The concept of social media began to gain popularity in the late 1990s and has played a significant role in connecting people across the globe. The constant addition of features to old social media platforms and the c...
详细信息
A heart disease diagnosis method has been proposed for effective heart disease diagnosis. In the proposed method machinelearning (ML) classifiers have been used for detection of heart disease. Chi square feature sele...
详细信息
ISBN:
(纸本)9780738142593
A heart disease diagnosis method has been proposed for effective heart disease diagnosis. In the proposed method machinelearning (ML) classifiers have been used for detection of heart disease. Chi square feature selection algorithm has been used for related feature selection to improve the prediction performance of machinelearning models. Cross validation, method Hold out has been employed for model hyper parameters tuning and best model selection. Furthermore, performance evaluation metrics, such as classification accuracy, specificity, sensitivity, Matthews' correlation coefficient and execution time have been used for model performance evaluation. The Cleveland heart disease data set has been used for testing of the proposed method. The experimental results demonstrated that proposed method has achieved high performance as compared to state of the art methods. Furthermore, the proposed method performance has been compared with deep learning model. Thus, the proposed method will support the medical professional to diagnosis heart disease efficiently and could easily incorporated in healthcare for diagnosis of heart disease.
Control and monitoring of asthma progress is highly important for patient's quality of life and healthcare management. Emerging tools for self-management of various chronic diseases have the potential to support p...
详细信息
ISBN:
(纸本)9781538603581
Control and monitoring of asthma progress is highly important for patient's quality of life and healthcare management. Emerging tools for self-management of various chronic diseases have the potential to support personalized patient guidance. This work presents the design aspects of the myAirCoach decision support system, with focus on the assessment of three machinelearning approaches as support tools the first prototype implementation.
Internet of Things (IoT) systems produce large amounts of raw data in the form of log files. This raw data must then be processed to extract useful information. machinelearning (ML) has proved to be an efficient tech...
详细信息
ISBN:
(纸本)9781538649800
Internet of Things (IoT) systems produce large amounts of raw data in the form of log files. This raw data must then be processed to extract useful information. machinelearning (ML) has proved to be an efficient technique for such tasks, but there are many different ML algorithms available, each suited to different types of scenarios. In this work, we compare the performance of 22 state-of-the-art supervised ML classification algorithms on different IoT datasets, when applied to the problem of anomaly detection. Our results show that there is no dominant solution, and that for each scenario, several candidate techniques perform similarly. Based on our results and a characterization of our datasets, we propose a recommendation framework which guides practitioners towards the subset of the 22 ML algorithms which is likely to perform best on their data.
Software defect prediction is a well renowned field of software engineering. Determination of defective classes early in the lifecycle of a software product helps software practitioners in effective allocation of reso...
详细信息
ISBN:
(纸本)9781509030125
Software defect prediction is a well renowned field of software engineering. Determination of defective classes early in the lifecycle of a software product helps software practitioners in effective allocation of resources. More resources are allocated to probable defective classes so that defects can be removed in the initial phases of the software product. Such a practice would lead to a good quality software product. Although, hundreds of defect prediction models have been developed and validated by researchers, there is still a need to develop and evaluate more models to draw generalized conclusions. Literature studies have found machinelearning (ML) algorithms to be effective classifiers in this domain. Thus, this study evaluates four ML algorithms on data collected from seven open source software projects for developing software defect prediction models. The results indicate superior performance of the Multilayer Perceptron algorithm over all the other investigated algorithms. The results of the study are also statistically evaluated to establish their effectiveness.
Recently, machinelearning is benefiting from advantages of quantum computing which has resulted in a new stream of algorithms known as quantum machine learning algorithms. This paper presents the literature describin...
详细信息
ISBN:
(纸本)9781665468282
Recently, machinelearning is benefiting from advantages of quantum computing which has resulted in a new stream of algorithms known as quantum machine learning algorithms. This paper presents the literature describing implementations of quantum machine learning algorithms in the various quantum machinelearning frameworks. In addition, for each of the observed algorithms and frameworks, the literature in which they are described is stated. To the best of our knowledge, this is so far the most comprehensive overview of the existing QML algorithms with their corresponding implementation frameworks.
