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Impact of Deep Learning on Transfer Learning : A Review Mohammed Jameel Barwary & Adnan Mohsin Abdulazeez

Impact of Deep Learning on Transfer Learning : A Review

Author (s)

Mohammed Jameel Barwary & Adnan Mohsin Abdulazeez

 

Abstract

­

Transfer learning and deep learning approaches have been utilised in several real-world applications and hierarchical systems for pattern recognition and classification tasks. However, in few of the real-world machine learning situations, this presumption does not sustain since there are instances where training data is costly or tough to gather and there is continually a necessity to produce high-performance learners competent with more easily attained data from diverse fields. The objective of this review is to determine more abstract qualities at the greater levels of the representation, by utilising deep learning to detach the variables in the outcomes, formally outline transfer learning, provide information on present solutions, and appraise applications employed in diverse facets of transfer learning and deep learning. This can be attained by rigorous literature exploration and discussion on all presently accessible techniques and prospective research studies on transfer learning solutions of independent as well as big data scale. The conclusions of this study could be an effectual platform directed at prospective directions for devising new deep learning patterns for different applications and dealing with the challenges concerned.

 Keywords: Machine Learning, Transfer Learning, Deep Learning, classifications, Supervised Learning techniques.

 

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Title: Impact of Deep Learning on Transfer Learning : A Review
Author: Mohammed Jameel Barwary & 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.4559668
Media: Online
Volume: 5
Issue: 3
Acceptance Date: 6/02/2021
Date of Publication: 24/02/2021
PDF URL: https://ijsab.com/wp-content/uploads/698.pdf
Free download: Available
Page: 204-216
First Page: 204
Last Page: 216
Paper Type: Literature Review
Current Status: Published

 

Cite This Article:

Barwary, M. J. & Abdulazeez, A. M. (2021). Impact of Deep Learning on Transfer Learning : A Review. International Journal of Science and Business, 5(3), 204-216. doi: https://doi.org/ 10.5281/zenodo.4559668

Retrieved from https://ijsab.com/wp-content/uploads/698.pdf

 

About Author (s)

Mohammed Jameel Barwary (corresponding author), Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq. Email: mohammed.jameel@uod.ac

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

 

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DOI: https://doi.org/10.5281/zenodo.4559668

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Reinforcement Learning and Modeling Techniques: A Review Hindreen Rashid Abdulqadir & Adnan Mohsin Abdulazeez

Reinforcement Learning and Modeling Techniques: A Review

Author (s)

Hindreen Rashid Abdulqadir & Adnan Mohsin Abdulazeez

Abstract

­

The Reinforcement learning (RL) algorithms solve a wide range of problems we faced. The topic of RL has achieved a new, complete standard of public opinion. High difficulty in large-scale real-world implementations is the effective use of large data sets previously obtained in augmented learning algorithms. Q-learning (QL), by learning a conservative Q function that allows a policy to be below the predicted value of the Q function, is introduced by us, which aims to circumvent these restrictions. We revealed technical reinforcement learning in this study. In principle, we demonstrate that QL creates a lower relation to current policy importance and that this can be correlated with guarantees of political learning theoretical change. In reality, QL strengthens the benchmark objective with a simple, standardized Q value which, in addition to existing Q-learning and essential applications, is quickly applied. The findings indicate that all algorithms are needed to learn how to play successfully. In comparison, all dual Q-learning variables have a significantly higher score compared with Q-learning, and the incremental reward function shows no improved effects than the normal reward function. We present an attack mechanism that uses the portability of competing tests to execute policy incentives and to prove their usefulness and consequences by means of a pilot study of a play learning scenario.

 Keywords: Machine learning, Reinforcement learning, Modelling – Technique, Q- learning.

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Title: Reinforcement Learning and Modeling Techniques: A Review
Author: Hindreen Rashid Abdulqadir & 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.4542638
Media: Online
Volume: 5
Issue: 3
Acceptance Date: 11/02/2021
Date of Publication: 16/02/2021
PDF URL: https://ijsab.com/wp-content/uploads/696.pdf
Free download: Available
Page: 174-189
First Page: 174
Last Page: 189
Paper Type: Literature Review
Current Status: Published

 

Cite This Article:

Abdulqadir, H. R. & Abdulazeez, A. M. (2021). Reinforcement Learning and Modeling Techniques: A Review. International Journal of Science and Business, 5(3), 174-189. doi: https://doi.org/10.5281/zenodo.4542638

Retrieved from https://ijsab.com/wp-content/uploads/696.pdf

 

About Author (s)

