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Similarity measurement is a fundamental problem that arise both on its own and as a key subroutine in more complex tasks, such as machine learning. However, in classical algorithms, the time used to similarity measurement usually increases exponentially as the amount of data and the number of data dimensions increase. In this paper, we presented three quantum algorithms based on Euclidean distance to measure the similarity between data sets. In the proposed algorithms, some special unitary operations are utilized to construct imperative quantum states from quantum random access memory. Then, a badly needed result for estimating the similarity between data sets, can be got by performing projective measurements. Furthermore, it is shown that these algorithms can achieve the exponential acceleration of the classical algorithm in the quantity or the dimension of data. [ABSTRACT FROM AUTHOR]

Recently, the problem of indoor localization based on WLAN signals is attracting increasing attention due to the development of mobile devices and the widespread construction of networks. However, no definitive solution for achieving a low-cost and accurate positioning system has been found. In most traditional approaches, solving the indoor localization problem requires the availability of a large number of labeled training samples, the collection of which requires considerable manual effort. Previous research has not provided a means of simultaneously reducing human calibration effort and improving location accuracy. This paper introduces fusion semi-supervised extreme learning machine (FSELM), a novel semi-supervised learning algorithm based on the fusion of information from Wi-Fi and Bluetooth Low Energy (BLE) signals. Unlike previous semi-supervised methods, which consider multiple signals individually, FSELM fuses multiple signals into a unified model. When applied to sparsely calibrated localization problems, our proposed method is advantageous in three respects. First, it can dramatically reduce the human calibration effort required when using a semi-supervised learning framework. Second, it utilizes fused Wi-Fi and BLE fingerprints to markedly improve the location accuracy. Third, it inherits the beneficial properties of ELMs with regard to training and testing speeds because the input weights and biases of hidden nodes can be generated randomly. As demonstrated by experimental results obtained on practical indoor localization datasets, FSELM possesses a better semi-supervised manifold learning ability and achieves higher location accuracy than several previous batch supervised learning approaches (ELM, BP and SVM) and semi-supervised learning approaches (SELM, S-RVFL and FS-RVFL). Moreover, FSELM needs less training and testing time, making it easier to apply in practice. We conclude through experiments that FSELM yields good results when applied to a multi-signal-based semi-supervised learning problem. The contributions of this paper can be summarized as follows: First, the findings indicate that effective multi-data fusion can be achieved not only through data-layer fusion, feature-layer fusion and decision-layer fusion but also through the fusion of constraints within a model. Second, for semi-supervised learning problems, it is necessary to combine the advantages of different types of data by optimizing the model’s parameters. [ABSTRACT FROM AUTHOR]

The qualities of new data used in the sequential learning phase of the online sequential extreme learning machine algorithm (OS-ELM) have a significant impact on the performance of OS-ELM. This paper proposes a novel data filter mechanism for OS-ELM from the perspective of fuzziness and a fuzziness-based online sequential extreme learning machine algorithm (FOS-ELM). In FOS-ELM, when new data arrive, a fuzzy classifier first picks out the meaningful data according to the fuzziness of each sample. Specifically, the new samples with high-output fuzziness are selected and then used in sequential learning. The experimental results on eight binary classification problems and three multiclass classification problems have shown that FOS-ELM updated by the new samples with high-output fuzziness has better generalization performance than OS-ELM. Since the unimportant data are discarded before sequential learning, FOS-ELM can save more memory and have higher computational efficiency. In addition, FOS-ELM can handle data one-by-one or chunk-by-chunk with fixed or varying sizes. The relationship between the fuzziness of new samples and the model performance is also studied in this paper, which is expected to provide some useful guidelines for improving the generalization ability of online sequential learning algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses learning in complex scenarios involving imbalance and overlap. We propose a novel measure, the Augmented R-value, for estimating the level of overlap in the data. It improves an existing model-based measure, by including the data imbalance in the estimation process. We provide both a theoretical demonstration and empirical validations of the new metric’s efficacy in estimating the overlap level. Another contribution of the present paper is to propose a collection of meta-features to be used in conjunction with a meta-learning strategy for predicting the most suitable classifier for a given problem. The evaluations performed on a well-known collection of benchmark problems have shown that the meta-learning approach achieves superior results to the manual classifier selection process normally carried out by data scientists. The analysis of the results obtained by the meta-feature selection step has confirmed the power of the Augmented R-value in predicting the expected performance of classifiers in such complex classification scenarios. Also, we found that the overlap is a more serious factor affecting the performance of classifiers than imbalance. [ABSTRACT FROM AUTHOR]

Artificial intelligence algorithms are booming in medicine, and the question of biases induced or perpetuated by these tools is a very important topic. There is a greater risk of these biases in radiology, which is now the primary diagnostic tool in modern treatment. Some authors have recently proposed an analysis framework for social inequalities and the biases at risk of being introduced into future algorithms. In our paper, we comment on the different strategies for resolving these biases. We warn that there is an even greater risk in mixing the notion of equity, the definition of which is socio-political, into the design stages of these algorithms. We believe that rather than being beneficial, this could in fact harm the main purpose of these artificial intelligence tools, which is the care of the patient. [ABSTRACT FROM AUTHOR]