There are limits in assessing healthy and abnormal swallowing by Videofluoroscopic swallowing study. Classification of accelerometric swallowing signals is much more efficient method to judge healthy swallowing. Howev...
详细信息
ISBN:
(纸本)9781665442312
There are limits in assessing healthy and abnormal swallowing by Videofluoroscopic swallowing study. Classification of accelerometric swallowing signals is much more efficient method to judge healthy swallowing. However, these methods have developed mostly with dual axis accelerometric signals and classifying two-class problems. This study is to examine classification methods with multi-class three-axis accelerometric signals. Swallowing signals of five foods are classified with both supervised learning algorithm and unsupervised learning algorithm. Three-axis signals denoised by 10-level discrete wavelet transform with soft thresholding before feature calculation. The result confirmed that classification with support vector machine and K-nearest neighbor can predict with 90% accuracy. However, Classification with fuzzy c-mean clustering produce low purity and normalized mutual information.
The progressive deployment of smart meters in Spain since 2015 has changed the retail electricity sector imposing a strong operational impact for agents participating in the wholesale market. Each residential customer...
详细信息
ISBN:
(纸本)9781538647226
The progressive deployment of smart meters in Spain since 2015 has changed the retail electricity sector imposing a strong operational impact for agents participating in the wholesale market. Each residential customer is characterized based on its real hourly consumption instead of the monthly aggregated consumption. New models are needed to forecast the hourly consumption of residential customers to buy the energy in the markets. This paper presents a robust and scalable methodology to predict the household's hourly energy consumption based on smart meters' data using machine learning algorithms such as Neural Gas, Classification Trees, Multilayer Perceptron Networks and XGBoost. First, a novel clustering methodology to aggregate consumers is presented. Secondly, a model to forecast the hourly consumption in the day-ahead is proposed for every cluster. Finally, a case example is used to illustrate the results, accuracy and robustness of the methodology.
In commercial projects, number of defects raised directly depends upon the releases. In order to get the idea of progress of the project it is necessary to estimate the average time required to fix the bug. This time ...
详细信息
ISBN:
(纸本)9781538680759
In commercial projects, number of defects raised directly depends upon the releases. In order to get the idea of progress of the project it is necessary to estimate the average time required to fix the bug. This time is referred as bug estimation time. It is essential to estimate the time of software bug for a proper project planning. In this paper, a new algorithm is proposed to determine bug estimation time with the help of machine learning algorithms. New developer is predicted and its average bug estimation time is calculated based on the bug estimation time of already existing developer. A comparative study for accuracy for different machine learning algorithms is carried out.
Breast cancer is a significant cause of morbidity and mortality among women, especially in developing countries. Early prediction, and accurate treatment are critical to reduce death rates. Using artificial intelligen...
详细信息
Breast cancer is a significant cause of morbidity and mortality among women, especially in developing countries. Early prediction, and accurate treatment are critical to reduce death rates. Using artificial intelligence and machinelearning techniques, this project will evaluate the performance of several models in predicting breast cancer. The prediction models such as Decision Tree, Gradient Boosting, Naive Bayes, Neural Network, SGD, kNN, and CN2 rule inducer are employed utilizing Orange tools. This study used publicly accessible secondary data from Keggle, enabling transparency and accessibility in the evaluation approach. The findings disply that the DT, GB, Neural Network, and CN2 rule inducer had higher accuracy ratings of 0.992, 0.998, 0.997, and 0.997, respectively, with exceptional AUC values of 0.989, 1.000, 1.000, and 0.990. Additionally, their recall and accuracy scores are 0.992, 0.987, 0.998, and 0.997, demonstrating their strong performance in breast cancer prediction. Among the most popular classifier models, GB and Neural Network, are the outperformed models than others.
暂无评论