Applied Machine Learning Techniques to Find Patterns and Trends in the Use of Bicycle Sharing Systems Influenced by Traffic Accidents and Violent Events in Guadalajara, Mexico

2021 ◽  
pp. 475-490
Author(s):  
Adrian Barradas ◽  
Andrea Gomez-Alfaro ◽  
Rosa-María Cantón-Croda
Keyword(s):  
Machine Learning ◽  
Traffic Accidents ◽  
Machine Learning Techniques ◽  
Applied Machine Learning ◽  
Learning Techniques ◽  
Violent Events

scholarly journals Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Majid Amirfakhrian ◽  
Mahboub Parhizkar
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Machine Vision ◽  
Traffic Accidents ◽  
Fuzzy Theory ◽  
Machine Learning Techniques ◽  
Industrial Setting ◽  
Technological Advances ◽  
Learning Techniques ◽  
Wide Range

AbstractIn the next decade, machine vision technology will have an enormous impact on industrial works because of the latest technological advances in this field. These advances are so significant that the use of this technology is now essential. Machine vision is the process of using a wide range of technologies and methods in providing automated inspections in an industrial setting based on imaging, process control, and robot guidance. One of the applications of machine vision is to diagnose traffic accidents. Moreover, car vision is utilized for detecting the amount of damage to vehicles during traffic accidents. In this article, using image processing and machine learning techniques, a new method is presented to improve the accuracy of detecting damaged areas in traffic accidents. Evaluating the proposed method and comparing it with previous works showed that the proposed method is more accurate in identifying damaged areas and it has a shorter execution time.

scholarly journals Meta-analysis of voice disorders databases and applied machine learning techniques

2020 ◽  
Vol 17 (6) ◽  
pp. 7958-7979
Author(s):  
Sidra Abid Syed ◽  
◽  
Munaf Rashid ◽  
Samreen Hussain ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Voice Disorders ◽  
Machine Learning Techniques ◽  
Applied Machine Learning ◽  
Learning Techniques

A pilot study of a machine-learning tool to assist in the diagnosis of hand arthritis (Preprint)

2020 ◽  
Author(s):  
Mark Daly Reed ◽  
Timothy James Le Souef ◽  
Elliot Rampono
Keyword(s):  
Machine Learning ◽  
Inflammatory Arthritis ◽  
Machine Learning Techniques ◽  
Patient Questionnaire ◽  
Machine Learning Methods ◽  
Applied Machine Learning ◽  
Learning Techniques ◽  
Examination Result ◽  
Delays In Diagnosis ◽  
Rheumatology Practice

BACKGROUND Arthritis is a common condition, which frequently involves the hands. Patients with inflammatory arthritis have been shown to experience significant delays in diagnosis. OBJECTIVE We sought to develop and test a screening tool combining an image of a patient’s hands, a short series of questions, and a single examination technique, to determine the most likely diagnosis in a patient presenting with hand arthritis. Machine learning techniques were used to develop separate algorithms for each component, which were combined to produce a diagnosis. METHODS 280 consecutive new patients presenting to a Rheumatology practice with hand arthritis were enrolled. Each patient completed a 9-part questionnaire, had photographs taken of each hand, and had a single examination result recorded. The Rheumatologist diagnosis was recorded following a 45-minute consultation. The photograph algorithm was developed from a library of 1000 images, and machine learning techniques were applied to the questionnaire results, training several models against the diagnosis from the Rheumatologist. RESULTS The combined algorithms in this study were able to predict inflammatory arthritis with an accuracy, precision, recall and specificity of 96·8%, 97·2%, 98·6% and 90·5% respectively. Similar results were found when inflammatory arthritis was subclassified into rheumatoid arthritis and psoriatic arthritis. The corresponding figures for osteoarthritis were 79·6%, 85·9%, 61·9% and 92·6%. CONCLUSIONS This study demonstrates a novel application of a combined image-processing and a patient questionnaire with applied machine-learning methods, to facilitate the diagnosis of patients presenting with hand arthritis. Preliminary results are encouraging for the application of such techniques in clinical practice. CLINICALTRIAL Not applicable.

scholarly journals Applied Machine Learning Techniques on Selection and Positioning of Human Resources in the Public Sector

2021 ◽  
Vol 09 (02) ◽  
pp. 536-556
Author(s):  
Panagiota Pampouktsi ◽  
Spyridon Avdimiotis ◽  
Manolis Μaragoudakis ◽  
Markos Avlonitis
Keyword(s):  
Machine Learning ◽  
Public Sector ◽  
Human Resources ◽  
Machine Learning Techniques ◽  
The Public ◽  
Applied Machine Learning ◽  
Learning Techniques

scholarly journals Machine Learning Methods to Classify Mushrooms for Edibility-A Review

