The paper focuses on the importance of prompt and efficient process fault detection in contempora... more The paper focuses on the importance of prompt and efficient process fault detection in contemporary manufacturing industries, where product quality and safety protocols are critical. The study compares the efficiencies of two techniques for process fault detection: Kernel Principal Component Analysis (KPCA) and the observer method. Both techniques are applied to observe water volume variation within a hydraulic system comprising three tanks. PCA is an unsupervised learning technique used for dimensionality reduction and pattern recognition. It is an extension of Principal Component Analysis (PCA) that utilizes kernel functions to transform data into higher-dimensional spaces, where it becomes easier to separate classes or identify patterns. In this paper, KPCA is applied to detect faults in the hydraulic system by analyzing the variation in water volume. The observer method originates from control theory and is utilized to estimate the internal states of a system based on its output...
This research investigates the potential benefits of integrating machine learning algorithms and ... more This research investigates the potential benefits of integrating machine learning algorithms and IoT sensors in modern agriculture. The focus is on optimizing crop production and reducing waste through informed decisions about planting, watering, and harvesting crops. The paper discusses the current state of machine learning and IoT in agriculture, highlighting key challenges and opportunities. It also presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, including real-time data from IoT sensors, farmers can make more informed verdicts about factors that affect crop growth. Eventually, the integration of these technologies can transform modern agriculture by increasing crop yields while minimizing waste. In our studies, we achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99. 46% using Naïve Bayes Classifier...
The American Journal of Tropical Medicine and Hygiene, 2021
A 3-year analysis released in August 2021 by the WHO indicated that more than 700 healthcare work... more A 3-year analysis released in August 2021 by the WHO indicated that more than 700 healthcare workers and patients have died (2,000 injured) as a result of attacks against health facilities since 2017. The COVID-19 pandemic has made the risks even worse for doctors, nurses, and support staff, unfortunately. According to the latest figures from the International Committee of the Red Cross, 848 COVID-19-related violent incidents were recorded in 2020, and this is likely an underrepresentation of a much more widespread phenomenon. In response to rises in COVID-19-related attacks against healthcare, some countries have taken action. In Algeria, for instance, the penal code was amended to increase protection for healthcare workers against attacks and to punish individuals who damage health facilities. In the United Kingdom, the police, crime, sentencing, and courts bill proposed increased the maximum penalty from 12 months to 2 years in prison for anyone who assaults an emergency worker. ...
In the Data Warehouse (DW) technology, On-line Analytical Processing (OLAP) is a good application... more In the Data Warehouse (DW) technology, On-line Analytical Processing (OLAP) is a good applications package that empowers decision makers to explore and navigate into a multidimensional structure of precomputed measures, which is referred to as a Data Cube. Though, OLAP is poorly equipped for forecasting and predicting empty measures of data cubes. Usually, empty measures translate inexistent facts in the DW and in most cases are a source of frustration for enterprise managements, especially when strategic decisions need to be taken. In the recent years, various studies have tried to add prediction capabilities to OLAP applications. For this purpose, generally, Data Mining and Machine Learning methods have been widely used to predict new measures' values in DWs. In this paper, we introduce a novel approach attempting to extend OLAP to a prediction application. Our approach operates in two main stages. The first one is a preprocessing one that makes use of the Principal Component Analysis (PCA) to reduce the dimensionality of the data cube and then generates ad hoc training sets. The second stage proposes a novel OLAP oriented architecture of Multilayer Perceptron Networks (MLP) that learns from each training set and comes out with predicted measures of inexistent facts. Carried out experiments demonstrate the effectiveness of our proposal and the performance of its predictive capabilities.
