scholarly journals Research of Vehicle Rear-End Collision Model considering Multiple Factors

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Qiang Luo ◽  
Xiaodong Zang ◽  
Jie Yuan ◽  
Xinqiang Chen ◽  
Junheng Yang ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Traffic Accident ◽  
Model Performance ◽  
Network Models ◽  
Neural Network Models ◽  
Collision Model ◽  
Car Following ◽  
Research Findings ◽  
Two Parameters ◽  
The Relationship

The accuracy of the rear-end collision models is crucial for the early warning of potential traffic accident identification, and thus analyzes of the main factors influencing the rear-end collision relevant models is an active topic in the field. The previous studies have tried to determine the single factor influence on the rear-end collision model performance. Less attention was paid to exploit mutual influences on the model performance. To bridge the gap, we proposed an improved vehicle rear-end collision model by integrating varied factors which influence two parameters (i.e., response time and road adhesion coefficient). The two parameters were solved with the integrated weighting and neural network models, respectively. After that we analyzed the relationship between varied factors and the minimum car-following distance. The research findings support both the theoretical and practical guidance for transportation regulations to release more reasonable minimum headway distance to enhance the roadway traffic safety.

scholarly journals Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis

2021 ◽  
Author(s):  
Robert J. O’Shea ◽  
Amy Rose Sharkey ◽  
Gary J. R. Cook ◽  
Vicky Goh
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Convolutional Neural Network ◽  
Cancer Diagnosis ◽  
Model Performance ◽  
Network Models ◽  
Imaging Studies ◽  
Neural Network Models ◽  
Eligibility Criteria ◽  
Data Partitions

Abstract Objectives To perform a systematic review of design and reporting of imaging studies applying convolutional neural network models for radiological cancer diagnosis. Methods A comprehensive search of PUBMED, EMBASE, MEDLINE and SCOPUS was performed for published studies applying convolutional neural network models to radiological cancer diagnosis from January 1, 2016, to August 1, 2020. Two independent reviewers measured compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Compliance was defined as the proportion of applicable CLAIM items satisfied. Results One hundred eighty-six of 655 screened studies were included. Many studies did not meet the criteria for current design and reporting guidelines. Twenty-seven percent of studies documented eligibility criteria for their data (50/186, 95% CI 21–34%), 31% reported demographics for their study population (58/186, 95% CI 25–39%) and 49% of studies assessed model performance on test data partitions (91/186, 95% CI 42–57%). Median CLAIM compliance was 0.40 (IQR 0.33–0.49). Compliance correlated positively with publication year (ρ = 0.15, p = .04) and journal H-index (ρ = 0.27, p < .001). Clinical journals demonstrated higher mean compliance than technical journals (0.44 vs. 0.37, p < .001). Conclusions Our findings highlight opportunities for improved design and reporting of convolutional neural network research for radiological cancer diagnosis. Key Points • Imaging studies applying convolutional neural networks (CNNs) for cancer diagnosis frequently omit key clinical information including eligibility criteria and population demographics. • Fewer than half of imaging studies assessed model performance on explicitly unobserved test data partitions. • Design and reporting standards have improved in CNN research for radiological cancer diagnosis, though many opportunities remain for further progress.

scholarly journals Towards Robust Representations of Spatial Networks Using Graph Neural Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6918
Author(s):  
Chidubem Iddianozie ◽  
Gavin McArdle
Keyword(s):  
Machine Learning ◽  
Model Performance ◽  
Network Models ◽  
Data Representation ◽  
Spatial Networks ◽  
Neural Network Models ◽  
Improve Model ◽  
Graph Neural Networks ◽  
Spatial Entities ◽  
Improve Model Performance

The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogeneous representation may benefit spatial networks and could improve model performance on certain tasks. Thus, we carry out an empirical study using Graph Neural Network models for two inference tasks on spatial networks. Our results demonstrate that heterogeneous representations improves model performance for down-stream inference tasks on spatial networks.

A Multi-Scale Modeling Method Based on Big Data

2015 ◽  
Vol 734 ◽  
pp. 447-450 ◽  
Author(s):  
Ji Wei Liu
Keyword(s):  
Big Data ◽  
Network Models ◽  
Modeling Method ◽  
Scale Model ◽  
Neural Network Models ◽  
Multi Scale ◽  
Scale Modeling ◽  
Input And Output ◽  
Multi Scale Modeling ◽  
The Relationship

A multi-scale modeling method based on big data was proposed to establish neural network models for complex plant. Wavelet transform was used to decompose input and output parameters into different scales. The relationship between these parameters were researched in every scale. Then models in each scale were established and added together to form a multi-scale model. A model of coal mill current in power plant was established using the multi-scale modeling method based on big data. The result shows that, the method is effective.

scholarly journals Road Traffic Accident Mortality and Economic Development: The Case of Bosnia and Herzegovina

2018 ◽  
Vol 3 (1-2) ◽  
Author(s):  
Mirsad Kulović ◽  
Belma Dogdibegović-Kovač
Keyword(s):  
Economic Development ◽  
Gross Domestic Product ◽  
Bosnia And Herzegovina ◽  
Traffic Safety ◽  
Road Traffic Accident ◽  
Traffic Accident ◽  
Road Traffic ◽  
Motor Vehicles ◽  
Traffic Mortality ◽  
The Relationship

This paper examines the relationship between the relevant parameters of traffic safety and the most important parameter of economic development, gross domestic product. In particular, the paper estimates the effects of the model of the rate of motorization and road traffic mortality in relation to the number of inhabitants and the number of motor vehicles, which are further used for the projection of mortality and the number of motor vehicles by 2030.