This paper presents an extreme learning machine-based super-twisting repetitive control (ELMSTRC) to improve the tracking accuracy of periodic signal with less chattering. The proposed algorithm is robust against the plant uncertainties caused by mass and viscous friction variations. Moreover, it compensates the nonlinear friction and the backlash by using extreme learning machine based super-twisting algorithm. Firstly, a repetitive control is designed to track the periodic reference and compensate the viscous friction. Then, a stable extreme learning machine-based super-twisting control is constructed to compensate the aperiodic disturbance, nonlinear friction, backlash and plant uncertainties. The stability of ELMSTRC system is analysed based on Lyapunov stability criteria. The proposed algorithm is verified on a brushless DC servo motor with various loading, backlash and friction conditions. The simulation and experimental comparisons highlight the advantages of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Fuzzy cognitive map (FCM) is an important approach for modeling the behavior of dynamic systems. FCM's ability to represent casual relationships between the concepts (factors, attributes, etc.) has attracted the interest of researchers from different disciplines. The construction process of FCMs is mostly initialized with expert knowledge because FCMs can conveniently incorporate available information and expertise in the determination of vital parameters and relations of the system. However, their higher dependence on expert knowledge may significantly influence the reliability of the model due to the increase in subjectivity. In order to avoid weaknesses depending on expert knowledge, learning algorithms that search for the appropriate relationships between the concepts have been used with FCM studies. In this paper, a FCM analysis was performed for tobacco use to understand the cause–effect relationships between demographic characteristics of people (such as gender, age range, and residence type) and likelihood to tobacco use. In order to reduce the impact of external interventions (from experts), a multi-stage learning procedure was applied by integrating two different learning algorithms (nonlinear Hebbian learning algorithm and extended Great Deluge algorithm). The results showed that the multi-stage learning procedure increased the accuracy of the model and provided more reliable maps for the studied system. They also proved that the multi-stage learning procedures can help to reduce the dependency to expert knowledge and improve the robustness of the study. [ABSTRACT FROM AUTHOR]

Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of attacks represent a serious threat to detection systems in environments having high noise rate or uncertainty, such as complex networks and Internet of Thing (IoT). Recent work in the literature has suggested using the K-nearest neighboring algorithm to defend against such attacks. However, such an approach can suffer from low to miss-classification rate accuracy. In this paper, we design an architecture to tackle the Android malware detection problem in IoT systems. We develop an attack mechanism based on silhouette clustering method, modified for mobile Android platforms. We proposed two convolutional neural network-type deep learning algorithms against this Silhouette Clustering-based Label Flipping Attack. We show the effectiveness of these two defense algorithms—label-based semi-supervised defense and clustering-based semi-supervised defense—in correcting labels being attacked. We evaluate the performance of the proposed algorithms by varying the various machine learning parameters on three Android datasets: Drebin, Contagio, and Genome and three types of features: API, intent, and permission. Our evaluation shows that using random forest feature selection and varying ratios of features can result in an improvement of up to 19% accuracy when compared with the state-of-the-art method in the literature. [ABSTRACT FROM AUTHOR]

This paper aims to improve data-driven prognostics by presenting a novel approach of directly estimating the remaining useful life (RUL) of aero-engines without requiring setting any failure threshold information or estimating degradation states. Specifically, based on the sensory data, RUL estimations are directly obtained through the universal function approximation capability of the extreme learning machine (ELM) algorithm. To achieve this, the features related with the RUL are first extracted from the sensory data as the inputs of the ELM model. Besides, to optimize the number of observed sensors, three evaluation metrics of correlation, monotonicity and robustness are defined and combined to automatically select the most relevant sensor values for more effective and efficient remaining useful life predictions. The validity and superiority of the proposed approach is evaluated by the widely used turbofan engine datasets from NASA Ames prognostics data repository. The proposed approach shows improved RUL estimation applicability at any time instant of the degradation process without determining the failure thresholds. This also simplifies the RUL estimation procedure. Moreover, the random properties of hidden nodes in the ELM learning mechanisms ensures the simplification and efficiency for real-time implementation. Therefore, the proposed approach suits to real-world applications in which prognostics estimations are required to be fast. [ABSTRACT FROM AUTHOR]

In this paper, a novel distributed elastic random weights network (DERWN) is proposed to achieve robust surface reconstruction from highly noisy point clouds sampled from real surface. The designed elastic regularization with l 1 and l 2 penalty items makes the network more resilient to noise and effectively capture the intrinsic shape of surface. Sparsity constraints of output weight vectors and threshold-based nodes removal are conducive to determining appropriate number of hidden nodes of network and optimizing the distribution of hidden nodes. The distributed optimization manner in DERWN on the basis of alternating direction method of multipliers solves the problem that traditional RWN learning algorithm suffers from the limitation of memory with large-scale data. The proposed DERWN achieves a solution to global problem by solving local subproblems coordinately. Experimental results show that the proposed DERWN algorithm can robustly reconstruct the unknown surface in case of highly noisy data with satisfying accuracy and smoothness. [ABSTRACT FROM AUTHOR]

Incremental extreme learning machine has been verified that it has the universal approximation capability. However, there are two major issues lowering its efficiency: one is that some "random" hidden nodes are inefficient which decrease the convergence rate and increase the structural complexity, the other is that the final output weight vector is not the minimum norm least-squares solution which decreases the generalization capability. To settle these issues, this paper proposes a simple and efficient algorithm in which the parameters of even hidden nodes are calculated by fitting the residual error vector in the previous phase, and then, all existing output weights are recursively updated based on inverse partitioned matrix. The algorithm can reduce the inefficient hidden nodes and obtain a preferable output weight vector which is always the minimum norm least-squares solution. Theoretical analyses and experimental results show that the proposed algorithm has better performance on convergence rate, generalization capability and structural complexity than other incremental extreme learning machine algorithms. [ABSTRACT FROM AUTHOR]

The growing volume and complexity of data has led to the development of the so-called linear algorithms for neural networks like ELM, which maintain the precision of classic algorithms but with higher training speed. This speed increase is due to a simpler architecture, the random fixing of the input weights without being trained and the analytical calculation of the output weights instead of the slowly classical iterative gradient methods as Backpropagation. However, the random fixing of the input weights increases the sensibility to input perturbations like noise. Recently, No-Propagation (No-Prop) algorithm has been introduced as another linear algorithm, which shares with ELM the architecture and the random input weights (hidden weights) initialization. In this paper, an exhaustive comparison of both algorithms and its regularized versions are presented. The simulations results suggest that No-Prop is a competitive alternative to the ELM algorithm. [ABSTRACT FROM AUTHOR]

