A NEURAL NETWORK BASED TRAFFIC-FLOW PREDICTION MODEL

Prediction of traffic-flow in Istanbul has been a great concern for planners of the city. Istanbul as being one of the most crowded cities in the Europe has a rural population of more than 10 million. The related transportation agencies ill Istanbul continuously collect data through many ways thanks to improvements in sensor technology and communication systems which allow to more closely monitor the condition of the city transportation system. Since monitoring alone cannot improve the safety or efficiency of the system, those agencies actively inform the drivers continuously through various media including television broadcasts, internet, and electronic display boards on many locations on the roads. Currently, the human expertise is employed to judge traffic-flow on the roads to inform the public. There is no reliance on past data and human experts give opinions only on the present condition without much idea on what will be the likely events in the next hours. Historical events such as school-timings, holidays and other periodic events cannot be utilized for judging the future traffic-flows. This paper makes a preliminary attempt to change scenario by using artificial neural networks (ANNs) to model the past historical data. It aims at the prediction of the traffic volume based on the historical data in each major junction in the city. ANNs have given very encouraging results with the suggested approach explained in the paper

Is bisphosphonate therapy for benign bone disease associated with impaired dental healing? A case-controlled study

<p>Abstract</p> <p>Background</p> <p>Bisphosphonates are common first line medications used for the management of benign bone disease. One of the most devastating complications associated with bisphosphonate use is osteonecrosis of the jaws which may be related to duration of exposure and hence cumulative dose, dental interventions, medical co-morbidities or in some circumstances with no identifiable aggravating factor. While jaw osteonecrosis is a devastating outcome which is currently difficult to manage, various forms of delayed dental healing may be a less dramatic and, therefore, poorly-recognised complications of bisphosphonate use for the treatment of osteoporosis. It is hypothesised that long-term (more than 1 year's duration) bisphosphonate use for the treatment of post-menopausal osteoporosis or other benign bone disease is associated with impaired dental healing.</p> <p>Methods/Design</p> <p>A case-control study has been chosen to test the hypothesis as the outcome event rate is likely to be very low. A total of 54 cases will be recruited into the study following review of all dental files from oral and maxillofacial surgeons and special needs dentists in Victoria where potential cases of delayed dental healing will be identified. Potential cases will be presented to an independent case adjudication panel to determine if they are definitive delayed dental healing cases. Two hundred and fifteen controls (1:4 cases:controls), matched for age and visit window period, will be selected from those who have attended local community based referring dental practices. The primary outcome will be the incidence of delayed dental healing that occurs either spontaneously or following dental treatment such as extractions, implant placement, or denture use.</p> <p>Discussion</p> <p>This study is the largest case-controlled study assessing the link between bisphosphonate use and delayed dental healing in Australia. It will provide invaluable data on the potential link between bisphosphonate use and osteonecrosis of the jaws.</p

neural networks

The aim of this study was to predict the effect of physical factors on tibial motion by making use of artificial neural networks (ANNs). Since assessment of the tibial motion by the conventional approaches is generally difficult, this study aimed at the prediction of the relations between several physical factors (gender, age, body mass, and height) and tibial motion in terms of the ANNs. Data collected for 484 healthy subjects have been analyzed by using the ANNs. The study has given encouraging results for such a purpose. This investigation has been made to predict the rotations; especially the RTER prediction is highly satisfactory and the ANNs have been found to be very promising processing systems for modelling in the tibial rotation data assessments. (C) 2009 Elsevier Ltd. All rights reserved

TIBIAL ROTATION ASSESSMENT USING ARTIFICIAL NEURAL NETWORKS

Assessment of the tibial rotations by the conventional approaches is generally difficult. An investigation has been made in this study to assess the tibial motions based on the prediction of the effects of physical factors as well as a portion of tibial measurements by making use of Artificial Neural Networks (ANN). Therefore, this study aimed at the prediction of the relations between several physical factors and tibial motion measurements in terms of Artificial Neural Networks. These factors include gender, age, weight, and height. Data collected for 484 healthy subjects have been analyzed by Artificial Neural Networks. Promising results showed that the ANN has been found to be appropriate for modeling and simulation in the data assessments. The paper gives detailed results regarding the use of ANN for modeling tibial rotations in terms of physical factors. The study shows the feasibility of ANN to predict the behaviour of knee joints

TIBIAL ROTATION ASSESSMENT USING ARTIFICIAL NEURAL NETWORKS

Assessment of the tibial rotations by the conventional approaches is generally difficult. An investigation has been made in this study to assess the tibial motions based on the prediction of the effects of physical factors as well as a portion of tibial measurements by making use of Artificial Neural Networks (ANN). Therefore, this study aimed at the prediction of the relations between several physical factors and tibial motion measurements in terms of Artificial Neural Networks. These factors include gender, age, weight, and height. Data collected for 484 healthy subjects have been analyzed by Artificial Neural Networks. Promising results showed that the ANN has been found to be appropriate for modeling and simulation in the data assessments. The paper gives detailed results regarding the use of ANN for modeling tibial rotations in terms of physical factors. The study shows the feasibility of ANN to predict the behaviour of knee joints

A NEURAL NETWORK BASED TRAFFIC-FLOW PREDICTION MODEL

Prediction of traffic-flow in Istanbul has been a great concern for planners of the city. Istanbul as being one of the most crowded cities in the Europe has a rural population of more than 10 million. The related transportation agencies ill Istanbul continuously collect data through many ways thanks to improvements in sensor technology and communication systems which allow to more closely monitor the condition of the city transportation system. Since monitoring alone cannot improve the safety or efficiency of the system, those agencies actively inform the drivers continuously through various media including television broadcasts, internet, and electronic display boards on many locations on the roads. Currently, the human expertise is employed to judge traffic-flow on the roads to inform the public. There is no reliance on past data and human experts give opinions only on the present condition without much idea on what will be the likely events in the next hours. Historical events such as school-timings, holidays and other periodic events cannot be utilized for judging the future traffic-flows. This paper makes a preliminary attempt to change scenario by using artificial neural networks (ANNs) to model the past historical data. It aims at the prediction of the traffic volume based on the historical data in each major junction in the city. ANNs have given very encouraging results with the suggested approach explained in the paper