Classification of data by support vector machine (SVM).

Classification of data by support vector machine (SVM).

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The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the cri...

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... training set can be separated by the hyperplane w T x i + b = 0, where w is the weight vector and b is the bias. The equations of the marginal hyperplanes, H 1 and H 2 (Figure 2), are: ...
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... distance between marginal hyperplanes (namely, the margin) is equal to 2 ||w|| . Any training samples that fall on hyperplanes H 1 or H 2 , the sides defining the margin, are support vectors, as shown in Figure 2. Thus, the problem is the maximizing of the margin by minimizing ||w|| 2 subject to Equation (1). ...

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... Compared with many non-entry mining methods (such as sub-level caving), entry-type mining methods (such as cut and fill) are more costly and complex (Garcia-Gonzalo et al., 2016). Although many mines use the non-entry mining methods to achieve cost savings, cut and fill is a better choice when the rock strength of the hanging wall is low or ore body contacts are quite irregular (Goh et al., 2017). ...
... There are a series of factors affecting the rock mass stability in entrytype underground excavations in mining operations, including the distribution of the discontinuity surface, the strength of the rock mass, the rock mass condition, the stress condition around the opening and the span of the opening (Garcia-Gonzalo et al., 2016), in which the rock mass condition and opening span play an important role. Accurate numerical description of rock mass conditions is difficult because rock mass conditions are a broad concept, including various indexes such as rock strength, rock mass integrity, weathering degree of rock and rock joint conditions. ...
... Traditional empirical design approaches, represented by the critical span graph developed by Lang (1994), typically require a database of historical cases and relate parameters such as rock mass characteristics and mine geometry to the stability of underground structures. This kind of method relies on the existing field observations to establish specific patterns that can be generalized to a wider range of data (Garcia- Gonzalo et al., 2016). However, empirical design methods are usually used only under engineering conditions similar to the data collected, which is clearly detrimental to subsequent mining operations. ...
Article
The stability evaluation of underground entry-type excavations is a prerequisite of the entry-type mining method, which directly affects whether workers can be provided with a safe and reliable working environment and whether subsequent mining operations can be carried out normally. The design and stability assessment of entry-type excavations in current mining engineering largely relies on an empirical design method called the critical span graph, which has been widely applied in the initial span design of various cut and fill stopes. In recent years, with the wide application of various intelligent algorithms in the field of mine engineering, models based on intelligent algorithms provide new research methods and ideas for the assessment of rock stability in entry-type excavations. This study aims to introduce several hybrid models based on the random forest (RF) algorithm into the stability evaluation work to find new data-driven methods with higher accuracy to update the critical span graph. To pursue better classification performance, this paper selects three optimization strategies, namely multi-verse optimizer (MVO), grey wolf optimizer (GWO) and moth-flame optimization (MFO) algorithm, to optimize two core parameters of RF, and establishes three corresponding hybrid models, abbreviated as MVO-RF, GWO-RF and MFO-RF, based on the database containing 399 samples from seven Canada mines. There are two input parameters in the database, i.e., opening span and rock mass condition (expressed as RMR), and the output parameter is rock mass stability, which is specifically divided into three categories: stable, potentially unstable and unstable. In addition, five commonly used measurement indexes applicable to multiclassification problems were adopted to verify the classification ability of the models, i.e., the accuracy (ACC), precision calculated using macro-average (PREM), recall calculated using macro-average (RECM), F1 score calculated using macro-average (F1M) and Kappa index (Kappa). The results indicate that the three hybrid models performed well based on the test set accounting for 25 % of the original database, in which the accuracy of the MFO-RF model was the highest: ACC = 0.9300; PREM = 0.9288; RECM = 0.8983; F1M = 0.9116; Kappa = 0.8666. To evaluate whether the three optimization strategies can effectively improve the performance of RF and judge the degree of improvement, the performance of an unoptimized RF model was discussed in this study. In addition, two support vector machine (SVM) models with different kernel functions were selected as references for performance evaluation. The results indicated that compared with the RF and two SVM models, the classification accuracy of the three hybrid models was obviously more satisfactory. The classification accuracy of the three hybrid models reached 0.91, which was sufficient to explain the excellent classification ability of these models. After tuning the RF hyperparameters of each hybrid model, the critical span graph was further updated according to the optimized classification models, which was the focus of this research. By comparing the critical span graphs obtained by the three hybrid models with the single RF model and two kinds of SVM models, it is certain that the three hybrid models proposed in this paper, MVO-RF, GWO-RF and MFO-RF, are promising in the study of evaluating the stability of entry-type excavations and may be deemed auxiliary decision tools to define the stability region of the critical span graph.
