scholarly journals Towards Adapting Cars to their Drivers

AI Magazine ◽  
2012 ◽  
Vol 33 (4) ◽  
pp. 46 ◽  
Author(s):  
Avi Rosenfeld ◽  
Zevi Bareket ◽  
Claudia V. Goldman ◽  
Sarit Kraus ◽  
David J. LeBlanc ◽  
...  
Keyword(s):  
Machine Learning Algorithms ◽  
Automated System ◽  
Expert Advice ◽  
Cruise Control ◽  
Learning Procedure ◽  
Combine Machine ◽  
Assistive Systems ◽  
Improved Learning ◽  
Current Systems ◽  
Interactive Activity

Traditionally, vehicles have been considered as machines that are controlled by humans for the purpose of transportation. A more modern view is to envision drivers and passengers as actively interacting with a complex automated system. Such interactive activity leads us to consider intelligent and advanced ways of interaction leading to cars that can adapt to their drivers.In this paper, we focus on the Adaptive Cruise Control (ACC) technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver’s preferences. Although individual drivers have different driving styles and preferences, current systems do not distinguish among users. We introduce a method to combine machine learning algorithms with demographic information and expert advice into existing automated assistive systems. This method can reduce the interactions between drivers and automated systems by adjusting parameters relevant to the operation of these systems based on their specific drivers and context of drive. We also learn when users tend to engage and disengage the automated system. This method sheds light on the kinds of dynamics that users develop while interacting with automation and can teach us how to improve these systems for the benefit of their users. While generic packages such as Weka were successful in learning drivers’ behavior, we found that improved learning models could be developed by adding information on drivers’ demographics and a previously developed model about different driver types. We present the general methodology of our learning procedure and suggest applications of our approach to other domains as well.

scholarly journals Trialstreamer: a living, automatically updated database of clinical trial reports

2020 ◽  
Author(s):  
Iain J Marshall ◽  
Benjamin Nye ◽  
Joël Kuiper ◽  
Anna Noel-Storr ◽  
Rachel Marshall ◽  
...  
Keyword(s):  
Automated System ◽  
Free Text ◽  
Gold Standard Method ◽  
Rule Based ◽  
Randomized Controlled ◽  
Structured Information ◽  
Combine Machine ◽  
Clinical Queries ◽  
Clinical Trials Registry ◽  
Extract Information

Objective Randomized controlled trials (RCTs) are the gold standard method for evaluating whether a treatment works in healthcare, but can be difficult to find and make use of. We describe the development and evaluation of a system to automatically find and categorize all new RCT reports. Materials and Methods Trialstreamer, continuously monitors PubMed and the WHO International Clinical Trials Registry Platform (ICTRP), looking for new RCTs in humans using a validated classifier. We combine machine learning and rule-based methods to extract information from the RCT abstracts, including free-text descriptions of trial populations, interventions and outcomes (the 'PICO') and map these snippets to normalised MeSH vocabulary terms. We additionally identify sample sizes, predict the risk of bias, and extract text conveying key findings. We store all extracted data in a database which we make freely available for download, and via a search portal, which allows users to enter structured clinical queries. Results are ranked automatically to prioritize larger and higher-quality studies. Results As of May 2020, we have indexed 669,895 publications of RCTs, of which 18,485 were published in the first four months of 2020 (144/day). We additionally include 303,319 trial registrations from ICTRP. The median trial sample size in the RCTs was 66. Conclusions We present an automated system for finding and categorising RCTs. This yields a novel resource: A database of structured information automatically extracted for all published RCTs in humans. We make daily updates of this database available on our website (trialstreamer.robotreviewer.net).

scholarly journals Automated Detection of Macular Hole in Optical Coherence Tomography Images using Depth-Check Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 105-108
Keyword(s):  
Optical Coherence Tomography ◽  
Macular Hole ◽  
Automated Detection ◽  
Machine Learning Algorithms ◽  
Automated System ◽  
Optical Coherence ◽  
Central Vision ◽  
Fiber Layer ◽  
Aged People ◽  
Depth Feature