Identifying the Direct Effect of Experience and the Moderating Effect of Satisfaction in the Greek Online Market

2013 ◽  
pp. 77-97 ◽  
Author(s):  
Michail N. Giannakos ◽  
Adamantia G. Pateli ◽  
Ilias O. Pappas
Keyword(s):  
Direct Effect ◽  
Self Efficacy ◽  
Model Performance ◽  
Moderating Effect ◽  
Shopping Behavior ◽  
Market Shares ◽  
Effort Expectancy ◽  
Two Parameters ◽  
Relationship Of ◽  
The Relationship

The scope of this paper is to examine the perceptions which induce the Greek customers to purchase over the Internet, testing the direct effect of experience and the moderating effect of satisfaction. A review of research conducted in the Greek online market demonstrates that satisfaction, self-efficacy, and trust keep a prominent role in the Greek customers’ shopping behavior. To increase understanding of this behavior, two parameters of the UTAUT model, performance expectancy and effort expectancy, are incorporated. The findings demonstrate that customers’ perceptions about all of the parameters do not remain constant, as the experience acquired from past purchases increases. Moreover, the relationship of experience with self-efficacy and intention to repurchase changes, as satisfaction gained from previous purchases increases. The implications of this study are interesting not only for the Greek but also for the Meditterranean researchers and e-retailers, since the Mediterranean ebusiness market shares several cultural similarities with the Greek market.

scholarly journals Deep-Learning-Based Prediction of High-Risk Taxi Drivers Using Wellness Data

2020 ◽  
Vol 17 (24) ◽  
pp. 9505
Author(s):  
Seolyoung Lee ◽  
Jae Hun Kim ◽  
Jiwon Park ◽  
Cheol Oh ◽  
Gunwoo Lee
Keyword(s):  
Deep Learning ◽  
High Risk ◽  
Traffic Safety ◽  
Network Models ◽  
Personal Characteristics ◽  
Taxi Driver ◽  
Neural Network Models ◽  
Factors Affecting ◽  
Taxi Drivers ◽  
Artificial Neural Network Models

Background: Factors related to the wellness of taxi drivers are important for identifying high-risk drivers based on human factors. The purpose of this study is to predict high-risk taxi drivers based on a deep learning method by identifying the wellness of a driver, which reflects the personal characteristics of the driver. Methods: In-depth interviews with taxi drivers are conducted to collect wellness data. The priorities of factors affecting the severity of accidents are derived through a random forest model. In addition, based on the derived priority of variables, various combinations of inputs are set as scenarios and optimal artificial neural network models are derived for each scenario. Finally, the model with the best performance for predicting high-risk taxi drivers is selected based on three criteria. Results: A model with variables up to the 16th priority as inputs is selected as the best model; this has a classification accuracy of 86% and an F1-score of 0.77. Conclusions: The wellness-based model for predicting high-risk taxi drivers presented in this study can be used for developing a taxi driver management system. In addition, it is expected to be useful when establishing customized traffic safety improvement measures for commercial vehicle drivers.

LONGER COMBINATION VEHICLES AND ROAD SAFETY

Transport ◽  
2010 ◽  
Vol 25 (3) ◽  
pp. 336-343 ◽  
Author(s):  
Aivis Grīslis
Keyword(s):  
Traffic Safety ◽  
Road Safety ◽  
Traffic Accident ◽  
Road Vehicles ◽  
Driver Training ◽  
Braking Performance ◽  
Safety Issues ◽  
Heavy Truck ◽  
The Relationship ◽  
Made In

The aim of this paper is to explore the relationship between the features of Longer Combination Vehicles (LCVs) and road safety issues. LCVs are road vehicles that exceed dimensions of a typical or standard heavy truck‐trailer or tractor‐semitrailer combination vehicles in length or length and weight. The systematization of LCVs is done. Several areas, which are likely to benefit through LCVs, are listed and described. The analysis of literature review is made in the areas where additional problems may be encountered using LCVs. Several engineering factors such as resistance to rollover, swept‐path parameters, vehicle capabilities of accelerating and maintaining speed as well as braking performance are analyzed. Several research projects on traffic accident analysis have been looked through to compare their conclusions about traffic safety of LCVs. The analysis of discussions related to LCVs traffic safety issues is provided. Some transportation experts and community groups have conflicting views about road safety issues of LCVs. The opinions and related arguments of both parties are discussed in this paper. Several technical improvements in designing LCVs and the importance of driver training programs are described.

