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big data mining and analytics journal impact factor

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With this method, the adversary can predict the unobserved genomes or traits of targeted individuals in a family genomic dataset where some individuals' genomes and traits are observed, relying on SNP-trait association from Genome-Wide Association Study (GWAS), Mendel's Laws, and statistical relations between SNPs. In this paper, a preconditioned VarDA model is presented, it is based on a reduced background error covariance matrix. However, the algorithms proposed on skyline community search can only work in the special case that the nodes have different values on each attribute, and the computation complexity degrades exponentially as the number of attributes increases. The benchmark will be beneficial for the development of symmetric protein docking algorithms. Results show that, compared with existing approaches, our proposed techniques can locate a more meaningful set of features with a high efficiency. and non-traditional mining methods (neural networks, deep … We also provide a summary of the Bayesian methods' applications toward these viruses' studies, where several important and useful results have been discovered. Our activation function is “sparse”, in that only two of the four possible outputs are active at a given time. ; Rapid Publication: manuscripts are peer-reviewed and a first … With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. First, we formulate the issue as a high-dimensional big data management problem and test different transmission rates to acquire the best QoE. Therefore, determining the symmetric oligomeric structure of subunits is crucial to investigate the molecular mechanism of the related processes. At the time of the disaster, detecting a target event is a challenging task. Second, we propose simple yet powerful attack frameworks against each of these categories of image captchas. We also examine the underground market for captcha-solving services, identifying 152 such services. This response can be a robot move, an answer to a question, etc. Traditional collaborative filtering methods such as matrix factorization, which regards user preferences as a linear combination of user and item latent vectors, have limited learning capacities and suffer from data sparsity and the cold-start problem. Then, we propose an approach to publish genomic data with differential privacy guarantee. However, using RSS data for localization needs to solve a fundamental problem, that is, how accurate are these methods? The proposed benchmark data set is available for download at In this paper, we propose a novel deep hybrid recommender system framework based on auto-encoders (DHA-RS) by integrating user and item side information to construct a hybrid recommender system and enhance performance. Experimental results are provided assuming observed values provided by sensors from positions mainly located on roofs of buildings. Spanning the life sciences, social sciences, engineering, physical and mathematical sciences, Big Data Analytics aims to provide a platform for … OMICS International organizes International conferences, World congresses, and Annual meetings in association with organizing committees across the globe. A preliminary docking test on the targets of cyclic groups symmetry with MZDOCK indicated that symmetric multimer docking remains challenging. The significance of our discovery has two folds: First, we present a general expression for localization error data analytics, which can explain and predict the accuracy of range-based localization algorithms; second, the further study on the general analytics expression and its minimum can be used to optimize current localization algorithms. The rapid progress and plummeting costs of human-genome sequencing enable the availability of large amount of personal biomedical information, leading to one of the most important concerns—genomic data privacy. Big data analytics and data mining are techniques used to analyze data and to extract hidden information. With the development of Chinese international trade, real-time processing systems based on ship trajectory have been used to cluster trajectory in real-time, so that the hot zone information of a sea ship can be discovered in real-time. For the CFD dataset, we show that the RReLU activation can reduce the number of epochs from 100 (using ReLU) to 10 while obtaining the same levels of performance. The exponentially increasing amounts of data being generated each year make getting useful information from that data more and more critical. Due to the inevitable measurement error, the analytics on the error data is critical to evaluate localization methods and to find the effective ones. Then, considering the wide range of clinical research and the diversity of data types, several deep learning applications for clinical data are illustrated: auxiliary diagnosis, prognosis, early warning, and other tasks. From the result, it is evident that the combination of frequency of hashtag and position of keyword features provides good classification results than the other combinations of features. The above two feature extraction operations are based on the LSTM networks and use their outputs. ... A survey of text … In order to further enhance the performance of machine learning based IDS, we propose the Big Data based Hierarchical Deep Learning System (BDHDLS). In the age of big data, services in the pervasive edge environment are expected to offer end-users better Quality-of-Experience (QoE) than that in a normal edge environment. Impact Factor: 2.673 ℹ Impact Factor: 2019: 2.673 The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. The primary objective of IJDMTA is to be an authoritative International forum for delivering both theoretical and innovative applied researches in the data mining concepts, to implementations. Relation classification is a crucial component in many Natural Language Processing (NLP) systems. ... How the Journal Impact Factor(JIF) and