scholarly journals Machine Learning Framework for the Estimation of Average Speed in Rural Road Networks with OpenStreetMap Data

2020 ◽  
Vol 9 (11) ◽  
pp. 638
Author(s):  
Sina Keller ◽  
Raoul Gabriel ◽  
Johanna Guth
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Road Network ◽  
Reference Data ◽  
Coefficient Of Determination ◽  
Rural Roads ◽  
Average Speed ◽  
Learning Framework ◽  
Input Feature ◽  
Better Than

Average speed information, which is essential for routing applications, is often missing in the freely available OpenStreetMap (OSM) road network. In this contribution, we propose an estimation framework, including different machine learning (ML) models that estimate rural roads’ average speed based on current road information in OSM. We rely on three datasets covering two regions in Chile and Australia. Google Directions API data serves as reference data. An appropriate estimation framework is presented, which involves supervised ML models, unsupervised clustering, and dimensionality reduction to generate new input features. The regression performance of each model with different input feature modes is evaluated on each dataset. The best performing model results in a coefficient of determination R2=80.43%, which is significantly better than previous approaches relying on domain-knowledge. Overall, the potential of the ML-based estimation framework to estimate the average speed with OSM road network data is demonstrated. This ML-based approach is data-driven and does not require any domain knowledge. In the future, we intend to focus on the generalization ability of the estimation framework concerning its application in different regions worldwide. The implementation of our estimation framework for an exemplary dataset is provided on GitHub.

scholarly journals Tensorflow vs R: A Comparative Study of Usability

2017 ◽  
Author(s):  
Luís Dias ◽  
Rosalvo Neto
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Comparative Study ◽  
Scientific Community ◽  
Deep Neural Network ◽  
Network Algorithms ◽  
Learning Framework ◽  
Usability Tests ◽  
Usability Analysis ◽  
Better Than

Google released on November of 2015 Tensorflow, an open source machine learning framework that can be used to implement Deep Neural Network algorithms, a class of algorithms that shows great potential in solving complex problems. Considering the importance of usability in software success, this research aims to perform a usability analysis on Tensorflow and to compare it with another widely used framework, R. The evaluation was performed through usability tests with university students. The study led do indications that Tensorflow usability is equal or better than the usability of traditional frameworks used by the scientific community.

scholarly journals Less Temptation To Exceed The Speed Limit Or Towards Vision Zero

2021 ◽  
Vol 1202 (1) ◽  
pp. 012042
Author(s):  
Viktoras Lapinas ◽  
Mantas Kišonas
Keyword(s):  
Traffic Safety ◽  
Road Network ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Speed Limit ◽  
Rural Roads ◽  
Average Speed ◽  
Safety Programme ◽  
The Impact ◽  
Vision Zero

Abstract In 2020 the Lithuanian Government approved traffic safety programme Vision Zero. One of the integral measures applied to improve traffic safety and to reduce the number of road traffic infringements is the development of average speed cameras’ network on state significance roads. It is planned that the network of average speed cameras will cover more than 800 km of state significance road network in Lithuania in 2020-2021. Initially, it was planned to implement these measures only on rural roads. However, taking into consideration the principles of road eligibility for average speed camera installation, some road sections crossing the so-called linear settlements were selected to test the impact of such systems on driving habits as well. It is presumed that from the beginning of exploitation of these systems the reduction in the consequences of severe traffic accidents on the selected most dangerous state significance road sections will be observed.

scholarly journals A machine-learning approach to map landscape connectivity inAedes aegyptiwith genetic and environmental data

2021 ◽  
Vol 118 (9) ◽  
pp. e2003201118
Author(s):  
Evlyn Pless ◽  
Norah P. Saarman ◽  
Jeffrey R. Powell ◽  
Adalgisa Caccone ◽  
Giuseppe Amatulli
Keyword(s):  
Machine Learning ◽  
Invasive Species ◽  
Genetic Distance ◽  
Landscape Connectivity ◽  
Environmental Data ◽  
Genetic Connectivity ◽  
Learning Framework ◽  
Machine Learning Approach ◽  
Primary Vector ◽  
Better Than

Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interacting and correlated environmental variables. To overcome these constraints, we combine the advantages of a machine-learning framework and an iterative optimization process to develop a method for integrating genetic and environmental (e.g., climate, land cover, human infrastructure) data. We validate and demonstrate this method for theAedes aegyptimosquito, an invasive species and the primary vector of dengue, yellow fever, chikungunya, and Zika. We test two contrasting metrics to approximate genetic distance and find Cavalli-Sforza–Edwards distance (CSE) performs better than linearized FST. The correlation (R) between the model’s predicted genetic distance and actual distance is 0.83. We produce a map of genetic connectivity forAe. aegypti’s range in North America and discuss which environmental and anthropogenic variables are most important for predicting gene flow, especially in the context of vector control.

