227 results on '"LAHMIRI, SALIM"'
Search Results
202. Multi-fluctuation nonlinear patterns of European financial markets based on adaptive filtering with application to family business, green, Islamic, common stocks, and comparison with Bitcoin, NASDAQ, and VIX.
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Lahmiri, Salim, Bekiros, Stelios, and Bezzina, Frank
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CAPITAL stock , *ADAPTIVE filters , *FINANCIAL markets , *FAMILY-owned business enterprises , *NASDAQ composite index , *HILBERT-Huang transform , *GREEN'S functions - Abstract
This paper investigates power-law correlations, chaos, and randomness in prices of family business, green (low Carbon), Islamic (Shariah), and common stock indices from the European zone. Specifically, the estimations of nonlinear patterns are performed in empirical mode decomposition domain to obtain time-scale computed values. The main findings follow. For all markets, price long term fluctuations are persistent, whilst price short term fluctuations are anti-persistent. In addition, short term fluctuations are chaotic, while long term fluctuations are not. Furthermore, short term fluctuations are less affected by randomness than long term fluctuations. Moreover, the level of anti-persistence and the information content in short term fluctuations are similar across all four European markets. Besides, computed nonlinear statistics from intermediate fluctuations are in general lower than those from short fluctuations, and are higher than those from long fluctuations. Our methodology is also applied to Bitcoin, NASDAQ, and VIX indices for comparison purpose. Some similarities in terms of randomness and dissimilarities in terms of long memory are clearly observed between European and US indices. Finally, it is found that the correlation between (i) long memory and chaos is positive, low, and not statistically significant, (ii) between long memory and randomness is positive, large, and statistically significant, and (iii) between chaos and randomness is negative, low, and not statistically significant. Active traders and portfolio managers can follow our research approach to determine specific trading strategies at short and long run horizons. • Focus is on family business, green, Islamic, and common stock indices. • Results from the European zone are compared to Bitcoin, NASDAQ, and VIX. • Analyses are performed in EMD domain to highlight distinct fluctuations in signals. • Empirical relationships between memory, chaos, and entropy are also examined. [ABSTRACT FROM AUTHOR]
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- 2020
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203. Characterization of infant healthy and pathological cry signals in cepstrum domain based on approximate entropy and correlation dimension.
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Lahmiri, Salim, Tadj, Chakib, Gargour, Christian, and Bekiros, Stelios
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INFANTS , *ENTROPY dimension , *MEDICAL physics , *STATISTICAL significance , *BIOMEDICAL engineering - Abstract
• Healthy and unhealthy infant cry signals are analyzed in by their respective cepstrums. • Approximate entropy and correlation dimension are estimated from cepstrums. • Statistical tests show evidence that approximate entropy and correlation dimension are different across healthy and unhealthy infant cry signals. • Approximate entropy and correlation dimension in cepstrum domain are promising biomarkers for pathology detection in infant cries. The analysis of infant cry signals is becoming an attractive field of research in biomedical physics and engineering for better understanding of the pathologies and appropriate medial diagnosis. The main purpose of the current study is to characterize infant normal and pathological cry signals by studying their respective oscillations by means of approximate entropy and correlation dimension estimated from their respective cepstrums. We analyzed two different sets. The first one is composed of 2638 expiration cry signals and the second set is composed of 1860 inspiration cry signals, both sets equally weighted. After estimating approximate entropy and correlation dimensions from cepstrums, three standard statistical tests are applied to them including the Student t -test, F -test, and two-sample Kolmogorov-Smirnov test. All statistical tests are performed at 5% statistical significance level. The empirical results follow. First, approximate entropy and correlation dimension measures exhibit different statistical characteristics across healthy and unhealthy infant cries from both expiration and inspiration sets. Second, the level of approximate entropy in cepstrums of healthy infant cries is statistically higher than that in cepstrums of unhealthy infant cries. Third, the level of correlation dimension in cepstrums of healthy infant cries is statistically higher than that in cepstrums of unhealthy infant cries. In other words, cepstrums of healthy infant cries show lower randomness and disorder compared to cepstrums of unhealthy infant cries. It is concluded that cepstrum-based approximate entropy and correlation dimension discriminate healthy from pathological infant cry signals and can be employed as effective biomarkers for biomedical diagnosis of cry records in clinical milieu. [ABSTRACT FROM AUTHOR]
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- 2021
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204. Renyi entropy and mutual information measurement of market expectations and investor fear during the COVID-19 pandemic.