Hindreen Rashid Abdulqadir  (corresponding author),   Information Technology Department,  Akre Technical College of Informatics, Duhok Polytechnic University, Duhok Kurdistan Region, Iraq. Email: Hindreen.rashid@dpu.edu.krd

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.4542638

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Machine Learning Powered IoT for Smart Applications Zhala Jameel Hamad & Shavan Askar

Machine Learning Powered IoT for Smart Applications  

Author (s)

Zhala Jameel Hamad & Shavan Askar

Abstract

­

With the coming of fast advancements, with the assistance of IoT, a great percentage of heterogeneous devices can be connected with each other. The technology with the relationship of different devices through the internet is named the internet of things (IoT), makes a wide number of different characteristics and qualities of data. IoT and Machine learning (ML) guarantees the widespread advancement to grow the insights of the IoT devices and applications. Over the final few years, artificial intelligence and machine learning have advanced very significantly. It allows a machine or system to learn more effectively than people learn on their own. When we learn some kind of system about the concept of our trial or the knowledge obtained after evaluating it. Combining IoT with rapidly advancing ML technologies can make ‘smart machines’ that mimic smart action to do well-informed resolve with little or no human involvement. There are at least two fundamental reasons, why machine learning is a suitable solution for the IoT world? The primary has got to do with the volume of data and the automation openings. The second is related to the prescient investigation. Therefore, this paper focuses on ML in different techniques and different domains that motivate and support IoT applications. Many previous works related to this subject and examples have been addressed, explained in detail. The results showed that ML plays a vital role in monitoring, processing, systematic investigation, and smart use of the expansive measure of data in several fields. It was also beneficial for helping users’ process massive data.

 Keywords: Machine Learning, Internet of things, SDN, Smart Applications.

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Title: Machine Learning Powered IoT for Smart Applications  
Author: Zhala Jameel Hamad & Shavan Askar
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.4497664
Media: Online
Volume: 5
Issue: 3
Acceptance Date: 01/02/2021
Date of Publication: 03/02/2021
PDF URL: https://ijsab.com/wp-content/uploads/689.pdf
Free download: Available
Page: 92-100
First Page: 92
Last Page: 100
Paper Type: Literature Review
Current Status: Published

 

Cite This Article:

Zhala Jameel Hamad & Shavan Askar (2021). Machine Learning Powered IoT for Smart Applications. International Journal of Science and Business, 5(3), 92-100. doi: https://doi.org/10.5281/zenodo.4497664

Retrieved from https://ijsab.com/wp-content/uploads/689.pdf

 

About Author (s)

Zhala Jameel Hamad  Information System Engineering, Erbil Polytechnic University, Erbil, Iraq. Email: zhalla.mei20@epu.edu.iq.

Shavan Askar (Corresponding Author), Assistant Professor, Erbil Polytechnic University, Erbil, Iraq. Email: shavan.askar@epu.edu.iq.

 

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DOI: https://doi.org/10.5281/zenodo.4497664

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Deep Learning Models for Cyber Security in IoT Networks: A Review Kosrat Dlshad Ahmed & Shavan Askar

Deep Learning Models for Cyber Security in IoT Networks: A Review

Author (s)

Kosrat Dlshad Ahmed & Shavan Askar

Abstract

­

The IoT systems and connectivity provide improved experience and improve the quality of service for the users in different perspectives. Recent development of the technological prospects and management of the sufficient aspects for the delivery of performance need to be ensured in this regard. The concept of IoT is related with the widely connected features, systems, data storage facilities, management processes, applications, devices, users, gateways, services and thousands of other elements. As the importance of IoT applications has been growing in recent times, the prospects for development and management are immense for the development opportunities. In recent times, cybersecurity and ensuring privacy for the users have attracted attention of the users. With growing popularity of the social media platforms, more and more people are becoming connected. With growing opportunity of connectivity, people need more secured space to connect. In this article, different aspects of the cybersecurity based on the deep learning models and analyzing the concepts of machine learning, understanding the concept of security and privacy, contributing to the development and management of cybersecurity etc. To demonstrate the understanding of cybersecurity in the IoT networks, effective deep learning models such as MLP, CNN, LSTP and a hybrid model of CNN and LSTP have been analyzed. To contribute to the learning process, future research opportunities have also been identified.

 Keywords: Deep Leaning, Machine Learning, Cyber Security, Internet of Things, Privacy, Cyber Security.

