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Research on 3D medical image surface reconstruction based on data mining and machine learning

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第8期37卷 4654-4669页

作者： Hua, Shanshan Liu, Qi Yin, Guanxiang Guan, Xiaohui Jiang, Nan Zhang, Yuejin East China Jiaotong Univ Sch Informat Engn Nanchang 330013 Jiangxi Peoples R China Nanchang Univ Natl Engn Res Ctr Bioengn Drugs & Technol Nanchang Jiangxi Peoples R China

Three-dimensional (3D) medical images are prone to overlap, and there are some problems, such as low detection efficiency and inconsistent with the actual situation. Therefore, a 3D medical image surface reconstruction method based on data mining and machine learning is proposed. The 3D medical images were classified according to different ways, the information frame of 3D medical images was established and the surface overlapping information model of 3D images was given. Based on this information framework, the nonlinear function of overlapping area information of 3D medical images was constructed. The weight of the nonlinear function was used to calculate the input and output results of overlapping area information. Combined with the input mode of 3D medical image information, the error between the information output and the expected output was set. The nonlinear function weight of the overlapping area information of 3D medical images was modified by using the learning rate and the use time of the overlapping area information, and the influence factors of the overlapping information detection were obtained by increasing the situation terms, so as to complete the detection of the surface reconstruction information of 3D medical images. The experimental results show that the information detection results of the proposed method fit well with the actual situation, and the information detection efficiency is high.

关键词： 3D medical images data mining algorithm image surface reconstruction machine learning algorithm

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data mining for inventory item selection with cross-selling considerations

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data mining AND KNOWLEDGE DISCOVERY 2005年第1期11卷 81-112页

作者： Wong, RCW Fu, AWC Wang, K Chinese Univ Hong Kong Dept Comp Sci & Engn Sha Tin 100083 Peoples R China Simon Fraser Univ Dept Comp Sci Burnaby BC V5A 1S6 Canada

Association rule mining, studied for over ten years in the literature of data mining, aims to help enterprises with sophisticated decision making, but the resulting rules typically cannot be directly applied and require further processing. In this paper, we propose a method for actionable recommendations from itemset analysis and investigate an application of the concepts of association rules-maximal-profit item selection with cross-selling effect (MPIS). This problem is about choosing a subset of items which can give the maximal profit with the consideration of cross-selling effect. A simple approach to this problem is shown to be NP-hard. A new approach is proposed with consideration of the loss rule-a rule similar to the association rule-to model the cross-selling effect. We show that MPIS can be approximated by a quadratic programming problem. We also propose a greedy approach and a genetic algorithm to deal with this problem. Experiments are conducted, which show that our proposed approaches are highly effective and efficient.

关键词： data mining algorithm cross-selling item selection association rule quadratic programming genetic algorithm

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The impact of training data tailoring on demand forecasting models in retail

The impact of training data tailoring on demand forecasting ...

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37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Karan, M. Pintar, D. Skocir, Z. Vranic, M. Alajkovic, A. Milojevic, J. Plesa, M. Univ Zagreb Fac Elect Engn & Comp Zagreb 41000 Croatia Konzum DD Zagreb Croatia

ISBN: (纸本)9789532330816

Demand forecasting plays a very important role in retail business. Retail information systems commonly store large amounts of data which are subsequently used by sophisticated data mining tools for building forecasting models. Quality of these models is usually measured through their predictive accuracy as their most important property, followed by other measures which consider average underestimate and overestimate costs etc. Even though the choice of data mining algorithm is usually paramount, training set cleansing and preparation has a significant influence on final model performance. This article discusses and analyses the impact of training set preparation and tailoring on a final forecasting model performance used in a real world example from the retail industry.

关键词： data mining demand forecasting retail data processing building forecasting models data mining algorithm data mining tools demand forecasting models final forecasting model retail business retail information systems training data tailoring training set preparation Analytical models Biological system modeling Buildings data models Forecasting Predictive models Training Demand Prediction data mining business data processing Biological system modeling data models ANALYSIS MODELS Buildings Prediction models Functional training Tailoring mining Equipment

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Running Genome Wide data Analysis Using a Parallel Approach on a Cloud Platform 15th

Running Genome Wide Data Analysis Using a Parallel Approach ...

