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5th international conference on Human Centered computing, HCC 2019

5th International Conference on Human Centered Computing, HC...

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5th international conference on Human Centered computing, HCC 2019

ISBN: (纸本)9783030374280

The proceedings contain 71 papers. The special focus in this conference is on Human Centered computing. The topics include: A real-time update approach for visualizing multidimensional big data;cultural psychology analysis on ancient chinese archive;real-time collaborative annotation system supporting separation of content and annotation;an advanced membrane evolutionary algorithm for constrained engineering design problems;aspect sentiment classification based on sequence to sequence reinforced learning;Performance-improved UCD-based hybrid precoding for millimeter-wave massive MIMO single user systems;evaluation of augmented reality occlusion scheme based on analytic hierarchy process;suicidal ideation detection via social media analytics;the latent semantic power of labels: Improving image classification via natural language semantic;clustering method for low voltage substation-area users based on edge computing architecture;mobile offloading in vehicular networks: A reward-based incentive caching scheme;different performances of speech and natural gait in identifying anxiety and depression;global anomaly detection based on a deep prediction neural network;SCHONA: A scholar persona system based on academic social network;customer evaluation-based automobile after-sale service multi-value-chain collaborative mechanism verification;research on the construction of sharing service model in fresh e-commerce cold storage;usability testing of a smartphone telecare application for informal caregivers;high efficiency permanent magnet synchronous motor used in electric vehicle;the method of urban intelligent address coding based on spatiotemporal semantics;advanced human centric 5G-IoT in a smart city: Requirements and challenges;Design of electrical equipment integration system for LNG single-fuel bulk carrier;early prediction of student success based on data mining and artificial neural network.

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intelligent Navigation System for the Visually Impaired - A Deep Learning Approach

Intelligent Navigation System for the Visually Impaired - A ...

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international conference on computing Methodologies and Communication (ICCMC)

作者： Deepak Kumar Yadav Somsankar Mookherji Joanne Gomes Siddhant Patil Department of Information Technology St. Francis Institute of Technology Mumbai University

ISBN: (数字)9781728148892

ISBN: (纸本)9781728148908

Visually impaired individuals have been gradually claiming a significant stake in the population demographics. The proposed autonomous device aims to provide a holistic solution by engineering a smart navigation system that relentlessly scans the environment, detects and classifies neighboring objects using a 4 layered Convolutional Neural Network (CNN) that has been trained on a data set containing 2513 permutations of various images of household objects that an individual may encounter in daily life. The CNN follows the 80-20 rule for testing and training the self-learning model enabling it to learn recursively from the error rate. The proposed system then calculates distances of neighboring objects from the user and provides adaptive solutions in real time to manoeuvre the user to safety by providing auditory input in a simplistic manner which considers 10-24 frames per second while drafting the kinematic response for the user. The device has achieved an unprecedented success rate of serving within a response time of less than 50 ms. The accuracy of the CNN algorithm being at 94.6%, also sets a distinguished benchmark as an object detection algorithm thereby contributing to the success in simulations of the proposed device in a constrained environment.

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A traceability set up using Digitalization of data and Accessibility

A traceability set up using Digitalization of Data and Acces...

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international conference on intelligent Sustainable Systems (ICISS)

作者： J. Aruna Jasmine V. Nisha Jenipher J. S. Richard Jimreeves K. Ravindran D. Dhinakaran Sri Sairam Engineering College St. Joseph's Institute of Technology Easwari Engineering College Velammal Institute of technology

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

The main intention of this development is to build a smart outcome for managing and maintaining data digitally using radiofrequency digitalization (RFID) tags and Quick Response code (QR) codes. The component traceability system either requires significant human effort and supervision or outsized amounts of subsidy and capital. This task provides better inventory supervision through enhanced clarity and organization of resources. The RFID-QR employed in this project is to afford admittance to the Records 24×7 that are collected via RFID tags-QR codes. Automated data compilation facilitates to improve the precision of conclusion building processes. RFID tool facilitates to evade vast disasters and upset origin by short of fact concerning the methodological state of rail coaches. The QR code supervision system mitigates some of the human efforts. This supervision system works via one application openly reachable to certified users. The Real-Time Dynamics monitoring System (RTDMS) solution is designed to manipulate manifold facilities or allow numerous users to scrutiny fact at a solitary competence. This method is employed in the cloud server and has password fortification, which averts unauthorized users from screening or revises facts.

