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Detecting MRSA Infections by Fusing Structured and Unstructured Electronic Health Record data 11th

Detecting MRSA Infections by Fusing Structured and Unstructu...

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11th international Joint conference on Biomedical engineering Systems and Technologies (BIOSTEC)

作者： Hartvigsen, thomas Sen, Cansu Rundensteiner, Elke A. Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (纸本)9783030291969;9783030291952

Methicillin-resistant Staphylococcus aureus (MRSA), an antibiotic resistant bacteria, is a common cause of one of the more devastating hospital-acquired infections (HAI) in the United States. In this work, we study the practicality of leveraging machine learning methods for early detection of MRSA infections based on a rich variety of patient information commonly available in modern Electronic Health Records (EHR). We explore heterogeneous types of data in EHRs including on-admission demographics, throughout-stay time series and free-form clinical notes. On-admission data capture non-clinical information (e.g., age, marital status) while throughout-stay data include vital signs, medications, laboratory studies, and other clinical assessments. Clinical notes, free-from text documents created by medical professionals, contain expert observations about patients. Our proposed system generates dense patient-level representations for each data type, extracting features from each of our data types. It then generates scores for each patient, indicating their risk of acquiring MRSA. We evaluate prediction performance achieved by core Machine learning methods, namely Logistic Regression, Support Vector Machine, and Random Forest, when mining these different types of EHR data retrospectively to detect patterns predictive of MRSA infection. We evaluate classification performance using MIMIC III a critical care data set comprised of 12 years of patient records from the Beth Israel Deaconess Medical Center Intensive Care Unit in Boston, MA. Our experiments show that while all types of data contain predictive signals, the fusion of all sources of data leads to the most effective prediction accuracy.

关键词： MRSA Machine learning Early prediction Feature fusion

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Real Time Weather Prediction System Using IOT and Machine learning

Real Time Weather Prediction System Using IOT and Machine Le...

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international conference on Signal Processing and Communication (ICSC)

作者： Gaurav Verma Pranjul Mittal Shaista Farheen Department of Electronics & Communication Jaypee Institute of Information Technology Noida U.P. India Department of Electronics & Communication Dayananda Sagar College of Engineering Bangalore Karnataka India

ISBN: (数字)9781728154930

ISBN: (纸本)9781728154947

Today's the focus has been shifted towards intelligent technologies like IoT and Machine learning. Many IoT hardware platforms are available for IoT implementations. ESP8266 chip is one of them. this paper implements the real time weather prediction system that can be used in number of applications like homes, industries, agriculture, stadiums etc. for predicting the weather information. the system utilizes a temperature and humidity sensor i.e. DHT11 and a light intensity sensor i.e. LDR. the sensed data from the sensors are uploaded to a thingSpeak cloud server using NodeMCU and ESP8266-01 module. the data is also displayed on a customized HTML webpage for monitoring the real time values. A logistic regression model is used for setting up the machine learning environment. this model is trained using the pre- recorded values of sensor data. Further, NodeMCU records the data from sensors i.e. temperature, humidity and light intensity and then the values are transferred to the Jupyter notebook that utilizes a python environment. this real time data is used to test the model and prediction is done for a particular value by blinking the led connected to NodeMCU.

关键词： Humidity Temperature measurement Temperature sensors Weather forecasting data mining Real-time systems

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Hierarchical Temporal Memory Based Machine learning for Real-Time, Unsupervised Anomaly Detection in Smart Grid: WiP Abstract

Hierarchical Temporal Memory Based Machine Learning for Real...

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IEEE/ACM international conference on Cyber-Physical Systems (ICCPS)

作者： Anomadarshi Barua Deepan Muthirayan Pramod P. Khargonekar Mohammad Abdullah Al Faruque Electrical Engineering and Computer Science University of California Irvine

ISBN: (数字)9781728155012

ISBN: (纸本)9781728155029

Micro-phasor measurement unit (μPMU) sensors in smart electric grids provide measurements of voltage and current at microsecond timescale across the network and have great potential value for grid diagnostics. In this work, we propose a novel neuro-cognitive inspired architecture based on Hierarchical Temporal Memory (HTM) for real-time anomaly detection in smart grid using μPMU data. the key technical idea is that the HTM learns a sparse distributed temporal representation of sequential data that turns out to be very useful for anomaly detection in *** numerical results show that the proposed HTM architecture can predict anomalies with 96%, 96%, and 98% accuracy for three different application profiles namely, Standard, Reward Few False Positive, Reward Few False Negative, respectively. the performance is compared with three state-of-the-art real-time anomaly detection algorithms and HTM demonstrates competitive score for real-time anomaly detection in μPMU data.

关键词： Anomaly detection Real-time systems Phasor measurement units Neurons Smart grids Computer architecture Sensors

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A Hybrid CNN Approach for Single Image Depth Estimation: A Case Study 11th

A Hybrid CNN Approach for Single Image Depth Estimation: A C...