Les outils de l'analyse en ligne (OLAP) permettent a l'utilisateur de realiser des tâches... more Les outils de l'analyse en ligne (OLAP) permettent a l'utilisateur de realiser des tâches exploratoires dans les cubes de donnees. Cependant, ils n'offrent aucun moyen pour la prediction ou l'explication des faits. En vue de renforcer le processus de l'aide a la decision, plusieurs travaux ont propose l'extension de l'analyse en ligne a des capacites plus avancees. Dans cet article, nous proposons une nouvelle approche d'extension de l'analyse en ligne a des capacites de prediction a deux phases. La premiere est une phase de reduction des dimensions des cubes de donnees, qui repose sur l'analyse en composantes principales (ACP). La deuxieme est une phase de prediction dans laquelle nous introduisons une nouvelle architecture de perceptrons multicouches (PMC). Notre etude experimentale a montre une capacite de prediction prometteuse, ainsi qu'une bonne robustesse dans le cas d'un cube epars
On-line Analytical Processing (OLAP) represents a good applications package to explore and naviga... more On-line Analytical Processing (OLAP) represents a good applications package to explore and navigate into data cubes. Though, it is limited to exploratory tasks. It does not assist the decision maker in performing information investigation. Thus, various studies have been trying to extend OLAP to new capabilities by coupling it with data mining algorithms. Our current proposal stands within this trend. It has two major contributions. First, a Multi-perspectives Cube Exploration Framework (MCEF) is introduced. It is a generalized framework designed to assist the application of classical data mining algorithm on OLAP cubes. Second, a Neural Approach for Prediction over High-dimensional Cubes (NAP-HC) is also introduced, which extends Modular Neural Networks (MNN)s architecture to multidimensional context of OLAP cubes, to predict non-existent measures. A preprocessing stage is embedded in NAP-HC to assist it in facing up the challenges arising from the particu-larity of OLAP cubes. It consists of an OLAP oriented cube exploration strategy coupled with a dimensions reduction step that reposes on the Principal Component Analysis (PCA). Carried out experiments highlight the efficiency of MCEF in assisting the application of MNNs on OLAP cubes and the high predictive capabilities of NAP-HC.
OLAP techniques provide efficient solutions to navigate through
data cubes. However, they are not... more OLAP techniques provide efficient solutions to navigate through data cubes. However, they are not equipped with frameworks that empower user investigation of interesting information. They are restricted to exploration tasks. Recently, various studies have been trying to extend OLAP to new capabilities by coupling it with data mining algorithms. However, most of these algorithms are not designed to deal with sparsity, which is an unavoidable consequence of the multidimensional structure of OLAP cubes. In [1], we proposed a novel approach that embeds Multilayer Perceptrons into OLAP environment to extend it to prediction. This approach has largely met its goals with limited sparsity cubes. However, its performances have decreased progressively with the increase of cube sparsity. In this paper, we propose a substantially modified version of our previous approach called NAP-SC (Neural Approach for Prediction over Sparse Cubes). Its main contribution consists in minimizing sparsity effect on measures prediction process through the application of a cube transformation step, based on a dedicated aggregation technique. Carried out experiments demonstrate the effectiveness and the robustness of NAP-SC against high sparsity data cubes.
In the Data Warehouse (DW) technology, On-line Analytical Processing (OLAP) is a good application... more In the Data Warehouse (DW) technology, On-line Analytical Processing (OLAP) is a good applications package that empowers decision makers to explore and navigate into a multidimensional structure of precomputed measures, which is referred to as a Data Cube. Though, OLAP is poorly equipped for forecasting and predicting empty measures of data cubes. Usually, empty measures translate inexistent facts in the DW and in most cases are a source of frustration for enterprise managements , especially when strategic decisions need to be taken. In the recent years, various studies have tried to add prediction capabilities to OLAP applications. For this purpose, generally, Data Mining and Machine Learning methods have been widely used to predict new measures' values in DWs. In this paper, we introduce a novel approach attempting to extend OLAP to a prediction application. Our approach operates in two main stages. The first one is a preprocessing one that makes use of the Principal Component Analysis (PCA) to reduce the dimensionality of the data cube and then generates ad hoc training sets. The second stage proposes a novel OLAP oriented architecture of Multilayer Per-ceptron Networks (MLP) that learns from each training set and comes out with predicted measures of inexistent facts. Carried out experiments demonstrate the effectiveness of our proposal and the performance of its predictive capabilities.
