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arXiv 2024年

作者： Allali, Karam Balde, Mouhamadou A.M.T. Ndiaye, Babacar M. Laboratory of Mathematics Computer Science and Applications Faculty of Sciences and Technologies University Hassan II of Casablanca PO BOX 146 Mohammedia Morocco Laboratory of Mathematics of Decision and Numerical Analysis University of Cheikh Anta Diop Dakar Senegal

This work will study an optimal control problem describing the two-strain SEIR epidemic model. The studied model is in the form of six nonlinear differential equations illustrating the dynamics of the susceptibles and the exposed, the infected, and the recovered individuals. The exposed and the infected compartments are each divided into two sub-classes representing the first and the second strain. The model includes two saturated rates and two treatments for each strain. We begin our study by showing the well-posedness of our problem. The basic reproduction number is calculated and depends mainly on the reproduction numbers of the first and second strains. The global stability of the disease-free equilibrium is fulfilled. The optimal control study is achieved by using the Pontryagin minimum principle. numerical simulations have shown the importance of therapy in minimizing the infection's effect. By administrating suitable therapies, the disease's severity decreases considerably. The estimation of parameters as well as a comparison study with COVID-19 clinical data are fulfilled. It was shown that the mathematical model results fits well the clinical data. © 2024, CC BY-NC-ND.

关键词： Nonlinear equations

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Evaluating Automatic Annotation Techniques for Fine-Tuning Large Language Models in Financial Sentiment analysis

Evaluating Automatic Annotation Techniques for Fine-Tuning L...

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Sentiment analysis and Deep Learning (ICSADL), International Conference on

作者： Ibtissam Youb Mohamed Hamlich Sebastian Ventura Feras Al-Obeidat Department of Computer Science and Numerical Analysis University of Cordoba Cordoba Spain Department of Electronic CCPS Laboratory ENSAM University of Hassan II Casablanca Morocco College of Technological Innovation Zayed University Abu Dhabi UAE

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Accurate sentiment analysis in financial contexts is crucial for market analysis and investment decisions. However, manually annotating large datasets for training models is time-consuming and costly. This study evaluates two automatic an-notation tools, VADER and TextBlob, for their effectiveness in generating labels for training large language models (LLMs) like BERT and GPT-2 in financial sentiment analysis. We used a large dataset of stock market-related tweets, including a manually annotated subset for benchmarking. The remaining tweets were automatically labeled using VADER and TextBlob. We trained BERT and GPT-2 models with these sentiment labels and compared their performance against the benchmark dataset. Our findings show that models trained with VADER annotations had a higher correlation with human-labeled data (62% accuracy) compared to those trained with TextBlob annotations (48% accuracy). These results suggest that VADER is more suitable for automatic annotation in financial sentiment analysis, providing more accurate and reliable sentiment labels, which can significantly improve the efficiency of training large language models for financial applications.

关键词： Training Sentiment analysis Accuracy Annotations Social networking (online) Large language models Benchmark testing Data models Reliability Investment

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Optimal mass transportation for noise minimization problem around an airport 11

Optimal mass transportation for noise minimization problem a...

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11th International Symposium on Signal, Image, Video and Communications, ISIVC 2022

作者： Barry, Mamadou Diallo, Coumba Ndiaye, Babacar Mbaye Cheikh Anta Diop University Departement of Mathematics and Computer Science Dakar Senegal Cheikh Anta Diop University Dakar Senegal Bp 45087 Dakar-Fann Laboratory of Mathematics of Decision and Numerical Analysis BP 45087 Dakar-Fann Dakar10700 Senegal

ISBN: (纸本)9781665487245

In this article, we develop, for a given aircraft,a new optimization model based on optimal mass transportation to minimize the noise perceived on the ground. We make an application for the airport of DIASS (Senegal) taking into account the constraint known as the mass conservation equation (one of Euler's equations). The problem is solved using an augmented Lagrangian algorithm introduced by Fortin [1]. Scenario simulations are carried out to illustrate that the sound level is decreasing, while the range and capacity of jets continue to increase. © 2022 IEEE.

关键词： Constrained optimization

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Android malware detection: An in-depth investigation of the impact of the use of imbalance datasets on the efficiency of machine learning models

Android malware detection: An in-depth investigation of the ...

