This page has only limited features, please log in for full access.

Mr. Naimat Ullah Khan
Shanghai University

Basic Info


Research Keywords & Expertise

0 Artificial Intelligence
0 Big Data
0 Data Analysis
0 Machine Learning
0 Smart City Technology

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 07 December 2020 in ISPRS International Journal of Geo-Information
Reads 0
Downloads 0

The main purpose of this research is to study the effect of various types of venues on the density distribution of residents and model check-in data from a Location-Based Social Network for the city of Shanghai, China by using combination of multiple temporal, spatial and visualization techniques by classifying users’ check-ins into different venue categories. This article investigates the use of Weibo for big data analysis and its efficiency in various categories instead of manually collected datasets, by exploring the relation between time, frequency, place and category of check-in based on location characteristics and their contributions. The data used in this research was acquired from a famous Chinese microblogs called Weibo, which was preprocessed to get the most significant and relevant attributes for the current study and transformed into Geographical Information Systems format, analyzed and, finally, presented with the help of graphs, tables and heat maps. The Kernel Density Estimation was used for spatial analysis. The venue categorization was based on nature of the physical locations within the city by comparing the name of venue extracted from Weibo dataset with the function such as education for schools or shopping for malls and so on. The results of usage patterns from hours to days, venue categories and frequency distribution into these categories as well as the density of check-in within the Shanghai and contribution of each venue category in its diversity are thoroughly demonstrated, uncovering interesting spatio-temporal patterns including frequency and density of users from different venues at different time intervals, and significance of using geo-data from Weibo to study human behavior in variety of studies like education, tourism and city dynamics based on location-based social networks. Our findings uncover various aspects of activity patterns in human behavior, the significance of venue classes and its effects in Shanghai, which can be applied in pattern analysis, recommendation systems and other interactive applications for these classes.

ACS Style

Naimat Ullah Khan; Wanggen Wan; Shui Yu; A. A. M. Muzahid; Sajid Khan; Li Hou. A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data. ISPRS International Journal of Geo-Information 2020, 9, 733 .

AMA Style

Naimat Ullah Khan, Wanggen Wan, Shui Yu, A. A. M. Muzahid, Sajid Khan, Li Hou. A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data. ISPRS International Journal of Geo-Information. 2020; 9 (12):733.

Chicago/Turabian Style

Naimat Ullah Khan; Wanggen Wan; Shui Yu; A. A. M. Muzahid; Sajid Khan; Li Hou. 2020. "A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data." ISPRS International Journal of Geo-Information 9, no. 12: 733.

Journal article
Published: 19 May 2020 in Electronics
Reads 0
Downloads 0

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: 11 February 2020 in IEEE Access
Reads 0
Downloads 0
ACS Style

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 Style

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.

Chicago/Turabian Style

Hidayat 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.

Journal article
Published: 29 January 2020 in ISPRS International Journal of Geo-Information
Reads 0
Downloads 0

The aim of the current study is to analyze and extract the useful patterns from Location-Based Social Network (LBSN) data in Shanghai, China, using different temporal and spatial analysis techniques, along with specific check-in venue categories. This article explores the applications of LBSN data by examining the association between time, frequency of check-ins, and venue classes, based on users’ check-in behavior and the city’s characteristics. The information regarding venue classes is created and categorized by using the nature of physical locations. We acquired the geo-location information from one of the most famous Chinese microblogs called Sina-Weibo (Weibo). The extracted data are translated into the Geographical Information Systems (GIS) format, and after analysis the results are presented in the form of statistical graphs, tables, and spatial heatmaps. SPSS is used for temporal analysis, and Kernel Density Estimation (KDE) is applied based on users’ check-ins with the help of ArcMap and OpenStreetMap for spatial analysis. The findings show various patterns, including more frequent use of LBSN while visiting entertainment and shopping locations, a substantial number of check-ins from educational institutions, and that the density extends to suburban areas mainly because of educational institutions and residential areas. Through analytical results, the usage patterns based on hours of the day, days of the week, and for an entire six months, including by gender, venue category, and frequency distribution of the classes, as well as check-in density all over Shanghai city, are thoroughly demonstrated.

ACS Style

Naimat Ullah Khan; Wanggen Wan; Shui Yu. Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China. ISPRS International Journal of Geo-Information 2020, 9, 76 .