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Lahmiri, Salim and Bekiros, Stelios
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COVID-19 pandemic , *EXPECTATION (Psychology) , *INFORMATION measurement , *PRECIOUS metals , *ELECTRONIC money , *INVESTOR confidence - Abstract
• Via Renyi entropy we analyze the multiscale entropy measurement of global markets. • By estimating mutual information we expose the dependencies and information flow. • Randomness and disorder are more observed in low probability events. • The information sharing network among markets was modified during COVID-19 pandemic. • We offer a valuable insight of the impact of COVID-19 pandemic upon expectations and investor fear. The COVID-19 pandemic has seriously affected world economies. In this regard, it is expected that information level and sharing between equity, digital currency, and energy markets has been altered due to the pandemic outbreak. Specifically, the resulting twisted risk among markets is presumed to rise during the abnormal state of world economy. The purpose of the current study is twofold. First, by using Renyi entropy, we analyze the multiscale entropy function in the return time series of Bitcoin, S&P500, WTI, Brent, Gas, Gold, Silver, and investor fear index represented by VIX. Second, by estimating mutual information, we analyze the information sharing between these markets. The analyses are conducted before and during the COVID-19 pandemic. The empirical results from Renyi entropy indicate that for all market indices, randomness and disorder are more concentrated in less probable events. The empirical results from mutual information showed that the information sharing network between markets has changed during the COVID-19 pandemic. From a managerial perspective, we conclude that during the pandemic (i) portfolios composed of Bitcoin and Silver, Bitcoin and WTI, Bitcoin and Gold, Bitcoin and Brent, or Bitcoin and S&P500 could be risky, (ii) diversification opportunities exist by investing in portfolios composed of Gas and Silver, Gold and Silver, Gold and Gas, Brent and Silver, Brent and Gold, or Bitcoin and Gas, and that (iii) the VIX exhibited the lowest level of information disorder at all scales before and during the pandemic. Thus, it seems that the pandemic has not influenced the expectations of investors. Our results provide an insight of the response of stocks, cryptocurrencies, energy, precious metal markets, to expectations of investors in the aftermath of the COVID-19 pandemic in terms of information ordering and sharing. [ABSTRACT FROM AUTHOR]
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- 2020
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205. The impact of COVID-19 pandemic upon stability and sequential irregularity of equity and cryptocurrency markets.
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Lahmiri, Salim and Bekiros, Stelios
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COVID-19 pandemic , *STOCK exchanges , *PANDEMICS , *CRYPTOCURRENCIES , *INTERNATIONAL markets , *LYAPUNOV exponents , *PRICE fluctuations - Abstract
• 45 cryptocurrency and 16 international stock markets are analyzed • A Big Data set is investigated before and during the COVID-19 pandemic period • We estimate stability and regularity metrics to infer on the predictability of price fluctuations • We find that cryptocurrencies embed higher instability and irregularity • Investing in digital assets during big crises could be considered riskier compared to equities We explore the evolution of the informational efficiency in 45 cryptocurrency markets and 16 international stock markets before and during COVID-19 pandemic. The measures of Largest Lyapunov Exponent (LLE) based on the Rosenstein's method and Approximate Entropy (ApEn), which are robust to small samples, are applied to price time series in order to estimate degrees of stability and irregularity in cryptocurrency and international stock markets. The amount of regularity infers on the unpredictability of fluctuations. The t -test and F -test are performed on estimated LLE and ApEn. In total, 36 statistical tests are performed to check for differences between time periods (pre- versus during COVID-19 pandemic samples) on the one hand, as well as check for differences between markets (cryptocurrencies versus stocks), on the other hand. During the COVID-19 pandemic period it was found that (a) the level of stability in cryptocurrency markets has significantly diminished while the irregularity level significantly augmented, (b) the level of stability in international equity markets has not changed but gained more irregularity, (c) cryptocurrencies became more volatile, (d) the variability in stability and irregularity in equities has not been affected, (e) cryptocurrency and stock markets exhibit a similar degree of stability in price dynamics, whilst finally (f) cryptocurrency exhibit a low level of regularity compared to international equity markets. We find that cryptos showed more instability and more irregularity during the COVID-19 pandemic compared to international stock markets. Thus, from an informational efficiency perspective, investing in digital assets during big crises as the COVID-19 pandemic, could be considered riskier as opposed to equities. [ABSTRACT FROM AUTHOR]
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- 2020
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206. Intelligent forecasting with machine learning trading systems in chaotic intraday Bitcoin market.
