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The distribution problem of unstructured data when solving data mining tasks on computer clusters 1

The distribution problem of unstructured data when solving d...

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1st international Conference of Information Systems and Design, ICID 2019

作者： Zein, Ali Najyevich Borisova, Svetlana Vyacheslavovna Moscow Power Engineering Institute National Research University Krasnokazarmennaya 14 Moscow Russia

In the modern world, the amount of information is growing exponentially, with every minute new data appears for further storage, processing and *** data sources generate huge amounts of data and most of them are presented in a format that does not correlate well with traditional structured database formats. The three main "commandments" of big data (3V) are Volume (large amount of information), Variety (heterogeneity and lack of structure) and Velocity (data processing speed) [1]. Imagine that there is a large amount of published information that needs to be sorted into sections, which will greatly speed up and simplify its reading and processing [2]. By machine learning can be attributed various methods of cluster analysis, with which you can automate the classification by finding patterns or patterns that describe this data. Using the method of cluster analysis of "K-means", it is possible to achieve the distribution of the K-number of clusters at the greatest possible distance from each other, thereby allowing us to assess how much they differ. Ideally, the differences will be quite large, which will show the quality of execution, thereby significantly accelerating the process of analysis and information content. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0)

关键词： data mining

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Association Rules mining Based Approach for Web Usage mining

Association Rules Mining Based Approach for Web Usage Mining

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12th IEEE international Conference on Information Reuse and Integration (IRI)/1st international workshop on Issues and Challenges in Social Computing (WICSOC)

作者： Nagi, Mohamad ElSheikh, Abdallah Sleiman, Iyad Peng, Peter Rifaie, Mohammad Kianmehr, Keivan Karampelas, Panagiotis Ridley, Mick Rokne, Jon Alhajj, Reda Univ Bradford Sch Comp Bradford BD7 1DP West Yorkshire England Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada RBC Royal Bank Toronto ON Canada Global Univ Dept Comp Sci Beirut Lebanon Hellenic American Univ Dept Informat Technol Manchester NH 03101 USA Univ Western Ontario Dept Elect & Comp Engn London ON Canada

ISBN: (纸本)9781457709661

The web has emerged rapidly into a valuable source of information. Web visitors leave trace behind them which is used by web site owners for knowledge discovery. The latter information guides site owners in deciding how to organize the information in their website and how to provide the best for their visitors in order to maximize their profit. Various mining techniques combined with machine learning models could be employed for effective knowledge discovery. The work described in this paper utilizes association rules mining integrated with fuzziness factor in order to analyze weblog data. The target is to find pages that are accessed together by majority of the users and hence should be linked in a proper way in order to maximize user satisfaction by providing to the users the access flow they expect. This way the number of visitors to the analyzed website will be maximized and hence the target will be achieved. Existing systems that are currently in use, such as AxisLogMiner and WebMiner, will be analyzed.

关键词： web mining weblog association rules mining fuzziness web structure

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Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet recognition Through the Lens of Robustness 1

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1st ICLR international workshop on Trustworthy machine learning for Healthcare (TML4H)

作者： Cheng, Yanqi Liu, Lihao Wang, Shujun Jin, Yueming Schonlieb, Carola-Bibiane Aviles-Rivero, Angelica, I Univ Cambridge Dept Appl Math & Theoret Phys Cambridge England UCL Wellcome EPSRC Ctr Intervent & Surg Sci London England UCL Dept Comp Sci London England

ISBN: (数字)9783031395390

ISBN: (纸本)9783031395383;9783031395390

Surgical action triplet recognition provides a better understanding of the surgical scene. This task is of high relevance as it provides the surgeon with context-aware support and safety. The current go-to strategy for improving performance is the development of new network mechanisms. However, the performance of current state-of-the-art techniques is substantially lower than other surgical tasks. Why is this happening? This is the question that we address in this work. We present the first study to understand the failure of existing deep learning models through the lens of robustness and explainability. Firstly, we study current existing models under weak and strong d- perturbations via an adversarial optimisation scheme. We then analyse the failure modes via feature based explanations. Our study reveals that the key to improving performance and increasing reliability is in the core and spurious attributes. Our work opens the door to more trustworthy and reliable deep learning models in surgical data science. https://***/robustIVT/.

关键词： Robustness Deep learning Surgical Triplet recognition

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1st international Conference on Advanced machine learning Technologies and Applications, AMLTA 2012

1st International Conference on Advanced Machine Learning Te...