H-Index are Calculated? : Perspectives on Data Mining. September 2019, issue 2; July 2019, issue 1; Volume 7 February - June 2019. Healthcare insurance fraud has caused billions of dollars in losses in public healthcare funds around the world. They also do not take into account the different medication stages of the same chronic disease, which is of great help when conducting healthcare insurance fraud detection and reducing healthcare costs. For the CIFAR-10 dataset, we use a deep baseline network that achieves 0.78 validation accuracy with 20 epochs but overfits the data. As real-world graphs are often evolving over time, interest in analyzing the temporal behavior of graphs has grown. The content copies delivered to a user may bring rewards to the CP if the content is adopted by the user. In this paper, we show how electric power data can be managed by using HBase, a distributed database maintained by Apache. In this paper, to achieve high QoE, we propose a QoE model for evaluating the qualities of services in the pervasive edge computing environment. Finally, we summarize the main findings based on the two taxonomies, analyze their usefulness, and discuss future directions in this area. To improve computation efficiency, we then propose a dimension reduction based algorithm, called P-MICS, using the maximum entropy method. ... Perspe ctives on Data Mining. The skyline community is defined as a maximal k-core that satisfies some influence constraints, which is very useful in depicting the communities that are not dominated by other communities in multi-valued networks. From the experimental results, it is clear that the proposed hybrid clustering algorithm is more accurate, and has better precision, recall, and F-measure values. Through mathematical techniques, the key factors that affect the accuracy of RSS-based localization methods are revealed, and the analytics expression that discloses the proportional relationship between the localization accuracy and these factors is derived. Moreover, we exploit linear reconstruction of neighborhood nodes to enable the model to get more structural information. Special Issue: Social data analytics in medicine and healthcare. Second, graph splitting further improves the worst-case query time, and reduces the performance variance introduced by splitting operations. With vast amounts of data being generated daily and the ever increasing interconnectivity of the world's internet infrastructures, a machine learning based Intrusion Detection Systems (IDS) has become a vital component to protect our economic and national security. Scalable graph data mining methods are getting increasingly popular and necessary due to increased graph complexities. Recently, many studies have shown that circRNAs can be regarded as micro RNA (miRNA) sponges, which are known to be associated with certain diseases. We first give an overview of a semantic parsing system, then we summary a general way to do semantic parsing in statistical learning. In this paper, we propose a novel bidirectional recurrent neural network architecture (using Long Short-Term Memory, LSTM, cells) for relation classification, with an attention layer for organizing the context information on the word level and a tensor layer for detecting complex connections between two entities. In particular, healthcare insurance fraud in chronic diseases is especially rampant. Intelligent data analysis provides powerful and effective tools for problem solving in a variety of business modelling tasks. Firstly, based on the analysis of the characteristics of clinical data, various types of clinical data (e.g., medical images, clinical notes, lab results, vital signs, and demographic informatics) are discussed and details provided of some public clinical datasets. The second part, “quantum classification algorithms”, introduces several classification problems that can be accelerated by using quantum subroutines and discusses the quantum methods used for classification. Such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. ... Their factor model was applied to a large data set of about 200 macroeconomic, financial and surveys indicators. We first derive Social Connection Pattern (SCP) data to handle the challenging opportunistic connections in IOSNs and statistically analyze the interest distribution of the users contacted or connected. DHA-RS combines stacked denoising auto-encoders with neural collaborative filtering, which corresponds to the process of learning user and item features from auxiliary information to predict user preferences. The health industry sector has been confronted by the need to manage the big data being produced by various sources, which are well known for producing high volumes of heterogeneous data. Our findings shed light on understanding the scale, impact, and commercial landscape of the underground market for captcha solving. There are many algorithms for solving complex problems in supervised manner. The NNs are iteratively trained as observational data is updated. F1000 Biology Reports DMM Disease Models and Mechanisms. In recent years, Spark has emerged to be the De Facto industry standard with its distributed in-memory computing capability. The recently proposed unsupervised deep learning models ignore the labels information. This article presents a comprehensive overview of studies that employ deep learning methods to deal with clinical data. We developed a new feature-selection method to address this challenge. The experimental results of vertex classification on two real-world network datasets demonstrate that SLLDNE outperforms the other state-of-the-art methods. Data mining tools can answer business questions that traditionally were time consuming to resolve. This algorithm can perform adaptive time-interval clustering according to the size of the real-time ship trajectory data stream, so that a ship's hot zone information can be found efficiently and in real-time. In this paper, we propose a novel automatic background subtraction algorithm for urban surveillance systems in which the computer can automatically renew