Deep fake news detection system based on concatenated and recurrent modalities
With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sedik, Ahmed [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Do denture processing techniques affect the mechanical properties of denture teeth? - Clements, Jody L. ELSEVIER, 2017, an international journal, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:208 ; year:2022 ; day:1 ; month:12 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.eswa.2022.117953 |
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ELV058806164 |
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| 264 | 1 | |c 2022transfer abstract | |
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| 520 | |a With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techniques that are used to detect FNs. Previously, great focus was given to online review classification and free internet posts based on social networks. This research proposes a Deep Learning-based FNs Detection method. This paper proposes a Deep Learning (DL)-based method for detecting FNs. The proposed system consists of three phases; text encoding, feature extraction, and classification. The text encoding process is carried out on the input news words using GLOVE for word representation. The encoded words are then embedded into a specific word length in order to be enrolled in the proposed DL models. The proposed DL models comprise both automatic feature extraction and classification tasks. Furthermore, this study proposes four different DL models, including Convolutional Neural Networks (CNNs) and Concatenated CNNs (C-CNNs), long short-term memory (LSTM), and Gated Recurrent Units, to find an optimal model prior to the section of FNs that outperforms previous works.The proposed DL models are carried out on FNs and FNC datasets which are provided by kaggle, and the suggested C-CNNs algorithm obtained an accuracy of 99.6% and trained faster than others. Multiple evaluation metrics such as precision, recall, F1, and accuracy have been utilized to evaluate the outcome of the proposed models. The experimental results demonstrated overall improvements in the subject of FND when compared with the current models and validated the potential of the proposed methodology for the detection of FNs on Social Media (SM). This study will help researchers to broaden the knowledge of applications of CNNs based on DL methods for FND. | ||
| 520 | |a With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techniques that are used to detect FNs. Previously, great focus was given to online review classification and free internet posts based on social networks. This research proposes a Deep Learning-based FNs Detection method. This paper proposes a Deep Learning (DL)-based method for detecting FNs. The proposed system consists of three phases; text encoding, feature extraction, and classification. The text encoding process is carried out on the input news words using GLOVE for word representation. The encoded words are then embedded into a specific word length in order to be enrolled in the proposed DL models. The proposed DL models comprise both automatic feature extraction and classification tasks. Furthermore, this study proposes four different DL models, including Convolutional Neural Networks (CNNs) and Concatenated CNNs (C-CNNs), long short-term memory (LSTM), and Gated Recurrent Units, to find an optimal model prior to the section of FNs that outperforms previous works.The proposed DL models are carried out on FNs and FNC datasets which are provided by kaggle, and the suggested C-CNNs algorithm obtained an accuracy of 99.6% and trained faster than others. Multiple evaluation metrics such as precision, recall, F1, and accuracy have been utilized to evaluate the outcome of the proposed models. The experimental results demonstrated overall improvements in the subject of FND when compared with the current models and validated the potential of the proposed methodology for the detection of FNs on Social Media (SM). This study will help researchers to broaden the knowledge of applications of CNNs based on DL methods for FND. | ||
| 650 | 7 | |a CNN |2 Elsevier | |
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| 650 | 7 | |a Fake News Detection |2 Elsevier | |
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| 700 | 1 | |a Abohany, Amr A. |4 oth | |
| 700 | 1 | |a Sallam, Karam M. |4 oth | |
| 700 | 1 | |a Munasinghe, Kumudu |4 oth | |
| 700 | 1 | |a Medhat, T. |4 oth | |
