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Speech Abusive Language Detection System Using MFCC Speech Feature Extraction and Convolutional Neural Network

Speech Abusive Language Detection System Using MFCC Speech F...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Yi-Chang Zhu Ying-Hsiu Hung Yen-Ching Chang Jhen-Kai Tang Wen-Kai Tsai Shin-Chi Lai The Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Department of Electrical Engineering Master's Program National Formosa University Huwei Taiwan Dept. Electrical Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

As bullying rapidly spreads in schools, many parents are becoming increasingly concerned about their children being subjected to bullying by peers, which could negatively impact their physical and mental health. This paper proposes an abusive language detection system that combines Mel-frequency Cepstrum Coefficients (MFCC) speech features with a Convolutional Neural Network (CNN). Audio files which contain daily abusive phrases in the Minnan dialect are initially preprocesses to eliminate non-speech interference. Subsequently, the MFCC algorithm is applied to extract speech features that closely capture auditory characteristics. The CNN classification model is trained to learn the MFCC features and classify the phrases into their respective categories. Experimental results indicate that the CNN model achieves an average recognition accuracy of 91.9%. Finally, the CNN model was deployed on an embedded platform, where it achieves an average recognition time of 0.54 seconds per phrase and an average recognition accuracy of 86.25%.

关键词： Pediatrics Accuracy Semantics Speech recognition Mental health Speech enhancement Feature extraction Convolutional neural networks Mel frequency cepstral coefficient Optimization

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The Use of Time and Frequency Features in Finger Movements Based on Electromyogram Recording

The Use of Time and Frequency Features in Finger Movements B...

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2022 International Conference for Advancement in Technology, ICONAT 2022

作者： Atmaji, Catur Lelono, Danang Harjoko, Agus Dharmawan, Andi Universitas Gadjah Mada Doctoral Program in Computer Science Yogyakarta Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9781665425773

Human movement is a direct result of brain commands to muscles. The muscle activity can be analyzed with an electromyograph device and produce a recording in the form of an electromyogram or EMG signal. There are two recording methods, namely intramuscular EMG and surface EMG or sEMG which are easier and more convenient to implement but have lower resolution. Analysis of the EMG record data will be better if the right method is chosen, one of which is the selection of feature extraction methods. This study performs basic finger motion classification with a limited number of electrodes, namely 4 electrodes. The purpose of this research is to compare the variation of features in the time or frequency domain that will be used for classification using artificial neural networks (ANN) and long short-term memory (LSTM). Data acquisition was carried out with the Ganglion Board device on 6 subjects aged 20-22 years who conducted experiments with two kinds of movements, namely opening and holding all fingers alternately. The results of this study indicate that the use of time domain features for classification with ANN produces better accuracy than LSTM. This can happen because the duration of the movement in this study is quite short, which is only for two seconds. The results of the use of the frequency domain feature show that the use of LSTM produces better accuracy, especially on the mean power and median frequency characteristics. © 2022 IEEE.

关键词： Frequency domain analysis

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A Study on Text Feature Selection Using Ant Colony and Grey Wolf Optimization 7

A Study on Text Feature Selection Using Ant Colony and Grey ...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Widians, Joan Angelina Wardoyo, Retantyo Hartati, Sri Universitas Gadjah Mada Doctoral Program Dept. of Computer Science and Electronics Indonesia Universitas Mulawarman Faculty of Engineering Dept. of Informatics Samarinda Indonesia Universitas Gadjah Mada Faculty of Mathematics and Natural Sciences Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350345711

Text classification (TC) is widely used for organizing digital documents. The issues in TC are numerous characteristics and high-element dimensions. Many pattern classification issues require feature selection (FS), which is pertinent. FS removes unneeded and redundant data from the dataset. The Ant Colony Optimization (ACO) and Grey Wolf Optimizer (GWO) for FS are the main topics of our thorough assessment of the literature on the Swarm Intelligence (SI) algorithm. Furthermore, it illustrates how the hybrid SI technique is used in FS across various sectors. The hybrid SI technique uses applicable data from various FS methods to find feature subsets with smaller sizes and better classification performance than those found by regular FS algorithms. © 2022 IEEE.

