al mining model

(BioNER)

2019-9-26 · A paper list for BioNER. Over the past decades, many automatic BioNER methods have been proposed and used to recognise biomeidcal entities. They can be categorised into dictionary-based, rule-based and machine learning-based methods. Recently, neural network-based machine learning methods exhibit promising results.

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Corporate Presentation

2021-7-6 · First Mining does not undertake to update any forward-looking statement, whether written or oral, that may be made from time to time by the Company or on our behalf, except as required by law. Hazel Mullin, P.Geo., Director, Data Management and Technical Services of First Mining Gold Corp., is a

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Application of data mining techniques in customer ...

2009-3-1 · The above seven models cover the generally mentioned data mining models in various articles (Ahmed, 2004, Carrier and Povel, 2003, Mitra et al., 2002, Shaw et al., 2001, Turban et al., 2007). There are numerous machine learning techniques available for each type of data mining model.

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Isolation Forest

2019-11-8 · Most existing model-based approaches to anomaly de-tection construct a profile of normal instances, then iden-tify instances that do not conform to the normal profile as anomalies. Notable examples such as statistical methods [11], classification-based methods [1], and clustering-based methods [5] all use this general approach. Two major draw-

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Deep Crossing: Web-Scale Modeling without Manually …

2021-2-9 · ditional combinatorial features to improve an existing model is a daunting task due to the large search space, and the slow turnaround in the training and evaluation cycle given billions of samples. Deep Learning [9, 20, 16] carries the promise of learning from individual features without manual intervention [17, 21].

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Jie Tang (Tang, Jie) ''s Homepage

2021-7-19 · Further, we incorporate user feedback into the prediction and propose a bandit learning model (Qi et al., NIPS''18). Datasets and codes: [NetMF] [DeepInf] Social Network Mining [IC2S2''19 Tutorial] [KDD''18 Tutorial] [Book] [Survey]

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Developing a novel Text mining Model for Exploring ...

2021-1-5 · In this research, we developed a text-mining model, which consist of two algorithms the first one for extracting Arabic root from diacritics texts and its inflection. The second algorithm determines the entities as verb type, formal noun, proper noun and stop word for text mining purpose. We applied our model to (Sahih Al-Bukhari)

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Deep-learning-based information mining from ocean …

Guo et al. used the DNN-based semantic-segmentation model to detect oil spills by using backscattering energy information. To demonstrate that the AI technology has great potential for robust oil-spill detection and characterization under various meteorological and SAR-acquisition conditions, we constructed a generalized AI framework to study ...

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A Reference Model for Learning Analytics

2014-8-30 · statistics, more recent LA approaches apply several other analytics methods such as data mining to guide learning improvement and performance support. Norris et al. (2008b) use the term action analytics to describe "the emergence of a new generation of tools, solutions, and behaviors that are giving rise to more powerful and effective utilities

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A Data Mining & Knowledge Discovery Process Model

2018-9-25 · Cios et al. s model was first proposed in 2000 (Cios et al., 2000). This model adapted the CRISP-DM model to the needs of the academic research community, providing a more general, research-oriented description of the steps. The KDD Roadmap (Howard et al., 2001) is a DM methodology used in the DM Witness Miner tool (Lanner Group, 2008).

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Model-based Overlapping Clustering

2005-9-9 · larly appealing model was proposed by Segal et al. [33] in the con-text of probabilistic relational models (PRMs) applied to the analy-sis of gene microarray data. In this paper, westart withthe basic ap-proach of Segal et al. and provide an alternative interpretation of the model as a generalization of mixture models, which makes it easily

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Modeling wine preferences by data mining from ...

2010-1-7 · model selection. The support vector machine achieved promising results, outper-forming the multiple regression and neural network methods. Such model is useful to support the oenologist wine tasting evaluations and improve wine production. Furthermore, similar techniques can help in target marketing by modeling consumer tastes from niche markets.

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Model Compression

2006-6-21 · The main idea behind model compression is to use a fast and compact model to approximate the function learned by a slower, larger, but better performing model. Unlike the true function that is unknown, the function learned by a high performing model is available and can be used to label large amounts of pseudo data. A fast, compact and expres-

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AMiner

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A Data Mining & Knowledge Discovery Process Model

2018-9-25 · A Data Mining & Knowledge Discovery Process Model 5 DMIE or Data Mining for Industrial Engineering (Solarte, 2002) is a methodology because it specifies how to do the tasks to develop a DM pr oject in the field of in dustrial engineering. It is an instance of CRISP-DM, which makes it a methodology, and it shares CRISP-DM s associated life cycle.

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"The Asian Development Model and Mining Reforms …

The aim of this paper is to provide accounting, marketing, management, finance and legal professionals who are engaged with emerging economies with an introduction to the ''Asian Development Model'' and to use the mining reforms in Indonesia as an example of the Model in operation. This will assist those professionals in recognising the challenges faced by businesses in Australia and New ...

