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Graphs describe and represent many complex structures in the field of social networks, biological, chemical, industrial and transport systems, and others. These graphs are not only connected but often also k-connected (or at least part of them). Different metrics are used to determine the distance between two nodes in the graph. In this article, we propose a novel metric that takes into account the higher degree of connectivity on the part of the graph (for example, biconnected fullerene graphs and fulleroids). Designed metric reflects the cyclical interdependencies among the nodes of the graph. Moreover, a new component model is derived, and the examples of various types of graphs are presented. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are a family of wireless networks that usually operate with irreplaceable batteries. The energy sources limitation raises the need for designing specific protocols to prolong the operational lifetime of such networks. These protocols are responsible for messages exchanging through the wireless communications medium from the sensors to the base station (sink node). Therefore, the determination of the optimal location of the sink node becomes crucial to assure both the prolongation of the network’s operation and the quality of the provided services. This paper proposes a novel algorithm based on a Particle Swarm Optimization (PSO) approach for designing an energy-aware topology control protocol. The deliverable of the algorithm is the optimal sink node location within a deployment area. The proposed objective function is based on a number of topology control protocol’s characteristics such as numbers of neighbors per node, the nodes’ residual energy, and how they are far from the center of the deployment area. The simulation results show that the proposed algorithm reveals significant effectiveness to both topology construction and maintenance phases of a topology control protocol in terms of the number of active nodes, the topology construction time, the number of topology reconstructions, and the operational network’s lifetime. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach is introduced to compare web sites by analysing their web page content. Each web page can be expressed as a set of entities called MicroGenres, which in turn are abstractions about design patterns and genres for representing the page content. This description is useful for web page and web site classification and for a deeper insight into the web site׳s social context. The web site comparison is useful for extracting patterns which can be used for improving Web search engine effectiveness, the identification of best practices in web site design and of course in the organization of web page content to personalize the web user experience on a web site. The effectiveness of the proposed approach was tested in a real world case, with e-shop web sites showing that a web site can be represented in a high level of abstraction by using MicroGenres, the contents of which can then be compared and given a measure corresponding to web site similarity. This measure is very useful for detecting web communities on the Web, i.e., a group of web sites sharing similar contents, and the result is essential in performing a focused and effective information search as well as minimizing web page retrieval. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19) is rapidly spreading worldwide. Recent studies show that radiological images contain accurate data for detecting the coronavirus. This paper proposes a pre-trained convolutional neural network (VGG16) with Capsule Neural Networks (CapsNet) to detect COVID-19 with unbalanced data sets. The CapsNet is proposed due to its ability to define features such as perspective, orientation, and size. Synthetic Minority Over-sampling Technique (SMOTE) was employed to ensure that new samples were generated close to the sample center, avoiding the production of outliers or changes in data distribution. As the results may change by changing capsule network parameters (Capsule dimensionality and routing number), the Gaussian optimization method has been used to optimize these parameters. Four experiments have been done, (1) CapsNet with the unbalanced data sets, (2) CapsNet with balanced data sets based on class weight, (3) CapsNet with balanced data sets based on SMOTE, and (4) CapsNet hyperparameters optimization with balanced data sets based on SMOTE. The performance has improved and achieved an accuracy rate of 96.58% and an F1- score of 97.08%, a competitive optimized model compared to other related models. [ABSTRACT FROM AUTHOR]

Large graphs are often used to simulate and model complex systems in various research and application fields. Because of its importance, frequent subgraph mining (FSM) in single large graphs is a vital issue, and recently, it has attracted numerous researchers, and played an important role in various tasks for both research and application purposes. FSM is aimed at finding all subgraphs whose number of appearances in a large graph is greater than or equal to a given frequency threshold. In most recent applications, the underlying graphs are very large, such as social networks, and therefore algorithms for FSM from a single large graph have been rapidly developed, but all of them have NP‐hard (nondeterministic polynomial time) complexity with huge search spaces, and therefore still need a lot of time and memory to restore and process. In this article, we present an overview of problems of FSM, important phases in FSM, main groups of FSM, as well as surveying many modern applied algorithms. This includes many practical applications and is a fundamental premise for many studies in the future. This article is categorized under:Algorithmic Development > Association RulesAlgorithmic Development > Structure Discovery [ABSTRACT FROM AUTHOR]

