scholarly journals Personalized Behavior Prediction: An Idiographic Person-Situation Test

2021 ◽  
Author(s):  
Emorie D Beck ◽  
Joshua James Jackson
Keyword(s):  
Prediction Accuracy ◽  
Time Of Day ◽  
Learning Approaches ◽  
Behavior Prediction ◽  
Affective States ◽  
Intensive Longitudinal Data ◽  
Psychological Phenomena ◽  
Diurnal Cycles ◽  
Future Behavior ◽  
Day Of The Week

A longstanding goal of psychology is to predict the things people do, but tools to accurately predict future behaviors remain elusive. In the present study, we used intensive longitudinal data (N = 104; total assessments = 5,971) and three machine learning approaches to investigate the degree to which two behaviors – loneliness and procrastination – could be predicted from past psychological (i.e. personality and affective states), situational (i.e. objective situations and psychological situation cues), and time (i.e. trends, diurnal cycles, time of day, and day of the week) phenomena from an idiographic, person-specific perspective. Rather than pitting persons against situations, such an approach allows psychological phenomena, situations, and time to jointly predict future behavior. We find (1) a striking degree of prediction accuracy across participants, (2) that a majority of participants’ future behaviors are predicted by both person and situation features, and (3) that the most important features vary greatly across people.

scholarly journals The system analysis and mathematical modeling road accidents in the metropolis

2019 ◽  
pp. 4-8
Author(s):  
A. G. Davidovsky ◽  
A. M. Linnik
Keyword(s):  
Mathematical Modeling ◽  
Traffic Safety ◽  
System Analysis ◽  
Safety Management ◽  
Road Traffic ◽  
Time Of Day ◽  
Road Accidents ◽  
Traffic Incidents ◽  
Day Of The Week ◽  
Pedestrian Collisions

The article presents the results of correlation analysis of the causes of road accidents in such a modern metropolis as Minsk. Has been identified the most frequent causes of road accidents, including pedestrian collisions caused by drivers, collisions at intersections, incidents at controlled and unregulated pedestrian crossings, as well as on the roadway. The dependence of transport incidents on the time of day, day of the week and month of the year was investigated. Shows the periods when road traffic incidents occur from 3.00 to 6.00 h, from 15.00 to 18.00 and from 21.00 to 24.00 on Monday, Friday and Sunday in January, March, June, September, October and November. Methods of correlation and multiple regression analysis can be the basis of preventive traffic safety management in a modern metropolis.

scholarly journals Prediction of Eye, Hair and Skin Color in Admixed Populations of Latin America

2020 ◽  
Author(s):  
Sagnik Palmal ◽  
Kaustubh Adhikari ◽  
Javier Mendoza-Revilla ◽  
Macarena Fuentes-Guajardo ◽  
Caio C. Silva de Cerqueira ◽  
...  
Keyword(s):  
Native American ◽  
Skin Color ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Skin Pigmentation ◽  
Genetic Ancestry ◽  
Learning Approaches ◽  
Latin Americans ◽  
Eye Color ◽  
The Impact

AbstractWe report an evaluation of prediction accuracy for eye, hair and skin pigmentation based on genomic and phenotypic data for over 6,500 admixed Latin Americans (the CANDELA dataset). We examined the impact on prediction accuracy of three main factors: (i) The methods of prediction, including classical statistical methods and machine learning approaches, (ii) The inclusion of non-genetic predictors, continental genetic ancestry and pigmentation SNPs in the prediction models, and (iii) Compared two sets of pigmentation SNPs: the commonly-used HIrisPlex-S set (developed in Europeans) and novel SNP sets we defined here based on genome-wide association results in the CANDELA sample. We find that Random Forest or regression are globally the best performing methods. Although continental genetic ancestry has substantial power for prediction of pigmentation in Latin Americans, the inclusion of pigmentation SNPs increases prediction accuracy considerably, particularly for skin color. For hair and eye color, HIrisPlex-S has a similar performance to the CANDELA-specific prediction SNP sets. However, for skin pigmentation the performance of HIrisPlex-S is markedly lower than the SNP set defined here, including predictions in an independent dataset of Native American data. These results reflect the relatively high variation in hair and eye color among Europeans for whom HIrisPlex-S was developed, whereas their variation in skin pigmentation is comparatively lower. Furthermore, we show that the dataset used in the training of prediction models strongly impacts on the portability of these models across Europeans and Native Americans.