The paper focuses on the importance of prompt and efficient process fault detection in contempora... more The paper focuses on the importance of prompt and efficient process fault detection in contemporary manufacturing industries, where product quality and safety protocols are critical. The study compares the efficiencies of two techniques for process fault detection: Kernel Principal Component Analysis (KPCA) and the observer method. Both techniques are applied to observe water volume variation within a hydraulic system comprising three tanks. PCA is an unsupervised learning technique used for dimensionality reduction and pattern recognition. It is an extension of Principal Component Analysis (PCA) that utilizes kernel functions to transform data into higher-dimensional spaces, where it becomes easier to separate classes or identify patterns. In this paper, KPCA is applied to detect faults in the hydraulic system by analyzing the variation in water volume. The observer method originates from control theory and is utilized to estimate the internal states of a system based on its output...
This research investigates the potential benefits of integrating machine learning algorithms and ... more This research investigates the potential benefits of integrating machine learning algorithms and IoT sensors in modern agriculture. The focus is on optimizing crop production and reducing waste through informed decisions about planting, watering, and harvesting crops. The paper discusses the current state of machine learning and IoT in agriculture, highlighting key challenges and opportunities. It also presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, including real-time data from IoT sensors, farmers can make more informed verdicts about factors that affect crop growth. Eventually, the integration of these technologies can transform modern agriculture by increasing crop yields while minimizing waste. In our studies, we achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99. 46% using Naïve Bayes Classifier...
The American Journal of Tropical Medicine and Hygiene, 2021
A 3-year analysis released in August 2021 by the WHO indicated that more than 700 healthcare work... more A 3-year analysis released in August 2021 by the WHO indicated that more than 700 healthcare workers and patients have died (2,000 injured) as a result of attacks against health facilities since 2017. The COVID-19 pandemic has made the risks even worse for doctors, nurses, and support staff, unfortunately. According to the latest figures from the International Committee of the Red Cross, 848 COVID-19-related violent incidents were recorded in 2020, and this is likely an underrepresentation of a much more widespread phenomenon. In response to rises in COVID-19-related attacks against healthcare, some countries have taken action. In Algeria, for instance, the penal code was amended to increase protection for healthcare workers against attacks and to punish individuals who damage health facilities. In the United Kingdom, the police, crime, sentencing, and courts bill proposed increased the maximum penalty from 12 months to 2 years in prison for anyone who assaults an emergency worker. ...
In the Data Warehouse (DW) technology, On-line Analytical Processing (OLAP) is a good application... more In the Data Warehouse (DW) technology, On-line Analytical Processing (OLAP) is a good applications package that empowers decision makers to explore and navigate into a multidimensional structure of precomputed measures, which is referred to as a Data Cube. Though, OLAP is poorly equipped for forecasting and predicting empty measures of data cubes. Usually, empty measures translate inexistent facts in the DW and in most cases are a source of frustration for enterprise managements, especially when strategic decisions need to be taken. In the recent years, various studies have tried to add prediction capabilities to OLAP applications. For this purpose, generally, Data Mining and Machine Learning methods have been widely used to predict new measures' values in DWs. In this paper, we introduce a novel approach attempting to extend OLAP to a prediction application. Our approach operates in two main stages. The first one is a preprocessing one that makes use of the Principal Component Analysis (PCA) to reduce the dimensionality of the data cube and then generates ad hoc training sets. The second stage proposes a novel OLAP oriented architecture of Multilayer Perceptron Networks (MLP) that learns from each training set and comes out with predicted measures of inexistent facts. Carried out experiments demonstrate the effectiveness of our proposal and the performance of its predictive capabilities.
