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Study of EEG signals for Focus Detection for Cocktail Party Phenomenon using Multiple Sources of Sound

Study of EEG signals for Focus Detection for Cocktail Party ...

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international conference and Workshop on Computing and Communication (IEMCON)

作者： Brandon Hoang Urmil Shah Ryan Villanueva Kiran George Computer Engineering California State University Fullerton Fullerton USA

ISBN: (数字)9781728184166

ISBN: (纸本)9781728184173

there are several scenarios where individuals must focus on one conversation while multiple conversations are happening around them. this phenomenon is commonly referred to as the cocktail party phenomenon. People with impaired hearing lack this ability. the research centers around finding a solution for this problem to eventually develop a hearing aid better suited for better directional intelligibility. A *** headset is used in this study to obtain EEG data. In this study, training data is extracted from a headset placed on a test subject's head and is analyzed using a machine learning algorithm. In this study, two experiments were conducted, and data was gathered from eight test subjects. Results show a significant change in terms of accuracy depending upon the environment. the data gathered presents the test subject's ability to listen to one auditory stimulus over the other when directed to do so.

关键词： Headphones Training data Predictive models Brain modeling Mobile communication Electroencephalography Reverberation

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Robust Text Extraction in Images for Personal Event Planner

Robust Text Extraction in Images for Personal Event Planner

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international conference on Computing and Networking Technology (ICCNT)

作者： Lavanya Bhaskar R. Ranjith Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Modern smartphones are having a large number of applications for personal event scheduling. the main mode of input to those applications are text, voice-input from the user or E-Mail updates provided by certain service providers. For certain events, the details of the event would be available as hard copies of different forms like paper print outs, banners and various modes of advertisements. In such cases, the user needs to manually locate the vital information from the image then needs to update his event schedule. To minimize the load on the user, an intelligent personal scheduler has been developed which takes an image input, extracts necessary information from the image and updates the schedule of the user with appropriate alert settings. this paper discusses about an event planner based on image processing and uses Google Calendar for scheduling the events. the application makes use of Optical Character Recognition (OCR) algorithm for identification of text in the images which acts as an input to the scheduler and Google Calendar is used to update the event planner.

关键词： data mining Schedules Internet Character recognition Text recognition Optical character recognition software Image segmentation

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A RNN Based Parallel Deep learning Framework for Detecting Sentiment Polarity from Twitter Derived Textual data

A RNN Based Parallel Deep Learning Framework for Detecting S...

引用

international conference on Electrical and Computer engineering (ICECE)

作者： Sakib Chowdhury Md. Latifur Rahman Shams Nafisa Ali Md. Jahin Alam Bangladesh University of Engineering and Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (数字)9781665422543

ISBN: (纸本)9781665446617

Social media platforms have become one of the primary mediums of communication nowadays. Along with communication, they are currently being utilized in a wide range of activities like digital marketing, customer care, e-learning etc. the unceasing use of social media is generating gigantic amount of textual data everyday. It is essential to properly analyze these data with the consideration of underlying human traits sentiments for exploring the full potential of these platforms. However, sentiment analysis from text has been considered as a challenging task because of the rapid use of informal and noisy words. Updated and powerful word embeddings are being invented almost every two years so that the machines could understand the underlying features of linguistics. Each of these embedding techniques excel in different aspects. In this paper, we present a novel RNN based sentiment polarity detection framework which feeds the power of three different word embeddings: Word2Vec, GloVe and SSWE into a single powerful network with three parallel branches. the proposed network effectively utilizes the semantic, syntactic and sentiment polarity wise embeddings in word vectors encoding three major aspect of language from the viewpoint of extracting sentiment information from text. Posts collected from twitter was used to train and validate the proposed network. the results demonstrate that the proposed network containing parallelly configured multiple word embeddings outperforms the single word vectorization techniques. Additionally, it shows comparable or better evaluation scores when compared to several contemporary state-of-the-art models.

关键词： Sentiment analysis Social networking (online) Blogs Semantics Syntactics Task analysis Optimization

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Deep learning Technique in Recognizing Hand Grasps using FMG signals

Deep Learning Technique in Recognizing Hand Grasps using FMG...

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international conference and Workshop on Computing and Communication (IEMCON)

作者： Umme Zakia Xianta Jiang Carlo Menon Menrva Research Group Schools of Mechatronic systems Engineering and Engineering Science Simon Fraser University Metro Vancouver BC Canada Memorial University of Newfoundland St. John's NL Canada

ISBN: (数字)9781728184166

ISBN: (纸本)9781728184173

Grasping objects are common phenomenon in daily human activities. Force myography (FMG) signal, a noninvasive technique can record muscle movements while a human participant grasps different objects and be categorized using machine learning (ML) algorithms. In this paper, a popular deep learning technique is presented for hand grasp recognition. A novel convolutional neural network (CNN) architecture was implemented in learning grasps via force myography. Twelve participants wearing an FMG band on dominant hand's forearm performed six hand grasps. Training dataset consisted of one-handed grasping small objects of different shapes and sizes either wrapping or pinching with fingers with a variety of arm poses. the proposed FMG-based CNN model obtained cross-trial classification accuracy of 96% (population mean) and was found comparable with other ML techniques. Pretranined Alexnet (with ImageNet dataset) through transfer learning was implemented to classify the hand grasps for comparison. the proposed model outperformed the pretrained Alexnet in terms of validation accuracy, loss, and training time. For future FMG-based practical applications, it would be advantageous to use the model for transfer learning where comparatively smaller datasets are desirable for training purpose.

