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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This paper proposes an on-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. This research aims to design and implement Neural Network like a digital PID using FPGA in order to generate the best value of the hydrogen partial pressure action (PH2) to control the stack terminal output voltage of the (PEMFC) model during a variable load current applied. The on-line Particle Swarm Optimization (PSO) algorithm is used for finding and tuning the optimal value of the digital PID-NN controller (kp, ki, and kd) parameters that improve the dynamic behavior of the closed-loop digital control fue
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A fluorescence microscopy considered as a powerful imaging tool in biology and medicine. In addition to useful signal obtained from fluorescence microscopy, there are some defects in its images such as random variation in brightness, noise that caused by photon detection and some background pixels in the acquired fluorescence microscopic images appear wrongly auto-fluorescence property. All these practical limitations have a negative impact on the correct vision and analysis of the fluorescent microscope users. Our research enters the field of automation of image processing and image analysis using image processing techniques and applying this processing and analysis on one of the very important experiments in biology science. This research
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The high cost of chemical analysis of water has necessitated various researches into finding alternative method of determining portable water quality. This paper is aimed at modelling the turbidity value as a water quality parameter. Mathematical models for turbidity removal were developed based on the relationships between water turbidity and other water criteria. Results showed that the turbidity of water is the cumulative effect of the individual parameters/factors affecting the system. A model equation for the evaluation and prediction of a clarifier’s performance was developed:
Model: T = T0(-1.36729 + 0.037101∙10λpH + 0.048928t + 0.00741387∙alk)
The developed model will aid the predictiv
... Show MorePlant extracts occupied a big place in diseases treatment and preserving human health because, they contain many active substances that can be exploited in the field of pharmaceutical manufacturing from natural materials. Therefore, this study was conducted to evaluate the effect of different concentrations of plant extracts for each of Nigella sativa, Alliumsativum and Allium cepa against the fungal growth of Candida albicans that cause many skin diseases and infections to humans as well as Trichophyton mentagrophytes, which affects the hair, skin and nails. These two fungi have been isolated and diagnosed from people who have skin infection. Both fungal isolates were treated with extracts of Nigella sativa, Alliumsativum and Allium cepa
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Total no. of patient (100) stool samples were collected, during the period from February to the end of May of 2008, for children under two years old suffering from non-bloody and bloody diarrhea at (Children Welfare Teaching Hospital) in Baghdad. The study evaluates the relationship between etiological agent of diarrhea and sex, age group, type of feeding, presence of blood in stool of the patients. All samples were examined microscopically to identify parasitic agent and serological test for Rotavirus to identify viral infection, also biochemical and serological tested for specimen's culture on different culture media and antibiotic sensitivity test. Results show from 100 cases 64] represents the etiological agent of diarrhea and
... Show MoreGlobal warming and environmental damage have become major problems. The production of Portland cement releases large quantities of gas, which cause pollution to the atmosphere. This problem can be solved via the use of sustainable materials, such as glass powder. This study investigates the effect of partial replacement of cement with sustainable glass powder at various percentages (0, 15, 20, and 25%) by weight of cement on some mechanical properties (compressive strength, flexural strength, absorption, and dry density) of Reactive Powder Concrete (RPC) containing a percentage of Polypropylene fibers (PRPC) of 1% by weight. Furthermore, steam curing was performed for 5 hours at 90oC after hardening the sample directly. The RPC was
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