DORA :: Browsing by Author "Hassani, Hossein"

Browsing by Author "Hassani, Hossein"

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Open Access
Banking with blockchain-ed big data
(Taylor and Francis, 2018-10-09) Hassani, Hossein; Huang, Xu; Silva, Emmanuel
Blockchain is disrupting the banking industry and contributing to the increased bigdata in banking. However, there exists a gap in research and development into blockchain-ed big data in banking from an academic perspective, and this gap is expected to have a significant negative impact on the adoption and development of blockchain technology for banking. In hope of motivating more active engagement by academics, researchers and bankers alike, we present the most comprehensive review of the impact of blockchain in banking to date by summarizing the opportunities and challenges from a bankers perspective. In addition, we also discuss the impact that big data from blockchain will have on banking data analytics in future and show the increasing importance of filtering and signal extraction for the banking industry. Whilst there is evidence of selected banks adopting blockchain technology in isolation or small groups, we find the need for extensive research and development into several aspects of banking with blockchain to overcome the challenges which are currently hindering its adoption in banking across the globe.
Open Access
Big Data and Causality
(Springer, 2017-08-01) Hassani, Hossein; Huang, Xu; Ghodsi, Mansi
Causality analysis continues to remain one of the fundamental research questions and the ultimate objective for a tremendous amount of scientific studies. In line with the rapid progress of science and technology, the age of big data has significantly influenced the causality analysis on various disciplines especially for the last decade due to the fact that the complexity and difficulty on identifying causality among big data has dramatically increased. Data mining, the process of uncovering hidden information from big data is now an important tool for causality analysis, and has been extensively exploited by scholars around the world. The primary aim of this paper is to provide a concise review of the causality analysis in big data. To this end the paper reviews recent significant applications of data mining techniques in causality analysis covering a substantial quantity of research to date, presented in chronological order with an overview table of data mining applications in causality analysis domain as a reference directory.
Open Access
Big Data and Climate Change
(MDPI, 2019-02-02) Hassani, Hossein; Huang, Xu; Silva, Emmanuel
Climate science as a data-intensive subject has overwhelmingly affected by the era of big data and relevant technological revolutions. The big successes of big data analytics in diverse areas over the past decade have also prompted the expectation of big data and its efficacy on the big problem—climate change. As an emerging topic, climate change has been at the forefront of the big climate data analytics implementations and exhaustive research have been carried out covering a variety of topics. This paper aims to present an outlook of big data in climate change studies over the recent years by investigating and summarising the current status of big data applications in climate change related studies. It is also expected to serve as a one-stop reference directory for researchers and stakeholders with an overview of this trending subject at a glance, which can be useful in guiding future research and improvements in the exploitation of big climate data.
Open Access
Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance
(MDPI, 2021-06-28) Hassani, Hossein; Huang, Xu; MacFeely, Steve; Entezarian, Mohammad Reza
The launch of the United Nations (UN) 17 Sustainable Development Goals (SDGs) in 2015 was a historic event, uniting countries around the world around the shared agenda of sustainable development with a more balanced relationship between human beings and the planet. The SDGs affect or impact almost all aspects of life, as indeed does the technological revolution, empowered by Big Data and their related technologies. It is inevitable that these two significant domains and their integration will play central roles in achieving the 2030 Agenda. This research aims to provide a comprehensive overview of how these domains are currently interacting, by illustrating the impact of Big Data on sustainable development in the context of each of the 17 UN SDGs.
