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Comparative Analysis of The Combined Model (Spatial and Temporal) and Regression Models for Predicting Murder Crime
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This research dealt with the analysis of murder crime data in Iraq in its temporal and spatial dimensions, then it focused on building a new model with an algorithm that combines the characteristics associated with time and spatial series so that this model can predict more accurately than other models by comparing them with this model, which we called the Combined Regression model (CR), which consists of merging two models, the time series regression model with the spatial regression model, and making them one model that can analyze data in its temporal and spatial dimensions. Several models were used for comparison with the integrated model, namely Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Random Forest Regression (RFR) and Neural Network Regression (NNR). The data used is about the monthly numbers of murder crimes for the police directorates in Baghdad and the governorates during the period from January 2015 to June 2023. The data was analyzed and then divided into two sets, a training and testing set, to perform these models in prediction. The accuracy of each modsl’s performance was evaluated using two statistical measures: RMSE and  in order to determine the best and most accurate performing model among the selected models. An important result was obtained in the comparison between these models, as the combined model obtained the most accurate performance than the other models, based on the values ​​of the performance accuracy metrics for each model in relation to the data used in the murder crimes.

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Publication Date
Tue Nov 01 2016
Journal Name
Renewable Energy
Biodiesel production by esterification of oleic acid over zeolite Y prepared from kaolin
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Publication Date
Sun Apr 12 2026
Journal Name
Waste And Biomass Valorization
Treatment of Recycled Concrete Aggregate with Sugarcane Molasses to Produce Durable Asphalt Mixtures
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The incorporation of recycled concrete aggregate (RCA) into asphalt concrete supports circular economy goals by reducing reliance on virgin materials and minimizing construction waste. However, RCA’s inherent limitations, such as high porosity, microcracking, and poor interfacial bonding, compromise the structural integrity and durability of asphalt mixtures. This study introduces sugarcane molasses (SCM), a naturally derived, carbohydrate-rich byproduct of sugarcane refining, as a novel and eco-friendly surface treatment for RCA aimed at enhancing its compatibility with asphalt binders. SCM was applied at 5-6% by weight of RCA replacing coarse aggregate at varying levels (0-100%) to assess its effect on asphalt mixture performance. A com

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Publication Date
Sat Apr 19 2025
Journal Name
Plos One
Early Detection of Autism Spectrum Disorder in Children Using Different Machine Learning Algorithms
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Abstract<p>Autism spectrum disorder(ASD) is a neurological condition marked by impaired communication abilities, social detachment, and repetitive behaviors in individuals. Global health organization facing difficulties in establishing an effective ASD diagnostic system that facilitates precise analysis and early autism prediction. It is a scientific issue that necessitates resolution. This research presents an approach for the early prediction of children with ASD utilizing significant variables through machine learning (ML) methods. Three stages comprise the suggested technique. First, a 1250-case ASD dataset was identified and preprocessed. Five extremely effective traits with high Pearson c</p> ... Show More
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Publication Date
Wed Jan 01 2025
Journal Name
Passer Journal Of Basic And Applied Sciences
Predictive Role of Key Pro-Inflammatory Cytokines in Patients with Acute Myeloid Leukemia
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Publication Date
Wed Jan 01 2025
Journal Name
Journal Of Engineering And Sustainable Development
Improving Performance Classification in Wireless Body Area Sensor Networks Based on Machine Learning Techniques
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Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s

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Publication Date
Mon Mar 31 2025
Journal Name
International Journal Of Advanced Technology And Engineering Exploration
Breast cancer survival rate prediction using multimodal deep learning with multigenetic features
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Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep

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Publication Date
Mon Aug 01 2022
Journal Name
Inorganic Chemistry Communications
Assembling [email protected] nanocomposites with an enhanced photocatalytic activity
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Herein, an efficient inorganic/organic hybrid photocatalyst composed of zeolitic imidazolate framework (ZIF-67) decorated with Cd0.5Zn0.5S solid solution semiconductor was constructed. The properties of prepared ZIF- [email protected] nanocomposite and its components (ZIF-67 and Cd0.5Zn0.5S) were investigated using XRD, FESEM, EDX, TEM, DRS and BET methods. The photocatalytic activity of fabricated [email protected] nanocomposite were measured toward removal of methyl violet (MV) dye as a simulated organic contaminant. Under visible-light and specific conditions (photocatalyst dose 1 g/l, MV dye 10 mg/l, unmodified solution pH 6.7 and reaction time 60 min.), the acquired [email protected] photocatalyst showed advanced photocatalytic activity

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Publication Date
Thu Jan 04 2024
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
VALUE CHAIN ANALYZE OF DAIRY PRODUCTS IN IRAQ CASE STUDY: ABU GHRAIB DAIRY ( FACTORIES)
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ABSTRACT
                  The research aims to analyze the value chain of dairy products in Iraq (Abu Ghraib/Study Case) factories for the year 2022, where value chain rings are identified to discuss and track the most important determinants and problems in the value chain rings of dairy products and their basic and secondary activities, as well as calculate the value added of the products by subtracting the total revenues of products from their variable costs. Research data were collected for the period 2022. Preliminary information and data from its field sources and personal interviews were collected through a questionnaire prepa

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Publication Date
Sun Sep 22 2019
Journal Name
Baghdad Science Journal
Thermal Conductivity Performance of Silica Aerogel after Exposition on Different Heating under Ambient Pressure
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         The varied thermal conductivity (insulation) of silica aerogel with heating for different pH has been investigated, it has been depended on ambient pressure drying method in the preparing silica aerogel samples, also six different pH of samples (1, 2, 3, 7, 8 and 9) were treated under five degree of heating with (50,100,150,200 and 250) ᴼC. This technique is important to test the carry-outs hydrophobic silica to temperature without high-quality material changes in the basic characteristics. The hot-wire technique is used in this work to examine the thermal conductivity, Fourier Transform Infrared Spectroscopy (FTIR) depended to characterize the bonds and their artificial by heating. Resu

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Publication Date
Wed Dec 11 2013
Journal Name
Proceeding Of The 2nd International Conference On Iraq Oil Studies
Diagnosing Complex Flow Characteristics of Mishrif Formation in Stimulated Well Using Production Logging Tool
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Production logging is used to diagnose well production problems by evaluating the flow profile, entries of unwanted fluids and downhole flow regimes. Evaluating wells production performance can be easily induce from production logs through interpretation of production log data to provide velocity profile and contribution of each zone on total production. Production logging results supply information for reservoir modeling, provide data to optimize the productivity of existing wells and plan drilling and completion strategies for future wells. Production logging was carried out in a production oil well from Mishrif formation of West Qurna field, with the objective to determine the flow profile and fluid contributions from the perforations af

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