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Design and Implementation of Campus Service System Platform Based on Big data 22

Design and Implementation of Campus Service System Platform ...

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22nd IEEE international conference on Computational Science and engineering (IEEE CSE) / 17th IEEE international conference on Embedded and Ubiquitous Computing (IEEE EUC)

作者： Zheng, Honglei Guo, Hong Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan Peoples R China Wuhan Univ Sci & Technol Coll Comp Sci & Technol Hubei Prov Key Lab Intelligent Informat Proc & Wuhan Peoples R China

ISBN: (纸本)9781728116648

In recent years, major universities have attached great importance to digital information construction, but the survey found that there are still areas for improvement in the design of the content blocks that students are most concerned about, and the processing speed of database data needs to be optimized. Campus service system platform based on the big - data studies the optimization method, and five optimization ideas are proposed. The first is schema optimization;the second is index optimization;the third is query performance optimization;the fourth is based on server optimization;the fifth is based on operating system optimization. Applying these optimization schemes, it is found that the speed of data processing is obviously improved, and the response time is shortened, which proves that the implementation of the system is feasible and the optimization effect is remarkable.

关键词： big data index query optimization

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2nd international conference on Computational Intelligence in Pattern Recognition, CIPR 2020

2nd International Conference on Computational Intelligence i...

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2nd international conference on Computational Intelligence in Pattern Recognition, CIPR 2020

ISBN: (纸本)9789811524486

The proceedings contain 50 papers. The special focus in this conference is on Computational Intelligence in Pattern Recognition. The topics include: Deep learning-Based automated Feature engineering for Rice Leaf Disease Prediction;literature Survey on Log-Based Anomaly Detection Framework in Cloud;feature Extraction and Disease Prediction from Paddy Crops Using data Mining Techniques;estimating Software Effort Using Neural Network: An Experimental Investigation;statistical Analysis on Efficacy of Yagya Therapy for Type-2 Diabetic Mellitus Patients Through Various Parameters;a Proposed Gene Selection Approach for Disease Detection;Deep Recurrent Neural Network (Deep-RNN) for Classification of Nonlinear data;elephant Herding Optimization for Multiprocessor Task Scheduling in Heterogeneous Environment;analysis of Parallel M5P and Random Forest Regression for Visualization of Traffic Behavior;nearest Neighbor-Based Differential Evolution for Reconstructing Gene Regulatory Network;software Effort Estimation Using Particle Swarm Optimization: Advances and Challenges;modern Trends in Monitoring and Analysis of Chemical Pesticides by Using Artificial Neural Networks;semi-supervised Change Detection Technique on Remote Sensing Images Using Posterior Probabilities;Feature engineering for Cardio-Thoracic Disease Detection from NIH Chest Radiographs;wireless Body Area Network: An Application of IoT and Its Issuses—A Survey;enhancing Security for Communication in Wireless Sensor Network;dimension Reduction of Microarray data Using Gene Ontology and Correlation Filter;modified Contrast Enhancement Algorithm Using Numerical Integration Simpson’s Method;WHMS: An Efficient Wireless NoC Design for Better Communication Efficiency;data Hiding Based on Decagon-Shaped Shell;inference-Based Statistical Analysis for Suspicious Activity Detection Using Facial Analysis;lightweight Secure Routing Over Vehicular Ad Hoc Networks with Traffic Status.

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Prediction of Cesarean Childbirth using Ensemble Machine learning Methods 20

Prediction of Cesarean Childbirth using Ensemble Machine Lea...

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Proceedings of the 22nd international conference on Information Integration and Web-based Applications & Services

作者： Nafiz Imtiaz Khan Tahasin Mahmud Muhammad Nazrul Islam Sumaiya Nuha Mustafina Dept. of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

ISBN: (纸本)9781450389228

Cesarean section around the world is increasing at an alarming rate. Cesarean section, on one hand, may introduce different short-term and long-term complications for mother; on another hand it may be a life-saving procedure for both mother and child, depending on childbirth complications. The purpose of this research is to predict whether or not the cesarean section is necessary with the help of data mining and consequently, increasing the safety of the mother and newborn during and after childbirth by avoiding unnecessary cesarean section. To attain the objective three different ensemble prediction models based on- XGBoost, AdaBoost and Catboost were developed. As an outcome XGBoost showed the highest accuracy-88.91% while AdaBoost showed 88.69% accuracy and Catboost showed 87.66% accuracy. This research also revealed that amniotic liquid, medical indication, fetal intrapartum ph, number of previous cesareans, pre-induction are the most influential features for predicting the target outcome accurately.