The study of power load forecasting is gaining greater significance nowadays, particularly with the use and integration of renewable power sources and external power stations. Power forecasting is an important task in the planning, control, and operation of utility power systems. In addition, load forecasting (LF) aims to estimate the power or energy needed to meet the required power or energy to supply the specific load. In this article, we introduce, review and compare different power load forecasting techniques. Our goal is to help in the process of explaining the problem of power load forecasting via brief descriptions of the proposed methods applied in the last decade. The study reviews previous research that deals with the design of intelligent systems for power forecasting using various methods. The methods are organized into five groups—Artificial Neural Network (ANN), Support Vector Regression, Decision Tree (DT), Linear Regression (LR), and Fuzzy Sets (FS). This way, the review provides a clear concept of power load forecasting for the purposes of future research and study. [ABSTRACT FROM AUTHOR]

The study of power load forecasting is gaining greater significance nowadays, particularly with the use and integration of renewable power sources and external power stations. Power forecasting is an important task in the planning, control, and operation of utility power systems. In addition, load forecasting (LF) aims to estimate the power or energy needed to meet the required power or energy to supply the specific load. In this article, we introduce, review and compare different power load forecasting techniques. Our goal is to help in the process of explaining the problem of power load forecasting via brief descriptions of the proposed methods applied in the last decade. The study reviews previous research that deals with the design of intelligent systems for power forecasting using various methods. The methods are organized into five groups—Artificial Neural Network (ANN), Support Vector Regression, Decision Tree (DT), Linear Regression (LR), and Fuzzy Sets (FS). This way, the review provides a clear concept of power load forecasting for the purposes of future research and study. [ABSTRACT FROM AUTHOR]

The neutrosophic fuzzy set (NF-set) is a unique hybrid structure that combines the essence of the fuzzy and neutrosophic sets. It is an effective mathematical tool for dealing with indeterminate and inconsistent information in situations where the data are imprecise or vague. This paper defines the concepts of similarity-based information measures, including entropy and cross-entropy, between NF-sets. It is the first time these concepts have been introduced since the NF-sets were defined. We provide their interesting properties through well-proven theorems. In addition, we also propose a novel and efficient algorithm to make multi-criteria decision(s) using these information measures with a clear step-by-step illustration. It overcomes the limitations of the original algorithm when evaluating the criteria in two aspects: qualitative and quantitative. A real-world experiment is then conducted to assist students in choosing the optimal subject group(s) for the Vietnamese national high school graduation examination. Experimental results show that our method outperforms the previous original method when giving recommendations with sixteen correct cases, three acceptable cases, and one noisy case out of twenty real-life cases. Finally, the experimental results are presented visually, analyzed rigorously, and discussed carefully, intending to verify the validity and feasibility of the proposed algorithm. The Python source code for experiments is publicly available on Github. [ABSTRACT FROM AUTHOR]

In real-world scenarios, identifying the optimal number of clusters in a dataset is a difficult task due to insufficient knowledge. Therefore, the indispensability of sophisticated automatic clustering algorithms for this purpose has been contemplated by some researchers. Several automatic clustering algorithms assisted by quantum-inspired metaheuristics have been developed in recent years. However, the literature lacks definitive documentation of the state-of-the-art quantum-inspired metaheuristic algorithms for automatically clustering datasets. This article presents a brief overview of the automatic clustering process to establish the importance of making the clustering process automatic. The fundamental concepts of the quantum computing paradigm are also presented to highlight the utility of quantum-inspired algorithms. This article thoroughly analyses some algorithms employed to address the automatic clustering of various datasets. The reviewed algorithms were classified according to their main sources of inspiration. In addition, some representative works of each classification were chosen from the existing works. Thirty-six such prominent algorithms were further critically analysed based on their aims, used mechanisms, data specifications, merits and demerits. Comparative results based on the performance and optimal computational time are also presented to critically analyse the reviewed algorithms. As such, this article promises to provide a detailed analysis of the state-of-the-art quantum-inspired metaheuristic algorithms, while highlighting their merits and demerits. [ABSTRACT FROM AUTHOR]