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ISBN: (纸本)9783642353253

The proceedings contain 59 papers. The special focus in this conference is on Rough Sets and Applications, machine learning in pattern recognition and Image Processing, machine learning in Multimedia Computing, Bioinformatics and Cheminformatics, data Classification and Clustering, Cloud Computing and Recommender Systems, Case-Based Reasoning and data Processing, Authentication, Digital Forensics and Plagiarism Detection. The topics include: Rough sets-based machine learning over non-deterministic data;learning a table from a table with non-deterministic information;parameterised fuzzy Petri nets for approximate reasoning in decision support systems;rough sets-based rules generation approach;automatic color image segmentation based on illumination invariant and superpixelization;wavelet based statistical adapted local binary patterns for recognizing avatar faces;solving avatar captchas automatically;comparative analysis of image fusion techniques in remote sensing;density based fuzzy thresholding for image segmentation;subjectivity and sentiment analysis of Arabic;support vector machine approach for detecting events in video streams;study of feature categories for musical instrument recognition;a genetic-CBR approach for cross-document relationship identification;improved action recognition using an efficient boosting method;towards smart Egypt -the role of large scale WSNs;language for writing descriptors of outline shape of molecules;web service based approach for viral hepatitis ontology sharing and diagnosing;sampleboost for capsule endoscopy categorization and abnormality detection;advanced parallel genetic algorithm with gene matrix for global optimization;semi-possibilistic biclustering applied to discrete and continuous data;a comparative study of localization algorithms in WSNs;test cases automatic generator (TCAG);support vector machines with weighted powered kernels for data classification;an enhanced cloud-based view materialization approach for peer-to-

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Search Space Complexity of Iteration Domain Based Instruction Embedding for Deep learning Accelerators 2nd

Search Space Complexity of Iteration Domain Based Instructio...

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2nd international workshop on IoT streams for data-Driven Predictive Maintenance, IoT streams 2020, and 1st international workshop on IoT, Edge, and Mobile for Embedded machine learning, ITEM 2020, co-located with ECML/PKDD 2020

作者： Rieber, Dennis Fröning, Holger Robert Bosch GmbH Stuttgart Germany Computing Systems Group Heidelberg University Heidelberg Germany

ISBN: (纸本)9783030667696

With the success of deep learning applications in many domains, the number of new deep learning operators and hardware accelerators increased significantly in recent years. Building efficient deep learning kernels from complex machine instructions like matrix-multiply is a task not yet automated by tools. Deciding which loop and memory transformations to perform and which part of the computation to implement with which machine instruction is mostly done by a human expert. Automating this task presents two challenges: 1) finding the right combination of transformations and 2) deciding where to embed which available instruction. Current work, such as ISAMIR [16] or TVM [3], offer some degree of automation for this task on iteration domain granularity and report results competitive with performance libraries. In this work we explore the search space complexity of this task with a novel graph-based Intermediate Representation (IR) that allows a hierarchical description of the n-dimensional tensor computations performed by Deep learning operators. We create an extensive search space across possible transformations of a given computation and then try to find possible implementations in a state of the art Deep learning instruction set using pattern matching. Our results show that further research into the design of embedding methods for specialized instruction is required to gracefully handle the vast search space of possible transformations. © 2020, Springer Nature Switzerland AG.

关键词： Tensors

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How to automate neural net based learning

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2nd international workshop on machine learning and data mining in pattern recognition, MLDM 2001

作者： Linder, Roland Pöppl, Siegfried J. Institute for Medical Informatics Medical University of Luebeck Ratzeburger Allee 160 D-23538 Luebeck Germany

ISBN: (纸本)3540423591

Although neural networks have many appealing properties, yet there is neither a systematic way how to set up the topology of a neural network nor how to determine its various learning parameters. Thus an expert is needed for fine tuning. If neural network applications should not be realisable only for publications but in real life, fine tuning must become unnecessary. In the present paper an approach is demonstrated fulfilling this demand. Moreover referring to six medical classification and approximation problems of the PROBEN1 benchmark collection this approach will be shown even to outperform fine tuned networks. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Medical problems

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Field-effect-transistor based biosensors: a review of their use in environmental monitoring applications 3

Field-effect-transistor based biosensors: a review of their ...

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1st IEEE international workshop on Metrology for the Agriculture and Forestry (IEEE MetroAgriFor)

作者： Elli, Giulia Ciocca, Manuela Lugli, Paolo Petti, Luisa Free Univ Bolzano Bozen Fac Sci & Technol I-39100 Bolzano Italy

ISBN: (纸本)9781665405331

Biosensors are analytical devices used to detect the presence of a specific bio-chemical substance (called analyte), through the combination of a biorecognition element with a physico-chemical transducer. Biosensors using field-effect transistor (FET) transducing platforms (called Bio-FETs) exploit the variations of the current between a source and a drain terminal upon specific (physical or chemical) interactions in presence of the analyte of interest. While Bio-FETs have mainly been used in various fields of application such as medical diagnostics, biomolecule detection (like glucose) and food safety, environmental monitoring is still a quite unexplored area. In particular for agricultural applications, it is extremely important to develop real-time, portable, and low-cost monitoring devices for various types of pesticides, especially considering that nowadays many of these are considered environmental contaminants and are being banned. Only recently some studies have reported the development of Bio-FETs for the detection of a particular type of pesticides: herbicides. In this review, we revise the use of Bio-FETs for environmental monitoring applications. First we highlight the working principles of Bio-FETs, presenting the most commonly used material and the possible device configurations. Then we present the most relevant studies employing Bio-FETs for environmental monitoring. Finally, we show a quite new approach for Bio-FET, which is the coupling of biosensors with artificial intelligence and machine learning algorithms, to analyse complex data.

