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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

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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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Impact of IoT Frameworks on Healthcare and Medical Systems Performance Rezgar Hasan Saeed, Hivi Ismat Dino, Lailan M. Haji, Daroon Mudhafer Hamed, Hanan M. Shukur, Omid H. Jader

Impact of IoT Frameworks on Healthcare and Medical Systems Performance

Author (s)

Rezgar Hasan Saeed, Hivi Ismat Dino, Lailan M. Haji, Daroon Mudhafer Hamed, Hanan M. Shukur, Omid H. Jader

Abstract

­

Internet of Thing (IoT) is a system of interconnected calculating equipment, electronically and mechanically and digital equipment delivered with Unique Identifiers and capability of data-transmission through a system without needful of Human-to-Human or Human-to-Computer communication. However, IoMT considered as IoT-program-implementation aimed at medicinal besides healthcare requirements, information gathering also investigation to be studied and observed. This led to proposing extensive scope of fascinating prospective consequences for enterprises: vehicles mileage-sensitivity as well auto-strategy support otherwise prepare it strongly establish in addition description predicted alighting periods towards taking-up travelers. This is due to that its standards are as of now being connected to improve access to the mind, increment the quality of care also, above all decrease the cost of care. An efficient IoT healthcare system aims to give continuous remote checking of patient health conditions, to counteract the basic patient conditions, and to improve personal satisfaction through a smart IoT environment. The trend of this paper is about displaying a detailed survey that addresses the closest previous studies to IoT roles in the healthcare sector. Giving inspiration, confinements looked by specialists, and recommendations proposed to examiners for improving this basic research field, according to detailed comparison among the addressed researches.

 Keywords: Internet of Things, Healthcare, Cloud Computing, Wireless Sensors.

 

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Title: Impact of IoT Frameworks on Healthcare and Medical Systems Performance
Author: Rezgar Hasan Saeed, Hivi Ismat Dino, Lailan M. Haji, Daroon Mudhafer Hamed, Hanan M. Shukur, Omid H. Jader
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.4423394
Media: Online
Volume: 5
Issue: 1
Acceptance Date: 26/12/2020
Date of Publication: 07/01/2021
PDF URL: https://ijsab.com/wp-content/uploads/661.pdf
Free download: Available
Page: 115-126
First Page: 115
Last Page: 126
Paper Type: Literature Review
Current Status: Published

 

Cite This Article:

Rezgar Hasan Saeed, Hivi Ismat Dino, Lailan M. Haji, Daroon Mudhafer Hamed, Hanan M. Shukur, Omid H. Jader (2020). Impact of IoT Frameworks on Healthcare and Medical Systems Performance. International Journal of Science and Business, 5(1), 115-126. doi: https://doi.org/10.5281/zenodo.4423394

Retrieved from https://ijsab.com/wp-content/uploads/661.pdf

 

About Author (s)

Rezgar Hasan Saeed (corresponding author), Computer and Technology Education Department, Near east university, Cyprus. rezgarhasan1992@gmail.com

Hivi Ismat Dino, Department of Computer Science, University of Zakho, Kurdistan Region – Iraq, hivi.dino@uoz.edu.krd

Lailan M. Haji, Department of Computer Science, University of Zakho, Kurdistan Region – Iraq, lailan.haji@uoz.edu.krd

Daroon Mudhafer Hamed, Lebanese French University, Erbil – Kurdistan Region – Iraq, daroon.m.h@gmail.com

Hanan M. Shukur, It Department, Al Kitab University, Kirkuk – Iraq, hanan89md@gmail.com

Omid H. Jader, Information System Engineering Dept. Erbil Polytechnic University, Kurdistan Region-Iraq, omid.jader@epu.edu.iq

 

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DOI: https://doi.org/10.5281/zenodo.4423394