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Title: Deep Learning Models for Cyber Security in IoT Networks: A Review
Author: Kosrat Dlshad Ahmed & Shavan Askar
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.4497017
Media: Online
Volume: 5
Issue: 3
Acceptance Date: 01/02/2021
Date of Publication: 03/02/2021
PDF URL: https://ijsab.com/wp-content/uploads/686.pdf
Free download: Available
Page: 61-70
First Page: 61
Last Page: 70
Paper Type: Literature Review
Current Status: Published

 

Cite This Article:

Kosrat Dlshad Ahmed, Shavan Askar (2021). Deep Learning Models for Cyber Security in IoT Networks: A Review. International Journal of Science and Business, 5(3), 61-70. doi: https://doi.org/10.5281/zenodo.4497017

Retrieved from https://ijsab.com/wp-content/uploads/686.pdf

 

About Author (s)

Kosrat Dlshad Ahmed, Information System Engineering, Erbil Polytechnic University, Erbil, Iraq. Email: kosrat.ahmed@epu.edu.iq.

Shavan Askar (Corresponding Author), Assistant Professor, Erbil Polytechnic University, Erbil, Iraq.  Email: shavan.askar@epu.edu.iq.

 

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DOI: https://doi.org/10.5281/zenodo.4497017

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Machine Learning for IoT HealthCare Applications: A Review Chnar Mustafa Mohammed & Shavan Askar

Machine Learning for IoT HealthCare Applications: A Review

Author (s)

Chnar Mustafa Mohammed & Shavan Askar

Abstract

­

Internet of Things and Machine Learning (ML) have wide applicability in many aspects of life, health care is one of them. With the rapid development and improvement of the internet, the conventional strategies for patient services diminished and supplanted with electronic healthcare systems. The use of IoT technology offers medical professionals and patients the most modern medical device environment. IoT things and Machine-Learning are valuable in various classifications from far off observing of the modern climate to mechanical mechanization. Moreover, medical care applications are principally indicating interest in IoT things in view of cost decrease, easy to understand and improve the personal satisfaction of patients. The latest applications for IoT medical treatment, investigated and still facing problems in the clinical environment, are needed for intellectual, creativity-based answers. In specific, portable, and implantable IoT model devices, investigated for calculating the data transmission. Implantable technologies lead to the natural substitution of the injured part of the human body. The creation of a wearable and implantable healthcare body area network faced several challenges that are illustrated in this study. In this paper, an overview of IoT and Machine Learning based on healthcare care demonstrated in detail, the applications that use in health care by incorporating Machine Learning (ML) for the Internet of Things (IoT) listed with all issues and challenges while using this application or devices for health care and their important usage. Also, algorithms used by Machine Learning in IoT for developing devices are indicated by showing previous work and classified each of them according to the used method.

 Keywords: Internet of Things, Machine Learning, Wearable devices, personalized health care, and implantable devices.

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Title: Machine Learning for IoT HealthCare Applications: A Review
Author: Chnar Mustafa Mohammed & Shavan Askar
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.4496904
Media: Online
Volume: 5
Issue: 3
Acceptance Date: 31/01/2021
Date of Publication: 03/02/2021
PDF URL: https://ijsab.com/wp-content/uploads/684.pdf
Free download: Available
Page: 42-51
First Page: 42
Last Page: 51
Paper Type: Literature Review
Current Status: Published

 

Cite This Article:

Chnar Mustafa Mohammed & Shavan Askar (2021). Machine Learning for IoT HealthCare Applications: A Review. International Journal of Science and Business, 5(3), 42-51. doi: https://doi.org/10.5281/zenodo.4496904

Retrieved from https://ijsab.com/wp-content/uploads/684.pdf

 

About Author (s)

Chnar Mustaf Mohammed,  Information System Engineering, Erbil Polytechnic University, Erbil, Iraq.

Shavan Askar (Corresponding Author), Assistant Professor, Erbil Polytechnic University, Erbil, Iraq. Email: shavan.askar@epu.edu.iq

 

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DOI: https://doi.org/10.5281/zenodo.4496904

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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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Machine Learning Classification Based on Radom Forest Algorithm: A Review Nasiba Mahdi Abdulkareem & Adnan Mohsin Abdulazeez

Machine Learning Classification Based on Radom Forest Algorithm: A Review

Author (s)

Nasiba Mahdi Abdulkareem & Adnan Mohsin Abdulazeez

Abstract

­Machine Learning is a significant technique to realize Artificial Intelligence. The Random Forest Algorithm can be considered as one of the Machine Learning’s representative algorithm, which is known for its simplicity and effectiveness. It is also can be defined as a Decision Tree-Based Classifier that chooses the best classification tree as the final classifier’s classification of the algorithm via voting. Random Forest is the most accepted group classification technique because of having excellent features such as Variable Importance Measure, Out-of-bag error, Proximities, etc. Currently, it is in the new classification, intrusion detection, content information filtering, and sentiment analysis that is why there is an extensive range of applications in image processing. In this paper, the construction process of Random Forests and the study status of Random Forests would primarily be introduced in terms of capacity enhancement and performance indicators. The use of Random Forest in different fields such as Medicine, Agriculture, Astronomy, etc. is often mentioned.

