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• Two-phase flow regime identification using ML with enhanced interpretability. • Evaluation of various dimensionality reduction techniques to optimize feature representation. • Kernel Fisher discriminant analysis is the best performing dimensionality reduction technique. • AutoML can impartially select the most effective ML classifier. • Our workflow can develop a virtual flow regime map to automate flow regime recognition. Accurate identification of flow regimes is paramount in several industries, especially in chemical and hydrocarbon sectors. This paper describes a comprehensive data-driven workflow for flow regime identification. The workflow encompasses: i) the collection of dynamic pressure signals using an experimentally verified numerical two-phase flow model for three different flow regimes: stratified, slug and annular flow, ii) feature extraction from pressure signals using Discrete Wavelet Transformation (DWT), iii) Evaluation and testing of 12 different Dimensionality Reduction (DR) techniques, iv) the application of an AutoML framework for automated Machine Learning classifier selection among K-Nearest Neighbors, Artificial Neural Networks, Support Vector Machines, Gradient Boosting, Random Forest, and Logistic Regression, with hyper-parameter tuning. Kernel Fisher Discriminant Analysis (KFDA) is the best DR technique, exhibiting superior goodness of clustering, while KNN proved to be the top classifier with an accuracy of 92.5 % and excellent repeatability. The combination of DWT, KFDA and KNN was used to produce a virtual flow regime map. The proposed workflow represents a significant step forward in automating flow regime identification and enhancing the interpretability of ML classifiers, allowing its application to opaque pipes fitted with pressure sensors for achieving flow assurance and automatic monitoring of two-phase flow in various process industries. [ABSTRACT FROM AUTHOR]

The temporal and spatial scale dependent relation of Convective Available Potential Energy (CAPE) and precipitation is investigated. Using the COSMO-REA6 data set, we ask which of the standard machine learning algorithms: perceptron, support vector machine, decision tree, random forest, k-nearest neighbor and a simple kept deep neural network algorithm can best relate these two variables. Then, we concentrate on decision trees and evaluate the relation of CAPE and precipitation across different scales. We investigate temporal resolutions of 1 hour to 24 hours and horizontal resolutions of 6 km up to 768 km. Regarding ten CAPE and two precipitation classes we find accuracy scores mostly of about 0.7 across all scales. Taking the Dynamic State Index (DSI) as additional predictor into account leads to an overall increase of the scores. We further introduce a theoretical relation of CAPE and precipitation based on the works of Hans Ertel (1933), which will be analyzed in future studies. Today it is natural to tackle complex atmospheric processes using machine learning methods. These data based methods are suggested as additional tool to complement the results gained by the governing equations of atmospheric motion. [ABSTRACT FROM AUTHOR]

In this paper, we present a new classifier called elastic net twin support vector machine (ETSVM). It resolves two smaller-sized quadratic programming problems (QPPs) similarly to the twin support vector machine (TSVM). The key difference between them is that ETSVM uses elastic net penalty for slack variables, which enhances classification performance. The dual QPPs of the ETSVM do not involve matrix inversion, in contrast to conventional TSVM. As a result, ETSVM can avoid the ill-conditioning case. We theoretically discuss its properties, including exploring the violation tolerance upper bound for the two QPPs of ETSVM. In order to increase computing efficiency, we derive a sequence of safe screening rules using variational inequalities to quicken the ETSVM parameter tuning process (SSR-ETSVM). We compare the proposed algorithm with SVM, nonparallel hyperplane SVM, elastic net SVM, and elastic net nonparallel hyperplane SVM. Numerical experiments confirm the rationality and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Quantum algorithms can enhance machine learning in different aspects. The quantum support vector machine was proposed to improve the performance, in which the Swap Test plays a crucial role in realizing the classification. However, as the Swap Test is destructive, the quantum support vector machine must be repeated in preparing qubits and manipulating operations. This paper proposes a quantum support vector machine based on the amplitude estimation (AE-QSVM) which gets rid of the constraint of repetitive process and saves the quantum resources. At first, a generalized quantum amplitude estimation is introduced in which the initial state can be arbitrary instead of being |0〉. Then, AE-QSVM is trained by the quantum singular value decomposition and a query sample is classified by the generalized quantum amplitude estimation. In AE-QSVM, a high accuracy can be achieved by adding auxiliary qubits instead of repeating the algorithm. The time and space complexity of AE-QSVM are reduced compared with other algorithms. Finally, we ran experiments on the IBM's quantum computer and experimental results demonstrate that classification with a 95% probability of success only uses 12 qubits. • The generalized amplitude estimation can take any quantum state as the initial state. • The complexity of AE-QSVM is reduced compared with other algorithms. • Quantum support vector machine gets rid of the constraint of repetitive process. • Classification is realized on the IBM quantum computer using 12 qubits. [ABSTRACT FROM AUTHOR]

In this study, a conductive hollow rotating conic object (H-RCO) is developed for convection control and thermal management in a 3D partially heated enclosure under uniform magnetic field with nanofluid considering two phase mixture formulation. Analysis is conducted for different parameters of interest as: Rayleigh number (Ra between 10 4 and 10 6), angular rotational speed of the H-RCO (Ω between −60 and 60), Hartmann number (Ha between 0 and 50), expansion ratio (r1 between 1.1 and 2.5) and conductivity ratio (KR between 0.01 and 50). The rotational speed and expansion ratio of the object contributes significantly to the overall performance improvements. At the highest speed of the H-RCO, the average Nusselt number (Nu) rises up to 38% when compared to cases of non-rotating object. When object with highest expansion ratio is used at rotational speed of Ω = − 40 , the average Nu rises by about 36%. The impacts of using magnetic field on the reduction of convective effects are stronger when rotations are active while up to 69% reduction of average Nu is seen at the highest strength. Thermal conductivity of the object at higher speeds contributes slightly to the overall heat transfer. Support vector machine based regression model is used for thermal performance predictions while model with third order polynomial kernel gives the best results as compared to high fidelity 3D computational results. [ABSTRACT FROM AUTHOR]