Open Access
Big-Crypto: Big Data, Blockchain and Cryptocurrency
(MDPI, 2018-10) Hassani, Hossein; Huang, Xu; Silva, Emmanuel
Cryptocurrency has been a trending topic over the past decade, pooling tremendous technological power and attracting investments valued over trillions of dollars on a global scale. The cryptocurrency technology and its network have been endowed with many superior features due to its unique architecture, which also determined its worldwide efﬁciency, applicability and data-intensive characteristics. This paper introduces and summarises the interactions between two signiﬁcantconceptsinthedigitalizedworld,i.e.,cryptocurrencyandBigData. Bothsubjectsareatthe forefront of technological research, and this paper focuses on their convergence and comprehensively reviews the very recent applications and developments after 2016. Accordingly, we aim to present a systematic review of the interactions between Big Data and cryptocurrency and serve as the one-stop reference directory for researchers with regard to identifying research gaps and directing future explorations.
Open Access
Causality analysis detects the regulatory role of maternal effect genes in the early Drosophila embryo
(Elsevier, 2016-11-22) Ghodsi, Zara; Huang, Xu; Hassani, Hossein
Open Access
Causality between Oil Prices and Tourist Arrivals
(MDPI, 2018-10-20) Huang, Xu; Silva, Emmanuel; Hassani, Hossein
This paper investigates the causal relationship between oil price and tourist arrivals to further explain the impact of oil price volatility on tourism-related economic activities. The analysis itself considers the time domain, frequency domain, and information theory domain perspectives. Data relating to the US and nine European countries are exploited in this paper with causality tests which include the time domain, frequency domain, and Convergent Cross Mapping (CCM). The CCM approach is nonparametric and therefore not restricted by assumptions. We contribute to existing research through the successful and introductory application of an advanced method and via the uncovering of significant causal links from oil prices to tourist arrivals.
Open Access
Classical Dynamic Consensus and Opinion Dynamics Models: A Survey of Recent Trends and Methodologies
(Elsevier, 2022-07-13) Hassani, Hossein; Razavi-Far, Roozbeh; Saif, Mehrdad; Chiclana, Francisco; Krejcar, Ondrej; Herrera-Viedma, Enrique
Consensus reaching is an iterative and dynamic process that supports group decision-making models by guiding decision-makers towards modifying their opinions through a feedback mechanism. Many attempts have been recently devoted to the design of efficient consensus reaching processes, especially when the dynamism is dependent on time, which aims to deal with opinion dynamics models. The emergence of novel methodologies in this field has been accelerated over recent years. In this regard, the present work is concerned with a systematic review of classical dynamic consensus and opinion dynamics models. The most recent trends of both models are identified and the developed methodologies are described in detail. Challenges of each model and open problems are discussed and worthwhile directions for future research are given. Our findings denote that due to technological advancements, a majority of recent literature works are concerned with the large-scale group decision-making models, where the interactions of decision-makers are enabled via social networks. Managing the behavior of decision-makers and consensus reaching with the minimum adjustment cost under social network analysis have been the top priorities for researchers in the design of classical consensus and opinion dynamics models.
Open Access
Deep Learning and Implementations in Banking
(Springer, 2020-06-29) Hassani, Hossein; Huang, Xu; Silva, Emmanuel; Ghodsi, Mansi
Data-driven technologies have been changing every aspect of human life and the fast-developing banking sector with its data-rich nature has become the implementation field of these fast-evolving technologies. Deep learning, as one of the emerging technologies in recent years, has also been inevitably adopted for various improvements in banking. To the best of our knowledge, there is no comprehensive literature review, which focuses on specifically deep learning and its implementations in banking. Therefore, this paper investigates the deep learning technology in-depth and summarizes the relevant applications in banking so to contribute to the existing literature. Moreover, by providing a reliable and up-to-date review, it is also aimed to serve as the one-stop repository for banks and researchers who are interested in embracing deep learning, whilst bringing insights for the directions of future research and implementation.
