Intelligent reorganized discrete cosine transform for reduced reference image quality assessment

Bashir, Tariq; Usman, Imran; Khan, Shah Nawaz; Rehman, Junaid ur

doi:10.3906/elk-1511-75

Cited by 14 publications

(3 citation statements)

References 15 publications

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“…Nonlinear autoregressive neural network for time series forecasting can overcome the nonlinearity and has the potential to forecast with minimum prediction error (Benmouiza and Cheknane 2016 ; Hill et al 1996 ). The use of neural networks can be found in various domains such as machine translation (Shahnawaz and Mishra 2013 ; Bye et al 2018 ), natural language processing (Khan et al 2018 ), sentiment analysis (Astya 2017 ), and image processing (Bashir et al 2017 ). Neural network forecasting models have been implemented to predict in several fields including CO 2 emission forecasting and air pollution estimation (Gallo et al 2014 ), for forecasting the intensity of emission by some of the top CO 2 emitters (Acheampong and Boateng 2019 ) and CO 2 emission estimation (Zhao and Mao 2012 ; Sun and Huang 2020 ), for predicting humidity and room temperature (Mustafaraj et al 2011 ), and for predicting energy consumption (Ruiz et al 2016 ; Usman and Hammar 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting carbon emissions due to electricity power generation in Bahrain

Qader

Khan

Kamal

et al. 2021

Environ Sci Pollut Res

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Global warming and climate change have become one of the most embarrassing and explosive problems/challenges all over the world, especially in third-world countries. It is due to a rapid increase in industrialization and urbanization process that has given the boost to the volume of greenhouse gases (GHGs) emissions. In this regard, carbon dioxide (CO 2 ) is considered a significant driver of GHGs and is the major contributing factor for global warming. Considering the goal of mitigating environmental pollution, this research has applied multiple methods such as neural network time series nonlinear autoregressive, Gaussian Process Regression, and Holt’s methods for forecasting CO 2 emission. It attempts to forecast the CO 2 emission of Bahrain. These methods are evaluated for performance. The neural network model has the root mean square errors (RMSE) of merely 0.206, while the Gaussian Process Regression Rational Quadratic (GPR-RQ) Model has RMSE of 1.0171, and Holt’s method has RMSE of 1.4096. Therefore, it can be concluded that the neural network time series nonlinear autoregressive model has performed better for forecasting the CO 2 emission in the case of Bahrain.

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting carbon emissions due to electricity power generation in Bahrain

Qader

Khan

Kamal

et al. 2021

Environ Sci Pollut Res

Self Cite

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“…Some examples of various techniques used in machine translation application domain of NLP are statistical approaches in which the model is built around the Bayesian probability theorem (Shahnawaz, 2013b), artificial neural network based approach which is one of the most popular approach nowadays as the use of deep learning is increasing (Khan, 2011;Mishra, 2012;Khan, 2019a), rule-based methods (Shahnawaz, 2013a), case-based reasoning approach which considers the previously available data as the case base for processing future incoming information (Shahnawaz, 2015) However the textual information received from the borrowers sometimes is not available in proper grammatical structure, therefore, NLP techniques are applied for getting the proper information because for the efficiency of the NLP algorithms the input provided to most of these algorithms must be in proper structure specifically for those algorithms which generates a parse tree for extracting the information from the input documents (Khan, 2018;Shahnawaz, 2011). The proposed system can also be developed for automatic assessment of the images and getting the preliminary information from the images and videos (Bashir, 2017). The researchers are working in the direction of developing a chatbot for the integration on Zakat platform which will be available online for the automatic response of the user queries (Khan, 2020;Khan, 2020b).…”

Section: Zakat and Qardh-al-hasan Platformmentioning

confidence: 99%

An Artificial Intelligence and NLP based Islamic FinTech Model Combining Zakat and Qardh-Al-Hasan for Countering the Adverse Impact of COVID 19 on SMEs and Individuals

