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Exploration of pattern recognition Methods for Motor Imagery EEG Signal with Convolutional Neural Network Approach 3

Exploration of Pattern Recognition Methods for Motor Imagery...

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3rd international conference on Emerging Electrical Energy, Electronics and Computing Technologies 2021, ICE4CT 2021

作者： Zahra, Hanina N Zakaria, Hasballah Hermanto, Beni R School of Electrical Engineering and Informatics Bandung Institute of Technology West Java Bandung40132 Indonesia

As an application of EEG, Motor Imagery based Brain-Computer Interface (MI BCI) plays a significant role in assisting patients with disability to communicate with their environment. MI BCI could now be realized through various methods such as machine learning. Many attempts using different machine learning approaches as MI BCI applications have been done with every one of them yielding various results. While some attempts managed to achieve agreeable results, some still failed. This failure may be caused by the separation of the feature extraction and classification steps, as this may lead to the loss of information which in turn causes lower classification accuracy. This problem can be solved by integrating feature extraction and classification by harnessing a classification algorithm that processed the input data as a whole until it produces the prediction, hence the use of convolutional neural network (CNN) approach which is known for its versatility in processing and classifying data all in one go. In this study, the CNN exploration involved a task to classify 5 different classes of fingers' imaginary movement (thumb, index, middle, ring, and pinky) based on the processed raw signal provided. The CNN performance was observed for both non-augmented and augmented data with the data augmentation techniques used include sliding window, noise addition, and the combination of those two methods. From these experiments, the results show that the CNN model managed to achieve an averaged accuracy of 47%, meanwhile with the help of augmentation techniques of sliding window, noise addition, and the combined methods, the model achieved even higher averaged accuracy of 57,1%, 47,2%, and 57,5% respectively. © Published under licence by IOP Publishing Ltd.

关键词： Brain computer interface

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Robust object recognition in wearable eye tracking system

Robust object recognition in wearable eye tracking system

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Asian conference on pattern recognition (ACPR)

作者： Mustafa Shdaifat Syed Saqib Bukhari Takumi Toyama Andreas Dengel German Research Center for Artificial Intelligence (DFKI) GmbH Kaiserslautern Germany

ISBN: (纸本)9781479961016

Object recognition is a versatile capability. Automatic guided tours and augmented reality are just two examples. Humans seem to do it subconsciously - unaware of the extensive processing required for it - while it is a complex task for machines. Methods based on SIFT features have proven to be robust for recognition. However, a prior detection step is required to limit confusion, caused by, e.g., scene clutter. We present an attention-guided method that offloads this to humans through eye tracking. Gaze data is used to extract candidate patches to recognize afterwards. It improves upon previous work by automatically selecting the dynamic size of said patch, instead of fixed large local region. Therefore increasing robustness and independence compared to fixed window size technique.

关键词： Object recognition Gaze tracking Feature extraction Image segmentation Databases Robustness Histograms

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3rd international Workshop on New Approaches for Multidimensional Signal Processing, NAMSP 2022

3rd International Workshop on New Approaches for Multidimens...

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3rd international Workshop on New Approaches for Multidimensional Signal Processing, NAMSP 2022

ISBN: (纸本)9789811978418

The proceedings contain 23 papers. The special focus in this conference is on New Approaches for Multidimensional Signal Processing. The topics include: Development of IoT Indoor Monitoring System for Independent Elderly;improving the Process of Evaluating User Stories Using the Paraconsistent Annotated Evidential Logic Eτ;neural Network Algorithm Applied in Electrical Engineering Automation;Truss Structure Optimization Design Based on FE-PSO-SQP Algorithm;practice System of Ant Colony Optimization Algorithm in Business Administration;literature Review of Smart Contracts Using Blockchain Technology;a Comprehensive Study of 5th Generation Scheduling Algorithms;a Comparative Analysis of Homogeneous and Heterogeneous Protocols to Maximize the Lifetime of Wireless Sensor Network for Precision Agriculture;the Integration Development and Upgrading Path of Industry 4.0 Architecture Industrial Engineering Network Driven by Big Data;video Tracing of Moving Objects by Fusing Three-Term Decompositions;SAAS Application Prospect Analysis in Hrm and Methods to Upgrade the Contemporary System;All Digital Phase Locked Loop (ADPLL) and Its Blocks—A Comprehensive Knowledge;on Realization of Smart Logistic Warehouse Management with Internet of Things;development and Design of Intelligent Traction System for Bulk Cargo Terminal;deep Learning Approaches for Classroom Audio Classification Using Mel Spectrograms;Tensor Spectral Pyramid for Color Video Sequences Representation, Based on 3D FO-AHKLT;electromyography Signal Acquisition, Processing, Optimization and Its Applications;research on the Radar Signal Classification Method Based on the Deep Faith Network Model;measuring machine intelligence Using Black-Box-Based Universal intelligence Metrics;COVID Detection Using ECG Image Reports: A Survey;disease Detection Techniques in Plants: Transition from Manual to Automation.

