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Zhang, F., Fleyeh, H., Wang, X. & Lu, M. (2019). Construction site accident analysis using text mining and natural language processing techniques. Automation in Construction, 99, 238-248
Open this publication in new window or tab >>Construction site accident analysis using text mining and natural language processing techniques
2019 (English)In: Automation in Construction, ISSN 0926-5805, E-ISSN 1872-7891, Vol. 99, p. 238-248Article in journal (Refereed) Published
Abstract [en]

Workplace safety is a major concern in many countries. Among various industries, construction sector is identified as the most hazardous work place. Construction accidents not only cause human sufferings but also result in huge financial loss. To prevent reoccurrence of similar accidents in the future and make scientific risk control plans, analysis of accidents is essential. In construction industry, fatality and catastrophe investigation summary reports are available for the past accidents. In this study, text mining and natural language process (NLP) techniques are applied to analyze the construction accident reports. To be more specific, five baseline models, support vector machine (SVM), linear regression (LR), K-nearest neighbor (KNN), decision tree (DT), Naive Bayes (NB) and an ensemble model are proposed to classify the causes of the accidents. Besides, Sequential Quadratic Programming (SQP) algorithm is utilized to optimize weight of each classifier involved in the ensemble model. Experiment results show that the optimized ensemble model outperforms rest models considered in this study in terms of average weighted F1 score. The result also shows that the proposed approach is more robust to cases of low support. Moreover, an unsupervised chunking approach is proposed to extract common objects which cause the accidents based on grammar rules identified in the reports. As harmful objects are one of the major factors leading to construction accidents, identifying such objects is extremely helpful to mitigate potential risks. Certain limitations of the proposed methods are discussed and suggestions and future improvements are provided.

Keywords
Construction site accident analysis, Machine learning, Natural language processing, Optimization, Sequential quadratic programming, Text mining
National Category
Computer and Information Sciences
Research subject
Energy and Built Environments
Identifiers
urn:nbn:se:du-29254 (URN)10.1016/j.autcon.2018.12.016 (DOI)000456759400020 ()2-s2.0-85058940383 (Scopus ID)
Available from: 2019-01-07 Created: 2019-01-07 Last updated: 2019-06-27Bibliographically approved
Paidi, V., Fleyeh, H. & Nyberg, R. G. (2019). Deep learning-based vehicle occupancy detection in an open parking lot using thermal camera.
Open this publication in new window or tab >>Deep learning-based vehicle occupancy detection in an open parking lot using thermal camera
2019 (English)In: Article in journal (Other academic) Submitted
Abstract [en]

Parking vehicle is a daunting task and a common problem in many cities around the globe. The search for parking space leads to congestion, frustration and increased air pollution. Information of a vacant parking space would facilitate to reduce congestion and subsequent air pollution. Therefore, aim of the paper is to acquire vehicle occupancy in an open parking lot using deep learning. Thermal camera was used to collect the data during varying environmental conditions such as; sunny, dusk, dawn, dark and snowy conditions. Vehicle detection with deep learning was implemented where image classification and object localization were performed for multi object detection. The dataset consists of 527 images which were manually labelled as there were no pre-labelled thermal images available. Multiple deep learning networks such as Yolo, ReNet18, ResNet50 and GoogleNet with varying layers and architectures were evaluated on vehicle detection. Yolo, GoogleNet and ResNet18 are computationally efficient detectors which took less processing time while Resnet50 produced better detection results compared to other detectors. However, ResNet18 also produced minimal miss rates and is suitable for real time vehicle detection. The detected results were compared with a template of parking spaces and IoU value is used to identify vehicle occupancy information.

National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30605 (URN)
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-08-14Bibliographically approved
Fleyeh, H. & Westin, J. (2019). Extracting Body Landmarks from Videos for Parkinson Gait Analysis. In: : . Paper presented at 32nd IEEE CBMS International Symposium on Computer-Based Medical Systems, Instituto Maimónides de Investigación Biomédica de Córdoba, Córdoba, Spain, 5-7 June 2019.
Open this publication in new window or tab >>Extracting Body Landmarks from Videos for Parkinson Gait Analysis
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Patients with Parkinson disease (PD) exhibit a gait disorder called festinating gait which is caused by deficiency of dopamine in the basal ganglia. To analyze gait of patients with PD, different spatiotemporal parameters such as stride length, cadence, and walking speed should be calculated. This paper aims to present a method to extract useful information represented by the positions of certain landmarks on the human body that can be used for analysis of PD patients’ gait. This method is tested using 132 videos collected from 7 PD patients and 7 healthy controls. The positions of 4 body landmarks, namely body’s center of gravity (COG), the position of the head, and the position of the feet, was computed using a total of more than 41000 of video frames. Results of object’s movement plots show high level of accuracy in the calculation of the body landmarks.