scholarly journals A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Negar Farzaneh ◽  
Craig A. Williamson ◽  
Jonathan Gryak ◽  
Kayvan Najarian
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Domain Knowledge ◽  
Characteristic Curve ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Learning Framework ◽  
Diagnosis And Prognosis ◽  
Learning Classifier

AbstractPrognosis of the long-term functional outcome of traumatic brain injury is essential for personalized management of that injury. Nonetheless, accurate prediction remains unavailable. Although machine learning has shown promise in many fields, including medical diagnosis and prognosis, such models are rarely deployed in real-world settings due to a lack of transparency and trustworthiness. To address these drawbacks, we propose a machine learning-based framework that is explainable and aligns with clinical domain knowledge. To build such a framework, additional layers of statistical inference and human expert validation are added to the model, which ensures the predicted risk score’s trustworthiness. Using 831 patients with moderate or severe traumatic brain injury to build a model using the proposed framework, an area under the receiver operating characteristic curve (AUC) and accuracy of 0.8085 and 0.7488 were achieved, respectively, in determining which patients will experience poor functional outcomes. The performance of the machine learning classifier is not adversely affected by the imposition of statistical and domain knowledge “checks and balances”. Finally, through a case study, we demonstrate how the decision made by a model might be biased if it is not audited carefully.

scholarly journals Approach to Kirana Store Product Arrangement Using Machine Learning

2021 ◽  
pp. 72-83
Author(s):  
Ajita Patel ◽  
Krishna Kumar Tiwari
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Domain Knowledge ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Detection Techniques ◽  
Store Product ◽  
Shopping Basket ◽  
Objective Detection ◽  
Better Than

Market Basket Analysis (MBA) is a method for determining the association between entities, and it has often been used to study the association between products in a shopping basket. Trained Computer vision models are able to recognize objects in photos so accurately that it can even outperform humans in some instances. This study shows that combining objective detection techniques with market basket analysis can assist Stores/Kirana in organizing the products effectively. With the use of MBA and Object detection, we formulated recommendations for store arrangements along with putting a recommendation engine on top to help shoppers. After deploying this to local Kirana stores, the Kirana store was able to see an increase of 7% in the sale. The recommendation engine performed better than just the domain knowledge of the kirana store.

scholarly journals A hierarchical machine learning framework for the identification of automated construction

2021 ◽  
Vol 26 ◽  
pp. 591-623
Author(s):  
Aparna Harichandran ◽  
Benny Raphael ◽  
Abhijit Mukherjee
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
Domain Knowledge ◽  
Progress Monitoring ◽  
Problem Formulation ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Hierarchical Framework ◽  
Automated Monitoring ◽  
Learning Framework

A robust monitoring system is essential for ensuring safety and reliability in automated construction. Activity recognition is one of the critical tasks in automated monitoring. Existing studies in this area have not fully exploited the potential for enhancing the performance of machine learning algorithms using domain knowledge, especially in problem formulation. This paper presents a hierarchical machine learning framework for improving the accuracy of identification of Automated Construction System (ACS) operations. The proposed identification framework arranges the operations to be identified in the form of a hierarchy and uses multiple classifiers that are organized hierarchically for separating the operation classes. It is tested on a laboratory prototype of an ACS, which follows a top-down construction method. The ACS consists of a set of lightweight and portable machinery designed to automate the construction of the structural frame of low-rise buildings . Accelerometers were deployed at critical locations on the structure. The acceleration data collected while operating the equipment were used to identify the operations through machine learning techniques. The performance of the proposed framework is compared with that of the conventional approach for equipment operation identification which involves a flat list of classes to be separated. The performance was comparable at the top level. However, the hierarchical framework outperformed the conventional one when fine levels of operations were identified. The versatility and noise tolerance of the hierarchical framework are also reported. Results demonstrate that the framework is robust, and it is feasible to identify the ACS operations precisely. Although the proposed framework is validated on a full-scale prototype of the ACS, the effects of strong ambient disturbances on actual construction sites have not been evaluated. This study will support the development of an automated monitoring system and assist the main operator to ensure safe operations. The high-level operation details collected for this purpose can also be utilised for project performance assessment and progress monitoring. The potential application of the proposed hierarchical framework in the operation recognition of conventional construction equipment is also outlined.

scholarly journals Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

2021 ◽  
Author(s):  
Giuseppe Guido ◽  
Sina Shaffiee Haghshenas ◽  
Sami Shaffiee Haghshenas ◽  
Alessandro Vitale ◽  
Vittorio Astarita ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rural Areas ◽  
Road Safety ◽  
Safety Management ◽  
Effective Parameters ◽  
Road Transportation ◽  
Contributing Factors ◽  
Rural Roads ◽  
Factors Affecting ◽  
Average Speed

Evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic of this issue and the presence of a large number of effective parameters on road safety. Therefore, evaluation and analysis of important contributing factors affecting the number of crashes play a key role in increasing the efficiency of road safety. For this purpose, in this research work, two machine learning algorithms including the group method of data handling (GMDH)-type neural network and a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA) are employed for evaluating the number of vehicles involved in the accident based on the seven factors affecting transport safety including the Daylight (DL), Weekday (W), Type of accident (TA), Location (L), Speed limit (SL), Average speed (AS) and Annual average daily traffic (AADT) of rural roads of Cosenza in southern Italy. In this study, 564 data sets of rural areas were investigated and relevant effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation for rural areas. The results obtained demonstrated that "Average speed" has the highest level and "Weekday" has the lowest level of importance in the investigated rural area. Finally, although the results of both algorithms were the same, the GOA-SVM model showed a better degree of accuracy and robustness than the GMDH model.

scholarly journals A stacking ensemble learning framework for genomic prediction

2020 ◽  
Author(s):  
Mang Liang ◽  
Tianpeng Chang ◽  
Bingxing An ◽  
Xinghai Duan ◽  
Lili Du ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Genomic Prediction ◽  
Milk Fat ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Fat Percentage ◽  
Prediction Ability ◽  
Learning Framework ◽  
Better Than

Abstract Background: Machine learning (ML) is perhaps the most useful for the interpretation of large genomic datasets. However, the performance of a single machine learning method in genomic selection (GS) was unsatisfactory in existing research. To improve the genomic predictions, we constructed a stacking ensemble learning framework (SELF) integrated three machine learning methods to predict genomic estimated breeding values (GEBVs). Results: We evaluated the prediction ability of SELF by three real datasets and compared the prediction accuracy of SELF, base learners, GBLUP and BayesB. For each trait, SELF performed better than base learners, which included support vector regression (SVR), kernel ridge regression (KRR) and elastic net (ENET). The prediction accuracy of SELF had an average 7.70% improvement compared with GBLUP in three datasets. Except for the milk fat percentage (MFP) traits of the German Holstein dairy cattle dataset, SELF more robust than BayesB in the remaining traits.Conclusions: In this study, we utilized a stacking ensemble learning framework (SELF) to genomic prediction and it performed much better than GBLUP and BayesB in three real datasets with different genetic architecture. Therefore, we believed SEFL had the potential to be promoted to estimate GEBVs in other animals and plants.

scholarly journals A physics-informed machine learning framework for predictive maintenance applied to turbomachinery assets

2021 ◽  
pp. 1-15
Author(s):  
Marzia Sepe ◽  
Antonino Graziano ◽  
Maciej Badora ◽  
Alessandro Di Stazio ◽  
Luca Bellani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Risk Model ◽  
Hybrid Approach ◽  
Predictive Maintenance ◽  
Learning Framework ◽  
Maintenance Service ◽  
Aging Phenomena ◽  
Long Term Prediction

The paper presents an overview of Baker Hughes digital framework for a predictive maintenance service boosted by Machine Learning and asset knowledge, applied to turbomachinery assets. Optimization of the maintenance scenario is performed through a risk model that assesses online health status and probability of failure, by detecting functional anomalies and aging phenomena and evaluating their impact on asset serviceability. Turbomachinery domain knowledge is used to create physics-based models, to configure a severity assessment layer and to properly map maintenance actions to anomaly types. The implemented analytics framework is able also to forecast engine behaviour over the future in order to optimize asset operation and maintenance, minimizing downtime and residual risk. Predictive capabilities are optimized thanks to the hybrid approach, where physics-based knowledge empowers long term prediction accuracy while data-driven analytics ensure fast-events prognostics. Accuracy of the hybrid approach is a differentiator for maintenance optimization, allowing activities to be planned properly and in early advance with respect to outage execution.

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

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