Mining association rules plays an important role in decision support systems. To mine strong association rules, it is necessary to mine frequent patterns. There are many algorithms that have been developed to efficiently mine frequent patterns, such as Apriori, Eclat, FP-Growth, PrePost, and FIN. However, these are only efficient with a small number of items in the database. When a database has a large number of items (from thousands to hundreds of thousands) but the number of transactions is small, these algorithms cannot run when the minimum support threshold is also small (because the search space is huge). This thus causes the problem of mining colossal patterns in high dimensional databases. In 2012, Sohrabi and Barforoush proposed the BVBUC algorithm for mining colossal patterns based on a bottom-up scheme. However, this needs more time to check subsets and supersets, because it generates a lot of candidates and consumes more memory to store these. In this paper we propose new, efficient algorithms for mining colossal patterns. Firstly, the CP (Colossal Pattern)-tree is designed. Next, we develop two theorems to rapidly compute patterns of nodes and prune nodes without the loss of information in colossal patterns. Based on the CP-tree and these theorems, an algorithm (named CP-Miner) is proposed to solve the problem of mining colossal patterns. A sorting strategy for efficiently mining colossal patterns is thus developed. This strategy helps to reduce the number of significant candidates and the time needed to check subsets and supersets. The PCP-Miner algorithm, which uses this strategy, is then proposed, and we also conduct experiments to show the efficiency of these algorithms. [ABSTRACT FROM AUTHOR]

On the outbreak of the global COVID-19 pandemic, high-risk and vulnerable groups in the population were at particular risk of severe disease progression. Pregnant women were one of these groups. The infectious disease endangered not only the physical health of pregnant women, but also their mental well-being. Improving the mental health of pregnant women and reducing their risk of an infectious disease could be achieved by using remote home monitoring solutions. These would allow the health of the mother and fetus to be monitored from the comfort of their home, a reduction in the number of physical visits to the doctor and thereby eliminate the need for the mother to venture into high-risk public places. The most commonly used technique in clinical practice, cardiotocography, suffers from low specificity and requires skilled personnel for the examination. For that and due to the intermittent and active nature of its measurements, it is inappropriate for continuous home monitoring. The pandemic has demonstrated that the future lies in accurate remote monitoring and it is therefore vital to search for an option for fetal monitoring based on state-of-the-art technology that would provide a safe, accurate, and reliable information regarding fetal and maternal health state. In this paper, we thus provide a technical and critical review of the latest literature and on this topic to provide the readers the insights to the applications and future directions in fetal monitoring. We extensively discuss the remaining challenges and obstacles in future research and in developing the fetal monitoring in the new era of Fetal monitoring 4.0, based on the pillars of Healthcare 4.0. [ABSTRACT FROM AUTHOR]

This paper introduces a novel algorithm for effective and accurate extraction of non-invasive fetal electrocardiogram (NI-fECG). In NI-fECG based monitoring, the useful signal is measured along with other signals generated by the pregnant women's body, especially maternal electrocardiogram (mECG). These signals are more distinct in magnitude and overlap in time and frequency domains, making the fECG extraction extremely challenging. The proposed extraction method combines the Grey wolf algorithm (GWO) with sequential analysis (SA). This innovative combination, forming the GWO-SA method, optimises the parameters required to create a template that matches the mECG, which leads to an accurate elimination of the said signal from the input composite signal. The extraction system was tested on two databases consisting of real signals, namely, Labour and Pregnancy. The databases used to test the algorithms are available on a server at the generalist repositories (figshare) integrated with Matonia et al. (Sci Data 7(1):1–14, 2020). The results show that the proposed method extracts the fetal ECG signal with an outstanding efficacy. The efficacy of the results was evaluated based on accurate detection of the fQRS complexes. The parameters used to evaluate are as follows: accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and F1 score. Due to the stochastic nature of the GWO algorithm, ten individual runs were performed for each record in the two databases to assure stability as well as repeatability. Using these parameters, for the Labour dataset, we achieved an average ACC of 94.60%, F1 of 96.82%, SE of 97.49%, and PPV of 98.96%. For the Pregnancy database, we achieved an average ACC of 95.66%, F1 of 97.44%, SE of 98.07%, and PPV of 97.44%. The obtained results show that the fHR related parameters were determined accurately for most of the records, outperforming the other state-of-the-art approaches. The poorer quality of certain signals have caused deviation from the estimated fHR for certain records in the databases. The proposed algorithm is compared with certain well established algorithms, and has proven to be accurate in its fECG extractions. [ABSTRACT FROM AUTHOR]