AMA Style

Naimat Ullah Khan, Wanggen Wan, Shui Yu. Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China. ISPRS International Journal of Geo-Information. 2020; 9 (2):76.

Chicago/Turabian Style

Naimat Ullah Khan; Wanggen Wan; Shui Yu. 2020. "Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China." ISPRS International Journal of Geo-Information 9, no. 2: 76.

Journal article
Published: 21 January 2020 in ISPRS International Journal of Geo-Information
Reads 0
Downloads 0

The aim of this study is to analyze and compare the patterns of behavior of tourists and residents from Location-Based Social Network (LBSN) data in Shanghai, China using various spatiotemporal analysis techniques at different venue categories. The paper presents the applications of location-based social network’s data by exploring the patterns in check-ins over a period of six months. We acquired the geo-location information from one of the most famous Chinese microblogs called Sina-Weibo (Weibo). The extracted data is translated into the Geographical Information Systems (GIS) format, and compared with the help of temporal statistical analysis and kernel density estimation. The venue classification is done by using information regarding the nature of physical locations. The findings reveal that the spatial activities of tourists are more concentrated as compared to those of residents, particularly in downtown, while the residents also visited suburban areas and the temporal activities of tourists varied significantly while the residents’ activities showed relatively stable behavior. These results can be applied in destination management, urban planning, and smart city development.

ACS Style

Naimat Ullah Khan; Wanggen Wan; Shui Yu. Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network’s Data from Weibo. ISPRS International Journal of Geo-Information 2020, 9, 70 .

AMA Style

Naimat Ullah Khan, Wanggen Wan, Shui Yu. Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network’s Data from Weibo. ISPRS International Journal of Geo-Information. 2020; 9 (2):70.

Chicago/Turabian Style

Naimat Ullah Khan; Wanggen Wan; Shui Yu. 2020. "Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network’s Data from Weibo." ISPRS International Journal of Geo-Information 9, no. 2: 70.

Journal article
Published: 21 January 2020 in IEEE Access
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (99):23802-23816.

Chicago/Turabian Style

A. 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.

Journal article
Published: 10 November 2019 in ISPRS International Journal of Geo-Information
Reads 0
Downloads 0

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.

Journal article
Published: 03 June 2019 in IEEE Access
Reads 0
Downloads 0

Image encryption is an efficient and vital way to protect classified and secret images. With the advancement of the processing power of the computer, AES, DES or chaotic series type just alike image encryption schemes are not as secure as before. So in this paper, we present a new hybrid image encryption method for protecting secret and imperative images by employing Logistic Sine System (LSS) together with two-dimensional Cellular Automata and FSM based DNA rule generator. Secure hash (SHA-256) algorithm is used to generate a secret key and to compute initial values for the LSS. In our proposed method there are three stages and each stage has its own rule. After the scrambling process, the first stage is the Feistel structure based bit inversion (FSBI) to change the pixels value. The second stage is 2D-CA with Moore neighborhood structure based local rules. The third is DNA conversion based on Finite State Machine (FSM-DNA) rule generator. The proposed encryption scheme is robust against well-known attacks such as statistical attacks, brute force attacks, differential attacks, and pixel correlation attacks and also possess strong key sensitivity. Results show that our three-layer hybrid image encryption technique is robust against many well-known attacks and can be applied directly to all type of classified gray-scale images to make them more secure from such cryptography attacks.

ACS Style

Sajid Khan; Lansheng Han; Hongwei Lu; Khushbu Khalid Butt; Guehguih Bachira; Naimat Ullah Khan. A New Hybrid Image Encryption Algorithm Based on 2D-CA, FSM-DNA Rule Generator, and FSBI. IEEE Access 2019, 7, 81333 -81350.

AMA Style

Sajid Khan, Lansheng Han, Hongwei Lu, Khushbu Khalid Butt, Guehguih Bachira, Naimat Ullah Khan. A New Hybrid Image Encryption Algorithm Based on 2D-CA, FSM-DNA Rule Generator, and FSBI. IEEE Access. 2019; 7 (99):81333-81350.

Chicago/Turabian Style

Sajid Khan; Lansheng Han; Hongwei Lu; Khushbu Khalid Butt; Guehguih Bachira; Naimat Ullah Khan. 2019. "A New Hybrid Image Encryption Algorithm Based on 2D-CA, FSM-DNA Rule Generator, and FSBI." IEEE Access 7, no. 99: 81333-81350.