Computer-aided diagnosis (CAD), a field of medical analysis, is rapidly advancing in a large range and is becoming more complex. Computer-aided recently, there has been a lot of interest in diagnostics for the reason ...
详细信息
Chronic kidney disease (CKD), is also known as chronic nephritic sickness. It defines constrains which affects your kidneys and reduces your potential to stay healthy. There will be various complication concerns like ...
详细信息
ISBN:
(纸本)9781538678084
Chronic kidney disease (CKD), is also known as chronic nephritic sickness. It defines constrains which affects your kidneys and reduces your potential to stay healthy. There will be various complication concerns like increased levels in your blood, anemia (low blood count), weak bones, and nerve injury. Detection and treatment should be done prior so it will typically keep chronic uropathy from obtaining a worse condition. data processing is the term used for information discovery from big databases. The task of knowledge mining is to generate regular patterns from historical data and emphasize future conclusions, follows from the convergence of many recent trends: the decreased value of huge knowledge storage devices and therefore the tremendous ease of aggregation knowledge over networks;the development of robust and economical machinelearning algorithms to method this data;and therefore the decrease value of machine power, enabling use of computationally intensive strategies for knowledge analysis. machinelearning is an important task as it benefits many applications such as analyzing life science outcomes, sleuthing fraud, sleuthing faux users etc. varied knowledge mining classification approaches and machinelearning algorithms are applied for prediction of chronic diseases. Therefore, this paper examines the performance of Naive Bayes, K-Nearest Neighbour (KNN) and Random Forest classifier on the basis of its accuracy, preciseness and execution time for CKD prediction. Finally, the outcome after conducted research is that the performance of Random Forest classifier is finest than Naive Bayes and KNN
Financial information extraction from big financial reports is a tedious task. This paper speaks about page-wise feature generation and applying learning algorithms for identifying financial information (balance sheet...
详细信息
ISBN:
(纸本)9781509036967
Financial information extraction from big financial reports is a tedious task. This paper speaks about page-wise feature generation and applying learning algorithms for identifying financial information (balance sheets, cash flows, and income statements) in Form 10-K or annual reports of companies. Balance sheets, cash flows, and income statements have some structure in them and are semi-structured information. This approach employs selection of unigrams and bigrams based on frequency of occurrence and expert advice, generation of page wise features, and applying learning models for identifying patterns of specific financial information. Different supervised learning models are applied yielding results with very high accuracy (greater than 99%).
Facial expression recognition is one of the technological capabilities in identifying a face image to follow up on research conducted by psychologists. The recognition of facial expressions is very important to know t...
详细信息
ISBN:
(纸本)9781728146102
Facial expression recognition is one of the technological capabilities in identifying a face image to follow up on research conducted by psychologists. The recognition of facial expressions is very important to know the emotions of someone who is experiencing it. In this study two datasets were used, namely the FER2013 and CK + datasets. The FER2013dataset and CK+ are datasets designed to identify facial expressions. At the feature extraction stage, it uses the Histogram of Oriented Gradient (HOG) feature dan Local Binary pattern (LBP) feature. Whereas in the classification stage, the Extreme learningmachine (ELM) classifier is used. The greatest accuracy by using HOG feature is 63.86% for the FER2013dataset and 99.79% for the CK + dataset with sigmoid as an activation function. And the greatest accuracy by using LBP feature is 55.11% for the FER2013dataset and 98.72% for the CK + dataset with RBF as an activation function.
Outlier explanation approaches are used to support analysts in investigating outliers, especially those detected by methods which are not intuitively interpretable such as deep learning or ensemble approaches. There h...
详细信息
Designing robust features for human activity recognition (HAR) that performs well across a wide range of users is a hard task. Therefore, more attention is being given to feature learning techniques, to automatically ...
详细信息
ISBN:
(纸本)9781728107882
Designing robust features for human activity recognition (HAR) that performs well across a wide range of users is a hard task. Therefore, more attention is being given to feature learning techniques, to automatically learn features from raw data. In this paper, we present a comparison study among feature learning methods for HAR. Using accelerometer data, we compare four methods for feature learning from raw-sensor data (PCA-based, clustering, matrix factorization, and LSTM networks) to the traditional hand-crafted feature extraction method. We focus on the performance degradation when each model is evaluated using a new user. According to our results, features learned with Principal Component Analysis are the more robust to the new user scenario. Our results evidence the importance of evaluation in unseen user, since the performance difference compared to a random split testing is big.
The article considers the task of classifying fractal time series based on the construction of their recurrence plots. Short realizations of EEG signals were used as input data. Two classification machinelearning met...
详细信息
Traditional kernelised classification methods Could not perforin well sometimes because of the using of a single and fixed kernel, especially oil sonic complicated data sets. In this paper. a novel optimal double-kern...
详细信息
ISBN:
(纸本)9783642030697
Traditional kernelised classification methods Could not perforin well sometimes because of the using of a single and fixed kernel, especially oil sonic complicated data sets. In this paper. a novel optimal double-kernel combination (ODKC) method is proposed for complicated classification tasks. Firstly, data sets are mapped by two basic kernels into different feature spaces respectively, and then three kinds of optimal composite kernels are constructed by integrating information of the two feature spaces. Comparative experiments demonstrate the effectiveness of our methods.
The brain is one of the most important parts of the human body, and the diagnosis of its diseases is of great significance to the treatment of diseases. With the rapid development of deep learning in recent years, its...
详细信息
ISBN:
(纸本)9781665417907
The brain is one of the most important parts of the human body, and the diagnosis of its diseases is of great significance to the treatment of diseases. With the rapid development of deep learning in recent years, its automatically extracted image features have significant advantages compared to traditional artificially extracted features. Therefore, more and more recognition methods based on deep learning are widely used in medical image recognition tasks (such as CT, MRI, PET-CT.). This paper will introduce the application of traditional methods and deep learning methods in various brain diseases. These methods are compared, analyzed, and summarized, then we explored their development status and future development trends.
In order to better analyze the damage characteristics of fiber materials under radiation environment, combined with datamining algorithm to calculate the degree of damage of material structure damage. Combine with ma...
详细信息
ISBN:
(纸本)9783030364021;9783030364014
In order to better analyze the damage characteristics of fiber materials under radiation environment, combined with datamining algorithm to calculate the degree of damage of material structure damage. Combine with machinelearning method to analyze the calculation results, obtain the damage range of fiber material structure, standardize material damage characteristics and Grade, accurately determine the damage of material structure, and finally improve the radiation damage characteristics of fiber materials. Experiments show that the research on radiation damage characteristics of fiber materials based on datamining and machinelearning is accurate and reasonable.
暂无评论