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5th IEEE international conference on Parallel, Distributed and Grid Computing (PDGC)

作者： Tyagi, Ankita Mehra, Ritika Saxena, Aditya Dit Univ Comp Applicat Dehra Dun Uttar Pradesh India Dit Univ Comp Sci & Engn Dehra Dun Uttar Pradesh India

ISBN: (纸本)9781728106465

thyroid disease is a major cause of formation in medical diagnosis and in theprediction, onset to which it is a difficult axiomin the medical research. thyroid gland is one of the most important organs in our body. the secretions of thyroid hormones are culpable in controlling the metabolism. Hyperthyroidism and hypothyroidism are one of the two common diseases of the thyroid that releases thyroid hormones in regulating the rate of body's metabolism. data cleansing techniques were applied to make the data primitive enough for performing analytics to show the risk of patients obtaining thyroid. the machine learning plays a decisive role in the process of disease prediction and this paper handles the analysis andclassificationmodels that are being used in the thyroid disease based on the information gathered from the dataset taken from UCI machine learning repository. It is important to ensure a decent knowledge base that can be entrenched and used as a hybrid model in solving complex learning task, such as in medical diagnosis and prognostic tasks. In this paper, we also proposed different machine learning techniques and diagnosis for the prevention of thyroid. machine learning Algorithms, support vector machine (SVM), K-NN, Decision Trees were used to predict the estimated risk on a patient's chance of obtaining thyroid disease.

关键词： thyroid Disease Prediction Model machine learning Algorithms

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Leveraging Edge Computing through Collaborative machine learning 5

Leveraging Edge Computing through Collaborative Machine Lear...

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IEEE 5th international conference on Future Internet of things and Cloud (FiCloud)

作者： Portelli, Kurt Anagnostopoulos, Christos Univ Glasgow Sch Comp Sci Glasgow G12 8QQ Lanark Scotland

ISBN: (纸本)9781538632819

the Internet of things (IoT) offers the ability to analyze and predict our surroundings through sensor networks at the network edge. To facilitate this predictive functionality, Edge Computing (EC) applications are developed by considering: power consumption, network lifetime and quality of context inference. Humongous contextual data from sensors provide data scientists better knowledge extraction, albeit coming at the expense of holistic data transfer that threatens the network feasibility and lifetime. To cope with this, collaborative machine learning is applied to EC devices to (i) extract the statistical relationships and (ii) construct regression (predictive) models to maximize communication efficiency. In this paper, we propose a learning methodology that improves the prediction accuracy by quantizing the input space and leveraging the local knowledge of the EC devices.

关键词： edge analytics predictive intelligence edge computing collaborative machine learning

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Detecting Cognitive Distortions through machine learning Text analytics 5

Detecting Cognitive Distortions Through Machine Learning Tex...

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5th IEEE international conference on Healthcare Informatics (ICHI)

作者： Simms, T. Ramstedt, C. Rich, M. Richards, M. Martinez, T. Giraud-Carrier, C. Brigham Young Univ Dept Comp Sci Provo UT 84602 USA

ISBN: (纸本)9781509048816

machine learning and text analytics have proven increasingly useful in a number of health-related applications, particularly in the context of analyzing online data for disease epidemics and warning signs of a variety of mental health issues. We follow in this tradition here, but focus our attention on cognitive distortion, a precursor and symptom of disruptive psychological disorders such as anxiety, anorexia and depression. We collected a number of personal blogs from the Tumblr API, and labeled them based on whether they exhibited distorted thought patterns. We then used LIWC to extract textual features and applied machine learning to the resulting vectors. Our findings show that it is possible to detect cognitive distortions automatically from personal blogs with relatively good accuracy (73.0%) and false negative rate (30.4%).

关键词： machine learning Social Media data Cognitive Distortion

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Building an IoT data Hub with Elasticsearch, Logstash and Kibana 5

Building an IoT Data Hub with Elasticsearch, Logstash and Ki...

