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Machine Learning Based Crop Yield Prediction Model in Rajasthan Region of India
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     The present study investigates the implementation of machine learning models on crop data to predict crop yield in Rajasthan state, India. The key objective of the study is to identify which machine learning model performs are better to provide the most accurate predictions. For this purpose, two machine learning models (decision tree and random forest regression) were implemented, and gradient boosting regression was used as an optimization algorithm. The result clarifies that using gradient boosting regression can reduce the yield prediction mean square error to 6%. Additionally, for the present data set, random forest regression performed better than other models. We reported the machine learning model's performance using Mean Squared Error, Mean Absolute Error and R-squared and identified that after the inclusion of gradient boosting regression, the accuracy increased to 92.77%. The MAE value decreased from 26.20 Mg/ha to 21.58 Mg/ha. The results indicate that machine learning models can improve the prediction of crop yield.

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Publication Date
Mon Aug 01 2022
Journal Name
Journal Of Engineering
Dynamic Behavior of Machine Foundations on layered sandy soil under Seismic Loadings
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In this paper, a dynamic investigation is done for strip, rectangular and square machine foundation at the top surface of two-layer dry sand with various states (i.e., loose on medium sand and dense on medium sand). The dynamic investigation is performed numerically using finite element programming, PLAXIS 3D. The soil is expected as a versatile totally plastic material that complies with the Mohr-Coulomb yield criterion. A harmonic load is applied at the base with an amplitude of 6 kPa at a frequency of (2 and 6) Hz, and seismic is applied with acceleration – time input of earthquake hit Halabjah city north of Iraq. A parametric study is done to evaluate the influence of changing L/B ratio (Length=12,6,3 m and width=3 m), type of sand

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Publication Date
Wed Jan 01 2020
Journal Name
Plant Archives
Isolation and identification of yersinia enterocolitica from local ovine meat in the middle region of Iraq
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Yersinia enterocolitica has ranked a third among the pathogens that most frequently cause gastrointestinal disorders transmitted to humans through food materials, especially contaminated meats. The meat infected with Yersinia enterocolitica had no change in apparent texture or smell. The aim of this research is to survey the frequency of Y. enterocolitica in ovine meat, compare their ratio of infection between the season, To carry out this study (125) samples of local ovine meat were collected by random sampling from the middle region of Iraq. The samples were divided into two groups steak and mince, then many microbiological tests (culture, & staining, biochemical Tests Api 20E, Vitik 2 and species-specific PCR amplicon for 16S RNA gene) w

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Publication Date
Sun Jun 20 2021
Journal Name
Bulletin Of The Iraq Natural History Museum (p-issn: 1017-8678 , E-issn: 2311-9799)
ON THE ECOLOGY AND SPECIES DIVERSITY OF THE FRESHWATER GASTROPODS OF SPRINGS IN ANDIJAN REGION, UZBEKISTAN
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This study examines the species composition, biodiversity, zoogeography, and ecology of freshwater gastropods of 12 springs in Andijan region of Uzbekistan. The study used generally accepted malacological, faunistic, ecological, analytical, and statistical methods. As a result of research in the springs, 14 species of freshwater gastropods belonging to 2 subclasses, 5 families, and 10 genera were recorded. 7 of them are endemic to Central Asia. When indicators of biodiversity of mollusks were analyzed according to the Shannon index, it was found that the highest value was recorded in the springs besides the hills. According to the biotope of distribution and bioecological features, they were divided into cryophilic, phytophilic, pelophil

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Publication Date
Sat Jun 03 2023
Journal Name
Iraqi Journal Of Science
Face Recognition Using Stationary wavelet transform and Neural Network with Support Vector Machine
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Face recognition is a type of biometric software application that can identify a specific
individual in a digital image by analyzing and comparing patterns. It is the process of
identifying an individual using their facial features and expressions.
In this paper we proposed a face recognition system using Stationary Wavelet Transform
(SWT) with Neural Network, the SWT are applied into five levels for feature facial
extraction with probabilistic Neural Network (PNN) , the system produced good results
and then we improved the system by using two manner in Neural Network (PNN) and
Support Vector Machine(SVM) so we find that the system performance is more better
after using SVM where the result shows the performance o

