A new proposed statistical feature extraction method in speech emotion recognition
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(35)
Publication Date
Mon Aug 01 2022
Journal Name
Telkomnika (telecommunication Computing Electronics And Control)
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(1)
Publication Date
Mon Dec 01 2014
Journal Name
2014 Ieee Student Conference On Research And Development
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(10)
Publication Date
Mon Aug 01 2016
Journal Name
2016 38th Annual International Conference Of The Ieee Engineering In Medicine And Biology Society (embc)
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(9)
Publication Date
Thu Oct 01 2020
Journal Name
Ieee Transactions On Artificial Intelligence
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Publication Date
Sun Jan 01 2017
Journal Name
Iec2017 Proceedings Book
Improving TF-IDF with Singular Value Decomposition (SVD) for Feature Extraction on Twitter
Feature Extraction
Retrieval Information
Classification
Singular Value
Decomposition
Dimension Reduction
TF-IDF.
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Publication Date
Thu Jan 01 2026
Journal Name
Ieee Transactions On Human-machine Systems
Publication Date
Thu Jan 01 2026
Journal Name
Ieee Transactions On Human-machine Systems
Publication Date
Sun May 01 2016
Journal Name
Iosr Journal Of Computer Engineering
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Publication Date
Wed Feb 01 2023
Journal Name
Baghdad Science Journal
Breast Cancer MRI Classification Based on Fractional Entropy Image Enhancement and Deep Feature Extraction
Breast MRI scans
Classification
CNN
Deep features
LSTM
Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature
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(11)
Publication Date
Tue Aug 01 2023
Journal Name
Baghdad Science Journal
Digital Data Encryption Using a Proposed W-Method Based on AES and DES Algorithms
Advanced Encryption Standard AES
Data Encryption Standard DES
Decryption
Encryption
Keys Encryption.
This paper proposes a new encryption method. It combines two cipher algorithms, i.e., DES and AES, to generate hybrid keys. This combination strengthens the proposed W-method by generating high randomized keys. Two points can represent the reliability of any encryption technique. Firstly, is the key generation; therefore, our approach merges 64 bits of DES with 64 bits of AES to produce 128 bits as a root key for all remaining keys that are 15. This complexity increases the level of the ciphering process. Moreover, it shifts the operation one bit only to the right. Secondly is the nature of the encryption process. It includes two keys and mixes one round of DES with one round of AES to reduce the performance time. The W-method deals with
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