Les outils de l'analyse en ligne (OLAP) permettent a l'utilisateur de realiser des tâches... more Les outils de l'analyse en ligne (OLAP) permettent a l'utilisateur de realiser des tâches exploratoires dans les cubes de donnees. Cependant, ils n'offrent aucun moyen pour la prediction ou l'explication des faits. En vue de renforcer le processus de l'aide a la decision, plusieurs travaux ont propose l'extension de l'analyse en ligne a des capacites plus avancees. Dans cet article, nous proposons une nouvelle approche d'extension de l'analyse en ligne a des capacites de prediction a deux phases. La premiere est une phase de reduction des dimensions des cubes de donnees, qui repose sur l'analyse en composantes principales (ACP). La deuxieme est une phase de prediction dans laquelle nous introduisons une nouvelle architecture de perceptrons multicouches (PMC). Notre etude experimentale a montre une capacite de prediction prometteuse, ainsi qu'une bonne robustesse dans le cas d'un cube epars
On-line Analytical Processing (OLAP) represents a good applications package to explore and naviga... more On-line Analytical Processing (OLAP) represents a good applications package to explore and navigate into data cubes. Though, it is limited to exploratory tasks. It does not assist the decision maker in performing information investigation. Thus, various studies have been trying to extend OLAP to new capabilities by coupling it with data mining algorithms. Our current proposal stands within this trend. It has two major contributions. First, a Multi-perspectives Cube Exploration Framework (MCEF) is introduced. It is a generalized framework designed to assist the application of classical data mining algorithm on OLAP cubes. Second, a Neural Approach for Prediction over High-dimensional Cubes (NAP-HC) is also introduced, which extends Modular Neural Networks (MNN)s architecture to multidimensional context of OLAP cubes, to predict non-existent measures. A preprocessing stage is embedded in NAP-HC to assist it in facing up the challenges arising from the particu-larity of OLAP cubes. It consists of an OLAP oriented cube exploration strategy coupled with a dimensions reduction step that reposes on the Principal Component Analysis (PCA). Carried out experiments highlight the efficiency of MCEF in assisting the application of MNNs on OLAP cubes and the high predictive capabilities of NAP-HC.
OLAP techniques provide efficient solutions to navigate through
data cubes. However, they are not... more OLAP techniques provide efficient solutions to navigate through data cubes. However, they are not equipped with frameworks that empower user investigation of interesting information. They are restricted to exploration tasks. Recently, various studies have been trying to extend OLAP to new capabilities by coupling it with data mining algorithms. However, most of these algorithms are not designed to deal with sparsity, which is an unavoidable consequence of the multidimensional structure of OLAP cubes. In [1], we proposed a novel approach that embeds Multilayer Perceptrons into OLAP environment to extend it to prediction. This approach has largely met its goals with limited sparsity cubes. However, its performances have decreased progressively with the increase of cube sparsity. In this paper, we propose a substantially modified version of our previous approach called NAP-SC (Neural Approach for Prediction over Sparse Cubes). Its main contribution consists in minimizing sparsity effect on measures prediction process through the application of a cube transformation step, based on a dedicated aggregation technique. Carried out experiments demonstrate the effectiveness and the robustness of NAP-SC against high sparsity data cubes.
In the Data Warehouse (DW) technology, On-line Analytical Processing (OLAP) is a good application... more In the Data Warehouse (DW) technology, On-line Analytical Processing (OLAP) is a good applications package that empowers decision makers to explore and navigate into a multidimensional structure of precomputed measures, which is referred to as a Data Cube. Though, OLAP is poorly equipped for forecasting and predicting empty measures of data cubes. Usually, empty measures translate inexistent facts in the DW and in most cases are a source of frustration for enterprise managements , especially when strategic decisions need to be taken. In the recent years, various studies have tried to add prediction capabilities to OLAP applications. For this purpose, generally, Data Mining and Machine Learning methods have been widely used to predict new measures' values in DWs. In this paper, we introduce a novel approach attempting to extend OLAP to a prediction application. Our approach operates in two main stages. The first one is a preprocessing one that makes use of the Principal Component Analysis (PCA) to reduce the dimensionality of the data cube and then generates ad hoc training sets. The second stage proposes a novel OLAP oriented architecture of Multilayer Per-ceptron Networks (MLP) that learns from each training set and comes out with predicted measures of inexistent facts. Carried out experiments demonstrate the effectiveness of our proposal and the performance of its predictive capabilities.
