M. OUALI Mohammed assam

Using density functional theory (DFT) with the GGA-PBESol, LDA, and TB-mBJ approximations, this paper thoroughly examines the structural, elastic, optoelectronic, and thermoelectric properties of chalcogenide-double perovskites Ba2NbBiS6 and Ba2TaSbS6, with the view to their potential use in optoelectronic and photovoltaic devices. Based on the calculation results, both compounds achieve the Born stability criteria and negative formation energy values, confirming their thermodynamic stability. Additionally, the elastic criteria analysis shows that the materials exhibit strong resistance to volume deformation while remaining ductile and demonstrate anisotropic characteristics, as seen through the ELATools software. Furthermore, the electronic band structure and density of states were investigated. The Ba2NbBiS6 compound exhibits an indirect band gap (L–X) of 1.58 eV (mBJ-GGA) and 1.45 eV (mBJ-LDA), while the Ba2TaSbS6 compound shows an indirect band gap (L–Γ) of 1.45 eV (mBJ-GGA) and 1.28 eV (mBJ-LDA). Analysis of the (DOS) and the electronic band structure revealed the contribution of 4d_Nd for Ba2NbBiS6 and 5d_Ta for Ba2TaSbS6. Furthermore, optical parameters derived from the dielectric function, including reflectivity, absorption coefficient, and refractive index were predicted. As a result, both compounds exhibit strong photon absorption extending from the visible to ultraviolet regions, with absorption coefficients of 342.03 × 104/cm for Ba2NbBiS6 and 333.92 × 104/cm for Ba2TaSbS6, indicating their high potential for optoelectronic devices. In addition, thermoelectric properties such as the Seebeck coefficient, electrical conductivity, thermal conductivity, ZT merit, and power factor were evaluated. The results provide valuable insights that may guide future experimental studies on these promising materials.

Predicting lattice constants is critical to advancing the discovery of functional materials. When dealing with highly nonlinear data, traditional techniques, such as Density Functional Theory (DFT), frequently suffer from restricted generalization and high computational cost. This study presents a hybrid predictive framework that merges an Autoregressive Type-3 Fuzzy Logic System with the Extended Kalman Filter (AR-T3FLS-EKF) to overcome these constraints and address the issue of restricted and scarce data. The autoregressive technique incorporates physical dependencies among compositional descriptors, whereas the Type-3 fuzzy system improves uncertainty modeling using three-dimensional membership functions. The extended kalman filter adaptively tunes the fuzzy model parameters, improving robustness and convergence. The proposed model is validated on three datasets of perovskite and double perovskite structures using features such as ionic radii, electronegativity, and tolerance factor. Compared with conventional machine learning methods, the AR-T3FLS-EKF achieves superior performance (R2 = 0.9999, MAE = 0.0015 Å, RMSE = 0.0012 Å). These results confirm the model's reliability for accurate lattice constant prediction, especially under limited and scarce data conditions.

Monitoring environmental data to ensure the safety and reliability of public resources has become a crucial task in data-driven systems. One key aspect of this monitoring is the detection of anomalies—data points or behaviors that significantly diverge from the norm. This study explores the use of a density-based clustering method, DBSCAN, to identify such anomalies within datasets collected from drinking water treatment facilities. DBSCAN's capability to recognize dense regions and isolate noise makes it well suited for flagging irregularities in complex, real-world data. By applying this method to extensive datasets with diverse attributes, the research aims to enhance the consistency and safety of drinking water production processes, contributing to improved public health outcomes and operational resilience in water management systems.

This study aims to examine the equilibrium Kesterite structure of Cu2BeSnS4, Cu2BeSnSe4, and Cu2BeSnTe4 by the application of density functional theory (DFT) and the Full-Potential Linearized Augmented Plane Wave (FP-LAPW) method. The study demonstrates that both Cu2BeSnS4 and Cu2BeSnSe4 compounds are semiconductors with direct band gaps at the Γ point, while Cu2BeSnTe4 has an indirect band gap (Γ→X). The electronic and optical characteristics of these materials indicate their potential utility in optoelectronic, photonic, and photovoltaic applications. Furthermore, a thorough comparison has been conducted between the obtained results and other experimental and theoretical data from the same chalcogenide family. In summary, the findings offer valuable information on the possible photovoltaic uses of these compounds.