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10.1016/j.eswa.2022.117953 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001889.pica (DE-627)ELV058806164 (ELSEVIER)S0957-4174(22)01189-7 DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Sedik, Ahmed verfasserin aut Deep fake news detection system based on concatenated and recurrent modalities 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techniques that are used to detect FNs. Previously, great focus was given to online review classification and free internet posts based on social networks. This research proposes a Deep Learning-based FNs Detection method. This paper proposes a Deep Learning (DL)-based method for detecting FNs. The proposed system consists of three phases; text encoding, feature extraction, and classification. The text encoding process is carried out on the input news words using GLOVE for word representation. The encoded words are then embedded into a specific word length in order to be enrolled in the proposed DL models. The proposed DL models comprise both automatic feature extraction and classification tasks. Furthermore, this study proposes four different DL models, including Convolutional Neural Networks (CNNs) and Concatenated CNNs (C-CNNs), long short-term memory (LSTM), and Gated Recurrent Units, to find an optimal model prior to the section of FNs that outperforms previous works.The proposed DL models are carried out on FNs and FNC datasets which are provided by kaggle, and the suggested C-CNNs algorithm obtained an accuracy of 99.6% and trained faster than others. Multiple evaluation metrics such as precision, recall, F1, and accuracy have been utilized to evaluate the outcome of the proposed models. The experimental results demonstrated overall improvements in the subject of FND when compared with the current models and validated the potential of the proposed methodology for the detection of FNs on Social Media (SM). This study will help researchers to broaden the knowledge of applications of CNNs based on DL methods for FND. With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techniques that are used to detect FNs. Previously, great focus was given to online review classification and free internet posts based on social networks. This research proposes a Deep Learning-based FNs Detection method. This paper proposes a Deep Learning (DL)-based method for detecting FNs. The proposed system consists of three phases; text encoding, feature extraction, and classification. The text encoding process is carried out on the input news words using GLOVE for word representation. The encoded words are then embedded into a specific word length in order to be enrolled in the proposed DL models. The proposed DL models comprise both automatic feature extraction and classification tasks. Furthermore, this study proposes four different DL models, including Convolutional Neural Networks (CNNs) and Concatenated CNNs (C-CNNs), long short-term memory (LSTM), and Gated Recurrent Units, to find an optimal model prior to the section of FNs that outperforms previous works.The proposed DL models are carried out on FNs and FNC datasets which are provided by kaggle, and the suggested C-CNNs algorithm obtained an accuracy of 99.6% and trained faster than others. Multiple evaluation metrics such as precision, recall, F1, and accuracy have been utilized to evaluate the outcome of the proposed models. The experimental results demonstrated overall improvements in the subject of FND when compared with the current models and validated the potential of the proposed methodology for the detection of FNs on Social Media (SM). This study will help researchers to broaden the knowledge of applications of CNNs based on DL methods for FND. CNN Elsevier LSTM Elsevier Fake News Detection Elsevier Concatenated CNN Elsevier GRU Elsevier CNNs-LSTM Elsevier Deep Learning Elsevier Abohany, Amr A. oth Sallam, Karam M. oth Munasinghe, Kumudu oth Medhat, T. oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:208 year:2022 day:1 month:12 pages:0 https://doi.org/10.1016/j.eswa.2022.117953 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 208 2022 1 1201 0 |
| spelling |
10.1016/j.eswa.2022.117953 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001889.pica (DE-627)ELV058806164 (ELSEVIER)S0957-4174(22)01189-7 DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Sedik, Ahmed verfasserin aut Deep fake news detection system based on concatenated and recurrent modalities 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techniques that are used to detect FNs. Previously, great focus was given to online review classification and free internet posts based on social networks. This research proposes a Deep Learning-based FNs Detection method. This paper proposes a Deep Learning (DL)-based method for detecting FNs. The proposed system consists of three phases; text encoding, feature extraction, and classification. The text encoding process is carried out on the input news words using GLOVE for word representation. The encoded words are then embedded into a specific word length in order to be enrolled in the proposed DL models. The proposed DL models comprise both automatic feature extraction and classification tasks. Furthermore, this study proposes four different DL models, including Convolutional Neural Networks (CNNs) and Concatenated CNNs (C-CNNs), long short-term memory (LSTM), and Gated Recurrent Units, to find an optimal model prior to the section of FNs that outperforms previous works.The proposed DL models are carried out on FNs and FNC datasets which are provided by kaggle, and the suggested C-CNNs algorithm obtained an accuracy of 99.6% and trained faster than others. Multiple evaluation metrics