— Several common classifiers can be adopted to classify plant species, such as Bayesian, decision tree [140], ... In SVM, a kernel or filter, such as radial basis …
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A hierarchical learning algorithm is developed for supporting large-scale plant species identification. A visual tree is first constructed for organizing large numbers of plant species hierarchically in a coarse-to-fine fashion. For the fine-grained plant species at the sibling leaf nodes under the same parent node, they share significant common visual properties …
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— Specifically formulated for sewage treatment plants, it consists of powerful and highly adaptable microbes able to degrade high organic loads efficiently and minimize sludge production. Furthermore, …
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— Plant disease has become a major threat to global food security [1].Plant diseases contribute 10–16% losses in the global harvest of crops each year costing an estimated US$220 billion [2].According to a report of the Food and Agriculture Organization (FAO) [3], our world population is anticipated to hit 9.1 billion in 2050.Therefore, …
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regrinding. Accordingly, the classifier blades' design can play an important role in improving the combustion efficiency inside the boiler. Figure 1 shows the selected classifier blade angles for the model of this study which are 70, 60, 50 and 40 degrees. The effect of the classifier blade angle shown in . Fig1 on the coal s
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— In this paper, a deep learning framework is developed to enable path-based tree classifier training for supporting large-scale plant species recognition, where a deep …
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— The method of combining classifiers can improve the accuracy of such predictions – in this case, predictions of genes involved in stress response in plants – and it effectively chooses the appropriate amount of dimensionality reduction automatically. BackgroundArabidopsis thaliana is the model species of current plant genomic research …
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DOVE Spiral Classifier, also referred to as Screw Classifier, or Spiral Mineral Separator, is highly efficient classifier designed for closed circuit wet classification and separation of the Slimes (Fines) from a sandy …
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Train a classifier function: Generate a classifier measurements object of the function applied to a test set: Get the accuracy from the function on the test set: Visualize the confusion matrix: Generate a classifier function whose input …
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— In this paper various classifiers are analyzed for the classification of various plant leaf disease. The classifiers used in the study are given below: K-Nearest Neighbor. Support Vector Machine. Decision Tree. 3.1 K-Nearest Neighbor. Machine learning algorithm and falls under the category of supervised learning techniques.
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— The Ore Concentration Plant, where the process of flotation is carried out as well as the final production of copper concentrate, plays a key role in the entire production line of KGHM Polska Miedź S.A. Majority of operations related to the run-of-mine preparation to copper flotation enrichment are carried out in a water environment.
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— The types of equipment that are used to accomplish this are called CLASSIFIERS. There are three basic kinds used. The first two, the RAKE classifier and the SPIRAL or screw classifier work on the same principal, and are not often used any more. These two types were popular for many years.
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Özer and Whiten [15] studied the coal breakage in compression tests aiming to obtain the appearance function. This was to give the distribution that results from the breakage of each coal particle type. The outcomes could be used in the ... Taylor's University INVESTIGATION ON CLASSIFICATION EFFICIENCY FOR COAL-FIRED POWER …
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— Identification of plant diseases plays an important and challenging role in the protection of agricultural crops and also their quality. Several works are in progress to improve the existing leaf image-based disease identification using deep learning. In this paper, we have studied some of the existing plant disease identification techniques and …
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— Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have …
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— The Pseudomonas syringae species complex (PSSC) is a diverse group of plant pathogens with a collective host range encompassing almost every food crop grown today. As a threat to global food ...
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A deep learning framework is proposed to recognize large-scale plant species by integrating attention-based deep feature extraction network and plant taxonomy-guided path-based tree classifier. First, a plant taxonomy is constructed for organizing large-scale fine-grained plant species hierarchically in a coarse-to-fine fashion. Second, a deep learning …
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In this paper multiple classifiers are used for automatic plant recognition based on the shape of leaf features which are extracted from the leaf images using different herbal plants. Four different classifiers have been used namely SVM, KNN, Random Forest, and Logistic regression. Shape feature is considered as an important feature among all other …
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Experimental results on the Orchid2608 plant dataset can prove that proposed deep attention network with path-based tree classifier can achieve improvements on large-scale plant species identification task. Abstract. A deep learning framework is proposed to recognize large-scale plant species by integrating attention-based deep feature …
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— Building Random Forest Algorithm in Python. In the Introductory article about random forest algorithm, we addressed how the random forest algorithm works with real life examples.As continues to that, In this article we are going to build the random forest algorithm in python with the help of one of the best Python machine learning library …
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— A Novel Res + LSTM Classifier-Based Tomato Plant Leaf Disease Detection Model with Artificial Bee Colony Algorithm. Conference paper; First Online: 02 December 2023; pp 25–47; ... Here, the hidden neurons and the activation functions such as ReLu, linear, softmax, sigmoid, and tanh functions are optimized in the ResNet structure, and …
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— View ALL our Spiral Classifiers Models. In Mineral Processing, the SPIRAL Classifier on the other hand is rotated through the ore. It doesn't lift out of the slurry but is revolved through it. The direction of rotation causes the slurry to be pulled up the inclined bed of the classifier in much the same manner as the rakes do.
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criterion {"gini", "entropy", "log_loss"}, default="gini". The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "log_loss" and "entropy" both for the Shannon information gain, see Mathematical formulation.Note: This parameter is …
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— Function f θ is a deep neural ... counting up to 10,000 plant species. State-of-the-art classifiers: The comparison of deep neural network classifiers in Section 5.1 shows the improvement in classification accuracy achieved by recent CNN architectures. The state-of-the-art Vision Transformers achieve even higher recognition scores: the …
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This paper proposed a model comprising of Auto-Color Correlogram as image filter and DL as classifiers with different activation functions for plant disease. This proposed model is implemented on four different datasets …
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— Plants can be seen everywhere in daily life and are closely connected with our lives. The recognition and classification of plants are of great significance to ecological and environmental protection. Traditional plant identification methods are complex, and experts cannot classify multiple plant species quickly. More and more researchers pay …
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— A deep CNN model with "Multilayer Perceptron" (MLP) classifier achieved 97.7% accuracy and improved to 98.1% accuracy with SVM classifier for the MalayaKew …
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— The spiral classifier is a commonly used equipment for mineral processing (sand washing). It is often paired with a ball mill to form a closed-circuit circulation to divert ore. It is also used in gravity concentrators to classify ore and fine mud, ore slurry in metal mineral processing processes, particle size classification, desliming, dehydration, and …
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— This ensemble classifier is an accurate tool that can be used to rank long non-protein coding RNA predictions for use in conjunction with gene expression studies. Selection of plant transcripts with a high potential for regulatory roles as long non-protein coding RNAs will advance research in the el …
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— Here performed an extensive experimental comparison of the proposed optimal feature set and other feature extraction methods using different classifiers and tested on different data sets (Swedish ...
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