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Article ; Online: Big datasets of optical-wireless cyber-physical systems for optimizing manufacturing services in the internet of things-enabled industry 4.0.

Faheem, Muhammad / Butt, Rizwan Aslam

2022 Volume 42, Page(s) 108026

Abstract: The Industry 4.0 revolution is aimed to optimize the product design according to the customers' demand, quality requirements and economic feasibility. Industry 4.0 employs advanced two-way communication technologies for optimizing the manufacturing ... ...

Abstract	The Industry 4.0 revolution is aimed to optimize the product design according to the customers' demand, quality requirements and economic feasibility. Industry 4.0 employs advanced two-way communication technologies for optimizing the manufacturing process to increase the sales of the products and revenues to cope the existing global economy issues. In Industry 4.0, big data obtained from the Internet of Things (IoT)-enabled industrial Cyber-Physical Systems (CPS) plays an important role in enhancing the system service performance to boost the productivity with enhanced quality of customer experience. This paper presents the big datasets obtained from the Internet of things (IoT)-enabled Optical-Wireless Sensor Networks (OWSNs) for optimizing service systems' performance in the electronics manufacturing Industry 4.0. The updated raw and analyzed big datasets of our published work [3] contain five values namely, data delivery, latency, congestion, throughput, and packet error rate in OWSNs. The obtained dataset are useful for optimizing the service system performance in the electronics manufacturing Industry 4.0.
Language	English
Publishing date	2022-03-09
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2786545-9
ISSN	2352-3409 ; 2352-3409
ISSN (online)	2352-3409
ISSN	2352-3409
DOI	10.1016/j.dib.2022.108026
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Big datasets of optical-wireless cyber-physical systems for optimizing manufacturing services in the internet of things-enabled industry 4.0

Faheem, Muhammad / Butt, Rizwan Aslam

Data in Brief. 2022 June, v. 42

2022

Abstract	The Industry 4.0 revolution is aimed to optimize the product design according to the customers' demand, quality requirements and economic feasibility. Industry 4.0 employs advanced two-way communication technologies for optimizing the manufacturing process to increase the sales of the products and revenues to cope the existing global economy issues. In Industry 4.0, big data obtained from the Internet of Things (IoT)-enabled industrial Cyber-Physical Systems (CPS) plays an important role in enhancing the system service performance to boost the productivity with enhanced quality of customer experience. This paper presents the big datasets obtained from the Internet of things (IoT)-enabled Optical-Wireless Sensor Networks (OWSNs) for optimizing service systems' performance in the electronics manufacturing Industry 4.0. The updated raw and analyzed big datasets of our published work [3] contain five values namely, data delivery, latency, congestion, throughput, and packet error rate in OWSNs. The obtained dataset are useful for optimizing the service system performance in the electronics manufacturing Industry 4.0.
Keywords	Internet ; data collection ; economic feasibility ; electronics
Language	English
Dates of publication	2022-06
Publishing place	Elsevier Inc.
Document type	Article
ZDB-ID	2786545-9
ISSN	2352-3409
ISSN	2352-3409
DOI	10.1016/j.dib.2022.108026
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Big datasets of optical-wireless cyber-physical systems for optimizing manufacturing services in the internet of things-enabled industry 4.0

Muhammad Faheem / Rizwan Aslam Butt

Data in Brief, Vol 42, Iss , Pp 108026- (2022)

