ORCID Profile
0000-0001-7181-5328
Current Organisation
The University of Newcastle
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 09-2020
Publisher: IEEE
Date: 07-2009
Publisher: Springer Science and Business Media LLC
Date: 13-04-2016
Publisher: Springer Science and Business Media LLC
Date: 12-08-2020
Publisher: Springer Science and Business Media LLC
Date: 30-10-2021
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer International Publishing
Date: 2019
Publisher: Oxford University Press (OUP)
Date: 15-11-2020
Abstract: Retrieval of arbitrary-shaped surrounding data objects has many potential applications in spatial databases including nearby arbitrary-shaped object-of-interests retrieval surrounding a user. In this paper, we propose directional zone concept to determine directional similarity among spatial data objects. Then, we propose a novel query, called direction-based spatial skyline (DSS), which retrieves non-dominated arbitrary-shaped surrounding data objects in spatial databases for a user. The proposed DSS query is rotationally invariant as well as fair. We develop efficient algorithms for processing DSS queries in spatial databases by designing novel data pruning techniques using R-Tree data indexing scheme. Finally, we demonstrate the effectiveness and efficiency of our approach by conducting extensive experiments with real datasets.
Publisher: ACM
Date: 24-10-2016
Publisher: ACM
Date: 24-10-2016
Publisher: Springer International Publishing
Date: 14-04-2020
Publisher: Springer Science and Business Media LLC
Date: 13-10-2018
Publisher: IEEE
Date: 12-2019
Publisher: IEEE
Date: 04-2013
Publisher: University of Illinois Libraries
Date: 21-10-2020
Abstract: The COVID-19 outbreak has focused attention on the use of social distancing as the primary defence against community infection. Forcing social animals to maintain physical distance has presented significant challenges for health authorities and law enforcement. Anecdotal media reports suggest widespread dissatisfaction with social distancing as a policy, yet there is little prior work aimed at measuring community acceptance of social distancing. In this paper, we propose a new approach to measuring attitudes towards social distancing by using social media and sentiment analysis. Over a four-month period, we found that 82.5 percent of tweets were in favour of social distancing. The results indicate a widespread acceptance of social distancing in a selected community. We examine options for estimating the optimal (minimal) social distance required at scale, and the implications for securing widespread community support and for appropriate crisis management during emergency health events.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 11-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 23-11-2022
Publisher: Science Alert
Date: 02-2009
Publisher: Springer Science and Business Media LLC
Date: 19-06-2020
Publisher: Elsevier BV
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer Singapore
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 11-2014
Publisher: Elsevier BV
Date: 09-2021
Publisher: ACM
Date: 27-06-2017
Publisher: Springer International Publishing
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2018
Publisher: IEEE
Date: 06-2014
Publisher: Elsevier BV
Date: 07-2022
Publisher: IEEE
Date: 12-2019
Publisher: Elsevier BV
Date: 06-2013
Publisher: MDPI AG
Date: 28-07-2020
DOI: 10.3390/S20154200
Abstract: Software-Defined Networking (SDN) offers an abstract view of the network and assists network operators to control the network traffic and the associated network resources more effectively. For the past few years, SDN has shown a lot of merits in erse fields of applications, an important one being the Wireless Body Area Network (WBAN) for healthcare services. With the amalgamation of SDN with WBAN (SDWBAN), the patient monitoring and management system has gained much more flexibility and scalability compared to the conventional WBAN. However, the performance of the SDWBAN framework largely depends on the controller which is a core element of the control plane. The reason is that an optimal number of controllers assures the satisfactory level of performance and control of the network traffic originating from the underlying data plane devices. This paper proposes a mathematical model to determine the optimal number of controllers for the SDWBAN framework in healthcare applications. To achieve this goal, the proposed mathematical model adopts the convex optimization method and incorporates three critical SDWBAN factors in the design process: number of controllers, latency and number of SDN-enabled switches (SDESW). The proposed analytical model is validated by means of simulations in Castalia 3.2 and the outcomes indicate that the network achieves high level of Packet Delivery Ratio (PDR) and low latency for optimal number of controllers as derived in the mathematical model.