The usefulness of machine learning algorithms has led to their widespread adoption prior to the development of a conceptual framework for making sense of them. One common response to this situation is to say that machine learning suffers from a "black box problem." That is, machine learning algorithms are "opaque" to human users, failing to be "interpretable" or "explicable" in terms that would render categorization procedures "understandable." The purpose of this paper is to challenge the widespread agreement about the existence and importance of a black box problem. The first section argues that "interpretability" and cognates lack precise meanings when applied to algorithms. This makes the concepts difficult to use when trying to solve the problems that have motivated the call for interpretability (etc.). Furthermore, since there is no adequate account of the concepts themselves, it is not possible to assess whether particular technical features supply formal definitions of those concepts. The second section argues that there are ways of being a responsible user of these algorithms that do not require interpretability (etc.). In many cases in which a black box problem is cited, interpretability is a means to a further end such as justification or non-discrimination. Since addressing these problems need not involve something that looks like an "interpretation" (etc.) of an algorithm, the focus on interpretability artificially constrains the solution space by characterizing one possible solution as the problem itself. Where possible, discussion should be reformulated in terms of the ends of interpretability. [ABSTRACT FROM AUTHOR]

Cloud computing provides utility computing in which clients pay the cost according to their demands and service use. There are some challenges to this technology. One of these issues in data centers is virtual machine (VM) placement so that mapping of these VMs to hosts is executed for a variety of objectives such as load balancing, reducing energy consumption, increasing resource utilization, shortening response time, etc. In this paper, a strategy is presented based on machine learning for VM replacement which aims to balance the load in host machines (HM). In this proposed strategy, the learning agent, in each learning episode by selecting an action from among the permissible actions and executing it on the environment receives a reward according to the desirability of the solution obtained by doing that action in the environment. Receiving a reward from the environment and updating the action value table enable the learner agent to learn in the following episodes that in each environment state, selecting and executing which action is better in the environment and this leads to further enhancement. Our proposed algorithm has, on average, improved the inter-HM load balance in terms of processor, memory, and bandwidth by 25%, 34%, and 32%, respectively, prior to the implementation of the algorithm. Our strategy was compared from diffrent aspects in three scenarios to the MOVMrB strategy. Finally, it was concluded that our proposed algorithm can be more effective in load balancing by having much less runtime and turning off more HMs. [ABSTRACT FROM AUTHOR]

In a large-scale data center, it is vital to precisely recognize the termination statuses of applications at an early stage. In recent years, many machine learning techniques have been applied to this issue, which is beneficial for optimizing the scheduling policy and improving the efficiency of resource utilization. However, if the application's dynamic information is insufficient at the early stage, the generalization performance of the machine learning model will be lessened, and the prediction accuracy could be low. To overcome this problem, a novel failure prediction method that is based on the association relationships between similar jobs is proposed in this paper to jointly predict task's termination statuses at an earlier stage. The similar jobs whose tasks have similar changing modes of consumed resources, an inherent structural correlation may exist, and the correlation information is significant for improving the prediction model's generalization performance. First, a job clustering algorithm is proposed for identifying the jobs with higher similarity from jobs that have various numbers of tasks. Second, based on the job clustering results, the robust multi-task learning algorithm is introduced to effectively utilize the domain information among jobs (i.e. interactional relationship among jobs on the termination statuses of task). Experiments are conducted on a Google cluster workload traces dataset. The results show that the proposed method can realize higher prediction accuracy, lower misjudgment rate, and higher predictive stability than several state-of-the-art methods at 1/3 the running time of the tasks. [ABSTRACT FROM AUTHOR]

Machine learning era began to rule almost all the technologies, which influences the improvement of the performance due to its intelligent computing methodologies. Especially the Deep Learning algorithm plays a vital role in computing a human-like decision, which is considered to be the superior breakthrough technology of the century. Deep Learning algorithms generate a massive sum of features which is stacked and learned by many other neurons of the network in the term of links. Links initiated from the input and ends in the output of the network connecting many neurons on its path. The significant limitation of this network is its thirst towards the high computation power. This paper represents a methodology to make the system consume less computation requirement during its training or testing phase. In this process, an effective clustering algorithm (fraternal K-median clustering) is used as the preprocessing strategies, and as the second phase the dropout regularization procedure is implemented (in the Convolutional Neural Network, a type of Deep Learning algorithm) to eliminate most of the insignificant data. The dropout strategies used in the process helps in the improvement of accuracy by making the network overfit the decision of CNN, obtaining state-of-the-art results. [ABSTRACT FROM AUTHOR]

Nowadays, it has become well known that efficient training of deep neural networks plays a vital role in various successful applications. To achieve this goal, it is impractical to use only one computer, especially when the scale of models is large and some efficient computing resources are available. In this paper, we present a distributed parallel computing framework for training deep belief networks (DBNs) by employing the great power of high-performance clusters (i.e., a system consists of many computers). Motivated by the greedy layer-wise learning algorithm of DBNs, the whole training process is divided layer by layer and distributed to different machines. At the same time, rough representations are exploited to parallelize the training process. By conducting experiments on several large-scale real datasets, the novel algorithms are shown to significantly accelerate the training speed of DBNs while achieving better or competitive prediction accuracy in comparison with the original algorithm. [ABSTRACT FROM AUTHOR]

Among the extensions of twin support vector machine (TSVM), some scholars have utilized K-nearest neighbor (KNN) graph to enhance TSVM's classification accuracy. However, these KNN-based TSVM classifiers have two major issues such as high computational cost and overfitting. In order to address these issues, this paper presents an enhanced regularized K-nearest neighbor-based twin support vector machine (RKNN-TSVM). It has three additional advantages: (1) Weight is given to each sample by considering the distance from its nearest neighbors. This further reduces the effect of noise and outliers on the output model. (2) An extra stabilizer term was added to each objective function. As a result, the learning rules of the proposed method are stable. (3) To reduce the computational cost of finding KNNs for all the samples, location difference of multiple distances-based K-nearest neighbors algorithm (LDMDBA) was embedded into the learning process of the proposed method. The extensive experimental results on several synthetic and benchmark datasets show the effectiveness of our proposed RKNN-TSVM in both classification accuracy and computational time. Moreover, the largest speedup in the proposed method reaches to 14 times. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is a widely used and reliable machine learning algorithm. It has been successfully applied to many real-world problems, with remarkable results. However, it has also been observed that SVM computational complexity increases with the increase in data size. Although many SVM speed optimization techniques have been proposed in the literature, there is still need for further improvement on the performance speed and accuracy of this algorithm. In this paper, a boundary detection algorithm for SVM speed optimization called ant colony optimization instance selection algorithm (ACOISA) is proposed. ACOISA is inspired by edge selection in ant colony optimization (ACO) algorithm, and it performs two primary functions: boundary detection and boundary instance selection. In the algorithm, ACO is used for boundary detection and k-nearest neighbor algorithm is used for boundary instance selection. Different sets of experiments are carried out to validate the efficiency of the proposed technique. All the experiments were performed on 35 datasets containing three well-known e-fraud types (credit card fraud, email spam and phishing email) and 31 other datasets available at UCI data repository. The experimental results showed that the proposed technique efficiently improved SVM training speed in 100% of the datasets used for evaluation, without significantly affecting SVM classification quality. Furthermore, the Freidman's and Holm's post hoc tests were conducted to statistically validate the credibility of the results. The statistical test results revealed that the proposed technique is significantly faster, compared to the standard SVM and some existing instance selection techniques, in all cases. [ABSTRACT FROM AUTHOR]