2022

Abstract	The Industry 4.0 revolution is aimed to optimize the product design according to the customers' demand, quality requirements and economic feasibility. Industry 4.0 employs advanced two-way communication technologies for optimizing the manufacturing process to increase the sales of the products and revenues to cope the existing global economy issues. In Industry 4.0, big data obtained from the Internet of Things (IoT)-enabled industrial Cyber-Physical Systems (CPS) plays an important role in enhancing the system service performance to boost the productivity with enhanced quality of customer experience. This paper presents the big datasets obtained from the Internet of things (IoT)-enabled Optical-Wireless Sensor Networks (OWSNs) for optimizing service systems' performance in the electronics manufacturing Industry 4.0. The updated raw and analyzed big datasets of our published work [3] contain five values namely, data delivery, latency, congestion, throughput, and packet error rate in OWSNs. The obtained dataset are useful for optimizing the service system performance in the electronics manufacturing Industry 4.0.
Keywords	Internet of things ; Big data ; Optical sensor network ; Wireless sensor network ; Industry 4.0 ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Science (General) ; Q1-390
Subject code	670
Language	English
Publishing date	2022-06-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Interplay of multifractal dynamics between shadow policy rates and stock markets.

Aslam, Faheem / Mohti, Wahbeeah / Ali, Haider / Ferreira, Paulo

Heliyon

2023 Volume 9, Issue 7, Page(s) e18114

Abstract: Stock markets are generally perceived as a barometer of the economy and respond to international monetary policies even before economic activities. Many central banks have turned to unconventional policy measures in response to various financial crises ... ...

Abstract	Stock markets are generally perceived as a barometer of the economy and respond to international monetary policies even before economic activities. Many central banks have turned to unconventional policy measures in response to various financial crises such as the global financial crisis of 2007-2009 or the recent crisis caused by COVID-19. To examine the cross-correlation of overall international monetary policies with stock markets, we employ the daily shadow short rate (SSR), which has the advantage of allowing comparison across unconventional and conventional regimes. The analysis is made through a multifractal context using multifractal detrended cross correlation analysis (MF-DXA), considering daily data from 1st January 2000 to 31st March 2022 and country specific SSR and the stock markets of eight developed economies. The main empirical findings are the following: (i) all the country specific pairs of SSR with stock markets have significant multifractal characteristics (ii) the pairs of NZ-SSR/NZX50, US-SSR/DJIA, and CN-SSR/S&P TSX have the highest multifractal patterns while EU-SSR/Euro-area Index has the lowest multifractal patterns (iii) Australian and New Zealand stock markets exhibit anti-persistent cross-correlation with SSR while the remainder have persistent cross-correlation in their multifractality. Lastly, the findings of this study have several important implications for central banks and stock market participants.
Language	English
Publishing date	2023-07-10
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2023.e18114
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Interplay of multifractal dynamics between shadow policy rates and stock markets

Faheem Aslam / Wahbeeah Mohti / Haider Ali / Paulo Ferreira

Heliyon, Vol 9, Iss 7, Pp e18114- (2023)

2023

Abstract	Stock markets are generally perceived as a barometer of the economy and respond to international monetary policies even before economic activities. Many central banks have turned to unconventional policy measures in response to various financial crises such as the global financial crisis of 2007–2009 or the recent crisis caused by COVID-19. To examine the cross-correlation of overall international monetary policies with stock markets, we employ the daily shadow short rate (SSR), which has the advantage of allowing comparison across unconventional and conventional regimes. The analysis is made through a multifractal context using multifractal detrended cross correlation analysis (MF-DXA), considering daily data from 1st January 2000 to 31st March 2022 and country specific SSR and the stock markets of eight developed economies. The main empirical findings are the following: (i) all the country specific pairs of SSR with stock markets have significant multifractal characteristics (ii) the pairs of NZ-SSR/NZX50, US-SSR/DJIA, and CN-SSR/S&P TSX have the highest multifractal patterns while EU-SSR/Euro-area Index has the lowest multifractal patterns (iii) Australian and New Zealand stock markets exhibit anti-persistent cross-correlation with SSR while the remainder have persistent cross-correlation in their multifractality. Lastly, the findings of this study have several important implications for central banks and stock market participants.
Keywords	Monetary policy ; Shadow short rates ; SSR ; Stock markets ; MF-DXA ; Cross correlation ; Science (General) ; Q1-390 ; Social sciences (General) ; H1-99
Subject code	332
Language	English
Publishing date	2023-07-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities.

