ORCID Profile
0000-0002-0735-9038
Current Organisation
Cardiff Metropolitan University
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Publisher: The Electromagnetics Academy
Date: 2011
Publisher: Public Library of Science (PLoS)
Date: 15-06-2023
DOI: 10.1371/JOURNAL.PONE.0287342
Abstract: The economic landscape of the United Kingdom has been significantly shaped by the intertwined issues of Brexit, COVID-19, and their interconnected impacts. Despite the country’s robust and erse economy, the disruptions caused by Brexit and the COVID-19 pandemic have created uncertainty and upheaval for both businesses and in iduals. Recognizing the magnitude of these challenges, academic literature has directed its attention toward conducting immediate research in this crucial area. This study sets out to investigate key economic factors that have influenced various sectors of the UK economy and have broader economic implications within the context of Brexit and COVID-19. The factors under scrutiny include the unemployment rate, GDP index, earnings, and trade. To accomplish this, a range of data analysis tools and techniques were employed, including the Box-Jenkins method, neural network modeling, Google Trend analysis, and Twitter-sentiment analysis. The analysis encompassed different periods: pre-Brexit (2011-2016), Brexit (2016-2020), the COVID-19 period, and post-Brexit (2020-2021). The findings of the analysis offer intriguing insights spanning the past decade. For instance, the unemployment rate displayed a downward trend until 2020 but experienced a spike in 2021, persisting for a six-month period. Meanwhile, total earnings per week exhibited a gradual increase over time, and the GDP index demonstrated an upward trajectory until 2020 but declined during the COVID-19 period. Notably, trade experienced the most significant decline following both Brexit and the COVID-19 pandemic. Furthermore, the impact of these events exhibited variations across the UK’s four regions and twelve industries. Wales and Northern Ireland emerged as the regions most affected by Brexit and COVID-19, with industries such as accommodation, construction, and wholesale trade particularly impacted in terms of earnings and employment levels. Conversely, industries such as finance, science, and health demonstrated an increased contribution to the UK’s total GDP in the post-Brexit period, indicating some positive outcomes. It is worth highlighting that the impact of these economic factors was more pronounced on men than on women. Among all the variables analyzed, trade suffered the most severe consequences in the UK. By early 2021, the macroeconomic situation in the country was characterized by a simple dynamic: economic demand rebounded at a faster pace than supply, leading to shortages, bottlenecks, and inflation. The findings of this research carry significant value for the UK government and businesses, empowering them to adapt and innovate based on forecasts to navigate the challenges posed by Brexit and COVID-19. By doing so, they can promote long-term economic growth and effectively address the disruptions caused by these interrelated issues.
Publisher: Elsevier BV
Date: 09-2016
Publisher: Wiley
Date: 11-05-2016
DOI: 10.1002/CTA.2222
Publisher: IEEE
Date: 12-2010
Publisher: Wiley
Date: 27-03-2015
DOI: 10.1002/MOP.29101
Publisher: MDPI AG
Date: 03-01-2023
DOI: 10.3390/S23010527
Abstract: Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence’s role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-shot learning studies that were utilized in several tasks related to COVID-19. In particular, genetic analysis, medical image analysis, clinical data analysis, sound analysis, biomedical data classification, socio-demographic data analysis, anomaly detection, health monitoring, personal protective equipment (PPE) observation, social control, and COVID-19 patients’ mortality risk approaches were used in this study to forecast the threatening factors of COVID-19. This study demonstrates that artificial-intelligence-based algorithms integrated into Internet of Things wearable devices were quite effective and efficient in COVID-19 detection and forecasting insights which were actionable through wide usage. The results produced by the study prove that artificial intelligence is a promising arena of research that can be applied for disease prognosis, disease forecasting, drug discovery, and to the development of the healthcare sector on a global scale. We prove that artificial intelligence indeed played a significantly important role in helping to fight against COVID-19, and the insightful knowledge provided here could be extremely beneficial for practitioners and research experts in the healthcare domain to implement the artificial-intelligence-based systems in curbing the next pandemic or healthcare disaster.
Publisher: Informa UK Limited
Date: 02-01-2020
Publisher: Springer Science and Business Media LLC
Date: 08-02-2023
DOI: 10.1007/S42979-022-01618-8
Abstract: Yoga has become an integral part of human life to maintain a healthy body and mind in recent times. With the growing, fast-paced life and work from home, it has become difficult for people to invest time in the gymnasium for exercises. Instead, they like to do assisted exercises at home where pose recognition techniques play the most vital role. Recognition of different poses is challenging due to proper dataset and classification architecture. In this work, we have proposed a deep learning-based model to identify five different yoga poses from comparatively fewer amounts of data. We have compared our model’s performance with some state-of-the-art image classification models-ResNet, InceptionNet, InceptionResNet, Xception and found our architecture superior. Our proposed architecture extracts spatial, and depth features from the image in idually and considers them for further calculation in classification. The experimental results show that it achieved 94.91% accuracy with 95.61% precision.