Network alignment, which is also known as user identity linkage, is a kind of network analysis task that predicts overlapping users between two different social networks. This research direction has attracted much attention from the research community, and it is considered to be one of the most important research directions in the field of social network analysis. There are many different models for finding users that overlap between two networks, but most of these models use separate and different techniques to solve prediction problems, with very little work that has combined them. In this paper, we propose a method that combines different embedding techniques to solve the network alignment problem. Each association network alignment technique has its advantages and disadvantages, so combining them together will take full advantage and can overcome those disadvantages. Our model combines three-level embedding techniques of text-based user attributes, a graph attention network, a graph-drawing embedding technique, and fuzzy c-mean clustering to embed each piece of network information into a low-dimensional representation. We then project them into a common space by using canonical correlation analysis and compute the similarity matrix between them to make predictions. We tested our network alignment model on two real-life datasets, and the experimental results showed that our method can considerably improve the accuracy by about 10–15% compared to the baseline models. In addition, when experimenting with different ratios of training data, our proposed model could also handle the over-fitting problem effectively. [ABSTRACT FROM AUTHOR]

To this aim, graph-based representations of news articles are constructed and a graph mining based pattern extraction method is proposed. This article belongs to the Topical Collection: Special Issue on Computational Aspects of Network Science Guests Editors: Apostolos N. Papadopoulos and Richard Chbeir Network Science is a rapidly evolving area with numerous practical applications spanning different fields like telecommunications, biology, sociology, computer science, just to name a few. Then, frequent subgraph mining and sequential rule mining algorithms are adapted for pattern prediction on graph sequences. [Extracted from the article]

Off-grid power systems are often used to supply electricity to remote households, cottages, or small industries, comprising small renewable energy systems, typically a photovoltaic plant whose energy supply is stochastic in nature, without electricity distributions. This approach is economically viable and conforms to the requirements of the European Green Deal and the Fit for 55 package. Furthermore, these systems are associated with a lower short circuit power as compared with distribution grid traditional power plants. The power quality parameters (PQPs) of such small-scale off-grid systems are largely determined by the inverter's ability to handle the impact of a device; however, this makes it difficult to accurately forecast the PQPs. To address this issue, this work compared prediction models for the PQPs as a function of the meteorological conditions regarding the off-grid systems for small-scale households in Central Europe. To this end, seven models—the artificial neural network (ANN), linear regression (LR), interaction linear regression (ILR), quadratic linear regression (QLR), pure quadratic linear regression (PQLR), the bagging decision tree (DT), and the boosting DT—were considered for forecasting four PQPs: frequency, the amplitude of the voltage, total harmonic distortion of the voltage ( T H D u ), and current ( T H D i ). The computation times of these forecasting models and their accuracies were also compared. Each forecasting model was used to forecast the PQPs for three sunny days in August. As a result of the study, the most accurate methods for forecasting are DTs. The ANN requires the longest computational time, and conversely, the LR takes the shortest computational time. Notably, this work aimed to predict poor PQPs that could cause all the equipment in off-grid systems to respond in advance to disturbances. This study is expected to be beneficial for the off-grid systems of small households and the substations included in existing smart grids. [ABSTRACT FROM AUTHOR]

Wireless power transfer (WPT) systems or simultaneous wireless information and power transfer (SWIPT) systems support multiple charging frequency standards and usually transmit two or more frequencies simultaneously. Multi-frequency sinusoidal pulse width modulation (MF-SPWM) technology can output multiple sine wave frequencies at the same time with only one inverter, which effectively reduces the system size and improves compatibility. However, MF-SPWM and dead-time make the MF-inverter highly nonlinearized, with significant effects on the desired frequencies and the total harmonic distortion (THD) of the multi-frequency output. In this paper, a double Fourier series-based method of harmonic analysis for the dual-frequency inverter (DF-inverter) is proposed, which can accurately calculate the magnitude of each of the harmonics in the output of the DF-inverter taking into account the influence of dead-time. The calculation methods of the minimum pulse width and voltage THD of the DF-inverter are provided. An analysis shows that when the dead-time is less than the minimum pulse width, its influence is negligible. Meanwhile, when the dead-time is slightly larger than the minimum pulse width, there is a trade-off between the desired frequency component and the THD. Experimental results are given and they are consistent with the analytical results of the proposed method, and the error is within 2%. [ABSTRACT FROM AUTHOR]