In the present research, the least squares support vector machine technique is employed to develop a relationship to predict the total useful exergy yield of a hybrid renewable energy system consisting of a building integrated photovoltaic thermal (PVT) system and an earth air heat exchanger (EAHX) system. This system is designed to bring the temperature of the ambient air to the desired temperature and also to supply the required electricity of the building. In the cold months of the year, the ambient air is preheated by passing through the PVT and EAHX systems. In the hot months of the year, the ambient air is pre-cooled by passing through the EAHX system, while the air leaving the building is also used to cool the photovoltaic panels. The electricity produced by photovoltaic panels is used throughout the year to meet the electricity needs of the building. The dimensions of different parts of the PVT and EAHE systems along with air flow rate are considered as the main variables and the annual exergy yield of the hybrid system is considered as the dependent variable. The LSSVM model in terms of (R = 0.9670, RMSE=1,152,165.21, and MAPE=29,264.80) resulted in promising outcomes to simulate the useful exergy. [ABSTRACT FROM AUTHOR]

In today's fuzzy community, the blending of fuzzy model and deep learning has become one hot topic for the development of more sophisticated and high-powered fuzzy systems. In this study, a rule-based deep fuzzy system with nonlinear fuzzy feature transform for data classification named by NFFT-DFSC is proposed, borrowing the "hierarchically stacked thought" originated from deep learning. Firstly, two fuzzy models with nonlinear fuzzy feature transform function and with decision-making function, and their neuro-fuzzy network implementations are proposed. Subsequently, we stack multiple fuzzy models with nonlinear fuzzy feature transform function and the fuzzy model with decision-making function together in a cascade way, which results in the formation of NFFT-DFSC. Here the stacked fuzzy models with nonlinear fuzzy feature transform function snoops and transforms features of raw data continuously to finally obtain the high-level latent fuzzy features, while the fuzzy model with decision-making function completes classification by exploring the multiple prototypes used to represent the high-level latent fuzzy features of each class. Besides, batch normalization operation and mini-batch gradient descent optimization rooted from the training of deep neural networks are also used to enhance the generalization of NFFT-DFSC and learn its parameters in end-to-end manner, respectively. Experiments between NFFT-DFSCs with three nonlinear mapping functions and 15 benchmark classifiers involving gaussian kernel support vector machine (GSVM), Takagi-Sugeno-Kang classifier (TSK), deep neural network (DNN) and state-of-the-art deep TSK ones on synthetic datasets and nineteen diverse real-world datasets demonstrate that the proposed NFFT-DFSC is a more competitive classifier on classification accuracy. [ABSTRACT FROM AUTHOR]

Compared with single-task learning, multi-tasks can obtain better classifiers by the information provided by each task. In the process of multi-task data collection, we always focus on the target task data in the training process, and ignore the non-target task data and unlabeled data that may be contained in the target task. In response to this issue, this paper introduces auxiliary or Universum into semi-supervised multi-task problem, and proposes a multi-task support vector machine (SU-MTLSVM) method based on semi-supervised learning to handle the case where each task contains the labeled, unlabeled, and Universum samples in the training set. This method introduces Universum as prior knowledge and provides high-dimensional information for semi-supervised learning, and builds a unique classifier from a large amount of unlabeled data. We then use KKT conditions and Lagrangian method to optimize the formulation of the model, and get the model parameters. Finally, we collect different data sets in the experiment part, and compare the performance of multiple baselines with the proposed method. Experiments prove that the method proposed in this paper is more effective for multi-task applications. [ABSTRACT FROM AUTHOR]

• A novel hesitant fuzzy set with SVM modeling scheme is proposed for FTSF. • Length-based discretization (LBD) method is proposed to compute number of interval. • Two set of UODs are used to handle the non-determinism in time series data. • FLRs are established using normalized crisp value with its mean of aggregated HFEs. • FLRs are modeled using SVM. Over the last three decades, several researchers have been putting their efforts into developing non-deterministic fuzzy time series (FTS) models using the traditional fuzzy set. However, considering a set of membership values to each element of the time series, the hesitant fuzzy set glorifies the chances to capture the fuzziness and uncertainty due to randomness better than the traditional fuzzy set. Motivated by this, the present study proposes a novel hesitant FTS forecasting model employing a support vector machine (HFTSF-SVM). In this model, to diminish the computational complexity and increase the forecasting accuracy, we have addressed three key issues by proposing three new methods such as (a) a length-based discretization technique is developed to compute the number of intervals, (b) two sets of universe of discourse are determined for the same time series to compute the non-membership and membership value for each observation (c) each observation along with its mean aggregated membership values are adopted to construct the fuzzy logical relationships (FLR). To assess the competency and reliability of the proposed HFTSF-SVM model, six promising FTS models from the recent literature and four promising time series forecasting (TSF) models are considered for comparison using sixteen time series datasets. Extensive statistical analyses of obtained results confirm the supremacy of the proposed HFTSF-SVM model. [ABSTRACT FROM AUTHOR]