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Lahmiri, Salim and Bekiros, Stelios
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MACHINE learning , *ARTIFICIAL intelligence , *ARTIFICIAL neural networks , *BITCOIN , *MACHINERY industry , *FEEDFORWARD neural networks - Abstract
• We employ SVR, GRP, RT, kNN, FFNN, BRNN and RBFNN machine learning models. • We comparatively evaluate AI systems in forecasting high-frequency Bitcoin prices via diverse metrics. • An entropy analysis reveals long memory traits, stochasticity and topological complexity. • The BRNN shows outstanding accuracy while its convergence is remarkably fast. • ANNs efficiently emulate parallel processing in human decision-making under noisy, nonlinear input-output variable spaces. Due to the remarkable boost in cryptocurrency trading on digital blockchain platforms, the utilization of advanced machine learning systems for robust prediction of highly nonlinear and noisy data, gains further popularity by individual and institutional market agents. The purpose of our study is to comparatively evaluate a plethora of Artificial Intelligence systems in forecasting high frequency Bitcoin price series. We employ three different sets of models, i.e., statistical machine learning approaches including support vector regressions (SVR) and Gaussian Poisson regressions (GRP), algorithmic models such as regression trees (RT) and the k -nearest neighbours (kNN) and finally artificial neural network topologies such as feedforward (FFNN), Bayesian regularization (BRNN) and radial basis function networks (RBFNN). To the best of our knowledge, this is the first time an extensive empirical investigation of the comparative predictability of various machine learning models is implemented in high-frequency trading of Bitcoin. The entropy analysis of training and testing samples reveals long memory traits, high levels of stochasticity, and topological complexity. The presence of inherent nonlinear dynamics of Bitcoin time series fully rationalizes the use of advanced machines learning techniques. The optimal parameter values for SVR, GRP and kNN are found via Bayesian optimization. Based on diverse performance metrics, our results show that the BRNN renders an outstanding accuracy in forecasting, while its convergence is unhindered and remarkably fast. The overall superiority of artificial neural networks is due to parallel processing features that efficiently emulate human decision-making in the presence of underlying nonlinear input-output relationships in noisy signal environments. [ABSTRACT FROM AUTHOR]
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- 2020
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207. Big data analytics using multi-fractal wavelet leaders in high-frequency Bitcoin markets.
