Narrow Results

This unique study examines the moderation effect of institutional quality (IQ) on the relationship between financial inclusion (FI) and financial development (FD) of 45 Organization of Islamic Cooperation (OIC) countries. For empirical analysis, panel data are used for the period 2000–2016. We use the Arellano–Bond generalized method of moments (GMM) and two-stage least-squares (2SLS) method in our estimations to draw multidimensional results. The empirical results confirm the significant positive relationship between FI, IQ and FD. Interestingly, we find that IQ moderates FI and has a significant positive impact on FD. Our findings are robust to alternative econometric specifications of FI, IQ and FD. Therefore, policymakers must sensibly understand the pivotal role of FI and IQ in establishing sustainable future development of OIC countries.

The world produces countless tonnes of food to feed everyone, yet the numbers of people who suffer from hunger remain high, especially in developing countries. This issue may highlight the importance of institutions as a foundation for the issue of food security. Hence, this study investigates the role of institutions on food supply in a panel of 56 developing countries. The dynamic generalized methods of moments results indicate that institutional factor plays a vital role in improving the availability of food and access to nutritious food for all people, thereby ameliorating the food supply problem. Therefore, the overall result suggests that policy-makers should improve the level of institutional quality so that it can form the fundamental ground toward alleviating hunger and improving the food supply.

The aim of this paper is to identify the factors that could contribute to an increase in a country’s GNP relative to its GDP. This represents a sequel to [Tan, EC, CF Tang and RD Palaniandi (2019). What could cause a country’s GNP to be greater than its GDP? Singapore Economic Review, https://doi.org/10.1142/S0217590819500073.] on what could cause a country’s GNP to exceed its GDP. Annual data of a panel of 52 countries from 1992 through 2016 are mobilized for the purpose, with the sample period split into five-year average intervals. The possible determinants of the relative position include the savings-investment gap, international reserves, state of technology, demography, unemployment, export-orientation, income inequality, size of the primary commodities sector, financial repression, tax incidence and the ease of doing business. Based upon the application of the system GMM technique to winsorized data and filtered data from Cook’s Distance Outlier Test, the savings-investment gap could enhance the GNP–GDP percentage of a country. The percentage could be lowered by export orientation, uneven income distribution and the size of the working age population.

Speech is a convenient medium for communication among human beings. Speaker recognition is a process of automatically recognizing the speaker by processing the information included in the speech signal. In this paper, a new approach is proposed for speaker recognition through speech signal. Here, a two-level approach is proposed. In the first-level, the gender of the speaker is recognized, and in the second-level speaker is recognized based on recognized gender at first-level. After recognizing the gender of the speaker, search space is reduced to half for the second-level as speaker recognition system searches only in a set of speech signals belonging to identified gender. To identify gender, gender-specific features: Mel Frequency Cepstral Coefficients (MFCC) and pitch are used. Speaker is recognized by using speaker specific features: MFCC, Pitch and RASTA-PLP. Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) classifiers are used for identifying the gender and recognizing the speaker, respectively. Experiments are performed on speech signals of two databases: “IIT-Madras speech synthesis and recognition” (containing speech samples spoken by eight male and eight female speakers of eight different regions in English language) and “ELSDSR” (containing speech samples spoken by five male and five female in English language). Experimentally, it is observed that by using two-level approach, time taken for speaker recognition is reduced by 30–32% as compared to the approach when speaker is recognized without identifying the gender (single-level approach). The accuracy of speaker recognition in this proposed approach is also improved from 99.7% to 99.9% as compared to single-level approach. It is concluded through the experiments that speech signal of a minimum 1.12 duration (after neglecting silence parts) is sufficient for recognizing the speaker.

Vehicle detection, as an important technology for urban intelligent transportation system, is having attracted increasingly interests of researchers in recent years. For the time cost problem of traditional road vehicles testing approach, a moving region extraction method based on Gaussian model is used to reduce the scanning area of the window, exclude some background noise and improve test speed. For the problem of traditional single feature, relatively lower detection rate and lack of ability to adapt to complex environment, a method based on the combination of Haar-like and 2bitBP (2bit Binary Pattern) features is adopted. Feature integration method enhances the expression of features. As a result, the improved classification performance of classifiers enables it to be adapted to different traffic environment. Firstly, a Gaussian mixture model is established to detect moving targets in overall region and then the Haar-like and 2bitBP features extraction are carried out in the region. At the end the action of cascading classification on samples achieve the detection of moving vehicles. The experimental results show that the method is effective for vehicle detection.