Deep neural mapping support vector machine (DNMSVM) has achieved good results in numerous tasks by mapping the input from a low-dimensional space to a high-dimensional space and then using support vector machine for classification. However, it did not consider the connection of different spaces and increased the model parameters. To improve the classification performance while reducing the number of model parameters, we propose a deep Convolutional Cross-connected Kernel Mapping Support Vector Machine framework based on SelectDropout (CCKMSVM-SD). It consists of a feature extraction module and a classification module. The feature extraction module maps the data from low-dimensional to high-dimensional space by fusing the representations of different dimensional spaces through convolutional layers with cross-connections. For some convolutional layers, we use the depthwise separable convolution to replace the original convolution to reduce the number of parameters. Besides, we use SelectDropout to improve its generalization capability. The classification module uses a soft margin support vector machine for classification. The results on three tasks with ten different datasets indicate that CCKMSVM-SD obtains higher classification accuracy than other models with fewer parameters, demonstrating its effectiveness. [ABSTRACT FROM AUTHOR]

• A novel non-parallel bounded support matrix machine (NPBSMM) is proposed. • A constraint norm group (CNG) is constructed, which can suppress negative influence of outliers and enhance robustness. • The dual problem of NPBSMM avoids the calculation of matrix inversion. • Multi-rank left and right projection matrices are employed to realize a better ability of data fitting. At present, the excellent performance of support vector machine (SVM) has made it successfully applied in many fields. However, when SVM is used for two-dimensional matrix data classification, vectorization of these data easily leads to dimension curse and the loss of structural information. Moreover, SVM is sensitive to outliers, which causes the hyperplane to move towards outliers. Therefore, this paper proposes a novel classification method for data in matrix-form, named non-parallel bounded support matrix machine (NPBSMM). In NPBSMM, a constraint norm group (CNG) is constructed and applied to objective function, which can not only suppress the negative impact of outliers on the model, but also make NPBSMM has better sparsity. By constructing CNG, the operation of matrix inversion in dual problem of traditional classification methods is avoided, so NPBSMM is more suitable for solving large-scale data problems. Further, to extract structure information of matrix for modeling, multi-rank left and right projection matrices are employed to establish objective function, which makes NPBSMM has a better ability of data fitting. Experiments performed on three roller bearing fault datasets show that the proposed NPBSMM method has powerful performance and robustness as compared with other typical classification methods. [ABSTRACT FROM AUTHOR]

Feature selection wrapper methods are powerful mechanisms for reducing the complexity of prediction models while preserving and even improving their precision. Meta-heuristic methods, such as multi-objective evolutionary algorithms, are commonly used as search strategies in feature selection wrapper methods since they allow minimizing the cardinality of the attribute subset and simultaneously maximizing the predictive capacity of the model. However, in high-dimensional problems, multi-objective evolutionary algorithms for wrapper-type feature selection may require excessive computational time, sometimes impractical, especially when the learning algorithm has a high computational cost, such as deep learning. To address this drawback, in this paper we propose a multi-surrogate assisted multi-objective evolutionary algorithm for feature selection, specially designed to improve generalization error. The proposed method has been compared with conventional feature selection wrapper methods that use random forest, support vector machine and long short-term memory learning algorithms to evaluate subsets of attributes. The experiments have been carried out with regression and classification problems with time series data for air quality forecasting in the south-east of Spain and for indoor temperature forecasting in a domotic house. The results demonstrate the superiority of the proposed multi-surrogate assisted method over conventional wrapper methods using the same run times. [ABSTRACT FROM AUTHOR]

In this study, we propose an oversampling method called the minority-predictive-probability-based synthetic minority oversampling technique (MPP-SMOTE) for imbalanced learning. First, MPP-SMOTE removes noisy samples from minority classes. Subsequently, it divides minority samples into two types (hard-to-learn and easy-to-learn) by predicting the probability of samples belonging to the minority class. For both sample types, we adopt a divide-and-conquer strategy. We separately calculate the probability of each sample being selected to generate a new synthetic sample. The relative density of a sample in both the majority and minority classes is considered in the method for calculating the selection probability of hard-to-learn samples, and the relative density of a sample in only the minority class is considered in that of easy-to-learn samples. Finally, according to the types and selection probabilities, MPP-SMOTE separately selects samples and generates synthetic samples based on them by using different sample-generation schemes. Experimental results reveal that the proposed method outperforms other oversampling methods in terms of three imbalanced-learning metrics for three common classifiers. According to the results, when a support vector machine classifier is applied, the area under the curve performance of the MPP-SMOTE improves by a factor of 1.44%. [ABSTRACT FROM AUTHOR]

One of the most important hydraulic structures that has been overlooked is culverts. Regular maintenance and inspections are required to ensure that these structures are used effectively and safely. Culvert inspection, on the other hand, takes a significant amount of time and resources, and agencies cannot afford to do it on a regular basis. To overcome physical inspection challenges, this study proposed employing machine learning algorithms to predict the condition of Utah's culverts based on historical data. This study assessed multiclass classification algorithms that had not been used before. The classification machine learning algorithms employed in this study are Random Forest, Decision Tree, Support Vector Machine, k-Nearest Neighbor, and Artificial Neural Network. Also, a combined dataset from four states was used to address Utah's limited culvert inventory data. The final dataset contained 2555 culvert records. Out of the developed prediction models, Random Forest had the highest accuracy of 82%. The findings demonstrated that Random Forests could be used to improve culvert network quality while reducing culvert maintenance expenses for UDOT. The proposed model may be applied to similarly existing culvert inventories located around the nation. [ABSTRACT FROM AUTHOR]