Sequential pattern mining (SPM) plays an important role in data mining, with broad applications such as in financial markets, education, medicine, and prediction. Although there are many efficient algorithms for SPM, the mining time is still high, especially for mining sequential patterns from huge databases, which require the use of a parallel technique. In this paper, we propose a parallel approach named MCM-SPADE (Multiple threads CM-SPADE), for use on a multi-core processor system as a multi-threading technique for SPM with very large database, to enhance the performance of the previous methods SPADE and CM-SPADE. The proposed algorithm uses the vertical data format and a data structure named CMAP (Co-occurrence MAP) for storing co-occurrence information. Based on the data structure CMAP, the proposed algorithm performs early pruning of the candidates to reduce the search space and it partitions the related tasks to each processor core by using the divide-and-conquer property. The proposed algorithm also uses dynamic scheduling to avoid task idling and achieve load balancing between processor cores. The experimental results show that MCM-SPADE attains good parallelization efficiency on various input databases. [ABSTRACT FROM AUTHOR]

Measurement and control of electric power quality (PQ) parameters in off-grid systems has played an important role in recent years. The purpose is to detect or forecast the presence of PQ parameter disturbances to be able to suppress or to avoid their negative effects on the power grid and appliances. This paper focuses on several PQ parameters in off-grid systems and it defines three evaluation criteria that are supposed to estimate the performance of a new forecasting model combining all the involved PQ parameters. These criteria are based on common statistical evaluations of computational models from the machine learning field of study. The studied PQ parameters are voltage, power frequency, total harmonic distortion, and flicker severity. The approach presented in this paper also applies a machine learning based model of random decision forest for PQ forecasting. The database applied in this task contains real off-grid data from long-term one-minute measurements. The hyperparameters of the model are optimized by multiobjective optimization toward the defined evaluation criteria. [ABSTRACT FROM AUTHOR]

Background: Systemic lupus erythematosus (SLE) is a remarkably heterogeneous autoimmune disease. Despite tremendous efforts, our knowledge of serum protein patterns in severe SLE phenotypes is still limited. We investigated the serum protein pattern of SLE, with special emphasis on irreversible organ damage and active lupus nephritis (LN) as assessed by renal Systemic Lupus Erythematosus Disease Activity Index. Methods: We used proximity extension immunoassay (PEA, Proseek Multiplex, Olink) to assess the serum levels of ninety-two inflammation-related proteins in Czech patients with SLE (n = 75) and age-matched healthy control subjects (n = 23). Subgroup analysis was carried out on the basis of organ damage (with/without, 42/33) and biopsyproven LN (with/without, 27/48; active LN, n = 13; inactive LN, n = 14). Results: Of thirty deregulated proteins between SLE and the healthy controls (Pcorr < 0.05), the top upregulated proteins in SLE were sirtuin 2, interleukin 18 (IL18), and caspase 8 (pcorr < 0.0006). Of these, sirtuin 2 and caspase 8 had not yet been reported with SLE. Elevated levels of IL8, CCL2/MCP1, CCL11, and MMP10 (pcorr < 0.05) were detected in patients with organ damage for which the serum levels of CCL11 and MMP10 were particularly informative in organ damage prediction. Comparing patients based on LN, elevated levels of CSF1, sIL15RA, sCD40, sCX3CL1, caspase 8, sIL18R1, bNGF, and GDNF (Pcorr < 0.05) were detected in active LN. Except GDNF, all LN-associated markers showed usefulness in prediction of active renal disease. Conclusions: This highly sensitive PEA analysis identified the serum pattern of SLE, organ damage, and active LN, with many novel candidate proteins detected. Their exact role and suitability as biomarkers in SLE deserve further investigation. [ABSTRACT FROM AUTHOR]

