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Saqib Ali Haidery
School of Communications and Information Engineering, Shanghai University, Shanghai 200444, China

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Journal article
Published: 28 July 2020 in Sustainability
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Ubiquitous online learning is continuing to expand, and the factors affecting success and educational sustainability need to be quantified. Procrastination is one of the compelling characteristics that students observe as a failure to achieve the weaker outcomes. Past studies have mainly assessed the behaviors of procrastination by describing explanatory work. Throughout this research, we concentrate on predictive measures to identify and forecast procrastinator students by using ensemble machine learning models (i.e., Logistic Regression, Decision Tree, Gradient Boosting, and Forest). Our results indicate that the Gradient Boosting autotuned is a predictive champion model of high precision compared to the other default and hyper-parameterized tuned models in the pipeline. The accuracy we enumerated for the VALIDATION partition dataset is 91.77 percent, based on the Kolmogorov–Smirnov statistics. Additionally, our model allows teachers to monitor each procrastinator student who interacts with the web-based e-learning platform and take corrective action on the next day of the class. The earlier prediction of such procrastination behaviors would assist teachers in classifying students before completing the task, homework, or mastery of a skill, which is useful and a path to developing a sustainable atmosphere for education or education for sustainable development.

ACS Style

Syed Muhammad Raza Abidi; Wu Zhang; Saqib Ali Haidery; Sanam Shahla Rizvi; Rabia Riaz; Hu Ding; Se Jin Kwon. Educational Sustainability through Big Data Assimilation to Quantify Academic Procrastination Using Ensemble Classifiers. Sustainability 2020, 12, 6074 .

AMA Style

Syed Muhammad Raza Abidi, Wu Zhang, Saqib Ali Haidery, Sanam Shahla Rizvi, Rabia Riaz, Hu Ding, Se Jin Kwon. Educational Sustainability through Big Data Assimilation to Quantify Academic Procrastination Using Ensemble Classifiers. Sustainability. 2020; 12 (15):6074.

Chicago/Turabian Style

Syed Muhammad Raza Abidi; Wu Zhang; Saqib Ali Haidery; Sanam Shahla Rizvi; Rabia Riaz; Hu Ding; Se Jin Kwon. 2020. "Educational Sustainability through Big Data Assimilation to Quantify Academic Procrastination Using Ensemble Classifiers." Sustainability 12, no. 15: 6074.

Journal article
Published: 22 June 2020 in Electronics
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Urban green spaces promote outdoor activities and social interaction, which make a significant contribution to the health and well-being of residents. This study presents an approach that focuses on the real spatial and temporal behavior of park visitors in different categories of green parks. We used the large dataset available from the Chinese micro-blog Sina Weibo (often simply referred to as “Weibo”) to analyze data samples, in order to describe the behavioral patterns of millions of people with access to green spaces. We select Shanghai as a case study because urban residential segregation has already taken place, which was expected to be followed by concerns of environmental sustainability. In this research, we utilized social media check-in data to measure and compare the number of visitations to different kinds of green parks. Furthermore, we divided the green spaces into different categories according to their characteristics, and our main findings were: (1) the most popular category based upon the check-in data; (2) changes in the number of visitors according to the time of day; (3) seasonal impacts on behavior in public in relation to the different categories of parks; and (4) gender-based differences. To the best of our knowledge, this is the first study carried out in Shanghai utilizing Weibo data to focus upon the categorization of green space. It is also the first to offer recommendations for planners regarding the type of facilities they should provide to residents in green spaces, and regarding the sustainability of urban environments and smart city architecture.

ACS Style

Qi Liu; Hidayat Ullah; Wanggen Wan; Zhangyou Peng; Li Hou; Sanam Shahla Rizvi; Saqib Ali Haidery; Tong Qu; A. A. M. Muzahid. Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data. Electronics 2020, 9, 1028 .

AMA Style

Qi Liu, Hidayat Ullah, Wanggen Wan, Zhangyou Peng, Li Hou, Sanam Shahla Rizvi, Saqib Ali Haidery, Tong Qu, A. A. M. Muzahid. Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data. Electronics. 2020; 9 (6):1028.

Chicago/Turabian Style

Qi Liu; Hidayat Ullah; Wanggen Wan; Zhangyou Peng; Li Hou; Sanam Shahla Rizvi; Saqib Ali Haidery; Tong Qu; A. A. M. Muzahid. 2020. "Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data." Electronics 9, no. 6: 1028.

Journal article
Published: 01 June 2020 in ISPRS International Journal of Geo-Information
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Green areas or parks are the best way to encourage people to take part in physical exercise. Traditional techniques of researching the attractiveness of green parks, such as surveys and questionnaires, are naturally time consuming and expensive, with less transferable outcomes and only site-specific findings. This research provides a factfinding study by means of location-based social network (LBSN) data to gather spatial and temporal patterns of green park visits in the city center of Shanghai, China. During the period from July 2014 to June 2017, we examined the spatiotemporal behavior of visitors in 71 green parks in Shanghai. We conducted an empirical investigation through kernel density estimation (KDE) and relative difference methods on the effects of green spaces on public behavior in Shanghai, and our main categories of findings are as follows: (i) check-in distribution of visitors in different green spaces, (ii) users’ transition based on the hours of a day, (iii) famous parks in the study area based upon the number of check-ins, and (iv) gender difference among green park visitors. Furthermore, the purpose of obtaining these outcomes can be utilized in urban planning of a smart city for green environment according to the preferences of visitors.

