The technology pertaining to gait generation, recognition and analysis is getting more sophisticated each day. The global demand for a gait-based dataset and its subsequent recognition, leading to extraction of valuab...
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ISBN:
(纸本)9781728107882
The technology pertaining to gait generation, recognition and analysis is getting more sophisticated each day. The global demand for a gait-based dataset and its subsequent recognition, leading to extraction of valuable information is now higher than ever. However, inertial sensor-based gait dataset is a comparatively new addition to the field of gait analysis. Consequently, most of the research works incorporating machinelearning algorithms into gait dataset are image based. In addition to that, most of the gait-based datasets have been analyzed for gait recognition. There remains very little research work on personal authentication from inertial sensor-based gait dataset. Personal authentication is of several types out of which, predicting gender and age is quite challenging. In this paper, we have tried to face these challenges and have manifested the process of predicting gender and age from the inertial sensor-based gait dataset which is a part of the vast Osaka University-ISIR Gait database. Finally, we have found that the models, Support Vector machine shows the highest accuracy for the problem of classifying gender and Decision Trees shows the highest variance score (R-2 value) for the problem of predicting age.
Current peer-review software lacks intelligence for responding to students39; reviewing performance. As an example of an additional intelligent assessment component to such software, we propose an evaluation system ...
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ISBN:
(纸本)9780615375298
Current peer-review software lacks intelligence for responding to students' reviewing performance. As an example of an additional intelligent assessment component to such software, we propose an evaluation system that generates assessment on reviewers' reviewing skills regarding the issue of problem localization. We take a datamining approach, using standard supervised machinelearning to build classifiers based on attributes extracted from peer-review data via Natural Language Processing techniques. Our work successfully shows it is feasible to provide intelligent support for peer-review systems to assess students' reviewing performance fully automatically.
Feature selection for datamining optimization receives quite a high demand especially on high-dimensional feature vectors of a data. Feature selection is a method used to select the best feature (or combination of fe...
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ISBN:
(纸本)9781612842127
Feature selection for datamining optimization receives quite a high demand especially on high-dimensional feature vectors of a data. Feature selection is a method used to select the best feature (or combination of features) for the data in order to achieve similar or better classification rate. Currently, there are three types of feature selection methods: filter, wrapper and embedded. This paper describes a genetic based wrapper approach that optimizes feature selection process embedded in a classification technique called a supervised Nearest Neighbour Distance Matrix (NNDM). This method is implemented and tested on several datasets obtained from the UCI machinelearning Repository and other datasets. The results demonstrate a significant impact on the predictive accuracy for feature selection combined with the supervised NNDM in classifying new instances. Therefore it can be used in other applications that require feature dimension reduction such as image and bioinformatics classifications.
Cardiovascular diseases are one of the main causes of mortality in the European Union. Cardiovascular diseases require a continuous follow-up in order to prevent complications that produce a decrease on the quality of...
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ISBN:
(纸本)9781509024551
Cardiovascular diseases are one of the main causes of mortality in the European Union. Cardiovascular diseases require a continuous follow-up in order to prevent complications that produce a decrease on the quality of life of patients and can even lead to their death. According to the Evidence Based Medicine thesis, the involvement of the patient in the care process will improve the Quality of Care provided and the efficiency of Care Plans. However, there is controversy in the literature about the fact that the Care Plans that Evidence Based Medicine provides are based on strict clinical cases to generate scientific evidence that is unclear whether they can be applied to personalized medicine. The use of an Interactive patternrecognition approach can create models controlled by human experts based on the biomedical data provided, in addition to medical decisions and the patient personality, providing a better way to find more tailored models. However, the use of Interactive patternrecognition techniques still requires the application of Human Understandable patternrecognition techniques. In this paper, an Interactive patternrecognition technique based on Process mining technology is presented as a solution to deal with this problem.
Feature extraction plays an essential role in hand written character recognition because of its effect on the capability of classifiers. This paper presents a framework for investigating and comparing the recognition ...
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ISBN:
(纸本)9781479928453
Feature extraction plays an essential role in hand written character recognition because of its effect on the capability of classifiers. This paper presents a framework for investigating and comparing the recognition ability of two classifiers: Deep-learning Feedforward-Backpropagation Neural Network (DFBNN) and Extreme learningmachine (ELM). Three data sets: Thai handwritten characters, Bangla handwritten numerals, and Devanagari handwritten numerals were studied. Each data set was divided into two categories: non-extracted and extracted features by Histograms of Oriented Gradients (HOG). The experimental results showed that using HOG to extract features can improve recognition rates of both of DFBNN and ELM. Furthermore, DFBNN provides higher slightly recognition rates than those of ELM.