such as precision, recall, F1, and accuracy have been utilized to evaluate the outcome of the proposed models. The experimental results demonstrated overall improvements in the subject of FND when compared with the current models and validated the potential of the proposed methodology for the detection of FNs on Social Media (SM). This study will help researchers to broaden the knowledge of applications of CNNs based on DL methods for FND. With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techniques that are used to detect FNs. Previously, great focus was given to online review classification and free internet posts based on social networks. This research proposes a Deep Learning-based FNs Detection method. This paper proposes a Deep Learning (DL)-based method for detecting FNs. The proposed system consists of three phases; text encoding, feature extraction, and classification. The text encoding process is carried out on the input news words using GLOVE for word representation. The encoded words are then embedded into a specific word length in order to be enrolled in the proposed DL models. The proposed DL models comprise both automatic feature extraction and classification tasks. Furthermore, this study proposes four different DL models, including Convolutional Neural Networks (CNNs) and Concatenated CNNs (C-CNNs), long short-term memory (LSTM), and Gated Recurrent Units, to find an optimal model prior to the section of FNs that outperforms previous works.The proposed DL models are carried out on FNs and FNC datasets which are provided by kaggle, and the suggested C-CNNs algorithm obtained an accuracy of 99.6% and trained faster than others. Multiple evaluation metrics such as precision, recall, F1, and accuracy have been utilized to evaluate the outcome of the proposed models. The experimental results demonstrated overall improvements in the subject of FND when compared with the current models and validated the potential of the proposed methodology for the detection of FNs on Social Media (SM). This study will help researchers to broaden the knowledge of applications of CNNs based on DL methods for FND. CNN Elsevier LSTM Elsevier Fake News Detection Elsevier Concatenated CNN Elsevier GRU Elsevier CNNs-LSTM Elsevier Deep Learning Elsevier Abohany, Amr A. oth Sallam, Karam M. oth Munasinghe, Kumudu oth Medhat, T. oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:208 year:2022 day:1 month:12 pages:0 https://doi.org/10.1016/j.eswa.2022.117953 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 208 2022 1 1201 0 |
| allfields_unstemmed |
10.1016/j.eswa.2022.117953 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001889.pica (DE-627)ELV058806164 (ELSEVIER)S0957-4174(22)01189-7 DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Sedik, Ahmed verfasserin aut Deep fake news detection system based on concatenated and recurrent modalities 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techniques that are used to detect FNs. Previously, great focus was given to online review classification and free internet posts based on social networks. This research proposes a Deep Learning-based FNs Detection method. This paper proposes a Deep Learning (DL)-based method for detecting FNs. The proposed system consists of three phases; text encoding, feature extraction, and classification. The text encoding process is carried out on the input news words using GLOVE for word representation. The encoded words are then embedded into a specific word length in order to be enrolled in the proposed DL models. The proposed DL models comprise both automatic feature extraction and classification tasks. Furthermore, this study proposes four different DL models, including Convolutional Neural Networks (CNNs) and Concatenated CNNs (C-CNNs), long short-term memory (LSTM), and Gated Recurrent Units, to find an optimal model prior to the section of FNs that outperforms previous works.The proposed DL models are carried out on FNs and FNC datasets which are provided by kaggle, and the suggested C-CNNs algorithm obtained an accuracy of 99.6% and trained faster than others. Multiple evaluation metrics such as precision, recall, F1, and accuracy have been utilized to evaluate the outcome of the proposed models. The experimental results demonstrated overall improvements in the subject of FND when compared with the current models and validated the potential of the proposed methodology for the detection of FNs on Social Media (SM). This study will help researchers to broaden the knowledge of applications of CNNs based on DL methods for FND. With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techniques that are used to detect FNs. Previously, great focus was given to online review classification and free internet posts based on social networks. This research proposes a Deep Learning-based FNs Detection method. This paper proposes a Deep Learning (DL)-based method for detecting FNs. The proposed system consists of three phases; text encoding, feature extraction, and classification. The text encoding process is carried out on the input news words using GLOVE for word