ACS Style

Qi Liu; Hidayat Ullah; Wanggen Wan; Zhangyou Peng; Li Hou; Tong Qu; Saqib Ali Haidery. Analysis of Green Spaces by Utilizing Big Data to Support Smart Cities and Environment: A Case Study About the City Center of Shanghai. ISPRS International Journal of Geo-Information 2020, 9, 360 .

AMA Style

Qi Liu, Hidayat Ullah, Wanggen Wan, Zhangyou Peng, Li Hou, Tong Qu, Saqib Ali Haidery. Analysis of Green Spaces by Utilizing Big Data to Support Smart Cities and Environment: A Case Study About the City Center of Shanghai. ISPRS International Journal of Geo-Information. 2020; 9 (6):360.

Chicago/Turabian Style

Qi Liu; Hidayat Ullah; Wanggen Wan; Zhangyou Peng; Li Hou; Tong Qu; Saqib Ali Haidery. 2020. "Analysis of Green Spaces by Utilizing Big Data to Support Smart Cities and Environment: A Case Study About the City Center of Shanghai." ISPRS International Journal of Geo-Information 9, no. 6: 360.

Journal article
Published: 19 May 2020 in Electronics
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In recent decades, a large amount of research has been carried out to analyze location-based social network data to highlight their application. These location-based social network datasets can be used to propose models and techniques that can analyze and reproduce the spatiotemporal structures and symmetries in user activities as well as density estimations. In the current study, different density estimation techniques are utilized to analyze the check-in frequency of users in more detail from location-based social network dataset acquired from Sina-Weibo, also referred as Weibo, over a specific period in 10 different districts of Shanghai, China. The aim of this study is to analyze the density of users in Shanghai city from geolocation data of Weibo as well as to compare their density through univariate and bivariate density estimation techniques; i.e., point density and kernel density estimation (KDE) respectively. The main findings of the study include the following: (i) characteristics of users’ spatial behavior, the center of activity based on their check-ins, (ii) the feasibility of check-in data to explain the relationship between users and social media, and (iii) the presentation of evident results for regulatory or managing authorities for urban planning. The current study shows that the point density and kernel density estimation. KDE methods provide useful insights for modeling spatial patterns using geo-spatial dataset. Finally, we can conclude that, by utilizing the KDE technique, we can examine the check-in behavior in more detail for an individual as well as broader patterns in the population as a whole for the development of smart city. The purpose of this article is to figure out the denser places so that the authorities can divide the mobility of people from the same routes or at least they can control the situation from any further inconvenience.

ACS Style

Saqib Ali Haidery; Hidayat Ullah; Naimat Ullah Khan; Kanwal Fatima; Sanam Shahla Rizvi; Se Jin Kwon. Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai. Electronics 2020, 9, 837 .

AMA Style

Saqib Ali Haidery, Hidayat Ullah, Naimat Ullah Khan, Kanwal Fatima, Sanam Shahla Rizvi, Se Jin Kwon. Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai. Electronics. 2020; 9 (5):837.

Chicago/Turabian Style

Saqib Ali Haidery; Hidayat Ullah; Naimat Ullah Khan; Kanwal Fatima; Sanam Shahla Rizvi; Se Jin Kwon. 2020. "Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai." Electronics 9, no. 5: 837.

Journal article
Published: 10 November 2019 in ISPRS International Journal of Geo-Information
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Green parks are vital public spaces and play a major role in urban living and well-being. Research on the attractiveness of green parks often relies on traditional techniques, such as questionnaires and in-situ surveys, but these methods are typically insignificant in scale, time-consuming, and expensive, with less transferable results and only site-specific outcomes. This article presents an investigative study that uses location-based social network (LBSN) data to collect spatial and temporal patterns of park visits in Shanghai metropolitan city. During the period from July 2016 to June 2017 in Shanghai, China, we analyzed the spatiotemporal behavior of park visitors for 157 green parks and conducted empirical research on the impacts of green spaces on the public’s behavior in Shanghai. Our main findings show (i) the check-in distribution of users in different green spaces; (ii) the seasonal effects on the public’s behavior toward green spaces; (iii) changes in the number of users based on the hour of the day, the intervals of the day (morning, afternoon, evening), and the day of the week; (iv) interesting user behavior variations that depend on temperature effects; and (v) gender-based differences in the number of green park visitors. These results can be used for the purpose of urban city planning for green spaces by accounting for the preferences of visitors.

ACS Style

Hidayat Ullah; Wanggen Wan; Saqib Ali Haidery; Naimat Ullah Khan; Zeinab Ebrahimpour; Tianhang Luo; Wan; Khan; Luo. Analyzing the Spatiotemporal Patterns in Green Spaces for Urban Studies Using Location-Based Social Media Data. ISPRS International Journal of Geo-Information 2019, 8, 506 .

AMA Style

Hidayat Ullah, Wanggen Wan, Saqib Ali Haidery, Naimat Ullah Khan, Zeinab Ebrahimpour, Tianhang Luo, Wan, Khan, Luo. Analyzing the Spatiotemporal Patterns in Green Spaces for Urban Studies Using Location-Based Social Media Data. ISPRS International Journal of Geo-Information. 2019; 8 (11):506.

Chicago/Turabian Style

Hidayat Ullah; Wanggen Wan; Saqib Ali Haidery; Naimat Ullah Khan; Zeinab Ebrahimpour; Tianhang Luo; Wan; Khan; Luo. 2019. "Analyzing the Spatiotemporal Patterns in Green Spaces for Urban Studies Using Location-Based Social Media Data." ISPRS International Journal of Geo-Information 8, no. 11: 506.