关键词： Servers RFID tags Cloud computing Rails Industries Task analysis Organizations

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Desirability/Rarity-based Ranks in the Prediction of Acceptability as a Presurfacing Step for Products

Desirability/Rarity-based Ranks in the Prediction of Accepta...

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international conference on Computer and Information Technology (ICCIT)

作者： Shithi Maitra Atanu Das Bapon Data Science Analytics and Insights — DS(Ai) Sheba Platform Limited (Sheba.xyz) Dhaka Bangladesh Ahsanullah University of Science and Technology Dhaka Bangladesh

ISBN: (数字)9781665422444

ISBN: (纸本)9781665446686

Artificially intelligent systems simulate humanoid qualities in terms of problem-solving by learning, precluding the need for explicit programming. This mimicry of human behavior can be greatly useful in Business Intelligence owing to its potential of predicting how a user group may react to a particular product. This constitutes a Machine Learning task (T), which improves its predictive performance (P) with the increase in experience (E). This paper attempts to build a User Experience-centric supply rating system to estimate Customer Satisfaction on a scale of one to five by taking into account three factors: (a) the opinions of the users from (b) variegated perspectives of the listings and (c) tangible features thereof. The research starts out by hard, statistical computing in that it initially constructs a ranking system that adjusts weights of five different criteria based on rarity, only after assuring their distinctiveness by a statistical ANOVA. Inferential statistics is further applied to sort attributes affecting the ranks after which a labeled dataset is prepared and divided by clubbing certain sequential ranks. Finally a learning-based soft computing algorithm is applied to recognize the patterns and assign the ratings, with zero rankspecific priors. A Neural Network was employed which could approximate with above 97% accuracy on each of its k-fold cross-validations. Thus the efficacy of the qualitative desirability (also, scarcity)-based ranker was established by a case study on travelogue data, showing great potential to be extended over other businesses as well.

关键词： Ranking (statistics) Neural networks Machine learning Programming Pattern recognition Problem-solving Task analysis

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4th international conference on Computational Intelligence in data Mining, ICCIDM 2017

4th International Conference on Computational Intelligence i...

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4th international conference on Computational Intelligence in data Mining, ICCIDM 2017

ISBN: (纸本)9789811080548

The proceedings contain 78 papers. The special focus in this conference is on Computational Intelligence in data Mining. The topics include: Analysis of Credit Card Fraud Detection Using Fusion Classifiers;an Efficient Swarm-Based Multicast Routing Technique—Review;a New Howard–Crandall–Douglas Algorithm for the American Option Problem in Computational Finance;graph Anonymization Using Hierarchical Clustering;Reevaluation of Ball-Race Conformity Effect on Rolling Element Bearing Life Using PSO;static Cost-Effective Analysis of a Shifted Completely Connected Network;dynamic Notifications in smart Cities for Disaster Management;application of Classification Techniques for Prediction and Analysis of Crime in India;improving Accuracy of Classification Based on C4.5 Decision Tree Algorithm Using Big data analytics;on Understanding the Release Patterns of Open Source Java Projects;Comparative Study of MPPT Control of Grid-Tied PV Generation by intelligent Techniques;Stability Analysis in RECS-Integrated Multi-area AGC System with SOS Algorithm Based Fuzzy Controller;log-Based Reward Field Function for Deep-Q-Learning for Online Mobile Robot Navigation;integrated Design for Assembly Approach Using Ant Colony Optimization Algorithm for Optimal Assembly Sequence Planning;Design and Performance Evaluation of Fractional Order PID Controller for Heat Flow System Using Particle Swarm Optimization;land Records data Mining: A Developmental Tool for Government of Odisha;Piecewise Modeling of ECG Signals Using Chebyshev Polynomials;Analysis of Supplementary Excitation Controller for Hydel Power System GT Dynamic Using Metaheuristic Techniques;Farthest SMOTE: A Modified SMOTE Approach;DKFCM: Kernelized Approach to Density-Oriented Clustering;A Study of High-Dimensional data Imputation Using Additive LASSO Regression Model;prediction of Gold Price Movement Using Discretization Procedure.