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Lahmiri, Salim and Bekiros, Stelios
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BITCOIN , *BIG data , *FRACTAL analysis , *MARKETS , *DATA analysis , *STATISTICS - Abstract
• A time-scale multi-fractal approach is used to investigate high-frequency Bitcoin prices & volume. • Big data analysis reveals heterogeneous multi-fractal dynamics in [5 mn–90 mn] & [120 mn–720 mn] intervals. • Wavelet leaders algorithmic technique provides robust description of the singularity spectrum. • Profitable high-frequency trading strategies in crypto-currency markets can be devised. We employ a time-scale multi-fractal decomposition approach to investigate the properties of Bitcoin prices and volume at different sampling rates using high-frequency data. We provide evidence of multi-fractality at all rates. The big data-driven analysis combined with statistical testing shows evidence of dominant multi-fractal traits within the intervals of 5 mn–90 mn, and 120 mn up to 720 mn. Wavelet leaders comprise a promising algorithmic technique that provides a richer description of the singularity spectrum. In particular, we reveal the distinct heterogeneity of the three log-cumulants for prices and volume between the two distinctive high-frequency sampling intervals. Our findings may assist in devising profitable high-frequency trading strategies in crypto-currency markets. [ABSTRACT FROM AUTHOR]
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- 2020
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208. Information, ondelettes, réseaux de neurones, méthodes numériques, et modélisation et prédiction des séries temporelles boursières : une étude comparative
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Lahmiri, Salim and Lahmiri, Salim
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L'intelligence artificielle connexionniste est devenue un outil de modélisation attrayant dans le contexte de la prédiction des séries temporelles boursières, grâce à la capacité des réseaux de neurones artificiels (RNA) de modéliser des séries temporelles bruitées ou incomplètes sans pour autant émettre des hypothèses fortes sur leurs distributions et la relation entre variables d'entrée et de sortie, contrairement aux modèles statistiques conventionnels. Par ailleurs, différentes catégories d'information peuvent servir d'entrées au système de prédiction. Ainsi plusieurs travaux empiriques montrent la possibilité de prévoir le marché boursier à partir d'information économique, technique ou historique (retards) tirée de séries temporelles boursières, ou de l'information extraite de l'analyse par ondelettes discrète (AOD). Pour réaliser la descente du gradient lors de l'entrainement du RNA, l'algorithme d'optimisation que l'on retrouve le plus souvent dans littérature sur la prédiction boursière est celui de Levenberg-Marquardt (L-M). Mais, il existe d'autres techniques d'optimisation plus avancées comme le Quasi-Netwon, l'algorithme du gradient conjugué de type Polak-Ribiére, Powell-Beale ou Fletcher-Reeves, et autres, et il n'a pas encore été établi quel l'algorithme donne les meilleures résultats. L'objectif de la thèse vise à répondre aux questions suivantes : Question l : Quel type d'information d'entrée peut mener aux meilleures prédictions? Question 2 : Une combinaison des différents types d'information peut-elle améliorer la qualité des prédictions ? Question 3 : Quel algorithme d'approximation numérique permet le meilleur apprentissage du RNA à retro-propagation d'erreur, et donc de donner les meilleurs résultats de prédiction? Question 4 : Peut-on améliorer les résultats des variables prédictives traditionnelles en introduisant une dimension de traitement temps-fréquence obtenue par l'analyse multi-résolution; en l'occurrence l'analyse par paquets d'ondelet
209. Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images
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Lahmiri, Salim, Boukadoum, Mounir, Lahmiri, Salim, and Boukadoum, Mounir
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A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform(DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images.The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction.
210. Alzheimer’s Disease Detection in Brain Magnetic Resonance Images Using Multiscale Fractal Analysis
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Lahmiri, Salim, Boukadoum, Mounir, Lahmiri, Salim, and Boukadoum, Mounir
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We present a new automated system for the detection of brain magnetic resonance images (MRI) affected by Alzheimer’s disease (AD).The MRI is analyzed by means of multiscale analysis (MSA) to obtain its fractals at six different scales. The extracted fractals are used as features to differentiate healthy brain MRI from those of AD by a support vector machine (SVM) classifier.The result of classifying 93 brain MRIs consisting of 51 images of healthy brains and 42 of brains affected by AD, using leave-one-out crossvalidation method, yielded 99.18% ± 0.01 classification accuracy, 100% sensitivity, and 98.20% ± 0.02 specificity. These results and a processing time of 5.64 seconds indicate that the proposed approach may be an efficient diagnostic aid for radiologists in the screening for AD.
