دانلود و نمایش مقالات مرتبط با Bayesian network::صفحه 4
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نتیجه جستجو - Bayesian network

تعداد مقالات یافته شده: 39
ردیف عنوان نوع
31 A parallel algorithm for Bayesian network structure learning from large data sets
الگوریتم موازی برای یادگیری ساختار شبکه بیزی از مجموعه داده های بزرگ-2017
This paper considers a parallel algorithm for Bayesian network structure learning from large data sets. The parallel algorithm is a variant of the well known PC algorithm. The PC algorithm is a constraint-based al gorithm consisting of five steps where the first step is to perform a set of (conditional) independence tests while the remaining four steps relate to identifying the structure of the Bayesian network using the results of the (conditional) independence tests. In this paper, we describe a new approach to paral lelization of the (conditional) independence testing as experiments illustrate that this is by far the most time consuming step. The proposed parallel PC algorithm is evaluated on data sets generated at ran dom from five different real-world Bayesian networks. The algorithm is also compared empirically with a process-based approach where each process manages a subset of the data over all the variables on the Bayesian network. The results demonstrate that significant time performance improvements are possible using both approaches.
Keywords: Bayesian network | PC algorithm | Parallelization
مقاله انگلیسی
32 Sample-Based Attribute Selective AnDE for Large Data
ande ویژگی انتخابی مبتنی بر نمونه برای داده های بزرگ-2017
More and more applications have come with large data sets in the past decade. However, existing algorithms cannot guarantee to scale well on large data. Averaged n-Dependence Estimators (AnDE) allows for flexible learning from out-of-core data, by varying the value of n (number of super parents). Hence, AnDE is especially appropriate for large data learning. In this paper, we propose a sample-based attribute selection technique for AnDE. It needs one more pass through the training data, in which a multitude of approximate AnDE models are built and efficiently assessed by leave-one-out cross validation. The use of a sample reduces the training time. Experiments on 15 large data sets demonstrate that the proposed technique significantly reduces AnDE’s error at the cost of a modest increase in training time. This efficient and scalable out-of-core approach delivers superior or comparable performance to typical in-core Bayesian network classifiers.
Index Terms: Bayesian network classifiers | large data |classification learning | attribute selection | averaged n-dependence estimators (AnDE) |leave-one-out cross validation
مقاله انگلیسی
33 A new reasoning and learning model for Cognitive Wireless Sensor Networks based on Bayesian networks and learning automata cooperation
یک مدل استدلال و یادگیری جدید برای شبکه های حسگر بی سیم شناختی مبتنی بر شبکه های بیزی و همکاری اتوماسیون یادگیری-2017
Adding cognition to existing Wireless Sensor Networks (WSNs) with a cognitive networking approach, which deals with using cognition to the entire network protocol stack to achieve end-to-end goals, brings about many benefits. However cognitive networking may be confused with cognitive radio or cross-layer design, it is a different concept; cognitive radios applies cognition only at the physical layer to overcome the problem of spectrum scarcity, and cross layer design usually focuses on linking at least two non consecutive specific layers, to achieve a particular goal. Indeed, it can be said that the cognitive radio and the cross layer design are two effective methods in cognitive networking. To the best of our knowledge, almost all of the existing researches on the Cognitive Wireless Sensor Networks (CWSNs) have focused on spectrum allocation and interference reduction in the physical layer. In this paper, we propose a new reasoning and learning model for CWSNs, in which firstly, a team of learning automata is employed to construct a Bayesian Network (BN) model of the parameters of the network protocol stack, and then the constructed BN is used to tune the controllable parameters. The BN represents the dependency relation ships between the parameters of the network protocol stack, and the BN-based reasoning is an efficient tool for cross-layer optimization, in order to maximize the perceived network performance. Simulations have been done to evaluate the performance of the proposed model. The results of the simulations show that the proposed model successively adds cognition to a WSN and improves the performance of the communication network.