This paper completely introduces an exhaustive and a comprehensive review of the so-called salp swarm algorithm (SSA) and discussions its main characteristics. SSA is one of the efficient recent meta-heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields, such as machine learning, engineering design, wireless networking, image processing, and power energy. This review shows the available literature on SSA, including its variants, like binary, modifications and multi-objective. Followed by its applications, assessment and evaluation, and finally the conclusions, which focus on the current works on SSA, suggest possible future research directions. [ABSTRACT FROM AUTHOR]

Quantum algorithms for the analysis of Boolean functions have received a lot of attention over the last few years. The algebraic normal form (ANF) of a linear Boolean function can be recovered by using the Bernstein–Vazirani (BV) algorithm. No research has been carried out on quantum algorithms for learning the ANF of general Boolean functions. In this paper, quantum algorithms for learning the ANF of quadratic Boolean functions are studied. We draw a conclusion about the influences of variables on quadratic functions, so that the BV algorithm can be run on them. We study the functions obtained by inversion and zero-setting of some variables in the quadratic function and show the construction of their quantum oracle. We introduce the concept of "club" to group variables that appear in quadratic terms and study the properties of clubs. Furthermore, we propose a bunch of algorithms for learning the full ANF of quadratic Boolean functions. The most efficient algorithm, among those we propose, provides an O(n) speedup over the classical one, and the number of queries is independent of the degenerate variables. [ABSTRACT FROM AUTHOR]

Due to the lack of experience and prior knowledge, the selection of the most informative features has become one of the challenging problems in many applications. Recently, many metaheuristic algorithms have widely used to solve the feature selection problem for classification tasks. In this paper, the chaotic atom search optimization (CASO) that integrates the chaotic maps into atom search optimization (ASO) is applied for wrapper feature selection. Twelve different chaotic maps are used to adjust the parameter of CASO through the optimization process, which is beneficial for enhancing the convergence rate and improving the efficiency of ASO algorithm. In this study, twenty benchmark datasets acquired from the UCI machine learning repository are used to validate the performance of CASO in feature selection. Several state-of-the-art metaheuristic algorithms are adopted to examine the efficacy and effectiveness of the proposed approach. Our results indicated that the Logistic-Tent map was the most suitable chaotic map to boost the performance of CASO. The experimental result shows the capability of CASO not only in finding the optimal solution but also in significantly improving the prediction accuracy and reducing the number of features. [ABSTRACT FROM AUTHOR]

Feature selection is one of the hottest machine learning topics in recent years. The main purposes of it are to simplify the original model, improve the readability of the model, and prevent over-fitting by searching for a suitable subset of features. There are many methods for this problem, including evolutionary algorithms and particle swarm optimization. Among them, the competitive swarm optimizer is a new optimization algorithm proposed in recent years, which is based on particle swarm optimization algorithm, and has achieved good results in high-dimensional feature selection problems, but it also has the problems of high computation time cost and easily being premature. Aiming at these problems, this paper proposes to add the crossover operator and mutation operator in the genetic algorithm to the competitive swarm optimization, so as to improve the generation speed of new individuals in the algorithm and prevent premature population. After testing on UC Irvine Machine Learning Repository, the new algorithm not only improves the computational efficiency, but also avoids the problem that the competitive swarm optimization algorithm is easy to fall into the local optimum, which greatly improves the calculation effect. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel quantum learning algorithm, based on Younes' quantum circuit, to find dependent variables of the Boolean function f : 0 , 1 n → 0 , 1 with one uncomplemented product of two variables. Typically, in the worst-case scenario, two dependent variables are found by evaluating the function O n times. However, our proposed quantum algorithm only requires O log 2 n function operations in the worst-case. Additionally, we evaluate the average number to perform the function. In the average case, our algorithm requires O 1 function operations. [ABSTRACT FROM AUTHOR]

Neural network (NN) finds role in variety of applications due to combined effect of feature extraction and classification availability in deep learning algorithms. In this paper, we have chosen SVM, logistic regression machine learning algorithms and NN for EEG signal classification. Two-layer LSTM and four-layer improved NN deep learning algorithms are proposed to improve the performance in EEG classification. Novelty lies in one-dimensional gradient descent activation functions with radial basis operations used in the initial layers of improved NN which help in achieving better performance. Statistical features namely mean, standard deviation, kurtosis and skewness are extracted for input EEG collected from Bonn database and then applied for various classification techniques. Accuracy, precision, recall and F1 score are the performance metrics used for analyzing the algorithms. Improved NN and LSTM give better performance compared to all other architectures. The simulations are carried out with variety of activation functions, optimizers and loss models to analyze the performance using Python in keras. [ABSTRACT FROM AUTHOR]