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International Conference on Advanced Communication Technology (ICACT)

作者： Zakaria Sawadogo Jean-Marie Dembele Gervais Mendy Samuel Ouya LITA(Laboratory of Computer Science Telecommunications and Applications) Cheikh Anta Diop University of Dakar Senegal LANI(Laboratory of Numerical Analysis and Computer Science) Gaston Berger University Senegal Cybersecurity Including Artificial at Gaston Berger University Computer Science at Gaston Berger University ESP Polytechnic School at Cheikh Anta Diop University LITA Laboratory at Cheikh Anta Diop University

Machine learning techniques have become an essential part of research into the detection and classification of malicious applications. There are several approaches or algorithms that learn from existing data and predict classes. Machine learning principles recommend a balance of classes in the training dataset, but the reality on the ground is quite different. The majority of datasets used for malicious application detection are unbalanced. Class imbalance degrades classifier performance, so it is a common problem in classification tasks. This observation is much more significant in the field of Android malware detection and classification. There is little work to our knowledge on the effects of unbalanced datasets in the field of Android malware detection. Our contribution focuses on the impact of unbalanced datasets on the performance of different algorithms and the relevance of using evaluation metrics in Android malware detection. And the state of the databases from which researchers typically draw datasets. We show that for malicious application detection, some classification algorithms are not suitable for unbalanced datasets. We also prove that some of the most widely used performance evaluation metrics in the literature (Accuracy, Precision, Recall) are not very well suited to unbalanced datasets. On the other hand, the metrics (Balanced Accuracy, Geometric mean) are more suitable. These results were obtained by evaluating the performances of eleven classification algorithms as well as the adequacy of the different evaluation metrics (Accuracy, Recall, Precision, F1_score, Balanced accuracy, Matthews corrcoef, Geometric mean, Fowlkes_mallows). Also not all databases are accessible by researchers and many of these databases are not updated.

关键词： Measurement Training Performance evaluation Machine learning algorithms Databases Machine learning Prediction algorithms

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Amazigh Digits Speech Recognition System Under Noise Car Environment 1st

Amazigh Digits Speech Recognition System Under Noise Car Env...

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1st International Conference on Embedded Systems and Artificial Intelligence, ESAI 2019

作者： Zealouk, Ouissam Hamidi, Mohamed Satori, Hassan Satori, Khalid Laboratory Computer Science Image Processing and Numerical Analysis Faculty of Sciences Dhar Mahraz USMBA Fez Morocco

ISBN: (纸本)9789811509469

Automatic speech recognition (ASR) in Amazigh speech, particularly Moroccan Tarifit accented speech, is a less researched area. Some efforts to develop ASR on Moroccan accented Amazigh speech in the clean environment have been studied in our previous works. In this paper, we analyze the effect of noise car at different signal-to-noise ratio (SNR) on Amazigh accented on the first ten digits with different decibel values. Various techniques are used for isolated speech recognition like hidden Markov model (HMMs) and Mel-frequency cepstral coefficients (MFCC). The experimental result presents that recognition rate of 88.22% in a clean environment, and 59.26% and 33.83% rates in noisy condition at SNR 10 dB and 20 dB, respectively. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Hidden Markov models

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Rational Gaussian wavelets and corresponding model driven neural networks

arXiv

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arXiv 2025年

作者： Ámon, Attila Miklós Fenech, Kristian Kovács, Péter Dózsa, Tamás Eötvös Loránd University Faculty of Informatics Department of Numerical Analysis Hungary Eötvös Loránd University Faculty of Informatics Department of Artificial Intelligence Hungary HUN-REN Institute for Computer Science and Control Systems and Control Laboratory Hungary University of Győr Hungary

In this paper we consider the continuous wavelet transform using Gaussian wavelets multiplied by an appropriate rational term. The zeros and poles of this rational modifier act as free parameters and their choice highly influences the shape of the mother wavelet. This allows the proposed construction to approximate signals with complex morphology using only a few wavelet coefficients. We show that the proposed rational Gaussian wavelets are admissible and provide numerical approximations of the wavelet coefficients using variable projection operators. In addition, we show how the proposed variable projection based rational Gaussian wavelet transform can be used in neural networks to obtain a highly interpretable feature learning layer. We demonstrate the effectiveness of the proposed scheme through a biomedical application, namely, the detection of ventricular ectopic beats (VEBs) in real ECG *** Codes 65D15 © 2025, CC BY.