Latif, Saima / Javaid, Nadeem / Aslam, Faheem / Aldegheishem, Abdulaziz / Alrajeh, Nabil / Bouk, Safdar Hussain

Heliyon

2024 Volume 10, Issue 6, Page(s) e27747

Abstract: Accurate predictions of stock markets are important for investors and other stakeholders of the equity markets to formulate profitable investment strategies. The improved accuracy of a prediction model even with a slight margin can translate into ... ...

Abstract	Accurate predictions of stock markets are important for investors and other stakeholders of the equity markets to formulate profitable investment strategies. The improved accuracy of a prediction model even with a slight margin can translate into considerable monetary returns. However, the stock markets' prediction is regarded as an intricate research problem for the noise, complexity and volatility of the stocks' data. In recent years, the deep learning models have been successful in providing robust forecasts for sequential data. We propose a novel deep learning-based hybrid classification model by combining peephole LSTM with temporal attention layer (TAL) to accurately predict the direction of stock markets. The daily data of four world indices including those of U.S., U.K., China and India, from 2005 to 2022, are examined. We present a comprehensive evaluation with preliminary data analysis, feature extraction and hyperparameters' optimization for the problem of stock market prediction. TAL is introduced post peephole LSTM to select the relevant information with respect to time and enhance the performance of the proposed model. The prediction performance of the proposed model is compared with that of the benchmark models CNN, LSTM, SVM and RF using evaluation metrics of accuracy, precision, recall, F1-score, AUC-ROC, PR-AUC and MCC. The experimental results show the superior performance of our proposed model achieving better scores than the benchmark models for most evaluation metrics and for all datasets. The accuracy of the proposed model is 96% and 88% for U.K. and Chinese stock markets respectively and it is 85% for both U.S. and Indian markets. Hence, the stock markets of U.K. and China are found to be more predictable than those of U.S. and India. Significant findings of our work include that the attention layer enables peephole LSTM to better identify the long-term dependencies and temporal patterns in the stock markets' data. Profitable and timely trading strategies can be formulated based on our proposed prediction model.
Language	English
Publishing date	2024-03-13
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2024.e27747
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Article: Are clean energy markets efficient? A multifractal scaling and herding behavior analysis of clean and renewable energy markets before and during the COVID19 pandemic.

Memon, Bilal Ahmed / Aslam, Faheem / Asadova, Shakhnoza / Ferreira, Paulo

Heliyon

2023 Volume 9, Issue 12, Page(s) e22694

Abstract: The literature lacks thorough and adequate evidence of the efficiency and herding behavior of clean and renewable energy markets. Therefore, the key objective of this paper is to explore the multifractality and efficiency of six clean energy markets by ... ...

Abstract	The literature lacks thorough and adequate evidence of the efficiency and herding behavior of clean and renewable energy markets. Therefore, the key objective of this paper is to explore the multifractality and efficiency of six clean energy markets by applying a robust method of Multifractal detrended fluctuation analysis (MFDFA) on daily data over a lengthy period. In addition, to examine the inner dynamics of clean energy markets around the global pandemic (COVID19), the data are further divided into two sub-periods of before and during COVID19. Our sampled clean energy markets exhibit multifractal behavior with a significant impact on the efficiency and intensified presence of multifractality during the COVID19 period. Overall, TXCT and BSEGRNX were the most efficient clean energy markets, but the ranking of TXCT deteriorated significantly in the sub-periods. The presence of multifractality and herding behavior symmetry intensified during the crisis period, which gives a potential for advancing portfolio management techniques. Moreover, our study provides practical implications and new insights for various market participants for better management and understanding of risks.
Language	English
Publishing date	2023-11-22
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2023.e22694
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Article ; Online: Are clean energy markets efficient? A multifractal scaling and herding behavior analysis of clean and renewable energy markets before and during the COVID19 pandemic