Publisher: IEEE
Date: 03-2014
Publisher: Springer Singapore
Date: 2019
Publisher: MDPI AG
Date: 27-04-2020
DOI: 10.3390/S20092464
Abstract: Over the last few decades, the proliferation of the Internet of Things (IoT) has produced an overwhelming flow of data and services, which has shifted the access control paradigm from a fixed desktop environment to dynamic cloud environments. Fog computing is associated with a new access control paradigm to reduce the overhead costs by moving the execution of application logic from the centre of the cloud data sources to the periphery of the IoT-oriented sensor networks. Indeed, accessing information and data resources from a variety of IoT sources has been plagued with inherent problems such as data heterogeneity, privacy, security and computational overheads. This paper presents an extensive survey of security, privacy and access control research, while highlighting several specific concerns in a wide range of contextual conditions (e.g., spatial, temporal and environmental contexts) which are gaining a lot of momentum in the area of industrial sensor and cloud networks. We present different taxonomies, such as contextual conditions and authorization models, based on the key issues in this area and discuss the existing context-sensitive access control approaches to tackle the aforementioned issues. With the aim of reducing administrative and computational overheads in the IoT sensor networks, we propose a new generation of Fog-Based Context-Aware Access Control (FB-CAAC) framework, combining the benefits of the cloud, IoT and context-aware computing and ensuring proper access control and security at the edge of the end-devices. Our goal is not only to control context-sensitive access to data resources in the cloud, but also to move the execution of an application logic from the cloud-level to an intermediary-level where necessary, through adding computational nodes at the edge of the IoT sensor network. A discussion of some open research issues pertaining to context-sensitive access control to data resources is provided, including several real-world case studies. We conclude the paper with an in-depth analysis of the research challenges that have not been adequately addressed in the literature and highlight directions for future work that has not been well aligned with currently available research.
Publisher: Oxford University Press (OUP)
Date: 18-07-2019
Publisher: Wiley
Date: 22-07-2022
DOI: 10.1002/CPE.5938
Abstract: Nowadays, public gatherings and social events are an integral part of a modern city life. To run such events seamlessly, it requires real time mining and monitoring of causally related events so that the management can make informed decisions and take appropriate actions. The automatic detection of event causality from short text such as tweets could be useful for event management in this context. However, detecting event causality from tweets is a challenging task. Tweets are short, unstructured, and often written in highly informal language which lacks enough contextual information to detect causality. The existing approaches apply different techniques including hand‐crafted linguistic rules and machine learning models. However, none of the approaches tackle the issue related to the lack of contextual information. In this paper, we detect event causality in tweets by applying a context word extension technique and a deep causal event detection model. The context word extension technique is driven by background knowledge extracted from one million news articles. Our model achieves 79.35% recall and 67.28% f1‐score, which are 17.39% and 2.33% improvements to the state‐of‐the‐art approach.
Publisher: IEEE
Date: 18-07-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: IEEE
Date: 04-2013
Publisher: Elsevier BV
Date: 06-2021
Publisher: IEEE
Date: 07-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2015
Publisher: Springer Science and Business Media LLC
Date: 15-11-2019
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 11-08-2019
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer International Publishing
Date: 15-03-2020
Publisher: IEEE
Date: 05-2016
Publisher: Springer Science and Business Media LLC
Date: 26-01-2022
DOI: 10.1038/S41467-021-27668-9
Abstract: Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national s les. Study 2 ( N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic ( r = −0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics.
Publisher: Informa UK Limited
Date: 09-2016
Publisher: IEEE
Date: 2009
Publisher: Springer Science and Business Media LLC
Date: 27-11-2022
Publisher: Springer Science and Business Media LLC
Date: 30-08-2019
Publisher: Springer International Publishing
Date: 2015
Publisher: Elsevier BV
Date: 07-2021
Publisher: IEEE
Date: 07-2020
Publisher: Elsevier BV
Date: 03-2022
Publisher: Elsevier BV
Date: 06-2020
No related grants have been discovered for Md Saiful Islam.