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The quality of sleep plays a crucial role in physical well-being, and individuals are becoming increasingly concerned about sleep quality and its associated health issues. Although various sleep monitoring devices exist, there remains a need for a highly accurate sleep state identification algorithm. To address this, we present a paper that utilizes machine learning techniques to identify human sleep states based on electroencephalogram (EEG) signals collected by an EEG instrument. We propose a model that incorporates two nonlinear characteristic parameters, MSE and PSE, extracted from artificially designed EEG signals as input. Additionally, we employ a Support Vector Machine (SVM) classifier algorithm to accurately identify sleep states, eliminating uncertainties associated with manually designed feature parameters. Experimental results demonstrate the superior accuracy of our proposed model for sleep state analysis, offering valuable insights for improving sleep quality and addressing related health concerns. [ABSTRACT FROM AUTHOR]

There is a widespread use of cluster-based routing in wireless sensor networks since it is the most energy-efficient. Idealizing cluster heads, on the other hand, is NP-hard and hence requires heuristic or metaheuristic approaches. However, while outperforming algorithms, metaheuristics computation time restricts its ability to respond to routing requests as rapidly as algorithms can today. A network's or an application's parameters can't be easily accommodated by routing methods. This paper offers the HMML, a combination model combining hybrid metaheuristics and machine-learning. Our HMML model makes use of an automated tuning metaheuristic (e.g. evolutionary algorithm) to fine-tune the heuristic technique for each specific configuration. For a variety of combinations, this is done. A network simulation is run using the modified heuristic algorithm in each configuration to arrive at a solution. As a result, a comprehensive dataset for a variety of conditions is produced (e.g., support vector machine). These characteristics include local (round-state), global (network-state), and application-specific aspects of the input feature vector. After training, the HMML model may be used to quickly cluster data. Machine learning's capacity to generalize helps us comprehend the metaheuristic algorithm's behavior in identifying optimal paths for previous configurations. Simulation studies show that HMML can adapt to varied applications while extending network life which increases upto 5% for total energy consumption. [ABSTRACT FROM AUTHOR]

There are currently few studies on the stress of athletes, so it is impossible to provide effective stadium guidance for athletes. Based on this, this study combines machine learning algorithms to identify athletes' pre-game emotions. At the same time, this study obtains the data related to the research through the survey access form and obtains the physiological parameters of the athletes under stress in the experimental way and processes the physiological parameters of the athletes with the machine learning algorithm. In order to improve the efficiency of data processing, this study improves the traditional machine learning algorithm, and combines the particle optimization algorithm with the support vector machine to realize the effective recognition of the athlete's physiological state. In addition, through the experimental method combined with the contrast method, this paper compares the performance of the improved algorithm with the traditional algorithm and combines the data analysis to analyze the test results. Finally, this study analyzes the effectiveness of the proposed algorithm by example analysis. The research shows that the proposed algorithm has better performance than the traditional algorithm and has certain practical significance and can provide theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

In some real applications, only limited labeled positive examples and many unlabeled examples are available, but there are no negative examples. Such learning is termed as positive and unlabeled (PU) learning. PU learning algorithm has been studied extensively in recent years. However, the classical ones based on the Support Vector Machines (SVMs) are assumed that labeled positive data is independent and identically distributed (i.i.d) and the sample size is large enough. It leads to two obvious shortcomings. On the one hand, the performance is not satisfactory, especially when the number of the labeled positive examples is small. On the other hand, classification results are not optimistic when datasets are Non-i.i.d. For this reason, this paper proposes a novel SVM classifier using Chebyshev distance to measure the empirical risk and designs an efficient iterative algorithm, named L∞ - BSVM in short. L∞ - BSVM includes the following merits: (1) it allows all sample points to participate in learning to prompt classification performance, especially in the case where the size of labeled data is small; (2) it minimizes the distance of the sample points that are (outliers in Non-i.i.d) farthest from the hyper-plane, where outliers are sufficiently taken into consideration (3) our iterative algorithm can solve large scale optimization problem with low time complexity and ensure the convergence of the optimum solution. Finally, extensive experiments on three types of datasets: artificial Non-i.i.d datasets, fault diagnosis of railway turnout with few labeled data (abnormal turnout) and six benchmark real-world datasets verify above opinions again and demonstrate that our classifier is much better than state-of-the-art competitors, such as B-SVM, LUHC, Pulce, B-LSSVM, NB and so on. [ABSTRACT FROM AUTHOR]

Accurate and efficient recognition of Parkinson's disease is one of the prominent issues in the field of healthcare. To address this problem, different methods have been proposed in the literature. However, existing methods are lacking in accurately recognizing the Parkinson's disease and suffer from efficiency problems. To overcome these problems faced by existing models, this paper presents a machine-learning-based model for Parkinson's disease recognition. Specifically, a hybrid feature selection algorithm has been designed by integrating the Relief and ant-colony optimization algorithms to select relevant features for training the model. Moreover, the support vector machine has been trained and tested on the selected features to achieve optimal classification accuracy. Additionally, the K-fold cross-validation technique has been employed for the optimal hyper-parameters value evaluation of the model.The experimental results on a real-world dataset, i.e., Parkinson's disease dataset is revealed that the proposed system outperforms baseline competitors by accurately recognizing the Parkinson's disease and achieving 99.50% accuracy on the selected features. Due to high performance is achieved our proposed method, we are highly recommended for the recognition of PD. [ABSTRACT FROM AUTHOR]

Binarized normed gradients (BING) can be utilized as a preprocessing step for generic object proposal generation, and has attracted great attention because of its fast running and appropriate generalization performance. Recently, although some modified schemes were presented to improve the proposal localization quality, the mechanism of enhancing the performance is still an open problem. In this paper, Adaptive weighted binary normed gradients plus (AWBING Plus) algorithm is proposed, based on the BING method, which replaces the support vector machine (SVM) with adaptive weighted extreme learning machine (Adaptive WELM) to reduce the number of proposals, as well as comparable performance, by using the multi-thresholding straddling expansion (MTSE) as the post-processing stage to enhance the localization quality. We explain the methodology of WELM applied to BING, and analyzed the effect of the improved WELM algorithm, which is named Adaptive WELM. The experimental results from PASCAL VOC2007, Microsoft COCO2014 and ILSVRC2013 show that the proposed approach achieved superior performance compared with other advanced methods on generic object proposal generation, and it runs faster as well. [ABSTRACT FROM AUTHOR]

This paper presents a multi-class Support Vector Machine (SVM) based algorithm for on-line static security assessment of the power systems. The proposed SVM based security assessment algorithm has a very small training time and space in comparison with the traditional machine learning methods such as Artificial Neural Networks (ANN) based algorithms. In addition, the proposed algorithm is faster than existing algorithms. One of the main points, to apply a machine learning method is feature selection. In this paper, a new Decision Tree (DT) based feature selection algorithm has been presented. The proposed SVM algorithm has been applied to New England 39-bus power system. The simulation results show the effectiveness and the stability of the proposed method for on-line static security assessment. The effectiveness of the proposed feature selection algorithm has been investigated, too. The proposed feature selection algorithm has been compared with different feature selection algorithms. The simulation results demonstrate the effectiveness of the proposed feature algorithm. [ABSTRACT FROM AUTHOR]