scholarly journals Vehicle Interaction Behavior Prediction with Self-Attention

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 429
Author(s):  
Linhui Li ◽  
Xin Sui ◽  
Jing Lian ◽  
Fengning Yu ◽  
Yafu Zhou
Keyword(s):  
Cluster Structure ◽  
Class Imbalance ◽  
Behavior Prediction ◽  
Time To Event ◽  
Future Behavior ◽  
Interaction Behavior ◽  
Proposed Model ◽  
High Interaction ◽  
Fully Connected ◽  
High Uncertainty

The structured road is a scene with high interaction between vehicles, but due to the high uncertainty of behavior, the prediction of vehicle interaction behavior is still a challenge. This prediction is significant for controlling the ego-vehicle. We propose an interaction behavior prediction model based on vehicle cluster (VC) by self-attention (VC-Attention) to improve the prediction performance. Firstly, a five-vehicle based cluster structure is designed to extract the interactive features between ego-vehicle and target vehicle, such as Deceleration Rate to Avoid a Crash (DRAC) and the lane gap. In addition, the proposed model utilizes the sliding window algorithm to extract VC behavior information. Then the temporal characteristics of the three interactive features mentioned above will be caught by two layers of self-attention encoder with six heads respectively. Finally, target vehicle’s future behavior will be predicted by a sub-network consists of a fully connected layer and SoftMax module. The experimental results show that this method has achieved accuracy, precision, recall, and F1 score of more than 92% and time to event of 2.9 s on a Next Generation Simulation (NGSIM) dataset. It accurately predicts the interactive behaviors in class-imbalance prediction and adapts to various driving scenarios.

scholarly journals Variation among Mechanical Turk Workers Across Time of Day Presents an Opportunity and a Challenge for Research

2019 ◽  
Author(s):  
Nathan Fordsham ◽  
Aaron J Moss ◽  
Sam Krumholtz ◽  
Thomas Roggina ◽  
Jonathan Robinson ◽  
...  
Keyword(s):  
Disordered Eating ◽  
Sampling Methods ◽  
Time Of Day ◽  
Mechanical Turk ◽  
Behavioral Characteristics ◽  
Online Platforms ◽  
Day Of The Week ◽  
Amazon's Mechanical Turk ◽  
Online Sampling ◽  
Anxiety Depression

Conducting behavioral research online allows researchers to gather more data in less time than conducting studies in person. But this efficiency may sometimes have a cost. Specifically, when researchers gather data within just a few hours, their study may be subject to a time of day bias. Because participants in online platforms are generally free to complete studies whenever they want, people who take studies in the morning may be different in important ways than those who take studies at night. We explored this possibility in two studies conducted on Amazon’s Mechanical Turk. In both studies, we sampled participants at different times of the day and examined whether morning and evening active people differed on a variety of psychological and behavioral characteristics known to correlate with a preference for either morningness or eveningness. We found that participants active in the morning and the evening reported different circadian typologies. Additionally, we found that participants active in the morning reported more conscientiousness and less anxiety, depression, procrastination, internet compulsion, disruptive sleep behaviors, disordered eating, and neuroticism than those sampled in the evening. Study 2 demonstrated that many signs of sub-clinical behavior were uniquely high among evening oriented people and that differences between morning and evening oriented people remained robust after controlling for local time zones and day of the week. Overall, our findings have important implications for online sampling methods and indicate that time of day differences in the composition of online samples represent both an opportunity and a challenge for research.

scholarly journals Nothing good happens after dark: the influence of temporal factors on motor carrier crash severity

2018 ◽  
Vol 28 (2) ◽  
pp. 7-18
Author(s):  
Misty Moody ◽  
S Scott Nadler ◽  
Doug Voss
Keyword(s):  
Public Policy ◽  
Time Of Day ◽  
Regulatory Agencies ◽  
Future Research ◽  
Crash Severity ◽  
Property Damage ◽  
Motor Carrier ◽  
Temporal Factors ◽  
Safety Outcomes ◽  
Day Of The Week