representation. The encoded words are then embedded into a specific word length in order to be enrolled in the proposed DL models. The proposed DL models comprise both automatic feature extraction and classification tasks. Furthermore, this study proposes four different DL models, including Convolutional Neural Networks (CNNs) and Concatenated CNNs (C-CNNs), long short-term memory (LSTM), and Gated Recurrent Units, to find an optimal model prior to the section of FNs that outperforms previous works.The proposed DL models are carried out on FNs and FNC datasets which are provided by kaggle, and the suggested C-CNNs algorithm obtained an accuracy of 99.6% and trained faster than others. Multiple evaluation metrics such as precision, recall, F1, and accuracy have been utilized to evaluate the outcome of the proposed models. The experimental results demonstrated overall improvements in the subject of FND when compared with the current models and validated the potential of the proposed methodology for the detection of FNs on Social Media (SM). This study will help researchers to broaden the knowledge of applications of CNNs based on DL methods for FND. CNN Elsevier LSTM Elsevier Fake News Detection Elsevier Concatenated CNN Elsevier GRU Elsevier CNNs-LSTM Elsevier Deep Learning Elsevier Abohany, Amr A. oth Sallam, Karam M. oth Munasinghe, Kumudu oth Medhat, T. oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:208 year:2022 day:1 month:12 pages:0 https://doi.org/10.1016/j.eswa.2022.117953 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 208 2022 1 1201 0 |
| allfieldsGer |
10.1016/j.eswa.2022.117953 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001889.pica (DE-627)ELV058806164 (ELSEVIER)S0957-4174(22)01189-7 DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Sedik, Ahmed verfasserin aut Deep fake news detection system based on concatenated and recurrent modalities 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techniques that are used to detect FNs. Previously, great focus was given to online review classification and free internet posts based on social networks. This research proposes a Deep Learning-based FNs Detection method. This paper proposes a Deep Learning (DL)-based method for detecting FNs. The proposed system consists of three phases; text encoding, feature extraction, and classification. The text encoding process is carried out on the input news words using GLOVE for word representation. The encoded words are then embedded into a specific word length in order to be enrolled in the proposed DL models. The proposed DL models comprise both automatic feature extraction and classification tasks. Furthermore, this study proposes four different DL models, including Convolutional Neural Networks (CNNs) and Concatenated CNNs (C-CNNs), long short-term memory (LSTM), and Gated Recurrent Units, to find an optimal model prior to the section of FNs that outperforms previous works.The proposed DL models are carried out on FNs and FNC datasets which are provided by kaggle, and the suggested C-CNNs algorithm obtained an accuracy of 99.6% and trained faster than others. Multiple evaluation metrics such as precision, recall, F1, and accuracy have been utilized to evaluate the outcome of the proposed models. The experimental results demonstrated overall improvements in the subject of FND when compared with the current models and validated the potential of the proposed methodology for the detection of FNs on Social Media (SM). This study will help researchers to broaden the knowledge of applications of CNNs based on DL methods for FND. With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techniques that are used to detect FNs. Previously, great focus was given to online review classification and free internet posts based on social networks. This research proposes a Deep Learning-based FNs Detection method. This paper proposes a Deep Learning (DL)-based method for detecting FNs. The proposed system consists of three phases; text encoding, feature extraction, and classification. The text encoding process is carried out on the input news words using GLOVE for word representation. The encoded words are then embedded into a specific word length in order to be enrolled in the proposed DL models. The proposed DL models comprise both automatic feature extraction and classification tasks. Furthermore, this study proposes four different DL models, including Convolutional Neural Networks (CNNs) and Concatenated CNNs (C-CNNs), long short-term memory (LSTM), and Gated Recurrent Units, to find an optimal model prior to the section of FNs that outperforms previous works.The proposed DL models are carried out on FNs and FNC datasets which are provided by kaggle, and the suggested C-CNNs algorithm obtained an accuracy of 99.6% and trained faster than others. Multiple evaluation metrics such as precision, recall, F1, and accuracy have been utilized to evaluate the outcome of the proposed models. The experimental results demonstrated overall improvements in the subject of FND when compared with the current models and validated the potential of the proposed methodology for the detection of FNs on Social Media (SM). This study will help researchers to broaden the knowledge of applications of CNNs based on DL methods for FND. CNN Elsevier LSTM Elsevier Fake News Detection Elsevier Concatenated CNN Elsevier GRU Elsevier CNNs-LSTM Elsevier Deep Learning Elsevier Abohany, Amr A. oth Sallam, Karam M. oth Munasinghe, Kumudu oth Medhat, T. oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:208 year:2022 day:1 month:12 pages:0 https://doi.org/10.1016/j.eswa.2022.117953 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 208 2022 1 1201 0 |
