Naoual Mouhni - Academia.edu (original) (raw)
Papers by Naoual Mouhni
Lecture notes in networks and systems, 2024
In the past, to answer a user query, we generally e xtract data from one centralized database or ... more In the past, to answer a user query, we generally e xtract data from one centralized database or from m ultiple sources with the same structure. then things have b een changed and we are facing the fact that in some cases, it is necessary to use a set of data sources to provide a complete information. these sources a r physically separated, but they are logically seen a s a single component to the final user. Besides the structure heterogeneity, there is another important point for what specialists are trying to find a so luti n which is the semantic heterogeneity of data sources . In this paper we are going to provide a list of d ifferent approaches that treated the query processing proble m on heterogeneous data sources under different ang les
In the domain of smart health devices, the accurate detection of physical indicators levels play... more In the domain of smart health devices, the accurate detection of physical
indicators levels plays a crucial role in enhancing safety and well-being.
This paper introduces a cross device federated learning framework using
hybrid deep learning model. Specifically, the paper presents a
comprehensive comparison of different combination of long short-term
memory (LSTM), gated recurrent unit (GRU), convolutional neural network
(CNN), random forest (RF), and extreme gradient boosting (XGBoost), in
order to forecast stress levels by utilizing time series information derived
from wearable smart gadgets. The LSTM-RF model demonstrated the
highest level of accuracy, achieving 93.53% for user 1, 99.40% for user 2,
and 97.88% for user 3. Similarly, the LSTM-XGBoost model yielded
favorable outcomes, with accuracy rates of 85.88%, 98.55%, and 92.02% for
users 1, 2, and 3, respectively, out of 23 users studied. These findings
highlight the efficacy of federated learning and the utilization of hybrid
models in stress detection. Unlike traditional centralized learning paradigms,
the presented federated approach ensures privacy preservation and reduces
data transmission requirements by processing data locally on Edge devices.
Electric power components and systems, May 16, 2024
Artificial Life and Robotics, Jan 5, 2024
International journal of intelligence science, 2022
With the increasing of data on the internet, data analysis has become inescapable to gain time an... more With the increasing of data on the internet, data analysis has become inescapable to gain time and efficiency, especially in bibliographic information retrieval systems. We can estimate the number of actual scientific journals points to around 40,000 with about four million articles published each year. Machine learning and deep learning applied to recommender systems had become unavoidable whether in industry or in research. In this current, we propose an optimized interface for bibliographic information retrieval as a running example, which allows different kind of researchers to find their needs following some relevant criteria through natural language understanding. Papers indexed in Web of Science and Scopus are in high demand. Natural language including text and linguistic-based techniques, such as tokenization, named entity recognition, syntactic and semantic analysis, are used to express natural language queries. Our Interface uses association rules to find more related papers for recommendation. Spanning trees are challenged to optimize the search process of the system.
Journal of Emerging Technologies in Web Intelligence, Feb 1, 2014
Business intelligence is based on the existence of main components including : data warehouses .T... more Business intelligence is based on the existence of main components including : data warehouses .The data warehouse is a specialized database which main task is to provide quick access to data in analysis objective. But in some cases it is necessary to use a set of data warehouses to provide a complete information. This structure is what we call federation, and even if the components are physically separated, they are logically seen as a single component. Generally, these items are heterogeneous which make it difficult to create the logical federation schema ,and the execution of user queries a complicated mission. In this paper, we will fill this gap by proposing a model for logical federation schema creation based on ontology, in order to treat different schema types (star , snow flack) including the treatment of hierarchies dimension too.
