With the expeditious growth of unstructured massive data on the World Wide Web (WWW), more advanced tools, techniques, methods, and approaches for information organization and retrieval are desired. Text mining is one...
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ISBN:
(纸本)9783319776996;9783319777009
With the expeditious growth of unstructured massive data on the World Wide Web (WWW), more advanced tools, techniques, methods, and approaches for information organization and retrieval are desired. Text mining is one such approach to achieve the above mentioned demand. One of the main text mining applications is how to classify data presented by different industries into groups. In this paper, the classification of data into various groups based on the choice of the users at any given point of time is proposed. Here, a support vector machine (SVM) based classification algorithm is established to classify the text data into two broad categories of Manufacturing and Non-Manufacturing suppliers. Later, the performance of the proposed classifier was tested experimentally using most commonly used accuracy measures such as precision, recall, and F-measure. Results proved the efficiency of the proposed approach for classification of the texts.
The paper describes algorithm for the classification of digital modulations and its testing with disturbed signals. 2ASK, 2FSK, 4FSK, MSK, BPSK, QPSK, 8PSK and 16QAM were chosen for recognition as the best-known digit...
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ISBN:
(纸本)9781457714115
The paper describes algorithm for the classification of digital modulations and its testing with disturbed signals. 2ASK, 2FSK, 4FSK, MSK, BPSK, QPSK, 8PSK and 16QAM were chosen for recognition as the best-known digital modulations used in modern communication technologies. The method designed uses ten features computed from parameters of recognized signal such as instantaneous amplitude, instantaneous phase, instantaneous frequency and spectrum characteristic. The GentleBoost algorithm was used to analyze the features and classify the modulations. We used multipath fading channel to model signal propagation and disturbed the signal by white Gaussian noise for the purpose of testing the algorithm.
This study aims to extract the most relevant set consisted of affective variables to the level of user satisfaction on engine sounds using classification algorithm. The affective variables for engine sounds were defin...
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ISBN:
(纸本)9781479964109
This study aims to extract the most relevant set consisted of affective variables to the level of user satisfaction on engine sounds using classification algorithm. The affective variables for engine sounds were defined by three axes, and two classification algorithms were used to determine the prediction accuracy for those affective axes. The study was consisted of three phases: 1) extracting sets of affective variables and the level of satisfaction on engine sounds, 2) preprocessing of engine sounds and experiment design, and 3) analysis of the most relevant sets of affective variables to user satisfaction. As a result, PA (Powerful-Affective) variable set showed the highest prediction accuracy of user satisfaction compared to other sets. Predicting the level of satisfaction based on classification algorithm could help to generalize the relationship between user satisfaction and affective variables more easily, beyond the limitation with a small size of subjects.
In this study, Artificial Bee Colony (ABC) algorithm based classifier is used. Also, in order to improve the effectiveness of ABC algorithm, some modifications are done. New method is called MABC algorithm. Both metho...
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ISBN:
(纸本)9781479975723
In this study, Artificial Bee Colony (ABC) algorithm based classifier is used. Also, in order to improve the effectiveness of ABC algorithm, some modifications are done. New method is called MABC algorithm. Both methods are applied on various real life data sets such as IRIS, WINE, PIMA, BUPA, ECG and results are compared. Those datasets are obtained from UCI Machine Learning Repository and MITBIH ECG database. In addition to it, validity indices and effects of some control parameters such as MCN, Limit are examined. It is observed that, selected features have significiant effect on classification success rate of classifier. If there is high overlap between the classes, success rate of classifier decreases. However observed results indicate that ABC algorithm can successfully be used for classification of multi dimensional datasets. By means of SCTR control parameter, MABC algorithm based classifier provides higher classification success rates versus ABC algorithm, independent from Limit and MCN values.
One of the most physically and psychologically damaging neurological conditions that affect people of all ages is an epileptic seizure. The abnormality should be recognized early so that the proper treatment is timely...
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One of the most physically and psychologically damaging neurological conditions that affect people of all ages is an epileptic seizure. The abnormality should be recognized early so that the proper treatment is timely. It is possible only with advanced signal processing techniques to distinguish and predict epileptic patterns in which substantial effort is invested. Therefore, efficient seizure detection and classification methods are proposed for machine learning and deep learning algorithms. The proposed method uses deep and machine-learning algorithms for seizure detection and classification. The objective is to analyze the performance and efficiency of deep and machine learning classifiers by comparing the various classifiers. This proposed work uses 11,500 EEG data samples from the UCI machine learning repository. To suggest an Improved Fitness Function Genetic algorithm (IGA) technique for optimal feature selection to improve the detection rate and CNN-RNN algorithm used as the classifier. The analysis proved that the hybrid CNN-RNN(LSTM with GRU) classifier with GA-based feGA-based-lection provides better densification accuracy results of 98% when compared with all other classifiers.
