Performance of Case-Based Reasoning Retrieval Using Classification Based on Associations versus Jcolibri and FreeCBR: A Further Validation Study

Aljuboori, Ahmed; Coenen, Frans; Nsaif, Mohammed; Parsons, David

doi:10.1088/1742-6596/1003/1/012130

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…However, a false non-match rate (FNMR) represents defining the target speaker as an impostor through the verification target trials. Moreover, the detection error trade-off (DET) curve is a very useful way to assessing the accuracy of the system in a linear plot of bit error rates on a standard scale, referred to by the NIST [28]. The critical area of the curve where the false match rate (FMR) and false non-match rate (FNMR) are equal is called the equal error rate (EER).…”

Section: Evaluation Methodsmentioning

confidence: 99%

Robust speaker verification by combining MFCC and entrocy in noisy conditions

2021

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Automatic speaker recognition may achieve remarkable performance in matched training and test conditions. Conversely, results drop significantly in incompatible noisy conditions. Furthermore, feature extraction significantly affects performance. Mel-frequency cepstral coefficients MFCCs are most commonly used in this field of study. The literature has reported that the conditions for training and testing are highly correlated. Taken together, these facts support strong recommendations for using MFCC features in similar environmental conditions (train/test) for speaker recognition. However, with noise and reverberation present, MFCC performance is not reliable. To address this, we propose a new feature 'entrocy' for accurate and robust speaker recognition, which we mainly employ to support MFCC coefficients in noisy environments. Entrocy is the fourier transform of the entropy, a measure of the fluctuation of the information in sound segments over time. Entrocy features are combined with MFCCs to generate a composite feature set which is tested using the gaussian mixture model (GMM) speaker recognition method. The proposed method shows improved recognition accuracy over a range of signal-to-noise ratios.

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Section: Evaluation Methodsmentioning

confidence: 99%

Robust speaker verification by combining MFCC and entrocy in noisy conditions

2021

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“…Several studies proved that integrating algorithms can increase performance compared to original algorithms [17][18][19] and 20 . This paper is an attempt to combine two cryptography algorithms.…”

Section: Brief Of Related Workmentioning

confidence: 99%

Digital Data Encryption Using a Proposed W-Method Based on AES and DES Algorithms

Shukur

Qurban²,

Aljuboori³

2023

Baghdad Sci.J

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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 Arabic and English texts with the same efficiency. The result showed that the proposed method performs faster and more securely when compared to standard DES and AES algorithms.

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“…This confirms the relation between hypertension & cholesterol as derived in Groups 3 & 4, in addition to thalassemia. Group 6, the next strongest rules relate to (testBPS=PreHypn, fbs>120=FALSE), are extracted in (11,12,15,16,17,19,21) display their association to class HeartDisease=yes. The highest confidence is attributed to 0.93 in Rule 3, followed by (0.91, 0.88, 0.88, 0.86, 0.84, 0.81) of the associated rules to this correlation.…”

Section: Mining Seci Model Secicarmentioning

confidence: 99%

“…In previous research, CARs have been used to demonstrate that classified rule patterns could be combined with a similar pattern, in the scope of; a colorectal cancer database [13], case-based reasoning. CARs have also been used in [14] to extract information concerning associations from a given case base using various approaches such as case-based reasoning [15,16]. Consequently, CAR as a DM tool can be used to discover significant relationships between risk factors implicated in target disease.…”

Section: Introductionmentioning

confidence: 99%

Pushing towards ehealth for iraqi hypertensives: an integrated class association rules into SECI model

Aljuboori

Tawfeeq

Al-Karawi

2021

IJEECS

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<span lang="EN-GB">This paper highlights the barriers that have led to a delay in the implementation of E-Health services in Iraq. A new framework is proposed to improve the E-Health sector using a SECI model which describes how explicit and tacit knowledge is generated, transferred, and recreated in organizations through main stages (socialization, externalization, combination and internalization). Class association rules (CARs) is integrated to mine the SECI model by extracting related rules which correspond to the medical advice. The proposed framework (SECICAR) can be done through a web portal to assemble healthcare professionals, patients in one environment. SECICAR will be applied to the hypertension community to show that disease if left untreated, frequently leads to serious illnesses such as heart disease. The SECICAR aims to facilitate the dissemination of tacit knowledge, which is explicit to hypertensives, in the form of strategies, guidelines and best practices. The validation of the SECICAR results displays satisfactory accuracy and reliability. Heuristic evaluation was used to test the web portal, the participants stating that there were no major issues regarding its usability.</span>

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Performance of Case-Based Reasoning Retrieval Using Classification Based on Associations versus Jcolibri and FreeCBR: A Further Validation Study

Cited by 4 publications

References 15 publications

Robust speaker verification by combining MFCC and entrocy in noisy conditions

Robust speaker verification by combining MFCC and entrocy in noisy conditions

Digital Data Encryption Using a Proposed W-Method Based on AES and DES Algorithms

Pushing towards ehealth for iraqi hypertensives: an integrated class association rules into SECI model

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