Energy Reports
Households and buildings have been utilizing the traditional electric network structure for the l... more Households and buildings have been utilizing the traditional electric network structure for the last decade, relying on energy supplied by manufacturing centers based on fossil fuels. Large energy use places a burden on such centers. In this perspective, smart grids are a new technology and a new generation of traditional electric networks that provide increased efficiency, dependability, and energy management based on demand optimization. The importance of smart grids can also be seen in the possibility of integrating communication systems for energy demand forecasting, to provide an optimal management of the combination of renewable energies and production centers energy. The authors present a comparative analysis of several deep learning models, notably Recurrent Neural Network (RNN) architectures such as basic RNN, Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU), in this paper. These architectures are trained and tested on the Smart Grid Smart City (SGSC) project's energy datasets (2010-2014) and assessed using a variety of indicators such as Root Mean Square Error (RMSE), Mean Absolute Error MAE, and R2 scores in order to analyze, compare and ultimately choose the most efficient model. As expected from the literature of RNN architectures, with the lowest value of RMSE error and the highest value of R2 Score among the three architectures, GRU outperformed both of basic RNN and LSTM, this result can be explained by several reasons the most important one is the ability of the GRU model to deal with the vanishing gradient problem and the impact of the number of parameters, used in building such a model, on the same problem.
Ingénierie des systèmes d information
If pandemics kill humans and spread too quickly, misinformation is another scourge that puts peop... more If pandemics kill humans and spread too quickly, misinformation is another scourge that puts people in danger. Health is what a person needs the most in the world to strive for great wealth and a bright future. The novel Coronavirus Disease (COVID-19) outbreak has threatened massively human health in the 21 century (precisely in 2020). The spreading of COVID-19 pandemic press specialists to do more efforts to find a cure. The same reason makes people perform billions of queries on search engines and social networks about comprehending the origin of the virus, the spread mechanisms and existent cures. The virus that causes the pandemic is the new Coronavirus appeared in a unique market in Wuhan, in China in December 2019. This new Coronavirus is named coronavirus (COVID-19). Throughout the ages, mankind has experienced many epidemics, but the distinction of the 21 century is technology development. The spread of misinformation is faster than that of the pandemic. With the advent of big data, we can analyze the huge information shared in a second in social networks and it contains millions of misinformation. In this current, we analyze the belief frequency of misinformation in three languages, English, French and Arabic shared on Twitter users' timelines. Misinformation urges people against vaccination in different ways; many people are spreading misinformation to be famous or make money through views and sharing. Scientists and Journalists are concerned to reduce the likelihood of susceptibility to misinformation by complying with WHO guidance measures in social networks.
Ingénierie des systèmes d information, 2022
Deep Neural networks algorithms are recently used to solve problems in medical imaging like no ti... more Deep Neural networks algorithms are recently used to solve problems in medical imaging like no time ever. However, one of the main challenges for training robust and accurate machine learning algorithms, such as Convolutional neural networks (CNNs) is to find a large dataset, which is, unfortunately, not available for public usage, or it is not available when it comes to a rare disease. Federated Learning (FL) could be a solution to data lack. It can make training and validation through multicenter datasets possible, without compromising the privacy and data protection. In this paper we summarize, discuss, and present an UpToDate overview of FL for medical image analysis solutions and related approaches.
International Journal of Geomate, 2016
In this paper, we discuss the most powerful techniques of tuning parallel/distributed databases. ... more In this paper, we discuss the most powerful techniques of tuning parallel/distributed databases. As in engineering, database tuning becomes an inescapable part of big projects since the conception phase of research projects. The needs of companies including big data have increased to databases optimization. Systems that not take into account the optimization rules become heavy after five years of their production; these reasons were of a paramount of importance to prepare this paper. Indexing is the most suitable way to optimize database systems, further one of the top ways of optimizing index is the application of parallelization. In this paper, we will discuss parallelization and we will practice it with different complex queries and sub-queries using different types of indexes; then we will compare the results gotten from each index. To top it all, the most suitable interference between the major types of index: B*Tree index, Bitmap index, composite parallel index, local parallel index and global parallel index.