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11th international conference on Multimedia and Network Information Systems (MISSI)

作者： Harsanyi, Karoly Kiss, Attila Majdik, Andras Sziranyi, Tamas MTA SZTAKI Machine Percept Res Lab Budapest Hungary BME Fac Transportat Engn & Vehicle Engn Budapest Hungary

ISBN: (纸本)9783319986784;9783319986777

three-dimensional scene understanding is an emerging field in many real-world applications. Autonomous driving, robotics, and continuous real-time tracking are hot topics within the engineering society. One essential component of this is to develop faster and more reliable algorithms being capable of predicting depths from RGB images. Generally, it is easier to install a system with fewer cameras because it requires less calibration. thus, our aim is to develop a strategy for predicting the depth on a single image as precisely as possible from one point of view. there are existing methods for this problem with promising results. the goal of this paper is to advance the state-of-the-art in the field of single-image depth prediction using convolutional neural networks. In order to do so, we modified an existing deep neural network to get improved results. the proposed architecture contains additional side-to-side connections between the encoding and decoding branches.

关键词： Depth estimation Deep learning CNN

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Chatbot: A Deep Neural Network Based Human to Machine Conversation Model

Chatbot: A Deep Neural Network Based Human to Machine Conver...

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international conference on Computing and Networking Technology (ICCNT)

作者： G Krishna Vamsi Akhtar Rasool Gaurav Hajela Computer science and engineering Maulana Azad National Institute Of Technology Bhopal India

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

A conversational agent (chatbot) is computer software capable of communicating with humans using natural language processing. the crucial part of building any chatbot is the development of conversation. Despite many developments in Natural Language Processing (NLP) and Artificial Intelligence (AI), creating a good chatbot model remains a significant challenge in this field even today. A conversational bot can be used for countless errands. In general, they need to understand the user's intent and deliver appropriate replies. this is a software program of a conversational interface that allows a user to converse in the same manner one would address a human. Hence, these are used in almost every customer communication platform, like social networks. At present, there are two basic models used in developing a chatbot. Generative based models and Retrieval based models. the recent advancements in deep learning and artificial intelligence, such as the end-to-end trainable neural networks have rapidly replaced earlier methods based on hand-written instructions and patterns or statistical methods. this paper proposes a new method of creating a chatbot using a deep neural learning method. In this method, a neural network with multiple layers is built to learn and process the data.

关键词： Chatbot Deep learning Software Medical services Training Task analysis Feature extraction

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Minimally produced inkjet-printed tactile sensor model for improved data reliability

Minimally produced inkjet-printed tactile sensor model for i...

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international conference on Electrical and Computer engineering (ICECE)

作者： Steven D. Gardner J. Iwan D. Alexander Yehia Massoud Mohammad R. Haider School of Engineering University of Alabama at Birmingham Birmingham AL USA School of Systems and Enterprises Stevens Institute of Technology Hoboken NJ USA

ISBN: (数字)9781665422543

ISBN: (纸本)9781665446617

Inkjet-printing as an on-the-go, inexpensive, and green method of creating instant flexible sensors and circuits will not proliferate until reliable device fabrication is possible outside the research environment. Shortfalls exist due to non-uniform fabrication/curing, environmental humidity/temperature influence, and uncontrollable deposition conditions, particularly in low-production setups. Electrical non-uniformity and variations from low-quality prints made by a minimally produced inkjet-printed sensor may be overcome by training a machine learning model to interpret the variabilities and output a high-confidence prediction of the signal. In this report, an inkjet-printed tactile sensor is modeled to simulate generate a rich data-set for training and testing an echo state network. the end goal of the reported work is to attach the echo state network to the imperfect, on-the-go, inkjet-printed sensor as an edge computing device, transforming the unreliable data into a more stable readout. In this way, the sensor design may be printed using any suitable inkjet-printer with minimal production effort and still extract reliable data. this enables inkjet-printers to be used at home by those in isolated/restrictive settings, poor communities, resource starved environments, or by enthusiasts. Applications include biometric, environmental, electro-chemical and -mechanical sensing, and the concept may be extended to inkjet-printed circuits for signal stabilization.

关键词： Training Computer network reliability Tactile sensors data models Sensors Mathematical model Integrated circuit reliability

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learning interpretable prefix-based patterns from demographic sequences 11th

Learning interpretable prefix-based patterns from demographi...

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11th international conference on intelligent data Processing, IDP 2016

作者： Gizdatullin, Danil Baixeries, Jaume Ignatov, Dmitry I. Mitrofanova, Ekaterina Muratova, Anna Espy, thomas H. National Research University Higher School of Economics Moscow Russia Universitat Politècnica de Catalunya Barcelona Spain University of Pittsburgh Pittsburgh United States St. Petersburg Department of Steklov Mathematical Institute of Russian Academy of Sciences Saint Petersburg Russia

ISBN: (纸本)9783030353995

there are many different methods for computing relevant patterns in sequential data and interpreting the results. In this paper, we compute emerging patterns (EP) in demographic sequences using sequence-based pattern structures, along with different algorithmic solutions. the purpose of this method is to meet the following domain requirement: the obtained patterns must be (closed) frequent contiguous prefixes of the input sequences. this is required in order for demographers to fully understand and interpret the results. © Springer Nature Switzerland AG 2019.