关键词： Training Deep learning Force Training data Grasping Convolutional neural networks Wrapping

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the Implementation of Pretrained AlexNet on PCG Classification 15th

The Implementation of Pretrained AlexNet on PCG Classificati...

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15th international conference on intelligent Computing, ICIC 2019

作者： Alaskar, Haya Alzhrani, Nada Hussain, Abir Almarshed, Fatma Computer Science Department College of Computer Engineering and Science Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia Liverpool John Moores University Liverpool United Kingdom

ISBN: (纸本)9783030267650

Heart sounds are essential components of cardiac diagnosis, in which heart conditions can be detected using phonocardiogram (PCG) signals. PCG signals provide useful information and can help in the early detection and diagnosis of heart diseases. Many studies have attempted to discover automated tools that analyse heart sounds by applying machine learning algorithms. Although tremendous efforts have been made in this area, no successful framework currently exists to detect pathology in signals because of issues with background noise or poor quality. One part of the evolution of machine learning is the development of deep learning networks, which are designed to exploit the compositional structure of data. this paper investigates the performance of a convolutional neural network called AlexNet, focusing on two approaches for distinguishing abnormality in PCG signals with data collected from the 2016 PhysioNet/CinC Challenge dataset. this dataset contains heart sound recordings collected from clinical and non-clinical environments. Our extensive simulation results indicated that using AlexNet as feature extractor and Support Vector Machine as classifier a 87% recognition accuracy was achieved, this is an improvement of 85% accuracy obtained by end-to-end learning AlexNet in comparison to the benchmarked techniques. © 2019, Springer Nature Switzerland AG.

关键词： Deep learning

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Google Trends Analysis of COVID-19 Pandemic

Google Trends Analysis of COVID-19 Pandemic

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IEEE international conference on Big data

作者： Zhenhe Pan Hoang Long Nguyen Hashim Abu-gellban Yuanlin Zhang Texas Tech University College of Engineering Texas Tech University

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

the World Health Organization (WHO) announced that COVID-19 was a pandemic disease on the 11th of March as there were 118K cases in several countries and territories. Numerous researchers worked on forecasting the number of confirmed cases since anticipating the growth of the cases helps governments adopting knotty decisions to ease the lockdowns orders for their countries. these orders help several people who have lost their jobs and support gravely impacted businesses. Our research aims to investigate the relation between Google search trends and the spreading of the novel coronavirus (COVID-19) over countries worldwide, to predict the number of cases. We perform a correlation analysis on the keywords of the related Google search trends according to the number of confirmed cases reported by the WHO. After that, we applied several machine learning techniques (Multiple Linear Regression, Nonnegative Integer Regression, Deep Neural Network), to forecast the number of confirmed cases globally based on historical data as well as the hybrid data (Google search trends). Our results show that Google search trends are highly associated with the number of reported confirmed cases, where the Deep learning approach outperforms other forecasting techniques. We believe that it is not only a promising approach for forecasting the confirmed cases of COVID-19, but also for similar forecasting problems that are associated with the related Google trends.

关键词： COVID-19 Pandemics Neural networks Predictive models Market research Internet Forecasting

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11th international conference on Wireless Internet , WiCON 2018

11th International Conference on Wireless Internet , WiCON 2...

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11th international conference on Wireless Internet , WiCON 2018

ISBN: (纸本)9783030061579

the proceedings contain 38 papers. the special focus in this conference is on Wireless Internet. the topics include: Audio event detection using wireless sensor networks based on deep learning;a robust remote authentication scheme for M-commerce environments;MIH-based eNB selection for untrusted networks;using instant messaging for collaboration: A study of the relationships among organizational trust, justice, and spirituality;application and challenges of blockchain technology to big data-based credit reference in China;a trust evaluation gateway for distributed blockchain iot network;personal data and identifiers: Some issues regarding general data protection regulations;A channel selection method for device to device (D2D) communication using the mobile edge computing (MEC) paradigm;using internet of things technology to improve patient safety in surgical instrument sterilization control;comparative study of two signal-to-noise ratio calculation methods in lte downlink simulations;reporting mechanisms for internet of things;container-based virtualization for real-time data streaming processing on the edge computing architecture;an efficient mobile ar navigation system using polygon approximation based data acquisition;location-based hotel recommendation system;developing a beacon-based location system using bluetooth low energy location fingerprinting for smart home device management;applying information quantity analysis to sold price of real estate;factors influencing online group buying in taiwan: an empirical study based on the tpb framework;empirical analysis on price-volume relation in the stock market of shanghai and shenzhen;heterogeneous goods, strategic investment, and first mover advantages: Real options theory and empirical study;analysis on excess return and risk of individual stock—the case study of China.

