Sustainable plant protection and the economy of plant crops worldwide depend heavily on the health of agriculture. In the modern world, one of the main factors influencing economic growth is the quality of agricultural produce. The need for future crop protection and production is growing as disease-affected plants have caused considerable agricultural losses in several crop categories. The crop yield must be increased while preserving food quality and security and having the most negligible negative environmental impact. To overcome these obstacles, early discovery of satisfactory plants is critical. The use of Advances in Intelligent Systems and information computer science effectively helps find more efficient and low-cost solutions. This paper proposed a multiclass classification model that aims to detect diseases in three types of fruit using the leaves plant images dataset. These three types of fruit are (Apple, Cherry, and Strawberry) where Apples have three disease dataset categories (Apple Scab, Black Rot, and Cedar Rust) as well as healthy apple dataset, Cherry have Powdery Mildew disease dataset category and healthy dataset, and Strawberry have leaf Scorch disease dataset category and healthy dataset. These datasets are based on the Kaggle website. These multiclass classifications need several steps of processing; the first step is preprocessing the dataset by resizing all images to the same size, segmentation, and removing noise; then, feature extraction from color and texture features; the next step is feature selection to find optimal features by using the Salp Swarm algorithm (SSA); and classification by using machine learning models (Random Forest), (CatBoost), and (XGBoost). In the final step, evaluation of the performance was used to select several matrices: Accuracy, precision, recall, and F1-score.
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Background: The overproduction of thyroid hormones is known as hyperthyroidism. Increased susceptibility to caries and periodontal disease are two potential oral symptoms. The interleukin-6 (IL-6) was observed to significantly increased in the hyperthyroid group. According to multiple research, IL-6 dysregulation has been linked to a number of oral disorders, including periodontal diseases. The study aimed to evaluate periodontal health status in relation to IL6 among hyperthyroidism patients. Subjects and Methods: The sample was composed of 90 female patients aged 25-45 years attending endocrine disorder |
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This research aims to solve the nonlinear model formulated in a system of differential equations with an initial value problem (IVP) represented in COVID-19 mathematical epidemiology model as an application using new approach: Approximate Shrunken are proposed to solve such model under investigation, which combines classic numerical method and numerical simulation techniques in an effective statistical form which is shrunken estimation formula. Two numerical simulation methods are used firstly to solve this model: Mean Monte Carlo Runge-Kutta and Mean Latin Hypercube Runge-Kutta Methods. Then two approximate simulation methods are proposed to solve the current study. The results of the proposed approximate shrunken methods and the numerical
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Manual probing and periodontal charting are the gold standard for periodontal diagnosis that have been used in practice over a century. These methods are affordable and reliable but they are associated with some drawbacks that cannot be avoided. Among these issues is their reliance on operator’s skills, time-consuming and tedious procedure, lack sensitivity especially in cases of early bone loss, and causing discomfort to the patient. Availability of a wide range of biomarkers in the oral biofluids, dental biofilm, and tissues that potentially reflect the periodontal health and disease accurately encouraged their use as predictive/diagnostic/monitoring tools. Analysing biomarkers during care-giving to the patient using chairside kits i
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Background: Chronic cough is often the key
symptom not only of chronic pulmonary diseases
but for other important extrapulmonary
pathologies, in particular upper airway and
gastrointestinal diseases.
Objective: This study was designed to
determine the etiology of chronic cough and the
usefulness of the available diagnostic tests in
reaching its causes.
Methods: One hundred patients presenting with
chronic cough at Baghdad Teaching Hospital
Outpatient Clinic were enrolled in this study. The
patients underwent a full clinical interview,
physical examination with indicated diagnostic
test(s) (such as chest x ray, bronchoscope, PFT,
GIT study, sinus X ray or CT).
Results: An etiology of chronic
In this paper, the general framework for calculating the stability of equilibria, Hopf bifurcation of a delayed prey-predator system with an SI type of disease in the prey population, is investigated. The impact of the incubation period delay on disease transmission utilizing a nonlinear incidence rate was taken into account. For the purpose of explaining the predation process, a modified Holling type II functional response was used. First, the existence, uniform boundedness, and positivity of the solutions of the considered model system, along with the behavior of equilibria and the existence of Hopf bifurcation, are studied. The critical values of the delay parameter for which stability switches and the nature of the Hopf bifurcat
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Eco-friendly concrete is produced using the waste of many industries. It reduces the fears concerning energy utilization, raw materials, and mass-produced cost of common concrete. Several stress-strain models documented in the literature can be utilized to estimate the ultimate strength of concrete components reinforced with fibers. Unfortunately, there is a lack of data on how non-metallic fibers, such as polypropylene (PP), affect the properties of concrete, especially eco-friendly concrete. This study presents a novel approach to modeling the stress-strain behavior of eco-friendly polypropylene fiber-reinforced concrete (PFRC) using meta-heuristic particle swarm optimization (PSO) employing 26 PFRC various mixtures. The cement was partia
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Gas and downhole water sink-assisted gravity drainage (GDWS-AGD) is a new process of enhanced oil recovery (EOR) in oil reservoirs underlain by large bottom aquifers. The process is capital intensive as it requires the construction of dual-completed wells for oil production and water drainage and additional multiple vertical gas-injection wells. The costs could be substantially reduced by eliminating the gas-injection wells and using triple-completed multi-functional wells. These wells are dubbed triple-completion-GDWS-AGD (TC-GDWS-AGD). In this work, we design and optimize the TC-GDWS-AGD oil recovery process in a fictitious oil reservoir (Punq-S3) that emulates a real North Sea oil field. The design aims at maximum oil recovery us
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An application of neural network technique was introduced in modeling the point efficiency of sieve tray, based on a
data bank of around 33l data points collected from the open literature.Two models proposed,using back-propagation
algorithm, the first model network consists: volumetric liquid flow rate (QL), F foctor for gas (FS), liquid density (pL),
gas density (pg), liquid viscosity (pL), gas viscosity (pg), hole diameter (dH), weir height (hw), pressure (P) and surface
tension between liquid phase and gas phase (o). In the second network, there are six parameters as dimensionless
group: Flowfactor (F), Reynolds number for liquid (ReL), Reynolds number for gas through hole (Reg), ratio of weir
height to hole diqmeter
<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c
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Corona virus sickness has become a big public health issue in 2019. Because of its contact-transparent characteristics, it is rapidly spreading. The use of a face mask is among the most efficient methods for preventing the transmission of the Covid-19 virus. Wearing the face mask alone can cut the chance of catching the virus by over 70\%. Consequently, World Health Organization (WHO) advised wearing masks in crowded places as precautionary measures. Because of the incorrect use of facial masks, illnesses have spread rapidly in some locations. To solve this challenge, we needed a reliable mask monitoring system. Numerous government entities are attempting to make wearing a face mask mandatory; this process can be facilitated by using face m
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