This paper focuses on the use of differential evolution to improve the approximation properties of function approximation models based on fuzzy partitions. Two cases are considered: Fuzzy transform and Fuzzy projection, and the design of hybrid evolutionary fuzzy systems, is studied. Even though function approximation techniques based on fuzzy partitions have been well studied, few papers consider the problem of centroid selection of the basis functions. Thus, in most cases uniform fuzzy partitions are considered. By using an evolutionary algorithm a systematic approach on the selection of the partition, is provided. The optimisation problem involves the determination of the model parameters, which in our case are the fuzzy partition's membership functions' locations. The proposed method is tested on the scattered data approximation problem, in a regression sense, that is given a set of sparse data the latent function is approximated. Numerical studies on one and two-dimensional test functions demonstrate that the evolutionary algorithm based fuzzy projection displays high performance in terms of approximation error. Moreover, the proposed approach shows high approximation capabilities with a small number of basis functions. Comparison results, with uniform fuzzy partition models, neural networks and support vector machines, are provided. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multi-class classification method using kernel supports and a dynamical system under differential privacy. For small datasets, kernel methods, such as kernel support vector machines (SVMs), show good generalization performance with high-dimensional feature mapping. However, kernel SVMs have a fundamental weakness in achieving differential privacy because they construct decision functions based on a subset of the training data called support vectors. Furthermore, multi-class SVMs must decompose the training data into a binary class, which requires multiple accesses to the same training data. To address these limitations, we develop a two-phase classification algorithm based on support vector data description (SVDD). We first generate and prove a differentially private SVDD (DP-SVDD) by perturbing the sphere center in a high-dimensional feature space. Next, we partition the input space using a dynamical system and classify the partitioned regions using a noisy count. The proposed method results in robust, fast, and user-friendly multi-class classification, even on small-sized datasets, where differential privacy performs poorly. [ABSTRACT FROM AUTHOR]

• A novel approach is proposed to evaluate health based on body area networks. • A multi-sign parameter fusion health assessment model is developed based on BPNN. • The effect of the number of nodes and activation function on the model is explored. • The optimized model is validated by comparing it with other machine learning methods. The widespread application of technologies such as the Internet of Things (IoT) and wireless sensors has promoted the development of body area networks (BAN) in the area of intelligent monitoring. However, current health assessment methods based on BAN still have problems such as a high false alarm rate and low efficiency in identifying signs and states, which not only increase the psychological burden of the ward but also bring unnecessary troubles to the medical staff. In response to this problem, this paper proposes a multi-sign parameter fusion health assessment model based on BP neural network (BPNN). Firstly, the blood pressure, heart rate, pulmonary hypertension, respiration rate, blood oxygen, and body temperature are obtained by sensors in real-time, and then these six parameters are fused by the BPNN. In addition, aiming at the problems of slow convergence speed and easy falling into a local minimum in BPNN, the structure of this model is optimized, and the influence of the number of neurons and activation function of the hidden layer on the performance of the model is explored. Results show that when the number of neurons in the hidden layer is 13 and the activation function is Logsit, the performance of the model is optimal. Among them, the recognition accuracy of the model is 95 %, and the running time is 2.798 s. Finally, comparing the recognition results of this model with support vector machines (SVM), genetic BP neural networks (GA-BPNN), and fuzzy neural networks (FNN), it is found that the accuracy of these three methods is 70 %, 70 % and 80 % respectively, which verifies the validity of the model proposed in this paper. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is a particularly powerful and flexible supervised learning model that analyzes data for both classification and regression, whose usual algorithm complexity scales polynomially with the dimension of data space and the number of data points. To tackle the big data challenge, a quantum SVM algorithm was proposed, which is claimed to achieve exponential speedup for least squares SVM (LS-SVM). Here, inspired by the quantum SVM algorithm, we present a quantum-inspired classical algorithm for LS-SVM. In our approach, an improved fast sampling technique, namely indirect sampling, is proposed for sampling the kernel matrix and classifying. We first consider the LS-SVM with a linear kernel, and then discuss the generalization of our method to nonlinear kernels. Theoretical analysis shows our algorithm can make classification with arbitrary success probability in logarithmic runtime of both the dimension of data space and the number of data points for low rank, low condition number, and high dimensional data matrix, matching the runtime of the quantum SVM. [ABSTRACT FROM AUTHOR]

Twin support vector machine (TWSVM), which constructs two nonparallel classifying hyperplanes, is widely applied to various fields. However, TWSVM solves two quadratic programming problems (QPPs) separately such that the final classifiers lack consistency and enough prediction accuracy. Moreover, by reason of only considering the 1-norm penalty for slack variables, TWSVM is not well defined in the geometrical view. In this article, we propose a novel elastic net nonparallel hyperplane support vector machine (ENNHSVM), which adopts elastic net penalty for slack variables and constructs two nonparallel separating hyperplanes simultaneously. We further discuss the properties of ENNHSVM theoretically and derive the violation tolerance upper bound to better demonstrate the relative violations of training samples in the same class. In particular, we design a safe screening rule for ENNHSVM to speed up the calculations. We finally compare the performance of ENNHSVM on both synthetic datasets and benchmark datasets with the Lagrangian SVM, the twin parametric-margin SVM, the elastic net SVM, the TWSVM, and the nonparallel hyperplane SVM. [ABSTRACT FROM AUTHOR]

One-shot semantic segmentation poses the challenging task of segmenting object regions from unseen categories with only one annotated example as guidance. Thus, how to effectively construct robust feature representations from the guidance image is crucial to the success of one-shot semantic segmentation. To this end, we propose in this article a simple, yet effective approach named rich embedding features (REFs). Given a reference image accompanied with its annotated mask, our REF constructs rich embedding features of the support object from three perspectives: 1) global embedding to capture the general characteristics; 2) peak embedding to capture the most discriminative information; 3) adaptive embedding to capture the internal long-range dependencies. By combining these informative features, we can easily harvest sufficient and rich guidance even from a single reference image. In addition to REF, we further propose a simple depth-priority context module to obtain useful contextual cues from the query image. This successfully raises the performance of one-shot semantic segmentation to a new level. We conduct experiments on pattern analysis, statical modeling and computational learning (Pascal) visual object classes (VOC) 2012 and common object in context (COCO) to demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