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Modelling and Optimization of the Cutting Parameters for the Milling Operation of Titanium Alloy (Ti6Al4V)

Modelling and Optimization of the Cutting Parameters for the...

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international conference on Mechanical and intelligent Manufacturing Technologies (ICMIMT)

作者： Daniyan Ilesanmi Isaac thlabadira Solomon Phokobye Siviwe Mrausi Khumbulani Mpofu Leonard Masu Department of Industrial Engineering Tshwane University of Technology Pretoria South Africa Department of Mechanical & Mechatronics Engineering Tshwane University of Technology Pretoria South Africa Institute of Advance Tooling Tshwane University of Technology Department of Mechanical Engineering Vaal University of Technology Vanderbijlpark South Africa

ISBN: (数字)9781728153322

ISBN: (纸本)9781728153339

Titanium alloy (Ti6Al4V) possesses excellent mechanical properties, but its machinability at high temperature and speed often lead to vibration and subsequently chatter during machining operations. In this work, the modelling and optimization of the cutting parameters for the milling operations of titanium alloy Ti6Al4V was carried out. the numerical experiment was conducted using the Response Surface Methodology (RSM). the process parameters considered include; the maximum chip thickness (0.1-0.2 mm), cutting speed (29000-34000 mm/min) and feed per tooth (0.14 - 0.28 mm). these process parameters were varied over different levels. the physical experiment was conducted on a DMU80monoBLOCK Deckel Maho 5-axis CNC milling with the stationary dynamometer (KISTLER 9257A 8-Channel Summation of Type 5001A Multichannel Amplifier) mounted directly to the machine table with the titanium alloy screwed to it. the response of the experiment; cutting force for each of the experimental trial was collected through the data Acquisition System (DAS) of the Kistler Dynamometer. the results obtained indicate significant model terms which implies that the developed model is suitable for predictive purpose and that the process parameters can significantly influence the magnitude of the cutting forces.

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A customized educational booster for online students in cybersecurity education 11

A customized educational booster for online students in cybe...

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11th international conference on Computer Supported Education, CSEDU 2019

作者： Rahouti, Mohamed Xiong, Kaiqi Department of Electrical Engineering University of South Florida TampaFL33620 United States Cyber Florida University of South Florida TampaFL33620 United States Department of Mathematics and Statistics University of South Florida TampaFL33620 United States Intelligent Computer Networking and Security Lab University of South Florida TampaFL33620 United States

ISBN: (纸本)9789897583674

Real-world lab experiments have been an integral part of computer science and engineering curriculums. However, human and computing resources as well as financial support to courses may be limited at many universities ranging from small to large universities and from liberal arts colleges to top research universities as there is a dramatic increase of student enrollments in computer science and engineering for the past ten years. Nowadays, like many other universities nation wide, University of South Florida (USF) in collaboration with the Cyber Florida center offers online cyber security degrees as well as certificates. throughout such programs, online students are expected to acquire knowledge via an interdisciplinary set of core courses prior to taking a deep dive into one of four following concentrations: cyber intelligence, digital forensics, information assurance and computer security fundamentals. However, when it comes to training student with real-world cyber security labs, both instructors and students face various challenges with regard to resources and virtual environment for exploring and running a broad range of security experiments and tests. In order to achieve the goal of our funded NSF project, in this paper we will discuss our teaching contributions to the development of a broad range of cyber security labs, facilitation of applied cryptography learning through experimental modules, and our customized virtual machine that fits the needs of various online computer networking and security courses. Specifically, we will first present our methodology for the design of our experimental modules and then present in details our pre-built Linux-based portable virtual machine. those learning and experimental modules have been developed at different levels to meet the need of students with different academic and industrial backgrounds. Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Virtual reality

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Validation of an optimization model based stochastic traffic flow fundamental diagram 11th

Validation of an optimization model based stochastic traffic...

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11th international KES conference on intelligent Interactive Multimedia: Systems and Services, KES-IIMSS 2018

作者： Zhang, Jin Wang, Xu Griffith School of Engineering Griffith University Gold CoastQLD4222 Australia

ISBN: (纸本)9783319922300

the fundamental diagram is used to represent the graphical layout and determine the mathematical relationships among traffic flow, speed and density. Based on the observational speed-density database, the distribution of speed is scattered in any given traffic state. In order to address the stochasticity of traffic flow, a new calibration approach has been proposed to generate stochastic traffic flow fundamental diagrams. With this proposed stochastic fundamental diagram, the residual and stochasticity of the performance of calibrated fundamental diagrams can be evaluated. As previous work only shows the validation of one model, in this paper, we will use field data to validate other stochastic models. Greenshields model, Greenberg model, and Newell model are chosen to evaluate the performance of the proposed stochastic model. Results show that the proposed methodology fits field data well. © Springer international Publishing AG, part of Springer Nature 2019.

关键词： Stochastic models

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