Motor carrier safety is a topic of great importance for both industry and makers of public policy. Regulatory agencies, such as the Federal Motor Carrier Safety Administration (FMCSA), regularly publish data detailing the circumstances surrounding roadway accidents. FMCSA’s Large Truck and Bus Crash Facts (LTBCF) data demonstrate an increase in accidents during daylight hours and on weekdays. Roadway risks are ever-present but differ by time of day and day of the week. These differences may potentially engender crashes of different severities at different times. This study analyzes FMCSA LTBCF data to determine when crashes of different severities are more likely to occur. Findings indicate that crashes resulting in property damage are more likely to occur during the day and on weekdays. However, fatal and injury crashes are significantly more likely during nights and weekends. Recommendations to improve safety outcomes are provided along with suggestions for future research.

Abstract TP458: High Accuracy of Predictive Models for SAH Using Different Machine Learning Approaches

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Paul Litvak ◽  
Jeevan Medikonda ◽  
Girish Menon ◽  
Pitchaiah Mandava
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Prediction Accuracy ◽  
Support Vector ◽  
World Federation ◽  
Learning Approaches ◽  
Flow Models ◽  
Multi Stage ◽  
Stage 1 ◽  
Categorical Scale

Background: Patients suffering from subarachnoid hemorrhage (SAH) have poor long-term outcomes. There are predictive models for ischemic and hemorrhagic stroke. However, there is paucity of models for SAH. Machine learning concepts were applied to build multi-stage Neural Networks (NN), Support Vector Machines (SVM) and Keras/Tensor Flow models to predict SAH outcomes. Methods: A database of ~800 aneurysmal SAH patients from Kasturba Medical College was utilized. Baseline variables of World Federation of Neurosurgeons 5-point scale (WFNS 1-5), age, gender, and presence/absence of hypertension and diabetes were considered in Stage 1. Stage 2 included all Stage 1 variables along with presence/absence of radiologic signs vasospasm and ischemia. Stage 3 includes earlier 2 stages and discharge Glasgow Outcome Scale (GOS 1-5). GOS at 3 months was predicted using 2-layer NN/SVM/Keras-TensorFlow models on the five point categorical scale as well as dichotomized to dead/alive and favorable (GOS 4-5) or unfavorable (GOS 1-3). Prediction accuracy of models was compared to the recorded GOS. Results: Prediction accuracy shown as percentages (See Table) for all three stages was similar for SVM, NN and Keras/TensorFlow models. Accuracy was remarkably higher with dichotomization compared to the complete five point GOS categorical scale. Conclusions: SVM, NN, and Keras-TensorFlow based machine learning models can be used to predict SAH outcomes to a high degree of accuracy. These powerful predictive models can be used to prognosticate and select patients into trials.

scholarly journals For Preventative Automated Driving System (PADS): Traffic Accident Context Analysis Based on Deep Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1829
Author(s):  
Minhee Kang ◽  
Jaein Song ◽  
Keeyeon Hwang
Keyword(s):  
Traffic Accident ◽  
Traffic Accidents ◽  
Deep Neural Networks ◽  
Time Of Day ◽  
Automated Driving ◽  
Context Analysis ◽  
Driving System ◽  
Vehicle Type ◽  
Fast Development ◽  
Day Of The Week

Automated Vehicles (AVs) are under development to reduce traffic accidents to a great extent. Therefore, safety will play a pivotal role to determine their social acceptability. Despite the fast development of AVs technologies, related accidents can occur even in an ideal environment. Therefore, measures to prevent traffic accidents in advance are essential. This study implemented a traffic accident context analysis based on the Deep Neural Network (DNNs) technique to design a Preventive Automated Driving System (PADS). The DNN-based analysis reveals that when a traffic accident occurs, the offender’s injury can be predicted with 85% accuracy and the victim’s case with 67%. In addition, to find out factors that decide the degree of injury to the offender and victim, a random forest analysis was implemented. The vehicle type and speed were identified as the most important factors to decide the degree of injury of the offender, while the importance for the victim is ordered by speed, time of day, vehicle type, and day of the week. The PADS proposed in this study is expected not only to contribute to improve the safety of AVs, but to prevent accidents in advance.

scholarly journals Economic Value of Improving Natural Gas Supply Reliability for Residential Consumers in South Korea