The brain–computer interface (BCI) P300 speller analyzes the P300 signals from the brain to achieve direct communication between humans and machines, which can assist patients with severe disabilities to control external machines or robots to complete expected tasks. Therefore, the classification method of P300 signals plays an important role in the development of BCI systems and technologies. In this article, a novel ensemble support vector recurrent neural network (E-SVRNN) framework is proposed and developed to acquire more accurate and efficient electroencephalogram (EEG) signal classification results. First, we construct a support vector machine (SVM) to formulate EEG signals recognizing model. Second, the SVM formulation is transformed into a standard convex quadratic programming (QP) problem. Third, the convex QP problem is solved by combining a varying parameter recurrent neural network (VPRNN) with a penalty function. Experimental results on BCI competition II and BCI competition III datasets demonstrate that the proposed E-SVRNN framework can achieve accuracy rates as high as 100% and 99%, respectively. In addition, the results of comparison experiments verify that the proposed E-SVRNN possesses the best recognition accuracy and information transfer rate (ITR) compared with most of the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The feature selection process plays an important role in different fields, particularly in bioinformatics and microarray gene expression data analysis, for choosing discriminative genes from high-dimensional datasets and selecting a subset of highly relevant features with low redundancy that may lead to build improved prediction models. Consequently, this study proposes a new feature selection method that integrates Preordonnances theory in terms of new Relevance and Complementarity criteria introduced here and also connectivity in undirected Weighted Graphs (PCRWG). The method can handle high-dimensional data. PCRWG retains the relevant and complementary features to select effective features in large scale gene datasets. The proposed algorithm operates in two phases: filtering and wrapping. The strength of the first phase is that it is preceded by a step that further reduces the number of predictors by removing those in disagreement with the target based on the new proposed relevance criterion. Then, the proposed heuristic uses the relevance-complementarity ratio between preordonnances to automatically update the compromise rule between relevance and complementarity. In the wrapping phase, the suggested graph-based approach using maximal clique is based on a powerful relevance-complementarity matrix to consolidate edges, two connected interdependent features are complementary to each other, and it is possible to have high discriminative power when they serve as a group. We highlight the fact that existing graph-based feature selection algorithms do not consider relevance and complementarity simultaneously. The experiments were carried out on three simulated scenarios and the thirteen most popular cancer microarray gene datasets. Formally, they are eight binary and five multi-class microarray data. A 10-fold cross validation was used to evaluate the Support Vector Machine (SVM), Naive Bayes (NB) and artificial Neural Network (NN) classifiers. The empirical results demonstrate the high performance of the proposed hybrid approach when compared to the most recently published articles. [ABSTRACT FROM AUTHOR]

Several techniques have long been proposed to enhance text classification performance, such as: classifier ensembles, feature selection, the integration of similarity measures with classifiers, and meta -heuristic algorithms. The integration of similarity measures with machine learning models (ML), however, has not yet received thorough analysis for text classification. As a result, in an effort to thoroughly investigate the impact of similarity measures integration with ML models, this work makes three major contributions: (1) proposing newly-integrated models and presenting benchmarking studies for integration methodology over balanced/imbalanced datasets; (2) offering detailed analysis for dozens of integrated models that are established, and experimentally proven, to significantly outperform state-of-the-art performance. The models' construction used fourteen similarity measures, three knowledge representations (BoW, TFIDF, and Word embedding), and five models (Support Vector Machine, N -Centroid-based Classifier, Multinomial Naïve Bayesian, Convolutional Neural Network, and Artificial Neural Network); and (3) introducing significantly-effective and highly-efficient variations of these five models. The evaluation study has been conducted internally for integrated models against their baselines, and externally against the state-of-the-art models. While the internal evaluation constantly showed a total enhancement rate of 49.3% and 59% over the balanced and imbalanced datasets, respectively, the external evaluation attested to the superiority of the integrated models. [ABSTRACT FROM AUTHOR]

• Propose an integrated method for product ranking through online reviews. • A complete framework to solve the ranking problem is developed. • A data-driven method of applying ER theory to sentiment analysis is proposed. • An MCDM-based method of ranking products with SD and PROMETHEE-II is proposed. • Propose an SMAA-PROMETHEE method for sensitivity analysis of the parameters. Online reviews play an important role in consumers' purchasing decisions. However, many online reviews confuse consumers when they wish to make a purchase but lack experience. To solve the problem of product ranking based on online reviews, two important issues must be addressed: sentiment analysis and product ranking based on multi-criteria decision-making (MCDM) methods. Therefore, this paper proposes an integrated MCDM method for product ranking through online reviews based on evidential reasoning (ER) theory and stochastic dominance (SD) rules. First, online reviews are preprocessed to obtain product attributes and weight values. Then, we use naive Bayes (NB), logistic regression (LR), and support vector machines (SVM) for the sentiment analysis of online reviews, and the results of the three classifiers are aggregated using ER theory. In addition, according to the confidence distribution matrix of sentiment orientations, SD rules are used to determine the stochastic dominance relations between pairwise alternatives for each attribute. Furthermore, we use the stochastic multi-criteria acceptability analysis (SMAA)-PROMETHEE method to obtain the final product ranking results and conduct sensitivity analysis. Finally, a case study on ranking computer products from JD Mall through online reviews is provided to illustrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Learning with label proportions (LLP) deals with the problem that the training data are provided as bags, where the label proportions of training bags rather than the labels of individual training instances are accessible. Existing LLP studies assume that the label proportions of all training bags are accessible. However, in many applications, it is time-consuming to mark all training bags with label proportions, which leads to the problem of learning with both marked and unmarked bags, namely, semisupervised LLP (SLLP). In this work, we propose semisupervised proportional support vector machine (SS- $\propto $ SVM), which extends the proportional SVM ($\propto $ SVM) model to its semisupervised version. To the best of our knowledge, SS- $\propto $ SVM is the first attempt to cope with the SLLP problem. Two realizations, alter-SS- $\propto $ SVM and conv-SS- $\propto $ SVM, which are based on alternating optimization and convex relaxation, respectively, are developed to solve the proposed SS- $\propto $ SVM model. Moreover, we design a cutting plane (CP) method to optimize conv-SS- $\propto $ SVM with a guaranteed convergence rate and present a fast accelerated proximal gradient method to solve the multiple kernel learning subproblem in conv-SS- $\propto $ SVM efficiently. Empirical experiments not only justify the superiority of SS- $\propto $ SVM over its supervised counterpart in classification accuracy but also demonstrate the high competitive computational efficiency of the CP optimization of conv-SS- $\propto $ SVM. [ABSTRACT FROM AUTHOR]