an image as the new background image when no object is detected. Third, we systematically evaluate our attack frameworks against 10 popular real-world image captchas, including captchas from,, and This paper aims to analyze some of the different analytics methods and tools which can be applied to big data, as well as the opportunities provided by the application of big data analytics in various decision domains. GEORGE M. GRANADOS, RENATO DAN A. PABLO II. Herein, we propose Auxo, a novel temporal graph management system to support temporal graph analysis. Our Group organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members. However, existing clustering algorithms, such as Such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. Data mining techniques such as decision trees, classification, and clustering can be used to solve the problem of Big Data. List of major data mining and related journals, List of major data mining and related conferences. However, actual network structures are complicated which means shallow models cannot obtain the high-dimensional nonlinear features of the network well. Auxo organizes temporal graph data in spatio-temporal chunks. Managing massive electric power data is a typical big data application because electric power systems generate millions or billions of status, debugging, and error records every single day. We test our activation function on the standard MNIST and CIFAR-10 datasets, which are classification problems, as well as on a novel Computational Fluid Dynamics (CFD) dataset which is posed as a regression problem. Wrapper based methods can select features independently from machine learning models but they often suffer from a high computational cost. The objectives of IJDATS are to promote discussions, deliberations and debates on different data analysis principles, architectures, techniques, methodologies, models, as well as the appropriate strategies and applications for various decision-making environments. Location prediction is the key technique in many location based services including route navigation, dining location recommendations, and traffic planning and control, to mention a few. First, we prove that, under the independent cascade model considering positive and negative influences, MPINS is APX-hard. Big data has emerged as an important area of study for both practitioners and researchers. This is a review of quantum methods for machine learning problems that consists of two parts. Journal Citation Reports (Clarivate Analytics, 2020) By extending one-layer model into multi-layer one with sparsity, we provided a hierarchical way to analyze big data and extract hidden features intuitively due to nonnegativity. OMICS International congresses include inspirational and informative sessions and presentations that enhance and update information about latest and current happenings in science, technology and Management disciplines. Focusing on the structural model uncertainty, we propose a framework for integration NN with the physical models by DA algorithms, to improve both the assimilation process and the forecasting results. Content of this site is available under  Creative Commons Attribution 4.0 License Copyright © Journal Citation Reports (Clarivate Analytics, 2020) 5-Year Impact Factor: 1.747 ℹ Five-Year Impact Factor: 2019: 1.747 Extensive experiments on real-world datasets demonstrate the efficiency and effectiveness of our algorithms. International Journal of Data Mining Techniques and Applications (IJDMTA) is a peer-reviewed bi-annual journal that publishes high-quality papers on all aspects of IJDMTA. Another important factor in our proposed method is that it can perform even in the absence of class labels. Empirical results on real-world networks show that the proposed algorithm can achieve higher accuracy prediction results in dynamic networks in comparison to other algorithms. The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. The main DA models used here are the Kalman filter and the variational approaches. We also explore the conceptual architecture of big data analytics for healthcare which involves the data gathering history of different branches, the genome database, electronic health records, text/imagery, and clinical decisions support system. Hence, usage of two features, namely, frequency of hashtag and position of the earthquake keyword reduces the event's detection time. Big Data Analytics is a multi-disciplinary open access, peer-reviewed journal, which welcomes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of big data science analytics. Microblogs, such as facebook and twitter, have much attention among the users and organizations. Previous shallow learning and deep learning strategies adopt the single learning model approach for intrusion detection. People often interacted with real-time events such as earthquakes and floods through twitter. In this article, we aim to provide a thorough review of the Bayesian-inference-based methods applied to Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), and Human Immunodeficiency Virus (HIV) studies with a focus on the detection of the viral mutations and various problems which are correlated to these mutations. To tackle these problems, some authors have considered the integration of a deep neural network to learn user and item features with traditional collaborative filtering. Experimental results show that the DAC-Stream algorithm improves the clustering effect and accelerates data processing compared with the fixed-time-interval clustering algorithm based on density grid (called DC-Stream). Unlike approaches using the Nystr¨om method, which randomly samples the training examples, we make use of random Fourier features, whose basis functions (i.e., cosine and sine ) are sampled from a distribution independent from the training sample set, to cluster preference data which appears extensively in recommender systems. Our system consists of clients, HBase database, status monitors, data migration modules, and data fragmentation modules.

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