2019 ◽  
Vol 11 (2) ◽  
pp. 515 ◽  
Author(s):  
Hyo-Jin Kim ◽  
Sung-Min Kim ◽  
Seung-Hoon Yoo
Keyword(s):  
Natural Gas ◽  
Economic Value ◽  
Time Of Day ◽  
Economic Damage ◽  
South Korean Government ◽  
South Korean ◽  
Day Of The Week ◽  
Gas Supply ◽  
Supply Reliability ◽  
Residential Consumers

An interruption to residential natural gas (NG) may cause considerable economic damage of the entire country. Thus, the South Korean government requires information about the economic value of improving residential NG supply reliability for planning NG supply. This article aims to measure the value using a specific case of South Korean residential consumers. The choice experiment (CE) approach was adopted for this purpose. The selected four attributes are the duration of interruption, the season of interruption, the time of day, and the day of the week. The value trade-off works among the four attributes and price attribute were sought and completed in a nationwide CE survey of 1000 households. The respondents revealed statistically significant willingness to pay for a decrease in the duration of interruption, avoiding interruption during winter rather than non-winter, and preventing interruption during off-daytime (18:00 to 09:00) rather than daytime (09:00 to 18:00). For example, they accepted a 0.10% increase in the residential NG bill for a one-minute reduction in interruption during NG supply interruption, a 5.16% increase in residential NG bill for avoiding interruption during winter rather than non-winter, and a 2.94% increase in residential NG bill for preventing interruption during off-daytime rather than daytime. However, they placed no importance on the day of the week. These results can be useful for policy-making and decision-making to improve residential NG supply reliability. It is necessary to conduct a study at regular intervals on the value of NG supply reliability because regarding NG supply reliability, it is difficult to maintain a specific value.

scholarly journals Monday Morning Blues: The Risk of No Show for out-Patient Endoscopy by Time of Day and Day of the Week

2011 ◽  
Vol 140 (5) ◽  
pp. S-419
Author(s):  
Dilhana S. Badurdeen ◽  
Andrew K. Sanderson ◽  
Momodu A. Jack ◽  
Getachew Mekasha ◽  
Rehana Begum ◽  
...  
Keyword(s):  
Time Of Day ◽  
Monday Morning ◽  
Day Of The Week

scholarly journals Accelerometer-Measured Diurnal Patterns of Sedentary Behavior among Japanese Workers: A Descriptive Epidemiological Study

2020 ◽  
Vol 17 (11) ◽  
pp. 3814
Author(s):  
Sayaka Kurosawa ◽  
Ai Shibata ◽  
Kaori Ishii ◽  
Mohammad Javad Koohsari ◽  
Koichiro Oka
Keyword(s):  
Sedentary Behavior ◽  
Multivariate Regression ◽  
Sedentary Time ◽  
Time Of Day ◽  
Work Related ◽  
Diurnal Patterns ◽  
Time Period ◽  
Japanese Workers ◽  
Sociodemographic Correlates ◽  
Day Of The Week

Increased sedentary behavior (SB) can adversely affect health. Understanding time-dependent patterns of SB and its correlates can inform targeted approaches for prevention. This study examined diurnal patterns of SB and its sociodemographic associations among Japanese workers. The proportion of sedentary time (% of wear time) and the number of breaks in SB (times/sedentary hour) of 405 workers (aged 40–64 years) were assessed using an accelerometer. SB patterns and sociodemographic associations between each time period (morning, afternoon, evening) on workdays and nonworkdays were examined in a series of multivariate regression analyses, adjusting for other sociodemographic associations. On both workdays and nonworkdays, the proportion of sedentary time was lowest in the morning and increased towards evening (b = 12.95, 95% CI: 11.28 to 14.62; b = 14.31, 95% CI: 12.73 to 15.88), with opposite trend for breaks. Being male was consistently correlated with SB. Other sociodemographic correlates differed depending on time-of-day and day-of-the-week. For instance, desk-based workstyles and urban residential area were associated with SB during workday mornings and afternoons, being single was related to mornings and evenings, workdays and nonworkdays. Initiatives to address SB should focus not only on work-related but time-of-day contexts, especially for at-risk subgroups during each period.

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