Multi-instance learning (MIL) has garnered considerable attention in recent years due to its favorable performance in various scenarios. Nonetheless, most previous studies have implicitly expressed the correlation between instances and bags. Moreover, the importance of negative instances has been largely overlooked. Hence, we seek to present an explicit and intuitively understandable method that can compensate for these deficiencies. In this article, we creatively introduce a metric-based multiconcept MIL approach based on two aspects. First, the triplet-based bag embedding method identifies instance categories and builds attention weights for every instance explicitly. Accordingly, bag embedding is accomplished under the limitation of weak supervision. Second, the developed instance correlation metric approach in the superbag considers the multiconcept issue to boost the model generalization performance. We have designed a rich variety of experiments to demonstrate the performance of our algorithm. The artificial data experiment reveals the interpretability of the proposed network. The results of the comparison experiment confirm that our method shows favorable performance in multiple tasks. Finally, we illustrate the motivation of the presented method by the ablation experiments. [ABSTRACT FROM AUTHOR]

In classification, the use of 0–1 loss is preferable since the minimizer of 0–1 risk leads to the Bayes optimal classifier. However, due to the nonconvexity of 0–1 loss, this optimization problem is NP-hard. Therefore, many convex surrogate loss functions have been adopted. Previous works have shown that if a Bayes-risk consistent loss function is used as a surrogate, the minimizer of the empirical surrogate risk can achieve the Bayes optimal classifier as the sample size tends to infinity. Nevertheless, the comparison of convergence rates of minimizers of different empirical surrogate risks to the Bayes optimal classifier has rarely been studied. Which characterization of the surrogate loss determines its convergence rate to the Bayes optimal classifier? Can we modify the loss function to achieve a faster convergence rate? In this article, we study the convergence rates of empirical surrogate minimizers to the Bayes optimal classifier. Specifically, we introduce the notions of consistency intensity and conductivity to characterize a surrogate loss function and exploit this notion to obtain the rate of convergence from an empirical surrogate risk minimizer to the Bayes optimal classifier, enabling fair comparisons of the excess risks of different surrogate risk minimizers. The main result of this article has practical implications including: 1) showing that hinge loss (SVM) is superior to logistic loss (Logistic regression) and exponential loss (Adaboost) in the sense that its empirical minimizer converges faster to the Bayes optimal classifier and 2) guiding the design of new loss functions to speed up the convergence rate to the Bayes optimal classifier with a data-dependent loss correction method inspired by our theorems. [ABSTRACT FROM AUTHOR]

In this article, anomaly detection is considered for hyperspectral imagery in the Gaussian background with an unknown covariance matrix. The anomaly to be detected occupies multiple pixels with an unknown pattern. Two adaptive detectors are proposed based on the generalized likelihood ratio test design procedure and ad hoc modification of it. Surprisingly, it turns out that the two proposed detectors are equivalent. Analytical expressions are derived for the probability of false alarm of the proposed detector, which exhibits a constant false alarm rate against the noise covariance matrix. Numerical examples using simulated data reveal how some system parameters (e.g., the background data size and pixel number) affect the performance of the proposed detector. Experiments are conducted on five real hyperspectral data sets, demonstrating that the proposed detector achieves better detection performance than its counterparts. [ABSTRACT FROM AUTHOR]

Data stream classification methods exploiting cohesion in a single data stream have demonstrated promising performance. However, scenarios involving multiple data streams are indeed common in practice. They involve several correlated data streams and can be viewed as multi-task data streams. Instead of considering them separately as individual data streams, it is beneficial to leverage the correlations among the multi-task data streams in data stream modeling. In this regard, a novel classification method called double-coupling support vector machines (DC-SVM) is proposed to classify multiple data streams simultaneously, where both the external correlations between multiple data streams and the internal relationships within each individual data stream are considered. Experimental results on synthetic and real-world multi-task data streams show that the proposed method outperforms traditional data stream classification methods. [ABSTRACT FROM AUTHOR]

• An approach is proposed for estimating monotonic change point in multistage process. • The quality of the process at each stage is characterized by simple linear profiles. • Two SVM algorithms are proposed for identifying the occurrence and type of change. • MLE is proposed for estimating both single and multiple monotonic change points. • The performance of the proposed approach is evaluated under various scenarios. In this paper, a hybrid method is proposed for estimating monotonic change points in multistage processes where, at each stage, the quality of the process is represented by simple linear profile models. In the case of monotonic changes, the change type is not known a priori, and the only assumption is that the changes are non-decreasing or non-increasing in nature. In the proposed method, a support vector machine (SVM) algorithm is first developed to identify the parameters experiencing a change in each process stage. Then, the second SVM algorithm is applied to identify the type of change occurring in the related parameters. Finally, considering the identified parameters and change types, the maximum likelihood estimator (MLE) is proposed to estimate the change points of the process under different scenarios, including single and multiple change points, upward and downward step changes, increasing and decreasing linear trends, shift in both parameters of the profiles, shift in different stages of the process, and weak and strong autocorrelation coefficients. The performance of the proposed method is evaluated through extensive simulation experiments. The results indicate that the proposed method recognizes the patterns and estimates the monotonic change points accurately. [ABSTRACT FROM AUTHOR]

