In this paper, we proposed sparse deep nonnegative matrix factorization models to analyze complex data for more accurate classification and better feature interpretation. However, these embedded approaches can only be applied to a small subset of machine learning models. The Journal Impact Quartile of Journal of Big Data Analytics in Transportation is still under caculation.The Journal Impact of an academic journal is a scientometric Metric that reflects the yearly average number of citations that recent articles published in a given journal received. Finally, we identify the potential challenges and future research directions in location prediction. Conference on Soft Computing & Machine Intelligence (ISCMI 2015) Hong Kong, China, 2015 International Conference on Computing Techniques and Mechanical Engineering (ICCTME'2015) Oct. 1-2, 2015 Phuket (Thailand) Phuket, Thailand, Conference on Systems Engineering Management & Innovation Washington DC, United States of America, 2015 International Conference on Computer, Electrical & Electronics Engineering (CEEE’15) Phuket, Thailand, nternational Conference on Engineering Technologies and Big Data Analytics (ETBDA’2015) Oct. 5-6, 2015 Bangkok (Thailand) Bangkok, Thailand, Big Data Analytics, Management, and Innovation 2015 Conference Boston, United States of America, 2015 The Fifth ASE International Conference on Big Data Kaohsiung, Taiwan, 2015 5th International Conference on Computer and Software Modeling - ICCSM 2015 Chengdu, China, The Second International Conference on Soft Computing and Data Mining (SCDM 2015 - AISC Springer) Malacca, Malaysia, 3rd Annual Congress of the European Society for Translational Medicine (EUSTM-2015) Vienna, Austria, 2015 The 2nd Multidisciplinary International Social Networks Conference Matsuyama, Japan, 3rd International Conference Recent treads in Engineering and Technology (ICRET'2015) Sept. 2-3, 2015 Istanbul (Turkey) Istanbul, Turkey, 2015 IEEE International Symposium on INnovations in Intelligent SysTems and Applications - INISTA 2015 Madrid, Spain, 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) Noida, India, 2015 1st International Conference on Next Generation Computing Technologies Dehradun, India, 2015 International Conference on Energy, Signal Processing and Computer Science (ESPCS'15) Istanbul, Turkey, ICBTS 2015 International Technology Logistics and Supply Chain Research Conference in London London, United Kingdom, Cybersecurity, Governance, and Network 2015 Conference Washington DC, United States of America, International conference on Innovative Engineering Technologies (ICIET'2015) August 7-8, 2015 Bangkok (Thailand) Bangkok, Thailand, 2015 International Conference on Data Mining, Electronics and Information Technology (DMEIT'15) Pattaya, Thailand, KDD 2015 Workshop on Interactive Data Exploration and Analysis Sydney, Australia, The 8th International Conference on Advanced Computer Theory and Engineering (ICACTE 2015) Berlin, Germany, 2015 7th International Conference on Education Technology and Computer (ICETC 2015) Berlin, Germany, 2015 4th International Conference on Advancements in Information Technology - ICAIT 2015 Toronto, Canada, This page was last updated on December 05, 2020. We also provide a summary of the Bayesian methods' applications toward these viruses' studies, where several important and useful results have been discovered. The amalgamation of time and structural information makes the method achieve prediction results that are more accurate. This response can be a robot move, an answer to a question, etc. 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. Data mining tools can answer business questions that traditionally were time consuming to resolve. 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. 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, … Therefore, although more challenging, it is also more practical to use implicit feedback for recommender systems. It is the main venue for a wide range of researchers and readers from computer science, network science, social sciences, mathematical sciences, medical and biological sciences, financial, management and political sciences. Our activation function is “sparse”, in that only two of the four possible outputs are active at a given time. Our aim in this research was to examine the dependencies between features and select the optimal feature set with respect to the original data structure. Nowadays, twitter is more popular because of its real-time nature. In terms of per-label accuracy, the single-label method has the best performance, although some multi-label methods approach the performance of single-label. These rules are further used to decompose the solving space from coarse granules to the optimal fine granules with a convergent and automated process. The NNs are iteratively trained as observational data is updated. The journal examines the challenges facing big data today and going forward including, but not limited to: data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing platforms; distributed file systems and databases; and … Thus, NNBCA provides a better classification result than other methods. First, in the conventional Random Walk Restart Heterogeneous (RWRH) algorithm, the computational method simply converts the circRNA/miRNA similarity network into the transition probability matrix; in contrast, we take the influence of the neighbor of the node in the network into account, which can suggest or stress some potential associations. To guarantee the safety and sustainability of electric power systems, massive electric power data need to be processed and analyzed quickly to make real-time decisions. In this paper, we identify the key … ; High Visibility: to be covered in Scopus. It has huge impacts on data-related problems. We developed a new feature-selection method to address this challenge. On the baseline network for the MNIST dataset, having two hidden layers, our activation function improves the validation accuracy from 0.09 to 0.97 compared to the well-known ReLU activation. For detecting the target event, a classifier is devised based on different combinations of