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15th Conference on Artificial Intelligence in Medicine (AIME)

作者： Demartini, Andrea Capozzi, Davide Malovini, Alberto Bellazzi, Riccardo Univ Pavia I-27100 Pavia Italy Biomeris Srl Pavia Italy IRCCS Fdn Salvatore Maugeri Pavia Italy Spindox Spa Milan Italy

ISBN: (纸本)9783319195513;9783319195506

Hierarchical Naive Bayes (HNB) is a multivariate classification algorithm that can be used to forecast the probability of a specific disease by analysing a set of Single Nucleotide Polymorphisms (SNPs). In this paper we present the implementation of HNB using a parallel approach based on the Map-Reduce paradigm built natively on the Hadoop framework, relying on the Amazon Cloud Infrastructure. We tested our approach on two GWAS datasets aimed at identifying the genetic bases of Type 1 (T1D) and Type 2 Diabetes (T2D). Both datasets include individual level data of 1,900 cases and 1,500 controls with similar to 420,000 SNPs. For T2D the best results were obtained using the complete set of SNPs, whereas for T1D the best performances were reached using few SNPs selected through standard univariate association tests. Our cloud-based implementation allows running genome wide simulations cutting down computational time and overall infrastructure costs.

关键词： Map reduce Cloud computing data mining algorithm Genome-wide association studies

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A Study on Various data mining Techniques for Crop Yield Prediction

A Study on Various Data Mining Techniques for Crop Yield Pre...

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International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT)

作者： Gandge, Yogesh Sandhya BKIT Bhalki Dept Master Comp Applicat Bidar India Visvesvaraya Technol Univ Dept Studies Comp Applicat Post Grad Ctr Mysuru India

ISBN: (纸本)9781538623619

India is a country where agriculture and agriculture related industries are the major source of living for the people. Agriculture is a major source of economy of the country. It is also one of the country which suffer from major natural calamities like drought or flood which damages the crop. This leads to huge financial loss for the farmers thus leading to the suicide. Predicting the crop yield well in advance prior to its harvest can help the farmers and Government organizations to make appropriate planning like storing, selling, fixing minimum support price, importing/exporting etc. Predicting a crop well in advance requires a systematic study of huge data coming from various variables like soil quality,pH ,EC,N,P,K etc. As Prediction of crop deals with large set of database thus making this prediction system a perfect candidate for application of data mining. Through data mining we extract the knowledge from the huge size of data. This paper presents the study about the various data mining techniques used for predicting the crop yield. The success of any crop yield prediction system heavily relies on how accurately the features have been extracted and how appropriately classifiers have been employed. This paper summarizes the results obtained by various algorithms which are being used by various authors for crop yield prediction, with their accuracy and recommendation.

关键词： crop yield prediction data mining data mining algorithm models accuracy and Recommendation

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data mining Techniques for Disease Risk Prediction Model: A Systematic Literature Review 3rd

Data Mining Techniques for Disease Risk Prediction Model: A ...

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3rd International Conference of Reliable Information and Communication Technology (IRICT)

作者： Ahmad, Wan Muhamad Taufik Wan Ab Ghani, Nur Laila Drus, Sulfeeza Mohd Univ Tenaga Nas Coll Comp Sci & Informat Technol Jalan IKRAM UNITEN Kajang 43000 Selangor Malaysia

ISBN: (纸本)9783319990071;9783319990064

Risk prediction model estimates event occurrence based on related data. Conventional statistical metrics that utilized primary data generates simple descriptive analysis that often provide insufficient knowledge for decision making. In contrast, data mining techniques that have the capability to find hidden pattern from the secondary data in large databases and create prediction for de-sired output has become a popular approach to develop any risk prediction model. In healthcare particularly, data mining techniques can be applied in disease risk prediction model to provide reliable prediction on the possibility of acquiring the disease based on individual's clinical and non-clinical data. Due to the increased use of data mining in healthcare, this study aims at identifying the data mining techniques and algorithms that are commonly implemented in studies related to various disease risk prediction model as well as finding the accuracy of the algorithms. The accuracy evaluation consists of various method, but this paper is focusing on overall accuracy which is measured by the total number of correctly predicted output over the total number of prediction. A systematic literature review approach that search across five databases found 170 articles, of which 7 articles were selected in the final process. This review found that most prediction model used classification technique, with a focus on decision tree, neural network, support vector machines, and Naive Bayes algorithms where heart-related disease is commonly studied. Further research can apply similar algorithms to develop risk prediction model for other types of diseases, such as infectious disease prediction.

关键词： Disease risk prediction data mining data mining techniques data mining algorithm

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mining Trailers data from YouTube for Predicting Gross Income of Movies 5

Mining Trailers Data from YouTube for Predicting Gross Incom...

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5th IEEE-Region-10 Humanitarian Technology Conference (R10-HTC)

作者： Rahim, Md Shamsur Chowdhury, A. Z. M. Ehtesham Islam, Md. Asiful Islam, Mir Riyanul Amer Int Univ Bangladesh Dept Comp Sci Dhaka Bangladesh

ISBN: (纸本)9781538621752

YouTube is the most popular video contents sharing platform around the world. As a consequence, YouTube has become one of the most preferred choices to the movie producers and studios for connecting/communicating with their potential viewers through sharing trailers and teasers. data regarding the trailers of a movie from YouTube can provide useful insights for predicting the gross income of movies. In this paper, we have prepared a dataset of 7988 movie trailers from YouTube. The dataset contains different attributes like opening income, number of views, number of likes, number of dislikes, number of comments. We prepared two prediction models and applied four regression techniques to find out the most suitable technique for predicting the gross income of a movie. The comparative analysis has depicted that linear regression is the most suitable method regarding the prediction of movies gross income using these attributes. Furthermore, we have provided future research issues from where our work has ended.

