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We consider the 3D object recognition problem from the perspective of the lack of labelled data. In this paper, we propose a novel progressive conditional generative adversarial network (PC-GAN) for 3D object recognition by conditioning the input with progressive learning strategies. PC-GAN is a powerful adversarial model whose generator automatically produces realistic 3D objects with annotations, and the discriminator distinguishes them from the training distribution and recognizes their categories. We train the discriminative classifier simultaneously with the generator to predict the class label by embedding a SoftMax classifier. Progressive learning uses input samples from lower to higher resolutions to increase the generator performance gradually and produce informative objects for a certain class of objects. The key idea of adopting progressing learning is to mitigate overshoots issues of the discriminator and increase variations in the generated objects by learning progressively. This strategy helps the generator to produce more realistic synthetic objects and improve the active classification performance of the discriminator. Our proposed PC-GAN is trained for object classification in a supervised manner and the performance is evaluated on two public datasets. Experimental results demonstrate that our adversarial PC-GAN outperforms the existing volumetric discriminative classifiers in term of classification accuracy.
A.A.M. Muzahid; Wan Wanggen; Ferdous Sohel; Mohammed Bennamoun; Li Hou; Hidayat Ullah. Progressive conditional GAN-based augmentation for 3D object recognition. Neurocomputing 2021, 460, 20 -30.
AMA StyleA.A.M. Muzahid, Wan Wanggen, Ferdous Sohel, Mohammed Bennamoun, Li Hou, Hidayat Ullah. Progressive conditional GAN-based augmentation for 3D object recognition. Neurocomputing. 2021; 460 ():20-30.
Chicago/Turabian StyleA.A.M. Muzahid; Wan Wanggen; Ferdous Sohel; Mohammed Bennamoun; Li Hou; Hidayat Ullah. 2021. "Progressive conditional GAN-based augmentation for 3D object recognition." Neurocomputing 460, no. : 20-30.
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.
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 StyleQi 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 StyleQi 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.
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.
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 StyleQi 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 StyleQi 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.
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.
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 StyleSaqib 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 StyleSaqib 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.
Hidayat Ullah; Wanggen Wan; Saqib Ali Haidery; Naimat Ullah Khan; Zeinab Ebrahimpour; A. A. M. Muzahid. Spatiotemporal Patterns of Visitors in Urban Green Parks by Mining Social Media Big Data Based Upon WHO Reports. IEEE Access 2020, 8, 39197 -39211.
AMA StyleHidayat Ullah, Wanggen Wan, Saqib Ali Haidery, Naimat Ullah Khan, Zeinab Ebrahimpour, A. A. M. Muzahid. Spatiotemporal Patterns of Visitors in Urban Green Parks by Mining Social Media Big Data Based Upon WHO Reports. IEEE Access. 2020; 8 ():39197-39211.
Chicago/Turabian StyleHidayat Ullah; Wanggen Wan; Saqib Ali Haidery; Naimat Ullah Khan; Zeinab Ebrahimpour; A. A. M. Muzahid. 2020. "Spatiotemporal Patterns of Visitors in Urban Green Parks by Mining Social Media Big Data Based Upon WHO Reports." IEEE Access 8, no. : 39197-39211.
We consider the recent challenges of 3D shape analysis based on a volumetric CNN that requires a huge computational power. This high-cost approach forces to reduce the volume resolutions when applying 3D CNN on volumetric data. In this context, we propose a multiorientation volumetric deep neural network (MV-DNN) for 3D object classification with octree generating low-cost volumetric features. In comparison to conventional octree representations, we propose to limit the octree partition to a certain depth to reserve all leaf octants with sparsity features. This allows for improved learning of complex 3D features and increased prediction of object labels at both low and high resolutions. Our auxiliary learning approach predicts object classes based on the subvolume parts of a 3D object that improve the classification accuracy compared to other existing 3D volumetric CNN methods. In addition, the influence of views and depths of the 3D model on the classification performance is investigated through extensive experiments applied to the ModelNet40 database. Our deep learning framework runs significantly faster and consumes less memory than full voxel representations and demonstrate the effectiveness of our octree-based auxiliary learning approach for exploring high resolution 3D models. Experimental results reveal the superiority of our MV-DNN that achieves better classification accuracy compared to state-of-art methods on two public databases.