Data warehouses are nowadays an important component in every competitive system, it's one of the ... more Data warehouses are nowadays an important component in every competitive system, it's one of the main components on which business intelligence is based. We can even say that many companies are climbing to the next level and use a set of Data warehouses to provide the complete information or it's generally due to fusion of two or many companies. these Data warehouses can be heterogeneous and geographically separated , this structure is what we call federation, and even if the components are physically separated, they are logically seen as a single component. generally, these items are heterogeneous which make it difficult to create the logical federation schema ,and the execution of user queries a complicated mission. In this paper, we will fill this gap by proposing an extension of an existent algorithm in order to treat different schema types (star , snow flack) including the treatment of hierarchies dimension using ontology
2014 Second World Conference on Complex Systems (WCCS), 2014
As in engineering and research, database tuning becomes an inescapable part of big projects since... more As in engineering and research, database tuning becomes an inescapable part of big projects since the conception phase. The needs of companies including big data have increased to databases optimization. A lot of systems cause many blockages at least the five years after the production, because they didn't take into account the optimization and its rules since the conception phase, these reasons were of a paramount of importance to prepare this paper. Indexing is the most suitable way to optimize databases system, further one of the best ways of optimizing the index is the application of parallelization. In this paper we will talk about parallelization, and we will use different query with different types of indexes and we will compare the results gotten from each index, to top it all, the most suitable interference between the major types of index: b*tree index, bitmap index, composite index, local index and global index.
Journal of Software Engineering and Applications, 2014
Cleaning duplicate data is a major problem that persists even though many works have been done to... more Cleaning duplicate data is a major problem that persists even though many works have been done to solve it, due to the exponential growth of data amount treated and the necessity to use scalable and speed algorithms. This problem depends on the type and quality of data, and differs according to the volume of data set manipulated. In this paper we are going to introduce a novel framework based on extended fuzzy C-means algorithm by using topic ontology. This work aims to improve the OLAP querying process over heterogeneous data warehouses that contain big data sets, by improving query results integration, eliminating redundancies by using the extended classification algorithm, and measuring the loss of information.
International Journal of Intelligence Science
With the increasing of data on the internet, data analysis has become inescapable to gain time an... more With the increasing of data on the internet, data analysis has become inescapable to gain time and efficiency, especially in bibliographic information retrieval systems. We can estimate the number of actual scientific journals points to around 40,000 with about four million articles published each year. Machine learning and deep learning applied to recommender systems had become unavoidable whether in industry or in research. In this current, we propose an optimized interface for bibliographic information retrieval as a running example, which allows different kind of researchers to find their needs following some relevant criteria through natural language understanding. Papers indexed in Web of Science and Scopus are in high demand. Natural language including text and linguistic-based techniques, such as tokenization, named entity recognition, syntactic and semantic analysis, are used to express natural language queries. Our Interface uses association rules to find more related papers for recommendation. Spanning trees are challenged to optimize the search process of the system.
Households and buildings have been utilizing the traditional electric network structure for the l... more Households and buildings have been utilizing the traditional electric network structure for the last decade, relying on energy supplied by manufacturing centers based on fossil fuels. Large energy use places a burden on such centers. In this perspective, smart grids are a new technology and a new generation of traditional electric networks that provide increased efficiency, dependability, and energy management based on demand optimization. The importance of smart grids can also be seen in the possibility of integrating communication systems for energy demand forecasting, to provide an optimal management of the combination of renewable energies and production centers energy. The authors present a comparative analysis of several deep learning models, notably Recurrent Neural Network (RNN) architectures such as basic RNN, Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU), in this paper. These architectures are trained and tested on the Smart Grid Smart City (SGSC) project's energy datasets (2010-2014) and assessed using a variety of indicators such as Root Mean Square Error (RMSE), Mean Absolute Error MAE, and R2 scores in order to analyze, compare and ultimately choose the most efficient model. As expected from the literature of RNN architectures, with the lowest value of RMSE error and the highest value of R2 Score among the three architectures, GRU outperformed both of basic RNN and LSTM, this result can be explained by several reasons the most important one is the ability of the GRU model to deal with the vanishing gradient problem and the impact of the number of parameters, used in building such a model, on the same problem.