In partial label learning (PLL) problem, each training sample corresponds to a group of candidate labels, in which only one label is the ground-truth label (correct label). Almost all the existing PLL algorithms attempt to eliminate the ambiguity of the candidate label sets by treating all labels indiscriminately. However, this kind of approach lacks the consideration of the complexity between the labels and the instances in the training process. Encouraged by the extensive researches of the self-paced learning (SPL) and transfer learning (TL) in various fields, this paper introduces SPL and TL together to address the PLL problem and proposes a new SPL framework, which is called self-paced method for transfer partial label learning (SPTPLL). The proposed model utilizes transfer learning model to share the parameters and regularization terms of the Support Vector Machine (SVM), which can transfer knowledge from the source task to the target task. Additionally, we implement the self-paced learning scheme by choosing a suitable self-paced function to enhance the robustness of the proposed model. In the process of learning iteration, the priority of training examples with their candidate labels is ranked through self-paced learning to control the learning process. Finally, we demonstrate the superior performance of the proposed method through a large number of experiments compared with state-of-the-art baseline methods. [ABSTRACT FROM AUTHOR]

Researchers from Qingdao Agricultural University in China have proposed a novel method for food safety testing based on infrared spectroscopy. The method, called negentropy-sorted kernel independent component analysis (NS-kICA), was used to analyze Fourier transform infrared spectroscopy data and rank the components based on non-Gaussian independent components. The researchers found that NS-kICA outperformed conventional feature selection methods, and the support vector machine model was the most effective for recognition performance. These findings are significant for food safety testing and can contribute to achieving high, stable, and efficient results. [Extracted from the article]

A study conducted in Warangal, India, by researchers from the School of Engineering has developed a hybrid deep learning method for accurately classifying breast lesions as benign or malignant. The method combines a modified Lipschitz-based image augmentation technique, k-fold cross-validation, and a hybrid convolutional neural network (CNN) approach. The deep features extracted from the CNNs are further reduced using a recursive feature elimination technique and then fed into machine learning classifiers. The results show that the proposed model outperforms existing techniques, achieving high accuracy, sensitivity, specificity, precision, and F1 score. This research has been peer-reviewed and provides valuable insights into breast cancer classification. [Extracted from the article]

A recent study conducted by researchers at Southeast University focused on predicting cervical cancer using a machine learning-based approach. Cervical cancer is a leading cause of premature death among women, with the majority of cases occurring in underdeveloped countries. The study utilized early screening and risk trends from individual health records to develop a predictive model. By employing machine learning classifiers and feature selection techniques, the researchers achieved an accuracy rate of 99.19%. The study highlights the effectiveness of the ensemble machine learning classifier in handling classification problems and addressing the issue of high-class imbalance. [Extracted from the article]

A new report discusses research findings on the integration of quantum computing and machine learning in healthcare. The study, conducted by researchers at Yonsei University in Seoul, South Korea, focused on predicting mortality in patients with early-onset colorectal cancer. The researchers compared the performance of quantum support vector machine (QSVM) models with classical support vector machine (SVM) models and found that the QSVM models outperformed the conventional SVM models in terms of predictive accuracy. The study highlights the potential of quantum computing to improve predictive modeling in healthcare, particularly for rare diseases with limited available data. [Extracted from the article]

A recent study conducted by the Higher Institute of Engineering and Technology explores the use of a wearable textile bra-tenna system for breast cancer detection. The researchers investigate the effectiveness of different polarization signals combined with machine learning techniques to enhance detection accuracy. The system utilizes machine learning algorithms to predict breast cancer in its early stages, achieving high accuracy rates. The study concludes that the Support Vector Machine algorithm performs exceptionally well in both tumor and non-tumor detection scenarios. The research provides valuable insights for doctors in effectively investigating breast cancer. [Extracted from the article]

A patent application has been submitted by inventors Renee Deehan-Kenney and Aaron Van Hooser for a disease spectrum classification system. The system uses machine learning algorithms to assess individuals for diseases, disorders, or conditions based on personalized data derived from biological samples. The algorithms generate classifications relative to a range of related classifications, such as severity or prognosis of a disease. The system also provides recommendations for treatment based on the determined progression of the disease and the individual's current classification. The patent application was filed by PatientsLikeMe LLC. [Extracted from the article]

A new report from Imam Mohammad Ibn Saud Islamic University in Riyadh, Saudi Arabia, discusses the use of artificial intelligence (AI) in predicting cervical cancer. The researchers propose a method called ViT-PSO-SVM, which integrates a Vision Transformer (ViT) with particle swarm optimization (PSO) and support vector machine (SVM) algorithms. The approach achieved high accuracy and F1-scores in predicting cervical cancer using two benchmark datasets. The researchers suggest that this AI-based diagnostic tool could provide a non-invasive and reliable method for early detection of cervical cancer, leading to improved healthcare outcomes. [Extracted from the article]

Researchers at Central South University have published a study on the detection of anemia using machine learning techniques. The study explores the use of classification models, such as logistic regression and decision trees, combined with innovative models incorporating attention modules and spatial attention. The proposed models achieved high accuracy, precision, recall, and F1 scores for both textual and image datasets, with an integrated approach outperforming individual modalities. The study presents a comprehensive and innovative framework for noninvasive anemia detection, offering valuable insights to the field. [Extracted from the article]

