Classifier detection runtime results on all datasets
A GENERATOR BASED ASSOCIATIVE CLASSIFIER FOR IMBALANCED DATASETS
... imbalanced datasets also, it is important to retain all the itemsets that satisfy the required minimum support ...The results of classifying synthetic imbalanced dataset is presented in Table ...
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MPI Runtime Error Detection with MUST: Advances in Deadlock Detection
... Application results We use the NAS Parallel Benchmarks (NPB) [1] ...MUST’s runtime deadlock detection ...for all operations that MUST analyzes to detect the ...
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Development of Automated Classifier of Diabetic Retinopathy using Datasets by Machine Learning
... The candidate pixel is classified as an exudates pixel if its intensity value is greater than its threshold t. VI. DETECTION OF OBJECTS WITH SHARP EDGES Our aim in this step is to detect all objects with ...
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L-classifier chains classification and variable selection for multi-label datasets
... benchmark datasets, and compared using six evaluation ...the results looked slightly different when another dataset was ...a classifier predicts the outcome, including the number of predictors and ...
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A Cascade-Classifier Approach for Fall Detection
... fall detection, especially those that use a sliding window, have a high com- putational cost because they need to compute the features from almost all ...of classifier (J48 Decision Tree, Logistic ...
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A survey of results on mobile phone datasets analysis
... the detection of communities may be influenced. All these additional considerations might significantly change the result of the detection of com- munities, including their internal ...
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Runtime Detection of Software Modification Using RSCA Method
... All RSMD methods that we reviewed have the advantage and disadvantage but none focuses on the PRSM and NRSM. We propose the RSCA method that not only detects the RSM but also can separate the PRSM and NRSM by ...
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Detection of Abusive Language: the Problem of Biased Datasets
... text classifier in the right-most ...different datasets was reported on the basis of different types of classifiers and also varying evaluation metrics, we ran the same classifier on all ...
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Improving Neural Metaphor Detection with Visual Datasets
... better results. All the means and vari- ances were normalized to have the same range as the visi- bility embeddings, and the results are shown in the last row of Table ...
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Intrusion Detection System And Mlsvm Classifier: A Survey
... by all of the IDSs and only individuals missed by one, two or the most them are ...of results illustrate that our process which is proposed is outperforms simultaneous methods by considerably dropping the ...
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Algorithms and hardness results for geometric problems on stochastic datasets
... Interestingly, this method for computing SCHMP is a generalization of the witness-edge method in [7] to the case d > 2, where the latter was the first known approach that solves this problem in R 2 and was thought to ...
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Runtime Detection and Prevention for Structure Query Language Injection Attacks
... The input checker component has been checked using various types of user input and multi forms of SQL injection attacks. The result matches with those of the expected results. However, these samples are not enough ...
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Studying Generalisability across Abusive Language Detection Datasets
... test results (accuracy and macro-F 1 ) for all dataset combinations, using the BERT ...other datasets. For all datasets, these were BERT models, but with vary- ing hyper-parameter ...
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CLOUDS: A Decision Tree Classifier for Large Datasets
... All the intervals with gini est > ginimin are eliminated (prune) to derive a list of potential candidate intervals (alive intervals). For an alive interval, we evalu[r] ...
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A Conic Section Classifier and its Application to Image Datasets ∗
... Each member class in the dataset is repre- sented by a prototype conic section in the feature space, and new data points are classified based on a distance measure to each such representa[r] ...
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MPI runtime error detection with MUST: Advances in deadlock detection
... shows the WFG for the deadlock from Fig. 1(b). This graph only uses the AND semantic that we illustrate with solid arcs. Fig. 3(b) shows the WFG for the deadlock from Fig. 1(d) for the interleaving that leads to ...
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Detection of Rice Disease Using Bayes Classifier and Minimum Distance Classifier
... disease detection on the basis of two parameters accuracy and detection ...inaccurate results for wrong input. On other side of KNN is effective classifier as compare to SVM and it would ...
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Classification and Anomaly Detection for Astronomical Datasets
... cron All Sky Survey (2MASS; Skrutskie et ...have all identified hundreds of millions of distinct ...can all be estimated reliably by using fairly simple moment-based approaches ...
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Detection and Deletion of Outliers from Large Datasets
... OUTLIER DETECTION METHOD In larger samplings of data, some data points will be further away from the sample mean than what is deemed ...outlier detection solving set algorithm ...from all the data ...
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MINING OF OUTLIER DETECTION IN LARGE CATEGORICAL DATASETS
... Outlier detection will typically be thought of as a pre-processing step for locating, throughout a data set, those objects that do not fits well-defined notions of expected ...outlier detection for ...
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