Lecture notes in networks and systems, 2024
In the past, to answer a user query, we generally e xtract data from one centralized database or ... more In the past, to answer a user query, we generally e xtract data from one centralized database or from m ultiple sources with the same structure. then things have b een changed and we are facing the fact that in some cases, it is necessary to use a set of data sources to provide a complete information. these sources a r physically separated, but they are logically seen a s a single component to the final user. Besides the structure heterogeneity, there is another important point for what specialists are trying to find a so luti n which is the semantic heterogeneity of data sources . In this paper we are going to provide a list of d ifferent approaches that treated the query processing proble m on heterogeneous data sources under different ang les
In the domain of smart health devices, the accurate detection of physical indicators levels play... more In the domain of smart health devices, the accurate detection of physical
indicators levels plays a crucial role in enhancing safety and well-being.
This paper introduces a cross device federated learning framework using
hybrid deep learning model. Specifically, the paper presents a
comprehensive comparison of different combination of long short-term
memory (LSTM), gated recurrent unit (GRU), convolutional neural network
(CNN), random forest (RF), and extreme gradient boosting (XGBoost), in
order to forecast stress levels by utilizing time series information derived
from wearable smart gadgets. The LSTM-RF model demonstrated the
highest level of accuracy, achieving 93.53% for user 1, 99.40% for user 2,
and 97.88% for user 3. Similarly, the LSTM-XGBoost model yielded
favorable outcomes, with accuracy rates of 85.88%, 98.55%, and 92.02% for
users 1, 2, and 3, respectively, out of 23 users studied. These findings
highlight the efficacy of federated learning and the utilization of hybrid
models in stress detection. Unlike traditional centralized learning paradigms,
the presented federated approach ensures privacy preservation and reduces
data transmission requirements by processing data locally on Edge devices.
Electric power components and systems, May 16, 2024
Artificial Life and Robotics, Jan 5, 2024
International journal of intelligence science, 2022
With the increasing of data on the internet, data analysis has become inescapable to gain time an... more With the increasing of data on the internet, data analysis has become inescapable to gain time and efficiency, especially in bibliographic information retrieval systems. We can estimate the number of actual scientific journals points to around 40,000 with about four million articles published each year. Machine learning and deep learning applied to recommender systems had become unavoidable whether in industry or in research. In this current, we propose an optimized interface for bibliographic information retrieval as a running example, which allows different kind of researchers to find their needs following some relevant criteria through natural language understanding. Papers indexed in Web of Science and Scopus are in high demand. Natural language including text and linguistic-based techniques, such as tokenization, named entity recognition, syntactic and semantic analysis, are used to express natural language queries. Our Interface uses association rules to find more related papers for recommendation. Spanning trees are challenged to optimize the search process of the system.
Journal of Emerging Technologies in Web Intelligence, Feb 1, 2014
Business intelligence is based on the existence of main components including : data warehouses .T... more Business intelligence is based on the existence of main components including : data warehouses .The data warehouse is a specialized database which main task is to provide quick access to data in analysis objective. But in some cases it is necessary to use a set of data warehouses to provide a complete information. This structure is what we call federation, and even if the components are physically separated, they are logically seen as a single component. Generally, these items are heterogeneous which make it difficult to create the logical federation schema ,and the execution of user queries a complicated mission. In this paper, we will fill this gap by proposing a model for logical federation schema creation based on ontology, in order to treat different schema types (star , snow flack) including the treatment of hierarchies dimension too.