Extension datamining is a new method that is based on the extension analysis method of Extenics. Extenics is a new disciplinary and a new branch of arfiticial intelligence. datamining techniques have their origins i...
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ISBN:
(纸本)0769523161
Extension datamining is a new method that is based on the extension analysis method of Extenics. Extenics is a new disciplinary and a new branch of arfiticial intelligence. datamining techniques have their origins in methods from statistics, patternrecognition, databases, artificial intelligence, high performance and parallel computing and visualization. This paper presents how to deal with multiple data formats and unify data representation based on extenics. Keyword: matter-element, databases, extension datamining, association rules.
In the last years, several researchers have interested in two-dimensional (2D) palmprint recognition. In order to enhance the security of biometric systems, recently, some works proposed to use three-dimensional (3D) ...
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ISBN:
(纸本)9781538642382
In the last years, several researchers have interested in two-dimensional (2D) palmprint recognition. In order to enhance the security of biometric systems, recently, some works proposed to use three-dimensional (3D) palmprint recognition. The advantage of using the 3D capture systems is that they capture the 2D and 3D palmprint at the same time, and they give different and complementary information. The 3D component contains the depth of the palm surface, whereas the 2D component contains the texture. In this paper, we proposed an efficient biometric identification system combining 2D and 3D palmprint by fusing them at matching score level. To exploit the 3D palmprint data, we converted it to grayscale images by using the Mean Curvature (MC) and the Gauss Curvature (GC). Feature extraction is made by a deep learning algorithm that is called the Discrete Cosine Transform Net (DCTNet). The experiments performed on a database containing 8000 samples show that the proposed scheme can achieve a high recognition rate.
Aiming at the problems of low data collaboration rate and weak storage capacity of police surveillance data in national border police cooperation, the research is based on Parallel Distributed Clustering with Semantic...
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ISBN:
(纸本)9798400709777
Aiming at the problems of low data collaboration rate and weak storage capacity of police surveillance data in national border police cooperation, the research is based on Parallel Distributed Clustering with Semantic Constraints (PDCSC) algorithm. Police surveillance data processing model is constructed. The research firstly utilizes local density clustering algorithm to mine and classify the surveillance data, and then introduces the concept of parallelism and constructs the processing model using PDCSC algorithm. The results show that the clustering purity of police surveillance data based on PDCSC method is 92.37% and the data clustering accuracy is 93.67%. Meanwhile, the surveillance data summary of PDCSC method is 7108 which is 1085 and 2241 higher than that of DBSCAN and K-means. This indicates that the PDCSC algorithm can effectively process and classify police surveillance data, providing accurate police incident identification and analysis. The research aims to provide strong support for international police cooperation and improve the efficiency and accuracy of police work.
Manufacturing process data collected over time are considered time-series data and can be arranged into control charts. Important applications can be centered around these data like, for example, recognition of specif...
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ISBN:
(纸本)9781424441358
Manufacturing process data collected over time are considered time-series data and can be arranged into control charts. Important applications can be centered around these data like, for example, recognition of specific patterns, pattern similarity, detecting anomalies, and clustering and classification of patterns. We study and evaluate a number of classification techniques for process control data. For pattern similarity, we examine distance measure with raw data and with new feature extracted from the data. The evaluation is conducted with common benchmark process control data for time series process variables. This paper shows that datamining and machinelearning can be extremely beneficial in acquiring and producing knowledge and discoveries form process data to benefit the industry.
The work presented here focuses on combining multiple classifiers to form single classifier for pattern classification, machinelearning for expert system, and datamining tasks. The basis of the combination is that e...
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ISBN:
(纸本)9783540770459
The work presented here focuses on combining multiple classifiers to form single classifier for pattern classification, machinelearning for expert system, and datamining tasks. The basis of the combination is that efficient concept learning is possible in many cases when the concepts learned from different approaches are combined to a more efficient concept. The experimental result of the algorithm, EMRL in a representative collection of different domain shows that it performs significantly better than the several state-of-the-art individual classifier, in case of 11 domains out of 25 data sets whereas the state-of-the-art individual classifier performs significantly better than EMRL only in 5 cases.
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