This paper presents a new scheme for dynamical systems and time series modeling and identification. It is based on artificial neural networks (ANN) and metaheuristic algorithms. This scheme combines the strength of ANN with the dexterity of metaheuristic algorithms. This fusion is renowned for its ability to detect complex patterns, which considerably improves accuracy, computational efficiency, and robustness. The proposed scheme deals with the curve fitting and addresses ANN's local minima problem. This approach introduces the identification concept using a fresh novel identification element, referred to as the error model. The proposed framework encompasses a parallel interconnection of two models. The principal sub-model is the elementary model, characterized by standard specifications and a lower resolution, designed for the data being examined. In order to address the resolution limitation and achieve heightened precision, a second sub-model, named the error model, is introduced. This error model captures the disparities between the primary model and considered data. The parameters of the proposed scheme are adjusted using metaheuristic algorithms. This technique is tested across many benchmark data sets to determine its efficacy. A comparative study along with benchmark approaches will be provided. Extensive computer studies show that the suggested strategy considerably increases convergence and resolution.

Predicting Lattice Constant for Complex Cubic Perovskite X2YY’O6 using LSTM machine learning method.

Perovskites have gained significant attention in recent years due to their unique and versatile material properties. The lattice parameters of the perovskite compounds play a crucial role in the engineering of layers and substrates for heteroepitaxial thin films. As an essential parameter in the cubic perovskite structure, the lattice constant, plays a significant role in the development of materials for specific technological applications and serves as a distinctive identifier of the crystal structure of the material. In the field of materials science, advanced Computational Intelligence (CI)-based techniques have become increasingly important for simulating the relationship between the physicochemical parameters of chemical elements and the physical properties of materials and compounds. Hence, this paper presents efficient techniques based on artificial neural network (ANN) and fuzzy logic to predict the lattice constants of pseudo-cubic and cubic perovskites. The identification of optimized parameters for the ANN and fuzzy logic models is accomplished using innovative metaheuristic algorithms such as, Particle Swarm Optimization (PSO), Invasive Weed Optimization (IWO) and Imperialist Competitive Algorithm (ICA). In the first part, the study assessed, the effectiveness of various metaheuristic algorithms (PSO-IWO-ICA) in tuning the parameters of the ANN prediction structure in order to get the optimal parameter of the ANN. Whereas in the second part, once we extracted the best optimization algorithm, we combined it with the fuzzy logic technique and then we compared the effectiveness of the two techniques, ANN and Fuzzy logic. On the basis of root mean square error (RMSE), mean absolute error (MAE) and the coefficient of determination (R2), the proposed PSO-ANN and PSO-Fuzzy based models are compared with existing and recent models such as Ubic, Sidey, and Owolabi. The proposed PSO-Fuzzy model performs better than our PSO-ANN model, the Ubic, Sidey, and Owolabi models, with performance improvement of 70.90%, 90.36%, 89.74% 84.46%, respectively on the basis of RMSE. Similarly, it attains performance improvement of 71.26%, 90.31%, 89.58%, and 85.02% on the basis of MAE. Furthermore, the developed PSO-ANN based model outperforms the Ubic, Sidey and Owolabi models with performance improvement of 66.86%, 64.74% and 46.60% respectively, on the basis of RMSE and percentage enhancement of 66.27%, 63.75%, and 47.90% when compared on the basis of MAE. Although the PSO-Fuzzy model has the best performance of all the compared models, the developed PSO-ANN based model possesses the advantage of easy implementation in addition to its moderate performance.

Classification and prediction of water quality index using deep learning techniques

Dispositif de mesure non invasive et prédiction de la glycémie

Double perovskite oxides have received a lot of interest in the last ten years because of their distinctive and adaptable material properties. Among the six parameters in the cubic structure, the lattice constant is the sole changeable parameter, which plays an important role in developing materials for particular technological applications and distinctively identifies the crystal structure of the material. In this paper, the extreme learning machine (ELM) is used to correlate the lattice constant of A+22BCO6 cubic perovskite compounds, such as their ionic radii, electronegativity, oxidation state, and lattice constant. We investigated 147 compounds with lattice constants between 7.700 and 8.890Å. The prediction method has a high level of accuracy and stability and provides accurate estimates of lattice constants.

Electrocardiogram (ECG) is one of the main tools to interpret and identify cardiovascular disease. ECG signals are frequently submitted to various noises, which alter the original signal and reduce its quality. ECG signal filtering enables cardiologists to assess heart health accurately. The present paper presents a newfound approach for ECG signal denoising built on two techniques which are EMD (Empirical Mode Decomposition) and AWC (Average Wavelet Coefficient method). The basic idea behind the suggested technique initially consists of deconstructing noisy ECG signal data on a restricted number of IMFs (Intrinsic Mode Functions) and then using the AWC technique to compute each IMF’s Hurst exponent. Finally, after a thresholding operation, the clean ECG signal is recovered by adding all IMFs, excluding those considered parts of noise. The suggested approach is assessed over experiments using the MIT-BIH databases. The experimental results reveal that the suggested method efficiently extracts ECG signals from noisy data samples.