Energy Reports
Households and buildings have been utilizing the traditional electric network structure for the l... more Households and buildings have been utilizing the traditional electric network structure for the last decade, relying on energy supplied by manufacturing centers based on fossil fuels. Large energy use places a burden on such centers. In this perspective, smart grids are a new technology and a new generation of traditional electric networks that provide increased efficiency, dependability, and energy management based on demand optimization. The importance of smart grids can also be seen in the possibility of integrating communication systems for energy demand forecasting, to provide an optimal management of the combination of renewable energies and production centers energy. The authors present a comparative analysis of several deep learning models, notably Recurrent Neural Network (RNN) architectures such as basic RNN, Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU), in this paper. These architectures are trained and tested on the Smart Grid Smart City (SGSC) project's energy datasets (2010-2014) and assessed using a variety of indicators such as Root Mean Square Error (RMSE), Mean Absolute Error MAE, and R2 scores in order to analyze, compare and ultimately choose the most efficient model. As expected from the literature of RNN architectures, with the lowest value of RMSE error and the highest value of R2 Score among the three architectures, GRU outperformed both of basic RNN and LSTM, this result can be explained by several reasons the most important one is the ability of the GRU model to deal with the vanishing gradient problem and the impact of the number of parameters, used in building such a model, on the same problem.
Ingénierie des systèmes d information
If pandemics kill humans and spread too quickly, misinformation is another scourge that puts peop... more If pandemics kill humans and spread too quickly, misinformation is another scourge that puts people in danger. Health is what a person needs the most in the world to strive for great wealth and a bright future. The novel Coronavirus Disease (COVID-19) outbreak has threatened massively human health in the 21 century (precisely in 2020). The spreading of COVID-19 pandemic press specialists to do more efforts to find a cure. The same reason makes people perform billions of queries on search engines and social networks about comprehending the origin of the virus, the spread mechanisms and existent cures. The virus that causes the pandemic is the new Coronavirus appeared in a unique market in Wuhan, in China in December 2019. This new Coronavirus is named coronavirus (COVID-19). Throughout the ages, mankind has experienced many epidemics, but the distinction of the 21 century is technology development. The spread of misinformation is faster than that of the pandemic. With the advent of big data, we can analyze the huge information shared in a second in social networks and it contains millions of misinformation. In this current, we analyze the belief frequency of misinformation in three languages, English, French and Arabic shared on Twitter users' timelines. Misinformation urges people against vaccination in different ways; many people are spreading misinformation to be famous or make money through views and sharing. Scientists and Journalists are concerned to reduce the likelihood of susceptibility to misinformation by complying with WHO guidance measures in social networks.
Ingénierie des systèmes d information, 2022
Deep Neural networks algorithms are recently used to solve problems in medical imaging like no ti... more Deep Neural networks algorithms are recently used to solve problems in medical imaging like no time ever. However, one of the main challenges for training robust and accurate machine learning algorithms, such as Convolutional neural networks (CNNs) is to find a large dataset, which is, unfortunately, not available for public usage, or it is not available when it comes to a rare disease. Federated Learning (FL) could be a solution to data lack. It can make training and validation through multicenter datasets possible, without compromising the privacy and data protection. In this paper we summarize, discuss, and present an UpToDate overview of FL for medical image analysis solutions and related approaches.
International Journal of Geomate, 2016
In this paper, we discuss the most powerful techniques of tuning parallel/distributed databases. ... more In this paper, we discuss the most powerful techniques of tuning parallel/distributed databases. As in engineering, database tuning becomes an inescapable part of big projects since the conception phase of research projects. The needs of companies including big data have increased to databases optimization. Systems that not take into account the optimization rules become heavy after five years of their production; these reasons were of a paramount of importance to prepare this paper. Indexing is the most suitable way to optimize database systems, further one of the top ways of optimizing index is the application of parallelization. In this paper, we will discuss parallelization and we will practice it with different complex queries and sub-queries using different types of indexes; then we will compare the results gotten from each index. To top it all, the most suitable interference between the major types of index: B*Tree index, Bitmap index, composite parallel index, local parallel index and global parallel index.