关键词： Couplings Transducers machine learning algorithms Target recognition Metrology Real-time systems Safety

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Conformal prediction for distribution-independent anomaly detection in streaming vessel data

Conformal prediction for distribution-independent anomaly de...

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1st international workshop on Novel data stream pattern mining Techniques, streamKDD'10, Held in Conjunction with the 16th ACM SIGKDD Conference on Knowledge Discovery and data mining

作者： Laxhammar, Rikard Falkman, Göran University of Skövde Skövde Sweden Security and Defense Solutions Saab AB Järfälla Sweden

ISBN: (纸本)9781450302265

This paper presents a novel application of the theory of conformal prediction for distribution-independent on-line learning and anomaly detection. We exploit the fact that confor-mal predictors give valid prediction sets at specified confidence levels under the relatively weak assumption that the (normal) training data together with (normal) observations to be predicted have been generated from the same distribution. If the actual observation is not included in the possibly empty prediction set, it is classified as anomalous at the corresponding significance level. Interpreting the significance level as an upper bound of the probability that a normal observation is mistakenly classified as anomalous, we can conveniently adjust the sensitivity to anomalies while controlling the rate of false alarms without having to find any application specific thresholds. The proposed method has been evaluated in the domain of sea surveillance using recorded data assumed to be normal. The validity of the prediction sets is justified by the empirical error rate which is just below the significance level. In addition, experiments with simulated anomalous data indicate that anomaly detection sensitivity is superior to that of two previously proposed methods. © 2010 ACM.

关键词： Forecasting

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A Fish Classification on Images using Transfer learning and Matlab 1

A Fish Classification on Images using Transfer Learning and ...

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1st international Conference of the Indonesian-Association-for-pattern-recognition (INAPR)

作者： Liawatimena, Suryadiputra Heryadi, Yaya Lukas Trisetyarso, Agung Wibowo, Antoni Abbas, Bahtiar Saleh Barlian, Erland Bina Nusantara Univ Fac Engn Comp Sci Dept Comp SciBINUS Grad Program Jakarta 11480 Indonesia Bina Nusantara Univ Fac Engn Dept Comp Engn Jakarta 11480 Indonesia Univ Katolik Indonesia Atma Jaya Fac Engn CERG Jakarta 12930 Indonesia Bina Nusantara Univ Comp Sci BINUS Grad Program Dept Comp Sci Jakarta 11480 Indonesia Bina Nusantara Univ Fac Engn Dept Ind Engn Jakarta 11480 Indonesia Bina Nusantara Univ Dept Management BINUS Business Sch Res Management Jakarta 11480 Indonesia

ISBN: (纸本)9781538694220

The Ministry of Marine Affairs and Fisheries (MMAF) carry out the responsibilities and features associated with the policy of marine and fisheries. MMAF carry out the responsibilities and features associated with marine and fisheries policy. One of their duties and functions are organized marine and fishery statistics, in accordance to Law of The Republic of Indonesia Number sixteen of 1997 regarding statistics. The main problem is the number of enumerators at each Basis Landing Of Fish. This paper proposed a fish classification on fish images using transfer learning and Matlab as the first stage of tackling the problem. FishNet is a modification from AlexNet to classify Katsuwonus Pelamis (Skipjack tuna or Cakalang), Euthynnus Affinis (Tongkol) and Coryphaena Hippurus (Mahi-mahi) that caught by fishermen. There are 15.120 images of 3 type of fishes, 5.040 for each fish. data is split into 70: 30 for training and validation set. The training process is done using Matlab 2018a on Windows 7 operating system in a notebook with single CPU i7 with 8 GB RAM for 124 minutes. The validation accuracy is 99.63%.

关键词： Image classification machine learning Marine animals

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machine learning Models for the recognition of Commands in Smart Home Technologies 1

Machine Learning Models for the Recognition of Commands in S...

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1st international Conference on Smart Automation and Robotics for Future Industry, SMARTINDUstRY 2024

作者： Mochurad, Lesia Babii, Viktoriia Greguš, Michal Lviv Polytechnic National University 12 Bandera street Lviv79013 Ukraine Faculty of Management Comenius University Bratislava Odbojárov 10 Bratislava 25820 05 Slovakia

Researching the possibilities of using smart home technologies to support the independent life of lonely elderly people involves considering various aspects that can improve their comfortable and safe stay at home. For instance, health monitoring systems, access to medical information resources, nutritional care, home security, assistance in everyday affairs. The main purpose of this work is to research the application of data analysis methods and machine learning to develop a model that can recognize commands and perform various functions to ensure a comfortable and safe life for older people. machine learning models used to recognize smart home commands such as Random Forest (RF), Support vector method (SVM), XGBoost, CatBoost, Multilayer Perceptron (MLP), Lightgradient boosted machine (LGBM) was analyzed. The models that were trained used data collected from Kaggle, a public repository of datasets. Optimal parameters in machine learning algorithms for the classification of smart home commands were determined. As a result of the comparative analysis conducted according to the experimental assessment, the MLP deep learning model demonstrated better results compared to other methods. The proposed model achieved accuracy at 91%. This indicator can be improved in the future by using different deep learning models. © 2024 Copyright for this paper by its authors.

关键词： Deep learning

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