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Arduino based smart and intelligent helmet system for two-wheelers

Arduino based smart and intelligent helmet system for two-wh...

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IEEE Distributed computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Mahesh S Gour Druva Kumar S Pradeep Kumara Manjunatha S Sunil Kumar K Chetan H CMR Institute of Technology Bengaluru India DSCE Bengaluru India VTU centre for PG studies Kalaburgi

ISBN: (数字)9781728198859

ISBN: (纸本)9781728198866

Road accidents are becoming very common in the country. The impact of road accidents can lead to the loss of many lives and can also damage many body parts. This situation becomes more serious if the riders won't wear the helmet which can be prevented by wearing the helmet and can reduce these impacts. While riding the bike, the government made it a mandatory rule to wear the helmet. Using this rule as a base, a smart helmet system is proposed which helps in providing safety to the riders and prevents accidents. The system mainly consists of Arduino Uno as a processor for processing the data, GSM & GPS modules for tracking the location and sending a message to authorized numbers, a wiper for wiping the raindrops on the helmet screen, a vibration sensor for alerting, in case the rider meets an accident and alcohol sensors as breath analyzer for the rider. The system will ensure a safe journey for riders and gives a helping hand in case of emergency. The cost of installing the whole system onto the helmet is affordable.

关键词： Safety Head Accidents Global Positioning System Vibrations GSM Radio frequency

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AMI smart Meter Big data analytics for Time Series of Electricity Consumption 17

AMI Smart Meter Big Data Analytics for Time Series of Electr...

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17th IEEE international conference on Trust, Security and Privacy in computing and Communications (IEEE TrustCom) / 12th IEEE international conference on Big data Science and Engineering (IEEE BigdataSE)

作者： Rashid, Mohammad Harun Pace Univ New York NY 10038 USA

ISBN: (纸本)9781538643877

AMI smart meters are the advanced meters capable of measuring customer energy consumption at a fine-grained time interval, e.g., every 5 minutes, 15 minutes etc. The data are very sizable, and might be from different sources, along with the other social-economic metrics, which make the data management very complex. A smart grid is an intelligent electricity grid that optimizes the generation, distribution and consumption of electricity through the introduction of Information and Communication Technologies on the electricity grid. Deployment of smart grids gives space to an occurrence of new methods of machine learning and data analysis. smart grids can contain a millions of smart meters, which produce a large amount of data of electricity consumption (long time series). Big data technologies offers suitable solutions for utilities. This paper presents a thorough analysis of 5-minutes 100 anonymized commercial buildings meter data sets to explore time series of electricity consumption and on the creation of a simple forecast model, which uses similar day approach. Our big data analytics can help energy companies to improve the management of energy and services, support intelligent grid control, make an accurate forecast or to detect anomalies.