Open Access
Digitalisation and Big Data Mining in Banking
(MDPI, 2018-07-20) Hassani, Hossein; Huang, Xu; Silva, Emmanuel
Banking as a data-intensive subject has been progressing continuously under the promoting influences of the era of big data. Exploring the advanced big data analytic tools like Data Mining (DM) techniques is key for the banking sector, which aims to reveal valuable information from the overwhelming volume of data and achieve better strategic management and customer satisfaction. In order to provide sound direction for the future research and development, a comprehensive and most up to date review of the current research status of DM in banking will be extremely beneficial. Since existing reviews only cover the applications until 2013, this paper aims to fill this research gap and presents the significant progressions and most recent DM implementations in banking post 2013. By collecting and analyzing the trends of research focus, data resources, technological aids, and data analytical tools, this paper contributes to bringing valuable insights with regard to the future developments of both DM and the banking sector along with a comprehensive one stop reference table. Moreover, we identify the key obstacles and present a summary for all interested parties that are facing the challenges of big data.
Open Access
Do trend extraction approaches affect causality detection in climate change studies?
(Physica A: Statistical Mechanics and its Applications, 2017-03-01) Huang, Xu; Hassani, Hossein; Ghodsi, Mansi; Mukherjee, Zinnia; Gupta, Rangan
Open Access
Does sunspot numbers cause global temperatures? A reconsideration using non-parametric causality tests.
(Elsevier, 2016-10-15) Hassani, Hossein; Huang, Xu; Gupta, Rangan; Ghodsi, Mansi
Open Access
Enabling Digital Twins to Support the UN SDGs
(MDPI, 2022-10-16) Hassani, Hossein; Huang, Xu; MacFeely, Steve
Digitalisation has enjoyed rapid acceleration during the COVID-19 pandemic on top of the already fast-paced expansion impacting almost every aspect of daily life. Digital twin technology, which is considered a building block of Metaverse and an important pillar of Industrial revolution 4.0, has also received growing interest. Apart from its significant contribution to intelligent manufacturing, there has been considerable discussion on its implementation and the as yet undiscovered potential. This paper reviews the current trajectory of digital twin applications in supporting general sustainability, in the context of the 17 UN SDGs. Furthermore, it connects researchers and readers from different fields with the aim of achieving a better understanding of emerging digital twin technologies, the current values this technology has brought to support UN SDGs, and identify areas with potential for future research to better contribute to achieving the remaining tasks of Agenda 2030.
Embargo
Forecasting tourism demand with denoised neural networks
(Elsevier, 2018-11-27) Silva, Emmanuel; Hassani, Hossein; Heravi, Saeed; Huang, Xu
The automated Neural Network Autoregressive (NNAR) algorithm from the forecast package in R generates sub-optimal forecasts when faced with seasonal tourism demand data. We propose denoising as a means of improving the accuracy of NNAR forecasts via an application into forecasting monthly tourism demand for ten European countries. Initially, we fit NNAR models on both raw and denoised (with Singular Spectrum Analysis) tourism demand series, generate forecasts and compare the results. Thereafter, the denoised NNAR forecasts are also compared with parametric and nonparametric benchmark forecasting models. Contrary to the deseasonalising hypothesis, we find statistically significant evidence which supports the denoising hypothesis for improving the accuracy of NNAR forecasts. Thus, it is noise and not seasonality which hinders NNAR forecasting capabilities.
Open Access
Fusing Big Data, Blockchain and Cryptocurrency: Their Individual and Combined Importance in the Digital Economy
(Palgrave Pivot, 2019-09) Hassani, Hossein; Huang, Xu; Silva, Emmanuel; Huang
As technology continues to revolutionise today’s economy, Big Data, Blockchain and Cryptocurrency are rapidly transforming themselves into mainstream functions within the financial services industry. This book examines each concept individually, analysing the opportunities and challenges they bring, and exploring the potential for future development. The authors further evaluate the fusion of these three important products of the FinTech revolution, illustrating their combined influence on the digital economy. Providing a comprehensive analysis of three innovative technologies, this timely book will appeal to scholars researching innovation in the finance industry, and financial services technology more specifically.