Macular hole is a tear or break in the macula. It is located in the center of the retina and affects central vision of aged people. Optical Coherence Tomography (OCT) enables accurate diagnosis of macular hole. Existing algorithms available to detect cysts and retinal layers, but identifying macular hole in an accurate manner is still a missing entity. Hence we propose an automated system for the accurate macular hole detection. The proposed system has six stages in process. The first stage starts with preprocessing the OCT image, then detecting Nerve Fiber Layer (NFL). The detected NFL layer is then processed and depth feature is extracted. Then the macular hole is detected in OCT images using our proposed system. The proposed system is evaluated with the healthy macula and macular hole OCT images. The proposed system is also compared with other machine learning algorithms. By experimentation results, the proposed algorithm provides 94% accuracy in finding macular hole.

scholarly journals Automation analysis X-ray of the spine to objectify the assessment of the severity of scoliotic deformity in idiopathic scoliosis: a preliminary report

2020 ◽  
Vol 8 (3) ◽  
pp. 317-326
Author(s):  
Grigory A. Lein ◽  
Natalia S. Nechaeva ◽  
Gulnar М. Mammadova ◽  
Andrey A. Smirnov ◽  
Maxim M. Statsenko
Keyword(s):  
Neural Network ◽  
Idiopathic Scoliosis ◽  
Cobb Angle ◽  
Machine Learning Algorithms ◽  
Automated System ◽  
X Rays ◽  
X Ray ◽  
Scoliotic Deformity ◽  
Window Method ◽  
Treatment Type

Background. A large number of studies have focused on automating the process of measuring the Cobb angle. Although there is no practical tool to assist doctors with estimating the severity of the curvature of the spine and determine the best suitable treatment type. Aim. We aimed to examine the algorithms used for distinguishing vertebral column, vertebrae, and for building a tangent on the X-ray photographs. The superior algorithms should be implemented into the clinical practice as an instrument of automatic analysis of the spine X-rays in scoliosis patients. Materials and methods. A total of 300 digital X-rays of the spine of children with idiopathic scoliosis were gathered. The X-rays were manually ruled by a radiologist to determine the Cobb angle. This data was included into the main dataset used for training and validating the neural network. In addition, the Sliding Window Method algorithm was implemented and compared with the machine learning algorithms, proving it to be vastly superior in the context of this research. Results. This research can serve as the foundation for the future development of an automated system for analyzing spine X-rays. This system allows processing of a large amount of data for achieving 85% in training neural network to determine the Cobb angle. Conclusions. This research is the first step toward the development of a modern innovative product that uses artificial intelligence for distinguishing the different portions of the spine on 2D X-ray images for building the lines required to determine the Cobb angle.

scholarly journals Woven Fabric Pattern Recognition and Classification Based on Deep Convolutional Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1048 ◽  
Author(s):  
Muhammad Ather Iqbal Hussain ◽  
Babar Khan ◽  
Zhijie Wang ◽  
Shenyi Ding
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Texture Features ◽  
Learning Model ◽  
Woven Fabrics ◽  
Machine Learning Algorithms ◽  
Woven Fabric ◽  
Automated System ◽  
Deep Convolutional Neural Networks ◽  
Deep Learning Model

The weave pattern (texture) of woven fabric is considered to be an important factor of the design and production of high-quality fabric. Traditionally, the recognition of woven fabric has a lot of challenges due to its manual visual inspection. Moreover, the approaches based on early machine learning algorithms directly depend on handcrafted features, which are time-consuming and error-prone processes. Hence, an automated system is needed for classification of woven fabric to improve productivity. In this paper, we propose a deep learning model based on data augmentation and transfer learning approach for the classification and recognition of woven fabrics. The model uses the residual network (ResNet), where the fabric texture features are extracted and classified automatically in an end-to-end fashion. We evaluated the results of our model using evaluation metrics such as accuracy, balanced accuracy, and F1-score. The experimental results show that the proposed model is robust and achieves state-of-the-art accuracy even when the physical properties of the fabric are changed. We compared our results with other baseline approaches and a pretrained VGGNet deep learning model which showed that the proposed method achieved higher accuracy when rotational orientations in fabric and proper lighting effects were considered.