With the development of remote sensing technology and the differences in remote sensing image classification, it is particularly important to be able to accurately use classification methods to classify images and to ...
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ISBN:
(纸本)9781510642768;9781510642751
With the development of remote sensing technology and the differences in remote sensing image classification, it is particularly important to be able to accurately use classification methods to classify images and to compare classification algorithms. In this paper, taking Yangshuo County as the research area, five common supervised classifications, namely support vector machine (SVM), maximum likelihood classification (MLC), neural network (NN), spectral angle mapping (SAM) and spectral information divergence (SID), are used to classify the land cover of remote sensing image data of GF-2. Landsat8 and its fusion in the same area. The classification results are obtained and compared. Moreover, the overall classification accuracy (OA) and Kappa coefficient are used to evaluate the performance of the image classification algorithm. The results show that both MLC and SVM perform best on these three data sets. For higher spatial resolution GF-2 and fusion data, the OA and Kappa coefficients of both image data classifiers is 10% higher than those of Landsat8 data with higher spectral resolution.
This paper proposes a solution for the most easily detectable type of cancer, skin cancer. Due to the ozone layer thinning, it was observed an increase in the number of skin lesion anomalies. Those anomalies are produ...
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ISBN:
(纸本)9781728133300
This paper proposes a solution for the most easily detectable type of cancer, skin cancer. Due to the ozone layer thinning, it was observed an increase in the number of skin lesion anomalies. Those anomalies are produced by the increasing UV radiation intensity. Good protection against sun is not always facile, mainly in the exposed regions of the skin like the face and the lower regions of the arms. Most types of cancers can be easily extracted via surgery if they are detected in one of the incipient development stages. Using a high resolution image, a classification algorithm can detect certain patterns in the nevi color, shape, veins or irregularities inside. Analyzing those aspects, a person can track their nevi development easily and intervene if it necessary. The originality of this paper consist in designing several convolutional neural networks, using the Python programming language, the Keras API and the Tensorflow framework alongside with proper dataset selection. The convolutional neural networks were trained using an open source dataset, with the purpose of formulating a diagnosis for new patients. The convolutional neural networks are provided with 7 classes of images, representing 7 types of skin lesions as input, and will output a diagnosis. The result of the research was a classifier with a convenient classification accuracy based on the dataset, and a medium accuracy based on new data.
Infrared target detection techniques have been widely used in infrared alarming and reconnaissance, and the detection task is often done by machine learning. Feature extraction and classification algorithm are two cor...
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ISBN:
(纸本)9781665409841
Infrared target detection techniques have been widely used in infrared alarming and reconnaissance, and the detection task is often done by machine learning. Feature extraction and classification algorithm are two core components of the machine learning. The selection of features and algorithm have a determinative effect on the detection result. Traditional target classification techniques only focus on only limited combinations of features and classification algorithm, which may result in a poor detection result. Based on this, this paper selects the gray features, statistical features, frequency domain features, and graphic features of the infrared target, and compares the three classification algorithms of KNN, Bayes, and SVM to give the optimal combination of features and classification algorithms by experiment.
In order to improve the accuracy and generalization performance of text sentiment analysis model, an integrated learning model is proposed in this paper, which includes three different classification algorithms - Logi...
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ISBN:
(纸本)9781728140681
In order to improve the accuracy and generalization performance of text sentiment analysis model, an integrated learning model is proposed in this paper, which includes three different classification algorithms - Logistic regression, support vector machine and K-Neighborhood algorithm. Compared with single classification algorithm, this algorithm shows better accuracy. The experimental results show that the model has good generalization performance and robustness.
Data mining is the process to predict future trends. Data mining involving searching of patterns whose general purpose is to extract information using intelligent methods from a set of data and information turn it int...
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ISBN:
(纸本)9781728183787
Data mining is the process to predict future trends. Data mining involving searching of patterns whose general purpose is to extract information using intelligent methods from a set of data and information turn it into an understandable structure for later use. Data mining using IoT becomes one of the leading providers applicable in several areas. The method of extracting usable data from larger set of arbitrary raw data is called data mining. This includes the analysis of sample data in large amounts of data with one or more programs. Concentrate on large amounts of data and databases to analyze, we use predictive analysis in the field of medicine. In this study, we examine the performance of algorithms alongwith the use of IoT that analyses large amounts of scattered information to make sense of it and turn it into knowledge. For this we used feature selection and classification algorithm on hepatitis data to get best result with minimum error rate and compare feature selection with classification algorithm so that to check which method is best according to results.
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