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Swarm intelligence optimization algorithm has been proved to perform well in the field of parameter optimization. In order to further improve the performance of intelligent optimization algorithm, this paper proposes an improved and adaptive tunicate swarm algorithm (IMATSA) based on tunicate swarm algorithm (TSA). IMATSA improves TSA in the following four aspects: population diversity, local search convergence speed, jumping out of local optimal position, and balancing global and local search. Firstly, IMATSA adopts Tent map and quadratic interpolation to initialize population and enhance the diversity. Secondly, IMATSA uses Golden-Sine algorithm to accelerate the convergence of local search. Thirdly, in the process of global development, IMATSA adopts Levy flight and the improved Gauss disturbance method to adaptively improves and coordinates the ability of global development and local search. Then, this paper verifies the performance of IMATSA based on 14 benchmark functions experiment, ablation experiment, parameter optimization experiments of Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT), Wilcoxon signed rank test and image multi-threshold segmentation experiment with the performance metrics are convergence speed, convergence value, significance level P-value, Peak Signal-to-Noise Ratio (PSNR) and Standard Deviation (STD). Experimental results show that IMATSA performs better in three kinds of benchmark functions; each component of IMATSA has a positive effect on the performance; IMATSA performs better in parameter optimization experiments of SVM experiment and GBDT; there is significant difference between IMATSA and other algorithms by Wilcoxon signed rank test; in image segmentation, the performance is directly proportional to the number of thresholds, and compared with other algorithms, IMATSA has better comprehensive performance. [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]

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]

Athlete's heart rate measurement has certain guiding significance for athlete training and competition intensity arrangement. At present, the accuracy and efficiency of the athlete's heart rate measurement method cannot meet the actual training needs of athletes. In view of this, based on support vector machine, this research combines with improved algorithm to build athlete heart rate measurement model. Moreover, in this study, the denoising algorithm of multi-channel spectral matrix decomposition is used to eliminate the interference factors. The heart rate measurement algorithm based on support vector machine (Mix-SVM) proposed by this paper mainly includes the following parts: preprocessing, preliminary filtering of motion noise, sparse signal reconstruction model, spectral subtraction, and heart rate spectral peak tracking method based on SVM. In addition, in order to verify the effectiveness of the algorithm in this study, a control experiment is designed to verify the efficiency and accuracy of the algorithm proposed by this study. The research results show that the algorithm proposed by this paper has certain advantages in accuracy and efficiency. [ABSTRACT FROM AUTHOR]

The imbalanced data problem is widespread in the real world. In the process of training machine learning models, ignoring imbalanced data problems will cause the performance of the model to deteriorate. At present, researchers have proposed many methods to deal with the imbalanced data problems, but these methods mainly focus on the imbalanced data problems in two-class classification tasks. Learning from multi-class imbalanced data sets is still an open problem. In this paper, an ensemble method for classifying multi-class imbalanced data sets is put forward, called multi-class WHMBoost. It is an extension of WHMBoost that we proposed earlier. We do not use the algorithm used in WHMBoost to process the data, but use random balance based on average size so as to balance the data distribution. The weak classifiers we use in the boosting algorithm are support vector machine and decision tree classifier. In the process of training the model, they participate in training with given weights in order to complement each other's advantages. On 18 multi-class imbalanced data sets, we compared the performance of multi-class WHMBoost with state of the art ensemble algorithms using MAUC, MG-mean and MMCC as evaluation criteria. The results demonstrate that it has obvious advantages compared with state of the art ensemble algorithms and can effectively deal with multi-class imbalanced data sets. [ABSTRACT FROM AUTHOR]

Multiple instance learning is a modification in supervised learning that handles the classification of collection instances, which called bags. Each bag contains a number of instances whose features are extracted. In multiple instance learning, the standard assumption is that a positive bag contains at least one positive instance, whereas a negative bag is only comprised of negative instances. The complexity of multiple instance learning relies heavily on the number of instances in the training datasets. Since we are usually confronted with a large instance space, it is important to design efficient instance selection techniques to speed up the training process, without compromising the performance. Firstly, a multiple instance learning model of support vector machine based on grey relational analysis is proposed in this paper. The data size can be reduced, and the importance of instances in the bag can be preliminarily judged. Secondly, this paper introduces an algorithm with the bag-representative selector that trains the support vector machine based on bag-level information. Finally, this paper shows how to generalize the algorithm for binary multiple instance learning to multiple class tasks. The experimental study evaluates and compares the performance of our method against 8 state-of-the-art multiple instance methods over 10 datasets, and then demonstrates that the proposed approach is competitive with the state-of-art multiple instance learning methods. [ABSTRACT FROM AUTHOR]

