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Human Pose Estimation Algorithm Using Optimized Symmetric Spatial Transformation Network
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Human posture estimation is a crucial topic in the computer vision field and has become a hotspot for research in many human behaviors related work. Human pose estimation can be understood as the human key point recognition and connection problem. The paper presents an optimized symmetric spatial transformation network designed to connect with single-person pose estimation network to propose high-quality human target frames from inaccurate human bounding boxes, and introduces parametric pose non-maximal suppression to eliminate redundant pose estimation, and applies an elimination rule to eliminate similar pose to obtain unique human pose estimation results. The exploratory outcomes demonstrate the way that the proposed technique can precisely recognize the human central issues, really work on the exactness of human posture assessment, and can adjust to the intricate scenes with thick individuals and impediment. Finally, the difficulties and possible future trends are described, and the development of the field is presented.

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Publication Date
Mon Jan 01 2018
Journal Name
Lecture Notes Of The Institute For Computer Sciences, Social Informatics And Telecommunications Engineering
Sensor Data Classification for the Indication of Lameness in Sheep
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Publication Date
Thu Jul 20 2023
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Applying Ensemble Classifier, K-Nearest Neighbor and Decision Tree for Predicting Oral Reading Rate Levels
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For many years, reading rate as word correct per minute (WCPM) has been investigated by many researchers as an indicator of learners’ level of oral reading speed, accuracy, and comprehension. The aim of the study is to predict the levels of WCPM using three machine learning algorithms which are Ensemble Classifier (EC), Decision Tree (DT), and K- Nearest Neighbor (KNN). The data of this study were collected from 100 Kurdish EFL students in the 2nd-year, English language department, at the University of Duhok in 2021. The outcomes showed that the ensemble classifier (EC) obtained the highest accuracy of testing results with a value of 94%. Also, EC recorded the highest precision, recall, and F1 scores with values of 0.92 for

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Publication Date
Fri Sep 30 2022
Journal Name
Iraqi Journal Of Science
Heart Disease Classification–Based on the Best Machine Learning Model
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    In recent years, predicting heart disease has become one of the most demanding tasks in medicine. In modern times, one person dies from heart disease every minute. Within the field of healthcare, data science is critical for analyzing large amounts of data. Because predicting heart disease is such a difficult task, it is necessary to automate the process in order to prevent the dangers connected with it and to assist health professionals in accurately and rapidly diagnosing heart disease. In this article, an efficient machine learning-based diagnosis system has been developed for the diagnosis of heart disease. The system is designed using machine learning classifiers such as Support Vector Machine (SVM), Nave Bayes (NB), and K-Ne

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Publication Date
Thu Jul 01 2021
Journal Name
Iraqi Journal Of Science
Implementation of Machine Learning Techniques for the Classification of Lung X-Ray Images Used to Detect COVID-19 in Humans
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COVID-19 (Coronavirus disease-2019), commonly called Coronavirus or CoV, is a dangerous disease caused by the SARS-CoV-2 virus. It is one of the most widespread zoonotic diseases around the world, which started from one of the wet markets in Wuhan city. Its symptoms are similar to those of the common flu, including cough, fever, muscle pain, shortness of breath, and fatigue. This article suggests implementing machine learning techniques (Random Forest, Logistic Regression, Naïve Bayes, Support Vector Machine) by Python to classify a series of chest X-ray images that include viral pneumonia, COVID-19, and healthy (Not infected) cases in humans. The study includes more than 1400 images that are collected from the Kaggle platform. The expe

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Publication Date
Wed Mar 01 2023
Journal Name
Baghdad Science Journal
Minimum Neighborhood Domination of Split Graph of Graphs
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Let  be a non-trivial simple graph. A dominating set in a graph is a set of vertices such that every vertex not in the set is adjacent to at least one vertex in the set. A subset  is a minimum neighborhood dominating set if  is a dominating set and if for every  holds. The minimum cardinality of the minimum neighborhood dominating set of a graph  is called as minimum neighborhood dominating number and it is denoted by  . A minimum neighborhood dominating set is a dominating set where the intersection of the neighborhoods of all vertices in the set is as small as possible, (i.e., ). The minimum neighborhood dominating number, denoted by , is the minimum cardinality of a minimum neighborhood dominating set. In other words, it is the

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Publication Date
Tue Dec 24 2019
Journal Name
Journal Of Legal Sciences
Investigation as a means of the parliamentary control over government actions
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Parliamentary investigation is one of the most important means of the parliament in its oversight on the work of the government. It is different from other types of investigations carried out by the Parliament itself, such as electoral or legislative investigation. It is also different from the investigation conducted by the administration or courts. This investigation is been conducted by a committee which consisted of some of members of parliament. The committee is looking to search the truth in a case related to the public interest and for that it usually has powers to access documents relevant to the case under investigation. Moreover, it has the right to request the presence of any government official before it. The committee usuall

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Publication Date
Sun Apr 02 2017
Journal Name
Journal Of Educational And Psychological Researches
أثر برنامج تدريبي لمدرسي الكيمياء على وفق جانبي الدماغ معا في التحصيل الدراسي لطلبتهم
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 This research aims to investigate the impact of a training program for teachers of chemistry according to the strategies for both sides of the brain together in academic achievement for their students, the sample consisted of a 12 teachers of both genders who are teaching chemistry for the students of the fifth scientific secondary schools of the General Directorate for the Education in Garmiyan/Sulaymaniyah governorate, Iraqi Kurdistan region, where five teachers of them randomly selected to involve the training program, and seven teachers did not participate in the proposed training program, where a sample of students was selected for each group of teachers about 147 male and female students for teachers of the experiment

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Publication Date
Sun Jan 04 2015
Journal Name
Journal Of Educational And Psychological Researches
Attitudes towards Mental illness among Pregnant Women AttendinGovernment on Women's Clinics in the Province of Ramallah and Al-Bireh
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This study aimed to identify attitudes towards mental illness in pregnant female clients to clinics women in the province of Ramallah and Al Bireh, for this purpose applied to study procedures on a sample of (200) of pregnant mothers were selected a sample available, have reached results no statistically significant differences in the level of attitudes towards mental illness due to the variable age in mothers pregnant female clients to clinics for women. Ther were astatistically significant differences in the level of these trends depending on the variable-level scientific research for the benefit of pregnant class university students and older and then high school and so on all areas except the area of social interaction, The results a

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
The Effect Of Optimizers On The Generalizability Additive Neural Attention For Customer Support Twitter Dataset In Chatbot Application
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When optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST dataset. The effectiveness of each optimizer is evaluat

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Publication Date
Sun May 12 2019
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Infection control measures to reduce hospital infection rates in the medical city burn center
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Background: The most serious problem in burn units is nosocomial infection (NI). In extensive burn patients, sepsis is considered the main cause of death; infection control program will help to lower NI and its subsequent high mortality rate.

Objective: To achieve the lowering of NI in burn units, by effect of infection control measures (ICMs).

Patients and methods: This is a prospective study conducted on patients admitted in Burn Center/Medical City in Baghdad from May 2012 to April 2015. A total of 1977 hospitalized patients were included in this study. This center receives burn patients with different severity. In around April 2012 an infection control program was sta

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