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Medical Text Classification Based on Convolutional Neural Network: A Review Hazha Saeed Yahia & Adnan Mohsin Abdulazeez

Medical Text Classification Based on Convolutional Neural Network: A Review

Author (s)

Hazha Saeed Yahia & Adnan Mohsin Abdulazeez

Abstract

­

Medical text classification has a significant impact on disease diagnosis, medical research, and the automatic development of disease ontology, acquiring knowledge of clinical results recorded in the medical literature. Hence, medical text classification is challenging because it contains terminologies that describe medical concepts and terminologies. Furthermore, the medical data mostly does not follow natural language grammar; it has inadequate grammatical sentences. The techniques used for text classification give different results comparing to medical text classifications, as extracting text and training sets are different. One of the most significant text classification models in general and medical text classification specifically is CNN-based models. In this paper, many papers on medical text classification have been reviewed, and the details of each article, such as algorithms, or approaches used, databases, classification techniques, and outcomes obtained, are evaluated and outlined thoroughly. Besides, discussions were carried out on all the studied papers, which profoundly influenced medical documents classification.

 Keywords: Text classification, Medical text classification, Neural networks, Convolutional neural networks, CNN architecture.

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Title: Medical Text Classification Based on Convolutional Neural Network: A Review
Author: Hazha Saeed Yahia & Adnan Mohsin Abdulazeez
Journal Name: International Journal of Science and Business
Website: ijsab.com
ISSN: ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)
DOI: https://doi.org/10.5281/zenodo.4483635
Media: Online
Volume: 5
Issue: 3
Acceptance Date: 30/01/2021
Date of Publication: 31/01/2021
PDF URL: https://ijsab.com/wp-content/uploads/683.pdf
Free download: Available
Page: 27-41
First Page: 27
Last Page: 41
Paper Type: Literature Review
Current Status: Published

 

Cite This Article:

Hazha Saeed Yahia & Adnan Mohsin Abdulazeez (2021). Medical Text Classification Based on Convolutional Neural Network: A Review. International Journal of Science and Business, 5(3), 27-41. doi: https://doi.org/10.5281/zenodo.4483635

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About Author (s)

Hazha Saeed Yahia (corresponding author), Poly Techniques University, Duhok, Kurdistan Region, Iraq. E-mail: Hazha.yahia@gmail.com

Professor Adnan Mohsin Abdulazeez, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.

 

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DOI: https://doi.org/10.5281/zenodo.4483635

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Identifying Speakers Using Deep Learning: A review Lawchak Fadhil Khalid & Adnan Mohsin Abdulazeez

Identifying Speakers Using Deep Learning: A review

Author (s)

Lawchak Fadhil Khalid & Adnan Mohsin Abdulazeez

Abstract

­With the advancement of technology and the increasing demand on smart systems and smart applications that provide a quality-of-life improvement, there has been a surge in the demand of more conscious applications, Machine Learning (ML) is considered one of the driving forces behind implementing these types of applications, and one of its implementations is Speaker Identification (SID). Deep Neural Networks (DNNs) and also Recurrent Neural Networks (RNNs) are two main types of Deep Learning that are being used in the implementation of such applications. Speaker Identification is being utilized more and more on daily basis and is being focused on by the research community as a result of this demand. In this paper, a review will be conducted to some of the most recent researches that were conducted in this area and compare their results while discussing their outcomes.

 Keywords: Machine Learning, Speaker Identification, Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks.

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Title: Identifying Speakers Using Deep Learning: A review
Author: Lawchak Fadhil Khalid & Adnan Mohsin Abdulazeez
Journal Name: International Journal of Science and Business
Website: ijsab.com
ISSN: ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)
DOI: https://doi.org/10.5281/zenodo.4481596
Media: Online
Volume: 5
Issue: 3
Acceptance Date: 28/01/2021
Date of Publication: 30/01/2021
PDF URL: https://ijsab.com/wp-content/uploads/682.pdf
Free download: Available
Page: 15-26
First Page: 15
Last Page: 26
Paper Type: Literature Review
Current Status: Published

 

Cite This Article:

Lawchak Fadhil Khalid & Adnan Mohsin Abdulazeez (2021). Identifying Speakers Using Deep Learning: A review. International Journal of Science and Business, 5(3), 15-26. doi: https://doi.org/10.5281/zenodo.4481596

Retrieved from https://ijsab.com/wp-content/uploads/682.pdf

 

About Author (s)

Lawchak Fadhil Khalid (corresponding author), Technical College of Informatics – Akre, Duhok Polytechnic University (DPU), Kurdistan Region, Iraq. Email: lawchak.fadhil@gmail.com

Adnan Mohsin Abdulazeez, Duhok Polytechnic University (DPU), Kurdistan Region, Iraq.