Syed¹,

Khan²,

Rabbani³

et al. 2020

IJEBA

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Pursose: The ongoing Corona virus (COVID 19) pandemic has already impacted almost everyone across the globe. The focus has now shifted from spread of the disease to the economic consequences it will bring to the society. The shortage of production will result into the shortage of supply and consequently will end as loss of jobs and employment for millions of people around the world. Two of the most important section of our society i.e., daily wage laborers and Small and Medium Enterprises (SMEs) will have to bear the major burnt of this crisis. The proposed integrated Artificial Intelligence and NLP based Islamic FinTech Model combining Zakat (Islamic tax) and Qardh-Al-Hasan (benevolent loan) can help the economy to minimize the adverse impact of COVID 19 on individuals and SMEs. Design/Methodology/Approach: The present study explores the possibility of Zakat and Qardh-Al-Hasan as a financing method to fight the adverse impact of Corona virus on poor individuls and SMEs. It provides the solution by proposing an Artificial Intelligence and NLP based Islamic FinTech Model combining Zakat and Qardh-Al-Hasan. Findings: The findings of the study reveals that Islamic finance has immense potential to fight any kind of situation/pandemic. Zakat and Qardh-Al-Hasan, if combined together can prove to be a deadly combination to fight the adverse effect of COVID 19. Practical Implications: To be used as an effective way to support individuals and SMEs in the period during and after the pandemic of COVID 19. Originality/value: There is no study combining Zakat and Qardh Al-Hasan to fight the adverse effect of poor individuals and SMEs. The study will contribute massively to the existing literature and will help the government and civil societies in fighting the economic impact of COVID 19 on individuals and SMEs.

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“…Non-linear autoregressive neural network for time series forecasting can overcome the non-linearity and has the potential to forecast with minimum prediction error (Benmouiza and Cheknane 2016;Hill et al 1996). The use of neural network can be found in various domain such as machine translation (Khan and Usman, 2019;Shahnawaz and Mishra 2013;Bye et al 2018), natural language processing (Khan et al 2018), sentiment analysis (Astya 2017) and image processing (Bashir et al 2017) etc. Neural network forecasting models have been implemented to predict in several fields including the for CO2 emission forecasting and air pollution estimation (Gallo et al 2014), for forecasting the intensity of emission by some of the top CO2 emitters…”

Section: Related Workmentioning

confidence: 99%

Forecasting CO2 Emissions Due To Electricity Power Generation In Bahrain

Qader

Khan

Kamal

et al. 2021

Preprint

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Global warming is one of the biggest challenges among the leaders and scientists from developed and developing countries. Rapid industrialization and urbanization have given the boost to the amount of greenhouse gases’ emission. Carbon dioxide (CO2) is a significant of greenhouse gases and is the major contributing factor for global warming. CO2 emissions concentrations in the atmosphere have increased by 47% over the past 170 years due to human activities. As per Doha amendment of the Kyoto protocol in 2012, the target for maximum CO2 emission per capita for Bahrain was set to 20.96 metric ton for 2020. However, the current amount of CO2 emission per capita is 21.64 metric ton as of 2019. This research has applied multiple methods such as neural network time series nonlinear autoregressive, Gaussian Process Regression and Holt’s methods for CO2 emission forecasting. It attempts to forecast the CO2 emission of Bahrain. These methods are evaluated for performance. Neural network model has the RMSE of merely 0.206 while the GPR-RQ model has RMSE of 1.0171 and Holt’s method has RMSE of 1.4096. Therefore, it can be concluded that neural network time series nonlinear autoregressive model has performed better for forecasting the CO2 emission of Bahrain.

show abstract

Intelligent reorganized discrete cosine transform for reduced reference image quality assessment

Cited by 14 publications

References 15 publications

Forecasting carbon emissions due to electricity power generation in Bahrain

Forecasting carbon emissions due to electricity power generation in Bahrain

An Artificial Intelligence and NLP based Islamic FinTech Model Combining Zakat and Qardh-Al-Hasan for Countering the Adverse Impact of COVID 19 on SMEs and Individuals

Forecasting CO2 Emissions Due To Electricity Power Generation In Bahrain

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