... The literature reveals a number of recent applications of artificial intelligence techniques related to stope design and underground excavation excavations. These include for instance, the design of underground excavation spans using artificial neural network (Wang et al. 2002); hard-rock stope span design in entry-type excavations using learning classifiers (García-Gonzalo et al. 2016); open stope stability analysis using the random forest algorithm (Qi et al. 2018a); prediction of stope stability based on several machine learning algorithms (Qi et al. 2018b(Qi et al. , 2018c; open stope stability assessment through artificial intelligence (Santos et al. 2020); and mine stope performance assessment through classifers (Adoko et al. 2019). While in these studies, the focus was mainly on the development of models capable of achieveing a high prediction capability, very limited effort was dedicated to the practical implementation of the ANN for open stope design. ...
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Assessing the stability of stopes is essential in open stope mine design as unstable hangingwalls and footwalls lead to sloughing, unplanned stope dilution, and safety concerns compromising the profitability of the mine. Over the past few decades, numerous empirical tools have been developed to dimension open stope in connection with its stability, using the stability graph method. However, one of the principal limitations of the stability graph method is to objectively determine the boundary of the stability zones, and gain a clear probabilistic interpretation of the graph. To overcome this issue, this paper aims to explore the feasibility of artificial neural network (ANN) based classifiers for the design of open stopes. A stope stability database was compiled and included the stope dimensions, rock mass properties, and the stope stability conditions. The main parameters included the modified stability number (N’), and the stope stability conditions (stable, unstable, and failed), and hydraulic radius (HR). A feed-forward neural network (FFNN) classifier containing two hidden layers (110 neurons each) was employed to identify the stope stability conditions. Overall, the outcome of the analysis showed good agreement with the field data; most stope surfaces were correctly predicted with an average accuracy of 91%. This shows an improvement over using the existing stability graph method. In addition, for a better interpretation of the results, the associated probability of occurrence of stable, unstable, or caved stope was determined and shown in iso-probability contour charts which were compared with the stability graph. The proposed FFNN-based classifier outperformed the conventional stability graph method in terms of accuracy and better prabablistic interpretation. It is suggested that the classifier could be a reliable tool that can complement the conventional stability graph for the design of open stopes.
... SVM is also known as one of the effective machine learning and has high classification efficiency [20]. Illustration of SVMs is shown in Figure 2 [21]. SVM is a machine learning algorithm for classification and regression, which was introduced by Vapnik (1990) [22]. ...
Article
Cervical cancer is the second most common cancer in women worldwide, and occurs when there are presences of abnormal cells in the cervix, which continue to grow uncontrollably. In the early stages, cervical cancer indications are not perceptible; however, it is easily detected with different forms of machine learning methods, such as the convolutional neural network (CNN). This is a popular method with a wide range of applications and known for its high accuracy value. Moreover, there is a support vector machine (SVM) with several kernel functions that is commonly used in the classification of diseases, and also known for its high accuracy value. Therefore, the combination of CNN-SVM with several linear kernels functions as classifier for the categorization of cervical cancer.
... where, w T represents transpose of weight vector and b represents bias [24]. ...
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Clickbaits are eye-catching headlines that are quite different from the actual content in the news. Clickbaits exaggerate the facts and lure users to click them. A dataset has been introduced that consists of Nepali news headlines and news body with label: clickbait and non-clickbait. A Machine learning model has been implemented using Support Vector Machine and Random Forest. The model uses cosine similarity metrics and TFIDF to compare between corresponding news headlines and news body, and classify them. The SVM model obtained an F1 score of 0.9483 where as RF obtained an F1 score of 0.9473. Cross validation has been used to validate the data.
... SVM, a binary typed classifier basis of the supervised learning approach for classifying data into two classes by drawing a hyperplane as Figure (2.4) [45]. ...
... The SVM hyperplane between two classes [45]. Also, can calculate weight and bias when obtaining the αi value, the weight calculation using the following formula: ...