关键词： Machine learning

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A hybrid predicting model for displacement of multifactor-triggered landslides 11

A hybrid predicting model for displacement of multifactor-tr...

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11th international conference on Advanced Computational Intelligence (ICACI)

作者： Deng, Honggao Guan, Shanwen Ji, Yuanfa Zhou, Li Luo, Xiaonan Guilin Univ Elect Technol Natl & Local Joint Engn Res Ctr Satellite Nav & L Guilin 541004 Peoples R China Guilin Univ Elect Technol Gunagxi Key Lab Precis Nav Technol & Applicat Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Intelligent Proc Comp Images & Gr Guilin 541004 Peoples R China

ISBN: (纸本)9781538677322

this paper presents a new hybrid model 14 landslide distance prediction. In the model, the cumulative displacement are divided into three parts: the trend term, the period term, and the random noise obtained by the wavelet domain de-nosing method and Hodrick-Prescott (IIP) filter. the trend term controlled by the geological conditions is generated using the double exponential smoothing (DES). the period term is predicted by the extreme learning machine (ELM) model, and the dynamic multi-swarm particle swarm optimizer (DMS-PSO) algorithm is applied to obtain optimal parameters of ELM. Case study involving real data collected from the Baishuihe landslide in China is used to verify that the hybrid approach enhances the ability to calculate the period term. Inputs of the proposed model include the period factors extracted from the seasonal triggers and displacement values which enhance excellently the robustness of the prediction model of the period displacement. Extensive experiments are carried out on the Baishuihe landslide dates. Comparing with the predictions obtained by the real original displacement , our model is efficient for predicting the landslide distance of multiple factors induced landslide.

关键词： Landslide displacement prediction Multiple factors Double exponential smoothing Extreme learning machine DMS-PSO

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11th Annual conference of the Prognostics and Health Management Society, PHM 2019

作者： Jia, Xiaodong Cai, Haoshu Hsu, Yuanming Li, Wenzhe Feng, Jianshe Lee, Jay NSF I/UCR Center for Intelligent Maintenance Systems Department of Mechanical Engineering University of Cincinnati PO Box 210072 CincinnatiOH45221-0072 United States

ISBN: (纸本)9781936263295

When abundant historical Run-to-Failure (R2F) data is available, the similarity-based method is one of the favored options for Remaining Useful Life (RUL) prediction due to its simplicity and satisfactory accuracy. In this study, a novel similarity-based methodology for RUL prediction is proposed. the proposed method has two important processing steps: Similarity matching and Weibull fitting. the similarity matching screens the historical records by a similarity testing called Kernel Two Sample Test (KTST), and only those records that pass KTST are adopted as references for RUL prediction. For the selected similar records, the RUL is predicted as the remaining time to failure. the Weibull fitting fuses the multiple RUL predictions given by similar historical records. the PDF of RUL is estimated as the fitted Weibull distribution. To demonstrate the effectiveness and superiority of the proposed method, the famous C-MAPSS (Modular Aero-Propulsion System Simulations) data about aero-engine degradation is adopted for model validation. the results demonstrate improved prediction accuracy comparing with other similarity-based approaches and the state-of-the-art deep learning predictors. © 2019 Prognostics and Health Management Society. All rights reserved.

关键词： Weibull distribution

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A neural network go rating model considering winning rate 3

A neural network go rating model considering winning rate

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3rd international conference on Computer Science and Artificial Intelligence, CSAI 2019 and its Workshop - the 11th international conference on Information and Multimedia Technology, ICIMT 2019

作者： Zhao, Rui Dang, Ruoqi Zhao, Yinliang School of Computer Science and Technology Xi'an Jiaotong University Xianning West Road Xi'an Shaanxi710049 China School of Aerospace Engineering Xi'an Jiaotong University Xianning West Road Xi'an Shaanxi710049 China

ISBN: (纸本)9781450376273

the existing rating system, as the foundation of estimating results of competitive sports and games, has problems of insufficient use of competing information and the unilateral considering of competing process modeling. In this paper, the Artificial Intelligence (AI) of go based on reinforcement learning and Monte Carlo Tree Search (MCTS) was used to obtain the winning rate during progress of the matches. Also, the Neural Network Go Rating (NNGR) model considering the winning rate was established on the basis of classical Bradley-Terry model, using Mini-Batch Gradient Descent Method to calculate efficiently. History decay method was adopted to improve the time efficiency when dealing with long-span match data. the experiment results and analysis on actual go matches indicated that NNGR model showed excellent objectivity and stability, and higher prediction accuracy compared with Elo, TrueSkill and Whole-History Rating (WHR) algorithms. © 2019 Association for Computing Machinery.

关键词： Neural network models

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