2018
Karech T, Benseghir A, Bouzid T.
The Behavior of Dam Foundation Reinforced by Stone Columns: Case Study of Kissir Dam-Jijel. International Journal of Civil and Environmental EngineeringInternational Journal of Civil and Environmental Engineering. 2018;11 :1187-1191.
Karech T, Benseghir A, Bouzid T.
The Behavior of Dam Foundation Reinforced by Stone Columns: Case Study of Kissir Dam-Jijel. International Journal of Civil and Environmental EngineeringInternational Journal of Civil and Environmental Engineering. 2018;11 :1187-1191.
Karech T, Benseghir A, Bouzid T.
The Behavior of Dam Foundation Reinforced by Stone Columns: Case Study of Kissir Dam-Jijel. International Journal of Civil and Environmental EngineeringInternational Journal of Civil and Environmental Engineering. 2018;11 :1187-1191.
Mechouma R, Mebarki H, Azoui B.
Behavior of nine levels NPC three-phase inverter topology interfacing photovoltaic system to the medium electric grid under variable irradiance. Springer-Verlag GmbH Germany, part of Springer Nature 2018, Electrical EngineeringSpringer-Verlag GmbH Germany, part of Springer Nature 2018, Electrical Engineering. 2018.
Mechouma R, Mebarki H, Azoui B.
Behavior of nine levels NPC three-phase inverter topology interfacing photovoltaic system to the medium electric grid under variable irradiance. Springer-Verlag GmbH Germany, part of Springer Nature 2018, Electrical EngineeringSpringer-Verlag GmbH Germany, part of Springer Nature 2018, Electrical Engineering. 2018.
Mechouma R, Mebarki H, Azoui B.
Behavior of nine levels NPC three-phase inverter topology interfacing photovoltaic system to the medium electric grid under variable irradiance. Springer-Verlag GmbH Germany, part of Springer Nature 2018, Electrical EngineeringSpringer-Verlag GmbH Germany, part of Springer Nature 2018, Electrical Engineering. 2018.
Khedidja A, Boudoukha A, Djenba S.
Bibliographic information. 2018.
Khedidja A, Boudoukha A, Djenba S.
Bibliographic information. 2018.
Khedidja A, Boudoukha A, Djenba S.
Bibliographic information. 2018.
Zerari N, Abdelhamid S, Bouzgou H, Raymond C.
Bi-directional recurrent end-to-end neural network classifier for spoken Arab digit recognition. 2018 2nd International Conference on Natural Language and Speech Processing (ICNLSP). 2018 :1-6.
Naima Z, Abdelhamid S, Bouzgou H, Raymond C.
Bi-directional Recurrent End-to-End Neural Network Classifier for Spoken Arab Digit Recognition. 2nd International Conference on Natural Language and Speech Processing (ICNLSP) [Internet]. 2018.
Publisher's VersionAbstractAutomatic Speech Recognition can be considered as a transcription of spoken utterances into text which can be used to monitor/command a specific system. In this paper, we propose a general end-to-end approach to sequence learning that uses Long Short-Term Memory (LSTM) to deal with the non-uniform sequence length of the speech utterances. The neural architecture can recognize the Arabic spoken digit spelling of an isolated Arabic word using a classification methodology, with the aim to enable natural human-machine interaction. The proposed system consists to, first, extract the relevant features from the input speech signal using Mel Frequency Cepstral Coefficients (MFCC) and then these features are processed by a deep neural network able to deal with the non uniformity of the sequences length. A recurrent LSTM or GRU architecture is used to encode sequences of MFCC features as a fixed size vector that will feed a multilayer perceptron network to perform the classification. The whole neural network classifier is trained in an end-to-end manner. The proposed system outperforms by a large gap the previous published results on the same database.
Zerari N, Abdelhamid S, Bouzgou H, Raymond C.
Bi-directional recurrent end-to-end neural network classifier for spoken Arab digit recognition. 2018 2nd International Conference on Natural Language and Speech Processing (ICNLSP). 2018 :1-6.
Naima Z, Abdelhamid S, Bouzgou H, Raymond C.
Bi-directional Recurrent End-to-End Neural Network Classifier for Spoken Arab Digit Recognition. 2nd International Conference on Natural Language and Speech Processing (ICNLSP) [Internet]. 2018.
Publisher's VersionAbstractAutomatic Speech Recognition can be considered as a transcription of spoken utterances into text which can be used to monitor/command a specific system. In this paper, we propose a general end-to-end approach to sequence learning that uses Long Short-Term Memory (LSTM) to deal with the non-uniform sequence length of the speech utterances. The neural architecture can recognize the Arabic spoken digit spelling of an isolated Arabic word using a classification methodology, with the aim to enable natural human-machine interaction. The proposed system consists to, first, extract the relevant features from the input speech signal using Mel Frequency Cepstral Coefficients (MFCC) and then these features are processed by a deep neural network able to deal with the non uniformity of the sequences length. A recurrent LSTM or GRU architecture is used to encode sequences of MFCC features as a fixed size vector that will feed a multilayer perceptron network to perform the classification. The whole neural network classifier is trained in an end-to-end manner. The proposed system outperforms by a large gap the previous published results on the same database.
Zerari N, Abdelhamid S, Bouzgou H, Raymond C.
Bi-directional recurrent end-to-end neural network classifier for spoken Arab digit recognition. 2018 2nd International Conference on Natural Language and Speech Processing (ICNLSP). 2018 :1-6.
Naima Z, Abdelhamid S, Bouzgou H, Raymond C.
