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Diabetes Prediction Using Machine Learning
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Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five attributes of the training process. The results of the second experiment showed improvement in the performance of the KNN and the Multilayer Perceptron. The results of the second experiment showed a slight decrease in the performance of the Random Forest with 97.5 % accuracy.

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Publication Date
Mon Dec 20 2021
Journal Name
Natural Volatiles & Essential Oils
Therapeutic Effects of Allicin against the Diabetes Mellitus Induced by Streptozotocin in Male Rats
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This study aimed to see how allicin (45mg/kg BW) affected diabetic Mellitus in male rats (DM). Forty male rats were utilized, and they were split into four groups at random for 42 days. T2 was treated with 45 mg/kg B.W of allicin dissolved in 1 ml of D.W daily and injected with a single dose of sodium citrate buffer (0.5ml Intra-Peritoneal IP), DM was induced in T1 and T2 by injection of a single dose of streptozotocin 50 mg/kg B.W IP, T1 was assigned as a positive control, T3 received 45 mg/kg B.W. of allicin dissolved in 1 ml D.W. every day, and a single dose of sodium citrate buffer was injected (0.5ml IP). When diabetic rats treated with allicin in T2 were compared to diabetic rats in T1, the findings indicated a significant increase (P

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Publication Date
Thu Aug 01 2019
Journal Name
Biochemical And Cellular Archives
PARTIAL PURIFICATION AND CHARACTERIZATION OF ACID PHOSPHATASE FROM SERA OF OBESE DIABETES MELLITUS PATIENTS
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The study aimed to purification of acid phosphatase (ACP) from sera of obesetype 2 diabetes mellitus patients, this study included from thirty T2DM patients and thirty control, purification process was done with several steps included precipitation with inorganic salt (NH4 ) 2SO4 30%-80%, dialysis, ion exchange chromatography by DEAE sepharose anion column and size exclusion chromatography by Sepharose 6B.ACP, BMI, FBS, HbA1c, Lipid profile, Urea, Creatinie, Insuline, Homa-IR were determined. Results showed the precipitate and concentrated protein appeared four peaks in ion exchange column. ACP located in the first and second peak with purification fold (21.1), (37.2) yield of enzyme and specific activity (173.3) IU/ml, which obtained a si

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Publication Date
Fri Jul 24 2020
Journal Name
Al-kindy College Medical Journal
Residual cardiovascular risk in diabetes and obesity: Targeting lipid abnormalities other than LDL cholesterol
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Background: The majorities of statin-treated patients, in whom low-density lipoprotein cholesterol (LDL-C) targets have been achieved, have had recurrent cardiovascular events (CVE) with an absolute rate remain even higher among patients with disorders of insulin resistance, metabolic syndrome (MetS) and type2 diabetes mellitus (T2DM) as compared to patients devoid of these conditions.Objectives: Provide updated key messages of lipid and lipoprotein abnormalities as indicator for cardiovascular disease (CVD) risk in patients with T2DM and obesity, as well as the current evidence-based treatment targets and interventions to reduce this risk.Key messages: The Residual Risk Reduction Initiative (R3I) emphasized atherogenic dyslipidemia (AD)

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Publication Date
Sat Jan 31 2026
Journal Name
International Journal Of Intelligent Engineering And Systems
Low-complexity Deep Learning for Joint Channel-type Identification and SNR Estimation in MIMO-OFDM Using CNN–BRNN with LUT Labels
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Channel estimation (CE) is essential for wireless links but becomes progressively onerous as Fifth Generation (5G) Multi-Input Multi-Output (MIMO) systems and extensive fading expand the search space and increase latency. This study redefines CE support as the process of learning to deduce channel type and signal-tonoise ratio (SNR) directly from per-tone Orthogonal Frequency-Division Multiplexing (OFDM) observations,with blind channel state information (CSI). We trained a dual deep model that combined Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks (BRNNs). We used a lookup table (LUT) label for channel type (class indices instead of per-tap values) and ordinal supervision for SNR (0–20 dB,5-dB steps). T

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Publication Date
Tue Jan 02 2018
Journal Name
Journal Of Educational And Psychological Researches
The Effect of Using Brainstorming Technique on the Essay Writing and Self- regulation Learning of the Iraqi Secondary Students
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         This study investigated the effect of using brainstorming as a teaching technique on the students’ performance in writing different kinds of essays and self regulation among the secondary students. The total population of this study, consisted of (51) female students of the 5th Secondary grade in Al –kawarzmi School in Erbil during the academic year 2015-2016. The chosen sample consisted of 40 female students, has been divided into two groups. Each one consists of (20) students to represent the experimental group and the control one. Brainstorming technique is used to teach the experimental group, and the conventional method is used to teach the control group. The study inst

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Publication Date
Fri Dec 01 2023
Journal Name
Applied Energy
Deep clustering of Lagrangian trajectory for multi-task learning to energy saving in intelligent buildings using cooperative multi-agent
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The intelligent buildings provided various incentives to get highly inefficient energy-saving caused by the non-stationary building environments. In the presence of such dynamic excitation with higher levels of nonlinearity and coupling effect of temperature and humidity, the HVAC system transitions from underdamped to overdamped indoor conditions. This led to the promotion of highly inefficient energy use and fluctuating indoor thermal comfort. To address these concerns, this study develops a novel framework based on deep clustering of lagrangian trajectories for multi-task learning (DCLTML) and adding a pre-cooling coil in the air handling unit (AHU) to alleviate a coupling issue. The proposed DCLTML exhibits great overall control and is

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Sport Sciences
The effect of using the McCarthy model according to cognitive style (rigid- flexibility) in learning some skills in artistic gymnastics for women
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The aim of the research is to identify the cognitive method (rigidity flexibility) of third-stage students in the collage of Physical Education and Sports Sciences at The University of Baghdad, as well as to recognize the impact of using the McCarthy model in learning some of skills in gymnastics, as well as to identify the best groups in learning skills, the experimental curriculum was used to design equal groups with pre test and post test and the research community was identified by third-stage students in academic year (2020-2021), the subject was randomly selected two divisions after which the measure of cognitive method was distributed to the sample, so the subject (32) students were distributed in four groups, and which the pre te

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Publication Date
Tue May 21 2019
Journal Name
The Journal Of Engineering
Performance of a tubular machine driven by an external‐combustion free‐piston engine
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Publication Date
Sat Feb 01 2020
Journal Name
International Journal Of Computer Science And Mobile Computing
Hierarchical Fixed Prediction of Mixed based for Medical Image Compression.
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Publication Date
Mon Mar 13 2017
Journal Name
Journal Of Baghdad College Of Dentistry
Computer Assisted Immunohistochemical Score Prediction Via Simplified Image Acquisition Technique
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Background: techniques of image analysis have been used extensively to minimize interobserver variation of immunohistochemical scoring, yet; image acquisition procedures are often demanding, expensive and laborious. This study aims to assess the validity of image analysis to predict human observer’s score with a simplified image acquisition technique. Materials and methods: formalin fixed- paraffin embedded tissue sections for ameloblastomas and basal cell carcinomas were immunohistochemically stained with monoclonal antibodies to MMP-2 and MMP-9. The extent of antibody positivity was quantified using Imagej® based application on low power photomicrographs obtained with a conventional camera. Results of the software were employed

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