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Analyzing the behavior of different classification algorithms in diabetes prediction
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<span lang="EN-US">Diabetes is one of the deadliest diseases in the world that can lead to stroke, blindness, organ failure, and amputation of lower limbs. Researches state that diabetes can be controlled if it is detected at an early stage. Scientists are becoming more interested in classification algorithms in diagnosing diseases. In this study, we have analyzed the performance of five classification algorithms namely naïve Bayes, support vector machine, multi layer perceptron artificial neural network, decision tree, and random forest using diabetes dataset that contains the information of 2000 female patients. Various metrics were applied in evaluating the performance of the classifiers such as precision, area under the curve (AUC), accuracy, receiver operating characteristic (ROC) curve, f-measure, and recall. Experimental results show that random forest is better than any other classifier in predicting diabetes with a 90.75% accuracy rate.</span>

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Publication Date
Sun Jan 01 2023
Journal Name
International Journal Of Nonlinear Analysis And Applications
Rock facies classification and its effect on the estimation of original oil in place based on petrophysical properties data
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The most significant function in oil exploration is determining the reservoir facies, which are based mostly on the primary features of rocks. Porosity, water saturation, and shale volume as well as sonic log and Bulk density are the types of input data utilized in Interactive Petrophysics software to compute rock facies. These data are used to create 15 clusters and four groups of rock facies. Furthermore, the accurate matching between core and well-log data is established by the neural network technique. In the current study, to evaluate the applicability of the cluster analysis approach, the result of rock facies from 29 wells derived from cluster analysis were utilized to redistribute the petrophysical properties for six units of Mishri

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Publication Date
Mon Mar 23 2020
Journal Name
Journal Of Engineering
prediction Capacity of Euphrates River at Assamawa City
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The reduction in the rivers capacity is one the most important issue to give the decision maker an idea during the flood season. The study area included the rivers of the Al Atshan, Al Sabeel and Euphrates, which are surveyed with a length of 21, 5 and 20 km respectively. The Euphrates , the Atshan and Al Sabeel rivers were simulated by using HEC-RAS 5.0.3 software to study the real condition within the city of Assamawa. As well as the simulation was implemented by modifying the cross sections of the Euphrates and Al Sabeel rivers to increase their capacity to 1300 and 1200 m3/s respectively which are a flood discharges100 year return periods. The results showed that the maximum discharge capacity under real conditions o

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Publication Date
Thu Dec 31 2015
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of Shear Wave velocity for carbonate rocks
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In many oil fields only the BHC logs (borehole compensated sonic tool) are available to provide interval transit time (Δtp), the reciprocal of compressional wave velocity VP.

   To calculate the rock elastic or inelastic properties, to detect gas-bearing formations, the shear wave velocity VS is needed. Also VS is useful in fluid identification and matrix mineral identification.

   Because of the lack of wells with shear wave velocity data, so many empirical models have been developed to predict the shear wave velocity from compressional wave velocity. Some are mathematical models others used the multiple regression method and neural network technique.

   In this study a number of em

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Publication Date
Thu Feb 05 2026
Journal Name
Plos Global Public Health
Exploring stakeholder perceptions of peer support initiatives in the management of diabetes in low- and middle-income countries: An online survey study
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Peer support is an effective strategy to promote self-management behaviors and improve well-being in those with cardiometabolic disease, including type 2 diabetes mellitus (T2DM). There is limited knowledge about stakeholder perceptions regarding peer support programs in low- and middle-income countries (LMICs). The study assessed stakeholders’ awareness and understanding of peer support initiatives for T2DM, and explored their perceived barriers and readiness for implementation. A cross-sectional, self-administered online survey with branching logic was distributed to stakeholders across macro- (health policy), meso (tertiary hospital), and micro (community) levels of LMIC healthcare systems from June 1 to December 15, 2023. Quan