We propose an efficient method for compressing Vietnamese text using n-gram dictionaries. It has a significant compression ratio in comparison with those of state-of-the-art methods on the same dataset. Given a text, first, the proposed method splits it into n-grams and then encodes them based on n-gram dictionaries. In the encoding phase, we use a sliding window with a size that ranges from bigram to five grams to obtain the best encoding stream. Each n-gram is encoded by two to four bytes accordingly based on its corresponding n-gram dictionary. We collected 2.5 GB text corpus from some Vietnamese news agencies to build n-gram dictionaries from unigram to five grams and achieve dictionaries with a size of 12 GB in total. In order to evaluate our method, we collected a testing set of 10 different text files with different sizes. The experimental results indicate that our method achieves compression ratio around 90% and outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a new and robust biometric-based approach to identify head of cattle. This approach used the Weber Local Descriptor (WLD) to extract robust features from cattle muzzle print images (images from 31 head of cattle were used). It also employed the AdaBoost classifier to identify head of cattle from their WLD features. To validate the results obtained by this classifier, other two classifiers ( k -Nearest Neighbor ( k -NN) and Fuzzy- k -Nearest Neighbor (F k -NN)) were used. The experimental results showed that the proposed approach achieved a promising accuracy result (approximately 99.5%) which is better than existed proposed solutions. Moreover, to evaluate the results of the proposed approach, four different assessment methods (Area Under Curve (AUC), Sensitivity and Specificity, accuracy rate, and Equal Error Rate (EER)) were used. The results of all these methods showed that the WLD along with AdaBoost algorithm gave very promising results compared to both of the k -NN and F k -NN algorithms. [ABSTRACT FROM AUTHOR]

Abstract: What is suggested is a new approach to Boolean factor analysis, which is an extension of the previously proposed Boolean factor analysis method: Hopfield-like attractor neural network with increasing activity. We increased its applicability and robustness when complementing this method by a maximization of the learning set likelihood function defined according to the Noisy-OR generative model. We demonstrated the efficiency of the new method using the data set generated according to the model. Successful application of the method to the real data is shown when analyzing the data from the Kyoto Encyclopedia of Genes and Genomes database which contains full genome sequencing for 1368 organisms. [Copyright &y& Elsevier]

In this article, we introduce a novel method for spam detection based on a combination of Bayesian filtering, signature trees, and data compression–based similarity. Bayesian filtering is one of the most popular and most efficient algorithms for dealing with spam detection. The problem with Bayesian filtering is that it is unable to classify any e-mail without doubt and sometimes spam e-mails are classified as regular e-mails. This novel method sorts out this problem by using signature trees and data compression–based similarity. The main result of this article is an up to 99% improvement in spam detection precision using this novel method. [ABSTRACT FROM AUTHOR]

Student evaluation is an essential part of education and is usually done through examinations. These examinations generally use tests consisting of several questions as crucial factors to determine the quality of the students. Test-making can be thought of as a multi-constraint optimization problem. However, the test-making process that is done by either manually or randomly picking questions from question banks still consumes much time and effort. Besides, the quality of the tests generated is usually not good enough. The tests may not entirely satisfy the given multiple constraints such as required test durations, number of questions, and question difficulties. In this paper, we propose parallel strategies, in which parallel migration is based on Pareto optimums, and applyan improved genetic algorithm called a genetic algorithm combined with simulated annealing, GASA, which improves diversity and accuracy of the individuals by encoding schemes and a new mutation operator of GA to handle the multiple objectives while generating multiple choice-tests from a large question bank. The proposed algorithms can use the ability to exploit historical information structure in the discovered tests, and use this to construct desired tests later. Experimental results show that the proposed approaches are efficient and effective in generating valuable tests that satisfy specified requirements. In addition, the results, when compared with those from traditional genetic algorithms, are improved in several criteria including execution time, search speed, accuracy, solution diversity, and algorithm stability. [ABSTRACT FROM AUTHOR]