| allfieldsSound |
10.1016/j.eswa.2022.117953 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001889.pica (DE-627)ELV058806164 (ELSEVIER)S0957-4174(22)01189-7 DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Sedik, Ahmed verfasserin aut Deep fake news detection system based on concatenated and recurrent modalities 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techniques that are used to detect FNs. Previously, great focus was given to online review classification and free internet posts based on social networks. This research proposes a Deep Learning-based FNs Detection method. This paper proposes a Deep Learning (DL)-based method for detecting FNs. The proposed system consists of three phases; text encoding, feature extraction, and classification. The text encoding process is carried out on the input news words using GLOVE for word representation. The encoded words are then embedded into a specific word length in order to be enrolled in the proposed DL models. The proposed DL models comprise both automatic feature extraction and classification tasks. Furthermore, this study proposes four different DL models, including Convolutional Neural Networks (CNNs) and Concatenated CNNs (C-CNNs), long short-term memory (LSTM), and Gated Recurrent Units, to find an optimal model prior to the section of FNs that outperforms previous works.The proposed DL models are carried out on FNs and FNC datasets which are provided by kaggle, and the suggested C-CNNs algorithm obtained an accuracy of 99.6% and trained faster than others. Multiple evaluation metrics such as precision, recall, F1, and accuracy have been utilized to evaluate the outcome of the proposed models. The experimental results demonstrated overall improvements in the subject of FND when compared with the current models and validated the potential of the proposed methodology for the detection of FNs on Social Media (SM). This study will help researchers to broaden the knowledge of applications of CNNs based on DL methods for FND. With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techniques that are used to detect FNs. Previously, great focus was given to online review classification and free internet posts based on social networks. This research proposes a Deep Learning-based FNs Detection method. This paper proposes a Deep Learning (DL)-based method for detecting FNs. The proposed system consists of three phases; text encoding, feature extraction, and classification. The text encoding process is carried out on the input news words using GLOVE for word representation. The encoded words are then embedded into a specific word length in order to be enrolled in the proposed DL models. The proposed DL models comprise both automatic feature extraction and classification tasks. Furthermore, this study proposes four different DL models, including Convolutional Neural Networks (CNNs) and Concatenated CNNs (C-CNNs), long short-term memory (LSTM), and Gated Recurrent Units, to find an optimal model prior to the section of FNs that outperforms previous works.The proposed DL models are carried out on FNs and FNC datasets which are provided by kaggle, and the suggested C-CNNs algorithm obtained an accuracy of 99.6% and trained faster than others. Multiple evaluation metrics such as precision, recall, F1, and accuracy have been utilized to evaluate the outcome of the proposed models. The experimental results demonstrated overall improvements in the subject of FND when compared with the current models and validated the potential of the proposed methodology for the detection of FNs on Social Media (SM). This study will help researchers to broaden the knowledge of applications of CNNs based on DL methods for FND. CNN Elsevier LSTM Elsevier Fake News Detection Elsevier Concatenated CNN Elsevier GRU Elsevier CNNs-LSTM Elsevier Deep Learning Elsevier Abohany, Amr A. oth Sallam, Karam M. oth Munasinghe, Kumudu oth Medhat, T. oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:208 year:2022 day:1 month:12 pages:0 https://doi.org/10.1016/j.eswa.2022.117953 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 208 2022 1 1201 0 |
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Enthalten in Do denture processing techniques affect the mechanical properties of denture teeth? Amsterdam [u.a.] volume:208 year:2022 day:1 month:12 pages:0 |