Publisher: Elsevier BV
Date: 08-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institution of Engineering and Technology (IET)
Date: 12-2015
Publisher: Wiley
Date: 28-08-2015
DOI: 10.1002/MOP.29407
Publisher: Springer Science and Business Media LLC
Date: 20-03-2023
DOI: 10.1007/S42979-023-01744-X
Abstract: Advanced persistent threat (APT) is a serious concern in cyber-security that has matured and grown over the years with the advent of technology. The main aim of this study is to establish an effective identification model for APT attacks to prevent and reduce their influence. Machine learning has the potential as well as substantial background to detect and predict cyber-security threats including APT. This study utilized several boosting-based machine learning methods to predict various types of APTs that are consistent in cyber-security domain. Furthermore, Explainable Artificial Intelligence (XAI) was coupled with the predictions to provide actionable insights to the domain stakeholders as well as practitioners in this domain. The results, particularly XGBoost with weighted F1 score of 0.97 and SHapley Additive exPlanations (SHAP)-based explanation, prove that boosting methods as well as machine learning models paired with XAI are indeed promising in handling cyber-security-related dataset problems which can be extrapolated towards new avenues of challenging research by effectively deploying boosting-based XAI models.
Publisher: IEEE
Date: 02-2009
Publisher: IEEE
Date: 12-2008
Publisher: Public Library of Science (PLoS)
Date: 15-09-2022
DOI: 10.1371/JOURNAL.PONE.0274538
Abstract: The devastating impact of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) pandemic almost halted the global economy and is responsible for 6 million deaths with infection rates of over 524 million. With significant reservations, initially, the SARS-CoV-2 virus was suspected to be infected by and closely related to Bats. However, over the periods of learning and critical development of experimental evidence, it is found to have some similarities with several gene clusters and virus proteins identified in animal-human transmission. Despite this substantial evidence and learnings, there is limited exploration regarding the SARS-CoV-2 genome to putative microRNAs (miRNAs) in the virus life cycle. In this context, this paper presents a detection method of SARS-CoV-2 precursor-miRNAs (pre-miRNAs) that helps to identify a quick detection of specific ribonucleic acid (RNAs). The approach employs an artificial neural network and proposes a model that estimated accuracy of 98.24%. The s ling technique includes a random selection of highly unbalanced datasets for reducing class imbalance following the application of matriculation artificial neural network that includes accuracy curve, loss curve, and confusion matrix. The classical approach to machine learning is then compared with the model and its performance. The proposed approach would be beneficial in identifying the target regions of RNA and better recognising of SARS-CoV-2 genome sequence to design oligonucleotide-based drugs against the genetic structure of the virus.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2016
Publisher: ACM
Date: 31-08-2009
Publisher: Public Library of Science (PLoS)
Date: 05-09-2023
Publisher: Walter de Gruyter GmbH
Date: 07-2016
Abstract: The design analysis and prototype of a compact 8×10-mm 2 planar microstrip line-fed patch antenna on a readily available, low-cost, reinforced-fiberglass polymer resin composite material substrate is presented in this article. The proposed compact-size antenna has been configured and numerically analyzed using the finite element method-based three-dimensional full-wave electromagnetic field simulator. The optimized design of the antenna has been fabricated on a printed circuit board (PCB), and experimental results have been collected for further analysis. The measurement results affirm the fractional impedance bandwidths of (return loss of less than -10 dB) of 38.78% (2.03–2.98 GHZ) and 16.3% (5.38–6.35 GHz), with average gains of 2.52 and 3.94 dBi at both lower and upper bands, respectively. The proposed dual resonant antenna shows the radiation efficiencies of 91.3% at 2.45 GHz and 87.7% at 5.95 GHz. The stable and almost symmetric radiation patterns and performance criteria of the antenna can successfully cover IEEE 802.11b/g/n, Bluetooth, WLAN, and C-band telecommunication satellite uplinks.
Publisher: Springer Science and Business Media LLC
Date: 31-10-2022
DOI: 10.1007/S42452-022-05205-7
Abstract: Wet dust on the Photovoltaic (PV) surface is a persistent problem that is merely considered for rooftop based PV cleaning under a high humid climate like Malaysia. This paper proposes an Automated Water Recycle (AWR) method encompassing a water recycling unit for rooftop PV cleaning with the aim to enhance the electrical performance. This study makes a major contribution by developing a new model to correlate output power ( $$P_{out}$$ P out ) and dust-fall factor. For model validation, we conducted an experiment of taking one set of Monocrystalline PV (mono) on a $$340\\frac{W}{m^{2}}$$ 340 W m 2 of medium luminance day. One mono module was cleaned by AWR - pressurized water sprayed through 11 small holes over its front surface, while the other module was left with no-cleaning. The dust-contaminated water was filtered and collected back to the tank for recycling process. The water loss per cleaning cycle was achieved 0.32%, which was normalized to net loss of 28.8% at a frequency of 1 cycle/day for 90 days of operation. We observed that $$P_{out}$$ P out of no-cleaning PV was decreased by 29.44% than that of AWR method. From this experimental data also, a unique and more accurate model is created for $$P_{out}$$ P out prediction, which is much simpler compared to multivariables equation. Our investigation offers important insights into the accuracy of this regression model demonstrated by $$R^{2}=0.744$$ R 2 = 0.744 or a strong negative quadratic relationship between $$P_{out}$$ P out and dust-fall. The cleaning of PV modules is expected to save significant energy to reduce the payback period. An automated water recycle method for cleaning dust-fall in rooftop photovoltaic module is proposed. Both simulation and experimental models are developed to predict output power of the photovoltaic module. Proposed method can produce 24.40% more output power than a no-cleaning system with a mere water loss of 0.32%/cycle.
Publisher: IEEE
Date: 05-2012
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for M Jasim Uddin.