Soft Sensing Modeling Based on Support Vector Machine and Self-Organaizing Maps Model Selection for Water Quality Monitoring

Sensor Anomaly Detection using Self Features Organizing Maps and Hierarchical-Clustring for Water Quality Assessment

EMD Based Average Wavelet coefficient method for ECG Signal Denoising

The electronic band gap energy is an essential photo-electronic parameter in the energy applications of engineering materials, particularly in solar cells and photo-catalysis domains. A prediction model that can correctly
predict this band gap energy is desirable. A new approach for predicting a band gap energy is suggested in this
paper. The proposed structure is based on artificial neural networks (ANN) and the particle swarm optimization
algorithm (PSO); this structure can solve the artificial neural network’s local minima issue while preserving the
fitting quality. Our technique will hasten the identification of novel chalcopyrite in photovoltaic solar cells with
improved resolution. The suggested model combines two sub-systems in a parallel configuration. A conventional
prediction system with a low resolution for the training data being considered makes up the first ANN subsystem. A second ANN sub-system, labelled the error model, is introduced to the primary system to address
the resolution quality issue, representing uncertainty in the primary model. The particle swarm optimization
algorithm is used to identify the parameters of the proposed neural system. The method’s effectiveness is assessed
in terms of several criteria, and the output of our system shows good performance compared to experimental and
other calculated results. Several benchmark approaches were compared with the proposed system in detail.
Numerous computer tests show that the suggested strategy can significantly enhance convergence and resolution.

EMD based average wavelet coefficient method for ECG signal denoising

The Effect of Bamboo and Hemp Natural Fibers on the Elastic Behavior of Composite Materials Based on PMMA Polymer Matrix

An appropriate hybrid technique for ECG signal denoising based on variational mode decomposition and average wavelet coefficient method

An improved ELM framework for dynamical system modeling and identification

Sensor Anomaly detection using self features organizing maps and hierarchical clustring for water quality assessment

Soft sensing modeling based on support vector machine and self organizing maps model selection for water quality monitoring

A New PSO-ANN Scheme for Composite Materials Properties Prediction

The objective of this article is the study of conventional techniques, such as Burg algorithm, for parameter estimation of autoregressive moving average ARMA. Then, we introduce optimization techniques based on Genetic Algorithms to improve the parameters estimation of ARMA model. ARMA-Burg procedure was applied and tested on the stator current signatures MCSA in order to detect broken rotor bar of induction motor. It provides a good estimate of the power spectral density (PSD) and refines the parameters estimation of an ARMA model. The test results show the importance and value of the GA in improving performances of parameters estimation of an ARMA model by adjustment.

Water quality control and monitoring is an important concern of countries over the world. We present in
this work, the use the self-organizing feature maps of Kohonen (SOFM) as features selection technique and
advanced classification techniques, such as: Long Short-Term Memory (LSTM) and Support Vector Machines
(SVM). This study involved the advanced assessment of surface water quality from Tilesdit dam in Algeria.
Typically, water quality status is determined by comparing collected data with water quality standards. LSTM and
SVM have been applied with SOFM-based features selection for water quality classification. In this work, the
training step is realized using the mentioned approaches to supervise the water quality from several physicochemical
parameters. Eleven of them were collected in 4 seasons during the period (2016-2018) from study area. Experiments
step using a mentioned dataset in terms of accuracy (training and test), running time and robustness, is carried out.
The performance of our approach is optimized by regulating the parameter values using a SFOM based features
selection method. The proposed approach outperforms current conventional methods, as this approach is a
combination of strong feature selection and classification techniques. Optimal input features are selected directly
from the original datasets, aiming to reduce the computational time and complexity. The impact of this result is
significant both technically (lower learning time) and economically (reduced the number of sensors) and can
improve obviously the performance of our monitoring system. The accuracy is more than 98% in training and testing
steps with features selection process for the LSTM and SVM models. The best results of sensitivity, specificity,
precision, and F-score of the two proposed models were ranged all between 96,99 % and 100%. In a nutshell, the
two comparative machine learning methods provide very high classification accuracy and make a considerable
solution for water quality control and monitoring.