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Publication Date
Wed Apr 15 2026
Journal Name
Experimental And Theoretical Nanotechnology
Utilization of multitemporal satellite imagery and forest canopy density (FCD) model for analyzing changes in forest density in Mosul province, Iraq
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Forest cover in Mosul Province experienced significant changes following the 2014 occupation. These changes can be effectively analyzed using multitemporal remote sensing imagery. This study aims to evaluate the ability of multi-temporal Landsat 8 images and the Forest Canopy Density (FCD) model to detect changes in forest canopy density in a protected forest in Mosul Governorate during the period from 2014 to 2025. The remote sensing data used in this research are Landsat 8 images captured on March 21, 2014, and April 4, 2025. The method employed is FCD modeling, which produces pixel-level canopy density estimates. The results of the FCD model are then used to analyze changes in canopy density following the occupation. The findings of this

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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Intelligent Systems
Optimizing genetic prediction: Define-by-run DL approach in DNA sequencing
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Abstract: The utility of DNA sequencing in diagnosing and prognosis of diseases is vital for assessing the risk of genetic disorders, particularly for asymptomatic individuals with a genetic predisposition. Such diagnostic approaches are integral in guiding health and lifestyle decisions and preparing families with the necessary foreknowledge to anticipate potential genetic abnormalities. The present study explores implementing a define-by-run deep learning (DL) model optimized using the Tree-structured Parzen estimator algorithm to enhance the precision of genetic diagnostic tools. Unlike conventional models, the define-by-run model bolsters accuracy through dynamic adaptation to data during the learning process and iterative optimization

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Publication Date
Sun Mar 03 2024
Journal Name
Mesopotamian Journal Of Cybersecurity
Using Information Technology for Comprehensive Analysis and Prediction in Forensic Evidence
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With the escalation of cybercriminal activities, the demand for forensic investigations into these crimeshas grown significantly. However, the concept of systematic pre-preparation for potential forensicexaminations during the software design phase, known as forensic readiness, has only recently gainedattention. Against the backdrop of surging urban crime rates, this study aims to conduct a rigorous andprecise analysis and forecast of crime rates in Los Angeles, employing advanced Artificial Intelligence(AI) technologies. This research amalgamates diverse datasets encompassing crime history, varioussocio-economic indicators, and geographical locations to attain a comprehensive understanding of howcrimes manifest within the city. Lev

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Publication Date
Fri Feb 20 2026
Journal Name
Frontiers In Global Women's Health
Cardiovascular risk prediction in women: rethinking traditional approaches through precision medicine
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Cardiovascular disease (CVD) remains the leading cause of mortality in women. Estimating cardiovascular risk using prediction models is essential for guiding preventive strategies. Despite progress, conventional risk models still omit critical women-specific factors, limiting their accuracy. Precision medicine, supported by artificial intelligence, provides a framework to integrate these overlooked determinants. This approach may help close existing gaps in cardiovascular risk prediction. Sex-specific biomarkers that contribute to overall cardiovascular risk can be incorporated into risk assessment tools to improve prevention strategies, early detection, and personalized intervention. The integration of imaging-derived variables enh

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
The Kinetic responses and foraging behavior of Drosophila melanogaster larvae
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Adaptive responses in larval behavior may be of two kinds: Taxis: This involves a change in direction relative to source of a stimulus. Kinesis: Kinesis has no directional component, but involves change in the rate of performance in response to a stimulus. Drosophila larvae exhibited flexible behavioral responses associated with food acquisition and selection for different environmental conditions. In this investigation, we are concerned explosively with kinetic responses to food viability. Third instar larvae were subjected to test for thirty minutes in each of the following conditions i) in distilled water, ii) in Ringer's solution, iii) in glucose solution and on live yeast suspension. In each case the larva was in a thin layer of solu

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Publication Date
Sun Jan 01 2023
Journal Name
: Revista Iberoamericana De Psicología Del Ejercicio Y El Deporte, , Vol. 17, Nº. 5, 2022, Págs. 319-322
THE ASSERTIVE BEHAVIOR OF DISABLED TENNIS AND TABLE TENNIS PLAYERS
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