The internet has provided numerous modes for secure data transmission from one end station to another, and email is one of those. The reason behind its popular usage is its cost-effectiveness and facility for fast communication. In the meantime, many undesirable emails are generated in a bulk format for a monetary benefit called spam. Despite the fact that people have the ability to promptly recognize an email as spam, performing such task may waste time. To simplify the classification task of a computer in an automated way, a machine learning method is used. Due to limited availability of datasets for email spam, constrained data and the text written in an informal way are the most feasible issues that forced the current algorithms to fail to meet the expectations during classification. This paper proposed a novel, spam mail detection method based on the document labeling concept which classifies the new ones into ham or spam. Moreover, algorithms like Naive Bayes, Decision Tree and Random Forest (RF) are used in the classification process. Three datasets are used to evaluate how the proposed algorithm works. Experimental results illustrate that RF has higher accuracy when compared with other methods. [ABSTRACT FROM AUTHOR]

Abstract : Currently, high hospital readmission rates have become a problem for mental health services, because it is directly associated with the quality of patient care. The development of predictive models with machine learning algorithms allows the assessment of readmission risk in hospitals. The main objective of this paper is to predict the readmission risk of patients with schizophrenia in a region of Spain, using machine learning algorithms. In this study, we used a dataset with 6089 electronic admission records corresponding to 3065 patients with schizophrenia disorders. Data were collected in the period 2005–2015 from acute units of 11 public hospitals in a Spain region. The Random Forest classifier obtained the best results in predicting the readmission risk, in the metrics accuracy = 0.817, recall = 0.887, F1-score = 0.877, and AUC = 0.879. This paper shows the algorithm with highest accuracy value and determines the factors associated with readmission risk of patients with schizophrenia in this population. It also shows that the development of predictive models with a machine learning approach can help improve patient care quality and develop preventive treatments. [ABSTRACT FROM AUTHOR]

Time-domain airborne electromagnetic (AEM) data are frequently subject to interference from various types of noise, which can reduce the data quality and affect data inversion and interpretation. Traditional denoising methods primarily deal with data directly, without analyzing the data in detail; thus, the results are not always satisfactory. In this paper, we propose a method based on dictionary learning for EM data denoising. This method uses dictionary learning to perform feature analysis and to extract and reconstruct the true signal. In the process of dictionary learning, the random noise is filtered out as residuals. To verily the effectiveness of this dictionary learning approach for denoising, we use a fixed overcomplete discrete cosine transform (ODCT) dictionary algorithm, the method-of-optimal-directions (MOD) dictionary learning algorithm, and the K-singular value decomposition (K-SVD) dictionary learning algorithm to denoise decay curves at single points and to denoise profile data for different time channels in time-domain AEM. The results show obvious differences among the three dictionaries for denoising AEM data, with the K-SVD dictionary achieving the best performance. [ABSTRACT FROM AUTHOR]

With the advent of powerful, low-cost IoT systems, processing data closer to where the data originates, known as edge computing, has become an increasingly viable option. In addition to lowering the cost of networking infrastructures, edge computing reduces edge-cloud delay, which is essential for mission-critical applications. In this paper, we show the feasibility and study the performance of image classification using IoT devices. Specifically, we explore the relationships between various factors of image classification algorithms that may affect energy consumption, such as dataset size, image resolution, algorithm type, algorithm phase, and device hardware. In order to provide a means of predicting the energy consumption of an edge device performing image classification, we investigate the usage of three machine learning algorithms using the data generated from our experiments. The performance as well as the trade-offs for using linear regression, Gaussian process, and random forests are discussed and validated. Our results indicate that the random forest model outperforms the two former algorithms, with an R-squared value of 0.95 and 0.79 for two different validation datasets. The random forest also served as a feature extraction mechanism which enabled us to identify which predictor variables influenced our model the most. [ABSTRACT FROM AUTHOR]

Job shop scheduling problem is a well-known NP problem. It is limited by various conditions. As the scale of the problem increases, the difficulty of finding the optimal solution will increase. It is a difficult combination problem. Limited by the constraints of the actual production environment, how to effectively arrange the processing order of each part will directly affect the production efficiency, the appropriate production scheduling algorithm can correctly and effectively plan the enterprise resources and rationally arrange the processing order and processing time of the workpiece. Proper use of existing resources, by optimizing production scheduling instructions, to meet the basic requirements of production scheduling, in order to obtain the optimization of total production time, has important theoretical significance for the actual production of enterprises. In this paper, the mathematical model is abstracted on the basis of the production scheduling problem. According to the different parts of the same machine and the different processes of the same part, the corresponding processing time and waiting time are obtained. At the same time, the genetic algorithm is improved by genetic algorithm. A dynamic genetic operator based on the number of iterations is proposed, which further enhances the convergence performance and search ability of the genetic algorithm. Through the simulation of MATLAB simulation program, combined with the scheduling standard example, the performance analysis of different algorithms is carried out, the search efficiency of genetic algorithm is improved, the convergence performance of the algorithm is improved, and different optimization choices are obtained for different time weights. The operation results of the system meet the requirements of production scheduling, which proves the feasibility and practicability of the improved genetic algorithm. [ABSTRACT FROM AUTHOR]

Seismic data in desert area generally have low signal-to-noise ratio (SNR) due to special surface conditions. Desert noise is characterized as low-frequency, non-Gaussian and non-stationary noise, which makes the noise suppression in desert area more challenging by conventional methods. Conventional methods are effective for the signal with high SNR, but in desert seismic signal, the SNR is low and the signal can easily be obliterated in desert noise. In this paper, we propose an approach that operates in synchrosqueezing transform (SST) domain and use classification techniques obtained from supervised machine learning to identify the coefficients associated with signal and noise. First of all, we transform the real desert seismic data into time–frequency domain by SST. Secondly, we select features by calculating the SST coefficients of signal and noise. And then, we train them in the Adaboost classifier. Finally, when the training is completed, we can obtain the final classifier that can effectively separate the signal from noise. We perform tests on synthetic and field records, and the results show great advantages in suppressing random noise as well as retaining effective signal amplitude. [ABSTRACT FROM AUTHOR]