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IEEE 5th international conference on Future Internet of things and Cloud (FiCloud)

作者： Bajer, Marcin ABB Corp Res Ctr Starowislna 13a PL-31038 Krakow Poland

ISBN: (纸本)9781538632819

the goal of this publication is to describe the implementation of an Elastisearch, Logstash and Kibana (ELK) stack to process IoT data. Although, those tools were designed to be used mainly for handling large number of log data, they can be applied also to store, search and visualise other type of information - including IoT data. In order to show practical implementation of the idea, selected devices installed in the building of ABB Corporate Research in Krakow has been used. In prepared system, real data generated by various subsystems in this building has been integrated into one Elasticsearch based solution for further processing. Later on, this data will be used to develop data analytics to extract and visualize meaningful insights about building operations. In addition, selected data is sent to the Azure cloud to use its abilities in big data processing and machine learning.

关键词： Indexes data visualization Buildings Internet of things Cloud computing Tools Buildings Internet of things data visualisation Cloud Computing WELL LOGGING data built structure ELAVL2 gene HUB EQUIPMENT

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Proceedings - 2017 IEEE international conference on Big data, Big data 2017

Proceedings - 2017 IEEE International Conference on Big Data...

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5th IEEE international conference on Big data, Big data 2017

ISBN: (纸本)9781538627143

the proceedings contain 661 papers. the topics discussed include: a more open efficient future for AI development and data science with an introduction to Julia;large-scale graph representation learning;being 'BYTES-oriented' in HPC leads to an open big data/AI ecosystem and further advances into the post-Moore era;collective subjective logic: scalable uncertainty-based opinion inference;contextual reinforcement learning;human-in-the-loop applied machine learning;quality-aware aggregation & predictive analytics at the edge;robust multi-label semi-supervised classification;standardizing big earth datacubes;compact multi-class boosted trees;constraint-aware dynamic truth discovery in big data social media sensing;and enhancing data quality by cleaning inconsistent big RDF data.

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A first estimation of the proportion of cybercriminal entities in the bitcoin ecosystem using supervised machine learning 5

A first estimation of the proportion of cybercriminal entiti...

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5th IEEE international conference on Big data, Big data 2017

作者： Sun Yin, Haohua Vatrapu, Ravi Centre for Business Data Analytics Copenhagen Business School Denmark Westerdals Oslo School of Arts Comm and Tech Norway

ISBN: (纸本)9781538627143

Bitcoin, a peer-to-peer payment system and digital currency, is often involved in illicit activities such as scamming, ransomware attacks, illegal goods trading, and thievery. At the time of writing, the Bitcoin ecosystem has not yet been mapped and as such there is no estimate of the share of illicit activities. this paper provides the first estimation of the portion of cyber-criminal entities in the Bitcoin ecosystem. Our dataset consists of 854 observations categorised into 12 classes (out of which 5 are cybercrime-related) and a total of 100,000 uncategorised observations. the dataset was obtained from the data provider who applied three types of clustering of Bitcoin transactions to categorise entities: co-spend, intelligence-based, and behaviour-based. thirteen supervised learning classifiers were then tested, of which four prevailed with a cross-validation accuracy of 77.38%, 76.47%, 78.46%, 80.76% respectively. From the top four classifiers, Bagging and Gradient Boosting classifiers were selected based on their weighted average and per class precision on the cybercrime-related categories. Both models were used to classify 100,000 uncategorised entities, showing that the share of cybercrime-related is 29.81% according to Bagging, and 10.95% according to Gradient Boosting with number of entities as the metric. With regard to the number of addresses and current coins held by this type of entities, the results are: 5.79% and 10.02% according to Bagging;and 3.16% and 1.45% according to Gradient Boosting. © 2017 IEEE.

关键词： Ecosystems

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A data driven approach to create an extensible EV driving data model 2016

A data driven approach to create an extensible EV driving da...

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3rd Multidisciplinary international Social Networks conference, MISNC 2016, 5th ASE international conference on Social Informatics, SocialInformatics 2016 and 7th ASE international conference on data Science, DS 2016

作者： Lee, Chung-Hong Wu, Chih-Hung Department of Electrical Engineering National Kaohsiung University of Applied Sciences Kaohsiung City Taiwan

ISBN: (纸本)9781450341295

this paper presents a machine learning approach to analyze driving behaviors that allows a better understanding of electric vehicle. We implement a pattern recognition process to model the driving pattern according to the energy consumption for both a single driver and a fleet. the growing hierarchical self-organizing maps (GHSOM) is applied to learn driver's behaviors gradually, and the experimental results show that the driving behaviors could be recognized with the increase of driving cycles. Moreover, the proposed framework would enhance the understanding of driver's behaviors and also facilitate the EV system design, the big data analytics for IoV and the implementation of advanced driver assistant system (ADAS). © 2016 ACM.