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Publication Date
Mon Jan 01 2018
Journal Name
International Journal Of Agricultural And Statistical Sciences
Response of broad bean growth and early yield to exposure periods of vernalization
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Publication Date
Fri Jan 01 2021
Journal Name
International Journal Of Agricultural And Statistical Sciences
Influence of yeast and intercropping system on growth and yield traits of pea
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Publication Date
Sun Jun 01 2008
Journal Name
Baghdad Science Journal
Effect of Nitrogen Fertilizer and Plant Density on Yield and Growth of Sunflower
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The effect of nitrogen fertilizer and the planting distance on growth and yield of the sunflower cultivar (Taka) was investigated. The experiment was conducted in the field using five nitrogen fertilizer levels (0, 50, 100, 150, 200) kg/donum and three planting distances (10, 20, 30) cm/plant. The experiment design was split-plot by using RCBD with four replicates. The level of fertilizer as the main plot, while the planting distance as the sub plot. Plant high and yield components were measured. Results indicated that using 200 kg/donum of nitrogen and 30 cm/plant of planting distance gave the highest rate of 1000 seeds weight and the number of seeds/ head. While using 200 kg/donum of nitrogen fertilizer with 10 cm/plant of planting dista

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Publication Date
Mon Dec 18 2017
Journal Name
Al-khwarizmi Engineering Journal
Prediction of Surface Roughness and Material Removal Rate in Electrochemical Machining Using Taguchi Method
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Electrochemical machining is one of the widely used non-conventional machining processes to machine complex and difficult shapes for electrically conducting materials, such as super alloys, Ti-alloys, alloy steel, tool steel and stainless steel.  Use of optimal ECM process conditions can significantly reduce the ECM operating, tooling, and maintenance cost and can produce components with higher accuracy. This paper studies the effect of process parameters on surface roughness (Ra) and material removal rate (MRR), and the optimization of process conditions in ECM. Experiments were conducted based on Taguchi’s L9 orthogonal array (OA) with three process parameters viz. current, electrolyte concentration, and inter-electrode gap. Sig

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Publication Date
Tue Jan 02 2018
Journal Name
Journal Of Educational And Psychological Researches
The impact of learning strategies in the collection of biology and their systemic thinking
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Research Summary The aim of the search for knowledge of the effect generative learning strategy in: 1 - Achievement of the second grade. 2 - Systemic thinking for the second grade students when studying the biology. The study sample increased (60) students distributed into two equal experimental and control groups. Prepare the test of 40 pieces of multiple choice type and prepare a test for systematic thinking according to three skills 1. Understand the relationships between the parts of the systemic form and complement the sentences given 2 - complement the relationships between parts of the systemic form 3. Building the systemic form. It was a search result 1- There is a difference of statistical significance (at level 0.05) between th

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Publication Date
Mon Nov 01 2021
Journal Name
Iop Conference Series: Earth And Environmental Science
Study of Seed Soaking and Foliar Application of Ascorbic Acid, Citric Acid and Humic Acid on Growth, Yield and Active Components In Maize
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Foliar application and seed soaking has been used as a means of supplying supplemental doses of nutrients, plant hormones, stimulants, and organic components. the effects of these applications have included yield increases, and improved drought tolerance, and enhanced crop quality, so A field experiment was carried out during spring seasons in 2019 and 2020 for styding Seed soaking and Foliar Application of Ascorbic acid, Citric acid and Humic acid on Growth, Yield and Active Components IN Maize. Randomized complete block design in split plots arrangement was used with three replicates. Main-plots were for seeds soaking with ascorbic, citric (100 mg l-1) frequently and humic at (1 ml l-1). Sub-plots were for vegetative parts nutrition with

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