211. Automated detection of circinate exudates in retina digital images using empirical mode decomposition and the entropy and uniformity of the intrinsic mode functions
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Lahmiri, Salim, Boukadoum, Mounir, Lahmiri, Salim, and Boukadoum, Mounir
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This work presents a new automated system to detect circinate exudates in retina digital images. It operates as follows: the true color image is converted to gray levels, and contrast-limited adaptive histogram equalization (CLAHE) is applied to it before undergoing empirical mode decomposition (EMD) as intrinsic mode functions (IMFs). The entropies and uniformities of the first two IMFs are then computed to form a feature vector that is fed to a support vector machine (SVM) for classification. The experimental results using a set of 45 images (23 normal images and 22 images with circinate exudates taken from the STARE database) and tenfold cross-validation indicate that the proposed approach outperforms previous works found in the literature, with perfect classification. In addition, the image processing time was < 4 min, making the presented circinate exudate detection system fit for use in a clinical environment.
212. Information, ondelettes, réseaux de neurones, méthodes numériques, et modélisation et prédiction des séries temporelles boursières : une étude comparative
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Lahmiri, Salim
- Subjects
- Algorithme, Bourse, Information, Machine à vecteurs de support, Modèle prédictif, Ondelette (Mathématiques), Prévision, Réseau neuronal (Informatique), Série chronologique, Simulation par ordinateur
- Abstract
L'intelligence artificielle connexionniste est devenue un outil de modélisation attrayant dans le contexte de la prédiction des séries temporelles boursières, grâce à la capacité des réseaux de neurones artificiels (RNA) de modéliser des séries temporelles bruitées ou incomplètes sans pour autant émettre des hypothèses fortes sur leurs distributions et la relation entre variables d'entrée et de sortie, contrairement aux modèles statistiques conventionnels. Par ailleurs, différentes catégories d'information peuvent servir d'entrées au système de prédiction. Ainsi plusieurs travaux empiriques montrent la possibilité de prévoir le marché boursier à partir d'information économique, technique ou historique (retards) tirée de séries temporelles boursières, ou de l'information extraite de l'analyse par ondelettes discrète (AOD). Pour réaliser la descente du gradient lors de l'entrainement du RNA, l'algorithme d'optimisation que l'on retrouve le plus souvent dans littérature sur la prédiction boursière est celui de Levenberg-Marquardt (L-M). Mais, il existe d'autres techniques d'optimisation plus avancées comme le Quasi-Netwon, l'algorithme du gradient conjugué de type Polak-Ribiére, Powell-Beale ou Fletcher-Reeves, et autres, et il n'a pas encore été établi quel l'algorithme donne les meilleures résultats. L'objectif de la thèse vise à répondre aux questions suivantes : Question l : Quel type d'information d'entrée peut mener aux meilleures prédictions? Question 2 : Une combinaison des différents types d'information peut-elle améliorer la qualité des prédictions ? Question 3 : Quel algorithme d'approximation numérique permet le meilleur apprentissage du RNA à retro-propagation d'erreur, et donc de donner les meilleurs résultats de prédiction? Question 4 : Peut-on améliorer les résultats des variables prédictives traditionnelles en introduisant une dimension de traitement temps-fréquence obtenue par l'analyse multi-résolution; en l'occurrence l'analyse par paquets d'ondelettes (APO)? L'indice boursier américain S&P500, le plus utilisé par les investisseurs américains et internationaux, est retenu pour effectuer différentes simulations. L'objectif est de prédire ses hausses et baisses, et les mesures de performance considérées sont le taux de classification (prédiction) correcte, la sensibilité et la sensibilité. Les machines à support de vecteurs (SVM) sont retenues pour être le modèle de référence vue leur capacité prédictive prouvée dans la littérature. Les simulations ont montré les résultats suivants : (a) parmi les catégories d'informations classiques, les informations historiques et les informations sur l'état psychologique du marché sont les plus pertinentes pour la prédiction des tendances futures; (b) la combinaison des différentes catégories d'informations classiques (économique, technique, historique, sentiment) n'améliore par la précision de la prédiction; (c) les différences entre les performances obtenues par