 Keywords: Machine Learning, Random Forest, Ensembles of Decision Tree, Classification.

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Title: Machine Learning Classification Based on Radom Forest Algorithm: A Review
Author: Nasiba Mahdi Abdulkareem & 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.4471118
Media: Online
Volume: 5
Issue: 2
Acceptance Date: 24/01/2021
Date of Publication: 27/01/2021
PDF URL: https://ijsab.com/wp-content/uploads/676.pdf
Free download: Available
Page: 128-142
First Page: 128
Last Page: 142
Paper Type: Literature Review
Current Status: Published

 

Cite This Article:

Nasiba Mahdi Abdulkareem & Adnan Mohsin Abdulazeez (2021). Machine Learning Classification Based on Radom Forest Algorithm: A Review. International Journal of Science and Business, 5(2), 128-142. doi: https://doi.org/10.5281/zenodo.4471118

Retrieved from https://ijsab.com/wp-content/uploads/676.pdf

 

About Author (s)

Nasiba Mahdi Abdulkareem (corresponding author), Information Technology Department, Akre Technical College of Informatics, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq. E-mail: nasiba.mahdi@dpu.edu.krd

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.4471118

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Deep Learning Convolutional Neural Network for Face Recognition: A Review Rondik J. Hassan & Adnan Mohsin Abdulazeez

Deep Learning Convolutional Neural Network for Face Recognition:

A Review

Author (s)

Rondik J. Hassan & Adnan Mohsin Abdulazeez

Abstract

­Face recognition is increasingly being used for solving various social-problems such as personal protection and authentication. As with other widely used biometric applications, facial recognition is a biometric instrument such as iris recognition, vein pattern recognition, and fingerprint recognition. Facial recognition identifies a person based on certain aspects of his physiology. Deep Learning (DL) is a branch of machine learning (ML) that can be used in image processing and pattern recognition to solve multiple problems, one of the applications is face recognition. With the advancement of deep learning, Convolution Neural Network (CNN) based facial recognition technology has been the dominant approach adopted in the field of face recognition. The purpose of this paper is to provide a review of face recognition approaches. Furthermore, the details of each paper, such as used datasets, algorithms, architecture, and achieved results are summarized and analyzed comprehensively.

 Keywords: Face Recognition, Machine Learning, Deep Learning, Convolution Neural Network, Feature Extraction, Feature Matching.

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Title: Deep Learning Convolutional Neural Network for Face Recognition: A Review
Author: Rondik J. Hassan & 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.4471013
Media: Online
Volume: 5
Issue: 2
Acceptance Date: 24/01/2021
Date of Publication: 27/01/2021
PDF URL: https://ijsab.com/wp-content/uploads/675.pdf
Free download: Available
Page: 114-127
First Page: 114
Last Page: 127
Paper Type: Literature Review
Current Status: Published

 

Cite This Article:

Rondik J. Hassan & Adnan Mohsin Abdulazeez (2021). Deep Learning Convolutional Neural Network for Face Recognition: A Review, International Journal of Science and Business, 5(2), 114-127. doi: https://doi.org/ 10.5281/zenodo.4471013

Retrieved from https://ijsab.com/wp-content/uploads/675.pdf

 

About Author (s)

Rondik J.Hassan (corresponding author), Information Technology Department, Akre Technical College of Informatics, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.  E-mail: rondik.jamaluddin@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.4471013

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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

Retrieved from https://ijsab.com/wp-content/uploads/674.pdf

 

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

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An Empirical Study of Various Machine Learning Approaches in Prediction of Chronic Kidney Disease Md. Shafiul Azam, Umme Kulsom, S. M. Hasan Sazzad Iqbal & Md. Toukir Ahmed

An Empirical Study of Various Machine Learning Approaches in Prediction of Chronic Kidney Disease

Author (s)

Md. Shafiul Azam, Umme Kulsom, S. M. Hasan Sazzad Iqbal & Md. Toukir Ahmed

Abstract

In today’s era everybody is trying to be conscious about health. Although, due to workload and busy schedule, one gives attention to the health when any major symptoms occur. But Chronic Kidney Disease (CKD) is a disease which doesn’t shows symptoms it is hard to predict, detect and prevent such a disease and this can lead to permanently health damage, but some machine learning algorithms can come handy in this aspect for their efficient prediction and analysis. By using data of CKD, patients with 25 attributes and 400 records we are going to use various machine learning techniques like Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree etc. The purposes of our work is to virtuously predicting Chronic Kidney disease and have a comparative analysis among some of the popular machine learning based approaches based on some performance metrics. In our work, it is found that the Random Forest algorithm outperforming other machine learning based approaches we used in the experiment.