关键词： Automobile drivers

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Probabilistic neural network based categorical data imputation

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NEUROCOMPUTING 2016年 218卷 17-25页

作者： Nishanth, Kancherla Jonah Ravi, Vadlamani IDRBT Ctr Excellence Analyt Castle Hills Rd 1 Hyderabad 500057 Andhra Pradesh India

Real world datasets contain both numerical and categorical attributes. Very often missing values are present in both numerical and categorical attributes. the missing data has to be imputed as the inferences made from complete data are often more accurate and reliable than those made from incomplete data [15]. Also, most of the data mining algorithms cannot work with incomplete datasets. the paper proposes a novel soft computing architecture for categorical data imputation. the proposed imputation technique employs Probabilistic Neural Network (PNN) preceded by mode for imputing the missing categorical data. the effectiveness of the proposed imputation technique is tested on 4 benchmark datasets under the 10 fold-cross validation framework. In all datasets, except Mushroom, which are complete, some values, which are randomly removed, are treated as missing values. the performance of the proposed imputation technique is compared with that of 3 statistical and 3 machine learning methods for data imputation. the comparison of the mode + PNN imputation technique with mode, K-Nearest Neighbor (K-NN), Hot Deck (HD), Naive Bayes, Random Forest (RF) and J48 (Decision Tree) imputation techniques demonstrates that the proposed method is efficient, especially when the percentage of missing values is high, for records having more than one missing value and for records having a large number of categories for each categorical variable. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Categorical data imputation Probabilistic Neural Network (PNN) Random Forest (RF) Decision Tree (DT)

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Biomarker Discovery Based on Large-Scale Feature Selection and MapReduce 5

Biomarker Discovery Based on Large-Scale Feature Selection a...

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5th IFIP TC 5 international conference on Computer Science and Its Applications (CIA)

作者： Kourid, Ahlam Batouche, Mohamed Constantine 2 Univ A Mehri Dept Comp Sci Coll NTIC Constantine 25000 Algeria

ISBN: (纸本)9783319195780;9783319195773

Large-scale feature selection is one of the most important fields in the big data domain that can solve real data problems, such as bioinformatics, where it is necessary to process huge amount of data. the efficiency of existing feature selection algorithms significantly downgrades, if not totally inapplicable, when data size exceeds hundreds of gigabytes, because most feature selection algorithms are designed for centralized computing architecture. For that, distributed computing techniques, such as MapReduce can be applied to handle very large data. Our approach is to scale the existing method for feature selection, Kmeans clustering and Signal to Noise Ratio (SNR) combined with optimization technique as Binary Particle Swarm Optimization (BPSO). the proposed method is divided into two stages. In the first stage, we have used parallel Kmeans on MapReduce for clustering features, and then we have applied iterative MapReduce that implement parallel SNR ranking for each cluster. After, we have selected the top ranked feature from each cluster. the top scored features from each cluster are gathered and a new feature subset is generated. In the second stage, the new feature subset is used as input to the proposed BPSO based on MapReduce which provides an optimized feature subset. the proposed method is implemented in a distributed environment, and its efficiency is illustrated through analyzing practical problems such as biomarker discovery.

关键词： Feature selection Large-scale machine learning Big data analytics Bioinformatics Biomarker discovery

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applied machine learning and data analytics 1

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丛书名： Communications in Computer and Information Science

1000年

作者： M. A. Jabbar Fernando Ortiz-Rodríguez Sanju Tiwari Patrick Siarry

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

this book constitutes the refereed proceedings of the 5th international conference on applied machine learning and data analytics, amlda 2022, held in Reynosa, Tamaulipas, Mexico, during December 22–23, 2022. the 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 89 submissions. they were organized in topical sections as follows: machine learning, Healthcare and medical imaging informatics; biometrics; forensics; precision agriculture; risk management; robotics and satellite imaging.

关键词： Artificial Intelligence Computer Applications Computer Imaging, Vision, Pattern Recognition and Graphics database Management

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