Keywords: Bayesian Networks | Cognitive networks | Learning automata | Reasoning | Wireless Sensor Network
مقاله انگلیسی
34 درباره استفاده از سیستم های چندعاملی برای نظارت بر سیستم های صنعتی
سال انتشار: 2016 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 17
هدف مقاله حاضر، ارائه سیستم هوشمند برای نظارت بر فرایند پیچیده، مبتنی بر تکنولوژی هوش مصنوعی است. هدف این سیستم، تحقق موفق وظایف نظارت بر فرایند پیچیده ای است که عبارتند از: آشکارسازی، تشخیص، شناسایی و پیکر بندی مجدد. بدین منظور، توسعه سیستم چندعاملی که چندین هوش را ترکیب کند همچون: نمودارهای کنترل چند متغیره، شبکه های عصبی، شبکه های بیزی و سیستم های خبره، یک ضرورت بوده است. سیستم پیشنهادی از نظر نظارت بر فرایند پیچیده Tennessee Eastman ارزیابی می شود.
واژه های کلیدی: فرایند چندمتغیری | نمودار کنترل هتلینگ T2 | سیستم چندعاملی | شبکه بیزی | شبکه عصبی.
مقاله ترجمه شده
35 کاربرد سیستم خبره در تشخیص پزشکی با استفاده از عامل های نرم افزاری
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 31
به منظور تسهیل تبادل اطلاعات در فرآیند تشخیص پزشکی، یک چهارچوب عوامل نرم افزاری ِ اشتراکی ارائه شده است. دستگاه های بدن انسان (مانند دستگاه تنفس، دستگاه قلبی عروقی) در داخل عوامل نرم افزاری متفاوتی قرار گرفته اند. دیدگاه کلی توسط عوامل متصل تبادل کنندۀ اطلاعات ارائه شده است. هدف این چهارچوب، ایجاد امکان تبادل خودکار اطلاعات میان متخصصان پزشکی ِ مختلف است. عامل اصلی تبادل، تشریک مفاهیم میان حوزه های تخصص است. هر متخصص دستگاههای بدن انسان فراتر از مفاهیم خود (مدارک، دلایل، تاثیرات) عمل میکند، با اینحال اطلاعات حاصل از دستگاههای دیگر نیز منطبق خواهد شد. چهارچوب پیشنهادی دارای سه جزء اصلی است: مدیریت دانش، استدلال عدم اطمینان، و عوامل نرم افزاری. برای بررسی دانش مدیریت دستگاههای بدن انسان، از آنتولوژی (نهاد شناسی) استفاده می شود. شبکۀ Bayesian، مدل گرافیکی ارائۀ دانش احتمالات و استدلال دربارۀ عقاید نسبی در شرایط عدم اطمینان می باشد. عوامل نرم افزاری، بعنوان شبکۀ اشتراک، مسئول ترویج عقاید در نمونه های سیستم هستند.
کلمات کلیدی: عوامل نرم افزاری | شبکۀ بیزین | ساختارشناسی
مقاله ترجمه شده
36 Development of a cyber security risk model using Bayesian networks
توسعه یک مدل ریسک امنیت سایبری با استفاده از شبکه های بیزی-2015
Cyber security is an emerging safety issue in the nuclear industry, especially in the instrumentation and control (I&C) field. To address the cyber security issue systematically, a model that can be used for cyber security evaluation is required. In this work, a cyber security risk model based on a Bayesian network is suggested for evaluating cyber security for nuclear facilities in an integrated manner. The suggested model enables the evaluation of both the procedural and technical aspects of cyber security, which are related to compliance with regulatory guides and system architectures, respectively. The activity-quality analysis model was developed to evaluate how well people and/or organizations comply with the regulatory guidance associated with cyber security. The architecture analysis model was created to evaluate vulnerabilities and mitigation measures with respect to their effect on cyber security. The two models are integrated into a single model, which is called the cyber security risk model, so that cyber security can be evaluated from procedural and technical viewpoints at the same time. The model was applied to evaluate the cyber security risk of the reactor protection system (RPS) of a research reactor and to demonstrate its usefulness and feasibility. Keywords: Cyber security Activity-quality Architecture analysis Bayesian network Reactor protection system Research reactor
مقاله انگلیسی
37 Gaining insight into regional coastal changes on La Réunion island through a Bayesian data mining approach
به دست آوردن بینش به تغییرات ساحلی منطقه در جزیره لا رئونیون از طریق یک رویکرد داده کاوی بیزی-2015