In high-dimensional data, many of the features are either irrelevant to the machine learning task or are redundant. These situations lead to two problems, firstly overfitting and secondly high computational overhead. The paper proposes a feature selection method to identify the relevant subset of features for the machine-learning task using wrapper approach. The wrapper approach uses the Binary Bat algorithm to select the set of features and One-pass Generalized Classifier Neural Network (OGCNN) to evaluate the selected set of features using a novel fitness function. The proposed fitness function accounts for the entropy of sensitivity and specificity along with accuracy of classifier and fraction of selected features. The fitness function is compared using four classifiers (Radial Basis Function Neural Network, Probabilistic Neural Network, Extreme Learning Machine and OGCNN) on six publicly available datasets. One-pass classifiers are chosen as these are computationally faster. The results suggest that OGCNN along with the novel fitness function performs well in the majority of cases. [ABSTRACT FROM AUTHOR]

Reconstructing gene regulatory networks (GRNs) from gene expression data is an important and challenging problem in system biology. In general, the problem of reconstructing GRNs can be modeled as an optimization problem. Recurrent neural network (RNN) has been widely used for GRNs. However, in a real GRN, the number of genes is very large and the relationships between genes are usually very sparse. In this paper, we design a memetic algorithm to learn partial parameters of RNN, and develop a framework based on the least absolute shrinkage and selection operator (LASSO) to reconstruct GRNs based on RNN, which is termed as MALASSORNN-GRN. In the LASSO, the task of reconstructing GRNs is decomposed into a sparse signal reconstructing problem. In the experiments, MALASSORNN-GRN is applied on synthetic data and well-known benchmark datasets DREAM3 and DREAM4. The effect of parameters on MALASSORNN-GRN is discussed, and MALASSORNN-GRN is compared with three other algorithms which are all state-of-the-art RNN learning algorithms. The results show that MALASSORNN-GRN performs best and is capable of reconstructing large-scale GRNs. [ABSTRACT FROM AUTHOR]

With age, our blood vessels are prone to aging, which induces cardiovascular disease. As an important basis for diagnosing heart disease and evaluating heart function, the electrocardiogram (ECG) records cardiac physiological electrical activity. Abnormalities in cardiac physiological activity are directly reflected in the ECG. Thus, ECG research is conducive to heart disease diagnosis. Considering the complexity of arrhythmia detection, we present an improved convolutional neural network (CNN) model for accurate classification. Compared with the traditional machine learning methods, CNN requires no additional feature extraction steps due to the automatic feature processing layers. In this paper, an improved CNN is proposed to automatically classify the heartbeat of arrhythmia. Firstly, all the heartbeats are divided from the original signals. After segmentation, the ECG heartbeats can be inputted into the first convolutional layers. In the proposed structure, kernels with different sizes are used in each convolution layer, which takes full advantage of the features in different scales. Then a max-pooling layer followed. The outputs of the last pooling layer are merged and as the input to fully-connected layers. Our experiment is in accordance with the AAMI inter-patient standard, which included normal beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F), and unknown beats (Q). For verification, the MIT arrhythmia database is introduced to confirm the accuracy of the proposed method, then, comparative experiments are conducted. The experiment demonstrates that our proposed method has high performance for arrhythmia detection, the accuracy is 99.06%. When properly trained, the proposed improved CNN model can be employed as a tool to automatically detect different kinds of arrhythmia from ECG. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to assess the potential of machine learning based on B-mode, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) radiomics for the localization of prostate cancer (PCa) lesions using transrectal ultrasound.Methods: This study was approved by the institutional review board and comprised 50 men with biopsy-confirmed PCa that were referred for radical prostatectomy. Prior to surgery, patients received transrectal ultrasound (TRUS), SWE, and DCE-US for three imaging planes. The images were automatically segmented and registered. First, model-based features related to contrast perfusion and dispersion were extracted from the DCE-US videos. Subsequently, radiomics were retrieved from all modalities. Machine learning was applied through a random forest classification algorithm, using the co-registered histopathology from the radical prostatectomy specimens as a reference to draw benign and malignant regions of interest. To avoid overfitting, the performance of the multiparametric classifier was assessed through leave-one-patient-out cross-validation.Results: The multiparametric classifier reached a region-wise area under the receiver operating characteristics curve (ROC-AUC) of 0.75 and 0.90 for PCa and Gleason > 3 + 4 significant PCa, respectively, thereby outperforming the best-performing single parameter (i.e., contrast velocity) yielding ROC-AUCs of 0.69 and 0.76, respectively. Machine learning revealed that combinations between perfusion-, dispersion-, and elasticity-related features were favored.Conclusions: In this paper, technical feasibility of multiparametric machine learning to improve upon single US modalities for the localization of PCa has been demonstrated. Extended datasets for training and testing may establish the clinical value of automatic multiparametric US classification in the early diagnosis of PCa.Key Points: • Combination of B-mode ultrasound, shear-wave elastography, and contrast ultrasound radiomics through machine learning is technically feasible. • Multiparametric ultrasound demonstrated a higher prostate cancer localization ability than single ultrasound modalities. • Computer-aided multiparametric ultrasound could help clinicians in biopsy targeting. [ABSTRACT FROM AUTHOR]

To provide a new numerical algorithm for solving elliptic partial differential equations (PDEs), the Legendre neural network (LNN) and improved extreme learning machine (IELM) algorithm are introduced to propose a Legendre improved extreme learning machine (L-IELM) method, which is applied to solving elliptic PDEs in this paper. The product of two Legendre polynomials is chosen as basis functions of hidden neurons. Single hidden layer LNN is used to construct approximate solutions and its derivatives of differential equations. IELM algorithm is used for network weights training, and the algorithm steps of the proposed L-IELM method are summarized. Finally, in order to evaluate the present algorithm, various test examples are selected and solved by the proposed approach to validate the calculation accuracy. Comparative study with the earlier methods in literature is described to verify the superiority of the presented L-IELM method. Experiment results show that the proposed L-IELM algorithm can perform well in terms of accuracy and execution time, which in addition provides a new algorithm for solving elliptic PDEs. [ABSTRACT FROM AUTHOR]