Realisation d'un appareil de mesure de l'indice de qualite de l'eau propre

A New AR-ANN-framework for time series Modeling and Identification enhanced using IWO and CMA-ES metaheuristics approaches: A pilot Study

This paper presents a novel type-2 fuzzy model for
nonlinear dynamical systems. This method can deal with the curve
fitting and computational time problems of type-2 fuzzy systems.
It is based on interval type-2 fuzzy systems and it is comprised of
a parallel interconnection of two type-2 sub fuzzy models. The first
sub fuzzy model is the primary model, which represents an
ordinary model with low resolution for the nonlinear dynamical
system under consideration. To overcome resolution quality
problem, and obtain a model with higher resolution, we will
introduce a second type-2 fuzzy sub model called error model
which will represent a model for the error modelling between the
primary model and the real nonlinear dynamical system. As the
error model represents uncertainty in the primary model, it’s
suitable to minimize this uncertainty by simple subtraction of the
error model output from the primary model output, which will
lead to a parallel interconnection between them, giving then a
unique whole final model possessing higher resolution. To apply
this approach successfully, the model’s representation and
identification are considered in this investigation using type-2
fuzzy auto regressive (T2FAR) and type-2 fuzzy auto regressive
moving average (T2FARMA) models. Identification is achieved by
innovative metaheuristic optimization algorithms, like as firefly
and biogeography-based optimization algorithms. To evaluate the
effectiveness of the proposed method, it will be tested on three
types of nonlinear dynamical systems. Computer investigations
indicate that the proposed model may significantly improves
convergence and resolution.

Optimization of SVM parameters with hybrid PCA-PSO methods for water quality monitoring

A novel type2-fuzzy adaptive filter is presented, which uses the concepts of type2-fuzzy logic, for electrocardiogram signals denoising. Type2-fuzzy adaptive filter is an information processor where both numerical and linguistic information are used as input-output pairs and fuzzy if-then rules, respectively. The proposed approach is based on an iterative procedure to achieve acceptable information extraction in the case where the statistical characteristics of the input-output signals are unknown. The proposed filter is presented as a dual-layered feedback system. Each layer has different function, the first layer being the type2-fuzzy autoregressive filter model. The second layer being responsible for training the membership function parameters. The second layer adjusts the type2-fuzzy adaptive filter parameters by using a teaching learning-based optimization algorithm (TLBO), which will allow the reaching of the required signal reconstruction by decreasing the criterion function. The proposed filter is validated and evaluated through some experimentations using the MIT-BIH ECGs databases where various artifacts were added to the ECGs signals; these included real and artificial noise. For comparison purposes, both model and non-model-based methods recently published are used. Furthermore, the effect of the proposed filter on the malformation of diagnostic features of the ECG was studied and compared with several benchmark schemes. The results show that the proposed method performs better denoising for all noise power levels and for a different criteria viewpoint.

An efficient method for Electrocardiogram (ECG) signal denoising based on
synchronous detection and Hilbert transform techniques is presented. The goal of the method
is to decompose a noisy ECG signal into two components classified according to their energy:
(1) component with high energy representing the dominant component which is the clean
ECG signal, and (2) component with low energy representing the sub-dominant component
which is the contaminant noise. The investigated approach is validated through out some
experimentations on MIT-BIH ECG database. Experimental results show that random noises
can be effectively suppressed from ECG signals.

Water quality monitoring are fundamental tools in the management of water resources and they provide essential information characterizing status of water resources, determining trends and changes over time, and identifying emerging water quality issues. This task consists of collecting quantitative information of different water parameters through a statistical sampling. For each parameters measurement, a sensor or a specific treatment is made. This makes the quality monitoring very expensive in money, time and labor. Therefore, it is important that water quality issues need to be understood in the framework of hydrological processes based on the water quality and hydrological monitoring.
To remedy this problem, we propose in this work an efficient system for water quality classification using Minimum Redundancy Maximum Relevance (mRMR) and Extreme Learning Machine (ELM). The mRMR is an algorithm frequently used in a method to accurately identify characteristics to reduce the number of input water parameters introduced in the ELM classifier and is usually described in its pairing with relevant feature selection. These subsets often contain material which is relevant but redundant and mRMR attempts to address this problem by removing those redundant subsets. mRMR has a variety of applications in many areas such as pattern recognition. As a special case, the "correlation" can be replaced by the statistical dependency between variables. Mutual information can be used to quantify the dependency. In this case, it is shown that mRMR is an approximation to maximizing the dependency between the joint distribution of the selected features and the classification variable. The ELM which is a technique for pattern classification has been widely used in many application areas such as water quality monitoring. A multi-class problem using ELM is a typical example for solving the mentioned problem.
In this work, Experimental results conducted on real dataset collected from Tilesdit dam of Bouira state (Algeria) were selected for this study. The proposed feature selection method can efficiently reduce the number of water parameters needed to classify its quality, which consequently causes a minimization in number of required sensor/treatment. Therefore, the water quality classification is perfectly insured with the trade-off between the low-cost and a high accuracy. Its performance is more competitive when compared with artificial neural networks. Furthermore, the results demonstrated that the proposed procedure has a great potential in water quality monitoring.