关键词： Ant colony optimization

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UFCity: A Software Architecture to Create Data Ecosystem in Smart Cities

UFCity: A Software Architecture to Create Data Ecosystem in ...

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2023 Symposium on Internet of Things, SIoT 2023

作者： Pereira, Danne Brayner, Angelo Federal University of Ceara Doctoral Program in Computer Science Fortaleza Brazil Federal University of Ceara Dept. Computer Science Fortaleza Brazil

ISBN: (纸本)9798350329438

Smart city platforms manage resources and information to offer services to citizens. Developing technological solutions for this scenario is not trivial, given data and resource heterogeneity and the need for platform flexibility and adaptability. This work presents UFCity, a software architecture for smart city data ecosystems using microservices, artificial intelligence, and a three-layer networking structure. As a proof of concept, we developed a prototype and tested it in usage scenarios with real data. These use cases explored how our solution solves identified problems, showing meeting essential requirements for smart city platforms. In this way, UFCity is an alternative for data management in a smart city, presenting advantages over other solutions listed in this work in meeting the platform requirements. © 2023 IEEE.

关键词： Smart city

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A segmentation-based automatic navigation technique for virtual endoscopy

A segmentation-based automatic navigation technique for virt...

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2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano

作者： Carvalho, Bruno M. Herman, Gabor T. Dept. of Comp. and Info. Science University of Pennsylvania Doctoral Program in Computer Science Graduate Center City University of New York

ISBN: (纸本)0780383885

Virtual endoscopy (VE) mimics the examination consisting of introducing an endoscope into a tubular structure for diagnostic purposes. One of the main problems in VE is the computattion of the navigation path that the virtual camera will travel while producing the animation that will be viewed by the specialist. We propose a technique that allows the automatic computation of navigation paths connecting a starting and one or more end points inside the structure to be visualized. The computation of the path uses the connectedness map produced by the segmentation algorithm that has been used to identify the structure to be visualized in the image. A simple smoothing operation is then applied to this path to produce the final navigation path. © 2004 IEEE.

关键词： Endoscopy

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A latency hiding framework for enhanced ubiquitous access to big data in a constrained digital ecosystem: Application to digital medical archives 12

A latency hiding framework for enhanced ubiquitous access to...

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International Conference on Management of Emergent Digital EcoSystems, MEDES 2012

作者： Mequanint, Dessalegn Brunie, Lionel Libsie, Mulugeta Coquil, David IT Doctoral Program Addis Ababa University Ethiopia France Dept. of Computer Science Addis Ababa University Ethiopia German-French Doctoral College- MDPS University of Passau Germany

ISBN: (纸本)9781450317559

This paper presents our latency hiding framework for access to big data in a constrained digital ecosystem with application to digital medical archives. Aiming to enhance ubiquitous access of big data such as patient-oriented access of medical archives, we apply complex/multi-context prefetching to reduce latency thereby improving response time. We propose a formal model for prefetch requests rate and network workload or stress bound that takes into account a diverse set of constraints a digital ecosystem could be in. In addition to that, components of our latency hiding framework such as a generic multi-context functional architecture, use case model, medical database model with emphasis on API (abstracted patient information) and a high-level system architecture have been designed. The development of a complex or multi-context prefetch algorithm that uses a patient's chief complaints, slackness sensitivity, popular content tag, user specified contexts and constraints is underway. A prototype system will also be developed to validate the proposed solutions. Moreover, input and output metrics will be developed to gauge the efficiency and effectiveness of the prefetch algorithm under development. Copyright © 2012 ACM.

关键词： Complex networks

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EEG-Based Emotion Classification Using Wavelet Decomposition and K-Nearest Neighbor 4

EEG-Based Emotion Classification Using Wavelet Decomposition...