关键词： predictive analytics trailer gross income gross income prediction movie data mining algorithm YouTube

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Sequential pattern mining in multi-databases via multiple alignment

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data mining AND KNOWLEDGE DISCOVERY 2006年第2-3期12卷 151-180页

作者： Kum, HC Chang, JH Wang, W Univ N Carolina Dept Comp Sci Chapel Hill NC 27514 USA Yonsei Univ Dept Comp Sci Seoul 120749 South Korea

To efficiently find global patterns from a multi-database, information in each local database must first be mined and summarized at the local level. Then only the summarized information is forwarded to the global mining process. However, conventional sequential pattern mining methods based on support cannot summarize the local information and is ineffective for global pattern mining from multiple data sources. In this paper, we present an alternative local mining approach for finding sequential patterns in the local databases of a multi-database. We propose the theme of approximate sequential pattern mining roughly defined as identifying patterns approximately shared by many sequences. Approximate sequential patterns can effectively summerize and represent the local databases by identifying the underlying trends in the data. We present a novel algorithm, ApproxMAP, to mine approximate sequential patterns, called consensus patterns, from large sequence databases in two steps. First, sequences are clustered by similarity. Then, consensus patterns are mined directly from each cluster through multiple alignment. We conduct an extensive and systematic performance study over synthetic and real data. The results demonstrate that ApproxMAP is effective and scalable in mining large sequences databases with long patterns. Hence, (A)pproxMAP can efficiently summarize a local database and reduce the cost for global mining. Furthremore, we present an elegant and uniform model to identify both high vote sequential patterns and exceptional sequential patterns from the collection of these consensus patterns from each local databases.

关键词： data mining algorithm sequential patterns approximate sequential pattern mining local pattern global sequential pattern multiple alignment

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mining minimal distinguishing subsequence patterns with gap constraints

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KNOWLEDGE AND INFORMATION SYSTEMS 2007年第3期11卷 259-286页

作者： Ji, Xiaonan Bailey, James Dong, Guozhu Univ Melbourne NICTA Victoria Lab Dept Comp Sci & Software Engn Melbourne Vic 3053 Australia Wright State Univ Dept Comp Sci & Engn Dayton OH 45435 USA

Discovering contrasts between collections of data is an important task in data mining. In this paper, we introduce a new type of contrast pattern, called a Minimal Distinguishing Subsequence (MDS). An MDS is a minimal subsequence that occurs frequently in one class of sequences and infrequently in sequences of another class. It is a natural way of representing strong and succinct contrast information between two sequential datasets and can be useful in applications such as protein comparison, document comparison and building sequential classification models. mining MDS patterns is a challenging task and is significantly different from mining contrasts between relational/transactional data. One particularly important type of constraint that can be integrated into the mining process is the gap constraint. We present an efficient algorithm called ConSGapMiner (Contrast Sequences with Gap Miner), to mine all MDSs satisfying a minimum and maximum gap constraint, plus a maximum length constraint. It employs highly efficient bitset and boolean operations, for powerful gap-based pruning within a prefix growth framework. A performance evaluation with both sparse and dense datasets, demonstrates the scalability of ConSGapMiner and shows its ability to mine patterns from high dimensional datasets at low supports.

关键词： data mining algorithm sequential pattern frequent pattern emerging pattern gap constraint contrast pattern

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Novel techniques and an efficient algorithm for closed pattern mining

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第11期41卷 5105-5114页

作者： Kiraly, Andras Laiho, Asta Abonyi, Janos Gyenesei, Attila Univ Pannonia Dept Proc Engn H-8200 Veszpreem Hungary Univ Turku Turku Ctr Biotechnol Finnish Microarray & Sequencing Ctr FIN-20520 Turku Finland Abo Akad Univ FIN-20520 Turku Finland

In this paper we show that frequent closed itemset mining and biclustering, the two most prominent application fields in pattern discovery, can be reduced to the same problem when dealing with binary (0-1) data. FCPMiner, a new powerful pattern mining method, is then introduced to mine such data efficiently. The uniqueness of the proposed method is its extendibility to non-binary data. The mining method is coupled with a novel visualization technique and a pattern aggregation method to detect the most meaningful, non-overlapping patterns. The proposed methods are rigorously tested on both synthetic and real data sets. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： Biclustering Closed frequent itemset mining Clustering visualization data mining algorithm Pattern detection

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