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.
Currently, with the huge increase in modern communication and network applications, the speed of transformation and storing data in compact forms are pressing issues. Daily an enormous amount of images are stored and shared among people every moment, especially in the social media realm, but unfortunately, even with these marvelous applications, the limited size of sent data is still the main restriction's, where essentially all these applications utilized the well-known Joint Photographic Experts Group (JPEG) standard techniques, in the same way, the need for construction of universally accepted standard compression systems urgently required to play a key role in the immense revolution. This review is concerned with Different
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Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet
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The impact of exposure to different sizes of particulate matter (PM1, PM2.5, PM7, and PM10) was evaluated in Babylon concrete plant workers who had been exposed to concrete dust for at least 10 years. The effects of these particles on the hematological parameters, malondialdehyde (MDA) levels, and antioxidant enzymes (catalase and glutathione peroxidase ) were examined. The results exhibited that the levels of PM2.5 and PM10 were higher than the acceptable limits approved by the National Ambient Air Quality Standards (NAAQS). The blood parameters, namely white blood cells (WBC), red blood cell (RBC) and platelets counts, demonstrated non-significant differences between workers exposed to the PM as compared to the control gro
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Abstract
Objective(s): To assess the job satisfaction during of covid-19 among the nurses in respiratory isolation units of coronavirus disease.
Methodology: A descriptive cross-sectional design was carried out in four hospitals at isolation units of coronavirus disease from the period (21th December, 2021 to 27th January, 2022). A non-probability (convenience) sampling method consists of (300) nurse was selected convenience based on the study criteria. The tool used to measure the job satisfaction is Job satisfaction scale for clinical nursing (JSS-CN). This tool consists of two parts, the first part is for demographic information and consists of 8 items, and the second
... Show MoreBackground: A great dental and biomedical interest had been paid to silver nanoparticles because of their antimicrobial activity. Objective: To evaluate the antimicrobial and cytotoxic activity of a newly developed Nano-silver fluoride that was synthesized from moringa oleifera leaf extract against S. mutants. Material and method: The green synthesis method was used to prepare Nano-silver fluoride from moringa oleifera leaf extract. The minimum inhibitory concentration and the minimum bactericidal concentration were evaluated using brain heart infusion plates, while the cytotoxicity was evaluated by the hemolytic activity. Results: Nano-silver fluoride had a bactericidal and bacteriostatic effect (MIC was 60 ppm a
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Green synthesis of silver nanoparticles (AgNPs) using different plant parts has shown a great potential in medicinal and industrial applications. In this study, AgNPs were in vitro green synthesized using A. graecorum, and its antifungal and antitumoractivities were investigated. Scanning electron microscopy (SEM) image result indicated spherical shape of AgNPs with a size range of 22-36 nm indicated by using Image J program. The functional groups indicated by Fourier-transform infrared spectroscopy (FTIR) represented the groups involved in the reduction of silver ion into nanoparticles. Alhagi graecorum AgNPs inhibited MCF-7 breast cancer cell line growth in increased concentration depend manner, significant differences shown at
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To assess the feasibility of using a faecal occult blood test (FOBT) kit as a screening tool for periodontal disease. Additionally, the study aimed to evaluate the test's sensitivity and specificity while exploring the influence of factors like age, sex and the number of teeth on its performance.
A prospective observational case–control study involving 150 participants divided into three groups on the basis of the basic perio
يعد تحليل العلاقات الكمية بين البنية والممتلكات/النشاط (QSPR/QSAR) أمرًا بالغ الأهمية لفهم خصائص المركبات المرتبطة بالمخدرات، خاصة في علاج مرض الزهايمر. في هذه الدراسة، هدفنا هو استخدام أساليب مبتكرة بما في ذلك استخدام المؤشرات الطوبولوجية -درجة و- درجة ، المستمدة من نظرية الرسم البياني، لتقييم الخصائص والخصائص الفيزيائية والكيميائية لخمسة أدوية لمرض الزهايمر: تاكرين، دونيبيزيل، ريفيستيجمين، جالانتامي
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The current study was carried out to study a high injection dose of the ethanolic extract thymus vulgaris leaf (500 ug /Kg) against the immune response combination with partially purified extracted Lipopolysaccharide ( LPS) from Proteus mirablis.Study groups were included four groups; Group I :treated with normal saline. Group II : treated with LPS antigen, Group III: injected subcutaneously ((500 ug /Kg) from ethanolic extract thymus vulgaris, group IV : injected subcutaneously (500 ug /Kg) from ethanolic extract thymus vulgaris leaf and LPS antigen, the immunological assays were measured through the phagocytic activity as (non specific immunity) after day 8 by using the phagocytic activity index.After day I4 the lymphocyte proliferations
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