différents algorithmes numériques pour l'entrainement du RNA sont minimes; cependant l'algorithme de Polak-Ribière semble être en général plus performant; (d) dans certains cas, l'algorithme L-M qui est le plus utilisé dans la littérature performe moins bien que les autres algorithmes considérés dans nos simulations; (e) contrairement à ce que l'on retrouve dans la littérature, le RNA performe mieux que le SVM; (f) l'information fréquentielle extraite par l'analyse multi-résolution par paquet d'ondelettes (APO) permet d'améliorer grandement la performance prédictive des RNA par rapport aux autres catégories d'information traditionnellement utilisées dans la littérature, et ce quelque soit l'algorithme numérique utilisé pour l'entraînement; (g) Finalement, l'usage de l'information extraite par APO permet d'obtenir une meilleure prédiction de la tendance future du marché S&P500 que l'information extraite par AOD utilisée dans la littérature. De ce fait, l'approche par l'APO est beaucoup plus simple à utiliser, contrairement aux approches traditionnelles qui requièrent beaucoup de prétraitements statistiques. ______________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : réseaux de neurones, algorithmes numériques, machines à supports de vecteurs, information, ondelette, prédiction.
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- 2014
213. Bayesian Inference for Inverse Gaussian Data with Emphasis on the Coefficient of Variation
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Chaubey, Yogendra P., Singh, Murari, Sen, Debaraj, Chaubey, Yogendra P., editor, Lahmiri, Salim, editor, Nebebe, Fassil, editor, and Sen, Arusharka, editor
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- 2021
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214. Modeling Obesity Rate with Spatial Auto-correlation: A Case Study
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Rana, Masud, Khan, Shahedul A., Feng, Cindy, Leatherdale, Scott T., Katapally, Tarun R., Pahwa, Punam, Chaubey, Yogendra P., editor, Lahmiri, Salim, editor, Nebebe, Fassil, editor, and Sen, Arusharka, editor
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- 2021
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215. A Spatiotemporal Investigation of the Cod Stock in the Northern Gulf of St. Lawrence
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Rivest, Louis-Paul, Boivin, Ariane, Benoît, Hugues, Chaubey, Yogendra P., editor, Lahmiri, Salim, editor, Nebebe, Fassil, editor, and Sen, Arusharka, editor
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- 2021
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216. Bayes Linear Emulation of Simulated Crop Yield
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Hasan, Muhammad Mahmudul, Cumming, Jonathan A., Chaubey, Yogendra P., editor, Lahmiri, Salim, editor, Nebebe, Fassil, editor, and Sen, Arusharka, editor
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- 2021
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217. A Markov Model of Polygenic Inheritance
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Murray, Jesse, Chaubey, Yogendra P., editor, Lahmiri, Salim, editor, Nebebe, Fassil, editor, and Sen, Arusharka, editor
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- 2021
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218. Estimation and Testing of a Common Coefficient of Variation from Inverse Gaussian Distributions
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Singh, Murari, Chaubey, Yogendra P., Sen, Debaraj, Sarker, Ashutosh, Chaubey, Yogendra P., editor, Lahmiri, Salim, editor, Nebebe, Fassil, editor, and Sen, Arusharka, editor
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- 2021
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219. Minimum Profile Hellinger Distance Estimation for Semiparametric Simple Linear Regression Model
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Li, Jiang, Wu, Jingjing, Chaubey, Yogendra P., editor, Lahmiri, Salim, editor, Nebebe, Fassil, editor, and Sen, Arusharka, editor
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- 2021
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220. Correction to: Estimation and Testing of a Common Coefficient of Variation from Inverse Gaussian Distributions
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Singh, Murari, Chaubey, Yogendra P., Sen, Debaraj, Sarker, Ashutosh, Chaubey, Yogendra P., editor, Lahmiri, Salim, editor, Nebebe, Fassil, editor, and Sen, Arusharka, editor
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- 2021
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221. The high frequency multifractal properties of Bitcoin.