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4th International Conference on science and Technology, ICST 2018

作者： Putra, Agfianto Eko Atmaji, Catur Ghaleb, Fajrul Dept. of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia Electronics and Instrumentations Study Program Universitates Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9781538658130

Research on the correlation of EEG signals to emotions based on high/low arousal and valence, has been done before. Some research using the Eigen-Emotion Pattern Kernel method and the Support Vector Machine. The others using the Higuchi Fractal Dimension (FD) Spectrum, the Multifractal Detrended Fluctuation Analysis (MDFA) and the Hidden Markov Model (HMM), but the accuracy is not too good. This research uses Wavelet Decomposition and k-Nearest Neighbor to improve accuracy. The results show that the optimum k values of the k-Nearest Neighbor parameters for this research are 21. Valence's classification accuracy results using Wavelet and k-NN, compared with previous research has the same relative accuracy, ie 57.5%. While the result of arousal classification accuracy using wavelet and k-NN is 64.0%, better than previous research. © 2018 IEEE.

关键词： Wavelet decomposition

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Three-Class Classification of EEG Signals Using Support Vector Machine Methods 4

Three-Class Classification of EEG Signals Using Support Vect...

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4th International Conference on science and Technology, ICST 2018

作者： Atmaji, Catur Putra, Agfianto Eko Tontowi, Irvan Albab Dept. of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia Electronics and Instrumentation Undergraduate Program Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9781538658130

Many research on how the human brain works has been done in the last century. The use of electroencephalogram signal generated from quantifying the brain wave have been developed in many areas including the development of brain computer interface (BCI) concept. One type of BCI that interesting for the future use is motor imagery (MI) based-BCI which only requiring imagination of a person to control an object. This study proposed a feature extraction in eight different channels using discrete wavelet (DWT) coefficients. The wavelet coefficient is transformed to frequency domain using discrete fourier transform (DFT) and then average power spectrum is calculated. Level 5 of detail component of the DWT is chosen because from 512Hz sampling frequency (8 - 16Hz), it resemble mu rhythm of brain wave (8 - 12Hz) which affected from motor imagery activity. The classification of three classes, which are imagination of right body movement, left movement, and random word using multiclass support vector machine (SVM) shows a promising result with sensitivity of 96.88%, 86.12% and 52.78% from three different subjects. © 2018 IEEE.

关键词： Power spectrum

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids 21

Integrating Noise Classification and Speech Enhancement Mode...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Hung, Ying-Hsiu Chang, Yen-Ching Lai, Shin-Chi Juang, Wen-Ho Sheu, Ming-Hwa Lee, Jeng-Dao National Yunlin University of Science and Technology Dept. Electronics Engineering Douliu Taiwan National Formosa University Doctoral Degree Program in Smart Industry Technology Research and Development Huwei Taiwan National Formosa University Dept. Automation Engineering Smart Machinery and Intelligent Manufacturing Research Center Huwei Taiwan National Formosa University Dept. Computer Science and Information Engineering Huwei Taiwan National Formosa University Dept. Automation Engineering Huwei Taiwan

ISBN: (纸本)9798350377088

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm. © 2024 IEEE.

关键词： Generative adversarial networks

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PeerWise: Exploring conflicting efficacy studies 11

PeerWise: Exploring conflicting efficacy studies

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7th International Computing Education Research Workshop, ICER 2011

作者： Denny, Paul Hanks, Brian Simon, Beth Bagley, Spencer Dept. of Computer Science University of Auckland Auckland New Zealand BFH Educational Consulting Seattle WA 98119 United States Computer Science and Engineering Dept. Univ. of California San Diego San Diego CA 92093 United States Math and Science Education Doctoral Program UCSD SDSU San Diego CA 92023 United States

ISBN: (纸本)9781450308298

PeerWise (PW) is an online tool that allows students in a course to collaborate and learn by creating, sharing, answering and discussing multiple-choice questions (MCQs). Previous studies of PW at the introductory level have shown that students in computing courses like it, and report statistically significant learning gains in courses taught by the investigators at different institutions. However, we recently conducted three quasi-experimental studies of PW use in upper-division computing courses in the U.S. and failed to replicate these positive results. In this paper we consider various factors that may impact the effectiveness of PW, including instructor engagement, usage requirements and subject-matter issues. We also report several positive results from other STEM courses at the same institution, discuss methodological issues pertaining to our recent studies and propose approaches for further investigation. © 2011 ACM.

关键词： Students

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