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Spatial Data Mining

2003-5-9 · Spatial data mining [Stolorz et al.1995, Shekhar & Chawla2002] is the process of discovering interesting and previously unknown, but potentially useful pat-terns from spatial databases. The complexity of spatial data and intrinsic spatial relationships limits the usefulness of conventional data mining tech-niques for extracting spatial patterns.

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HEART DISEASE PREDICTION USING DATA MINING …

2021-7-8 · A. Sheik Abdullah et al [10], " A Data mining Model for predicting the Coronary Heart Disease using Random Forest Classifier", a Data mining model has been developed using Random Forest classifier to increase the forecast accuracy and to examine several events related to CHD. [3] DATA SET

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CRISP-DM 1

2020-5-17 · model to service the data mining community. Over the next two and a half years, we worked to develop and refine CRISP-DM. We ran trials in live, large-scale data mining projects at Mercedes-Benz and at our insurance sector partner, OHRA. We worked on the integration of CRISP-DM with commercial data mining tools.

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(BioNER)

2019-9-26 · BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Lee J, Yoon W, Kim S, et al. Bioinformatics, Advance article, 2019. [paper] [code] HUNER: Improving Biomedical NER with Pretraining. Weber L, Münchmeyer J[paper]

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Using data mining techniques to model primary …

2019-2-8 · Data mining, as a part of the Knowledge Discovery in Databases (KDD) process,uses machine learning and statisti-cal methods in order to find interesting patterns in data (Fayyad et al. 1996). The goal of data mining is to extract information from datasets that is intelligible and useful in an understandable and easily interpretable format. Different data

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The Dual-Sparse Topic Model: Mining Focused Topics and ...

2014-5-30 · The Dual-Sparse Topic Model: Mining Focused Topics and Focused Terms in Short Text Tianyi Lin∗ The Chinese University of Hong Kong [email protected] .hk Wentao Tian∗ The Chinese University of Hong Kong [email protected] .hk Qiaozhu Mei School of Information University of Michigan [email protected] Hong Cheng The Chinese University of Hong Kong ...

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Energy forecasting based on predictive data mining ...

2018-10-10 · This process can improve the model performance and provide faster decisions. The authors of (Saleh et al. 2016) implemented a data mining-based load forecasting strategy and divided the whole process in two parts data pre-processing and load estimation. The data pre-processing step performed outlier rejection to eliminate the bad data using a distance-based outlier rejection and feature selection …

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What Is Text Mining? A Beginner''s Guide

Text mining makes teams more efficient by freeing them from manual tasks and allowing them to focus on the things they do best. You can let a machine learning model take care of tagging all the incoming support tickets, while you focus on providing fast and …

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A Reference Model for Learning Analytics

2014-8-30 · Educational Data Mining'' (Romero et al., 2010a). EDM is concerned with developing methods to explore the unique types of data that come from an educational context and, using these methods, to better understand students and the settings in which they learn (Romero et al., 2010a). From a

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[PDF] CRISP-DM 1.0: Step-by-step data mining guide ...

CRISP-DM 1.0: Step-by-step data mining guide. This document describes the CRISP-DM process model, including an introduction to the CRISP-DM methodology, the CRISP-DM reference model, the CRISP-DM user guide and the CRISP-DM reports, as well as an appendix with additional useful and related information. This document and information herein, are ...

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VirSorter: mining viral signal from microbial genomic …

2  · Viruses of microbes impact all ecosystems where microbes drive key energy and substrate transformations including the oceans, humans and industrial fermenters. However, despite this recognized importance, our understanding of viral diversity and impacts remains limited by too few model …

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AI in Mining – Mineral Exploration, Autonomous Drills, …

2019-2-2 · Mining is a major worldwide industry producing everything from coal to gold. According to a PWC annual report, the top 40 mining companies have a market capitalization of $748 billion as of April 2017.The industry as a whole saw a slump in 2015 but since then the sector has recovered due to rising commodity prices.

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TOPIC MODELS

2016-6-9 · This is illustrated as a directed graphical model in Figure 2. The hidden topical structure of a collection is represented in the hidden randomvariables: thetopicsβE 1:K, theper-documenttopicproportionsθE 1:D, and the per-word topic assignments z1:D,1:N. With these variables, LDA is a type of mixed-membership model (Erosheva et al., 2004 ...

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Data Mining Using RFM Analysis

2018-9-25 · data mining with RFM variables include different data mining techniques such as neural network and decision tree (Olson et al., 2009), rough set theory (Cheng & Chen, 2009), self organizing map (Li et al., 2008), CHAID (McCarty and Hastak, 2007), genetic algorithm (Chan, 2008) and sequential pattern mining (C hen et al., 2009; Liu et al., 2009).

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