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Machine learning models for predicting morphological traits and optimizing genotype and planting date in roselle (Hibiscus Sabdariffa L.)
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Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. The dataset was generated from a field experiment involving ten Roselle genotypes and five planting dates. Both RF and MLP exhibited robust predictive capabilities; however, RF (R² = 0.84) demonstrated superior performance compared to MLP (R² = 0.80), underscoring its efficacy in capturing the nonlinear genotype-by-environment interactions. Permutation-based feature importance analysis further revealed that planting date had a more significant impact on trait variation than genotype. To identify optimal combinations of genotype and planting date for maximizing morphological traits, the RF model was integrated with the Non-dominated Sorting Genetic Algorithm II (NSGA-II). According to the RF–NSGA-II optimization results, the optimal values, including 26 branches per plant, a growth period of 176 days, 116 bolls per plant, and 1517 seed numbers per plant, were achieved with the Qaleganj genotype planted on May 5. Collectively, these findings highlight the potential of integrating machine learning and evolutionary optimization algorithms as powerful computational tools for crop improvement and agronomic decision-making.

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Publication Date
Fri Feb 15 2019
Journal Name
Route Educational & Social Science Journal
The effect of the 4-H model on self-regulated learning and life skills for female chemistry students in the second intermediate year
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Publication Date
Thu Mar 01 2012
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Predicting changes on budget expenditures using Markov chains with practical application
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The researchers have a special interest in studying  Markov  chains as one of the probability samples which has many applications in different fields. This study comes to deal with the changes issue that happen on budget expenditures by using statistical methods, and Markov chains is the best expression about that as they are regarded reliable  samples in the prediction process. A transitional matrix is built for three expenditure cases (increase ,decrease ,stability) for one of budget expenditure items (base salary) for three directorates (Baghdad ,Nineveh , Diyala) of one  of the ministries. Results are analyzed by applying  Maximum likelihood estimation  and Ordinary least squares  methods resulting

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Publication Date
Mon Dec 30 2019
Journal Name
College Of Islamic Sciences
The matter, its wisdom, and models of its applications In Ahmadiyya interpretations Research drawn from a doctoral thesis
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This study examined the tagged (the matter, its ruling, and examples of its applications in Ahmadiyya interpretations), the study of devising formulas of commissioning, especially the matter from the book of interpretation, which is: (Ahmadiyyeh’s interpretations), and the contemplative of the nature of (Ahmadiyya’s interpretations) realizes that the cognitive, linguistic and juristic knowledge product and the verses of judgments And other is the focus of this immortal travel, as it is truly a comprehensive book containing what the students of knowledge, scholars and the general public need in terms of interpreting the legal verses, and it is not possible to reach an understanding of the Book of God Almighty, and to clarify his purpo

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
EFFECT OF SALINE WATER AND POTASH FERTILIZER ON PROLINE CONTENT AND WATER POTENTAL IN PISUM SATIVUM L. (VAR.SENADOR CAMBADOS)
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Filed experiment was conducted to test the effect of saline water and potassium fertilizers rate on proline and water potential of Pisum sativum L. (Var.Senador Cambados ) leaves . Treatments of the experiment included two levels of water salinity( 2, 7 dSm-1) as a main plot and fertilizer rates as a sub plot. Results indicated that irrigation of plant with saline water 7 dSm-1 and fertilization 150 kg/donum increased proline accumulation and water potential 0.31 mmol/g,-17.00 bar at 9 AM morning and 0.62 mmol/g , -21.00 bar at 3 PM afternoon ,Irrigating plant with a 2 dSm-1 and fertilization 300 kg/donum decreased proline accumulation and water potential of leaves 0.22 mmol/g, -16.00 bar at 9 A

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Publication Date
Tue Apr 30 2024
Journal Name
International Journal On Technical And Physical Problems Of Engineering
Deep Learning Techniques For Skull Stripping of Brain MR Images
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Deep Learning Techniques For Skull Stripping of Brain MR Images

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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
Comparative analysis of deep learning techniques for lung cancer identification
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One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

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Publication Date
Sun Jan 01 2023
Journal Name
Computers, Materials & Continua
Hybrid Deep Learning Enabled Load Prediction for Energy Storage Systems
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Publication Date
Thu Jun 01 2023
Journal Name
International Journal Of Electrical And Computer Engineering (ijece)
An optimized deep learning model for optical character recognition applications
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The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog

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Publication Date
Wed Mar 15 2023
Journal Name
Al-adab Journal
Models of Phonological Loanword Adaptation
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Borrowing in linguistics refers to the process whereby a group of speakers incorporates certain foreign linguistic components into their home language via a process known as linguistic borrowing. The process by which these foreign linguistic elements, known as loanwords, go through phonological, morphological, or semantic changes in order for them to fit the grammar of the recipient language is referred to as loanword adaptation. Loanwords go through these changes in order for them to become compatible with the grammar of the recipient language. One of the most divisive topics in loanword phonology is whether adaptations occur at the phonemic or phonetic levels, and current literature distinguishes three primary viewpoints: nativiza

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Publication Date
Fri Jan 01 2016
Journal Name
Statistics And Its Interface
Search for risk haplotype segments with GWAS data by use of finite mixture models
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The region-based association analysis has been proposed to capture the collective behavior of sets of variants by testing the association of each set instead of individual variants with the disease. Such an analysis typically involves a list of unphased multiple-locus genotypes with potentially sparse frequencies in cases and controls. To tackle the problem of the sparse distribution, a two-stage approach was proposed in literature: In the first stage, haplotypes are computationally inferred from genotypes, followed by a haplotype coclassification. In the second stage, the association analysis is performed on the inferred haplotype groups. If a haplotype is unevenly distributed between the case and control samples, this haplotype is labeled

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