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A missing data imputation method based on salp swarm algorithm for diabetes disease
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Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve Bayesian classifier (NBC) have been enhanced as compared to the dataset before applying the proposed method. Moreover, the results indicated that issa was performed better than the statistical imputation techniques such as deleting the samples with missing values, replacing the missing values with zeros, mean, or random values.

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Publication Date
Fri Jul 12 2024
Journal Name
World Water Policy
The effect of natural factors on changing soil uses in the marshes: An experimental study using Landsat satellite data
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The study aimed to analyze the effect of meteorological factors (rainfall rate and temperature) on the change in land use in the marshes of the Al‐Majar Al‐Kabir region in southern Iraq. Satellite images from Landsat 7 for 2012 and Landsat 8 for 2022 were used to monitor changes in the land coverings, the images taken from the Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) sensors of the Landsat satellite. Geometric correction was used to convert images into a format with precise geographic coordinates using ArcMap 10.5. The maximum likelihood classification method was used to examine satellite image data using a supervised approach, and the data were analyzed statistically. We obtained clear images of the area,

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Publication Date
Tue Jan 01 2019
Journal Name
Iraqi Journal Of Agricultural Sciences
Role of calcium-regulating hormones, adipocytokines and renal function test in the progress of type 2 diabetes mellitus in a sample of Iraqi patients
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A cross-sectional study was conducted on 80 type 2 diabetic patients aged 20-60 years in Baghdad and 20 non diabetic persons as controls. Laboratory assessment of glucose related parameters; Fasting blood sugar (FBS), Glycated hemoglobin (HbA1c), Insulin and Insulin resistance (IR), renal function test; Blood urea, serum creatinine, Calcium (Ca) and Phosphorus (P), Calcium regulating hormones; Parathyroid hormone (PTH), calcitonin and vitamin D, cytokines, Adiponectin and Tumor necrosis factor (TNF-α) and comparison these parameters between patients and controls. The results: a high significant (p˂0.01) increase in FBG level in the patients (211.34 ± 11.20 mg/dl) as compared with control (85.89 ± 3.07 mg/dl). A high significant (p˂0.01

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Publication Date
Fri Oct 31 2025
Journal Name
Sustainability
Upgrading Sustainability in Clean Energy: Optimization for Proton Exchange Membrane Fuel Cells Using Heterogeneous Comprehensive Learning Bald Eagle Search Algorithm
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Clean energy applications widely recognize Proton Exchange Membrane Fuel Cells (PEMFCs) for their high efficiency and environmental compatibility. Accurate parameter identification of PEMFC models is essential for enhancing system performance and reliability, particularly under dynamic operating conditions. This paper presents a novel optimization-based approach called Heterogeneous Comprehensive Learning-Bald Eagle Search (HCLBES) with enhanced exploration and exploitation capabilities for the effective modeling of PEMFC. The algorithm combines the exploration strength of the Bald Eagle Search with comprehensive learning and heterogeneity mechanisms to achieve a balanced global and local search space. In this algorithm, the number

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Publication Date
Tue Jul 01 2014
Journal Name
Computer Engineering And Intelligent Systems
Static Analysis Based Behavioral API for Malware Detection using Markov Chain
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Researchers employ behavior based malware detection models that depend on API tracking and analyzing features to identify suspected PE applications. Those malware behavior models become more efficient than the signature based malware detection systems for detecting unknown malwares. This is because a simple polymorphic or metamorphic malware can defeat signature based detection systems easily. The growing number of computer malwares and the detection of malware have been the concern for security researchers for a large period of time. The use of logic formulae to model the malware behaviors is one of the most encouraging recent developments in malware research, which provides alternatives to classic virus detection methods. To address the l

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Publication Date
Sun Sep 01 2019
Journal Name
2019 11th Computer Science And Electronic Engineering (ceec)
ANN based Measurement for No-Reference Video Quality of Experience Metric
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Publication Date
Sun Aug 06 2023
Journal Name
Journal Of Economics And Administrative Sciences
Probit and Improved Probit Transform-Based Kernel Estimator for Copula Density
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Copula modeling is widely used in modern statistics. The boundary bias problem is one of the problems faced when estimating by nonparametric methods, as kernel estimators are the most common in nonparametric estimation. In this paper, the copula density function was estimated using the probit transformation nonparametric method in order to get rid of the boundary bias problem that the kernel estimators suffer from. Using simulation for three nonparametric methods to estimate the copula density function and we proposed a new method that is better than the rest of the methods by five types of copulas with different sample sizes and different levels of correlation between the copula variables and the different parameters for the function. The

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Publication Date
Sun Jul 01 2018
Journal Name
2018 2nd International Conference On Imaging, Signal Processing And Communication (icispc)
Analogy-based Common-Sense Knowledge for Opinion-Target Identification and Aggregation
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The development of Web 2.0 has improved people's ability to share their opinions. These opinions serve as an important piece of knowledge for other reviewers. To figure out what the opinions is all about, an automatic system of analysis is needed. Aspect-based sentiment analysis is the most important research topic conducted to extract reviewers-opinions about certain attribute, for instance opinion-target (aspect). In aspect-based tasks, the identification of the implicit aspect such as aspects implicitly implied in a review, is the most challenging task to accomplish. However, this paper strives to identify the implicit aspects based on hierarchical algorithm incorporated with common-sense knowledge by means of dimensionality reduction.

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Publication Date
Fri Apr 28 2023
Journal Name
Surgical Neurology International
Neurosurgery theater-based learning: Etiquette and preparation tips for medical students
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Publication Date
Sun Feb 13 2022
Journal Name
Petroleum & Coal
Laboratory-Based Correlations to Estimate Geomechanical Properties for Carbonate Tight Reservoir.
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Rock mechanical properties are critical parameters for many development techniques related to tight reservoirs, such as hydraulic fracturing design and detecting failure criteria in wellbore instability assessment. When direct measurements of mechanical properties are not available, it is helpful to find sufficient correlations to estimate these parameters. This study summarized experimentally derived correlations for estimating the shear velocity, Young's modulus, Poisson's ratio, and compressive strength. Also, a useful correlation is introduced to convert dynamic elastic properties from log data to static elastic properties. Most of the derived equations in this paper show good fitting to measured data, while some equations show scatters

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Publication Date
Mon Nov 09 2020
Journal Name
Construction Research Congress 2020
Alternative Risk Models for Optimal Investment in Portfolio-Based Community Solar
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