With the rapid development of computer technology and network technology, it has become possible to build a large-scale networked video surveillance system. The video surveillance system has become a new type of infrastructure necessary for modern cities. In this paper, the problem of foreground extraction and motion recognition in intelligent video surveillance is studied. The three key sub-problems, namely the extraction of motion foreground in video, the deblurring of motion foreground and the recognition of human motion, are studied and corresponding solutions are proposed. A background modeling technique based on video block is proposed. The background is modeled at the block level, which greatly reduces the spatial complexity of the algorithm. It solves the problem that the traditional Gaussian model (GMM) moving target enters the static state and is integrated into the background process. The target starts to move for a long time and there are ghosts and other problems, which reduce the processing efficiency of the lifting algorithm. The test results on the Weizmann dataset show that the proposed algorithm can achieve high human motion recognition accuracy and recognition with low computational complexity. The rate can reach 100%; the local constrained group sparse representation classification (LGSRC) model is used to classify it. The experimental results on Weizmann, KTH, UCF sports and other test datasets confirm the validity of the algorithm in this chapter. KNN, SRC voting classification accuracy.

Coastline detection using a Gaussian Mixture Model (GMM) applied to synthetic aperture radar (SAR) imagery is usually inaccurate due to the inherent noise of SAR data. In addition, the traditional active counter model is sensitive to the initial position of the contour line and requires a large number of iterations to converge to a solution. In this study, we first used the GMM algorithm to segment the SAR images and obtain a coarse land and sea segmentation map. This map is then used as the initial position for a subsequent active contour model. The K distribution was introduced into the local statistical active contour model to better model the SAR image. The Gaussian distribution-based local active contour model and the algorithm detailed in this paper were used to perform coastline extraction experiments on four SAR images. Four GF-3 SAR images with different modes were collected to validate the efficiency of the proposed method. The experimental results show that the coastline extraction methods from SAR images based on the GMM algorithm and the K distribution-based local statistical active contour model (LKDACM) overcame the shortcomings of the traditional active contour model to accurately and quickly detect coastlines, thus enabling the detection of coastline changes.

This work aims to inspect the common debate about the implication of derivative instruments in amplifying the last financial crisis. To reach this goal, the study chooses a sample of banks entirely from emerging countries — over the whole period 2003–2011 — in which we examine the impact of derivatives simultaneously on performance, risk and stability during the ordinary period “the pre-crisis period”, 2003–2006, and the unstable period “the crisis and post crisis period”, 2007–2011. The regressions are estimated by generalized methods of moments (GMM) as developed by Blundell and Bond (1998). The major conclusion reveals that only swaps can be considered as implicated in the intensification of the last financial crisis. Therefore, the rest of derivatives instruments cannot be responsible in the amplification of the recent financial crisis. Indeed, the widespread idea accusing all derivatives to be in part responsible of the intensification of the last financial crisis should be revised.

Using panel data estimation, we evaluate the effects of financial soundness indicators, financial openness and bank liquidity on financial development across 40 countries. According to our dynamic panel estimates, the following variables are found to significantly affect financial development: capital to asset ratio, nonperforming loans and direct foreign investment. Our result indicated that the change in capitalization ratio was the main driver of financial development. Moreover, nonperforming loan shock has a negative and significant influence on financial development. This study emphasizes the important role of financial soundness and financial openness in maintaining financial stability and development of banking system.

This paper investigates the implications of government borrowing for corporate financing and capital structure of the firms. In doing so, we explore the effects of government debt, macroeconomic and firm-specific factors on firm’s choice of financing and capital structure. We draw on the 10-year data (2007–2017) of 225 non-financial firms listed on the Ho Chi Minh Stock Exchange (HoSE) and employ the system Generalized Method of Moments (system-GMM) for estimation. Our key findings suggest that the government borrowing and debt financing for the Vietnamese listed companies have a negative relationship. Specifically, the short-term corporate leverage structure is influenced more strongly than the long-term leverage structure. We also define the threshold for the association between government borrowing and corporate financing decisions by capturing a U-shaped relationship i.e., Crowding out Kuznets Curve (CKC). Furthermore, macroeconomic factors also show a statistically significant impact on corporate financing decisions. Our findings have profound implications for the fiscal and public policymakers, investors as well as corporate finance managers and firms.

This study aims to identify the role of contextual variables, especially the interest rate, in affecting the relationship between a firm’s capital structure and firm value. This study investigates the capital structure of Pakistani-listed firms in light of rising interest rates, declining “Domestic credit to the private sector” and emerging Islamic banking in the country. The study uses GMM (Two-Step) to examine the linear, and dynamic Panel threshold model to examine the quadratic relationship between leverage firm value and how other contextual variables affect this relationship. The study found that there is a negative relationship between leverage and firm value in the presence of the majority of contextual variables. Except for tax, depreciation, and free cash flow, leverage shows a negative relationship with firm value in presence of all other contextual variables. Further results show that there is a quadratic relationship present between leverage and firm value. Also, the interest rate and inflation has a negative effect on firm value in long term, while in short term this relationship is positive. The study supports the pecking order & Trade-off theory but does not support the agency theory. The study is using new methodologies, just as the panel threshold model which is never used before for Pakistani industries. The panel threshold model is using some variables for the first time in research. Previously only size and debt were used in panel threshold models, this time we used debt, firm value, profitability, tax, and tangibility, which will be a significant contribution to the literature.

