Machine learning-based multi-target regression to effectively predict turning movements at signalized intersections

Effective prediction of turning movement counts at intersections through efficient and accurate methods is essential and needed for various applications. Commonly predictive methods require extensive data collection, calibration, and modeling efforts to estimate turning movements. In this study, three models were proposed to estimate turning movements at signalized intersections using approach volumes. Two sets of data from the United States and Canada were obtained to develop and test the proposed models. Machine learning-based regression models, including random forest regressor (RFR) and multioutput regressor (MOR) in addition to an artificial neural network (ANN) model, were developed and trained to analyze the relationship between approach volumes and corresponding turning movements. Multiple evaluation measurements were utilized to compare the models. All models produced satisfactory results. The RFR regression model outperformed the MOR model. However, the ANN model had the best performance when compared to the other models. The proposed models provide traffic engineers and planners with reliable and fast methods to estimate turning movements. 2022 Tongji University, Tongji University PressThe authors would like to thank the reviewers for their dedicated work and insightful comments and recommendations.Scopu

Comparative Evaluation of Sentiment Analysis Methods Across Arabic Dialects

Sentiment analysis in Arabic is challenging due to the complex morphology of the language. The task becomes more challenging when considering Twitter data that contain significant amounts of noise such as the use of Arabizi, code-switching and different dialects that varies significantly across the Arab world, the use of non-Textual objects to express sentiments, and the frequent occurrence of misspellings and grammatical mistakes. Modeling sentiment in Twitter should become easier when we understand the characteristics of Twitter data and how its usage varies from one Arab region to another. We describe our effort to create the first Multi-Dialect Arabic Sentiment Twitter Dataset (MD-ArSenTD) that is composed of tweets collected from 12 Arab countries, annotated for sentiment and dialect. We use this dataset to analyze tweets collected from Egypt and the United Arab Emirates (UAE), with the aim of discovering distinctive features that may facilitate sentiment analysis. We also perform a comparative evaluation of different sentiment models on Egyptian and UAE tweets. These models are based on feature engineering and deep learning, and have already achieved state-of-The-Art accuracies in English sentiment analysis. Results indicate the superior performance of deep learning models, the importance of morphological features in Arabic NLP, and that handling dialectal Arabic leads to different outcomes depending on the country from which the tweets are collected.This work was made possible by NPRP 6-716-1-138 grant from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Research-based learning in computing courses for senior engineering students

This paper reports on the experience and lessons learned from introducing a constructivist inquiry-based learning (IBL) in advanced computing courses. The paper describes an iterative problem-centric process and gives details on its activities, procedures, assessment methods, and related management aspects. Research-based projects are one of the IBL practices that are particularly suitable for higher education. Research-based IBL has been conducted in two different higher education contexts; at the University of Waterloo and at Qatar University. Measurement data provides information relative to the strengths and weaknesses of the adopted methodology in satisfying the pedagogical objectives being addressed in the paper. 2012 IEEE.Scopu

Interest-determining web browser

This paper investigates the application of data-mining techniques on a user's browsing history for the purpose of determining the user's interests. More specifically, a system is outlined that attempts to determine certain keywords that a user may or may not be interested in. This is done by first applying a term-frequency/inverse-document frequency filter to extract keywords from webpages in the user's history, after which a Self-Organizing Map (SOM) neural network is utilized to determine if these keywords are of interest to the user. Such a system could enable web-browsers to highlight areas of web pages that may be of higher interest to the user. It is found that while the system is indeed successful in identifying many keywords of user-interest, it also mis-classifies many uninteresting words boasting only a 62% accuracy rate. 2010 Springer-Verlag Berlin Heidelberg.Scopu

Global stabilization of autonomous underactuated underwater vehicles in 3D space

In this paper, global asymptotic stabilization of an autonomous underactuated underwater vehicle (AUUV) is investigated, where the number of actuators of the AUUV is less than the vehicle's degrees of freedom. The model that is considered describes both the kinematics and dynamics of the AUUV with six degrees of freedom and four actuators. To cope with the underactuation characteristics of AUUV a state transformation is proposed to change the model of the vehicle to a cascade nonlinear system. Then a switching control algorithm is proposed where the stability of the whole vehicle is guaranteed based on the stability properties of cascade systems. To illustrate the performance of the proposed approach, simulation results are provided. 2016 IEEE.Scopu

Urban air pollution monitoring system with forecasting models

A system for monitoring and forecasting urban air pollution is presented in this paper. The system uses low-cost air-quality monitoring motes that are equipped with an array of gaseous and meteorological sensors. These motes wirelessly communicate to an intelligent sensing platform that consists of several modules. The modules are responsible for receiving and storing the data, preprocessing and converting the data into useful information, forecasting the pollutants based on historical information, and finally presenting the acquired information through different channels, such as mobile application, Web portal, and short message service. The focus of this paper is on the monitoring system and its forecasting module. Three machine learning (ML) algorithms are investigated to build accurate forecasting models for one-step and multi-step ahead of concentrations of ground-level ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2). These ML algorithms are support vector machines, M5P model trees, and artificial neural networks (ANN). Two types of modeling are pursued: 1) univariate and 2) multivariate. The performance evaluation measures used are prediction trend accuracy and root mean square error (RMSE). The results show that using different features in multivariate modeling with M5P algorithm yields the best forecasting performances. For example, using M5P, RMSE is at its lowest, reaching 31.4, when hydrogen sulfide (H2S) is used to predict SO2. Contrarily, the worst performance, i.e., RMSE of 62.4, for SO2 is when using ANN in univariate modeling. The outcome of this paper can be significantly useful for alarming applications in areas with high air pollution levels. 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.Scopu

CLASENTI: A class-specific sentiment analysis framework

Arabic text sentiment analysis suffers from low accuracy due to Arabic-specific challenges (e.g., limited resources, morphological complexity, and dialects) and general linguistic issues (e.g., fuzziness, implicit sentiment, sarcasm, and spam). The limited resources problem requires efforts to build new and improved Arabic corpora and lexica. We propose a class-specific sentiment analysis (CLASENTI) framework. The framework includes a new annotation approach to build multi-faceted Arabic corpus and lexicon allowing for simultaneous annotation of different facets, including domains, dialects, linguistic issues, and polarity strengths. Each of these facets has multiple classes (e.g., the nine classes representing dialects found in the Arab world). The new corpus and lexicon annotations facilitate the development of new class-specific classification models and polarity strength calculation. For the new sentiment classification models, we propose a hybrid model combining corpus-based and lexicon-based models. The corpus-based model has two interrelated phases to build; (1) full-corpus classification models for all facets; and (2) class-specific models trained on filtered subsets of the corpus according to the performances of the full-corpus models. To calculate polarity strengths, the lexicon-based model filters the annotated lexicon based on the specific classes of the domain and dialect. As a case study, we collect and annotate 15274 reviews from various sources, including surveys, Facebook comments, and Twitter posts, pertaining to governmental services. In addition, we develop a new web-based application to apply the proposed framework on the case study. CLASENTI framework reaches up to 95% accuracy and 93% F1-Score surpassing the best-known sentiment classifiers implemented in Scikit-learn library that achieve 82% accuracy and 81% F1-Score for Arabic when tested on the same dataset