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Role of Artificial intelligence and machine Learning in scalable Internet of Things Applications

Role of Artificial Intelligence and Machine Learning in scal...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Neeraj Singla Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Anything non-living that can take decisions on its own is called artificially intelligent. It is a technique designed to mimic human behaviour. On the other hand machine learning if a subset of artificial intelligence. It is a statistic method used to enable machine to improve with experience. They both go hand in hand. Now IoT has been there in existence for an enormous amount of time but during recent times due to large scale good connectivity for efficient data transfer, machine learning and analytics and Conversional Artificial intelligence has made IoT possible.

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Development of Light-Weight High Entropy Alloys for Aerospace Applications: Perspective of Artificial intelligence 1

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3rd international conference on Innovation in Engineering, ICIE 2024

作者： Balaji, V. Xavior, M. Anthony Machado, Jose Joel, J. Jeyapandiarajan, P. Batako, Andre Burduk, Anna Vellore Institute of Technology Vellore632014 India University of Minho Braga Portugal Liverpool John Moores University Liverpool United Kingdom Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (数字)9783031626845

ISBN: (纸本)9783031626838

The primary driver of growing demand for innovation in materials particularly for aerospace applications is energy efficient and lightweight without sacrificing mechanical properties. One of such group of materials developed and researched across the globe is the high entropy alloys (HEAs). These alloys comprise a minimum of five elements to a maximum of thirteen elements in it. As the name indicates, the level of entropy has to decide the stability of the alloy system followed by Gibbs free energy. To increase the entropy level, more alloying elements can be added this is the simple concept of High Entropy alloy. This state-of-the-art article focuses on HEAs based on lightweight materials, their development, and mechanical property evaluation, and the application of Artificial intelligence (AI) techniques in predicting the properties and performance based on various machine learning techniques based on sustainable development goals. The role of artificial intelligence in optimizing the composition, the number of elements for designing the HEAs, the predicted properties, and the performance of the same are compiled and presented in this review article. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sustainable development

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machine Learning Application to Priority Scheduling in Smart Microgrids 16

Machine Learning Application to Priority Scheduling in Smart...

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16th IEEE international Wireless Communications and Mobile Computing conference (IEEE IWCMC)

作者： Dridi, Aicha Moungla, Hassine Afifi, Hossam Badosa, Jordi Ossart, Florence Kamal, A. E. Inst Polytech Paris Telecom SudParis F-91120 Palaiseau France Sorbonne Univ PSL Univ CNRS Inst Polytech ParisLMD IPSLEcole PolytechENS Palaiseau France Centrale Supelec GEEPS Gif Sur Yvette France Iowa State Univ Iowa City IA USA

ISBN: (数字)9781728131290

ISBN: (纸本)9781728131290

The need to integrate flexible and intelligent mechanisms for energy management becomes a necessity. In this paper, we are considering a microgrid with infrastructures having production capacities and consumption needs. Several data and constraints related to the microgrid consumption have been collected, in addition to data concerning the production of renewable energy from Photovoltaic panels (PV). Data history is used as input to a neural network to predict one day ahead of consumption and production. Then, a prioritized scheduling family of algorithms is presented. First, we introduce a mathematical formulation to our problem. Then, we propose various scenarios that go from an exact solution to heuristic-based use cases, including scheduling of several energy classes with a maximum scheduling time lapse. Results show that prioritized scheduling, including time lapse based on predictions, can give more reliable results than scheduling based on bin packing.

关键词： Artificial intelligence (AI) Deep Learning (DL) Long Short-Term Memory (LSTM) Bin Packing (BP) Smart Microgrid Optimization

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Social Robotics - Third international conference, ICSR 2011, Proceedings

Social Robotics - Third International Conference, ICSR 2011,...

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3rd international conference on Social Robotics, ICSR 2011

ISBN: (纸本)9783642255038

The proceedings contain 23 papers. The topics discussed include: interaction scenarios for HRI in public space;MAWARI: a social interface to reduce the workload of the conversation;design of robust robotic proxemic behavior;effects of gesture on the perception of psychological anthropomorphism: a case study with a humanoid robot;eight lessons learned about non-verbal interactions through robot theater;proxemic feature recognition for interactive robots: automating metrics from the social sciences;children interpretation of emotional body language displayed by a robot;making robots persuasive: the influence of combining persuasive strategies (gazing and gestures) by a storytelling robot on its persuasive power;BEHAVE: a set of measures to assess users' attitudinal and non-verbal behavioral responses to a robot's social behaviors;and initial formation of trust: designing an interaction with Geminoid-DK to promote a positive attitude for cooperation.

