Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
46,654
result(s) for
"Big data analysis"
Sort by:
Big data in eHealthcare : challenges and perspectives
\"This book focuses on the different aspects of handling big data in healthcare. It showcases the current state-of-the-art technology used for storing health records and health data models. It also focuses on the research challenges in big data acquisition, storage, management and analysis\"-- Provided by publisher.
Book
Cluster Analysis and Discriminant Analysis for Determining Post-Earthquake Road Recovery Patterns
The transport network in eastern Japan was severely damaged by the 2011 Tohoku earthquake. To understand the road recovery conditions after a large earthquake, a large amount of time is needed to collect information on the extent of the damage and road usage. In our previous study, we applied cluster analysis to analyze the data on driving vehicles in Fukushima prefecture to classify the road recovery conditions among municipalities within the first six months after the earthquake. However, the results of the cluster analysis and relevant factors affecting road recovery from that study were not validated. In this study, we proposed a framework for determining post-earthquake road recovery patterns and validated the cluster analysis results by using discriminant analysis and observing them on a map to identify their common characteristics. In addition, our analysis of objective data reflecting regional characteristics showed that the road recovery conditions were similar according to the topography and the importance of roads.
Journal Article
Data mining approaches for big data and sentiment analysis in social media
\"This book explores the key concepts of data mining and utilizing them on online social media platforms, offering valuable insight into data mining approaches for big data and sentiment analysis in online social media and covering many important security and other aspects and current trends\"-- Provided by publisher.
Book
Solid‐Phase Upcycling Toward the Production of Ultrahigh‐Loading Single‐Atom Catalysts
The recovery of valuable transition metals from deactivated catalysts is crucial for alleviating the challenges of resource scarcity and environmental pollution. Guided by AI‐powered big data analysis, we identified an important research gap in the sustainable recovery of early transition metals and proposed a solid‐phase upcycling strategy to transform waste catalysts into highly valuable single‐atom catalysts (SACs). This involves a heat‐induced redispersion of metal aggregates into single atoms on the polycrystalline carbon nitride (PCN) support, producing highly active M1‐PCN SACs up to 20 wt% (M = Cu, Fe, Co, and Ni). Subsequent techno‐economic analysis confirms a two‐thirds reduction in production cost and greenhouse gas emissions compared to conventional hydrometallurgical and pyrometallurgical processes, thus paving a new path in the development of sustainable technologies for metal recovery. Guided by AI‐driven big‐data analysis, this study reports a solid‐phase upcycling strategy to transform spent catalysts into ultrahigh‐loading single‐atom catalysts (SACs) with superior catalytic performance, addressing the critical imbalance between metal demand and resource conservation in catalysis.
Journal Article
Networks of networks in biology : concepts, tools and applications
Biological systems are extremely complex and have emergent properties that cannot be explained or even predicted by studying their individual parts in isolation. The reductionist approach, although successful in the early days of molecular biology, underestimates this complexity. As the amount of available data grows, so it will become increasingly important to be able to analyse and integrate these large data sets. This book introduces novel approaches and solutions to the Big Data problem in biomedicine, and presents new techniques in the field of graph theory for handling and processing multi-type large data sets. By discussing cutting-edge problems and techniques, researchers from a wide range of fields will be able to gain insights for exploiting big heterogonous data in the life sciences through the concept of 'network of networks'.
BOOK
Parallelly Running and Privacy-Preserving k-Nearest Neighbor Classification in Outsourced Cloud Computing Environments
Classification is used in various areas where k-nearest neighbor classification is the most popular as it produces efficient results. Cloud computing with powerful resources is one reliable option for handling large-scale data efficiently, but many companies are reluctant to outsource data due to privacy concerns. This paper aims to implement a privacy-preserving k-nearest neighbor classification (PkNC) in an outsourced environment. Existing work proposed a secure protocol (SkLE/SkSE) to compute k data with the largest/smallest value privately, but this work discloses information. Moreover, SkLE/SkSE requires a secure comparison protocol, and the existing protocols also contain information disclosure problems. In this paper, we propose a new secure comparison and SkLE/SkSE protocols to solve the abovementioned information disclosure problems and implement PkNC with these novel protocols. Our proposed protocols disclose no information and we prove the security formally. Then, through extensive experiments, we demonstrate that the PkNC applying the proposed protocols is also efficient. Especially, the PkNC is suitable for big data analysis to handle large amounts of data, since our SkLE/SkSE is executed for each dataset in parallel. Although the proposed protocols do require efficiency sacrifices to improve security, the running time of our PkNC is still significantly more efficient compared with previously proposed PkNCs.
Journal Article
Big Data Analysis Supports the Research and Pilot Application of Distribution Network Planning Technology
The construction of the enterprise data center has met the automatic access of the massive data generated by the operation and management of the distribution network, and realized the integration and unification of the professional data of dispatching, operation inspection, and marketing, providing big data technology to support the planning and management of the distribution network The application of new technical conditions such as broadband carrier has greatly improved the authenticity, reliability and timeliness of big data in the distribution network. Through the design of distribution network monitoring and early warning, diagnosis management, quality assessment, portrait research, power outage optimization, load forecasting and other intelligent application models, the accurate processing of distribution data is realized, and the state of the distribution network can be accurately evaluated. Provide technical support for grid structure optimization. Based on this research background, papers, articles have studied and proposed big data-based distribution network planning, planning auxiliary decision-making system: Based on unified technical principles, a unified planning platform is adopted to realize unified project construction. Practice shows that the plan, the auxiliary decision-making system has improved the quality of planning scheme preparation, improved the review effect, and raised the lean level of investment decision-making.
Journal Article
Handbook of research on opinion mining and text analytics on literary works and social media
\"This book uses artificial intelligence and big data analytics to conduct opinion mining and text analytics on literary works and social media, focusing on theories, method, applications and approaches of data analytic techniques that can be used to extract and analyze data from literary books and social media, in a meaningful pattern\"-- Provided by publisher.
Book
Comparative study of microarray and experimental data on Schwann cells in peripheral nerve degeneration and regeneration: big data analysis
A Schwann cell has regenerative capabilities and is an important cell in the peripheral nervous system. This microarray study is part of a bioinformatics study that focuses mainly on Schwann cells. Microarray data provide information on differences between microarray-based and experiment-based gene expression analyses. According to microarray data, several genes exhibit increased expression (fold change) but they are weakly expressed in experimental studies (based on morphology, protein and mRNA levels). In contrast, some genes are weakly expressed in microarray data and highly expressed in experimental studies; such genes may represent future target genes in Schwann cell studies. These studies allow us to learn about additional genes that could be used to achieve targeted results from experimental studies. In the current big data study by retrieving more than 5000 scientific articles from PubMed or NCBI, Google Scholar, and Google, 1016 (up- and downregulated) genes were determined to be related to Schwann cells. However, no experiment was performed in the laboratory; rather, the present study is part of a big data analysis. Our study will contribute to our understanding of Schwann cell biology by aiding in the identification of genes. Based on a comparative analysis of all microarray data, we conclude that the microarray could be a good tool for predicting the expression and intensity of different genes of interest in actual experiments.
Journal Article