Signal multiple encodings by using autoencoder deep learning

Anaz, Ammar Sameer; Al-Ridha, Moatasem Yaseen; Al-Nima, Raıd Rafı Omar

doi:10.11591/eei.v12i1.4229

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…It also shows outperformances compared to other models or networks for the same training and testing samples used in Table 1, the comparisons are detailed in Table 2. That is, the suggested algorithm outperformed previous deep learning models of the stacked autoencoder (SA) [23], deep autoencoder network (DAN) [24], and autoencoder deep learning (ADL) [25] in terms of its flexibility, training time, mean square error (MSE) and its ability to recognize parents. Regarding the flexibility, the SDDL can be enlarged or reduced without the requiring of re-train again.…”

Section: Sddl Performancesmentioning

confidence: 96%

Identifying deoxyribonucleic acids of individuals based on their chromosomes by proposing a special deep learning model

Omar Al-Nima,

Salahaldeen Al-Kaltakchi,

A. Abdulla

2024

Bulletin EEI

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One of the most significant physiological biometrics is the deoxyribonucleic acid (DNA). It can be found in every human cell as in hair, blood, and skin. In this paper, a special DNA deep learning (SDDL) is proposed as a novel machine learning (ML) model to identify persons depending on their DNAs. The proposed model is designed to collect DNA chromosomes of parents for an individual. It is flexible (can be enlarged or reduced) and it can identify one or both parents of a person, based on the provided chromosomes. The SDDL is so fast in training compared to other traditional deep learning models. Two real datasets from Iraq are utilized called: Real Iraqi Dataset for Kurd (RIDK) and Real Iraqi Dataset for Arab (RIDA). The results yield that the suggested SDDL model achieves 100% testing accuracy for each of the employed datasets.

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Section: Sddl Performancesmentioning

confidence: 96%

Identifying deoxyribonucleic acids of individuals based on their chromosomes by proposing a special deep learning model

Omar Al-Nima,

Salahaldeen Al-Kaltakchi,

A. Abdulla

2024

Bulletin EEI

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“…The DDLN consistently outperforms these models across various aspects, as detailed in Table II. Notably, it excels previous deep learning models of the Stacked Autoencoder [26], Deep Autoencoder Network [27], and Autoencoder Deep Learning [28] in terms of flexibility, training time, Mean Square Error (MSE), and the ability to identify parents. The DDLN's flexibility stands out as it can be adjusted in size without necessitating re-training, unlike other networks that require specific parameters such as hidden layer counts and neurons.…”

Section: B Ddln Performancesmentioning

confidence: 99%

DNA recognition using Novel Deep Learning Model

Al-Kaltakchi,

Abdulla,

Al-Nima

2024

International Journal of Electronics and Telecommunications

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DNA, a significant physiological biometric, is present in all human cells like hair, blood, and skin. This research introduces a new approach called the Deep DNA Learning Network (DDLN) for person identification based on their DNA. This novel Machine Learning model is designed to gather DNA chromosomes from an individual’s parents. The model’s flexibility allows it to expand or contract and has the capability to determine one or both parents of an individual using the provided chromosomes. Notably, the DDLN model offers quick training in comparison to traditional deep learning methods. The study employs two real datasets from Iraq: the Real Iraqi Dataset for Kurds (RIDK) and the Real Iraqi Dataset for Arabs (RIDA). The outcomes demonstrate that the proposed DDLN model achieves an Equal Error Rate (EER) of 0 for both datasets, indicating highly accurate performance.

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“…Many academic subjects including image recognitions, analyses and classifications have recently benefited from the Deep Learning (DL) techniques as in [8] [9][10] [11] [12][13] [14] [15] [16] [17] [18]. There are two types of methods that can be applied to the SL recognition: the first type is by using sensors and the second type is based on images [19].…”

Section: Introductionmentioning

confidence: 99%

Interpreting arabic sign alphabet by using the deep learning

Al-Obaidi,

Al-Nima,

Han

2023

INTELLIGENT BIOTECHNOLOGIES OF NATURAL AND SYNTHETIC BIOLOGICALLY ACTIVE SUBSTANCES: XIV Narochanskie Readings

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Sign Language (SL) is a communication method between people. It is an essential language; especially for people who are speech impaired and hearing impaired, it can be considered as their mother tongues. Hand gestures form the nonverbal communication of this language. We focus on interpreting Arabic Sign Alphabet (ASA) in this study and, as a case study, the recognition of alphabet in Iraqi Sign Language (IrSL) is carried out with the help of specialists from the "Al-Amal Institute for the Deaf and Dumb". A new ASA dataset of various hand gestures was created and adopted. In addition, a deep learning model named the Deep Arabic Sign Alphabet (DASA) is proposed, which is a developed version of the Convolutional Neural Network (CNN). It can efficiently interpret the ASA, achieving a high interpretation accuracy of 95.25%.

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Signal multiple encodings by using autoencoder deep learning

Cited by 3 publications

References 17 publications

Identifying deoxyribonucleic acids of individuals based on their chromosomes by proposing a special deep learning model

Identifying deoxyribonucleic acids of individuals based on their chromosomes by proposing a special deep learning model

DNA recognition using Novel Deep Learning Model

Interpreting arabic sign alphabet by using the deep learning

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