In this paper classification algorithms will be used to investigate the presence of tumours in the breast, from signals collected with a radar microwave imaging prototype from the University of Bristol. A number of fe...
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ISBN:
(纸本)9788890701863
In this paper classification algorithms will be used to investigate the presence of tumours in the breast, from signals collected with a radar microwave imaging prototype from the University of Bristol. A number of features will be extracted from the scattering of breast tumours and will then be used in classification algorithms such as Linear Discriminant Analysis or Quadratic Discriminant Analysis. The results from the classifier will allow creating an image of the considered synthetic breast phantom in which normal breast tissue is classified as a "miss" and tumour tissue is classified as a "hit".
A comparative analysis of classification algorithms of iCub platform humanoid hand tactile sensors is presented. The experimental data were analyzed with different learning supervised classification algorithms: Decisi...
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ISBN:
(纸本)9781509015504
A comparative analysis of classification algorithms of iCub platform humanoid hand tactile sensors is presented. The experimental data were analyzed with different learning supervised classification algorithms: Decision Trees Classifiers, k-Nearest Neighbors Classifiers (kNN), and Support Vector Machines (SVM). The best result was obtained with a Gaussian SVM kernel, which allowed 97.4% accuracy using 20% data for holdout validation. The results indicate the potential of categorization and learning of robotic hands for object grasping and manipulation.
This short paper presents preliminary results on the evaluation of a classification algorithms Framework Domain-Specific Language (CAF DSL) that was previously developed. In our previous work, a domain-specific langua...
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ISBN:
(纸本)9798350326970
This short paper presents preliminary results on the evaluation of a classification algorithms Framework Domain-Specific Language (CAF DSL) that was previously developed. In our previous work, a domain-specific language to provide a framework for choosing-evaluating classification algorithms but with a system-level and simplified interface approach was presented. However, the evaluation that took place was restricted to data scientists and language engineers. In this work, the evaluation of the same DSL but by finance-accountant professionals (domain-experts) took place in a small scale. The DSL was presented and feedback regarding its usability and learnability took place. Future plans consist of scaling this attempt further and extending it to different domains such as health care. The long-term plan consists of bringing AI - classification algorithms to professionals and to the wider public.
Since they transform electrical energy into mechanical energy, three-phase induction motors are one of the main assets that companies have. Therefore, good monitoring of their conditions and diagnosing their faults is...
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ISBN:
(数字)9781665488549
ISBN:
(纸本)9781665488549
Since they transform electrical energy into mechanical energy, three-phase induction motors are one of the main assets that companies have. Therefore, good monitoring of their conditions and diagnosing their faults is essential. In this article, we propose a curve fitting technique and classification algorithms for a current motor phase to detect broken bars inside the motor. The data set is in the IEEE database, where the data was acquired, simulating the conditions of healthy and broken bars by varying the load condition. The curve fitting technique gives me essential attributes such as the signal's amplitude, frequency, and phase shift, supported by the Fourier transform, which informs how the signal power is a function of frequency. Furthermore, we extracted attributes to train the classifiers, achieving 85% accuracy in classifying the number of broken bars within the engine.
Five classification algorithms namely J48, Naive Bayes, Multi layer Perceptron, IBK and Bayes Net are evaluated using Mc Nemar's test over datasets including both nominal and numeric attributes. It was found that ...
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ISBN:
(纸本)9788132210375
Five classification algorithms namely J48, Naive Bayes, Multi layer Perceptron, IBK and Bayes Net are evaluated using Mc Nemar's test over datasets including both nominal and numeric attributes. It was found that Multi layer Perceptron performed better than the two other classification methods for both nominal and numerical datasets. Furthermore, it was observed that the results of our evaluation concur with Kappa statistic and Root Mean Squared Error, two well-known metrics used for evaluating machine learning algorithms.
Machine Learning is one of the most rapidly developing technologies, and it is currently being utilized in every kind of application. The Healthcare industry has been benefiting exponentially from this emerging techno...
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ISBN:
(纸本)9781665467490
Machine Learning is one of the most rapidly developing technologies, and it is currently being utilized in every kind of application. The Healthcare industry has been benefiting exponentially from this emerging technology. Among the number of advantages heart failure prediction is one of them. Almost 17.9 million people die from heart disease every year. This research predicts heart failure with seven machine learning classification algorithms by the factors or features of health conditions such as age, resting blood pressure, chest pain type, cholesterol, fasting blood sugar, resting electro diagram, maximum heart rate, etc. Then, it is accompanied by a comparative performance analysis of these algorithms. The research shows that Naive-bias, Random Forest, and Support Vector Machines are outperformed to predict heart failure. The accuracy of these algorithms is almost 85-86%.
This paper attempts to lay bare the underlying ideas used in various pattern classification algorithms reported in the literature. It is shown that these algorithms can be classified according to the type of input inf...
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Peptide-binding proteins prediction is important in understanding biological interaction, protein performance analysis, cellular processes, drug design, and even cancer prediction, so using experimental predictive met...
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ISBN:
(纸本)9781728195742
Peptide-binding proteins prediction is important in understanding biological interaction, protein performance analysis, cellular processes, drug design, and even cancer prediction, so using experimental predictive methods, despite their operational capabilities, has limitations such as being costly and need to spend more time, differences between unrecognized protein structures and sequences, so design and development of computational systems for maintenance, optimal models for representing biological knowledge, management and the analysis of big biological data is so important that the authors used machine learning-based techniques such as Support Vector Machine (SVM),Random Forest (RF),Decision Tree (C4.5), Decision Tree (ID3),Gradient Boosting classifiers, which evaluated experimental results to optimize Support Vector Machine(SVM) classifier (Radial Basis Function kernel) with significant evaluation parameters such as accuracy(ACC) is equal to 0.7401 and 0.7599 for 10 - fold cross validation and independent test set and also specificity (SPE) is equal to 0.7966 and 0.8088 for 10- fold cross validation and independent test set (respectively) by using various Structure- Based and Sequence -Based features.
Through analyzing the limitations of modeling and evaluating the cost-sensitive multiclass classification algorithms, a series of models based on three classification algorithms are presented. On this basis, expected ...
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ISBN:
(纸本)9783319495682;9783319495675
Through analyzing the limitations of modeling and evaluating the cost-sensitive multiclass classification algorithms, a series of models based on three classification algorithms are presented. On this basis, expected cost of misclassification as a cost-sensitive metric, which is introduced for evaluating the more cost details of models.
Hyperspectral remote sensing technology is applied to many fields because of its super-multiband,high resolution and vast *** classification technology is a research hotspot *** information is not utilized fully in tr...
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ISBN:
(纸本)9781467397155
Hyperspectral remote sensing technology is applied to many fields because of its super-multiband,high resolution and vast *** classification technology is a research hotspot *** information is not utilized fully in traditional remote sensing image classification method;so many improved algorithms are disappeared in order to enhance efficiency,accuracy and *** hyperspectral remote sensing image processing flow is ***,demerits and development tendency of classification method are clarified.
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