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Stavroyiannis, Stavros, Babalos, Vassilios, Bekiros, Stelios, Lahmiri, Salim, and Uddin, Gazi Salah
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MULTIFRACTALS , *BITCOIN , *PROPERTY , *INTERNATIONAL trade - Abstract
Abstract Following the new advances in encryption and network computing, Bitcoin emerged as a private sector system facilitating peer-to-peer exchange via distributed ledgers based on blockchains, driving a transformational change towards a global economy outside the core financial system. The main purpose of this paper is to examine the multifractal properties of the Bitcoin price using high frequency data. The methods used are the wavelet transform modulus maxima and the multifractal detrended fluctuation analysis. The results indicate that Bitcoin exhibits a large degree of multifractality in all examined time intervals, and the main source of multifractality is attributed to the high kurtosis and the fat distributional tails of the series returns. Highlights • Encryption & network computing enabled a transformational change in markets via Bitcoin. • We explore the fractal dynamics of Bitcoin using high frequency data. • A novel hybrid of wavelet modulus maxima & detrended fluctuation analysis is used. • Fractality is observed in all time intervals. • The main drivers are high kurtosis & fat distributional tails. [ABSTRACT FROM AUTHOR]
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- 2019
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222. Artificial macro-economics: A chaotic discrete-time fractional-order laboratory model.
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Chu, Yu-Ming, Bekiros, Stelios, Zambrano-Serrano, Ernesto, Orozco-López, Onofre, Lahmiri, Salim, Jahanshahi, Hadi, and Aly, Ayman A.
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CHAOS theory , *MACROECONOMIC models , *BIFURCATION diagrams , *LYAPUNOV exponents , *FRACTIONAL calculus - Abstract
• We introduce for the first time in the literature a fractional-calculus-based artificial macroeconomic model. • A new system of fractional difference-order equations is proposed for the Goodwin model. • Using bifurcation diagrams, phase portraits and Lyapunov exponents among other measures, the characteristics of the new system are depicted. • We implement extensive simulations via a new hardware laboratory realization set up. In this novel research, through dynamical analysis, we introduce for the first time a fractional-calculus based artificial macroeconomic model, actually implemented in the Laboratory via a new hardware set up. Firstly, we propose a new model of a discrete-time macroeconomic system where fractional derivatives are incorporated into the system of equations. Using well-known tools and methods, including bifurcation diagrams and Lyapunov exponents, the characteristics of the system are disclosed, and the importance of the fractional-order derivative in the modeling of the system is shown. After that, a laboratory hardware realization is also carried out for the proposed system that provides further insight and a better understanding of the properties of the system. For the hardware realization an Arduino Due™ is chosen in which possess two analog output pins. Experimental results conspicuously illustrate the chaotic behavior of the system. Through the results of the hardware realization, phase portraits and bifurcation diagram of the system are demonstrated, and the effects of the parameters and fractional derivatives are studied. We believe the presented study and its results pave the way for future studies on the incorporation of fractional calculus into macroeconomic models. [ABSTRACT FROM AUTHOR]
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- 2021
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223. Bayes Linear Emulation of Simulated Crop Yield
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Hasan, Muhammad Mahmudul, Cumming, Jonathan A., Chaubey, Yogendra P., Lahmiri, Salim, Nebebe, Fassil, and Sen, Arusharka
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The analysis of the output from a large-scale computer simulation experiment can pose a challenging problem in terms of size and computation. We consider output in the form of simulated crop yields from the Environmental Policy Integrated Climate (EPIC) model, which requires a large number of inputs—such as fertilizer levels, weather conditions, and crop rotations—inducing a high dimensional input space. In this paper, we adopt a Bayes linear approach to efficiently emulate crop yield as a function of the simulator fertilizer inputs. We explore emulator diagnostics and present the results from emulation of a subset of the simulated EPIC data output.