 

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DOI: https://doi.org/10.5281/zenodo.4481596

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Deep Learning Convolutional Neural Network for Speech Recognition: A Review Kazheen Ismael Taher & Adnan Mohsin Abdulazeez

Deep Learning Convolutional Neural Network for Speech Recognition: A Review

Author (s)

Kazheen Ismael Taher & Adnan Mohsin Abdulazeez

Abstract

­In the last few decades, there has been considerable amount of research on the use of Machine Learning (ML) for speech recognition based on Convolutional Neural Network (CNN). These studies are generally focused on using CNN for applications related to speech recognition. Additionally, various works are discussed that are based on deep learning since its emergence in the speech recognition applications. Comparing to other approaches, the approaches based on deep learning are showing rather interesting outcomes in several applications including speech recognition, and therefore, it attracts a lot of researches and studies. In this paper, a review is presented on the developments that occurred in this field while also discussing the current researches that are being based on the topic currently.

 Keywords: Machine Learning, Speech Recognition, Convolutional Neural Networks, Deep Learning, Word Error Rate (WER).

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Title: Deep Learning Convolutional Neural Network for Speech Recognition: A Review
Author: Kazheen Ismael Taher & Adnan Mohsin Abdulazeez
Journal Name: International Journal of Science and Business
Website: ijsab.com
ISSN: ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)
DOI: https://doi.org/10.5281/zenodo.4475361
Media: Online
Volume: 5
Issue: 3
Acceptance Date: 24/01/2021
Date of Publication: 28/01/2021
PDF URL: https://ijsab.com/wp-content/uploads/681.pdf
Free download: Available
Page: 1-14
First Page: 1
Last Page: 14
Paper Type: Literature Review
Current Status: Published

 

Cite This Article:

Kazheen Ismael Taher & Adnan Mohsin Abdulazeez (2021). Deep Learning Convolutional Neural Network for Speech Recognition: A Review. International Journal of Science and Business, 5(3), 1-14. doi: https://doi.org/10.5281/zenodo.4475361

Retrieved from https://ijsab.com/wp-content/uploads/681.pdf

 

About Author (s)

Kazheen Ismael Taher (corresponding author), Information Technology Department, Akre Technical College of Informatics, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq. E-mail: kajeen.ismael@gmail.com

Professor Adnan Mohsin Abdulazeez, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq. E-mail: adnan.mohsin@dpu.edu.krd

 

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DOI: https://doi.org/10.5281/zenodo.4475361

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Human Diseases Detection Based On Machine Learning Algorithms: A Review Nareen O. M. Salim & Adnan Mohsin Abdulazeez

Human Diseases Detection Based On Machine Learning Algorithms: A Review

Author (s)

Nareen O. M. Salim & Adnan Mohsin Abdulazeez

Abstract

­One of the most significant subjects of society is human healthcare. It is looking for the best one and robust disease diagnosis to get the care they need as soon as possible. Other fields, such as statistics and computer science, are needed for the health aspect of searching since this recognition is often complicated. The task of following new approaches is challenging these disciplines, moving beyond the conventional ones. The actual number of new techniques makes it possible to provide a broad overview that avoids particular aspects. To this end, we suggest a systematic analysis of human diseases related to machine learning. This research concentrates on existing techniques related to machine learning growth applied to the diagnosis of human illnesses in the medical field to discover exciting trends, make unimportant predictions, and help decision-making. This paper analyzes unique machine learning algorithms used for healthcare applications to create adequate decision support. This paper intends to reduce the research gap in creating a realistic decision support system for medical applications.

 Keywords: Human disease, Healthcare, Machine learning, Deep learning, Convolutional Neural Networks.

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Title: Human Diseases Detection Based On Machine Learning Algorithms: A Review
Author: Nareen O. M. Salim & Adnan Mohsin Abdulazeez
Journal Name: International Journal of Science and Business
Website: ijsab.com
ISSN: ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)
DOI: https://doi.org/10.5281/zenodo.4467510
Media: Online
Volume: 5
Issue: 2
Acceptance Date: 19/01/2021
Date of Publication: 25/01/2021
PDF URL: https://ijsab.com/wp-content/uploads/674.pdf
Free download: Available
Page: 102-113
First Page: 102
Last Page: 113
Paper Type: Literature Review
Current Status: Published

 

Cite This Article:

Nareen O. M. Salim & Adnan Mohsin Abdulazeez (2021). Human Diseases Detection Based On Machine Learning Algorithms: A Review. International Journal of Science and Business, 5(2), 102-113. doi: https://doi.org/10.5281/zenodo.4462858

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About Author (s)

Nareen O. M. Salim, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.

Adnan Mohsin Abdulazeez (corresponding author), Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq. Email: nareen.mohameed@dpu.edu.krd

 

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DOI: https://doi.org/10.5281/zenodo.4467510