Mining frequent subgraphs is an important issue in graph mining. It is defined as finding all subgraphs whose occurrences in the dataset are greater than or equal to a given frequency threshold. In recent applications, such as social networks, the underlying graphs are very large. Algorithms for mining frequent subgraphs from a single large graph have been developing rapidly lately. Among all such algorithms, GraMi is considered the state-of-the-art. However, GraMi still consumes a lot of time and memory in the mining of a large graph. In this paper, we propose two effective strategies to optimize the GraMi algorithm, which help to increase performance as well as reduce memory consumption during execution. Firstly, GraMi only lists all frequent subgraphs, without computing the support of each mined subgraph. This is disadvantageous in decision support systems, which require information about the support of all subgraphs. Therefore, we optimize GraMi to compute the support values during the mining process. Secondly, we apply the strategy of sorting all edges in graphs by their frequencies, which means that edges with low frequencies will be mined first, and vice versa. This sorting strategy can reduce the number of possibly infrequent subgraph candidates, especially on large subgraphs that are usually derived from those edges with high frequency. Thirdly, we apply a parallel processing technique, in which each frequent edge is executed simultaneously in a separate thread, and improve our parallel strategy by combination with the sorting strategy. Our experiments were performed on three real datasets and the results showed that the performance, as well as memory requirements, are better than those of the original GraMi algorithm [ABSTRACT FROM AUTHOR]

Using topology in data analysis, known as Topological Data Analysis (TDA), is now a promising new area of data mining research. One of the important and foundational tools of TDA is the Mapper algorithm. During the past two decades, this algorithm has proven its useful and robust abilities in extracting insights and meaningful information from high-dimensional datasets. Nevertheless, several alterations in the choices of parameters, such as lens, cover and clustering, can be used to develop this algorithm. In this paper, we propose the F-Mapper algorithm, based on the foundation of the Mapper algorithm, to solve the problem of automating when dividing cover intervals with an arbitrary percentage of overlap. To clarify the efficiency of this enhanced algorithm, experiments were carried out on three datasets, including the Unit Circle, Reaven and Miller Diabetes, and NKI Breast Cancer. The experimental results will be analyzed and compared with those of the original method, the Mapper algorithm, through the output image and silhouette coefficient score in the evaluation of clustering. • The Mapper algorithm is optimized in choosing cover automatically with random percentage. • The F-Mapper outputs are rather similar to those of the Mapper in the topological sense. • The F-Mapper results are well-clustered based on the silhouette coefficient score. [ABSTRACT FROM AUTHOR]

Introduction Motor imagery (MI) has been shown to be an effective tool in rehabilitation of post-stroke patients. MI can be enhanced by kinesthetic feedback provided by brain-computer-exoskeleton interfaces. Description and analysis of brain activity during the kinesthetic imagery of movements in healthy subjects and post-stroke patients with the subcortical location of the damage was performed in the current study. Methods Seven right-handed healthy volunteers (age 29 [22.0; 35.0] years) and nine post-stroke patients (age 52.0 [36.0; 58.0] years, time since stroke 6.0 [4.0; 13.0] months) were enrolled. They were trained in kinesthetic motor imagery using brain-computer interface (BCI) and a controllable exoskeleton. All participants had 20 training sessions with a duration of up to 15 min. The BCI was controlled using a Bayessian classifier, which was preset to recognize the imagery of left or right hand extension and the rest state. Processing of EEG recordings was performed using the independent component analysis. The digitization of EEG-electrode positions for obtaining their coordinates in the MRI-related system was made using an NBS Eximia Nexstim device for navigational transcranial magnetic stimulation. This study used the AMICA method to find the sources of electrophysiological brain activity during motor imagery. For each component determined, the EEG inverse problem was solved according to the sLORETA method. Results Components that are most frequently identified by the AMICA method were revealed in the same areas in both groups: SIL (the primary somatosensory left cortex), SIR (the primary somatosensory right cortex), PRC (the precuneus), PRM (the premotor left cortex), and SMA (the supplementary motor area). There were differences in the activity characteristics when comparing healthy controls and patients: the μ -rhythm in the somatosensory area was characterized by a lower frequency in the studied poststroke patients than in the healthy subjects; it had a poorly pronounced peak in all mental states and was only slightly suppressed during contralateral hand motor imagery. The rhythm in the α -band was also characterized by a low frequency with a poorly pronounced peak in the precuneus and premotor area; however, unlike in the healthy subjects, this rhythm increased, i.e., was synchronized, during motor imagery. Conclusion This study is an extension of previous investigations of EEGs during motor imagery in healthy subjects. Only two symmetrical sources in the primary somatosensory cortex were previously discussed. This study revealed three more sources: the premotor area, precuneus, and supplementary motor area. The characteristics of the EEG sources during motor imagery are fundamentally different in post-stroke patients comparing to healthy controls. Funding was provided by grant RFMEF N I60715X0128. [ABSTRACT FROM AUTHOR]