The Use of Artificial Neural Networks to Assess the Capacity of Transport Measures

 

In the area of logistics management both managers and engineers rely primarily on proven computational
algorithms, for this reason, it is often difficult to convince them to the use of artificial neural networks in solving decision problems. The paper presents the possibilities of using the FANN library in building of a computer application applied in the area of logistics. The possibilities of the component are presented on the example of applications of artificial neural networks to estimate the capacity of transport vehicles based on their dimensions. The example presented in the work was solved with the use of a multi-network Layered Perceptron. The example depicted not only the possibility of using artificial neural networks for solving poorly structured tasks but also practical application of the TFannNetwork component.
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Fast Artificial Neural Network library (FANN) is an open-source project that implements a
multi-layer one-way neural network networks with support for both full and weakly
connected networks [14]. FANN is easy to use, comprehensive, well documented and fast in
acting [15], [16]. There are links to over 15 programming languages, including Delphi 7
program.
TFannNetwork is a component of Delphi (created by Pereira Maia) that connects the
application with FANN library. Undoubtedly it is not necessary to install TFannNetwork to
use FANN in Delphi but the component makes the library more friendly for Delphi
environment [14].

The Use of Artificial Neural Networks to Assess the Capacity of Transport Measures

Artur Duchaczek / Dariusz Skorupka

General Tadeusz Kościuszko Military Academy of Land Forces Faculty of Management

SSP - JOURNAL OF CIVIL ENGINEERING Vol. 10, Issue 1, 2015

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Staged Tuning: A Hybrid (Compile/Install-time) Technique for Improving Utilization of Performance-asymmetric Multicores

Emerging trends towards performance-asymmetric multicore pro-
cessors (AMPs) are posing new challenges, because for effective
utilization of AMPs, code sections of a program must be assigned
to cores such that the resource needs of the code sections closely
match  the  resources  available  at  the  assigned  core.  Computing
this assignment can be difficult especially in the presence of un-
known or many target AMPs. We observe that finding a mapping
between the code segment characteristics and the core character-
istics is inexpensive enough, compared to finding a mapping be-
tween the code segments and the cores, that it can be deferred un-
til installation-time for more precise decision. We present staged
tuning which combines extensive compile time  analysis  with in-
telligent binary customization at install-time. Staged tuning is like
staged compilation, just for core assignment. Our evaluation shows
that staged tuning is effective in improving the utilization of AMPs.
We see a 23% speedup over untuned workloads.

 
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Neural Network Training
We  use  the  FANN  library  [32]  for constructing and training our neural networks. In our experiments,we  compute  a  grouping  (i.e.,  core  assignment)  for  each  individual benchmark.

Staged Tuning: A Hybrid (Compile/Install-time) Technique for Improving Utilization of Performance-asymmetric Multicores

tyler Sondag ( Intel Labs ) Hridesh Rajan ( Iowa State University )

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YSA ile Maç Sonucu Tahmini

YSA ile Maç Sonucu Tahmini

Bu çalışmada FannTool kullanarak Futbol Maçları için sonuç tahmini yapan YSA modelinin dizayn edip eğitim ve test aşamaları adım adım açıklanmış, sonuçlar değerlendirilmiştir. Öncelikle dizayn edeceğimiz YSA modeli için problemi tanımlayalım. Futbol maçlarının olası 3 sonucu vardır; Galibiyet Beraberlik Mağlubiyet. Girdi olarak Ev Sahibi ve Misafir takıma ait sezon boyunca oynadığı maçlar sonucunda çıkan 9 farklı performans kriteri kullanılacaktır. YSA dan beklenen eğitim sonunda soru olarak verilen Ev Sahibi ve Misafir Takım performansları için muhtemel maç sonucunu hesaplamak.

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 Her ne kadar Futbol maçları sonuçlarını yüksek doğrulukla tahmin edemesek te Yapay Sinir Ağlarının, FANN kütüphanesinin, FanntTool yazılımının her alanda nasıl kullanılabileceğine örnek olabilecek bir çalışma yapmış olduk.

KARAR DESTEK SİSTEMLERİ DERSİ
FİNAL ÖDEVİ
YAPAY SİNİR AĞLARI UYGULAMASI
 İdris AKBIYIK

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Integration of Neonatal Mortality Prediction Models into a Clinical Decision Support System

Integration of Neonatal Mortality Prediction Models into a Clinical Decision Support System
by
Hasmik Martirosyan, B.Sc.

A thesis submitted to the Faculty of Graduate and Postdoctoral Affairs
in partial fulfillment of the requirements for the degree of
Master of Applied Science
in
Biomedical Engineering
Ottawa - Carleton Institute for Biomedical Engineering (OCIBME)
Carleton University
Ottawa, Ontario © 2015

https://curve.carleton.ca/system/files/theses/32040.pdf

Yenidoğan Ölüm Tahmin Modellerinin Klinik Karar Destek Sistemine  Entegrasyonu.

Abstract
This thesis describes the development of neonatal mortality risk estimation models using Artificial Neural Networks (ANNs), the integration of these models into the Physician-Parent Decision Support (PPADS) tool, and the pilot study to test the PPADS tool.
A set of data mining programs were created to automate the data preparation, the development of ANN models and the selection of models that satisfy the usefulness criteria specified by our clinician partners. These programs were used to classify neonatal mortality data (6% mortality rate) with the average sensitivity and specificity of 81% and 98% respectively.
The mortality models were integrated with the PPADS tool to provide predictions about the risk of mortality for neonates admitted to the Neonatal Intensive Care Unit (NICU).
The observational and survey study conducted with parents whose infant did not graduate (died) from the NICU gave encouraging results regarding the usefulness of the PPADS tool.
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Fast Artificial Neural Network library
In order to create ANN models, and make predictions in real time environment, we have explored several open sources libraries. We found the Fast Artificial Neural Network (FANN) library to be suitable to our work for the following reasons: firstly, the library implements feed-forward networks, which our research group has identified to be well performing machine learning methods for our medical datasets; secondly, the fast performance is one of the main features of the library, which is important in processing and analyzing real-time data; lastly, the library is implemented in C language which makes the FANN library based applications compatible with many software environments and portable to many different computer architectures or platforms (Nissen, 2007), (FANN, 2014).
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