In the context of computer experiments, metamodels are largely used to represent the output of computer codes. Among these models, Gaussian process regression (kriging) is very efficient see e.g Snelson (Flexible and efficient Gaussian process models for machine learning. ProQuest LLC, Ann Arbor, MI. Thesis (Ph.D.)–University of London, University College London, London, 2008). In high dimension that is with a large number of input variables, but with few observations, the estimation of the parameters with a classical anisotropic kriging can be completely inaccurate. Because there are equal numbers of ranges and input variables the optimization space becomes too large compared to available information. One way to overcome this drawback is to use an isotropic kernel that only depends on one parameter. However this model is too restrictive. The aim of this paper is twofold. Our first objective is to propose a smooth kernel with as few parameters as warranted. We introduce a kernel which is a tensor product of few isotropic kernels built on well-chosen subgroup of variables. The main difficulty is to find the number and the composition of the groups. Our second objective is to propose algorithmic strategies to overcome this difficulty. Four forward strategies are proposed. They all start with the simplest isotropic kernel and stop when the best model according to BIC criterion is found. They all show very good accuracy results on simulation test cases. But one of them is more efficient. Tested on a real data set, our kernel shows very good prediction results. [ABSTRACT FROM AUTHOR]

The traditional way of lexical interpretation is to use some words to explain other words. These semantic information cannot be used to solve the problem of natural language processing oriented to the physical situation. Based on this, this paper proposes a new method based on the feature vector Word semantic representation model and its corresponding learning algorithm Sal-F. This algorithm applies the idea of cross-scenario learning to the alignment of "word-shape features" and uses the result of feature selection under MSAV to construct a visual semantic model Lsm-G, A semantic dictionary based on graph features is constructed. Using machine-oriented evaluation method, the average selection accuracy of this algorithm is about 70%, and the accuracy of sentence selection is up to 16%. [ABSTRACT FROM AUTHOR]

This paper proposes a hybridized machine-learning framework called Extreme Learning Machine using self-adaptive multi-population-based Jaya algorithm for forecasting the currency exchange value. This learning technique attempts to take the advantages of generalization ability of Extreme Learning Machines (ELMs) along with the multi-population search scheme of Jaya optimization technique. This model can very well forecast the exchange price of USD–INR and USD–EURO based on statistical measures, technical indicators and combination of both measures over a time frame varying from 1 day to 1 month ahead. Proposed model has been compared with original ELM and ELM-Jaya along with technical analysis method such as discrete wavelet neural network optimized with self-adaptive multi-population-based Jaya and the comparison of different performance measures like MAPE, Theil's U, ARV and MAE reveal that ELM using self-adaptive multi-population-based Jaya hybrid models possesses superior compared to the rest predictive models. Comparison of different features demonstrates technical indicators outperform other two features such as statistical measures and combination of both technical indicators and statistical measures. [ABSTRACT FROM AUTHOR]

Very large-scale social networks are typically sparse and dynamic and often have millions of nodes and billions of links. Link prediction in very large-scale networks is a challenging task for most existing methods. This paper investigates the link prediction problem in very large-scale networks. We propose a model, namely, a very large-scale network co-occurrence embedding algorithm (Hsem), which learns the co-occurrence features of node pairs to embed nodes into a vector with a lower and fixed dimension. A damping-based random walk algorithm is also developed to extract the hierarchical structural information of node pairs. Moreover, we design an incremental learning algorithm based on Hsem to meet the requirements of highly dynamic evolution in very large-scale networks, which significantly improves learning speed while maintaining accuracy. Finally, we present controlled experiments employing the proposed and baseline methods on eight challenging real-world large-scale datasets with a fixed dropout percentage and calculate the time overhead and receiver operating characteristics. The results show that Hsem performs comparably to current state-of-the-art methods and consistently outperforms the baselines in different experimental settings. [ABSTRACT FROM AUTHOR]

In situ monitoring and accurate detecting of welding quality have been one of the common challenges of automatic welding process. This paper contributes an intelligent decision-making framework for the weld penetration prediction from the keyhole dynamic behavior under time-varying VPPAW pools. Initially, a series of dynamic experiments under different welding conditions were conducted to acquire the backside images of keyhole and corresponding backside bead width. Then, the geometry appearance of keyhole was described by the supervised descent method (SDM)–based image processing algorithm. Subsequently, the internal correlation between the keyhole characteristics and the backside width was further derived to help understand the nonlinear and time-varying VPPAW process. Finally, a novel dynamic model based on an online sequential extreme learning machine (OS-ELM) was designed to predict the weld penetration as measured by the backside bead width in real time. Extensive experiment results further verify and validate that the proposed dynamic OS-ELM model is significantly better than other state-of-the-art algorithms in terms of predicting accuracy, efficiency, and robustness. [ABSTRACT FROM AUTHOR]

Movie box-office research is an important work for the rapid development of the film industry, and it is also a challenging task. Our study focuses on finding the regular box-office revenue patterns. Clustering algorithm is unsupervised machine learning algorithm which classifies the data in the absence of early knowledge of the classes. Unlike static data, the time series data vary with time. The work focused on time series clustering analysis is relatively less than those focused on static data. In this paper, the sparse subspace clustering (SSC) algorithm is introduced to analyze the time series data. The SSC algorithm has a better performance both on the artificial data set and the daily box-office data than recently developed well-known clustering algorithm such as K-means and spectral clustering algorithm. On the artificial data set, SSC is more suitable for time series, whether from the angle of clustering error or visualization. On the actual data, movies are divided into five clusters by SSC algorithm, and each cluster represents a distinct type of distribution pattern. And these patterns can be used in movie recommendation, film evaluation and can guide theater exhibitors and distributors. In addition, this is the first time to apply SSC to deal with time series clustering problem and get a pleasant effect. [ABSTRACT FROM AUTHOR]

