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This study aims to explore the linkage between fluctuations in the global crude oil price and equity market in fast emerging economies of India and China.

The present research uses wavelet decomposition and maximal overlap discrete wavelet transform (MODWT), which decompose the time series into various frequencies of short, medium and long-term nature. The paper further uses continuous and cross wavelet transform to analyze the variance among the variables and wavelet coherence analysis and wavelet-based Granger causality analysis to examine the direction of causality between the variables.

The continuous wavelet transform indicates strong variance in WTIR (return series of West Texas Instrument crude oil price) in short, medium and long run at various time periods. The variance in CNX Nifty is observed in the short and medium run at various time periods. The Chinese stock index, i.e. SCIR, experiences very little variance in short run and significant variance in the long and medium run. The causality between the changes in crude oil price and CNX Nifty is insignificant and there exists a bi-directional causality between global crude oil price fluctuations and the Chinese equity market.

To the best of the authors’ knowledge, very limited work has been done where the researchers have analyzed the linkage between the equity market and crude oil price fluctuations under the framework of discrete wavelet transform, which overlooks the bottleneck of non-stationarity nature of the time series. To bridge this gap, the present research uses wavelet decomposition and MODWT, which decompose the time series into various frequencies of short, medium and long-term nature.

The purpose of this paper is to estimate the years the European Muslim population will be majority among 30 European countries.

The methodology/approach is to forecast the population of 30 European countries with wavelet analysis combined with the Burg model which fits a pth order autoregressive model to the input signal by minimizing (least squares) the forward and backward prediction errors while constraining the autoregressive parameters to satisfy the Levinson–Durbin recursion, then relies on an infinite impulse response prediction error filter. Three scenarios are considered: the zero-migration scenario where the authors assume that the Muslim population has a higher fertility (one child more per woman, on average) than other Europeans, mirroring a global pattern; a 2017 migration scenario: to the Muslim population obtained in the zero-migration scenario, the authors add a continuous flow of migrants every year based on year 2017; the mid-point migration scenario is obtained by averaging the data of the two previous scenarios.

Among three scenarios, the most likely mid-point migration scenario identifies 13 countries where the Muslim population will be majority between years 2085 and 2215: Cyprus (in year 2085), Sweden (2125), France (2135), Greece (2135), Belgium (2140), Bulgaria (2140), Italy (2175), Luxembourg (2175), the UK (2180), Slovenia (2190), Switzerland (2195), Ireland (2200) and Lithuania (2215). The 17 remaining countries will never reach majority in the next 200 years.

The growing Muslim population will change the face of Europe socially, politically and economically. This paper will provide a better insight and understanding of Muslim population dynamics to European governments, policymakers, as well as social and economic planners.

Current technology makes it easy for humans to take an image and convert it to digital content, but sometimes there is additional noise in the image so it looks damaged. The damage that often occurs, like blurring and excessive noise in digital images, can certainly affect the meaning and quality of the image. Image restoration is a process used to restore the image to its original state before the image damage occurs. In this research, we proposed an image restoration method by combining Wavelet transformation and Akamatsu transformation. Based on previous research Akamatsu's transformation only works well on blurred images. In order not to focus solely on blurry images, Akamatsu's transformation will be applied based on Wavelet transformations on high-low (HL), low-high (LH), and high-high (HH) subunits. The result of the proposed method will be comparable with the previous methods. PSNR is used as a measure of image quality restoration. Based on the results the proposed method can improve the quality of the restoration on image noise, such as Gaussian, salt and pepper, and also works well on blurred images. The average increase is around 2 dB based on the PSNR calculation.

The purpose of this paper is to answer the following two questions: Will Saudi Arabia get older? Will its pension system be sustainable?

The methodology/approach is to forecast KSA’s population with wavelet analysis combined with the Burg model which fits a pth order autoregressive model to the input signal by minimizing (least squares) the forward and backward prediction errors while constraining the autoregressive parameters to satisfy the Levinson-Durbin recursion, then relies on an infinite impulse response prediction error filter.

Spectral analysis projections of Saudi age groups are more optimistic than the Bayesian probabilistic model sponsored by the United Nations Population Division: Saudi Arabia will not get older as fast as projected by the United Nations model. The KSA’s pension system will stay sustainable based on spectral analysis, whereas it will not based on the U.N. model.

Spectral analysis will provide better insight and understanding of population dynamics for Saudi government policymakers, as well as economic, health and pension planners.

The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.

Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.

The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.

The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.

The purpose of this paper is to show that major reversals of an index (specifically BIST-30 index) can be detected uniquely on the date of reversal by checking the extreme outliers in the rate of change series using daily closing prices.

The extreme outliers are determined by checking if either the rate of change series or the volatility of the rate of change series displays more than two standard deviations on the date of reversal. Furthermore; wavelet analysis is also utilized for this purpose by checking the extreme outlier characteristics of the A1 (approximation level 1) and D3 (detail level 3) wavelet components.

Paper investigates ten major reversals of BIST-30 index during a five year period. It conclusively shows that all these major reversals are characterized by extreme outliers mentioned above. The paper also checks if these major reversals are unique in the sense of being observed only on the date of reversal but not before. The empirical results confirm the uniqueness. The paper also demonstrates empirically the fact that extreme outliers are associated only with major reversals but not minor ones.