Automatic image annotation is an important and challenging job for image analysis and understanding such as content-based image retrieval (CBIR). The relationship between the keywords and visual features is too complicated due to the semantic gap. We present an approach of automatic image annotation based on scene analysis. With the constrain of scene semantics, the correlation between keywords and visual features becomes simpler and clearer. Our model has two stages of process. The first stage is training process which groups training image data set into semantic scenes using the extracted semantic feature and visual scenes constructed from the calculation distances of visual features for every pairs of training images by using Earth mover's distance (EMD). Then, combine a pair of semantic and visual scene together and apply Gaussian mixture model (GMM) for all scenes. The second stage is to test and annotate keywords for test image data set. Using the visual features provided by Duygulu, experimental results show that our model outperforms probabilistic latent semantic analysis (PLSA) & GMM (PLSA&GMM) model on Corel5K database.

A computer-based intelligent system for the classification of eye diseases can be very useful for their diagnosis and management. With age, the incidence of ocular pathology rises, thereby decreasing normal eye function. The most common causes of age-related eye disorders and visual impairment in the elderly are cataracts and iridocyclitis (inflammation of the iris, i.e. the colored part of the eye, and of the ciliary body). For proper care and management of eyes, we need a system which can automatically classify these eye diseases. The method proposed in this study is based on higher-order spectral (HOS) features that capture contour and shape information, while providing robustness to shift, rotation, changes in size, and noise. The parameters are extracted from the raw images using the HOS techniques, and fed to the classifiers for classification. This paper presents the classification of three kinds of eye classes using four-layer feedforward and Gaussian mixture model (GMM) classifiers. Our protocol used 122 subjects who had three different kinds of eye disease conditions. We demonstrated a sensitivity of 100% for the classifier, with a specificity of 90%. Our systems are clinically ready to test on large data sets.

Epilepsy is a brain disorder causing people to have recurring seizures. Electroencephalogram (EEG) is the electrical activity of the brain signals that can be used to diagnose the epilepsy. The EEG signal is highly nonlinear and nonstationary in nature and may contain indicators of current disease, or warnings about impending diseases. The chaotic measures like correlation dimension (CD), Hurst exponent (H), and approximate entropy (ApEn) can be used to characterize the signal. These features extracted can be used for automatic diagnosis of seizure onsets which would help the patients to take appropriate precautions. These nonlinear features have been reported to be a promising approach to differentiate among normal, pre-ictal (background), and epileptic EEG signals. In this work, these features were used to train both Gaussian mixture model (GMM) and support vector machine (SVM) classifiers. The performance of the two classifiers were evaluated using the receiver operating characteristics (ROC) curves. Our results show that the GMM classifier performed better with average classification efficiency of 95%, sensitivity and specificity of 92.22% and 100%, respectively.

The sum total of millions of cardiac cell depolarization potentials can be represented using an electrocardiogram (ECG). By inspecting the P-QRS-T wave in the ECG of a patient, the cardiac health can be diagnosed. Since the amplitude and duration of the ECG signal are too small, subtle changes in the ECG signal are very difficult to be deciphered. In this work, the heart rate variability (HRV) signal has been used as the base signal to observe the functioning of the heart. The HRV signal is non-linear and non-stationary. Recurrence quantification analysis (RQA) has been used to extract the important features from the heart rate signals. These features were fed to the fuzzy, Gaussian mixture model (GMM), and probabilistic neural network (PNN) classifiers for automated classification of cardiac bio-electrical contractile disorders. Receiver operating characteristics (ROC) was used to test the performance of the classifiers. In our work, the Fuzzy classifier performed better than the other classifiers and demonstrated an average classification accuracy, sensitivity, specificity, and positive predictive value of more than 83%. The developed system is suitable to evaluate large datasets.