A. A. M. Muzahid; Wanggen Wan; Ferdous Sohel; Naimat Ullah Khan; Ofelia Delfina Cervantes Villagomez; Hidayat Ullah. 3D Object Classification Using a Volumetric Deep Neural Network: An Efficient Octree Guided Auxiliary Learning Approach. IEEE Access 2020, 8, 23802 -23816.
AMA StyleA. A. M. Muzahid, Wanggen Wan, Ferdous Sohel, Naimat Ullah Khan, Ofelia Delfina Cervantes Villagomez, Hidayat Ullah. 3D Object Classification Using a Volumetric Deep Neural Network: An Efficient Octree Guided Auxiliary Learning Approach. IEEE Access. 2020; 8 (99):23802-23816.
Chicago/Turabian StyleA. A. M. Muzahid; Wanggen Wan; Ferdous Sohel; Naimat Ullah Khan; Ofelia Delfina Cervantes Villagomez; Hidayat Ullah. 2020. "3D Object Classification Using a Volumetric Deep Neural Network: An Efficient Octree Guided Auxiliary Learning Approach." IEEE Access 8, no. 99: 23802-23816.
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.
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 StyleHidayat 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 StyleHidayat 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.
Color image encryption has enticed a lot of attention in recent years. Many authors proposed a chaotic system-based encryption algorithms for that purpose. However, due to the shortcomings of the low dimensional chaotic systems, similar rule structure for RGB channels, and the small keyspace, many of those were cryptanalyzed by chosen-plaintext or other well-known attacks. A Security vulnerability exists because of the same method being applied over the RGB channels. This paper aims to introduce a new three-channel three rules (3C3R) image encryption algorithm along with two novel mathematical models for DNA rule generator and bit inversion. A different rule structure was applied in the different RGB-channels. In the R-channel, a novel Block-based Bit Inversion (BBI) is introduced, in the G-channel Von-Neumann (VN) and Rotated Von-Neumann (RVN)- based 2D-cellular structure is applied. In the B-channel, a novel bidirectional State Machine-based DNA rule generator (SM-DNA) is introduced. Simulations and results show that the proposed 3C3R encryption algorithm is robust against all well-known attacks particularly for the known-plaintext attacks, statistical attacks, brute-force attacks, differential attacks, and occlusion attacks, etc. Also, unlike earlier encryption algorithms, the 3C3R has no security vulnerability.
Sajid Khan; Lansheng Han; Ghulam Mudassir; Bachira Guehguih; Hidayat Ullah. 3C3R, an Image Encryption Algorithm Based on BBI, 2D-CA, and SM-DNA. Entropy 2019, 21, 1075 .
AMA StyleSajid Khan, Lansheng Han, Ghulam Mudassir, Bachira Guehguih, Hidayat Ullah. 3C3R, an Image Encryption Algorithm Based on BBI, 2D-CA, and SM-DNA. Entropy. 2019; 21 (11):1075.
Chicago/Turabian StyleSajid Khan; Lansheng Han; Ghulam Mudassir; Bachira Guehguih; Hidayat Ullah. 2019. "3C3R, an Image Encryption Algorithm Based on BBI, 2D-CA, and SM-DNA." Entropy 21, no. 11: 1075.
Extracting features from crowd flow analysis has become an important research challenge due to its social cost and the impact of inadequate planning of high-quality services and security monitoring on the lives of citizens. This paper descriptively reviews and compares existing crowd analysis approaches based on different data sources. This survey provides the fundamentals of crowd analysis and considers three main approaches: crowd video analysis, crowd spatio-temporal analysis, and crowd social media analysis. The key research contributions in each approach are presented, and the most significant techniques and algorithms used to improve the precision of results that could be integrated into solutions to enhance the quality of services in a smart city are analyzed.
Zeinab Ebrahimpour; Wanggen Wan; Ofelia Cervantes; Tianhang Luo; Hidayat Ullah; Wan; Luo. Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis. ISPRS International Journal of Geo-Information 2019, 8, 440 .
AMA StyleZeinab Ebrahimpour, Wanggen Wan, Ofelia Cervantes, Tianhang Luo, Hidayat Ullah, Wan, Luo. Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis. ISPRS International Journal of Geo-Information. 2019; 8 (10):440.
Chicago/Turabian StyleZeinab Ebrahimpour; Wanggen Wan; Ofelia Cervantes; Tianhang Luo; Hidayat Ullah; Wan; Luo. 2019. "Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis." ISPRS International Journal of Geo-Information 8, no. 10: 440.