doi:10.24996/ijs.2021.SI.1.1

Details

Publication Date

Wed Jan 13 2021

Journal Name

Iraqi Journal Of Science

DOI

10.24996/ijs.2021.SI.1.1

Choose Citation Style

Statistics

View publication

4

Abstract Views

250

Galley Views

216

Statistics

(3)

Embedded Electronics Applied in Remote Laboratories Using NodeJs

e-learning

embedded systems

online manipulation

PcDuino

Remote labs

Amine Moulay Taj

Abdelmoula

Naima Taifi

Abdessamad Malaoui

...Show More Authors

A description of the implementation of integrated practical work in a remote laboratory was presented in this paper. The student, in real time, can access an online web page in order to manipulate a practical work of digital electronics. This work is based on the use of an embedded system PcDuino. The hardware architecture and software solutions are described, as well as the supervision tool that allows the student to follow changes in the output states of the Practical Work remotely.

View Publication Preview PDF

Quick Preview PDF

Publication Date

Tue Feb 28 2023

Journal Name

Iraqi Journal Of Science

Benchmarking Framework for COVID-19 Classification Machine Learning Method Based on Fuzzy Decision by Opinion Score Method

COVID-19

Evaluation and benchmarking

Machine learning

multi-criteria decision making

MCDM

Fuzzy Decision by Opinion Score Method

Mahmood M.

M. A.

Baidaa

Kahlan F.

Moceheb Lazam

Z.T.

...Show More Authors

Coronavirus disease (COVID-19), which is caused by SARS-CoV-2, has been announced as a global pandemic by the World Health Organization (WHO), which results in the collapsing of the healthcare systems in several countries around the globe. Machine learning (ML) methods are one of the most utilized approaches in artificial intelligence (AI) to classify COVID-19 images. However, there are many machine-learning methods used to classify COVID-19. The question is: which machine learning method is best over multi-criteria evaluation? Therefore, this research presents benchmarking of COVID-19 machine learning methods, which is recognized as a multi-criteria decision-making (MCDM) problem. In the recent century, the trend of developing

View Publication Preview PDF

(9)

(7)

Publication Date

Sat Jun 01 2024

Journal Name

Journal Of Engineering

Intelligent Dust Monitoring System Based on IoT

IoT (Internet of Things)

Dust prediction

WSN (Wireless Sensor Network)

Deep Learning (DL)

Long Short-Term Memory (LSTM)

Muna Hadi

...Show More Authors

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

View Publication

Publication Date

Wed May 04 2022

Journal Name

Int. J. Nonlinear Anal. Appl.

Knee Meniscus Segmentation and Tear Detection Based On Magnitic Resonacis Images: A Review of Literature

Magnetic Resonance Imaging (M.R.I.)

Convolutional Neural Network (C.N.N.)

Deep learning (D.L.)

Musculoskeletal (M.S.K.)

Knee

Meniscus

Tear

Area Under the Curve (A.U.C.)

Mohammed

...Show More Authors

The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when

Publication Date

Fri Apr 01 2016

Journal Name

Journal Of Economics And Administrative Sciences

Comparing Bayes Estimators With others , for scale parameter and Reliability function of two parameters Frechet distribution

مقدر الإمكان الأعظم (MLE)

مقدر Bayes الأول

مقدر Bayes الثاني.

Maximum likelihood estimator (MLE) Bayes (1) and Bayes (2) Mean square error (E) is shape parameter is scale parameter under Quadratic loss function under General Entropy loss function

ميسون حميد

...Show More Authors

View Publication Preview PDF

1 2 3 4 ... 1822 1823 1824 1825