scholarly journals Estimating Spatiotemporal Information from Behavioral Sensing Data of Wheelchair Users by Machine Learning Technologies

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 114 ◽  
Author(s):  
Ikuko Yairi ◽  
Hiroki Takahashi ◽  
Takumi Watanabe ◽  
Kouya Nagamine ◽  
Yusuke Fukushima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Surface Condition ◽  
Learning Technologies ◽  
Road Surface ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Prototype System ◽  
Wheelchair Users ◽  
The Road

Recent expansion of intelligent gadgets, such as smartphones and smart watches, familiarizes humans with sensing their activities. We have been developing a road accessibility evaluation system inspired by human sensing technologies. This paper introduces our methodology to estimate road accessibility from the three-axis acceleration data obtained by a smart phone attached on a wheelchair seat, such as environmental factors, e.g., curbs and gaps, which directly influence wheelchair bodies, and human factors, e.g., wheelchair users’ feelings of tiredness and strain. Our goal is to realize a system that provides the road accessibility visualization services to users by online/offline pattern matching using impersonal models, while gradually learning to improve service accuracy using new data provided by users. As the first step, this paper evaluates features acquired by the DCNN (deep convolutional neural network), which learns the state of the road surface from the data in supervised machine learning techniques. The evaluated results show that the features can capture the difference of the road surface condition in more detail than the label attached by us and are effective as the means for quantitatively expressing the road surface condition. This paper developed and evaluated a prototype system that estimated types of ground surfaces focusing on knowledge extraction and visualization.

scholarly journals Research on Road Adhesion Condition Identification Based on an Improved ALexNet Model

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
QiMing Wang ◽  
JinMing Xu ◽  
Tao Sun ◽  
ZhiChao Lv ◽  
GaoQiang Zong
Keyword(s):  
Machine Learning ◽  
Surface Condition ◽  
Image Features ◽  
Road Surface ◽  
Driving Safety ◽  
Error Performance ◽  
Training Time ◽  
The Road ◽  
Network Training ◽  
Proposed Model

Automotive intelligence has become a revolutionary trend in automotive technology. Complex road driving conditions directly affect driving safety and comfort. Therefore, by improving the recognition accuracy of road type or road adhesion coefficient, the ability of vehicles to perceive the surrounding environment will be enhanced. This will further contribute to vehicle intelligence. In this paper, considering that the process of manually extracting image features is complicated and that the extraction method is random for everyone, road surface condition identification method based on an improved ALexNet model, namely, the road surface recognition model (RSRM), is proposed. First, the ALexNet network model is pretrained on the ImageNet dataset offline. Second, the weights of the shallow network structure after training, including the convolutional layer, are saved and migrated to the proposed model. In addition, the fully connected layer fixed to the shallow network is replaced by 2 to 3, which improves the training accuracy and shortens the training time. Finally, the traditional machine learning and improved ALexNet model are compared, focusing on adaptability, prediction output, and error performance, among others. The results show that the accuracy of the proposed model is better than that of the traditional machine learning method by 10% and the ALexNet model by 3%, and it is 0.3 h faster than ALexNet in training speed. It is verified that RSRM effectively improves the network training speed and accuracy of road image recognition.

scholarly journals Analysed potential of big data and supervised machine learning techniques in effectively forecasting travel times from fused data

2015 ◽  
Vol 27 (6) ◽  
pp. 515-528 ◽  
Author(s):  
Ivana Šemanjski
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Random Forest ◽  
Data Sources ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Travel Times ◽  
The Road ◽  
Learning Techniques ◽  
Nearest Neighbours

Travel time forecasting is an interesting topic for many ITS services. Increased availability of data collection sensors increases the availability of the predictor variables but also highlights the high processing issues related to this big data availability. In this paper we aimed to analyse the potential of big data and supervised machine learning techniques in effectively forecasting travel times. For this purpose we used fused data from three data sources (Global Positioning System vehicles tracks, road network infrastructure data and meteorological data) and four machine learning techniques (k-nearest neighbours, support vector machines, boosting trees and random forest). To evaluate the forecasting results we compared them in-between different road classes in the context of absolute values, measured in minutes, and the mean squared percentage error. For the road classes with the high average speed and long road segments, machine learning techniques forecasted travel times with small relative error, while for the road classes with the small average speeds and segment lengths this was a more demanding task. All three data sources were proven itself to have a high impact on the travel time forecast accuracy and the best results (taking into account all road classes) were achieved for the k-nearest neighbours and random forest techniques.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

2020 ◽  
Vol 28 (2) ◽  
pp. 253-265 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Amauri Duarte da Silva ◽  
Walter Filgueira de Azevedo
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Predictive Performance ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Cyclin Dependent Kinase ◽  
Learning Models ◽  
Learning Techniques ◽  
Machine Learning Models

Background: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. Objective: Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. Methods: We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. Results: Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. Conclusion: Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

scholarly journals Local mortality estimates during the COVID-19 pandemic in Italy

2021 ◽  
Author(s):  
Augusto Cerqua ◽  
Roberta Di Stefano ◽  
Marco Letta ◽  
Sara Miccoli
Keyword(s):  
Machine Learning ◽  
Excess Mortality ◽  
Control Method ◽  
Local Level ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Mortality Data ◽  
Official Method ◽  
Learning Techniques ◽  
Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

scholarly journals Supervised Machine Learning Techniques: An Overview with Applications to Banking

2021 ◽  
Author(s):  
Linwei Hu ◽  
Jie Chen ◽  
Joel Vaughan ◽  
Soroush Aramideh ◽  
Hanyu Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques
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