Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
88
result(s) for
"Datamining"
Sort by:
Big data : a guide to big data trends, artificial intelligence, machine learning, predictive analytics, internet of things, data science, data analytics, business intelligence, and data mining
In this book, we will investigate big data from a bird's-eye view, covering the subject from a beginner's perspective and introducing its many applications. This will include not only mundane topics like targeted advertising but also an exploration of machine learning and artificial intelligence. Many of the applications of big data have been incorporated into business intelligence and data analytics, and the process of data mining. These topics will be investigated in this book. --back cover
Book
Data mining with decision trees : theory and applications
This is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique.
Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. The area is of great importance because it enables modeling and knowledge extraction from the abundance of data available. Both theoreticians and practitioners are continually seeking techniques to make the process more efficient, cost-effective and accurate. Decision trees, originally implemented in decision theory and statistics, are highly effective tools in other areas such as data mining, text mining, information extraction, machine learning, and pattern recognition. This book invites readers to explore the many benefits in data mining that decision trees offer:
eBook
TwitterSensing: An Event-Based Approach for Wireless Sensor Networks Optimization Exploiting Social Media in Smart City Applications
Modern cities are subject to periodic or unexpected critical events, which may bring economic losses or even put people in danger. When some monitoring systems based on wireless sensor networks are deployed, sensing and transmission configurations of sensor nodes may be adjusted exploiting the relevance of the considered events, but efficient detection and classification of events of interest may be hard to achieve. In Smart City environments, several people spontaneously post information in social media about some event that is being observed and such information may be mined and processed for detection and classification of critical events. This article proposes an integrated approach to detect and classify events of interest posted in social media, notably in Twitter, and the assignment of sensing priorities to source nodes. By doing so, wireless sensor networks deployed in Smart City scenarios can be optimized for higher efficiency when monitoring areas under the influence of the detected events.
Journal Article
Practical text mining and statistical analysis for non-structured text data applications
Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis.Winner of a 2012 PROSE Award in Computing and Information Sciences from the Association of American Publishers.
eBook
One for All? Hitting Multiple Alzheimer's Disease Targets with One Drug
HIGHLIGHTS Many AD target combinations are being explored for multi-target drug design.New databases and models increase the potential of computational drug designLiraglutide and other antidiabetics are strong candidates for repurposing to AD.Donecopride a dual 5-HT/AChE inhibitor shows promise in pre-clinical studies Alzheimer's Disease is a complex and multifactorial disease for which the mechanism is still not fully understood. As new insights into disease progression are discovered, new drugs must be designed to target those aspects of the disease that cause neuronal damage rather than just the symptoms currently addressed by single target drugs. It is becoming possible to target several aspects of the disease pathology at once using multi-target drugs (MTDs). Intended as an introduction for non-experts, this review describes the key MTD design approaches, namely structure-based, in silico, and data-mining, to evaluate what is preventing compounds progressing through the clinic to the market. Repurposing current drugs using their off-target effects reduces the cost of development, time to launch, and the uncertainty associated with safety and pharmacokinetics. The most promising drugs currently being investigated for repurposing to Alzheimer's Disease are rasagiline, originally developed for the treatment of Parkinson's Disease, and liraglutide, an antidiabetic. Rational drug design can combine pharmacophores of multiple drugs, systematically change functional groups, and rank them by virtual screening. Hits confirmed experimentally are rationally modified to generate an effective multi-potent lead compound. Examples from this approach are ASS234 with properties similar to rasagiline, and donecopride, a hybrid of an acetylcholinesterase inhibitor and a 5-HT4 receptor agonist with pro-cognitive effects. Exploiting these interdisciplinary approaches, public-private collaborative lead factories promise faster delivery of new drugs to the clinic.
Journal Article
Knowledge Graph in Smart Education: A Case Study of Entrepreneurship Scientific Publication Management
In recent years, with the rapid growth of science and innovation, plenty of constantly-updated scientific achievements containing innovative knowledge can be acquired and used to solve problems. However, most undergraduate students and non-researchers cannot use them efficiently. In traditional teacher-centric education, education for sustainability is often marginalized and the interdisciplinary demand is neglected. Additionally, it fails to provide education for learners to connect abstract knowledge with actual world problems. This paper presents the design of a scientific publication management model to integrate scientific metadata based on the knowledge graph and data analysis technologies. Based on this model, an interdisciplinary transregional multiple application platform could be realized for scientific resource retrieval and analysis, the purpose of which is to enhance scientific retrieval efficiency and reduce learning difficulty in the scientific domains and encourage non-researchers to utilize scientific resources in their study and work. Finally, to evaluate this model, the use of the case of an entrepreneurship scientific publication management prototype system was implemented. This work not only favors student’s learning for sustainability through analysis and knowledge management functions, but also promotes their awareness, comprehensive thinking, and the skills to deal with the issues of sustainability in their future work.