Open Access
Fusing Nature with Computational Science for Optimal Signal Extraction
(MDPI, 2021-01-19) Hassani, Hossein; Yeganegi, Mohammad Reza; Huang, Xu
Fusing nature with computational science has been proved paramount importance and researchers have also shown growing enthusiasm on inventing and developing nature inspired algorithms for solving complex problems across subjects. Inevitably, these advancements have rapidly promoted the development of data science, where nature-inspired algorithms are changing the traditional way of data processing. This paper proposes the hybrid approach, namely SSA-GA, which incorporates the optimization merits of genetic algorithm (GA) for the advancements of Singular Spectrum Analysis (SSA). This approach further boosts the performance of SSA forecasting via better and more efficient grouping. Given the performances of SSA-GA on 100 real-time series data across various subjects, this newly proposed SSA-GA approach is proved to be computationally efficient and robust with improved forecasting performance.
Open Access
The Human Digitalisation Journey: Technology First at the Expense of Humans?
(MDPI, 2021-06-29) Hassani, Hossein; Huang, Xu; Silva, Emmanuel
The ongoing COVID-19 pandemic has enhanced the impact of digitalisation as a driver of transformation and advancements across almost every aspect of human life. With the majority actively embracing smart technologies and their benefits, the journey of human digitalisation has begun. Will human beings continue to remain solitary unaffected beings in the middle of the whirlpool—a gateway to the completely digitalised future? This journey of human digitalisation probably started much earlier, before we even realised. This paper, in the format of an objective review and discussion, aims to investigate the journey of human digitalisation, explore the reality of domination between technology and humans, provide a better understanding of the human value and human vulnerability in this fast transforming digital era, so as to achieve valuable and insightful suggestion on the future direction of the human digitalisation journey.
Open Access
Hydrological natural inflow and climate variables: Time and frequency causality analysis
(Elsevier., 2018-10-30) Huang, Xu; Macaira, Paula; Hassani, Hossein; Cyrino Oliveira, Fernando Luiz; Dhesi, Gurjeet
Numbers of studies have proved the significant influence of climate variables on hydrological series. Considering the pivotal role of the hydroelectric power plants play in the electricity production in Brazil this paper considers the natural hydrological inflow data from 15 major basins and 8 climate variables containing 7 El Niño Southern Oscillation proxies and the sunspot numbers. The causal relationships between hydrological natural inflows and climate variables are investigated by adopting and comparing 5 different causality detection methods (Granger Causality test, Frequency Domain Causality test, Convergent Cross Mapping Causality test, Single Spectrum Analysis (SSA) Causality test and Periodic Autoregressive Model Causality test) that cover both well established and novel empirical approaches. Both time domain and frequency domain causality tests gain valid evidences of unidirectional causality for a group of series; CCM achieved unidirectional causality for 18% of pairs and overwhelmingly indicated the opposite direction of causality; a mixture of results are concluded by SSA causality test; PAR based causality test obtained six unidirectional causality, but only one is really significant.
Open Access
Impactful Digital Twin in the Healthcare Revolution
(MDPI, 2022-08-08) Hassani, Hossein; Huang, Xu; MacFeely, Steve
Open Access
Is there a Causal Relationship between Oil Prices and Tourist Arrivals?
(Taylor & Francis, 2020) Hassani, Hossein; Ghodsi, Mansi; Huang, Xu; Silva, Emmanuel
This application note investigates the causal relationship between oil price and tourist arrivals to further explain the impact of oil price volatility on tourism related economic activities. The analysis itself considers the time domain, frequency domain and information theory domain perspectives. Data relating to the US and nine European countries are exploited in this paper with causality tests which include time domain, frequency domain, and Convergent Cross Mapping (CCM). The CCM approach is nonparametric and therefore not restricted by assumptions. We contribute to existing research through the successful and introductory application of an advanced method, and via the uncovering of signiﬁcant causal links from oil prices to tourist arrivals. Keywords: Oil price; tourist arrivals; causality; convergent cross mapping; granger causality