Data warehouses are nowadays an important component in every competitive system, it's one of the ... more Data warehouses are nowadays an important component in every competitive system, it's one of the main components on which business intelligence is based. We can even say that many companies are climbing to the next level and use a set of Data warehouses to provide the complete information or it's generally due to fusion of two or many companies. these Data warehouses can be heterogeneous and geographically separated , this structure is what we call federation, and even if the components are physically separated, they are logically seen as a single component. generally, these items are heterogeneous which make it difficult to create the logical federation schema ,and the execution of user queries a complicated mission. In this paper, we will fill this gap by proposing an extension of an existent algorithm in order to treat different schema types (star , snow flack) including the treatment of hierarchies dimension using ontology
2014 Second World Conference on Complex Systems (WCCS), 2014
As in engineering and research, database tuning becomes an inescapable part of big projects since... more As in engineering and research, database tuning becomes an inescapable part of big projects since the conception phase. The needs of companies including big data have increased to databases optimization. A lot of systems cause many blockages at least the five years after the production, because they didn't take into account the optimization and its rules since the conception phase, these reasons were of a paramount of importance to prepare this paper. Indexing is the most suitable way to optimize databases system, further one of the best ways of optimizing the index is the application of parallelization. In this paper we will talk about parallelization, and we will use different query with different types of indexes and we will compare the results gotten from each index, to top it all, the most suitable interference between the major types of index: b*tree index, bitmap index, composite index, local index and global index.
Journal of Software Engineering and Applications, 2014
Cleaning duplicate data is a major problem that persists even though many works have been done to... more Cleaning duplicate data is a major problem that persists even though many works have been done to solve it, due to the exponential growth of data amount treated and the necessity to use scalable and speed algorithms. This problem depends on the type and quality of data, and differs according to the volume of data set manipulated. In this paper we are going to introduce a novel framework based on extended fuzzy C-means algorithm by using topic ontology. This work aims to improve the OLAP querying process over heterogeneous data warehouses that contain big data sets, by improving query results integration, eliminating redundancies by using the extended classification algorithm, and measuring the loss of information.
International Journal of Intelligence Science
With the increasing of data on the internet, data analysis has become inescapable to gain time an... more With the increasing of data on the internet, data analysis has become inescapable to gain time and efficiency, especially in bibliographic information retrieval systems. We can estimate the number of actual scientific journals points to around 40,000 with about four million articles published each year. Machine learning and deep learning applied to recommender systems had become unavoidable whether in industry or in research. In this current, we propose an optimized interface for bibliographic information retrieval as a running example, which allows different kind of researchers to find their needs following some relevant criteria through natural language understanding. Papers indexed in Web of Science and Scopus are in high demand. Natural language including text and linguistic-based techniques, such as tokenization, named entity recognition, syntactic and semantic analysis, are used to express natural language queries. Our Interface uses association rules to find more related papers for recommendation. Spanning trees are challenged to optimize the search process of the system.
Households and buildings have been utilizing the traditional electric network structure for the l... more Households and buildings have been utilizing the traditional electric network structure for the last decade, relying on energy supplied by manufacturing centers based on fossil fuels. Large energy use places a burden on such centers. In this perspective, smart grids are a new technology and a new generation of traditional electric networks that provide increased efficiency, dependability, and energy management based on demand optimization. The importance of smart grids can also be seen in the possibility of integrating communication systems for energy demand forecasting, to provide an optimal management of the combination of renewable energies and production centers energy. The authors present a comparative analysis of several deep learning models, notably Recurrent Neural Network (RNN) architectures such as basic RNN, Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU), in this paper. These architectures are trained and tested on the Smart Grid Smart City (SGSC) project's energy datasets (2010-2014) and assessed using a variety of indicators such as Root Mean Square Error (RMSE), Mean Absolute Error MAE, and R2 scores in order to analyze, compare and ultimately choose the most efficient model. As expected from the literature of RNN architectures, with the lowest value of RMSE error and the highest value of R2 Score among the three architectures, GRU outperformed both of basic RNN and LSTM, this result can be explained by several reasons the most important one is the ability of the GRU model to deal with the vanishing gradient problem and the impact of the number of parameters, used in building such a model, on the same problem.