National Category
Medical Image Processing
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30583 (URN)
Conference
32nd IEEE CBMS International Symposium on Computer-Based Medical Systems, Instituto Maimónides de Investigación Biomédica de Córdoba, Córdoba, Spain, 5-7 June 2019
Available from: 2019-07-29 Created: 2019-07-29 Last updated: 2019-07-30Bibliographically approved
Aghanavesi, S., Fleyeh, H., Memedi, M. & Dougherty, M. (2019). Feasibility of using smartphones for quantification of Parkinson’s disease motor states during hand rotation tests. In: : . Paper presented at 41st International Engineering in Medicine and Biology Conference, Berlin, Germany, July 23–27, 2019.
Open this publication in new window or tab >>Feasibility of using smartphones for quantification of Parkinson’s disease motor states during hand rotation tests
2019 (English)Conference paper, Published paper (Refereed)
National Category
Medical Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-29543 (URN)
Conference
41st International Engineering in Medicine and Biology Conference, Berlin, Germany, July 23–27, 2019
Available from: 2019-02-21 Created: 2019-02-21 Last updated: 2019-06-05Bibliographically approved
Paidi, V. & Fleyeh, H. (2019). Parking Occupancy Detection Using Thermal Camera. In: Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS: . Paper presented at 5th International Conference on Vehicle Technology and Intelligent Transport Systems, May 3-5 2019, Heraklion, Greece (pp. 483-490).
Open this publication in new window or tab >>Parking Occupancy Detection Using Thermal Camera
2019 (English)In: Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, 2019, p. 483-490Conference paper, Published paper (Refereed)
Abstract [en]

Parking a vehicle is a daunting task during peak hours. The search for a parking space leads to congestion and increased air pollution. Information of a vacant parking space would facilitate to reduce congestion and subsequent air pollution. This paper aims to identify parking occupancy in an open parking lot which consists of free parking spaces using a thermal camera. A thermal camera is capable of detecting vehicles in any weather and light conditions based on emitted heat and it can also be installed in public places with less restrictions. However, a thermal camera is expensive compared to a colour camera. A thermal camera can detect vehicles based on the emitted heat without any illumination. Vehicles appear bright or dark based on heat emitted by the vehicles. In order to identify vehicles, pre-trained vehicle detection algorithms, Histogram of Oriented Gradient detectors, Faster Regional Convolutional Neural Network (FRCNN) and modified Faster RCNN deep learning networks were implemented in this paper. The detection rates of the detectors reduced with diminishing of heat in the vehicles. Modified Faster RCNN deep learning network produced better detection results compared to other detectors. However, the detection rates can further be improved with larger and diverse training dataset.

Keywords
Convolutional Neural Network, Detectors, Thermal Camera
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30153 (URN)10.5220/0007726804830490 (DOI)2-s2.0-85067576912 (Scopus ID)978-989-758-374-2 (ISBN)
Conference
5th International Conference on Vehicle Technology and Intelligent Transport Systems, May 3-5 2019, Heraklion, Greece
Available from: 2019-06-10 Created: 2019-06-10 Last updated: 2019-07-01Bibliographically approved
Zhang, F. & Fleyeh, H. (2018). A review on electricity price forecasting using neural network based models.
Open this publication in new window or tab >>A review on electricity price forecasting using neural network based models
2018 (English)Report (Other (popular science, discussion, etc.))
Publisher
p. 100
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-28694 (URN)
Available from: 2018-10-12 Created: 2018-10-12 Last updated: 2018-12-17Bibliographically approved
Zhang, F. & Fleyeh, H. (2018). Short term electricity price forecasting using CatBoost and bidirectional long short term memory neural network.
Open this publication in new window or tab >>Short term electricity price forecasting using CatBoost and bidirectional long short term memory neural network
2018 (English)Report (Other (popular science, discussion, etc.))
Publisher
p. 37
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-28693 (URN)
Available from: 2018-10-12 Created: 2018-10-12 Last updated: 2018-12-17Bibliographically approved
Paidi, V., Fleyeh, H., Håkansson, J. & Nyberg, R. G. (2018). Smart parking sensors, technologies and applications for open parking lots: a review. IET Intelligent Transport Systems, 12(8), 735-741
Open this publication in new window or tab >>Smart parking sensors, technologies and applications for open parking lots: a review
2018 (English)In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 12, no 8, p. 735-741Article in journal (Refereed) Published
Abstract [en]

Parking a vehicle in traffic dense environments often leads to excess time of driving in search for free space which leads to congestions and environmental pollution. Lack of guidance information to vacant parking spaces is one reason for inefficient parking behaviour. Smart parking sensors and technologies facilitate guidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensors or technologies is in use for open parking lot. This paper reviews the literature on the usage of smart parking sensors, technologies, applications and evaluate their applicability to open parking lots. Magnetometers, ultrasonic sensors and machine vision were few of the widely used sensors and technologies on closed parking lots. However, this paper suggests a combination of machine vision, convolutional neural network or multi-agent systems suitable for open parking lots due to less expenditure and resistance to varied environmental conditions. Few smart parking applications show drivers the location of common open parking lots. No application provided real time parking occupancy information, which is a necessity to guide them along the shortest route to free space. To develop smart parking applications for open parking lots, further research is needed in the fields of deep learning and multi-agent systems.