关键词： ensemble methods XGBoost data mining machine learning Catboost cesarean delivery prediction childbirth AdaBoost

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Bridging the gap between smart home platforms and machine learning using relational reference attribute grammars 19

Bridging the gap between smart home platforms and machine le...

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Proceedings of the 22nd international conference on Model Driven engineering Languages and Systems

作者： René Schöne Johannes Mey Boqi Ren Uwe Aßmann Technische Universität Dresden

ISBN: (纸本)9781728151250

Machine learning improves the development of self-adaptive systems, i.e., to react to unforeseen situations. However, state-of-the-art smart home platforms lack the ability to integrate machine learning models directly into their knowledge base. While there are machine learning approaches for smart home environments, they are usually not integrated nor represented in the middleware of smart environments. Instead, they rely on data obtained previously and use it in a form prepared for a single use case. To remedy this, our aim is to extend existing middleware platforms to integrate machine learning algorithms as first-class concepts of the knowledge base. To achieve this, we employ relational Reference Attribute Grammars to design and implement an integrated runtime model, where machine learning models can be represented and related to elements of the extended knowledge base, e.g., physical entities, location, users, and activities. Consequently, this enables using a state-of-the-art middleware to build a self-adaptive system, which integrates machine learning algorithms enabling both context-awareness and self-awareness. To showcase the feasibility of our approach, we implemented a small smart home scenario using openHAB as a middleware, in which the system learns the preferences of a user using neural networks.

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Construction of human-robot cooperation assembly simulation system based on augmented reality 12th

Construction of human-robot cooperation assembly simulation ...

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12th international conference on Virtual, Augmented and Mixed Reality, VAMR 2020, held as part of the 22nd international conference on Human-Computer Interaction, HCII 2020

作者： Wang, Qiang Fan, Xiumin Luo, Mingyu Yin, Xuyue Zhu, Wenmin Institute of Intelligent Manufacturing and Information Engineering School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Lab of Advanced Manufacturing Environment Shanghai200030 China

ISBN: (纸本)9783030496944

Human-Robot cooperation (HRC) is the developing trend in the field of industrial assembly. Design and evaluation of the HRC assembly workstation considering the human factor is very important. In order to evaluate the transformational construction scenario of a manual assembly workstation to a HRC workstation fast and safely, a HRC assembly simulation system is constructed which is based on Augmented Reality (AR) with human-in-loop interaction. It enables a real operator to interact with virtual robot in a real scene, and the assembly steps of real workers can be restored and mapped to a virtual human model for further ergonomic analysis. Kinect and LeapMotion are used as the sensors for human-robot interaction decision and feedback. An automobile gearbox assembly is taken as an example for different assembly task verification, operators’ data are collected and analyzed by RULA scores and NASA-TLX questionnaires. The result shows that the simulation system can be used for the human factor evaluation of different HRC task configuration schemes. © Springer Nature Switzerland AG 2020.

关键词： Augmented reality

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SciBERT-based semantification of bioassays in the open research knowledge graph 22

SciBERT-based semantification of bioassays in the open resea...

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22nd international conference on Knowledge engineering and Knowledge Management - Posters and Demonstrations Session, EKAW-PD 2020

作者： Anteghini, Marco D'Souza, Jennifer Dos Santos, Vitor A.P. Martins Auer, Sören Lifeglimmer GmbH Markelstr. 38 Berlin12163 Germany Wageningen University and Research Laboratory of Systems and Synthetic Biology Stippeneng 4 Wageningen6708 WE Netherlands TIB Leibniz Information Centre for Science and Technology Hannover Germany

As a novel contribution to the problem of semantifying bio- logical assays, in this paper, we propose a neural-network-based approach to automatically semantify, thereby structure, unstructured bioassay text descriptions. Experimental evaluations, to this end, show promise as the neural-based semantification significantly outperforms a naive frequencybased baseline approach. Specifically, the neural method attains 72% F1 versus 47% F1 from the frequency-based method. The work in this paper aligns with the present cutting-edge trend of the scholarly knowledge digitalization impetus which aim to convert the long-standing document-based format of scholarly content into knowledge graphs (KG). To this end, our selected data domain of bioassays are a prime candidate for structuring into KGs. © 2020 CEUR-WS. All rights reserved.

关键词： Machine learning

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Generative Pseudorehearsal Strategy for Fault Classification Under an Incremental learning 22

Generative Pseudorehearsal Strategy for Fault Classification...