Researchers at Beth Israel Deaconess Medical Center in Boston have developed a machine learning algorithm that can accurately distinguish between myeloid malignancies and leukemoid reactions in adults with neutrophil-predominant leukocytosis. The algorithm uses demographic and lab data to generate predictive models, with the best model achieving a sensitivity of 96% and a specificity of 95.9%. The study also found a significant difference in outcomes between the two diagnoses, with a six-fold increase in 12-month mortality for patients with leukemoid reactions. These findings highlight the potential of predictive models to improve timely and accurate diagnosis and ultimately enhance patient outcomes. [Extracted from the article]

A study conducted by researchers from the Department of ECE in Salem, India, focuses on the early detection of breast cancer using machine learning techniques. The study proposes a modified U-Net model and an optimized XGBoost-SVM classifier to improve accuracy in classifying and segmenting breast cancer histopathological images. The results show a high accuracy rate of 99% in various performance metrics. The researchers suggest that this system could be used in real-time to assist medical experts in early breast cancer prognosis. [Extracted from the article]

A new report discusses research on melanoma conducted by Aristotle University. The study focused on using machine learning algorithms, specifically convolutional neural networks, to predict melanoma metastasis based on dermatoscopy deep features. The researchers conducted an international multicenter cohort study involving cutaneous melanoma patients from three continents. The study found that dermatoscopy deep features could potentially serve as a biomarker for predicting melanoma metastasis, offering a way to prioritize high-risk patients for treatment. The findings suggest that this advancement could have significant clinical importance in guiding treatment decisions for melanoma patients. [Extracted from the article]

Researchers from Hangzhou Medical College in Zhejiang, China have developed a deep-learning-based 3D super-resolution CT radiomics model to predict the presence of micropapillary/solid components in invasive lung adenocarcinoma (ILA). The model, which uses radiomic features extracted from CT and super-resolution CT (SR-CT) images, outperformed conventional CT radiomics models in predicting these components. The study suggests that this model could improve preoperative identification of ILA with poor prognosis, leading to more accurate and individualized treatment planning. Further research and validation are needed to fully harness the clinical potential of this model. [Extracted from the article]

This study develops an oversampling method designed for rebalancing datasets in impact damage classification of reinforced concrete walls. The proposed method addresses class imbalance and overlap issues using the Wasserstein Generative Adversarial Network with Gradient Penalty (WGANGP) approach, aiming to enhance classification accuracy. Initially, sample weights are calculated for overlapped and non-overlapped samples based on their proximity to the decision boundary via an initial classification process using the Support Vector Machine algorithm. Then, these weighted samples are combined with the original dataset to form a training dataset for the WGANGP model. To effectively learn from limited data samples and stabilize the training process, the Model Agnostic Meta-learning (MAML) method is incorporated into the WGANGP algorithm. New samples are generated to balance the impact damage dataset using the generator network of the WGANGP model. The proposed method is compared to advanced oversampling methods handling overlapping, noisy, near-borderline samples between the classes. Four well-known classifiers are trained on the impact damage dataset, including Multilayer Perceptron (MLP), Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM). Implementation results demonstrate the efficacy of the proposed oversampling technique on the impact damage dataset, outperforming others in terms of F1 score. [ABSTRACT FROM AUTHOR]

Multi-view imbalanced learning is to handle the datasets with multi-view representations and imbalanced classes. Existing multi-view imbalanced learning methods can be divided into two main categories: multi-view ensemble learning and multi-view cost-sensitive learning. However, these methods suffer from the following problems: 1) neglecting either consensus or complementary information, 2) complex preprocessing and information fusion in multi-view ensemble learning and manual assignment of misclassification costs in multi-view cost-sensitive learning, and 3) limited ability to handle noisy samples. Therefore, we aim to design a concise and unified framework to grapple with the multi-view representations, imbalanced classes and noisy samples simultaneously. Inspired by the merits of support vector machine (SVM) and quadratic type squared error (QTSE) loss function, we propose a robust multi-view instance-level cost-sensitive SVM with QTSE loss (MVQS) for imbalanced data classification. The consensus regularization term and combination weight strategy are employed to fully exploit multi-view information. The QTSE loss can adaptively impose instance-level penalties to the misclassification of samples, and make MVQS be robust to noisy samples. We solve MVQS with the alternating direction method of multipliers (ADMM) and the gradient descent (GD) algorithm. Comprehensive experiments validate that MVQS is more competitive and robust than other benchmark approaches. • MVQS handles multiple views, imbalanced classes and noise simultaneously. • QTSE loss helps achieve instance-level cost-sensitivity and noise robustness. • Comprehensive experiments on various datasets validate the superiority of MVQS. [ABSTRACT FROM AUTHOR]

A report from Harbin Medical University in China discusses the use of computer-assisted endoscopic ultrasonography to differentiate between gastrointestinal stromal tumors (GISTs) and benign gastric mesenchymal tumors. The study found that texture features extracted from endoscopic ultrasonography images were effective in distinguishing GISTs from other tumors. Support vector machine models using gray-gradient co-occurrence matrix-based texture features showed the best diagnostic performance for identifying GISTs. The findings suggest that computer-assisted endoscopic ultrasonography could be a valuable tool in diagnosing GISTs. [Extracted from the article]

A study conducted by researchers at Universitas Diponegoro in Semarang, Indonesia, explored breast cancer classification using statistical methods. The study found that early detection and accurate diagnosis are crucial for effective breast cancer management. The researchers used two classification methods, Support Vector Machine (SVM) and LightGBM, and found that the SVM method had the highest accuracy rate of 97.9% in classifying breast cancer into malignant and benign categories. This research provides valuable insights into the development of diagnostic models for breast cancer. [Extracted from the article]