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Classification of Diseases in Oil Palm Leaves Using the GoogLeNet Model
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The general health of palm trees, encompassing the roots, stems, and leaves, significantly impacts palm oil production, therefore, meticulous attention is needed to achieve optimal yield. One of the challenges encountered in sustaining productive crops is the prevalence of pests and diseases afflicting oil palm plants. These diseases can detrimentally influence growth and development, leading to decreased productivity. Oil palm productivity is closely related to the conditions of its leaves, which play a vital role in photosynthesis. This research employed a comprehensive dataset of 1,230 images, consisting of 410 showing leaves, another 410 depicting bagworm infestations, and an additional 410 displaying caterpillar infestations. Furthermore, the major objective was to formulate a deep learning model for the identification of diseases and pests affecting oil palm leaves, using image analysis techniques to facilitate pest management practices. To address the core problem under investigation, the GoogLeNet deep learning approach was applied, alongside various hyperparameters. The classification experiments were executed across 16 trials, each capped at a computational timeframe of 10 minutes, and the predominant duration spanned from 2 to 7 minutes. The results, particularly derived from the superior performance in Model 4 (M4), showed evaluation accuracy, precision, recall, and F1-score rates of 93.22%, 93.33%, 93.95%, and 93.15%, respectively. These were highly satisfactory, warranting their application in oil palm companies to enhance the management of pest and disease attacks.

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Publication Date
Fri Mar 01 2019
Journal Name
Al-khwarizmi Engineering Journal
Improving Wear Properties of 392 Al Alloy Using Centrifugal Casting
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The microstructure and wear properties of 392 Al alloy with different Mg contents were studied using centrifugal casting. All melted alloys were heated to 800 ºC and poured into the preheated centrifugal casting mold (200-250 ºC) at different mould rotational speeds (1500, 1900 and 2300 r.p.m).  It is clear from the results obtained that wear rate was dependent on the Mg content, applied load and mould rotational speed. Furthermore, wear test showed that the minimum wear rate was found in the inner layer of produced rings at mould rotational speed of 1900 r.p.m and Mg content of 5%.

 

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Publication Date
Fri Dec 01 2023
Journal Name
Al-khwarizmi Engineering Journal
An Overview of Audio-Visual Source Separation Using Deep Learning
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    In this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test A

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Publication Date
Tue Jul 09 2024
Journal Name
Diagnostics
A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification
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Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea

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Publication Date
Thu Nov 21 2019
Journal Name
Al-kindy College Medical Journal
Effect Of Pomegrante Peels And Bay Leaves On Multidrug Resistant Bacteria Isolated From Urinary Tract Infection Patients
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Background: Alternative natural therapy by plants extracts had opened  wide  door   for   the    use  of  natural products as an alternative therapy instead of many antibiotics and drugs , which had many harmful side effects.Also, an increased interest has been centered on the industrial wastes, especially plant raw materials which contain phenols (e.g. Pomegranate peel and Bay leaves) which is a sources of natural antioxidants ,which are on the contrary  of  synthetic  antioxidants that  had restrict use  due to  their  health  risks , carcinogenesis and toxicity .

Objectives :This study was done to fi

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Publication Date
Wed Jun 01 2022
Journal Name
Baghdad Science Journal
Kinetic, Isotherm and Thermodynamic Studies on the Ciprofloxacin Adsorption from Aqueous Solution Using Aleppo bentonite
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          Aleppo bentonite was investigated to remove ciprofloxacin hydrochloride from aqueous solution. Batch adsorption experiments were conducted to study the several factors affecting the removal process, including contact time, pH of solution, bentonite dosage, ion strength, and temperature. The optimum contact time, pH of solution and bentonite dosage were determined to be 60 minutes, 6 and 0.15 g/50 ml, respectively. The bentonite efficiency in removing CIP decreased from 89.9% to 53.21% with increasing Ionic strength from 0 to 500mM, and it increased from 89% to 96.9% when the temperature increased from 298 to 318 K. Kinetic studies showed that the pseudo second-order model was the best in describing  the adsorption sys

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Publication Date
Sun Oct 18 2020
Journal Name
Diagnostics
Diagnostic Accuracy of Oral Fluids Biomarker Profile to Determine the Current and Future Status of Periodontal and Peri-Implant Diseases
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Severe periodontitis is ranked as the sixth most prevalent disease affecting humankind, with an estimated 740 million people affected worldwide. The diagnosis of periodontal diseases mainly relies upon assessment of conventional clinical parameters. However, these parameters reflect past, rather than current, clinical status or future disease progression and, likely, outcome of periodontal treatment. Specific and sensitive biomarkers for periodontal diseases have been examined widely to address these issues and some biomarkers have been translated as point-of-care (PoC) tests. The aim of this review was to provide an update on PoC tests for use in the diagnosis and management of periodontal diseases. Among the PoC tests developed so

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Publication Date
Thu Jan 16 2020
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Suggested Model for activating the role of the regulators for the auditing profession in achieving quality audit
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The concept of quality of auditing profession comes on top of the concerns of the international business community and international institutions particularly now following the impact of the several failures and financial hardships suffered by the major companies in the recent collapse of money markets in some countries of the world and fear of their recurrence in the future.An observer of the local and international rules and standards (or principles) finds that these include such implications have direct or indirect effects on the performance of the service of the accountant and auditor, which should upgrade their professional performance in these services to a high level of quality so as to be in line with the requirements, principles

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Publication Date
Tue Aug 01 2023
Journal Name
Iop Conference Series: Earth And Environmental Science
Estimate Raw Water Salinity for the Tigris River for a Long Time Using a Mathematical Model
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Abstract<p>The measurement data of the raw water quality of Tigris River were statistically analyzed to measure the salinity value in relation to the selected raw water quality parameters. The analyzed data were collected from five water treatment plants (WTPs) assembled alongside of the Tigris River in Baghdad: Al-Karkh, Al-Karama, Al-Qadisiya, Al-Dora, and Al-Wihda for the period from 2015 to 2021. The selected parameters are total dissolved solid (TDS), electrical conductivity (EC), pH and temperature. The main objective of this research is to predicate a mathematical model using SPSS software to calculate the value of salinity along the river, in addition, the effect of electrical conductivi</p> ... Show More
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Publication Date
Mon May 27 2019
Journal Name
Al-academy
Revival of the Heritage Using Porcelain Units Inspired by Traditional Sadou Decoration and Kufic Calligraphy in Fashion Design: تهاني ناصر العجاجي ---وجدان محمد الفليج
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The purpose of this research is to identify heritage and highlight its value by drawing on its decorative vocabulary and integrating it with the Arabic calligraphy to revive the heritage in a contemporary style, and to create decorative design units inspired by Sadou and Arabic calligraphy and to employ them in the modern fashion designs. The applied descriptive approach has been used in this research, and the research community is made of women in Riyadh area. The tools used in this research were the questionnaire and the observation. The most important results of the research are: the design of decorative units from the integration of Arabic calligraphy and the decorations of the Saduo, and the use of these units in the design of the o

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Publication Date
Fri Mar 29 2024
Journal Name
Iraqi Journal Of Science
Evaluating the Performance and Behavior of CNN, LSTM, and GRU for Classification and Prediction Tasks
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     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

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