scholarly journals Evaluation of multi-target deep neural network models for compound potency prediction under increasingly challenging test conditions

2021 ◽  
Author(s):  
Raquel Rodríguez-Pérez ◽  
Jürgen Bajorath
Keyword(s):  
Large Scale ◽  
Model Performance ◽  
Network Models ◽  
Data Partitioning ◽  
Superior Performance ◽  
Support Vector ◽  
Neural Network Models ◽  
Multiple Targets ◽  
Single Target ◽  
Qsar Models

AbstractMachine learning (ML) enables modeling of quantitative structure–activity relationships (QSAR) and compound potency predictions. Recently, multi-target QSAR models have been gaining increasing attention. Simultaneous compound potency predictions for multiple targets can be carried out using ensembles of independently derived target-based QSAR models or in a more integrated and advanced manner using multi-target deep neural networks (MT-DNNs). Herein, single-target and multi-target ML models were systematically compared on a large scale in compound potency value predictions for 270 human targets. By design, this large-magnitude evaluation has been a special feature of our study. To these ends, MT-DNN, single-target DNN (ST-DNN), support vector regression (SVR), and random forest regression (RFR) models were implemented. Different test systems were defined to benchmark these ML methods under conditions of varying complexity. Source compounds were divided into training and test sets in a compound- or analog series-based manner taking target information into account. Data partitioning approaches used for model training and evaluation were shown to influence the relative performance of ML methods, especially for the most challenging compound data sets. For example, the performance of MT-DNNs with per-target models yielded superior performance compared to single-target models. For a test compound or its analogs, the availability of potency measurements for multiple targets affected model performance, revealing the influence of ML synergies.

Identifying the Direct Effect of Experience and the Moderating Effect of Satisfaction in the Greek Online Market

2011 ◽  
Vol 3 (2) ◽  
pp. 39-58 ◽  
Author(s):  
Michail N. Giannakos ◽  
Adamantia G. Pateli ◽  
Ilias O. Pappas
Keyword(s):  
Direct Effect ◽  
Self Efficacy ◽  
Model Performance ◽  
Moderating Effect ◽  
Shopping Behavior ◽  
Market Shares ◽  
Effort Expectancy ◽  
Two Parameters ◽  
Relationship Of ◽  
The Relationship

The scope of this paper is to examine the perceptions which induce the Greek customers to purchase over the Internet, testing the direct effect of experience and the moderating effect of satisfaction. A review of research conducted in the Greek online market demonstrates that satisfaction, self-efficacy, and trust keep a prominent role in the Greek customers’ shopping behavior. To increase understanding of this behavior, two parameters of the UTAUT model, performance expectancy and effort expectancy, are incorporated. The findings demonstrate that customers’ perceptions about all of the parameters do not remain constant, as the experience acquired from past purchases increases. Moreover, the relationship of experience with self-efficacy and intention to repurchase changes, as satisfaction gained from previous purchases increases. The implications of this study are interesting not only for the Greek but also for the Meditterranean researchers and e-retailers, since the Mediterranean ebusiness market shares several cultural similarities with the Greek market.

How Optimal Stimuli for Sensory Neurons Are Constrained by Network Architecture

2008 ◽  
Vol 20 (3) ◽  
pp. 668-708 ◽  
Author(s):  
Christopher DiMattina ◽  
Kechen Zhang
Keyword(s):  
Firing Rate ◽  
Network Architecture ◽  
Single Neuron ◽  
Network Models ◽  
Synaptic Connections ◽  
Trial And Error ◽  
Neural Network Models ◽  
Stimulus Response ◽  
The Relationship ◽  
Topological Boundary

Identifying the optimal stimuli for a sensory neuron is often a difficult process involving trial and error. By analyzing the relationship between stimuli and responses in feedforward and stable recurrent neural network models, we find that the stimulus yielding the maximum firing rate response always lies on the topological boundary of the collection of all allowable stimuli, provided that individual neurons have increasing input-output relations or gain functions and that the synaptic connections are convergent between layers with nondegenerate weight matrices. This result suggests that in neurophysiological experiments under these conditions, only stimuli on the boundary need to be tested in order to maximize the response, thereby potentially reducing the number of trials needed for finding the most effective stimuli. Even when the gain functions allow firing rate cutoff or saturation, a peak still cannot exist in the stimulus-response relation in the sense that moving away from the optimum stimulus always reduces the response. We further demonstrate that the condition for nondegenerate synaptic connections also implies that proper stimuli can independently perturb the activities of all neurons in the same layer. One example of this type of manipulation is changing the activity of a single neuron in a given processing layer while keeping that of all others constant. Such stimulus perturbations might help experimentally isolate the interactions of selected neurons within a network.

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