2024

1. 

   Deep Umbra: A Generative Approach for Sunlight Access Computation in Urban Spaces

   Kazi Shahrukh Omar, Gustavo Moreira, Daniel Hodczak, and 4 more authors

   IEEE Transactions on Big Data, 2024

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   Sunlight and shadow play critical roles in how urban spaces are utilized, thrive, and grow. While access to sunlight is essential to the success of urban environment, shadows can provide shaded places to stay during the hot seasons, mitigate heat island effect, and increase pedestrian comfort levels. Properly quantifying sunlight access and shadows in large urban environments is key in tackling some of the important challenges facing cities today. In this paper, we propose Deep Umbra, a novel computational framework that enables the quantification of sunlight access and shadows at a global scale. Our framework is based on a conditional generative adversarial network that considers the physical form of cities to compute high-resolution spatial information of accumulated sunlight access for the different seasons of the year. We use data from seven different cities to train our model, and show, through an extensive set of experiments, its low overall RMSE (below 0.1) as well as its extensibility to cities that were not part of the training set. Additionally, we contribute a set of case studies and a comprehensive dataset with sunlight access information for more than 100 cities across six continents of the world. Deep Umbra is available at urbantk.org/shadows

   @article{omar2024deep,
     title = {Deep Umbra: A Generative Approach for Sunlight Access Computation in Urban Spaces},
     doi = {https://doi.org/10.1109/TBDATA.2024.3382964},
     author = {Omar, Kazi Shahrukh and Moreira, Gustavo and Hodczak, Daniel and Hosseini, Maryam and Colaninno, Nicola and Lage, Marcos and Miranda, Fabio},
     journal = {IEEE Transactions on Big Data},
     year = {2024},
   }

2022

1. 

   Crowdsourcing and Sidewalk Data: A Preliminary Study on the Trustworthiness of OpenStreetMap Data in the US

   Kazi Shahrukh Omar, Gustavo Moreira, Daniel Hodczak, and 2 more authors

   ASSETS UrbanAccess Workshop, 2022

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   Sidewalks play a pivotal role in urban mobility of everyday life. Ideally, sidewalks provide a safe walkway for pedestrians, link public transportation facilities, and equip people with routing and navigation services. However, there is a scarcity of open sidewalk data, which not only impacts the accessibility and walkability of cities but also limits policymakers in generating insightful measures to improve the current state of pedestrian facilities. As one of the most famous crowdsourced data repositories, OpenStreetMap (OSM) could aid the lack of open sidewalk data to a large extent. However, completeness and quality of OSM data have long been a major issue. In this paper, we offer a preliminary study on the availability and trustworthiness of OSM sidewalk data. First, we compare OSM sidewalk data coverage in over 50 major cities in the United States. Then, we select three major cities (Seattle, Chicago, and New York City) to further analyze the completeness of sidewalk data and its features, and to compute a trustworthiness index leveraging historical OSM sidewalk data.

   @article{omar2022crowdsourcing,
     title = {Crowdsourcing and Sidewalk Data: A Preliminary Study on the Trustworthiness of OpenStreetMap Data in the US},
     author = {Omar, Kazi Shahrukh and Moreira, Gustavo and Hodczak, Daniel and Hosseini, Maryam and Miranda, Fabio},
     journal = {ASSETS UrbanAccess Workshop},
     year = {2022},
   }

2021

1. 

   Exploring tree-based machine learning methods to predict autism spectrum disorder

   Kazi Shahrukh Omar, Muhammad Nazrul Islam, and Nabila Shahnaz Khan

   In Neural Engineering Techniques for Autism Spectrum Disorder, 2021

   Bib

   @incollection{omar2021exploring,
     title = {Exploring tree-based machine learning methods to predict autism spectrum disorder},
     doi = {https://doi.org/10.1016/B978-0-12-822822-7.00009-0},
     author = {Omar, Kazi Shahrukh and Islam, Muhammad Nazrul and Khan, Nabila Shahnaz},
     booktitle = {Neural Engineering Techniques for Autism Spectrum Disorder},
     pages = {165--183},
     year = {2021},
     publisher = {Elsevier},
   }

2019

1. 

   A machine learning approach to predict autism spectrum disorder

   Kazi Shahrukh Omar, Prodipta Mondal, Nabila Shahnaz Khan, and 2 more authors

   In International conference on electrical, computer and communication engineering (ECCE), 2019

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   In present day Autism Spectrum Disorder (ASD) is gaining its momentum faster than ever. Detecting autism traits through screening tests is very expensive and time consuming. With the advancement of artificial intelligence and machine learning (ML), autism can be predicted at quite early stage. Though number of studies have been carried out using different techniques, these studies didn’t provide any definitive conclusion about predicting autism traits in terms of different age groups. Therefore this paper aims to propose an effective prediction model based on ML technique and to develop a mobile application for predicting ASD for people of any age. As outcomes of this research, an autism prediction model was developed by merging Random Forest-CART (Classification and Regression Trees) and Random Forest-ID3 (Iterative Dichotomiser 3) and also a mobile application was developed based on the proposed prediction model. The proposed model was evaluated with AQ10 dataset and 250 real dataset collected from people with and without autistic traits. The evaluation results showed that the proposed prediction model provide better results in terms of accuracy, specificity, sensitivity, precision and false positive rate (FPR) for both kinds of datasets.

   @inproceedings{omar2019machine,
     title = {A machine learning approach to predict autism spectrum disorder},
     doi = {https://ieeexplore.ieee.org/document/8679454},
     author = {Omar, Kazi Shahrukh and Mondal, Prodipta and Khan, Nabila Shahnaz and Rizvi, Md Rezaul Karim and Islam, Md Nazrul},
     booktitle = {International conference on electrical, computer and communication engineering (ECCE)},
     pages = {1--6},
     year = {2019},
     organization = {IEEE},
   }

2. 

   An intelligent assistive tool for alzheimer’s patient

   Kazi Shahrukh Omar, Afia Anjum, Tahrima Oannahary, and 7 more authors

   In International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 2019

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   Alzheimer’s Disease (AD) is a chronic neurodegenerative disease that causes to develop dementia. Alzheimer’s patients find it hard to remember recent events, reason and even to recognize people they know. As the disease advances, symptoms can include difficulty with language, disorientation including getting lost, mood swings, loss of motivation, lack of self-awareness and overall behavior. Though a limited number of IT based solutions exist to provide support for Alzheimer’s patients, but most of these provide very isolated services either for the patients or for the caregivers. The objective of this research is to propose an assistive tool for Alzheimer’s patients and their caregivers to provide support like health monitoring, assist to find lost items, provide reminder to take medicine and assist to monitor patient’s location. A light-weighted evaluation study was carried out with 15 participants. The evaluation study showed that the proposed system was effective and usable for the patients and their caregivers.

   @inproceedings{omar2019intelligent,
     title = {An intelligent assistive tool for alzheimer’s patient},
     doi = {https://ieeexplore.ieee.org/document/8934542},
     author = {Omar, Kazi Shahrukh and Anjum, Afia and Oannahary, Tahrima and Rizvi, Rezaul Karim and Shahrin, Diana and Anannya, Tasmiah Tamzid and Tumpa, Sanjida Nasreen and Karim, MMahboob and Islam, Muhammad Nazrul and Rabbi, Fazle},
     booktitle = {International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)},
     pages = {1--6},
     year = {2019},
     organization = {IEEE},
   }