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Papers by Wiem Abdelbaki
réaliser des tâches exploratoires dans les cubes de données. Cependant, ils n’offrent
aucun moyen pour la prédiction ou l’explication des faits. En vue de renforcer
le processus de l’aide à la décision, plusieurs travaux ont proposé l’extension
de l’analyse en ligne à des capacités plus avancées. Dans cet article, nous pro-
posons une nouvelle approche d’extension de l’analyse en ligne à des capacités
de prédiction à deux phases. La première est une phase de réduction des dimen-
sions des cubes de données, qui repose sur l’analyse en composantes principales
(ACP). La deuxième est une phase de prédiction dans laquelle nous introdui-
sons une nouvelle architecture de percéptrons multicouches (PMC). Notre étude
expérimentale a montré une capacité de prédiction prometteuse, ainsi qu’une
bonne robustesse dans le cas d’un cube épars.
data cubes. However, they are not equipped with frameworks that empower
user investigation of interesting information. They are restricted
to exploration tasks.
Recently, various studies have been trying to extend OLAP to new capabilities
by coupling it with data mining algorithms. However, most of
these algorithms are not designed to deal with sparsity, which is an unavoidable
consequence of the multidimensional structure of OLAP cubes.
In [1], we proposed a novel approach that embeds Multilayer Perceptrons
into OLAP environment to extend it to prediction. This approach has
largely met its goals with limited sparsity cubes. However, its performances
have decreased progressively with the increase of cube sparsity.
In this paper, we propose a substantially modified version of our previous
approach called NAP-SC (Neural Approach for Prediction over Sparse
Cubes). Its main contribution consists in minimizing sparsity effect on
measures prediction process through the application of a cube transformation
step, based on a dedicated aggregation technique.
Carried out experiments demonstrate the effectiveness and the robustness
of NAP-SC against high sparsity data cubes.
Other by Wiem Abdelbaki
réaliser des tâches exploratoires dans les cubes de données. Cependant, ils n’offrent
aucun moyen pour la prédiction ou l’explication des faits. En vue de renforcer
le processus de l’aide à la décision, plusieurs travaux ont proposé l’extension
de l’analyse en ligne à des capacités plus avancées. Dans cet article, nous pro-
posons une nouvelle approche d’extension de l’analyse en ligne à des capacités
de prédiction à deux phases. La première est une phase de réduction des dimen-
sions des cubes de données, qui repose sur l’analyse en composantes principales
(ACP). La deuxième est une phase de prédiction dans laquelle nous introdui-
sons une nouvelle architecture de percéptrons multicouches (PMC). Notre étude
expérimentale a montré une capacité de prédiction prometteuse, ainsi qu’une
bonne robustesse dans le cas d’un cube épars.
data cubes. However, they are not equipped with frameworks that empower
user investigation of interesting information. They are restricted
to exploration tasks.
Recently, various studies have been trying to extend OLAP to new capabilities
by coupling it with data mining algorithms. However, most of
these algorithms are not designed to deal with sparsity, which is an unavoidable
consequence of the multidimensional structure of OLAP cubes.
In [1], we proposed a novel approach that embeds Multilayer Perceptrons
into OLAP environment to extend it to prediction. This approach has
largely met its goals with limited sparsity cubes. However, its performances
have decreased progressively with the increase of cube sparsity.
In this paper, we propose a substantially modified version of our previous
approach called NAP-SC (Neural Approach for Prediction over Sparse
Cubes). Its main contribution consists in minimizing sparsity effect on
measures prediction process through the application of a cube transformation
step, based on a dedicated aggregation technique.
Carried out experiments demonstrate the effectiveness and the robustness
of NAP-SC against high sparsity data cubes.