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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]

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]

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]

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]

Due to the rapid growth of web platforms such as blogs, discussion forums, peer-to-peer networks, and various other types of social media, Sentiment Polarity Detection (SPD) (classifying texts by "positive" or "negative" orientation) has become more important and challenging task in recent years. There is a growing need for management and study of SPD not only in English, but also in other languages. The key reason for using Machine Learning (ML) for SPD lies in engineering a representative set of features. This paper explores different (byte, character and word) n -gram based text representation models in order to determine the most valuable model for the representation of text documents in various languages, which can be used successfully by ML classification techniques for solving SPD task. Proposed n -gram models were used in conjunction with k Nearest Neighbourhood (kNN), Support Vector Machine (SVM) and Maximum Entropy (MaxEnt) algorithms to determine opinion polarity of the proposed movie reviews. The effectiveness and language independence of the proposed n -gram models were demonstrated in experiments performed on seven publicly available movie review benchmarks in Arabic, Czech, English, French, Spanish,Turkish, and Serbian being the authors' mother tongue. Formal evaluation has confirmed that the proposed byte and character n -gram models outperform word n -gram model, and in conjunction with the presented MaxEnt algorithm outperform other ML supervised techniques used with more complex document representation approaches. In some cases (Arabic, Czech, French, Serbian and Turkish), signficant improvements over the baselines have been achieved. Despite their simplicity and broad applicability, byte and character n -grams have been shown to be able to capture information on different levels – lexical and syntactic. [ABSTRACT FROM AUTHOR]

BACKGROUND: The goal of this work is to develop intelligent systems to monitor the wellbeing of individuals in their home environments.OBJECTIVE: This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality. METHODS: This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants. RESULTS: Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve=0.80, g-mean=0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy. CONCLUSIONS: The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained. [ABSTRACT FROM AUTHOR]

The Support Vector Machine (SVM) is a powerful technique for data classification. For linearly separable data points, the SVM constructs an optimal separating hyper-plane as a decision surface, to divide the data points of different categories in the vector space. For the non-linearly separable data points, the Kernel functions are used to extend the concept of the optimal separating hyper-plane so that the data can be linearly separable. The different kernel functions have different characteristics and hence the performance of the SVM is highly influenced by the selection of kernel functions. This paper presents the classification algorithm that uses the SVM in the training phase and the Mahalanobis distance in the testing phase, in order to design a classifier which has low impact of kernel function on the classification accuracy, positively. The Mahalanobis distance is used to replace the optimal separating hyper-plane as the classification decision making function in the SVM. The proposed approach is compared with Euclidean-SVM, which uses Euclidean distance function to replace the optimal separating hyper-plane as the classification boundary. It has also been evaluated against conventional SVM too. The experimental results show that the accuracy of the EuDiC (Euclidean Distance towards the Center of data) SVM classifier has a low impact on the implementation of kernel functions. The EuDiC SVM also achieves the drastic reduction in the classification time since it only depends on the mean of Support Vectors (SVs) of each category for classification. To prove its effectiveness on other types of data, the time series data have also been used. Due to robust design of the EuDiC, it also performs well for time series data too. [ABSTRACT FROM AUTHOR]

The major problem of SVMs is the dependence of the nonlinear separating surface on the entire dataset which creates unwieldy storage problems. This paper proposes a novel design algorithm, called the extractive support vector algorithm, which provides improved learning speed and a vastly improved performance. Instead of learning and training with all input patterns, the proposed algorithm selects support vectors from the input patterns and uses these support vectors as the training patterns. Experimental results reveal that our proposed algorithmprovides near optimal solutions and outperforms the existing design algorithms. In addition, a significant framework which is based on extractive support vector algorithm is proposed for image restoration. In the framework, input patterns are classified by three filters: weighted order statistics filter, alpha-trimmed mean filter and identity filter. Our proposed filter can achieve three objectives: noise attenuation, chromaticity retention, and preservation of edges and details. Extensive simulation results illustrate that our proposed filter not only achieves these three objectives but also possesses robust and adaptive capabilities, and outperforms other proposed filtering techniques. [ABSTRACT FROM AUTHOR]