A Case Study of Career Counseling for ICT

2021 ◽  
pp. 162-184
Author(s):  
Rana Muhammad Amir Latif ◽  
Javed Ferzund ◽  
Muhammad Farhan ◽  
N. Z. Jhanjhi ◽  
Muhammad Umer
Keyword(s):  
Career Counseling ◽  
Effective Means ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Automated System ◽  
Kappa Statistics ◽  
Short Course ◽  
The Individual ◽  
Career Options ◽  
Individual Choices

In the education system, the students may find counselors, but student-to-counselor ratio is higher, which forces us to implement an automated system for the guidance of the students. Career counseling can be useful for students to evaluate their careers and select the best direction for the future. This chapter aims to explore, develop, and implement the effective means of analyzing student career counseling, guidelines, and decision making. The authors have developed a realistic dataset from a different mindset of students. The research started once the student provides the machine input about the individual choices about taking admission for matriculation, intermediate, and or short course. The machine learning algorithms like logistic model tree, naïve Bayes, J48, and random forest are used to predict career options. In evaluated results, they found the best algorithm based on the accuracy of kappa statistics, mean absolute error, and correctly classified or incorrectly classified for career-related problems.

scholarly journals A Review : Classification of Food Grains and Quality Prediction

2020 ◽  
pp. 34-37
Author(s):  
Mahalaxmi P P ◽  
Kavita D. Hanabaratti
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Automated System ◽  
Quality Prediction ◽  
Food Grains ◽  
Geometrical Features ◽  
Image Procession

This review paper discuss about recent techniques and methods used for grain classification and grading. Grains are important source of nutrients and they play important role in healthy diet. The production of grains across worldwide each year is in terms of hundreds of millions. The common method to classify these hugely produced grains is manual which is mind-numbing and not accurate. So the automated system is required which can classify the verities and predict the quality (i.e. grade A, grade B) of grain fast and accurate. As machine learning had done most of the difficult things easy, many machine learning algorithms can be used which can easily classify and predict the quality of grains. The system uses colour and geometrical features like size and area of grains as attributes for classification and quality prediction. Here, several image procession methods and machine learning algorithms are reviewed.

scholarly journals How Do Drivers Respond to Silent Automation Failures? Driving Simulator Study and Comparison of Computational Driver Braking Models

2019 ◽  
Vol 62 (7) ◽  
pp. 1212-1229
Author(s):  
Giulio Bianchi Piccinini ◽  
Esko Lehtonen ◽  
Fabio Forcolin ◽  
Johan Engström ◽  
Deike Albers ◽  
...  
Keyword(s):  
Prediction Model ◽  
Computational Models ◽  
Driving Simulator ◽  
Reaction Times ◽  
Automated System ◽  
Cruise Control ◽  
Automated Driving ◽  
Driver Response ◽  
Driving Condition ◽  
Simulator Study

Objective This paper aims to describe and test novel computational driver models, predicting drivers’ brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC). Background Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving. Method Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers’ arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study. Results The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study. Conclusion Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data. Application Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving.

scholarly journals Frontal electroencephalogram based drug, sex, and age independent sedation level prediction using non-linear machine learning algorithms

2020 ◽  
Author(s):  
S. M. Ramaswamy ◽  
M. H. Kuizenga ◽  
M. A. S. Weerink ◽  
H. E. M. Vereecke ◽  
M. M. R. F. Struys ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Age Groups ◽  
Machine Learning Algorithms ◽  
Automated System ◽  
Patient Specific ◽  
Learning Models ◽  
Sedation Level ◽  
Prediction Probability ◽  
Machine Learning Models

AbstractBrain monitors which track quantitative electroencephalogram (EEG) signatures to monitor sedation levels are drug and patient specific. There is a need for robust sedation level monitoring systems to accurately track sedation levels across all drug classes, sex and age groups. Forty-four quantitative features estimated from a pooled dataset of 204 EEG recordings from 66 healthy adult volunteers who received either propofol, dexmedetomidine, or sevoflurane (all with and without remifentanil) were used in a machine learning based automated system to estimate the depth of sedation. Model training and evaluation were performed using leave-one-out cross validation methodology. We trained four machine learning models to predict sedation levels and evaluated the influence of remifentanil, age, and sex on the prediction performance. The area under the receiver-operator characteristic curve (AUC) was used to assess the performance of the prediction model. The ensemble tree with bagging outperformed other machine learning models and predicted sedation levels with an AUC = 0.88 (0.81–0.90). There were significant differences in the prediction probability of the automated systems when trained and tested across different age groups and sex. The performance of the EEG based sedation level prediction system is drug, sex, and age specific. Nonlinear machine-learning models using quantitative EEG features can accurately predict sedation levels. The results obtained in this study may provide a useful reference for developing next generation EEG based sedation level prediction systems using advanced machine learning algorithms.Clinical trial registration: NCT 02043938 and NCT 03143972.

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