Place, publisher, year, edition, pages
Institution of Engineering and Technology, 2018
National Category
Computer Systems
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-27619 (URN)10.1049/iet-its.2017.0406 (DOI)000444389300001 ()2-s2.0-85053198237 (Scopus ID)
Available from: 2018-05-04 Created: 2018-05-04 Last updated: 2019-08-14Bibliographically approved
Paidi, V., Fleyeh, H., Håkansson, J. & Nyberg, R. G. (2018). Smart Parking Tools Suitability for Open Parking Lots: A Review. In: Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems: . Paper presented at 4th International Conference on Vehicle Technology and Intelligent Transport Systems, March 16-18 2018, Funchal, Madeira (pp. 600-609). Madeira
Open this publication in new window or tab >>Smart Parking Tools Suitability for Open Parking Lots: A Review
2018 (English)In: Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems, Madeira, 2018, p. 600-609Conference paper, Published paper (Refereed)
Abstract [en]

Parking a vehicle in traffic dense environments is a common issue in many parts of the world which oftenleads to congestion and environmental pollution. Lack of guidance information to vacant parking spaces isone of the reasons for inefficient parking behaviour. Smart parking sensors and technologies facilitateguidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensorsor technologies are in use for the common open parking lot. This paper reviews the literature on the usage ofsmart parking sensors, technologies, applications and evaluate their suitability to open parking lots. Suitabilitywas made in terms of expenditure and reliability. Magnetometers, ultrasonic sensors and machine vision werefew of the widely used sensors and technologies used in closed parking lots. However, this paper suggests acombination of machine vision, fuzzy logic or multi-agent systems suitable for open parking lots due to lessexpenditure and resistance to varied environmental conditions. No application provided real time parkingoccupancy information of open parking lots, which is a necessity to guide them along the shortest route tofree space. To develop smart parking applications for open parking lots, further research is needed in the fieldsof deep learning.

Place, publisher, year, edition, pages
Madeira: , 2018
Keywords
Decision support system, sensors, technologies, applications
National Category
Computer Systems
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-27616 (URN)10.5220/0006812006000609 (DOI)2-s2.0-85051933085 (Scopus ID)978-989-758-293-6 (ISBN)
Conference
4th International Conference on Vehicle Technology and Intelligent Transport Systems, March 16-18 2018, Funchal, Madeira
Available from: 2018-05-04 Created: 2018-05-04 Last updated: 2018-12-17Bibliographically approved
Li, Y. & Fleyeh, H. (2018). Twitter Sentiment Analysis of New IKEA Stores Using Machine Learning. In: 2018 International Conference on Computer and Applications, ICCA 2018: . Paper presented at International Conference on Computer and Applications, July 25th-26th, 2018, Beirut, Lebanon (pp. 4-11). , Article ID 8460277.
Open this publication in new window or tab >>Twitter Sentiment Analysis of New IKEA Stores Using Machine Learning
2018 (English)In: 2018 International Conference on Computer and Applications, ICCA 2018, 2018, p. 4-11, article id 8460277Conference paper, Published paper (Refereed)
Abstract [en]

This paper studied public emotion and opinion concerning the opening of new IKEA stores, specifically, how much attention are attracted, how much positive and negative emotion are aroused, what IKEA-related topics are talked due to this event. Emotion is difficult to measure in retail due to data availability and limited quantitative tools. Twitter texts, written by the public to express their opinion concerning this event, are used as a suitable data source to implement sentiment analysis. Around IKEA opening days, local people post IKEA related tweets to express their emotion and opinions on that. Such “IKEA” contained tweets are collected for opinion mining in this work. To compute sentiment polarity of tweets, lexiconbased approach is used for English tweets, and machine learning methods for Swedish tweets. The conclusion is new IKEA store are paid much attention indicated by significant increasing tweets frequency, most of them are positive emotions, and four studied cities have different topics and interests related IKEA. This paper extends knowledge of consumption emotion studies of prepurchase, provide empirical analysis of IKEA entry effect on emotion. Moreover, it develops a Swedish sentiment prediction model, elastic net method, to compute Swedish tweets’ sentiment polarity which has been rarely conducted.  

Keywords
big-box effect, opinion analysis, customer emotion, elastic net model, text mining, natural language processing
National Category
Computer Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-27646 (URN)10.1109/COMAPP.2018.8460277 (DOI)000450258100002 ()2-s2.0-85054497923 (Scopus ID)9781538643716 (ISBN)
Conference
International Conference on Computer and Applications, July 25th-26th, 2018, Beirut, Lebanon
Available from: 2018-05-08 Created: 2018-05-08 Last updated: 2018-11-29Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1429-2345

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