The results are important for fund managers for whom the timely identification of the initial phase of a major bullish or bearish trend is crucial. Such timely identification of the major reversals is also important for the hedging applications since a major issue in the practical implementation of the stock index futures as a hedging instrument is the correct timing of derivatives positions.

To the best of the author’ knowledge; this is the first study dealing with the issue of major reversal identification. This is evidently so for the BIST-30 index and the use of extreme outliers for this purpose is also a novelty in the sense that neither the use of rate of change extremity nor the use of wavelet decomposition for this purpose was addressed before in the international literature.

This paper aims to analyze the connectedness between Gulf Cooperation Council (GCC) stock market index and cryptocurrencies. It investigates the relevant impact of RavenPack COVID sentiment on the dynamic of stock market indices and conventional cryptocurrencies as well as their Islamic counterparts during the onset of the COVID-19 crisis.

The authors rely on the methodology of Diebold and Yilmaz (2012, 2014) to construct network-associated measures. Then, the wavelet coherence model was applied to explore co-movements between GCC stock markets, cryptocurrencies and RavenPack COVID sentiment. As a robustness check, the authors used the time-frequency connectedness developed by Barunik and Krehlik (2018) to verify the direction and scale connectedness among these markets.

The results illustrate the effect of COVID-19 on all cryptocurrency markets. The time variations of stock returns display stylized fact tails and volatility clustering for all return series. This stressful period increased investor pessimism and fears and generated negative emotions. The findings also highlight a high spillover of shocks between RavenPack COVID sentiment, Islamic and conventional stock return indices and cryptocurrencies. In addition, we find that RavenPack COVID sentiment is the main net transmitter of shocks for all conventional market indices and that most Islamic indices and cryptocurrencies are net receivers.

This study provides two main types of implications: On the one hand, it helps fund managers adjust the risk exposure of their portfolio by including stocks that significantly respond to COVID-19 sentiment and those that do not. On the other hand, the volatility mechanism and investor sentiment can be interesting for investors as it allows them to consider the dynamics of each market and thus optimize the asset portfolio allocation.

This finding suggests that the RavenPack COVID sentiment is a net transmitter of shocks. It is considered a prominent channel of shock spillovers during the health crisis, which confirms the behavioral contagion. This study also identifies the contribution of particular interest to fund managers and investors. In fact, it helps them design their portfolio strategy accordingly.

This paper examines the impact of political instability on the investors' behavior, measured by Google search queries, and on the dynamics of stock market returns.

First, by using the DCC-GARCH model, the authors examine the effect of investor sentiment on the Tunisian stock market return. Second, the authors employ the fully modified dynamic ordinary least square method (FMOL) to estimate the long-term relationship between investor sentiment and Tunisian stock market return. Finally, the authors use the wavelet coherence model to test the co-movement between investor sentiment measured by Google Trends and Tunisian stock market return.

Using the dynamic conditional correlation (DCC), the authors find that Google search queries index has the ability to reflect political events especially the Tunisian revolution. In addition, empirical results of fully modified ordinary least square (FMOLS) method reveal that Google search queries index has a slightly higher effect on Tunindex return after the Tunisian revolution than before this revolution. Furthermore, by employing wavelet coherence model, the authors find strong comovement between Google search queries index and return index during the period of the Tunisian revolution political instability. Moreover, in the frequency domain, strong coherence can be found in less than four months and in 16–32 months during the Tunisian revolution which show that the Google search queries measure was leading over Tunindex return. In fact, wavelet coherence analysis confirms the result of DCC that Google search queries index has the ability to detect the behavior of Tunisian investors especially during the period of political instability.

This study provides empirical evidence to portfolio managers that may use Google search queries index as a robust measure of investor's sentiment to select a suitable investment and to make an optimal investments decisions.

The important research question of how political instability affects stock market dynamics has been neglected by scholars. This paper attempts principally to fill this void by investigating the time-varying interactions between market returns, volatility and Google search based index, especially during Tunisian revolution.

This study aims to analyze the differences of electrocardiograph (ECG) characteristics for female drivers in calm and anxious states during driving.

The authors used various materials (e.g. visual materials, auditory materials and olfactory materials) to induce drivers’ mood states (calm and anxious), and then conducted the real driving experiments and driving simulations to collect driver’s ECG signal dynamic data. Physiological changes in ECG during the stimulus process were recorded using PSYLAB software. The paired T-test analysis was conducted to determine if there is a significant difference in driver’s ECG characteristics between calm and anxious states during driving.

The results show significant differences in the characteristic parameters of female driver’s ECG signals, including (average heart rate), (atrioventricular interval), (percentage of NN intervals > 50ms), (R wave average peak), (Root mean square of successive), (Q wave average peak) and ( S wave average peak), in time domain, frequency domain and waveform in emotional states of calmness and anxiety.

Findings of this work show that ECG can be used to identify driver’s anxious and calm states during driving. It can be used for the development of personalized driver assistance system and driver warning system.

Only a few attempts have been made on the influence of human emotions on physiological signals in the transportation field. Hence, there is a need for transport scholars to begin to identify driver’s ECG characteristics under different emotional states. This study will analyze the differences of ECG characteristics for female drivers in calm and anxious states during driving to provide a theoretical basis for developing the intelligent and connected vehicles.