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- 2012
224. Fractals in Neuroimaging.
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Lahmiri S, Boukadoum M, and Di Ieva A
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- Humans, Magnetic Resonance Imaging, Fractals, Neuroimaging
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Several natural phenomena can be described by studying their statistical scaling patterns, hence leading to simple geometrical interpretation. In this regard, fractal geometry is a powerful tool to describe the irregular or fragmented shape of natural features, using spatial or time-domain statistical scaling laws (power-law behavior) to characterize real-world physical systems. This chapter presents some works on the usefulness of fractal features, mainly the fractal dimension and the related Hurst exponent, in the characterization and identification of pathologies and radiological features in neuroimaging, mainly, magnetic resonance imaging., (© 2024. The Author(s), under exclusive license to Springer Nature Switzerland AG.)
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- 2024
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225. Automated detection of circinate exudates in retina digital images using empirical mode decomposition and the entropy and uniformity of the intrinsic mode functions.
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Lahmiri S and Boukadoum M
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- Angiomatosis, Artificial Intelligence, Diabetic Retinopathy etiology, Early Diagnosis, Entropy, Humans, Reproducibility of Results, Sensitivity and Specificity, Symptom Assessment methods, Algorithms, Diabetic Retinopathy pathology, Exudates and Transudates cytology, Image Interpretation, Computer-Assisted methods, Pattern Recognition, Automated methods, Retina pathology, Retinoscopy methods
- Abstract
This work presents a new automated system to detect circinate exudates in retina digital images. It operates as follows: the true color image is converted to gray levels, and contrast-limited adaptive histogram equalization (CLAHE) is applied to it before undergoing empirical mode decomposition (EMD) as intrinsic mode functions (IMFs). The entropies and uniformities of the first two IMFs are then computed to form a feature vector that is fed to a support vector machine (SVM) for classification. The experimental results using a set of 45 images (23 normal images and 22 images with circinate exudates taken from the STARE database) and tenfold cross-validation indicate that the proposed approach outperforms previous works found in the literature, with perfect classification. In addition, the image processing time was <4 min, making the presented circinate exudate detection system fit for use in a clinical environment.
- Published
- 2014
- Full Text
- View/download PDF
226. Automatic brain MR images diagnosis based on edge fractal dimension and spectral energy signature.
- Author
-
Lahmiri S and Boukadoum M
- Subjects
- Humans, Automation, Brain Diseases diagnosis, Fractals, Magnetic Resonance Imaging methods
- Abstract
A new automatic system to detect pathologies in human brain magnetic resonance (MR) images is presented. The goal is to classify normal versus abnormal images affected by Alzheimer, Glioma, Herpes, Metastatic, and Multiple Sclerosis. The extracted features are the fractal dimension of edges in the Hilbert domain, and the skewness and kurtosis of their spectral energy distribution. The proposed system (FDSE) outperforms the popular discrete wavelet transform (DWT) and principal component analysis (PCA).
- Published
- 2012
- Full Text
- View/download PDF
227. Hybrid cosine and Radon transform-based processing for digital mammogram feature extraction and classification with SVM.
- Author
-
Lahmiri S and Boukadoum M
- Subjects
- Female, Humans, Reproducibility of Results, Sensitivity and Specificity, Algorithms, Breast Neoplasms diagnostic imaging, Mammography methods, Pattern Recognition, Automated methods, Radiographic Image Enhancement methods, Radiographic Image Interpretation, Computer-Assisted methods, Support Vector Machine
- Abstract
A new methodology to automatically extract features from mammograms and classify them is presented. It relies on a hybrid processing system that sequentially uses the discrete cosine transform (DCT) to obtain the high frequency component of the mammogram and then applies the Radon transform to the obtained DCT image in order to extract its directional features. The features are subsequently fed to a support vector machine for classification. The approach was tested on a database of one hundred images and shows improved classification accuracy in comparison to using the discrete cosine transform or the Radon transform alone, as done in others works.
- Published
- 2011
- Full Text
- View/download PDF
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