Diabetes Mellitus (DM) is a chronic metabolic disorder that hampers the body’s energy absorption capacity from the food. It is either caused by pancreatic malfunctioning or the body cells being inactive to insulin production. Prolonged diabetes results in severe complications, such as retinopathy, neuropathy, cardiomyopathy and cardiovascular diseases. DM is an incurable disorder. Thus, diagnosis and monitoring of diabetes is essential to prevent the body organs from severe damage. Heart Rate Variability (HRV) signal processing can be used as one of the methods for the diagnosis of DM. Our paper introduces a noninvasive technique of automated diabetic diagnosis using HRV signals. The R-R interval signals are decomposed using Shearlet transforms integrated with Continuous Wavelet Transform (CWT), and their characteristic features are extracted by using Shannon’s, Renyi’s, Kapur entropies, energy and Higher Order Spectra (HOS). Then, Locality Sensitive Discriminant Analysis (LSDA) is employed to remove insignificant features and reduce the number of employed features. These redundant features are eliminated by using six feature selection algorithms: Student’s t-test, Receiver Operating Characteristic Curve (ROC), Wilcoxon signed-rank test, Bhattacharyya distance, Information entropy and Fuzzy Max-Relevance and Min-Redundancy (MRMR). This step is followed by classification of normal and diabetic signals using different classifiers, such as discriminant classifiers, Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Naïve Bayes (NB), Fuzzy Sugeno (FSC), Gaussian Mixture Model (GMM), AdaBoost and k-Nearest Neighbor (k-NN) classifier. In these classifiers, the selected features are employed to distinguish diabetic signals from normal signals. These classifiers are trained and then tested to validate their accuracy to make accurate diagnosis. The FSC classifier is shown to have the highest (100%) accuracy. Nevertheless, we go one step further in formulating another novel classifier in the form of the diabetic index, and have shown how distinctly it is able to separate diabetic signals from normal signals.

In this study, a bio-inspired approach for extracting efficient features prior to the recognition of scenes is proposed. It is highly inspired from the model of the mammals visual system. The retina contains many levels of neurons (bipolar, amacrine, horizontal and ganglion cells) accurately organized from cones and rods to the optic nerve up till the lateral geniculate nucleus (LGN) which is the main thalamic relay for inputs to the visual cortex. This structure probably eases other brain areas tasks in preprocessing the visual information. This paper is focusing on the study of these specific structures, relying on a bottom up approach to propose a comprehensive mathematical model of the low level image processing performed within the eye. The presented system takes into account the foveolar structure of the retina to produce a low-resolution representation of observed images by decomposing them into a local summation of elementary gaussian color histograms. This representation corresponds to the LGN biological organization. It has been thought that due to short timings, some very quick localization tasks involving particularly fast information processing pathways cannot be provided by the classical ones passing through higher level cortical areas. This work proposes a model of retinal coding and LGN-visual representation that we show provides reliable and sufficient early features for scenes recognition and localization. Experiments on real scenes using the developed model are presented showing the efficiency of the approach on localization.

In this paper, we present empirical estimations of the new Keynesian Phillips curve (NKPC) for Tunisia using the generalized method of moments. We study empirical issues related to measuring variables of the theoretical model, and the impact of choices made at this level on its empirical validation. Our results support the hybrid version of the NKPC, with higher fraction of the forward-looking component. In addition, we establish the sensitivity of the estimation results to the choice of the driving variable measure. Our finding is that the success of the output gap variable in the validation of the theoretical relationship is conditional on the implemented measure, and that the deviation of the real exchange rate as a driving variable could be a good choice, due to the openness degree of the Tunisian economy.

The failure of energy economists and planners to comprehend the dynamics and paradigm shift in the finance and institutional quality domain that drive energy use is blamed for the ongoing energy consumption concerns. Consequently, this study revisits and contributes to repositories by examining the relationship between finance-renewable energy consumption and institution-renewable energy consumption. The research question raised is: Do governance indicators moderate the impact of finance on renewable energy consumption? With panel dataset of 46 countries in sub-Saharan Africa spanning from 2010 to 2020 and using political stability, voice and accountability, government effectiveness, and regulatory quality indicators of governance, the research output is as follows: (i) Financial development exerts a significant positive impact on renewable energy consumption and intensity, but the level of impact is weak (i.e., at a 10% level significant). (ii) The governance indicators significantly drag renewable energy consumption and intensity. (iii) The negative interaction between financial development and governance indicators is sufficient to worsen the weak relationship between finance and renewable energy in sub-Saharan Africa. (iv) Governance threshold eroded the weak positive effect of financial development on renewable energy consumption and intensity, leading to negative synergy effect in some cases, and (v) The net effect from the moderating impact of governance indicators on finance is significantly different across model specification. The study demonstrates the undeveloped nature of finance and institutional framework in sub-Saharan Africa, considering the weak association between the key variables.

This study explores the productivity growth effects of horizontal, backward, and forward FDI spillovers in the Türkiye manufacturing sector using industry-level data for the period 2008–2018. While controlling for capital intensity and human capital as proxies for the absorption capacity, we apply system GMM and bootstrapped LSDV estimator to estimate the models. Our findings reveal that backward linkages significantly hamper industrial productivity due to the rudimentary nature of the products in the downstream sector or lack of absorption capacity. While horizontal spillovers retard the productivity growth; forward linkages enhance the growth, though all insignificant. The results depict that all the interactions with capital intensity strongly promote industrial productivity growth.