A Smart Machine Learning Model for the Detection of Brain Hemorrhage Diagnosis Based Internet of Things in Smart Cities
المؤلفون المشاركون
Chen, Hang
Hussain, Anwar
Khan, Sulaiman
Kou, Bo
Nazir, Shah
Liu, Wei
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-09-15
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
Generally, the emergence of Internet of Things enabled applications inspired the world during the last few years, providing state-of-the-art and novel-based solutions for different problems.
This evolutionary field is mainly lead by wireless sensor network, radio frequency identification, and smart mobile technologies.
Among others, the IoT plays a key role in the form of smart medical devices and wearables, with the ability to collect varied and longitudinal patient-generated health data, and at the same time also offering preliminary diagnosis options.
In terms of efforts made for helping the patients using IoT-based solutions, experts exploit capabilities of the machine learning algorithms to provide efficient solutions in hemorrhage diagnosis.
To reduce the death rates and propose accurate treatment, this paper presents a smart IoT-based application using machine learning algorithms for the human brain hemorrhage diagnosis.
Based on the computerized tomography scan images for intracranial dataset, the support vector machine and feedforward neural network have been applied for the classification purposes.
Overall, classification results of 80.67% and 86.7% are calculated for the support vector machine and feedforward neural network, respectively.
It is concluded from the resultant analysis that the feedforward neural network outperforms in classifying intracranial images.
The output generated from the classification tool gives information about the type of brain hemorrhage that ultimately helps in validating expert’s diagnosis and is treated as a learning tool for trainee radiologists to minimize the errors in the available systems.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Chen, Hang& Khan, Sulaiman& Kou, Bo& Nazir, Shah& Liu, Wei& Hussain, Anwar. 2020. A Smart Machine Learning Model for the Detection of Brain Hemorrhage Diagnosis Based Internet of Things in Smart Cities. Complexity،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1141250
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Chen, Hang…[et al.]. A Smart Machine Learning Model for the Detection of Brain Hemorrhage Diagnosis Based Internet of Things in Smart Cities. Complexity No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1141250
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Chen, Hang& Khan, Sulaiman& Kou, Bo& Nazir, Shah& Liu, Wei& Hussain, Anwar. A Smart Machine Learning Model for the Detection of Brain Hemorrhage Diagnosis Based Internet of Things in Smart Cities. Complexity. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1141250
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1141250
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر