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.
A substantial percentage of the world’s energy consumption (almost 40%) and carbon dioxide (CO2) emissions (around 37%) come from the construction industry, especially schools. This work presents a new hybrid artificial intelligence (AI) engineering model that aims to maximize energy performance on campuses in a holistic way. Modules for data-driven forecasting, metaheuristic optimization, and real-time adaptive control are all part of the concept. A thorough energy simulation of a university campus building is used in conjunction with the AI model to assess its performance through a co-simulation framework. Findings show that yearly peak electricity demand may be reduced by 18.7% and total site energy consumption by 22.4% when co
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In order to promote sustainable steel-concrete composite structures, special shear connectors that can facilitate deconstruction are needed. A lockbolt demountable shear connector (LB-DSC), including a grout-filled steel tube embedded in the concrete slab and fastened to a geometrically compatible partial-thread bolt, which is bolted on the steel section's top flange of a composite beam, was proposed. The main drawback of previous similar demountable bolts is the sudden slip of the bolt inside its hole. This bolt has a locked conical seat lug that is secured inside a predrilled compatible counter-sunk hole in the steel section's flange to provide a non-slip bolt-flange connection. Deconstruction is achieved by demounting the tube from the t
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Transportation is one of the aspects that enable us to achieve sustainability on a university campus, by taking environmental, social, and economic requirements. Walking is a green mode that can be essential to promoting sustainable transport. This study aims to evaluate the ability of campus physical development planning at Diyala University in creating sustainable transport on campus by determining the problems that exist. The research problem was identified in the absence of a comprehensive view of the importance of greenway network connectivity in the sustainability of the campus and the most important barriers that prevent it from being achieved and the incentives to be activated. The methodology used in this study was the quantitative
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The integration of Artificial Intelligence with Big Data Analytics is one of the most groundbreaking developments that could change the face of educational sustainability in higher education.. Using AI and Big Data technologies not only makes the educational process more efficient but also changes the way people learn and thus opens the door for educators and institutions to make decisions based on the data. The document imparts the manner that the use of AI and the digital revolution can remove student requirements, execute the efficiency of the curriculum, and acquire the balance of educational resources through a majority of instances and the latest developments in that field. Furthermore, the paper, along with the issues of morality wit
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The current study included the isolation, purification and cultivation of blue-green alga Oscillatoria pseudogeminata G.Schmidle from soil using the BG-11liquid culture medium for 60 days of cultivation. The growth constant (k) and generation time (G) were measured which (K=0.144) and (G=2.09 days).
Microcystins were purified and determined qualitatively and quantitatively from this alga by using the technique of enzyme linked immunosorbent assay (Elisa Kits). The alga showed the ability to produce microcystins in concentration reached 1.47 µg/L for each 50 mg DW. Tomato plants (Lycopersicon esculentum) aged two months were irrigated with three concentrations of purified microcystins 0.5 , 3.0 and 6.0
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Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with
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There are many methods of searching large amount of data to find one particular piece of information. Such as find name of person in record of mobile. Certain methods of organizing data make the search process more efficient the objective of these methods is to find the element with least cost (least time). Binary search algorithm is faster than sequential and other commonly used search algorithms. This research develops binary search algorithm by using new structure called Triple, structure in this structure data are represented as triple. It consists of three locations (1-Top, 2-Left, and 3-Right) Binary search algorithm divide the search interval in half, this process makes the maximum number of comparisons (Average case com
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