The lack of effective evaluation of online education is a worldwide malpractice, and it is impossible to help students improve the correctness of online learning choices through existing reviews. Based on the current mainstream sentiment lexicon and text sentiment analysis, the authors use machine learning method to analyze the sentiment orientation of the legal course review text, through method that combines PMI and SVM. At the same time, this paper uses LibSVM tool to train and predict data, collect and pre-process data through network data collection, and, based on traditional algorithms, propose improved experimental scheme based on their respective advantages and disadvantages. In addition, the model proposed in this study is used to classify and process the emotional text, and the two methods are combined to obtain the final result. Finally, this paper combines experiments to analyze the performance of the comprehensive model proposed in this study. The research shows that the classification effect of the text sentiment analysis of model is good, it can be applied to practice, and it can provide theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

The evolution of technology has brought new challenges and opportunities for the different dimensions of feature space. The higher dimension of the feature space is one of the most critical issues in e-mail classification problems due to accuracy considerations. The problem of finding the subset features that significantly influence the performance of e-mail spam classification has become one of the important challenges. This paper proposes to overcome such a problem, an intelligent approach to Binary Differential Evolution Support Vector Machine (BDE-SVM). The proposed approach enhances the Binary Differential Evolution (BDE) algorithm based on the correlation coefficient as a fitness function to select the significant subset feature evaluated by an SVM classifier. To our best of knowledge, the correlation coefficient as the fitness function has not been used in the differential evolution algorithm before. The selected subset feature is used to assess the most features that contribute to the reliability of the email spam classification. The finding of the enhanced BDE is to present a powerful accuracy. The tests were conducted using "Spambase" and "SpamAssassin." Identified benchmark datasets are to assess the feasibility of the proposed solution. The result with full-feature accuracy was 93.55 percent compared to the proposed BDE-SVM approach, which is 93.99 percent. Empirical findings also show that our method is capable of effectively increasing the number of features required to enhance the reliability of the email spam classification. [ABSTRACT FROM AUTHOR]

In recent times, driver drowsiness is one of the major reasons for road accidents that leads to severe physical injuries, deaths and significant economic losses. Hence, the existing driver drowsiness detection systems require a countermeasure device for the prevention of sleepiness related accident. This research paper aims to perform drowsiness detection with the help of driver's eye state, head pose, and mouth state information. Initially, the input data were collected from the public drowsy driver database. Then, the Camera Response Model (CRM) was applied to improve the quality of collected data. Also, viola-jones, and Kanade-Lucas-Tomasi (KLT) approaches were used to detect and track the driver's face, eye, and mouth regions from the input video. In this research study, Online Region-Based Active Contour Model (ORACM) algorithm was used to segment the driver's mouth region in order to obtain the threshold value. Successively, feature extraction; Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) was applied to extract the features from the detected eye region. The extracted features of the eye region were combined with the threshold value of mouth region and head pose angle. After extracting the feature vectors, infinite approach was utilized to choose the relevant feature vectors. Finally, the selected features were classified using Support Vector Machine (SVM) for classifying the stages of drowsiness detection. Simulation outcome illustrated that the proposed system increased the classification accuracy up to 5.52% as related to hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). [ABSTRACT FROM AUTHOR]

This paper proposed a new semi-supervised algorithm combined with Mutual-cross Imperial Competition Algorithm (MCICA) optimizing Support Vector Machine (SVM) for motion imagination EEG classification, which not only reduces the tedious and time-consuming training process and enhances the adaptability of Brain Computer Interface (BCI), but also utilizes the MCICA to optimize the parameters of SVM in the semi-supervised process. This algorithm combines mutual information and cross validation to construct objective function in the semi-supervised training process, and uses the constructed objective function to establish the semi-supervised model of MCICA for optimizing the parameters of SVM, and finally applies the selected optimal parameters to the data set Iva of 2005 BCI competition to verify its effectiveness. The results showed that the proposed algorithm is effective in optimizing parameters and has good robustness and generalization in solving small sample classification problems. [ABSTRACT FROM AUTHOR]