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ABSTRACT The process of identifying each letter separately is very important to understand. With this, sign language recognition has become an important technology in artificial intelligence (AI) and machine learning (ML). This thesis presents two proposed systems for static hand gesture recognition (HGR) based on ML and Deep Learning (DL) algorithms in which several steps are used in the form of phases; image acquisition, image preprocessing, feature extraction, and classification. In the first proposed system, a histogram of oriented gradients (HOG) is utilized for extracting features from each image and then a multi-class support vector machine (MSVM) is applied using the result of the HOG of images to perform the classification process. In the second proposed system, the convolution neural network (CNN) is used through which recognition of static hand gestures is accomplished according to a special structure of this algorithm that consisting of several layers. The Previous works and researches in that field had a lot of complexity with different accuracy. The obtained results, the second proposed system which adopted DL by using the CNN model outperforms the first system in terms of performance and accuracy, the accuracy rate obtained from the second proposed system was (99.71%) for American Sign Language (ASL) and (99.03%) for Arabic sign language (ArSL), While the accuracy rate obtained from the first proposed system was (95.58%) and (96.16%) for ArSL
... SVM model separates the data into classes by creating a line called a hyperplane. method aims to maximize the margin to find the optimal hyperplane as shown in Figure 4. Figure 4. Illustration of Optimal Hyperplane on SVM [23] As for the optimization, on the classification process this research implements Synthetic Minority Oversampling Technique (SMOTE). The SMOTE optimization is beneficial for handling imbalanced data and allows increasing the quality of the SVM Classifier [22]. ...
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Personality provides a deep insight of someone and has an important part in someone’s job performance. Predicting personality through social media has been studied on several research. The problem is how to improve the performance of personality prediction system. The purpose of this research is to predict personality on Twitter users and increase the performance of the personality prediction system. An online survey using Big Five Inventory (BFI) questionnaire has been distributed and gathered 295 Twitter users with 511,617 tweets data. In this research, we experiment on two different methods using Support Vector Machine (SVM), and the combination of SVM and BERT as the semantic approach. This research also implements Linguistic Inquiry Word Count (LIWC) as the linguistic feature for personality prediction system. The results showed that combination of these two methods achieve 79.35% accuracy score and with the implementation of LIWC can improve the accuracy score up to 80.07%. Overall, these results showed that the combination of SVM and BERT as the semantic approach with the implementation of LIWC is recommended to gain a better performance for the personality prediction system.
... The best decision boundary is called a hyperplane. To find the hyperplane, SVM chooses the extreme points/vectors which are called support vectors. Figure 2.8: Classification of data using Supportvector machine [37]. ...
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Cybercrime is an evolving and growing threat that heavily bothers Internet users and the relevant authorities. Due to the rapid adoption of technology, Cybercrime Incidents have been increasing at an enormous rate. Cybercrime has several types and various targets, depending on what the offender wants to achieve. It is well-established that the prevention and confrontation of such incidents is a challenging problem since they have a highly complex nature that is constantly developing. This thesis aims to propose a Cybercrime Classification System (CCS) that automatically classifies Cybercrimes into a specific type/class. This process will help to group similar incidents together, propose appropriate counter-measures, design an effective response policy and find recurring patterns between the incidents. The CCS consists of three Components and each serves a different purpose. In order to classify Cybercrime Incidents, the Cybercrime Classification System uses a feature-based approach, which means that each incident is characterized by some specific, distinctive features. These features will determine the Cybercrime Class that the incident falls into. Machine Learning techniques, such as Data Mining, are used by the CCS for the Classification process. The results of the presented system are decent and prove that a practical and real-world system which uses the proposed approach could be developed, and it could be proven critical for mitigating Cybercrime. The CCS could be extended in order to provide more functionalities, such as automatically assess threat severity or automatically apply the respective counter-actions.
... Infogain is considered a symmetrical measure of the variation (reduction) of the entropy in the variable Y when increasing the information from variable X [44]. It has been used in studies that designed tools for indoor localization in wireless environments [45], chronic disease prediction (osteoporosis and arthritis) [46] and bioluminescent protein prediction (BLProt) [47]. ...
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... Hyper-plane in SVM [27]. ...
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The Dissolved Gas Analysis (DGA) is utilized as a test for the detection of incipient problems in transformers, and condition monitoring of transformers using software-based diagnosis tools has become crucial. This research uses dissolved gas analysis as an intelligent fault classification of a transformer. The Multilayer SVM technique is used to determine the classification of faults and the name of the gas. The learned classifier in the multilayer SVM is trained with the training samples and can classify the state as normal or fault state, which contains six fault categories. In this paper, polynomial and Gaussian functions are utilized to assess the effectiveness of SVM diagnosis. The results demonstrate that the combination ratios and graphical representation technique is more suitable as a gas signature and that the SVM with the Gaussian function outperforms the other kernel functions in diagnosis accuracy.
... Exemplo de uma SVM. Adaptado de[13]. ...
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