Journal Article
Taxonomic diversity in the global wheat phyllosphere mycobiome – a meta analysis
Wheat ( Triticum aestivum L. ) is a major crop grown on all continents. Due to environmental concerns, it is desirable to reduce the inputs of both chemical pesticides and inorganic fertilizers. However, yield reduction must be expected when switching to low-input systems. To mitigate such losses, the use of natural or introduced microbiomes may provide the key to maintaining sustainable yield. Phyllosphere fungi, both endophytic and phylloplane-associated, colonize aboveground plant structures, some of which have the potential to mitigate biotic and abiotic stressors. A first step toward realizing the potential of the wheat microbiome is to map the current knowledge on wheat phyllosphere fungi. This meta-analysis aims to map the diversity and abundance of fungal taxa associated with the wheat phyllosphere across global wheat-producing areas. To this end, we searched previous published literature and retrieved fungal community data from relevant studies. Retrieved studies included both culturing-based and metabarcoding amplicon sequence-based studies. We retrieved and analyzed 33 studies from five regions across the world, which differed greatly in their taxonomic composition. Across all regions, we found that while the majority of identified genera were unique to individual studies, some genera occurred across all five wheat growing regions, specifically Alternaria, Aspergillus, Bipolaris, Candida, Chaetomium, Cladosporium, Epicoccum, Fusarium, Nigrospora, Penicillium, Pyrenophora, Stemphylium and Trichoderma. Furthermore, we identified that while community composition differed between wheat growing regions, the identification method used was the most significant factor determining the depiction of community composition. We also highlight a lack of research in important wheat growing regions that are important for global wheat production. These considerations and other knowledge gaps are used to pinpoint future research.
Journal Article
A novel observation points‐based positive‐unlabeled learning algorithm
In this study, an observation points‐based positive‐unlabeled learning algorithm (hence called OP‐PUL) is proposed to deal with positive‐unlabeled learning (PUL) tasks by judiciously assigning highly credible labels to unlabeled samples. The proposed OP‐PUL algorithm has three components. First, an observation point classifier ensemble (OPCE) algorithm is constructed to divide unlabeled samples into two categories, which are temporary positive and permanent negative samples. Second, a temporary OPC (TOPC) is trained based on the combination of original positive samples and permanent negative samples and then the permanent positive samples that are correctly classified with TOPC are retained from the temporary positive samples. Third, a permanent OPC (POPC) is finally trained based on the combination of original positive samples, permanent positive samples and permanent negative samples. An exhaustive experimental evaluation is conducted to validate the feasibility, rationality and effectiveness of the OP‐PUL algorithm, using 30 benchmark PU data sets. Results show that (1) the OP‐PUL algorithm is stable and robust as unlabeled samples and positive samples are increased in unlabeled data sets and (2) the permanent positive samples have a consistent probability distribution with the original positive samples. Moreover, a statistical analysis reveals that POPC in the OP‐PUL algorithm can yield better PUL performances on the 30 data sets in comparison with four well‐known PUL algorithms. This demonstrates that OP‐PUL is a viable algorithm to deal with PUL tasks.
Journal Article
Simulation of high-frequency dissolved oxygen dynamics in a shallow estuary, the Corsica River, Chesapeake Bay
Understanding shallow water biogeochemical dynamics is a challenge in coastal regions, due to the presence of highly variable land-water interface fluxes, tight coupling with sediment processes, tidal dynamics, and diurnal variability in biogeochemical processes. While the deployment of continuous monitoring devices has improved our understanding of high-frequency (12 - 24 hours) variability and spatial heterogeneity in shallow regions, mechanistic modeling of these dynamics has lagged behind conceptual and empirical models. The inherent complexity of shallow water systems is represented in the Corsica River estuary, a small basin within the Chesapeake Bay ecosystem, where abundant monitoring data have been collected from long-term monitoring stations, continuous monitoring sensors, synoptic sensor surveys, and measurements of sediment-water fluxes. A state-of-the-art modeling system, the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM), was applied to the Corsica domain with a high-resolution grid and nutrient loads from the most recent version of the Chesapeake Bay watershed model. The Corsica SCHISM model reproduced observed high-frequency variability in dissolved oxygen, as well as seasonal variability in chlorophyll-a and sediment-water fluxes. Time-series signal analyses using Empirical Model Decomposition and spectral analysis revealed that the diurnal and M2 tide frequencies are the dominant high-frequency modes and physical transport contributes a larger share to dissolved oxygen budgets than biogeochemical processes on an hourly time scale. Heterogeneity and patchiness in dissolved oxygen resulting from phytoplankton distributions and geometry-driven eddies amplify the physical transport effect, and on longer time scales oxygen is controlled more by photosynthesis and respiration. Our simulation demonstrates that interactions among physical and biological dynamics generate complex high-frequency variability in water quality and non-linear reposes to nutrient loading and environmental forcing in shallow water systems.
Journal Article
Profiling Immune Escape in Hodgkin’s and Diffuse large B-Cell Lymphomas Using the Transcriptome and Immunostaining
Therapeutic blockade of PD-1/PD-L1 shows promising results in Hodgkin’s lymphoma (HL) and in some diffuse large B-cell lymphoma (DLBCL) patients, but biomarkers predicting such responses are still lacking. To this end, we recently developed a transcriptional scoring of immune escape (IE) in cancer biopsies. Using this method in DLBCL, we identified four stages of IE correlated with overall survival, but whether Hodgkin’s lymphomas (HL) also display this partition was unknown. Thus, we explored the transcriptomic profiles of ~1000 HL and DLBCL using a comparative meta-analysis of their bulk microarrays. Relative to DLBCL, the HL co-clustered at the advanced stage of immune escape, displaying significant enrichment of both IE and T-cell activation genes. Analyses via transcriptome deconvolution and immunohistochemistry showed more CD3+ and CD4+ tumor-infiltrating lymphocytes (TILs) in HL than DLBCL. Both HL and non-GCB DLBCL shared a high abundance of infiltrating CD8+ T-cells, but HL had less CD68+CD163+ macrophages. The same cellular distribution of PD-1 and TIM-3 was observed in HL and DLBCL, though HL had more PD-L1 tumor cells and LAG-3 ME cells. This study illuminates the advanced stage of immune activation and escape in HL, consistent with the response to checkpoint blockade therapies for this type of lymphoma.
Journal Article