Leveraging Deep Learning and SNA approaches for Smart City Policing in the Developing World

Hassan, Saeed‐Ul; Shabbir, Mudassir; Iqbal, Sehrish; Said, Anwar; Kamiran, Faisal; Nawaz, Raheel; Saif, Umar

doi:10.1016/j.ijinfomgt.2019.102045

Cited by 27 publications

(9 citation statements)

References 43 publications

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“…A citation network is a graph, its nodes being articles and its edges citation links between them [24,25]. In recent years, a number of studies have been conducted to leverage deep neural networks and word embedding to extract entities from web and social network datasets [26][27][28][29][30].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network

2021

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We investigated the scientific research dissemination by analyzing the publications and citation data, implying that not all citations are significantly important. Therefore, as alluded to existing state-of-the-art models that employ feature-based techniques to measure the scholarly research dissemination between multiple entities, our model implements the convolutional neural network (CNN) with fastText-based pre-trained embedding vectors, utilizes only the citation context as its input to distinguish between important and non-important citations. Moreover, we speculate using focal-loss and class weight methods to address the inherited class imbalance problems in citation classification datasets. Using a dataset of 10 K annotated citation contexts, we achieved an accuracy of 90.7% along with a 90.6% f1-score, in the case of binary classification. Finally, we present a case study to measure the comprehensiveness of our deployed model on a dataset of 3100 K citations taken from the ACL Anthology Reference Corpus. We employed state-of-the-art graph visualization open-source tool Gephi to analyze the various aspects of citation network graphs, for each respective citation behavior.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network

2021

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“…The diffusion of information technology in the policing services lies in the entire operations. From the back-end strategies, the use of the information technology includes data security and data protection (Roseline & Geetha, 2021), distinguishing criminals from non-criminals by tapping into the power of social network analysis (Hassan et al, 2019), and early predictions of crimes by real-time analysis (Manjunatha & Annappa, 2020). Likewise, the success of the IT adoption and usage is also evaluated against the perception of the police officers.…”

Section: Introductionmentioning

confidence: 99%

Exploring Citizens’ Perception of Abu Dhabi Policing e-Service Quality

Alblooshi¹,

Kassim²

2021

IJARBSS

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The purpose of the study is to explore citizens' perception on the quality of service of the newly transformed e-policing system in Abu Dhabi. The Abu Dhabi policing system has undergone a major change by utilizing information and communication technology in all aspects of service operations. The changes require an evaluation from the citizens as an assessment of the success. Hence, it is timely to assess the citizens' perceptions on the policing system's integrity, transparency, responsivity, interactivity and serviceability and to compare for gender differences in the perception. The study participants were citizens of Abu Dhabi. The sample was selected based on the convenience sampling technique. The data was collected from 260 respondents via an online survey. The results of the descriptive analysis show an acceptable level of e-service quality perception on integrity, transparency, responsivity, interactivity and serviceability. In addition, the results also indicate there is no significant difference in the perception of Abu Dhabi policing e-service between male and female respondents. At present, studies on Abu Dhabi policing are rather limited. Therefore, the findings demonstrate how smart policing could drive a greater acceptance of the e-service quality especially for the United Arab Emirates (UAE). In addition, the high perception of the policing integrity, transparency, responsivity, interactivity and serviceability will provide greater opportunities for future research to embark on how society-authority could work together for sustainable peace and harmony.

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“…These solutions must be compatible with standard communication protocols. IoT devices generate vast quantities of data during industrial operations, which can make an IIoT system a favorite target for attackers [7], [8]. Due to the large quantity of data, traditional data processing techniques are not suitable for IoT and IIoT applications.…”

Section: Introductionmentioning

confidence: 99%

A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network

et al. 2020

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The Industrial Internet of Things (IIoT) brings together many sensors, machines, industrial applications, databases, services, and people at work. The IIoT is improving our lives in several ways including smarter cities, agriculture, and e-healthcare, etc. Although the IIoT shares several characteristics with the consumer IoT, different cybersecurity mechanisms are adopted for both networks. Unlike consumer IoT solutions that are used by an individual user for a single purpose, IIoT solutions tend to be integrated into larger operational systems. As a result, IIoT security solutions require additional planning and awareness to ensure the security and privacy of the system. In this paper, different cybersecurity attacks such as denial of service (DoS), malicious operation, malicious control, data type probing, spying, scan, and wrong setup are predicted by applying machine learning techniques. To predict the aforementioned attacks, a novel lightweight random neural network (RaNN)-based prediction model has been proposed in this article. To investigate the performance of the RaNN-based prediction model, several evaluation parameters such as accuracy, precision, recall, and F1 score were calculated and compared with the traditional artificial neural network (ANN), support vector machine (SVM) and decision tree (DT). The evaluation results show that the proposed RaNN model achieves an accuracy of 99.20% for a learning rate of 0.01, with a prediction time of 34.51 milliseconds. Other performance parameters such as the precision, recall, and F1 score were 99.11%, 99.13%, and 99.20%, respectively. The proposed scheme improves the attack detection accuracy by an average of 5.65% compared to that of state-of-the-art machine learning schemes for IoT security. INDEX TERMS Artificial neural network, cybersecurity, industrial Internet of Things, random neural network, support vector machine.

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Leveraging Deep Learning and SNA approaches for Smart City Policing in the Developing World

Cited by 27 publications

References 43 publications

A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network

A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network

Exploring Citizens’ Perception of Abu Dhabi Policing e-Service Quality

A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network

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