关键词： smart meter AMI smart grid time series big data electric consumptions forcast data analytics load profile

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A Framework for Big data Analysis in smart Cities 3rd

A Framework for Big Data Analysis in Smart Cities

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3rd international conference on Advanced Machine Learning Technologies and Applications (AMLTA)

作者： Elhoseny, Hisham Elhoseny, Mohamed Riad, A. M. Hassanien, Aboul Ella Mansoura Univ Fac Comp & Informat Mansoura Egypt Cairo Univ Fac Comp & Informat Giza Egypt Sci Res Grp Egypt SRGE Cairo Egypt

ISBN: (纸本)9783319746906;9783319746890

Due to the rapid change in technologies, new data forms exist which lead to a huge data size on the internet. As a result, some learning platforms such as e-learning systems must change their methodologies for data processing to be smarter. This paper proposes a framework for smoothly adapt the traditional e-learning systems to be suitable for smart cities applications. Learning analytics (LA) has turned into a noticeable worldview with regards to instruction of late which embraces the current progressions of innovation, for example, cloud computing, big data processing, and Internet of Things. LA additionally requires a concentrated measure of preparing assets to create applicable investigative outcomes. Be that as it may, the customary methodologies have been wasteful at handling LA difficulties.

关键词： Big data E-learning smart learning smart systems smart cities Learning analytics Internet of Things

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Towards Context-Aware Social Behavioral analytics 2019

Towards Context-Aware Social Behavioral Analytics

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Proceedings of the 17th international conference on Advances in Mobile computing & Multimedia

作者： Amin Beheshti Vahid Moraveji Hashemi Shahpar Yakhchi Macquarie University Sydney Australia

ISBN: (纸本)9781450371780

The confluence of technological and societal advances, and more specifically, engagement with Web, social media, and smart devices has the potential to affect the mental behavior of the individuals. Examples include extremist and criminal behaviors such as radicalization and cyber-bullying, which are causing serious issues for humanity. Major barriers to the effective understanding of behavioral disorders on social networks includes the ability to understand the content and context of social documents, as well as the activity of social users. Understanding the patterns of behavioral disorders (e.g., criminal and extremist activities) on social networks, is challenging and requires techniques to contextualize the content of social documents based on the time-aware analysis of personality, behaviour and past activities of social users. In this context, semantic information extraction and enrichment from social documents has the potential to become a vital asset to explore the sign of behavioral disorders and prevent serious issues such as cyber-bullying, suicidal related behavior and radicalization. To address this challenge, in this paper, we present a novel social document analysis pipeline to enable analysts engage with social documents (e.g., a Tweet in Twitter or a post on Facebook) to explore cognitive aspects of behavioral disorders. We implement the pipeline as an extensible and scalable architecture and present the evaluation results.

关键词： Behavioral analytics data Curation Context-aware Applications

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Metadata-Driven Industrial-Grade ETL System

Metadata-Driven Industrial-Grade ETL System

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IEEE international conference on Big data

作者： Alexander Suleykin Peter Panfilov Korus Consulting & Russian Academy of Sciences’ V. A. Trapeznikov Institute of Control Sciences Moscow Russia Graduate School of Business HSE University Moscow Russia

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Digital transformation of a railway system based on big data technologies relies on integrating large volumes of streaming data into digitally enabled enterprise systems to form a comprehensive and efficient intelligent transportation system. data requirements of the smart railway transportation involve a large number of unstructured data and semi-structured data including railway KPI data. Traditional ETL technology cannot cope with fast growing demands of processing large volumes of real-time data collected from heterogeneous sources both inside the system and in the environment. According to the characteristics of the railway KPI data, this paper proposes the designs of an automated ETL system with higher versatility and efficiency of data processing. To reach the goals, we optimize the workflow of the ETL using a proprietary designed metadata management framework. Making ETL suitable for big data-driven railway transportation environment, requires redesigning the ETL processing rules by using metadata model and then optimizing the extracting, transforming and loading processes of the ETL system. Our experimental results with actual railway KPI data show that the proposed metadata supported automated ETL system can effectively serve the railway KPI data processing using open source distributed big data technologies. The proposed metadata framework proved to be efficient in processing complex data structures and large data capacity of big data.

关键词： Distributed databases Transportation Cluster computing Metadata Big data data processing Rail transportation

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