make it possible to predict the short-term and long-term consequences that may occur during energy transition by varying these factors. Recommendations are given for further development of the model, based on Bayesian networks.

This text is a reprint of the seminal 1989 book Probabilistic Reasoning in Expert systems: Theory and Algorithms, which helped serve to create the field we now call Bayesian networks. It introduces the properties of Bayesian networks (called causal networks in the text), discusses algorithms for doing inference in Bayesian networks, covers abductive inference, and provides an introduction to decision analysis. Furthermore, it compares rule-base experts systems to ones based on Bayesian networks, and it introduces the frequentist and Bayesian approaches to probability. Finally, it provides a critique of the maximum entropy formalism. Probabilistic Reasoning in Expert Systems was written from the perspective of a mathematician with the emphasis being on the development of theorems and algorithms. Every effort was made to make the material accessible. There are ample examples throughout the text. This text is important reading for anyone interested in both the fundamentals of Bayesian networks and in the history of how they came to be. It also provides an insightful comparison of the two most prominent approaches to probability.

We present here an approach to battlefield situation assessment based on a level 2 fusion processing of incoming information via probabilistic Bayesian Belief Network technology. A belief network (BN) can be thought of as a graphical program script representing causal relationships among various battlefield concepts represented as nodes to which observed significant events are posted as evidence. In our approach, each BN can be constructed in real-time from a library of smaller component-like BNs to assess a specific high-level situation or address mission-specific high-level intelligence requirements. Furthermore, by distributing components of a BN across a set of networked computers, we enhance inferencing efficiency and allow computation at various levels of abstraction suitable for military hierarchical organizations. We demonstrate them effectiveness of our approach by modeling the situation assessment tasks in the context of a battlefield scenario and implementing on our in-house software engine BNet 2000.

Most real world problems involve some kind of uncertainty, like randomness, vagueness or ignorance. In the computational intelligence area there are a number of techniques capable of dealing with each kind of uncertainty. However, it is possible that a problem involves more than one kind of uncertainty at the same time, and in this case, hybrid solutions must be sought. Bayesian networks are probabilistic graphical models capable of modeling statistical uncertainty and are widely applied in many practical problems. Despite bayesian networks being able to deal with randomness, they are unable to model linguistic vagueness. Fuzzy systems, on the other hand, are a well known technique capable of dealing with linguistic vagueness by representing knowledge with simple and interpretable rules and membership functions. As classical fuzzy systems are unable to model statistical uncertainty, Probabilistic Fuzzy Systems were developed in order to account for both kind of uncertainties. In this work we propose the Probabilistic Fuzzy Bayesian Network as a combination of both probabilistic fuzzy systems and bayesian networks, also capable of simultaneously modeling both kinds of uncertainty using the concepts of bayesian inference and probabilistic fuzzy systems. The proposed model is firstly applied in a very simple classification problem in order to show its potential and advantage over traditional naive bayes classifiers. For validation of the proposed model, experiments are done using benchmark classification data sets from the UCI machine learning repository and the results are then compared with other machine learning techniques.

The blast furnace is the principal method of producing cast iron. In the production of cast iron, the control of silicon is vital because this impurity is harmful to almost all steels. Artificial neural networks with Bayesian regularization are more robust than traditional back-propagation networks and can reduce or eliminate the need for tedious cross-validation. Bayesian regularization is a mathematical process that converts a nonlinear regression into a "well-posed" statistical problem in the manner of ridge regression. The main objective of this work was to develop an artificial neural network to predict silicon content in hot metal by varying the number of neurons in the hidden layer by 10, 20, 25, 30, 40, 50, 75, and 100 neurons. The results show that all neural networks converged and presented reliable results, neural networks with 20, 25, and 30 neurons showed the best overall results. However, In short, Bayesian neural networks can be used in practice because the actual values correlate excellently with the values calculated by the neural network.

Legal cases involve reasoning with evidence and with the development of a software support tool in mind, a formal foundation for evidential reasoning is required. Three approaches to evidential reasoning have been prominent in the literature: argumentation, narrative and probabilistic reasoning. In this paper a combination of the latter two is proposed.In recent research on Bayesian networks applied to legal cases, a number of legal idioms have been developed as recurring structures in legal Bayesian networks. A Bayesian network quantifies how various variables in a case interact. In the narrative approach, scenarios provide a context for the evidence in a case. A method that integrates the quantitative, numerical techniques of Bayesian networks with the qualitative, holistic approach of scenarios is lacking.In this paper, a method is proposed for modeling several scenarios in a single Bayesian network. The method is tested by doing a case study. Two new idioms are introduced: the scenario idiom and the merged scenarios idiom. The resulting network is meant to assist a judge or jury, helping to maintain a good overview of the interactions between relevant variables in a case and preventing tunnel vision by comparing various scenarios.