 Keywords: CKD, KNN, Machine Learning, Prediction, Performance Metrics.

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Title: An Empirical Study of Various Machine Learning Approaches in Prediction of Chronic Kidney Disease
Author: Md. Shafiul Azam, Umme Kulsom, S. M. Hasan Sazzad Iqbal & Md. Toukir Ahmed
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.4244468
Media: Online
Volume: 4
Issue: 11
Acceptance Date: 27/10/2020
Date of Publication: 28/10/2020
PDF URL: https://ijsab.com/wp-content/uploads/615.pdf
Free download: Available
Page: 101-110
First Page: 101
Last Page: 110
Paper Type: Research article
Current Status: Published

 

Cite This Article:

Md. Shafiul Azam, Umme Kulsom, S. M. Hasan Sazzad Iqbal & Md. Toukir Ahmed (2020). An Empirical Study of Various Machine Learning Approaches in Prediction of Chronic Kidney Disease, International Journal of Science and Business, 4(11), 1-8. doi: https://doi.org/10.5281/zenodo.4244468

Retrieved from https://ijsab.com/wp-content/uploads/615.pdf

 

About Author (s)

Md. Shafiul Azam Assistant Professor,  Dept. of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh.

Umme Kulsom, Student, Dept. of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh

M. Hasan Sazzad Iqbal, Assistant Professor, Dept. of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh

Md. Toukir Ahmed (Corresponding Author), Lecturer,  Dept. of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh.

 

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DOI: https://doi.org/10.5281/zenodo.4244468

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Deep Learning Models Based on Image Classification: A Review Kavi B. Obaid, Subhi R. M. Zeebaree & Omar M. Ahmed

Deep Learning Models Based on Image Classification: A Review

Author (s)

Kavi B. Obaid, Subhi R. M. Zeebaree & Omar M. Ahmed

Abstract

With the development of the big data age, deep learning developed to become having a more complex network structure and more powerful feature learning and feature expression abilities than traditional machine learning methods. The model trained by the deep learning algorithm has made remarkable achievements in many large-scale identification tasks in the field of computer vision since its introduction. This paper first introduces the deep learning, and then the latest model that has been used for image classification by deep learning are reviewed.  Finally, all used deep learning models in the literature have been compared to each other in terms of accuracy for the two most challenging datasets CIFAR-10 and CIFAR-100.

 Keywords: Deep Learning, Image Classification, Machine Learning, Models.

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Title: Deep Learning Models Based on Image Classification: A Review
Author: Kavi B. Obaid, Subhi R. M. Zeebaree & Omar M. Ahmed
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.4108433
Media: Online
Volume: 4
Issue: 11
Acceptance Date: 13/10/2020
Date of Publication: 20/10/2020
PDF URL: https://ijsab.com/wp-content/uploads/612.pdf
Free download: Available
Page: 75-81
First Page: 75
Last Page: 81
Paper Type: Research article
Current Status: Published

 

Cite This Article:

Kavi B. Obaid, Subhi R. M. Zeebaree & Omar M. Ahmed (2020). Deep Learning Models Based on Image Classification: A Review. International Journal of Science and Business, 4(11), 75-81. doi: https://doi.org/10.5281/zenodo.4108433

Retrieved from https://ijsab.com/wp-content/uploads/612.pdf

 

About Author (s)

Kavi B. Obaid, Computer Science Department, College of Science, University of Zakho, Iraq (e-mail: kavi.obaid@uoz.edu.krd).

Subhi R. M. Zeebaree, Duhok Polytechnic University, Iraq (e-mail: subhi.rafeeq@dpu.edu.krd).

Omar M. Ahmed, (Corresponding author),Information Technology Department, Zakho Technical Institute, Duhok Polytechnic University, Iraq (e-mail: omar.alzakholi@uoz.edu.krd).

 

 

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DOI: https://doi.org/10.5281/zenodo.4108433