statistical features such as the position of the keyword in a tweet, length of a tweet, the frequency of hashtag, and frequency of user mentions and the URL. Auxo organizes temporal graph data in spatio-temporal chunks. Such a representation learning problem is referred to as network embedding, and it has attracted significant attention in recent years. However, the combined impact of the storage, delivery, and sensors used in various types of edge devices in this environment is producing volumes of high-dimensional big data that are increasingly pervasive and redundant. Additionally, different classifiers such as Artificial Neural Networks (ANN), decision tree, and K-Nearest Neighbor (KNN) are compared by using these two features. Then, the improved DPC algorithm is used to construct the initial decomposition solving space with multi-granularity theory. Our model allows end-to-end learning from the raw sentences in the dataset, without trimming or reconstructing them. 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. Submit an article Journal homepage. Both processes require either sifting through an immense amount of material, or intelligently probing it to find where the value resides. Firstly, the DPC algorithm is modified to nullify its essential defects such as the difficulty of locating correct clustering centers and classifying them accurately. The model is then used to analyze the challenges that multi-class classification presents and to highlight possible future enhancements to multi-class classification accuracy. Data mining with big data Abstract: Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. 3,518 Views 42 CrossRef citations to date Altmetric Articles ... engineering, education and other areas. Our evaluation results show that (1) each of the popular image captchas that we study is vulnerable to our attacks; (2) our attacks yield the highest captcha-breaking success rate compared with state-of-the-art methods in almost all scenarios; and (3) our attacks achieve almost as high a success rate as human labor while being much faster. Scalable graph data mining methods are getting increasingly popular and necessary due to increased graph complexities. Although there are challenges involved in applying deep learning techniques to clinical data, it is still worthwhile to look forward to a promising future for deep learning applications in clinical big data in the direction of precision medicine. The first part, “quantum tools”, presented some of the fundamentals and introduced several quantum tools based on known quantum search algorithms. In recent years, huge amounts of structured, unstructured, and semi-structured data have been generated by various institutions around the world and, collectively, this heterogeneous data is referred to as big data. People often interacted with real-time events such as earthquakes and floods through twitter. 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. However, most studies ignore negative influences among individuals and groups. Existing genome inferences have relatively high computational complexity with the input of tens of millions of SNPs and human traits. 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. 1) The Google Ranking of data mining and analysis … 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. Big data analysis of economic news: Hints to forecast macroeconomic indicators Show all authors. Big Data and Cognitive Computing (ISSN 2504-2289) is an international, scientific, peer-reviewed, open access journal of big data and cognitive computing published quarterly online by MDPI.. Open Access —free for readers, with article processing charges (APC) paid by authors or their institutions. List of Scopus indexed Journals … Based on our evaluation, we identify some design flaws in these popular schemes, along with some best practices and design principles for more secure captchas. We also analyzed the computing complexity of our framework to demonstrate their efficiency. 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. In this paper, we propose automatic breadth searching and attention searching adjustment approaches to further speedup randomized wrapper based feature selection. Then, with respect to accuracy, we propose a Tensor-Fast Convolutional Neural Network (TF-CNN) algorithm based on deep learning, which is suitable for high-dimensional big data analysis in the pervasive edge computing environment. The above two feature extraction operations are based on the LSTM networks and use their outputs. Experimental results show that the proposed TFNR algorithm performs well in network vertex classification and visualization tasks on three real citation network datasets. ABSTRACT . Big Data Analytics and Deep Learning are two high-focus of data science. 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. For the CIFAR-10 dataset, we use a deep baseline network that achieves 0.78 validation accuracy with 20 epochs but overfits the data. At the time of the disaster, detecting a target event is a challenging task. In this framework, we make use of random Fourier features to map the preference matrix into the feature matrix, soon afterwards, utilize the traditional k-means approach to cluster preference data in the transformed feature space. Compared with traditional preference clustering, our method solves the problem of insufficient memory and greatly improves the efficiency of the operation. Data mining techniques such as decision trees, classification, and clustering can be used to solve the problem of Big Data. Our dataset based simulation shows that our SCPD algorithm is effective and efficient to disseminate the authorized content in IOSNs. Healthcare insurance fraud has caused billions of dollars in losses in public healthcare funds around the world. Findings indicate that this new algorithm provides a good classification result without artificially selecting the neighborhood parameter. To tackle this problem, many main memory-based methods were proposed, which proved to be inefficient as the data size grew exponentially over time. 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