A Research Model on Social Media-Enabled Public Value: A Refinement Using an Online Focus Group

Government agencies, including municipalities around the globe, have begun using various social media applications to provide useful and even innovative services to citizens through fostering an improved engagement with them. The success of government agencies in creating and delivering innovative services is often interpreted using the lens of public value. Despite a growth in the scholarly literature on social media and public value, scant attention has so far been paid by IS and e-government scholars to explain how citizens government officials alike perceive public value created by social media applications. We thus report on the development of an initial research model to explain public value creation using social media applications. We further refine the model using an online focus group (OFG) comprising eight participants from three groups: academics, government officials, and citizen representatives

PUBLIC VALUE CREATION USING SOCIAL MEDIA APPLICATIONS FOR THE LOCAL GOVERNMENT CONTEXT

In recent years, the use of various social media applications has received growing attention from local government agencies. This is because social media applications have the potential to offer public value to those agencies as well as citizens through enhancing public engagement and public services innovation. Despite the growth in the literature on social media, there is still a limited understanding of how the key stakeholders of local government agencies, around the world in general and Saudi Arabia in particular, can receive public value created through us-ing various social media applications. To address this concern, this proposed study is initiated to develop a model for investigating public value creation using social media applications. The model is influenced by multiple theoretical lenses (e.g. trust in social media, social media capability, public engagement, public services innovation, public value theory, and stakeholder theory). This proposed research is based on a qualitative methodology with several phases of research (e.g. pilot study, multiple-case study and domain expert panel) for the Saudi Arabian local government context. The expected contribution of this research is a model with constructive associations between several variables identified from multiple streams of literature (e.g. social media, information systems literature and public administration literature). Furthermore, a classification of public services innovation associated with four types of public value are proposed. The findings of the study are expected to benefit public managers as well as citizens to better utilise social media for public value creation. Keywords: Trust in social media, social media capability, public engagement, public service innovation, public value, stakeholder theory

Public value creation using social media applications for the local government context: a pilot case study

The use of social media applications is receiving a growing attention from the local government agencies. This is because social media applications have the potential to offer many public values to those agencies as well as benefit citizens through enhancing public engagement and public services innovation. Despite the growth in the literature on social media, there is still limited understanding on how public value created through using various social media applications for local government context. To address this concern, we report on the development of a model to investigate public value creation using social media applications. The model is evaluated using a pilot case study at a large Saudi Arabian municipality. The model and empirical evidence together contribute towards establishing a theoretical foundation for research into the impact of social media applications for public value creation. In addition, council managers can learn useful lessons drawing on our findings

Realisation of Social Media Enabled Public Value at a Saudi Municipality Council: A Perspective of Citizen Representatives

Government agencies including municipalities around the globe have begun using various social media applications to provide useful and even innovative services and create engagement with citizens. The success of government agencies to creating engagement and delivery of services is often interpreted using the lens of public value. Despite a growth in the scholarly literature on social media and public value, scant attention has so far been paid to explain how citizens themselves or their representatives (known as citizen representatives) perceive public value created by social media through delivery services and fostering citizen-government engagement. To address this concern, in this paper we report on the development of a conceptual model to explain public value creation using social media applications. We further present an empirical evaluation of the model using the viewpoints of citizen representatives at a large municipality council in Saudi Arabia. The implications of the findings are then outlined

Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images

Diagnostic and predictive models of disease have been growing rapidly due to developments in the field of healthcare. Accurate and early diagnosis of COVID-19 is an underlying process for controlling the spread of this deadly disease and its death rates. The chest radiology (CT) scan is an effective device for the diagnosis and earlier management of COVID-19, meanwhile, the virus mainly targets the respiratory system. Chest X-ray (CXR) images are extremely helpful in the effective diagnosis of COVID-19 due to their rapid outcomes, cost-effectiveness, and availability. Although the radiological image-based diagnosis method seems faster and accomplishes a better recognition rate in the early phase of the epidemic, it requires healthcare experts to interpret the images. Thus, Artificial Intelligence (AI) technologies, such as the deep learning (DL) model, play an integral part in developing automated diagnosis process using CXR images. Therefore, this study designs a sine cosine optimization with DL-based disease detection and classification (SCODL-DDC) for COVID-19 on CXR images. The proposed SCODL-DDC technique examines the CXR images to identify and classify the occurrence of COVID-19. In particular, the SCODL-DDC technique uses the EfficientNet model for feature vector generation, and its hyperparameters can be adjusted by the SCO algorithm. Furthermore, the quantum neural network (QNN) model can be employed for an accurate COVID-19 classification process. Finally, the equilibrium optimizer (EO) is exploited for optimum parameter selection of the QNN model, showing the novelty of the work. The experimental results of the SCODL-DDC method exhibit the superior performance of the SCODL-DDC technique over other approaches

Hybrid Hunter–Prey Optimization with Deep Learning-Based Fintech for Predicting Financial Crises in the Economy and Society

Financial technology (Fintech) plays a pivotal role in driving contemporary technology, society, economies, and many other fields. The new-generation Fintech is Smart Fintech, mainly empowered and inspired by data science and artificial intelligence (DSAI) technologies. Smart Fintech combines DSAI and transforms finance and economies for driving automated, intelligent, personalized financial and economic businesses, services and systems, and the whole of business. The strength and growth of the country’s economy were evaluated with the accurate prediction of how many companies will succeed and how many will fail. Financial crisis prediction (FCP) has a considerable effect on the economy. Prior research focuses mainly on deep learning (DL), machine learning (ML), and statistical approaches for forecasting the financial health of a company. Thus, this study presents a hybrid hunter–prey optimization with a deep learning-based FCP (HHPODL-FCP) technique. The objective of the HHPODL-FCP algorithm lies in the effective identification of the financial crisis in enterprises or organizations. To accomplish this, the HHPODL-FCP method makes use of the HHPO algorithm for the feature subset selection process. In addition, the HHPODL-FCP technique employs the gated attention recurrent network (GARN) model for the identification and classification of financial and non-financial crises. The HHPODL-FCP method exploits a sparrow search algorithm (SSA)-based hyperparameter tuning process to enrich the performance of the GARN model. The simulation results of the HHPODL-FCP method are tested on different financial datasets. A wide range of experiments highlighted the remarkable performance of the HHPODL-FCP method over recent techniques under various measures

Enhancing Cybersecurity in the Internet of Things Environment Using Bald Eagle Search Optimization With Hybrid Deep Learning

Nowadays, the Internet of Things (IoT) has become a rapid development; it can be employed by cyber threats in IoT devices. A correct system to recognize malicious attacks at IoT platforms became of major importance to minimize security threats in IoT devices. Botnet attacks have more severe and common attacks and it is threaten IoT devices. These threats interrupt IoT alteration by interrupting networks and services for IoT devices. Several existing methods present themselves to determine unknown patterns in IoT networks for improving security. Recent analysis presents DL and ML methods for classifying and detecting botnet attacks from the IoT environment. Consequently, this paper develops a Bald Eagle Search Optimization with a Hybrid Deep Learning based botnet detection (BESO-HDLBD) algorithm in an IoT platform. The presented BESO-HDLBD approach aims to resolve the security issue by identifying the botnets in the IoT environment. To reduce the high dimensionality problem, the BESO-HDLBD method uses the BESO system for the feature selection process. For botnet detection purposes, the BESO-HDLBD algorithm uses HDL, which is an integration of convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), and attention concept. The desire for the HDL technique in botnet detection utilises the intricate nature of botnet attacks that frequently contain difficult and developing patterns. Combining CNNs permits for effectual feature extraction from spatial data, BiLSTM networks capture temporal dependencies, and attention mechanisms improve the model’s capability to concentrate on fundamental patterns. The selection of hyperparameters of the HDL approach takes place using the dragonfly algorithm (DFA). The experimental analysis of the BESO-HDLBD system could be examined under a benchmark botnet dataset. The obtained outcome infers a better outcome of the BESO-HDLBD technique compared to the recent detection system with respect to distinct estimation measures