Recent works have highlighted the interest in coastal geographical databases – collected for coastal management purposes – for obtaining insight into current shoreline changes. On La Réunion, a tropical volcanic high island located in the Southern Indian Ocean, a dataset is available which describes shoreline changes, the coastal geomorphology and the presence of anthropic structures. This database is first supplemented with information on the exposure of each coastal segment to energetic waves and to estuarine sediment inputs. To incorporate relative sea-level changes along the coast in the database, levelling data are analysed in combination with GPS, satellite altimetry and sea-level reconstructions. Finally, a method based on Bayesian networks is used to assess the probabilistic relationships between the variables in the database. The results highlight the high degree of dependency between variables: a retrospective model is able to reproduce 81% of the observations of shoreline mobility. Importantly, we report coastal ground motions for La Réunion island of the order of 1 to 2 mm/year along the coast. However, the resulting differing rates of relative sea-level rise do not significantly impact on shoreline changes. Instead, the results suggest a major control of geological processes and local coastal geomor- phic settings on shoreline evolution. While any exploration of a coastal database needs to be complemented with human reasoning to interpret the results in terms of physical processes, this study highlights the significance of revisiting other datasets to gain insight into coastal processes and factors causing shoreline changes, including sea-level changes.
Keywords: Shoreline changes | Bayesian networks | Sea-level rise | Vertical ground motions | Coastal databases | La Réunion island
مقاله انگلیسی
38 Study of Spanish mining accidents using data mining techniques
مطالعه کاوش تصادفات اسپانیایی با استفاده از تکنیک های داده کاوی-2015
Mining is an economic sector with a high number of accidents. Mines are hazardous places and workers can suffer a wide variety of injuries. Utilizing a database composed of almost 70,000 occupational accidents and fatality reports corresponding to the decade 2003–2012 in the Spanish mining sector, the paper analyzes the main causes of those accidents. To carry out the study, powerful statistical tools have been applied, such as Bayesian classifiers, decision trees or contingency tables, among other data mining techniques. Statistical analyses have been performed using Weka software and behavioral patterns based on certain rules have been obtained. From these rules, some conclusions are extracted which can help to develop suitable prevention policies to reduce injuries and fatalities.© 2015 Elsevier Ltd. All rights reserved.
Keywords: Mining accidents | Data mining | Bayesian network | Classification methods
مقاله انگلیسی
39 System of Systems and Big Data analytics – Bridging the gap
سیستمی از سیستم ها و تجزیه و تحلیل ترافیک داده های بزرگ - حذف فاصله-2014
Large data has been accumulating in all aspects of our lives for quite some time. Advances in sensor technology, the Internet, wireless communication, and inexpensive memory have all contributed to an explosion of ‘‘Big Data’’. System of Systems (SoS) integrate independently operating, non-homogeneous systems to achieve a higher goal than the sum of the parts. Today’s SoS are also contributing to the existence of unmanageable ‘‘Big Data’’. Recent efforts have developed a promising approach, called ‘‘Data Analytics’’, which uses statistical and computational intelligence (CI) tools such as principal component analysis (PCA), clustering, fuzzy logic, neuro-computing, evolutionary computation (such as genetic algorithms), Bayesian networks, etc. to reduce the size of ‘‘Big Data’’ to a manageable size and apply these tools to (a) extract information, (b) build a knowledge base using the derived data, and (c) eventually develop a non-parametric model for the ‘‘Big Data’’. This paper demonstrates how to construct a bridge between SoS and Data Analytics to develop reliable models for such systems. The subject material for this demonstration is using data analytics to generate a model to forecast produced photovoltaic energy to assist in the optimization of a micro grid SoS. Tools like fuzzy interference, neural networks, PCA, and genetic algorithms are used.
مقاله انگلیسی
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