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22nd IEEE international conference on Computational Science and engineering (IEEE CSE) / 17th IEEE international conference on Embedded and Ubiquitous Computing (IEEE EUC)

作者： Lee, Subin Baek, Jun-Geol Korea Univ Div Ind Management Engn Seoul South Korea

ISBN: (纸本)9781728116648

As fault classification becomes more important in manufacturing industry, the state-of-art machine learning methods have been utilized. However, owing to the problem called catastrophic forgetting, the networks tend to forget the former knowledge. Thus, it is evident that overall classification performance has fallen, when training the existing model with new classes. We propose classification model that retains previous information using generative pseudorehearsal networks. In this method, newly arrived fault classes could be trained on same network which is parameterized by former data. The proposed method shows significant experimental results comparing to non-incremental methods, while achieving memory efficiency and solving the class imbalance problem.

关键词： incremental learning pseudorehearsal strategy generative networks fault diagnostics and classification

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When DevOps meets meta-learning: a portfolio to rule them all 19

When DevOps meets meta-learning: a portfolio to rule them al...

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Proceedings of the 22nd international conference on Model Driven engineering Languages and Systems

作者： Benjamin Benni Mireille Blay-Fornarino Sébastien Mosser Frédéric Précioso Günther Jungbluth Université Côte d'Azur France Université du Québec à Montréal Canada

ISBN: (纸本)9781728151250

The Machine learning (ML) world is in constant evolution, as the amount of different algorithms in this context is evolving quickly. Until now, it is the responsibility of data scientists to create ad-hoc ML pipelines for each situation they encounter, gaining knowledge about the adequacy between their context and the chosen pipeline. Considering that it is not possible at a human scale to analyze the exponential number of potential pipelines, picking the right pipeline that combines the proper preprocessing and algorithms is a hard task that requires knowledge and experience. In front of the complexity of building a right ML pipeline, algorithm portfolios aim to drive algorithm selection, learning from the past in a continuous process. However, building a portfolio requires that (i) data scientists develop and test pipelines and (ii) portfolio maintainers ensure the quality of the portfolio and enrich it. The firsts are the developers, while the seconds are the operators. In this paper, we present a set of criteria to be respected, and propose a pipeline-based meta-model, to support a DevOps approach in the context of Machine learning Pipelines. The exploitation of this meta-model, both as a graph and as a logical expression, serves to ensure continuity between Dev and Ops. We depict our proposition through the simplified study of two primary use cases, one with developer's point-of-view, the other with ops'.

关键词： composition meta-learning machine learning pipeline portfolio generation

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Collaborative Health Care Plan through Crowdsource data using Ambient Application 22

Collaborative Health Care Plan through Crowdsource Data usin...

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22nd IEEE international Multi Topic conference (INMIC)

作者： Sarwar, Muhammad Usman Javed, Abdul Rehman Natl Univ Comp & Emerging Sci Dept Comp Sci Islamabad Pakistan

ISBN: (纸本)9781728140018

A collaborative health care plan using a multi-agent system assists adult individuals to live an independent healthy life by analyzing their routine life activities. Robust recognition of activities provides services such as health monitoring and fitness assessment. In this paper, we propose Collaborative Health Care Plan a novel system, to improve the independent living of an individual using a smartphone sensor, machine learning algorithm, multiple agents i.e. doctor, gym trainer, guardian, and intelligent ranker agent. The novelty of the devised approach is that it shares the daily life assessment of activities among care provides which in return provides a care plan or recommendation to ensure good health of the individual. We use a machine learning algorithm to recognize the adult individual's daily life physical activities. This system provides recommendations by care providers as well as suggest an optimal care plan decided by an intelligent autonomous agent using crowdsource data.

关键词： ambient intelligence multi-agent system wellness detection human-agent interaction crowdsourcing healthcare activity recognition

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Safe Facial Multi-attribute Feature Fusion Analysis Based on Embedded Multi-Task learning 22

Safe Facial Multi-attribute Feature Fusion Analysis Based on...

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22nd IEEE international conference on Computational Science and engineering (IEEE CSE) / 17th IEEE international conference on Embedded and Ubiquitous Computing (IEEE EUC)

作者： Liu, Haibo Li, Qianmu Meng, Shunmei Rong, Zhenbang Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Wuyi Univ Intelligent Mfg Dept Jiangmen Peoples R China

ISBN: (纸本)9781728116648

As the main branch of face analysis, facial multi-attribute analysis has a very broad application prospect in the fields of public security monitoring and human-computer interaction. Most deep learning methods have problems such as face alignment, complex network structure, and low computation speed. This paper analyses the potential correlation between facial multi-attributes, and proposes a Embedded Multi-Task learning-based Safe Facial Multi-attribute Feature Fusion analysis model. Using Embedded Multi-Task learning strategy and feature fusion method, a single neural network is used to compute the facial region bounding box and analysis multiple facial attributes. This method realizes rapid analysis of multi-attributes of unconstrained faces.

关键词： Public safety Facial multi-attribute Feature fusion Embedded Multi-Task learning

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