Nédra Mellouli

@article{bignoumba_2463,

title = {A new efficient ALignment-driven Neural Network for Mortality Prediction from Irregular Multivariate Time Series data},

author = {Nzamba Bignoumba and Nédra Mellouli and Sadok Ben Yahia},

url = {https://www.sciencedirect.com/science/article/pii/S0957417423026507?via%3Dihub},

year  = {2024},

date = {2024-03-01},

journal = {Expert Systems With Applications},

volume = {238},

number = {Part E},

pages = {122148},

abstract = {The irregularity of the time interval between observations in and across the stream is a key factor that leads to a drop in performance when classical machine learning or deep learning models are used for a downstream task requiring multivariate time series. Indeed, irregular multivariate time series not only increase the rate of missing values but also lead to data sparsity, which consequently makes the data almost unleverageable and/or ineffective for models. To tackle this scorching challenge, most of the pioneering approaches apply imputation or interpolation in their core, which might lead to embedding data with noise. To especially address this irregular multivariate time series issue, we introduce, in this paper, a new deep neural network model called ALignment-driven Neural Network. The innovative idea of our model is to transform the irregular multivariate time series into pseudo-aligned (or pseudo-regular) latent values. The latter are shown as a matrix, where the coefficients are the latent values of each feature at user-defined reference time points that are evenly spaced. They are obtained through a duplication process driven by an exponential decay mechanism. The obtained output is then passed to a Recurrent Neural Network model, which is undoubtedly the must-use model for regular time series data. To show that our model added value, we looked at the Intensive Care Unit mortality prediction task. In this unit, the physiological measurements used to make decisions have a problem with time irregularity. Leveraging the publicly available MIMIC-III, we compare the performance of our model to that of flagship models. In addition, we also performed extensive ablation studies to highlight the importance of specific components in our model. Interestingly enough, whenever data is collected 24 and 48 h after a patient's admission, we outperform our pioneering competitors, i.e., +1.1% and +1.5% for the AUC score, +2.3% and for the AUPRC score and +0.6% and +1.7%

 for the F1-score.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{waga_2351,

title = {Analytical and Deep Learning Approaches for Solving the Inverse kinematic Problem of a High Degrees of Freedom Robotic Arm},

author = {Nesrine Waga and Hichem Kallel and Nédra Mellouli},

url = {https://www.sciencedirect.com/science/article/pii/S0952197623004852?via%3Dihub},

year  = {2023},

date = {2023-08-01},

journal = {Engineering Applications Of Artificial Intelligence},

volume = {123},

number = {Part B},

pages = {106301},

abstract = {Inverse kinematics is the basis for controlling the motion of robotic manipulators. It defines the required joint variables for the robotic end-effector accurately reach the desired location. Due to the derivation difficulty, computation complexity, singularity problem, and redundancy, analytical Inverse kinematics solutions pose numerous challenges to the operation of many robotic arms, especially for a manipulator with a high degree of freedom. This paper develops different Deep Learning networks for solving the Inverse kinematics problem of six- Degrees of Freedom robotic manipulators. The implemented neural architectures are Artificial Neural Network, Convolutional Neural Network, Long-Short Term Memory, Gated Recurrent Unit, and Bidirectional Long-Short Term Memory. In this context, we associate the proposed results with a specific tuning of Deep Learning network hyper-parameters (number of hidden layers, learning rate, Loss function, optimization algorithm, number of epochs, etc.). The Bidirectional Long-Short Term Memory network outperformed all proposed architectures. To be close as possible to the experimental results, we have included two types of noise in the training data set to validate which of the five proposed neural networks is more efficient. Furthermore, in this study, we compare the performance of analytical and soft computing solutions in generating robots' trajectories. We include this scenario, focusing on the advantage of implementing neural networks in avoiding the singularity problem that can occur using the analytical approach. In addition, we used the RoboDK simulator to show simulation results with real-world meaning. The performance of Deep Learning models depends on the complexity of the posed problem. Moreover, the complexity of the Inverse Kinematics problem is related to the number of Degrees of Freedom. At the end of this work, we evaluate the influence of the complexity of robotic manipulators on the proposed Deep Learning networks' performance. The results show that the implemented Deep Learning mechanisms performed well in reaching the desired pose of the end-effector. The proposed inverse kinematics strategies apply to other manipulators with different numbers of Degrees of Freedom.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{balti_2397,

title = {Spatio-temporal Heterogeneous Graph using Multivariate Earth Observation Time Series: Application for drought forecasting},

author = {Hanen Balti and Ali Ben Abbes and Yanfang Sang and Nédra Mellouli and Imed Riadh Faraha},

url = {https://www.sciencedirect.com/science/article/pii/S0098300423001395?via%3Dihub},

year  = {2023},

date = {2023-08-01},

journal = {Computers & Geosciences},

volume = {179},

number = {october 20},

pages = {105435},

note = {Accurate forecasting is required for the effective risk management of drought disasters. Many machine learning- and deep learning-based models have been proposed for drought forecasting, however, they cannot handle the temporal and/or spatial dependencies in the input data, causing unexpected forecasting results. In order to solve the challenging issue, in this paper we proposed the Heterogeneous Spatio-Temporal Graph (HetSPGraph), for drought forecasting. It includes three major layers: spatial aggregations including inter and intra aggregations, temporal aggregation, and a forecasting network. The main function of HetSPGraph is to learn the dynamic spatiotemporal correlations between the regions and to further predict the drought in different regions, based on which accurate drought forecasting can be achieved. Experimental forecasting results of the Standardized Precipitation Evapotranspiration Index (SPEI) in China indicated that the HetSPGraph model outperformed the traditional baseline methods including the Long Short-Term Memory model (LSTM), Convolutional Neural Network-LSTM (CNN-LSTM), Gated Recurrent Unit (GRU), Spatio-Temporal Graph Convolutional Networks (STGCN) and Geographic-Semantic-Temporal Hypergraph Convolutional Network (GST-HCN). Even for long-term forecasting (12 months), more accurate forecasting results, with the coefficient of determination 

 higher than 0.89, can also be obtained by HetSPGraph compared to the other three models. The proposed HetSPGraph model has the potential for wider use in forecasting drought and other natural disasters.},

keywords = {},

pubstate = {online},

tppubtype = {article}

}