The aim of this study is to propose a high-accuracy and high-efficiency sleep staging algorithm using single-channel electroencephalograms (EEGs). The process consists four parts: signal preprocessing, feature extraction, feature selection, and classification algorithms. In the preconditioning of EEG, wavelet function and IIR filter are used for noise reduction. In feature selection, 15 feature algorithms in time domain, time-frequency domain, and nonlinearity are selected to obtain 30 feature parameters. Feature selection is very important for eliminating irrelevant and redundant features. Feature selection algorithms as Fisher score, Sequential Forward Selection (SFS), Sequential Floating Forward Selection (SFFS), and Fast Correlation-Based Filter Solution (FCBF) were used. The paper establishes a new ensemble learning algorithm based on stacking model. The basic layers are k-Nearest Neighbor (KNN), Random Forest (RF), Extremely Randomized Trees (ERT), Multi-layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost) and the second layer is a Logistic regression. Comparing classification of RF, Gradient Boosting Decision Tree (GBDT), and XGBoost, the accuracies and kappa coefficients are 96.67% and 0.96 using the proposed method. It is higher than other classification algorithms.The results show that the proposed method can accurately sleep staging using single-channel EEG and has a high ability to predict sleep staging. Graphical abstract. [ABSTRACT FROM AUTHOR]

Linear equations with infinite or no solutions play key roles in machine learning and optimization. However, the existed quantum algorithms cannot be applied directly for these classes of equations. In this paper, based on the modification of algorithm (Wossnig et al., Phys. Rev. Lett. 120, 050502 2017), we describe a quantum algorithm to compute the minimal norm solution or minimum norm least-squares solution for equations with infinite or no solutions, respectively. It can be shown that the presented algorithm can achieve an exponential speedup over the best classical algorithm. Furthermore, the corresponding quantum circuit is designed on a quantum computer. [ABSTRACT FROM AUTHOR]

Consider the classical problem of solving a general linear system of equations A x = b . It is well known that the (successively over relaxed) Gauss–Seidel scheme and many of its variants may not converge when A is neither diagonally dominant nor symmetric positive definite. Can we have a linearly convergent G–S type algorithm that works for anyA? In this paper we answer this question affirmatively by proposing a doubly stochastic G–S algorithm that is provably linearly convergent (in the mean square error sense) for any feasible linear system of equations. The key in the algorithm design is to introduce a nonuniform double stochastic scheme for picking the equation and the variable in each update step as well as a stepsize rule. These techniques also generalize to certain iterative alternating projection algorithms for solving the linear feasibility problem A x ≤ b with an arbitrary A, as well as high-dimensional minimization problems for training over-parameterized models in machine learning. Our results demonstrate that a carefully designed randomization scheme can make an otherwise divergent G–S algorithm converge. [ABSTRACT FROM AUTHOR]

In this paper, we propose a perturbed proximal primal–dual algorithm (PProx-PDA) for an important class of linearly constrained optimization problems, whose objective is the sum of smooth (possibly nonconvex) and convex (possibly nonsmooth) functions. This family of problems can be used to model many statistical and engineering applications, such as high-dimensional subspace estimation and the distributed machine learning. The proposed method is of the Uzawa type, in which a primal gradient descent step is performed followed by an (approximate) dual gradient ascent step. One distinctive feature of the proposed algorithm is that the primal and dual steps are both perturbed appropriately using past iterates so that a number of asymptotic convergence and rate of convergence results (to first-order stationary solutions) can be obtained. Finally, we conduct extensive numerical experiments to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper considers nonconvex distributed constrained optimization over networks, modeled as directed (possibly time-varying) graphs. We introduce the first algorithmic framework for the minimization of the sum of a smooth nonconvex (nonseparable) function—the agent's sum-utility—plus a difference-of-convex function (with nonsmooth convex part). This general formulation arises in many applications, from statistical machine learning to engineering. The proposed distributed method combines successive convex approximation techniques with a judiciously designed perturbed push-sum consensus mechanism that aims to track locally the gradient of the (smooth part of the) sum-utility. Sublinear convergence rate is proved when a fixed step-size (possibly different among the agents) is employed whereas asymptotic convergence to stationary solutions is proved using a diminishing step-size. Numerical results show that our algorithms compare favorably with current schemes on both convex and nonconvex problems. [ABSTRACT FROM AUTHOR]

Latest studies done on huge data collected from aging features proved that the performance of facial image based age estimation is low and need to be improved. One of the significant biometric traits for human recognition or search is Human age. Age assessment is very much exigent over other pattern recognition problems since the aging differs from person to person. This paper proposes a new framework that uses periocular region for age feature extraction and application of hybrid algorithm for age recognition. Firstly, preprocessing and periocular region normalization is done to acquire age invariant features. Secondly, the periocular region that underwent preprocessing is analyzed using hybrid approach, a novel machine algorithm that combines both SVM and kNN. The proposed technique generates the best recognition outputs. [ABSTRACT FROM AUTHOR]

Aiming at the shortage of classic ID3 decision tree and C4.5 decision tree algorithm in ability of time series data mining, this paper increases Bayesian classification algorithm in the node of the decision tree algorithm as a preprocessing Bayesian node to form incremental learning decision tree. In incremental learning, the decision tree is more powerful in the mining of time series data, and the result is more robust and accurate. In order to verify the effectiveness of the algorithm, time series data mining simulation based on UCR time series data sets have been conducted, and the performance and efficiency of ID3 decision tree, Bayesian algorithm and incremental decision tree algorithm have been contrasted respectively in non-incremental learning and incremental learning. The experimental results show that in both the incremental and non-incremental time series data mining, the incremental decision tree algorithm based on Bayesian nodes optimization which can improve the classification accuracy. When optimizing the parameters of Bayesian nodes, the consumption of pruning time will be decreased, and the efficiency of the data mining is greatly improved. [ABSTRACT FROM AUTHOR]

The global k-means algorithm is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure from suitable initial positions, and employs k-means to minimize the sum of the intra-cluster variances. However the global k-means algorithm sometimes results singleton clusters and the initial positions sometimes are bad, after a bad initialization, poor local optimal can be easily obtained by k-means algorithm. In this paper, we modified the global k-means algorithm to eliminate the singleton clusters at first, and then we apply MinMax k-means clustering error method to global k-means algorithm to overcome the effect of bad initialization, proposed the global Minmax k-means algorithm. The proposed clustering method is tested on some popular data sets and compared to the k-means algorithm, the global k-means algorithm and the MinMax k-means algorithm. The experiment results show our proposed algorithm outperforms other algorithms mentioned in the paper. [ABSTRACT FROM AUTHOR]