Mobile Ad hock Networks (MANETs) are currently used for developing the privacy and accuracy of modern networks. Furthermore, MANET applications are fit to be data-oriented systems, that introduce a secure and more robust data transmission protocol making it a topmost priority in the design. The lack of infrastructure in the existence of dynamic topology as well as limited resources of MANET is a major challenge facing those interested in the field. Further, the nonexistence of a formerly authorized trust relationship within the connected nodes produces instability of the detection process in MANETs. Basically, by adding adapted LEACH routing protocol to MANET, enhancement of the preserved nodes vitality will be achieved, moreover, the load balancing with data loss reduction provides MANET ability to tracks along with shortest and limited paths. This paper proposes a newly developed detection scheme for both active and passive black-hole attacks in MANETs. Moreover, the scheme deals with assessing a group of selected features for each node-based AdaBoost-SVM algorithm. These features are collected from cluster members nodes based on Ad hoc On-demand Multipath Distance Vector (OMDV) with LEACH routing protocol clustering approaches. Although SVM is considered a more stable classifier, there are great influences of the AdaBoost weight adaption algorithm to enhance the classification process in terms of strengthening the weights of extracted features. This hybrid algorithm is essential for active black-hole attacks as well as for identifying passive black-hole attacks in MANET. The proposed scheme is tested against the effect of mobility variation to determine the accuracy of the detection process including the routing overhead protocol. The experimental results investigated that the accuracy of detecting both active and passive black-holes attacks in MANET reached 97% with a promising time complexity for different mobility conditions. Moreover, the proposed scheme provides an accurate decision about malicious vs benign node dropping behavior using an adjustable threshold value. [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: Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer.Objective: To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required.Methods: In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection. The diversified pattern of training data is partitioned into several groups based on EM algorithm. Then a series of fuzzy SVM are integrated for classification with each group of samples from the MC lesions and normal breast tissues.Results: From DDSM database, a total of 1,064 suspicious regions are selected from 239 mammography, and the measurement of Accuracy, True Positive Rate (TPR), False Positive Rate (FPR) and EVL = TPR* 1-FPR are 0.82, 0.78, 0.14 and 0.72, respectively.Conclusion: The proposed method incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification. Experimental results from synthetic data and DDSM database demonstrate that our integrated classification framework reduces the false positive rate significantly while maintaining the true positive rate. [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]

In this paper, we develop a novel support vector algorithm with fuzzy hyperplane for pattern classification. We first introduce the concepts of fuzzy hyperplane and fuzzy linear separability. Then, the proposed approach seeks a fuzzy hyperplane that best separates the positive class from the negative class with the widest margin in the feature space. Further, the decision function of the proposed approach is generalized so the values assigned to the individuals fall within a specified range and indicate the membership degree of these individuals in a given category. This integration preserves the benefits of fuzzy set theory and SVM theory, where the use of the fuzzy hyperplane provides the SVM with effective means for capturing the approximate, imprecise nature of the realworld. On the other hand, the SVM provides the advantage to minimize the structural risk and effectively generalize the unseen data. Experimental results are then presented which indicate the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

The existing multi-class classification algorithms based on support vector machine (SVM) generally decompose the original problem into smaller subproblems. However, the decomposition approach raises the problems of unreliable and unbalanced training of individual classifiers. The aim of this work is to alleviate these problems. In this paper, a novel multiclass classification algorithm based on one-class SVM is presented. The distinguishing point of our proposed algorithm is that the algorithm solves a one-class classification problem rather than decomposing the problem into several smaller subproblems. The proposed algorithm solves a multi-class classification problem by expanding the training input vector with the class label and constructing a one-class SVM with the expanded input vectors. In the test phase, test input vectors are assessed by fit to the decision boundary under the assumption of belonging to a class. We conducted experiments on several real-world benchmark datasets. Experimental results showed that the proposed algorithm outperforms other SVM-based multi-class classification algorithms. [ABSTRACT FROM AUTHOR]

This paper studies pedestrian detection algorithm based on trapezoidal feature of local model, and applied to histogram intersection kernel support vector machine (HIKSVM) for verification. In comparison with traditional asymmetry, rectangle and triangle features, the experimental results from trapezoid feature of local model indicate that it can more effectively describe the pedestrian's postures and promote the accuracy and robustness of pedestrian detection. Experimental results show that the proposed algorithm is robust and accurate against cluttered dynamical background, occlusion and the object deformation, and tested in many pedestrian datasets and achieved good results. [ABSTRACT FROM AUTHOR]

Background: In recent times there has been a significant change in lifestyle in many parts of the world, with most people experiencing a more sedentary existence combined with an abundance of food. This has resulted in the modern epidemic of obesity and consequent hyperinsulinemia - situations which in women may precipitate expression of fertility problems; effective methods to evaluate the fertility status are required. Ultrasonographic imaging is an effective, easy to use, safe, and readily available noninvasive means to evaluate fertility potential.Objective: Manual recognition of the follicles in terms of area measurement and counting the number of follicles is laborious; often fatigue may lead to error-prone conclusions. The paper attempts an automated classification of the ovaries based on the biomarking done by the physician. Also, biomarked data correlates with the hormones values such as androgen, testosterone and leutinizing hormone.Methods: Despeckled images are segmented by improved active contour with split-Bregman optimization. The features are extracted from images using geometric and intensity method. The significant features selected by particle swarm optimization and dimension reduction by principal component analysis and classification by probabilistic neural network.Results: Proposed probabilistic neural network achieves maximum efficiency of 97% compared to SVM 92% and RBF 88%.Conclusions: The results obtained show that using a very large number of features combined with a feature selection approach allows us to achieve high classification rates. [ABSTRACT FROM AUTHOR]