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Artificial intelligence Applications and Innovations 1

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丛书名： IFIP Advances in Information and Communication Technology

1000年

作者： John MacIntyre Ilias Maglogiannis Lazaros Iliadis Elias Pimenidis

ISBN: (数字)9783030198237

ISBN: (纸本)9783030198220

This book constitutes the refereed proceedings of the 15th IFIP WG 12.5 international conference on Artificial intelligence Applications and Innovations, AIAI 2019, held in Hersonissos, Crete, Greece, in May 2019.;The 49 full papers and 6 short papers presented were carefully reviewed and selected from 101 submissions. They cover a broad range of topics such as deep learning ANN; genetic algorithms - optimization; constraints modeling; ANN training algorithms; social media intelligent modeling; text mining/machine translation; fuzzy modeling; biomedical and bioinformatics algorithms and systems; feature selection; emotion recognition; hybrid Intelligent models; classification - pattern recognition; intelligent security modeling; complex stochastic games; unsupervised machine learning; ANN in industry; intelligent clustering; convolutional and recurrent ANN; recommender systems; intelligent telecommunications modeling; and intelligent hybrid systems using Internet of Things. The papers are organized in the following topical sections:;AI anomaly detection - active learning; autonomous vehicles - aerial vehicles; biomedical AI; classification - clustering; constraint programming - brain inspired modeling; deep learning - convolutional ANN; fuzzy modeling; learning automata - logic based reasoning; machine learning - natural language; multi agent - IoT; nature inspired flight and robot; control - machine vision; and recommendation systems.

关键词： Artificial intelligence Data Mining and Knowledge Discovery Image Processing and Computer Vision Information Storage and Retrieval Information Systems Applications (incl. Internet) Mathematical Logic and Formal Languages

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Analysing the Need for 5G Networks based on Smartphone Market Penetration 3

Analysing the Need for 5G Networks based on Smartphone Marke...

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3rd international conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Panse, Chetan Chaskar, Aayush Sibm Symbiosis International Deemed University Pune India

ISBN: (纸本)9781665453578

The term '5G' refers to the latest advancements in mobile technology. The next key stage of mobile communications standards after the soon-to-be-implemented 4G standards is 5G. Due to the development of 5G technology, the bulk of high bandwidth users will change how they access their phones. With 5G pushed via a VOIP-equipped smartphone, people would experience a volume of calls and data transfer unlike anything else. [1] The word '5G' relates to the generation of wireless telecommunications, which will change multiple areas of our daily life. Due to emerging mobile technologies like virtual reality applications, HD video consumption, and cloud-based entertainment, mobile users and their usage is expanding very quickly. The rate of increase in demand and the predicted trends of new technologies, such as driverless vehicles and virtual reality, will far exceed the capacity of 4G networks in a few years Consequently, multiple attempts have been made by industry experts and research associations to make 5G networks a reality soon. Academicians and thought leaders have agreed that these new network systems would employ highly exciting, developed technologies like SDN and NFV to realize their objectives. The transmission speed of 5G is a lot greater than that of the present network Data transmission speeds up to 10Gbps, which are 10 to 100 times faster than 4G and 4G- LTE will be available with 5G. To facilitate the development of new services, 5G is anticipated to merge ultra-broadband networks and incorporate new-age technologies like the Internet of Things (IoT), blockchain, big data, artificial intelligence, machine learning and cloud computing. © 2023 IEEE.

关键词： 5G mobile communication systems

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Evolutionary feature extraction to infer behavioral patterns in ambient intelligence 3rd

Evolutionary feature extraction to infer behavioral patterns...

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3rd international Joint conference on Ambient intelligence, AmI 2012

作者： Shafti, Leila S. Haya, Pablo A. García-Herranz, Manuel Pérez, Eduardo Computer Science and Engineering Dept. Universidad Carlos III de Madrid Spain Knowledge Engineering Institute Universidad Autónoma de Madrid Spain Computer Engineering Dept. Universidad Autónoma de Madrid Spain

ISBN: (纸本)9783642348976

machine learning methods have been applied to infer activities of users. However, the small number of training samples and their primitive representation often complicates the learning task. In order to correctly infer inhabitant’s behavior a long time of observation and data collection is needed. This article suggests the use of MFE3/GADR, an evolutionary constructive induction method. Constructive induction has been used to improve learning accuracy through transforming the primitive representation of data into a new one where regularities are more apparent. The use of MFE3/GADR is expected to improve the representation of data and behavior learning process in an intelligent environment. The results of the research show that by applying MFE3/GADR a standard learner needs considerably less data to correctly infer user’s behavior. © Springer-Verlag Berlin Heidelberg 2012.

关键词： Genetic algorithms

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