Support Vector Machine (SVM) is a machine learning with excellent classification performance, which has been widely used in various fields such as data mining, text classification, face recognition and etc. However, when data volume scales to a certain level, the computational time becomes too long and the efficiency becomes low. To address this issue, we propose a parallel balanced SVM algorithm based on Spark, named PB-SVM, which is optimized on the basis of the traditional Cascade SVM algorithm. PB-SVM contains three parts, i.e., Clustering Equal Division, Balancing Shuffle and Iteration Termination, which solves the problems of data skew of Cascade SVM and the large difference between local support vector and global support vector. We implement PB-SVM in AliCloud Spark distributed cluster with five kinds of public datasets. Our experimental results show that in the two-classification test on the dataset covtype, compared with MLlib-SVM and Cascade SVM on Spark, PB-SVM improves efficiency by 38.9% and 75.4%, and the accuracy is improved by 7.16% and 8.38%. Moreover, in the multi-classification test, compared with Cascade SVM on Spark on the dataset covtype, PB-SVM improves efficiency and accuracy by 94.8% and 18.26% respectively. [ABSTRACT FROM AUTHOR]

As a supervised learning algorithm, Support Vector Machine (SVM) is very popularly used for classification. However, the traditional SVM is error-prone because of easy to fall into local optimal solution. To overcome the problem, a new SVM algorithm based on Relief algorithm and particle swarm optimization-genetic algorithm (Relief-PGS) is proposed for feature selection and data classification, where the penalty factor and kernel function of SVM and the extracted feature of Relief algorithm are encoded as the particles of particle swarm optimization-genetic algorithm (PSO-GA) and optimized by iteratively searching for optimal subset of features. To evaluate the quality of features, Relief algorithm is used to screen the feature set to reduce the irrelevant features and effectively select the feature subset from multiple attributes. The advantage of Relief-PGS algorithm is that it can optimize both feature subset selection and SVM parameters including the penalty factor and the kernel parameter simultaneously. Numerical experimental results indicated that the classification accuracy and efficiency of Relief-PGS are superior to those of other algorithms including traditional SVM, PSO-GA-SVM, Relief-SVM, ACO-SVM, etc. [ABSTRACT FROM AUTHOR]

In this research work, machine learning techniques are used to classify magnetic resonance imaging brain scans of people with Alzheimer's disease. This work deals with binary classification between Alzheimer's disease and cognitively normal. Supervised learning algorithms were used to train classifiers in which the accuracies are being compared. The database used is from The Alzheimer's Disease Neuroimaging Initiative (ADNI). Histogram is used for all slices of all images. Based on the highest performance, specific slices were selected for further examination. Majority voting and weighted voting is applied in which the accuracy is calculated and the best result is 69.5% for majority voting. [ABSTRACT FROM AUTHOR]

Traffic sign detection and recognition has been a topic of research for at least the last two decades. Efforts are being made to reliably detect candidate traffic signs in natural uncontrolled environment and to recognize their contents. For detection, a large proportion of relevant literature discusses color based segmentation by either sticking to a predefined color space (e.g., RGB, HSI, YCbCr etc.) or make use of empirically selected subset of eigen space to achieve partially data dependent segmentation. Since, the input RGB data for various color classes and the background is not linearly separable, none of the existing methods guarantee to achieve complete separation among pixels corresponding to traffic signs and the background objects. To tackle this problem, we propose a completely data driven segmentation technique that adaptively selects an optimized color space based on available training data. To recognize the contents of potential traffic signs, we present a hybrid spatio-frequency radial feature extraction technique with an emphasis on the regions containing useful information. We explore the energy compaction property of steerable discrete cosine transform for feature extraction and augment it with well known circular histogram of oriented gradients in a pyramid. Using our proposed method, experiments on (1) German Traffic Sign Detection Benchmark, (2) our self collected dataset and on a (3) hand crafted version of the combination of the two provide competitive performance compared to various latest and state of the art methods by achieving up to 0.978 precision and 0.98 recall values at an expense of only an insignificant additional computational cost. The method also obtained 0.81 precision on traffic signs partially occluded by other objects. [ABSTRACT FROM AUTHOR]

Active learning has proven to be quite effective in a vast array of machine learning tasks. Despite the lower labeling cost of active learning, it has been shown that active learning still can not reach state-of-the-art performance on several classification tasks and is sensitive to initial state. In this work, we propose a novel algorithm to improve the performance of active learning and it’s robustness to initial state. More specifically, we integrate low-rank transformation (LRT) with active learning. In each iteration, LRT is applied to project original high dimensional data to a feature space where data are easier to be classified and then support vector machines classifier is updated in this feature space. As iteration goes on, active learning’s propriety of labeling data improves the performance of LRT, which further promotes the accuracy of SVM classifier. Experiment on several benchmark binary classification datasets results showed the proposed algorithm outperforms other active learning methods in accuracy and robustness. [ABSTRACT FROM AUTHOR]