2020 ◽  
Vol 06 (09) ◽  
pp. 54-58
Author(s):  
Rakesh Kumar Y and Dr. V. Chandrasekhar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Literature Review ◽  
Edible Mushroom ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Applied Machine Learning ◽  
Learning Techniques ◽  
Artificial Neural Network Ann

There are thousands of species of Mushrooms in the world; they are edible and non-edible being poisonous. It is difficult for non-expertise person to Identify poisonous and edible mushroom of all the species manually. So a computer aided system with software or algorithm is required to classify poisonous and nonpoisonous mushrooms. In this paper a literature review is presented on classification of poisonous and nonpoisonous mushrooms. Most of the research works to classify the type of mushroom have applied, machine learning techniques like Naïve Bayes, K-Neural Network, Support vector Machine(SVM), Artificial Neural Network(ANN), Decision Tree techniques. In this literature review, a summary and comparisons of all different techniques of mushroom classification in terms of its performance parameters, merits and demerits faced during the classification of mushrooms using machine learning techniques.

Ensemble of SVM Classifiers for Spam Filtering

2011 ◽  
pp. 561-566
Author(s):  
Ángela Blanco ◽  
Manuel Martín-Merino
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Applied Machine Learning ◽  
Internet Users ◽  
Learning Techniques ◽  
Svm Algorithm ◽  
Misclassification Errors ◽  
Voting Strategy

Unsolicited commercial email also known as Spam is becoming a serious problem for Internet users and providers (Fawcett, 2003). Several researchers have applied machine learning techniques in order to improve the detection of spam messages. Naive Bayes models are the most popular (Androutsopoulos, 2000) but other authors have applied Support Vector Machines (SVM) (Drucker, 1999), boosting and decision trees (Carreras, 2001) with remarkable results. SVM has revealed particularly attractive in this application because it is robust against noise and is able to handle a large number of features (Vapnik, 1998). Errors in anti-spam email filtering are strongly asymmetric. Thus, false positive errors or valid messages that are blocked, are prohibitively expensive. Several authors have proposed new versions of the original SVM algorithm that help to reduce the false positive errors (Kolz, 2001, Valentini, 2004 & Kittler, 1998). In particular, it has been suggested that combining non-optimal classifiers can help to reduce particularly the variance of the predictor (Valentini, 2004 & Kittler, 1998) and consequently the misclassification errors. In order to achieve this goal, different versions of the classifier are usually built by sampling the patterns or the features (Breiman, 1996). However, in our application it is expected that the aggregation of strong classifiers will help to reduce more the false positive errors (Provost, 2001 & Hershop, 2005). In this paper, we address the problem of reducing the false positive errors by combining classifiers based on multiple dissimilarities. To this aim, a diversity of classifiers is built considering dissimilarities that reflect different features of the data. The dissimilarities are first embedded into an Euclidean space where a SVM is adjusted for each measure. Next, the classifiers are aggregated using a voting strategy (Kittler, 1998). The method proposed has been applied to the Spam UCI machine learning database (Hastie, 2001) with remarkable results.

scholarly journals Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 372
Author(s):  
Luca Ronzio ◽  
Federico Cabitza ◽  
Alessandro Barbaro ◽  
Giuseppe Banfi
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Laboratory Medicine ◽  
Machine Learning Techniques ◽  
Prognostic Performance ◽  
Applied Machine Learning ◽  
Previous Review ◽  
Learning Techniques

This article presents a systematic literature review that expands and updates a previous review on the application of machine learning to laboratory medicine. We used Scopus and PubMed to collect, select and analyse the papers published from 2017 to the present in order to highlight the main studies that have applied machine learning techniques to haematochemical parameters and to review their diagnostic and prognostic performance. In doing so, we aim to address the question we asked three years ago about the potential of these techniques in laboratory medicine and the need to leverage a tool that was still under-utilised at that time.

scholarly journals A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3174 ◽  
Author(s):  
Renato Torres ◽  
Orlando Ohashi ◽  
Gustavo Pessin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Traffic Accidents ◽  
Driver Distraction ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Learning Approach ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Smartphone Sensors

Driver distraction is one of the major causes of traffic accidents. In recent years, given the advance in connectivity and social networks, the use of smartphones while driving has become more frequent and a serious problem for safety. Texting, calling, and reading while driving are types of distractions caused by the use of smartphones. In this paper, we propose a non-intrusive technique that uses only data from smartphone sensors and machine learning to automatically distinguish between drivers and passengers while reading a message in a vehicle. We model and evaluate seven cutting-edge machine-learning techniques in different scenarios. The Convolutional Neural Network and Gradient Boosting were the models with the best results in our experiments. Results show accuracy, precision, recall, F1-score, and kappa metrics superior to 0.95.

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