Bilal Ahmed Memon / Faheem Aslam / Shakhnoza Asadova / Paulo Ferreira

Heliyon, Vol 9, Iss 12, Pp e22694- (2023)

2023

Abstract	The literature lacks thorough and adequate evidence of the efficiency and herding behavior of clean and renewable energy markets. Therefore, the key objective of this paper is to explore the multifractality and efficiency of six clean energy markets by applying a robust method of Multifractal detrended fluctuation analysis (MFDFA) on daily data over a lengthy period. In addition, to examine the inner dynamics of clean energy markets around the global pandemic (COVID19), the data are further divided into two sub-periods of before and during COVID19. Our sampled clean energy markets exhibit multifractal behavior with a significant impact on the efficiency and intensified presence of multifractality during the COVID19 period. Overall, TXCT and BSEGRNX were the most efficient clean energy markets, but the ranking of TXCT deteriorated significantly in the sub-periods. The presence of multifractality and herding behavior symmetry intensified during the crisis period, which gives a potential for advancing portfolio management techniques. Moreover, our study provides practical implications and new insights for various market participants for better management and understanding of risks.
Keywords	Multifractal detrended fluctuation analysis ; Generalized Hurst exponent ; COVID19 ; MLM ; Herd behavior ; Clean and renewable energy ; Science (General) ; Q1-390 ; Social sciences (General) ; H1-99
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Prediction of daily COVID-19 cases in European countries using automatic ARIMA model.

Awan, Tahir Mumtaz / Aslam, Faheem

Journal of public health research

2020 Volume 9, Issue 3, Page(s) 1765

Abstract: The recent pandemic (COVID-19) emerged in Wuhan city of China and after causing a lot of destruction there recently changed its epicenter to Europe. There are countless people affected and reported cases are increasing day by day. Predictive models need ... ...

Abstract	The recent pandemic (COVID-19) emerged in Wuhan city of China and after causing a lot of destruction there recently changed its epicenter to Europe. There are countless people affected and reported cases are increasing day by day. Predictive models need to consider previous reported cases and forecast the upcoming number of cases. Automatic ARIMA, one of the predictive models used for forecasting contagions, was used in this study to predict the number of confirmed cases for next 10 days in four top European countries through R package "forecast". The study finds that Auto ARIMA applied on the sample satisfactorily forecasts the confirmed cases of coronavirus for next ten days. The confirmed cases for the four countries show an increasing trend for the next ten days with Spain with a highest number of expected new confirmed cases, followed by Germany and France. Italy is expected to have lowest number of new confirmed cases among the four countries.
Keywords	covid19
Language	English
Publishing date	2020-07-08
Publishing country	United States
Document type	Journal Article
ISSN	2279-9028
ISSN	2279-9028
DOI	10.4081/jphr.2020.1765
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Article ; Online: Prediction of daily COVID-19 cases in European countries using automatic ARIMA model

Tahir Mumtaz Awan / Faheem Aslam

Journal of Public Health Research, Vol 9, Iss

2020 Volume 3

Abstract	The recent pandemic (COVID-19) emerged in Wuhan city of China and after causing a lot of destruction there recently changed its epicenter to Europe. There are countless people affected and reported cases are increasing day by day. Predictive models need to consider previous reported cases and forecast the upcoming number of cases. Automatic ARIMA, one of the predictive models used for forecasting contagions, was used in this study to predict the number of confirmed cases for next 10 days in four top European countries through R package “forecast”. The study finds that Auto ARIMA applied on the sample satisfactorily forecasts the confirmed cases of coronavirus for next ten days. The confirmed cases for the four countries show an increasing trend for the next ten days with Spain with a highest number of expected new confirmed cases, followed by Germany and France. Italy is expected to have lowest number of new confirmed cases among the four countries.
Keywords	Prediction ; COVID-19 ; Auto ARIMA ; Europe ; Public aspects of medicine ; RA1-1270 ; covid19
Language	English
Publishing date	2020-07-01T00:00:00Z
Publisher	PAGEPress Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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