关键词： Electrocardiography

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Conservation Properties of the Augmented Basis Update & Galerkin Integrator for Kinetic Problems

SSRN

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SSRN 2023年

作者： Einkemmer, Lukas Daniel Kusch, Jonas Schotthöfer, Steffen University of Innsbruck Numerical Analysis and Scientific Computing Innsbruck Austria Norwegian University of Life Sciences Scientific Computing Ås Norway Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

numerical simulations of kinetic problems can become prohibitively expensive due to their large memory footprint and computational costs. A method that has proven to successfully reduce these costs is the dynamical low-rank approximation (DLRA). One key question when using DLRA methods is the construction of robust time integrators that preserve the invariances and associated conservation laws of the original problem. In this work, we demonstrate that the augmented basis update & Galerkin integrator (BUG) preserves solution invariances and the associated conservation laws when using a conservative truncation step and an appropriate time and space discretization. We present numerical comparisons to existing conservative integrators and discuss advantages and disadvantages. © 2023, The Authors. All rights reserved.

关键词： Integral equations

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Conservation properties of the augmented basis update & Galerkin integrator for kinetic problems

arXiv

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arXiv 2023年

作者： Einkemmer, Lukas Kusch, Jonas Schotthöfer, Steffen University of Innsbruck Numerical Analysis and Scientific Computing Innsbruck Austria Norwegian University of Life Sciences Scientific Computing Ås Norway Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

关键词： Kinetics

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Continuous Wavelet Transform Based Variable Projection Networks

Continuous Wavelet Transform Based Variable Projection Netwo...

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European Signal Processing Conference (EUSIPCO)

作者： Attila Miklós Ámon Kristian Fenech Péter Kovács Tamás Dózsa Department of Numerical Analysis Eötvös Loránd University Budapest Department of Artificial Intelligence Eötvös Loránd University Budapest Széchenyi Itsván University and HUN-REN Institute for Computer Science and Control Vehicle Industry Research Center Budapest Systems and Control Laboratory Budapest

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

A novel feature extraction layer using the continuous wavelet transform is developed for use with neural networks. The learned parameters (scales and shifts in time) of the proposed wavelet layer can be interpreted by humans. In addition, using the concept of adaptive orthogonal projections to approximate wavelet coefficients, a sparse representation of the input is achieved. The proposed method is applicable with any analytic mother wavelet. As a case study, the application of bearing fault detection is considered to demonstrate the efficiency of the method, using a benchmark dataset. The results clearly show that the proposed model can be used to solve fault detection problems achieving near perfect classification accuracy. Qualitative and quantitative comparisons are made to a previous wavelet based neural network architecture. It is shown that the proposed method matches or outperforms previous approaches while using significantly less parameters. This sparsity improves model performance from the point of view of interpretability as well as model complexity.

关键词： Adaptation models Continuous wavelet transforms Sparse approximation Fault detection Neural networks Europe Wavelet analysis Feature extraction numerical models Wavelet coefficients

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Pathological Detection Using HMM Speech Recognition-Based Amazigh Digits 1st

Pathological Detection Using HMM Speech Recognition-Based Am...

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1st International Conference on Embedded Systems and Artificial Intelligence, ESAI 2019

作者： Zealouk, Ouissam Satori, Hassan Hamidi, Mohamed Satori, Khalid Laboratory Computer Science Image Processing and Numerical Analysis Faculty of Sciences Dhar Mahraz Sidi Mohamed Ben Abdellah University Fés Morocco

ISBN: (纸本)9789811509469

In the last decade, the automatic speech pathology detection systems based on voice production theory are evolving up to date. Overall, there have not been much speech technology research studies for persons regarding voice disorders which center on Amazigh language. This research project focuses on the build of an automatic speech recognition system based on Sphinx-4 that permits to detect the differences between normal and pathological voices based on the produced speech. The performance in our system was measured using the combinations of different Hidden Markov Models and Gaussian mixture distributions. Results show that the maximum accuracy with the normal voices is greater than the maximum accuracy obtained from the pathological speaker. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Hidden Markov models

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