Bi-directional Recurrent End-to-End Neural Network Classifier for Spoken Arab Digit Recognition. 2nd International Conference on Natural Language and Speech Processing (ICNLSP) [Internet]. 2018.
Publisher's VersionAbstractAutomatic Speech Recognition can be considered as a transcription of spoken utterances into text which can be used to monitor/command a specific system. In this paper, we propose a general end-to-end approach to sequence learning that uses Long Short-Term Memory (LSTM) to deal with the non-uniform sequence length of the speech utterances. The neural architecture can recognize the Arabic spoken digit spelling of an isolated Arabic word using a classification methodology, with the aim to enable natural human-machine interaction. The proposed system consists to, first, extract the relevant features from the input speech signal using Mel Frequency Cepstral Coefficients (MFCC) and then these features are processed by a deep neural network able to deal with the non uniformity of the sequences length. A recurrent LSTM or GRU architecture is used to encode sequences of MFCC features as a fixed size vector that will feed a multilayer perceptron network to perform the classification. The whole neural network classifier is trained in an end-to-end manner. The proposed system outperforms by a large gap the previous published results on the same database.
Naima Z, Abdelhamid S, Bouzgou H, Raymond C.
Bi-directional Recurrent End-to-End Neural Network Classifier for Spoken Arab Digit Recognition. 2nd International Conference on Natural Language and Speech Processing (ICNLSP) [Internet]. 2018.
Publisher's VersionAbstractAutomatic Speech Recognition can be considered as a transcription of spoken utterances into text which can be used to monitor/command a specific system. In this paper, we propose a general end-to-end approach to sequence learning that uses Long Short-Term Memory (LSTM) to deal with the non-uniform sequence length of the speech utterances. The neural architecture can recognize the Arabic spoken digit spelling of an isolated Arabic word using a classification methodology, with the aim to enable natural human-machine interaction. The proposed system consists to, first, extract the relevant features from the input speech signal using Mel Frequency Cepstral Coefficients (MFCC) and then these features are processed by a deep neural network able to deal with the non uniformity of the sequences length. A recurrent LSTM or GRU architecture is used to encode sequences of MFCC features as a fixed size vector that will feed a multilayer perceptron network to perform the classification. The whole neural network classifier is trained in an end-to-end manner. The proposed system outperforms by a large gap the previous published results on the same database.
Zerari N, Abdelhamid S, Bouzgou H, Raymond C.
Bi-directional recurrent end-to-end neural network classifier for spoken Arab digit recognition. 2018 2nd International Conference on Natural Language and Speech Processing (ICNLSP). 2018 :1-6.
Boubiche S, Boubiche DE, Bilami A, Toral-Cruz H.
Big Data Challenges and Data Aggregation Strategies in Wireless Sensor Networks. IEEE AccessIEEE Access. 2018;6 :20558 - 20571.
AbstractThe emergence of new data handling technologies and analytics enabled the organization of big data in processes as an innovative aspect in wireless sensor networks (WSNs). Big data paradigm, combined with WSN technology, involves new challenges that are necessary to resolve in parallel. Data aggregation is a rapidly emerging research area. It represents one of the processing challenges of big sensor networks. This paper introduces the big data paradigm, its main dimensions that represent one of the most challenging concepts, and its principle analytic tools which are more and more introduced in the WSNs technology. The paper also presents the big data challenges that must be overcome to efficiently manipulate the voluminous data, and proposes a new classification of these challenges based on the necessities and the challenges of WSNs. As the big data aggregation challenge represents the center of our interest, this paper surveys its proposed strategies in WSNs.
Boubiche S, Boubiche DE, Bilami A, Toral-Cruz H.
Big Data Challenges and Data Aggregation Strategies in Wireless Sensor Networks. IEEE AccessIEEE Access. 2018;6 :20558 - 20571.
AbstractThe emergence of new data handling technologies and analytics enabled the organization of big data in processes as an innovative aspect in wireless sensor networks (WSNs). Big data paradigm, combined with WSN technology, involves new challenges that are necessary to resolve in parallel. Data aggregation is a rapidly emerging research area. It represents one of the processing challenges of big sensor networks. This paper introduces the big data paradigm, its main dimensions that represent one of the most challenging concepts, and its principle analytic tools which are more and more introduced in the WSNs technology. The paper also presents the big data challenges that must be overcome to efficiently manipulate the voluminous data, and proposes a new classification of these challenges based on the necessities and the challenges of WSNs. As the big data aggregation challenge represents the center of our interest, this paper surveys its proposed strategies in WSNs.
Boubiche S, Boubiche DE, Bilami A, Toral-Cruz H.
Big Data Challenges and Data Aggregation Strategies in Wireless Sensor Networks. IEEE AccessIEEE Access. 2018;6 :20558 - 20571.
AbstractThe emergence of new data handling technologies and analytics enabled the organization of big data in processes as an innovative aspect in wireless sensor networks (WSNs). Big data paradigm, combined with WSN technology, involves new challenges that are necessary to resolve in parallel. Data aggregation is a rapidly emerging research area. It represents one of the processing challenges of big sensor networks. This paper introduces the big data paradigm, its main dimensions that represent one of the most challenging concepts, and its principle analytic tools which are more and more introduced in the WSNs technology. The paper also presents the big data challenges that must be overcome to efficiently manipulate the voluminous data, and proposes a new classification of these challenges based on the necessities and the challenges of WSNs. As the big data aggregation challenge represents the center of our interest, this paper surveys its proposed strategies in WSNs.