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With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techniques that are used to detect FNs. Previously, great focus was given to online review classification and free internet posts based on social networks. This research proposes a Deep Learning-based FNs Detection method. This paper proposes a Deep Learning (DL)-based method for detecting FNs. The proposed system consists of three phases; text encoding, feature extraction, and classification. The text encoding process is carried out on the input news words using GLOVE for word representation. The encoded words are then embedded into a specific word length in order to be enrolled in the proposed DL models. The proposed DL models comprise both automatic feature extraction and classification tasks. Furthermore, this study proposes four different DL models, including Convolutional Neural Networks (CNNs) and Concatenated CNNs (C-CNNs), long short-term memory (LSTM), and Gated Recurrent Units, to find an optimal model prior to the section of FNs that outperforms previous works.The proposed DL models are carried out on FNs and FNC datasets which are provided by kaggle, and the suggested C-CNNs algorithm obtained an accuracy of 99.6% and trained faster than others. Multiple evaluation metrics such as precision, recall, F1, and accuracy have been utilized to evaluate the outcome of the proposed models. The experimental results demonstrated overall improvements in the subject of FND when compared with the current models and validated the potential of the proposed methodology for the detection of FNs on Social Media (SM). This study will help researchers to broaden the knowledge of applications of CNNs based on DL methods for FND. |
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With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techniques that are used to detect FNs. Previously, great focus was given to online review classification and free internet posts based on social networks. This research proposes a Deep Learning-based FNs Detection method. This paper proposes a Deep Learning (DL)-based method for detecting FNs. The proposed system consists of three phases; text encoding, feature extraction, and classification. The text encoding process is carried out on the input news words using GLOVE for word representation. The encoded words are then embedded into a specific word length in order to be enrolled in the proposed DL models. The proposed DL models comprise both automatic feature extraction and classification tasks. Furthermore, this study proposes four different DL models, including Convolutional Neural Networks (CNNs) and Concatenated CNNs (C-CNNs), long short-term memory (LSTM), and Gated Recurrent Units, to find an optimal model prior to the section of FNs that outperforms previous works.The proposed DL models are carried out on FNs and FNC datasets which are provided by kaggle, and the suggested C-CNNs algorithm obtained an accuracy of 99.6% and trained faster than others. Multiple evaluation metrics such as precision, recall, F1, and accuracy have been utilized to evaluate the outcome of the proposed models. The experimental results demonstrated overall improvements in the subject of FND when compared with the current models and validated the potential of the proposed methodology for the detection of FNs on Social Media (SM). This study will help researchers to broaden the knowledge of applications of CNNs based on DL methods for FND. |
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With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techniques that are used to detect FNs. Previously, great focus was given to online review classification and free internet posts based on social networks. This research proposes a Deep Learning-based FNs Detection method. This paper proposes a Deep Learning (DL)-based method for detecting FNs. The proposed system consists of three phases; text encoding, feature extraction, and classification. The text encoding process is carried out on the input news words using GLOVE for word representation. The encoded words are then embedded into a specific word length in order to be enrolled in the proposed DL models. The proposed DL models comprise both automatic feature extraction and classification tasks. Furthermore, this study proposes four different DL models, including Convolutional Neural Networks (CNNs) and Concatenated CNNs (C-CNNs), long short-term memory (LSTM), and Gated Recurrent Units, to find an optimal model prior to the section of FNs that outperforms previous works.The proposed DL models are carried out on FNs and FNC datasets which are provided by kaggle, and the suggested C-CNNs algorithm obtained an accuracy of 99.6% and trained faster than others. Multiple evaluation metrics such as precision, recall, F1, and accuracy have been utilized to evaluate the outcome of the proposed models. The experimental results demonstrated overall improvements in the subject of FND when compared with the current models and validated the potential of the proposed methodology for the detection of FNs on Social Media (SM). This study will help researchers to broaden the knowledge of applications of CNNs based on DL methods for FND. |
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