In this investigation a novel type-2 fuzzy model for Time series is presented. It is based on interval type-2 fuzzy systems. The proposed method deals with the curve fitting and computational time problems of type-2 fuzzy systems. This approach will significantly reduce the number of type-2 fuzzy rules and simultaneously preserves the fitting quality. The proposed model comprises a parallel interconnection of two type-2 sub-fuzzy models. The first one is the primary model, which represents an ordinary model with a low resolution for the time series under consideration. To overcome resolution quality problem and obtain a model with higher resolution, we introduce the second model called the error model. This model represents the error modelling between the primary model and the real time series model. The error model characterizes the uncertainty in the primary model which can be minimized by a simple subtraction of the error model output from the primary model output. The result is a parallel interconnection between the two sub models. Thus, a unique and entire final model possessing higher resolution is realized. The model's representation and identification are implemented by using type-2 fuzzy auto regressive moving average (T2FARMA) models. Identification is achieved by innovative metaheuristic optimization algorithm such as biogeography-based optimization (BBO). The effectiveness of the method is evaluated by testing the proposed model with the reference time series models. In addition, a detailed comparative study with several reference methods will be presented. The results of the experiments that have been conducted confirm that the proposed method can considerably improve convergence, resolution and computation time.

A simple and efficient method to remove white Gaussian noises and physiological
noises from electrocardiogram (ECG) signals is presented. It is based on simple tools usually
used in digital signal processing like moving average filter, median filter, baseline drift
removal and peak detection. We show by several simulations that the proposed algorithm
outperforms significantly conventional median filter and moving average filter and can be
considered as a valid concurrent to the standard wavelet-based method

aseline wander (BW) is a common low frequency artifact in electrocardiogram (ECG) signals. The prime cause from which BW arises is the patient's breathing and movement. To facilitate reliable visual interpretation of the ECG and to discern particular patterns in the ECG signal, BW needs to be removed. In this paper, a novel BW removal method is presented. The hypothesis is based on the observation that ECG signal variation covaries with its BW. As such, the P, Q, R, S, and T peaks will follow the baseline drift. On this basis, the following proposition is true: a reliable approximation of the baseline drift can be obtained from the shape derived from the interpolation of one form of the ECG signal peak (peak envelope). The simulation was performed by adding artificial BW to ECG signal recordings. The signal-to-noise ratio, mean squared error, and improvement factor criteria were used to numerically evaluate the performance of the proposed approach. The technique was compared to that of the Hilbert vibration decomposition method, an empirical-mode decomposition technique and mathematical morphology. The results of the simulation indicate that the proposed technique is most effective in situations where there is a considerable distortion in the baseline wandering.

In this investigation a novel type-2 fuzzy model for electrophysiological signals is presented. It is based on interval type-2 fuzzy systems. This method can deal with the curve fitting and computational time problems of type-2 fuzzy systems. This approach will significantly reduce the number of type-2 fuzzy rules and simultaneously preserve the fitting quality. The proposed model comprises a parallel interconnection of two type-2 sub-fuzzy models. The first is the primary model, which represents an ordinary model with a low resolution for the electrophysiological signal under consideration, the second is a fuzzy sub-model called the error model, which represents uncertainty in the primary model. Identification is achieved by innovative metaheuristic optimisation algorithms. The method's effectiveness is evaluated through testing on synthetic and real ECG signals. In addition, a detailed comparative study with several benchmark methods will be given. Intensive computer experimentations confirm that the proposed method can significantly improve convergence and resolution.

In this paper an efficient filtering procedure based on the Singular Value Decomposition (SVD) has been proposed. SVD, a high resolution spectrum estimation tools, is used to decompose the ECG data matrix into orthogonal subspaces. Due to the energy-preserving orthogonal transformation in the SVD, these subspaces correspond to the signal and noise components contained in the ECG data. Projection of the data onto the desired subspace eliminates the noise and the unwanted signal components.

The separation of the maternal and fetal Electrocardi-
ograms (ECGs) from skin electrodes located on the mother’s
body can be considered as a blind source separation (BSS)
problem. In this paper, we propose to apply singular value
decomposition (SVD) to separate the maternal and fetal ECG
signals.

In this paper a combination of Extended Kalman Filter (EKF) and a dynamic model of a synthetic electrocardiogram (ECG) for ECG denoising is proposed. Experimental results show that the proposed algorithm is very efficient for the extraction of the ECG signals from noisy data measurements.